Health at A Glance Asia Pacific 2022

Health at a Glance:
Asia/Pacific 2022
MEASURING PROGRESS TOWARDS UNIVERSAL
HEALTH COVERAGE
Health at a Glance:
Asia/Pacific
2022
MEASURING PROGRESS TOWARDS UNIVERSAL

HEALTH COVERAGE
This work is published under the responsibility of the Secretary-General of the OECD and the Director-General of the
WHO. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the
member countries of the OECD or those of the World Health Organization.
This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over
any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.
The names and representation of countries and territories used in this joint publication follow the practice of the OECD.
Please cite this publication as:

OECD/WHO (2022), Health at a Glance: Asia/Pacific 2022: Measuring Progress Towards Universal Health Coverage, OECD
Publishing, Paris, https://doi.org/10.1787/c7467f62-en.
ISBN 978-92-64-42245-2 (print)

ISBN 978-92-64-39628-9 (pdf)
ISBN 978-92-64-70834-1 (HTML)
ISBN 978-92-64-62422-1 (epub)
Health at a Glance: Asia/Pacific

ISSN 2305-4956 (print)
ISSN 2305-4964 (online)
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3
Foreword
The COVID-19 pandemic has had a significant and lasting impact on health systems and economies in
most countries around the world, including those in the Asia-Pacific region. Since January 2020,
over 1 million people have died due to the COVID-19 pandemic in the Asia-Pacific region, and more than
80 million lost their jobs in 2020. Amid the recovery is under way, it is critical that stakeholders identify
lessons learned, while at the same time leveraging heightened awareness of the importance of health
resilience and preparedness to propel investment, commitment, and action towards building resilient health
systems that are adequately prepared for the complex health challenges of the future.
Lessons learned across Asia-Pacific during the pandemic
In response to the pandemic, most Asia-Pacific countries introduced rapid and far-reaching measures to
protect people’s health and livelihoods, ranging from effective contact tracing strategies, to smart
containment measures, and later to successful vaccination campaigns. However, the crisis has also
exposed underlying health system shortcomings and social and economic inequities often further
exacerbating them. As this report outlines, limited access to essential health care services, in particular for
disadvantaged groups such as women living in low-income households or rural areas, and high levels of
out-of-pocket and catastrophic health spending remain significant issues in Asia-Pacific.
To remedy these and other challenges, universal health coverage ensures that all people can access
quality health services, without financial hardship. It is the foundation of a resilient health system, and
ensures that when acute events occur, essential health services can be maintained. Building equitable and
resilient health systems not only protects people’s lives, especially in times of crisis, but also pave the way
towards inclusive recovery, social justice and sustainable development.
How to prepare for the future
The COVID-19 pandemic has clearly demonstrated that when health is at risk, everything is at risk. This
suggests that ensuring greater resilience and preparedness to shocks – and the required investment to
achieve these goals – should be a key element of governments’ overall commitment to sustainable social
and economic development.
Only with significant and sustained financial investment and political commitment can countries mobilise
the whole-of-government capacity needed to tackle the increasingly complex health challenges of our time.
In the months, years and decades ahead, key priorities include investing in innovative health and social
care service delivery models, including patient-centred and integrated primary health care; adopting digital
health interventions; and creating healthy environments and lifestyles to promote healthy ageing.
HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

4
Given that health is intricately linked to social, economic and cultural life, delivering this agenda and
working towards more just and equitable societies and health systems requires a multidisciplinary, cross-
sectorial and collaborative approach. Ultimately, investments to achieve quality and accessible health care
for all, without financial hardship, are investments in overall economic and social development that will
translate into healthier, more resilient and cohesive societies that are future-ready.
Poonam Khetrapal Singh Zsuzsanna Jakab, Stefano Scarpetta,

Regional Director, Officer-in-Charge, Director,
WHO Regional Office WHO Regional Office Directorate for Employment,
for South-East Asia for the Western Pacific Labour and Social Affairs, OECD

5
Table of contents
Foreword 3
Reader’s guide 8
Acronyms and abbreviations 12
Executive summary 13
1 Country and territory dashboards 14
Health status 15
Risk factors 16
Quality of care 17
Health care resources 18
2 The health impact of COVID-19 19

The direct impact of COVID-19 20
Disruptions of health services during the pandemic 31
Countries responded differently to the pandemic based on the pandemic situations and national
capabilities and contexts 38
Conclusions 41
References 42
Note 45
3 Health status 46
Life expectancy at birth and survival rate to age 65 47
Neonatal mortality 50
Infant mortality 53
Under age 5 mortality 55
Mortality from all causes 58
Mortality from cardiovascular disease 61
Mortality from cancer 64
Mortality from injuries 67
Maternal mortality 70
Tuberculosis 73
HIV/AIDS 76
Malaria 78
Diabetes 81
Ageing 83

6
4 Determinants of health 86
Family planning 87
Infant and young child feeding 90
Child malnutrition (including undernutrition and overweight) 93
Water and sanitation 96
Tobacco 99
5 Health care resources and utilisation 102

Doctors and nurses 103
Consultations with doctors 106
Medical technologies 108
Hospital care 111
Pregnancy and birth 113
Infant and child health 115
Mental health care 117
Access to health care 120
6 Health expenditure and financing 122

Health expenditure per capita and in relation to GDP 123
Financing of health care from government and compulsory health insurance schemes 126
Financing of health care from households’ out-of-pocket payments and voluntary payment
schemes 129
Health expenditure by type of service 131
7 Quality of care 133

Childhood vaccination 134
In-hospital mortality following acute myocardial infarction and stroke 137
Screening, survival and mortality for breast cancer 140
Vaccination, survival and mortality for cervical cancer 143
Survival for other cancers 146
Annex A. National data sources 149

Annex B. Additional information on demographic and economic context 151

7
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8
Reader’s guide
Health at a Glance: Asia/Pacific presents a set of key indicators on health and health systems for 27 Asia-
Pacific countries and territories. It builds on the format used in previous editions of Health at a Glance to
present comparable data on health status and its determinants, health care resources and utilisation,
health care expenditure and financing and health care quality.
This publication was prepared jointly by the WHO Regional Office for the Western Pacific (WHO/WPRO),
the WHO Regional Office for South-East Asia (WHO/SEARO), the OECD Health Division and the
OECD/Korea Policy Centre, under the co-ordination of Luca Lorenzoni from the OECD Health Division.
Chapter 1 and Chapter 2 were prepared by Luca Lorenzoni, Yoshiaki Hori, Gabriel Di Paolantonio and
Tom Raitzik Zonenschein from the OECD Health Division, with support from Ayodele Akinnawo,
Eriko Anzai, Benjamin Bayutas, Emma Callon, Mengjuan Duan, Kareena Hundal, Chung Won Lee,
Ji Young Lee, Kayla Mae Mariano, Tamano Matsui, Sangjun Moon, Manilay Phengxay,
Ariuntuya Ochirpurev, Babatunde Olowokure, Jinho Shin, Alpha Grace Tabanao, Martin Vandendyck,
Ding Wang, Xiaojun Wang, Tracy Yuen, Masahiro Zakoji, and Angel Grace Zorilla from WHO/WPRO.
Chapter 3, Chapter 4 and Chapter 5 were prepared by Luca Lorenzoni, Gabriel Di Paolantonio and
Tom Raitzik Zonenschein from the OECD Health Division, with support from Robert Ryan Arciaga,
Sahar Bajis, Mengjuan Duan, Kiyohiko Izumi, James Kelley, April Siwon Lee, Virginia Macdonald,
Ada Moadsiri, Fukushi Morishita, Jinho Shin, Alpha Grace Tabanao, Josaia Tiko, Juliawati Untoro,
Martin Vandendyck, Delgermaa Vanya, Manami Yanagawa, Tracy Yuen, and Masahiro Zakoji from
WHO/WPRO. Chapter 6 was written by Luca Lorenzoni, with support from Natalja Eigo and Andrew Siroka
from WHO headquarters, Fe Dy-Liacco and Ding Wang from WHO/WPRO. Chapter 7 was prepared by
Rie Fujisawa and Anamaria Verdugo (OECD Health Division), with support from Hardeep Sandhu and
Josaia Tiko from WHO/WPRO.
Valuable input was received from Mengjuan Duan, Alpha Grace Tabanao and Tracy Yuen from
WHO/WPRO, Rakesh Mani Rastogi, Ruchita Rajbhandary and Tarun Jain from WHO/SEARO, and
Frederico Guanais (OECD Health Division).
This publication benefited from the comments and suggestions of Kidong Park (Director, Data, Strategy
and Innovation, WHO/WPRO), Manoj Jhalani (Director, Health Systems Development, WHO/SEARO),
Jeong Joung-hun (Director General, Health and Social Policy Programme, OECD/Korea Policy Centre)
and Francesca Colombo (Head of OECD Health Division).
Thanks go to Lucy Hulett (OECD) for editorial assistance.

9
Structure of the publication
Health at a Glance: Asia-Pacific 2022 is divided into seven chapters:

Chapter 1 Dashboards shows a set of key indicators to compare performance across countries in each of
the following dimensions: health status, risk factors, quality of care and health care resources. For each
dimension, a set of indicators is presented in the form of country dashboards. The indicators are selected
based on their policy relevance, but also on data availability and interpretability.
Chapter 2 on The health impact of COVID-19 provides an overview of the direct and indirect health impact
of COVID-19. It discussed the direct impact of the pandemic in terms of number of COVID-19 cases,
reported deaths and excess mortality. It then discusses the disruptions of health services during the
pandemic and reviews how countries responded to the pandemic based on the epidemiological scenari
and national capabilities and contexts.
Chapter 3 on Health status highlights the variations across countries and territories in life expectancy,
neonatal, infant and childhood mortality and major causes of mortality and morbidity, including both
communicable and non-communicable diseases.
Chapter 4 on Determinants of health focuses on determinants of health. It features the health of mothers
and babies, through family planning issues, low birthweight and breastfeeding. It also includes lifestyle and
behavioural indicators such as smoking and underweight and overweight, as well as water and sanitation.
Chapter 5 on Health care resources, utilisation and access reviews some of the inputs, outputs and
outcomes of health care systems. This includes the supply of doctors and nurses and hospital beds, as
well as the provision of primary and secondary health care services, such as doctor consultations and
hospital discharges, as well as a range of services surrounding pregnancy, childbirth and infancy.
Chapter 6 on Health care expenditure and financing examines trends in health spending across Asia-
Pacific countries. It looks at how health services and goods are paid for, and the different mix between
public funding, private health insurance, direct out-of-pocket payments by households and external
resources.
Chapter 7 on Health care quality builds on the indicators used in the OECD’s Health Care Quality Indicator
programme to examine trends in health care quality improvement across Asia-Pacific countries and
territories.
Annex A provides the list of national data sources used for this publication.
Annex B provides some additional tables on the demographic context within which different health systems
operate.
Asia-Pacific countries and territories
For this seventh edition of Health at a Glance: Asia/Pacific, 27 regional countries and territories were
compared: 22 in Asia (Bangladesh, Brunei Darussalam, Cambodia, China, Democratic People’s Republic
of Korea, Hong Kong (China), India, Indonesia, Japan, Korea, Lao People’s Democratic Republic, Macau
(China), Malaysia, Mongolia, Myanmar, Nepal, Pakistan, Philippines, Singapore, Sri Lanka, Thailand and
Viet Nam) and five in the Pacific region (Australia, Fiji, New Zealand, Papua New Guinea and Solomon
Islands).
We follow OECD guidelines concerning the names of countries and territories, and those guidelines may
differ from those of the WHO.

10 
Selection and presentation of indicators
The indicators have been selected on the basis of being relevant to monitoring health systems
performance, taking into account the availability and comparability of existing data in the Asia/Pacific
region. The publication takes advantage of the routine administrative and programme data collected by
the World Health Organization, especially the Regional Offices for the Western Pacific and South-East
Asia, as well as special country population surveys collecting demographic and health information.
The indicators are presented in the form of easy-to-read figures and explanatory narratives. Each of the
topics covered in this publication is presented over two or three pages. The first page (s) defines the
indicator and notes any methodological or contextual concerns which might affect data comparability. It
also provides brief commentary highlighting the key findings from the data analyses. On the facing page
is a set of figures. These typically show the latest levels of the indicator across countries and, where
possible, trends over time. When appropriate, an additional figure describing the relationship between two
comparable indicators variable is included.
The cut-off date for all the data reported in this publication was Friday 21 October 2022.
Averages
Countries and territories are classified into four income groups – high, upper-middle, lower-middle, and
low – based on their Gross National Income (GNI) per capita (current USD) calculated using the Atlas
method (World Bank). The classification reported in the table below and used in this publication is the one
updated on the 1 July 2021.
In text and figures, Asia Pacific-H refers to the unweighted average for high-income reporting Asia-Pacific
countries and territories, Asia Pacific-UM refers to the unweighted average for upper-middle income
reporting Asia Pacific countries and territories, and Asia Pacific-LM/L refers to the unweighted average for
lower-middle and low-income reporting countries and territories.
“OECD” refers to the unweighted average for the 38 OECD member countries. It includes Australia, Japan,
New Zealand and Korea. Data for OECD countries are generally extracted from OECD sources, unless
stated otherwise. For some indicators where data is not available for all 38 OECD countries, averages
were calculated based on information available and are denoted in figures using OECDXX, where XX
represents the number of OECD countries included in the average.
Even if from a statistical viewpoint the use of a population-weighted average is sound, the unweighted
average used in this report allows for a better representation of levels and trends observed in countries
and territories with small population numbers.

 11
Country and territory ISO codes, GNI per capita and classification by income level
Country/territory name Country/territory short ISO code GNI per capita World Bank Classification
name (used hereafter) in international $ classification by used in this
(2020) income level report
Australia Australia AUS 52 230 High H
Bangladesh Bangladesh BGD 6 240 Lower-middle LM/L
Brunei Darussalam Brunei Darussalam BRN 67 580 High H
Cambodia Cambodia KHM 4 250 Lower-middle LM/L
People’s Republic of China China CHN 17 070 Upper-middle UM
Democratic People’s Republic of Korea DPRK PRK Low LM/L
Fiji Fiji FJI 11 420 Upper-middle UM
Hong Kong (China) Hong Kong (China) HKG 62 420 High H
India India IND 6 440 Lower-middle LM/L
Indonesia Indonesia IDN 11 750 Lower-middle LM/L
Japan Japan JPN 43 630 High H
Korea Korea KOR 45 570 High H
Lao People’s Democratic Republic Lao PDR LAO 7 750 Lower-middle LM/L
Macau (China) Macau (China) MAC 72 260 High H
Malaysia Malaysia MYS 27 360 Upper-middle UM
Mongolia Mongolia MNG 11 200 Lower-middle LM/L
Myanmar Myanmar MMR 4 960 Lower-middle LM/L
Nepal Nepal NPL 4 040 Lower-middle LM/L
New Zealand New Zealand NZL 43 890 High H
Pakistan Pakistan PAK 5 330 Lower-middle LM/L
Papua New Guinea Papua New Guinea PNG 4 240 Lower-middle LM/L
Philippines Philippines PHL 9 030 Lower-middle LM/L
Singapore Singapore SGP 86 340 High H
Solomon Islands Solomon Islands SLB 2 680 Lower-middle LM/L
Sri Lanka Sri Lanka LKA 12 850 Lower-middle LM/L
Thailand Thailand THA 17 780 Upper-middle UM
Viet Nam Viet Nam VNM 10 410 Lower-middle LM/L

12 
Acronyms and abbreviations
AIDS Acquired immunodeficiency syndrome

ALOS Average length of stay
ART Antiretroviral treatment
BMI Body mass index
DALYs Disability-adjusted life years
DHS Demographic and Health Surveys
DTP Diphtheria-tetanus-pertussis
FAO Food and Agriculture Organization of the United Nations
GBD Global burden of disease
GDP Gross domestic product
HIV Human immunodeficiency virus
IARC International Agency for Research on Cancer
IDF International Diabetes Federation
IHD Ischemic heart disease
MDG Millennium Development Goals
MMR Maternal mortality ratio
OECD Organisation for Economic Co-operation and Development
PPP Purchasing power parities
SEARO WHO Regional Office for South-East Asia
SHA System of Health Accounts
TB Tuberculosis
UN United Nations
UNAIDS Joint United Nations Programme on HIV/AIDS
UNDESA United Nations, Department of Economic and Social Affairs, Population Division
UNESCAP United Nations Economic and Social Commission for Asia and the Pacific
UNICEF United Nations Children’s Fund
WHO World Health Organization
WPRO WHO Regional Office for the Western Pacific

 13
Executive summary
Health at a Glance: Asia/Pacific 2022 presents key indicators on health status, determinants of health,
health care resources and utilisation, health expenditure and financing, and quality of care for 27 Asia-
Pacific countries and territories. Countries and territories in the Asia-Pacific region are diverse, and their
health issues and health systems differ. However, these indicators provide a concise overview of the
progress of countries towards achieving universal health coverage for their population.
Life expectancy decreased by 1 year during the COVID-19 pandemic and

maternal mortality ratio is still twice the Sustainable Development Goal target in
lower-middle and low-income countries in the region
 During the COVID-19 pandemic, life expectancy has decreased by one year in lower-middle and
low-income Asia-Pacific countries from 2019 to 2021, while it decreased by 0.4 years un upper-
middle income countries and slightly increased in high-income countries during the same period.
 In 2020, the average neonatal mortality rate amongst lower-middle and low-income countries in
Asia-Pacific was 15.8 deaths per 1 000 live births, almost halving the rate observed in 2000 but
still above the SDG target of 12 deaths or less per 1 000 live births.
 Maternal mortality ratio averaged around 140 deaths per 100 000 live births in lower-middle and
low-income Asia-Pacific countries and territories in 2019, still two times higher than the SDG target
of less than 70 death per 100 000 live births.
Almost half of total health spending came from payments made by households
out-of-pocket in lower-middle and low-income countries
 In 2019, lower-middle and low-income Asia-Pacific countries spend – after adjusting for differences
in purchasing power across countries – USD 285 per person per year on health, against USD 822
and USD 3 891 in upper-middle income and high-income Asia-Pacific countries respectively.
 The share of public spending in total health spending increased – on average – in all Asia-Pacific
country income groups from 2010 to 2019, but the increase was much smaller in lower-middle and
low-income Asia-Pacific countries compared to upper-middle and high-income countries: 41.4%
compared to 62.5% and 74.1%, respectively.
 On average, household out-of-pocket expenditure (that is, payments made directly by households
for health services and goods) accounted for 49% of total health expenditure in lower-middle and
low-income Asia-Pacific countries in 2019, a slight decrease in the percentage share of total health
expenditure but an increase in level from 2010.

14 
1 Country and territory dashboards

The aim of this chapter is to show a set of key indicators to compare performance across countries and
territories in each of the following dimensions:
 Health status
 Risk factors for health
 Quality of care
 Health care resources
For each dimension, a set of indicators is presented in the form of country and territories dashboards. The
indicators are selected based on their policy relevance, but also on data availability and interpretability.
Indicators where the availability of recent data for Asia-Pacific countries and territories is highest are
therefore prioritised.
In order to assess the comparative performance across countries and territory, each country/territory is
classified for every indicator based on how it compares against the median of the income group it
categorised into. Therefore, countries and territories significantly - defined as one median absolute
deviation - above/below their respective group median will be classified as better/worse than median
(▲/▼), with the remaining countries and territories classified as close to the median (⦿).
Methodology
In order to allow for cross-country comparisons of performance, countries and territories are split
according to their income group (high income, upper-middle income, lower-middle and low income).
The central tendency measures presented, for all indicators and income groups, are medians.
In order to classify countries and territories as “better than”, “close to”, or “worse than” the central
tendency of any indicator, a measure of statistical dispersion is needed to compute the reasonable
range for values close to the central tendency value, with anything above or below classified
accordingly. The preferred measure is the Median Absolute Deviation (MAD), since it is a robust
measure that is both more efficient and less biased than a simple standard deviation when outliers are
present.
Countries and territories are classified as “better than median” if they lie above the median + 1 MAD,
“worse than median” if they lie below the median – 1 MAD, and “close to the median” if they lie within
± 1 MAD from the median. Given the nature of the indicators presented, for “under age 5 mortality rate”
and “smoking”, “alcohol consumption” and “children and adolescent overweight”, countries and
territories are classified as “better than median” if they lie below the median - 1 MAD, “worse than
median” if they lie above the median + 1 MAD, and “close to the median” if they lie within ± 1 MAD from
the median.

 15
Health status
The five (5) indicators used to compare health status are life expectancy at birth for females (2020), life
expectancy at birth for males (2020), survival to age 65 for females (2020), survival to age 65 for males
(2020), and under age 5 mortality rate per 1 000 live births (2020).
Table 1.1. Dashboard on health status

▲ Better than ⦿ Close to ▼ Worse than group-specific central tendency
Country Life expectancy Life expectancy Survival to Survival to Under age 5
(F) at birth (M) at birth age 65 (F) age 65 (M) mortality rate
In years In years % % Per 1 000 live births
High income 86.3 81.3 94.5 89.7 3.0
Australia 85.3 ⦿ 81.2 ⦿ 93.8 ⦿ 89.9 ⦿ 3.7 ⦿
Brunei Darussalam 77.3 ▼ 74.9 ▼ 84.8 ▼ 78.5 ▼ 11.5 ▼
Hong Kong (China) 88.0 ⦿ 82.9 ▲ 94.9 ⦿ 90.3 ⦿ 2.0 ⦿
Japan 87.7 ⦿ 81.6 ⦿ 94.7 ⦿ 89.5 ⦿ 2.5 ⦿
Korea 86.5 ⦿ 80.5 ⦿ 95.3 ⦿ 88.4 ⦿ 3.0 ⦿
Macau (China) 87.3 ⦿ 81.4 ⦿ 96.2 ⦿ 90.7 ⦿
New Zealand 84.1 ▼ 80.5 ⦿ 92.5 ▼ 89.1 ⦿ 4.7 ⦿
Singapore 86.1 ⦿ 81.5 ⦿ 94.4 ⦿ 90.4 ⦿ 2.2 ⦿
Upper-middle income 78.9 74.0 87.5 76.1 8.6
China 79.4 ⦿ 75.0 ⦿ 89.4 ⦿ 83.8 ▲ 7.3 ⦿
Fiji 69.5 ▼ 65.8 ▼ 73.6 ▼ 62.5 ▼ 27.4 ▼
Malaysia 78.5 ⦿ 74.4 ⦿ 87.1 ⦿ 77.2 ⦿ 8.6 ⦿
Thailand 81.1 ⦿ 73.7 ⦿ 88.0 ⦿ 75.0 ⦿ 8.7 ⦿
Lower-middle and low income 74.2 68.7 79.9 69.2 26.4
Bangladesh 74.9 ⦿ 71.1 ▲ 80.4 ⦿ 74.6 ▲ 29.1 ⦿
Cambodia 72.2 ⦿ 67.7 ⦿ 78.3 ⦿ 68.7 ⦿ 25.7 ⦿
DPRK 75.9 ⦿ 68.8 ⦿ 83.3 ⦿ 71.4 ⦿ 16.5 ⦿
India 71.2 ▼ 68.7 ⦿ 75.4 ▼ 69.2 ⦿ 32.6 ⦿
Indonesia 74.2 ⦿ 69.8 ⦿ 80.3 ⦿ 72.0 ⦿ 23.0 ⦿
Lao PDR 70.1 ▼ 66.4 ▼ 74.9 ▼ 67.2 ⦿ 44.1 ▼
Mongolia 74.3 ⦿ 66.0 ▼ 80.0 ⦿ 60.1 ▼ 15.4 ▲
Myanmar 70.3 ▼ 64.3 ▼ 75.3 ▼ 62.2 ▼ 43.7 ▼
Nepal 72.5 ⦿ 69.5 ⦿ 79.2 ⦿ 72.9 ⦿ 28.2 ⦿
Pakistan 68.5 ▼ 66.5 ⦿ 73.4 ▼ 69.0 ⦿ 65.2 ▼
Papua New Guinea 66.1 ▼ 63.5 ▼ 69.3 ▼ 61.1 ▼ 43.9 ▼
Philippines 75.6 ⦿ 67.4 ⦿ 79.9 ⦿ 65.4 ⦿ 26.4 ⦿
Solomon Islands 75.0 ⦿ 71.4 ▲ 80.7 ⦿ 74.3 ▲ 19.4 ⦿
Sri Lanka 80.4 ▲ 73.8 ▲ 90.9 ▲ 78.5 ▲ 6.9 ▲
Viet Nam 79.6 ▲ 71.4 ▲ 87.0 ▲ 72.4 ⦿ 20.9 ⦿
Note: F, females; M, males.
Source: Life expectancy at birth by sex, UN World Population Prospects 2022 edition. Survival to age 65, see Figure 3.3. Under age 5 mortality,
see Figure 3.9.

16 
Risk factors
The five (5) indicators used to compare risk factors are the age-standardised prevalence estimates for
daily tobacco use among persons aged 15 and above for females (2020), the age-standardised prevalence
estimates for daily tobacco use among persons aged 15 and above for males (2020), the share of
population living in rural areas with access to basic sanitation (latest year available), the share of population
living in rural areas with access to basic drinking water (latest year available) and the prevalence of
overweight among children under age 5 (latest year available).
Table 1.2. Dashboard on risk factors for health

Country Tobacco use (F) Tobacco use Access to basic Access to basic Children under
(M) sanitation (rural drinking water age 5
areas) (rural areas) overweight
% of daily tobacco use % of daily tobacco use % population % population % population under
age 5
High income 8.0 29.0 8.3
Australia 11.5 ⦿ 15.6 ▲ 22.0 ▼
Brunei Darussalam 2.3 ▲ 30.0 ⦿ 8.3 ⦿
Japan 10.0 ⦿ 30.1 ⦿
Korea 5.9 ⦿ 35.7 ▼ 7.3 ⦿
New Zealand 15.0 ▼ 15.0 ▲
Singapore 5.0 ⦿ 28.0 ⦿
Upper-middle income 2.3 42.6 98.3 89.9 6.9
China 1.7 ⦿ 49.4 ▼ 87.9 ▼ 89.7 ⦿ 8.5 ⦿
Fiji 10.5 ▼ 35.6 ▲ 99.3 ⦿ 89.1 ⦿ 5.1 ⦿
Malaysia 1.1 ⦿ 43.8 ⦿ 90.2 ⦿ 5.2 ⦿
Thailand 2.9 ⦿ 41.3 ⦿ 98.3 ⦿ 100.0 ▲ 9.2 ▼
Lower-middle and low income 7.3 47.9 69.1 88.6 2.6
Bangladesh 17.1 ▼ 52.2 ⦿ 55.0 ⦿ 97.9 ⦿ 2.4 ⦿
Cambodia 6.0 ⦿ 36.1 ▲ 61.0 ⦿ 65.1 ▼ 2.2 ⦿
DPRK 0.0 ▲ 34.8 ▲ 73.1 ⦿ 88.8 ⦿ 2.3 ⦿
India 13.0 ⦿ 41.3 ⦿ 67.0 ⦿ 88.8 ⦿ 1.6 ⦿
Indonesia 3.7 ⦿ 71.4 ▼ 79.9 ⦿ 85.7 ⦿ 8.0 ▼
Lao PDR 10.3 ⦿ 53.3 ⦿ 69.1 ⦿ 78.5 ⦿ 3.5 ⦿
Mongolia 7.1 ⦿ 51.7 ⦿ 50.6 ▼ 61.1 ▼ 10.5 ▼
Myanmar 19.7 ▼ 68.5 ▼ 71.0 ⦿ 78.4 ⦿ 0.8 ⦿
Nepal 12.8 ⦿ 47.9 ⦿ 76.7 ⦿ 90.2 ⦿ 2.6 ⦿
Pakistan 7.3 ⦿ 33 ▲ 60.2 ⦿ 88.6 ⦿ 2.5 ⦿
Papua New Guinea 25.1 ▼ 53.5 ⦿ 14.7 ▼ 39.1 ▼ 13.7 ▼
Philippines 6.5 ⦿ 39.3 ⦿ 82.2 ⦿ 91.1 ⦿ 4.0 ⦿
Solomon Islands 19.2 ▼ 53.8 ⦿ 20.6 ▼ 59.4 ▼ 4.5 ⦿
Sri Lanka 2.6 ⦿ 41.4 ⦿ 93.9 ▲ 90.5 ⦿ 2.0 ⦿
Viet Nam 2.2 ⦿ 47.4 ⦿ 85.2 ▲ 95.5 ⦿ 5.9 ▼
Note: F, females; M, males.

Source: Tobacco use, see Figure 4.11. Access to basic sanitation, see Figure 4.9. Access to drinking water, see Figure 4.10. Children
overweight, see Figure 4.8.

 17
Quality of care
The four (4) indicators used to compare quality of care are the age-standardised breast cancer mortality
rate (2020), the age-standardised cervical cancer mortality rate (2020), and vaccination coverage for three
doses of diphtheria tetanus toxoid and pertussis (DTP3) and for 1st dose of measles (MCV) among children
(2021).
Table 1.3. Dashboard on quality of care

Country Breast cancer Cervical cancer Vaccination for DTP3 Vaccination for
mortality mortality measles, 1st dose
Age-standardised rates per Age-standardised rates per Coverage (%), children Coverage (%), children
100 000 women 100 000 women
High income 12.1 2.5 96.0 96.5
Australia 11.7 ⦿ 1.5 ⦿ 95.0 ⦿ 93.0 ▼
Brunei Darussalam 12.5 ⦿ 5.7 ▼ 99.0 ▲ 99.0 ⦿
Japan 9.9 ⦿ 2.9 ⦿ 96.0 ⦿ 98.0 ⦿
Korea 6.4 ▲ 1.8 ⦿ 98.0 ⦿ 98.0 ⦿
New Zealand 14.1 ⦿ 2.0 ⦿ 90.0 ▼ 91.0 ▼
Singapore 17.8 ▼ 3.3 ⦿ 96.0 ⦿ 95.0 ⦿
Upper-middle income 16.7 6.6 98.0 96.0
China 10.0 ⦿ 5.3 ⦿ 99.0 ⦿ 99.0 ▲
Fiji 41.0 ▼ 20.7 ▼ 99.0 ⦿ 96.0 ⦿
Malaysia 20.7 ⦿ 5.8 ⦿ 95.0 ▼ 96.0 ⦿
Thailand 12.7 ⦿ 7.4 ⦿ 97.0 ⦿ 96.0 ⦿
Lower-middle and low income 13.3 8.3 83.0 81.0
Bangladesh 9.3 ⦿ 6.7 ⦿ 98.0 ⦿ 97.0 ⦿
Cambodia 10.3 ⦿ 8.3 ⦿ 92.0 ⦿ 84.0 ⦿
DPRK 10.0 ⦿ 6.5 ⦿ 41.0 ▼ 42.0 ▼
India 13.3 ⦿ 11.4 ⦿ 85.0 ⦿ 89.0 ⦿
Indonesia 15.3 ⦿ 14.4 ▼ 67.0 ⦿ 72.0 ⦿
Lao PDR 15.8 ⦿ 6.7 ⦿ 75.0 ⦿ 73.0 ⦿
Mongolia 3.9 ▲ 11.6 ⦿ 95.0 ⦿ 95.0 ⦿
Myanmar 9.6 ⦿ 14.4 ▼ 37.0 ▼ 44.0 ▼
Nepal 7.6 ▲ 11.1 ⦿ 91.0 ⦿ 90.0 ⦿
Pakistan 18.8 ▼ 4.0 ▲ 83.0 ⦿ 81.0 ⦿
Papua New Guinea 27.7 ▼ 19.1 ▼ 31.0 ▼ 38.0 ▼
Philippines 19.3 ▼ 7.9 ⦿ 57.0 ▼ 57.0 ▼
Solomon Islands 18.9 ▼ 16.4 ▼ 87.0 ⦿ 67.0 ⦿
Sri Lanka 11.0 ⦿ 4.9 ⦿ 96.0 ⦿ 97.0 ⦿
Viet Nam 13.8 ⦿ 3.4 ▲ 83.0 ⦿ 89.0 ⦿
Source: Breast cancer mortality, see Figure 7.9. Cervical cancer mortality, see Figure 7.12. Vaccination for DTP3, see Figure 7.10. Vaccination
for measles, see Figure 7.1.

18 
Health care resources
The five (5) indicators used to compare health care resources are health expenditure per capita in
international dollars (USD PPPs) (2019), the share of out-of-pocket (OOP) spending in total current health
spending (2019), the number of doctors per 1 000 population (latest year available), the number of nurses
per 1 000 population (latest year available), and the number of hospital beds per 1 000 population (latest
year available). Given the nature of the indicators presented, where a higher or lower value may not be
indicative of better or worse performance, the arrows simply imply that the values are significantly higher
or lower than the median using the same methodology.
Table 1.4. Dashboard on health care resources

▲ Higher than ⦿ Close to ▼ Lower than central tendency
Country Health spending Out-of-pocket Doctors per 1 000 Nurses per 1 000 Hospital beds
spending population population per 1 000
population
International dollars Share of total health Number Number Number
(USD PPPs) per capita expenditure
High income 4 271 14.5 2.5 7.1 3.4
Australia 5 294 ▲ 16.0 ⦿ 3.8 ▲ 12.2 ▲ 3.8 ⦿
Brunei Darussalam 1 401 ▼ 5.7 ▼ 1.6 ▼ 5.8 ⦿ 2.9 ⦿
Hong Kong (China) 2.0 ⦿ 6.2 ⦿ 4.1 ⦿
Japan 4 587 ⦿ 12.9 ⦿ 2.5 ⦿ 11.8 ▲ 12.6 ▲
Korea 3 521 ⦿ 30.2 ▲ 2.5 ⦿ 7.9 ⦿ 12.7 ▲
Macau (China) 2.6 ⦿ 3.8 ▼ 2.5 ⦿
New Zealand 4 439 ⦿ 12.2 ⦿ 3.4 ▲ 10.6 ▲ 2.7 ⦿
Singapore 4 102 ⦿ 30.2 ▲ 2.5 ⦿ 6.2 ⦿ 2.0 ⦿
Upper-middle income 805 23.9 1.6 3.2 2.0
China 880 ⦿ 35.2 ⦿ 2.2 ▲ 3.1 ⦿ 5.0 ▲
Fiji 545 ▼ 13.2 ⦿ 0.9 ⦿ 3.5 ▲ 2.0 ⦿
Malaysia 1 133 ▲ 34.6 ⦿ 2.3 ▲ 3.4 ⦿ 1.3 ⦿
Thailand 730 ⦿ 8.7 ▼ 1.0 ⦿ 3.1 ⦿ 2.1 ⦿
Lower-middle and low income 227 48.6 0.7 1.7 1.1
Bangladesh 119 ⦿ 72.7 ▲ 0.7 ⦿ 0.4 ▼ 0.9 ⦿
Cambodia 316 ⦿ 64.4 ▲ 0.2 ⦿ 0.6 ▼ 0.9 ⦿
DPRK 3.7 ▲ 4.1 ▲ 14.3 ▲
India 210 ⦿ 54.8 ⦿ 0.7 ⦿ 1.7 ⦿ 0.5 ⦿
Indonesia 358 ▲ 34.2 ▼ 0.6 ⦿ 2.3 ⦿ 1.0 ⦿
Lao PDR 212 ⦿ 41.8 ⦿ 0.4 ⦿ 1.0 ⦿ 1.5 ⦿
Mongolia 484 ▲ 34.8 ▼ 3.9 ▲ 3.9 ▲ 8.0 ▲
Myanmar 227 ⦿ 76.0 ▲ 0.7 ⦿ 0.8 ⦿ 1.0 ⦿
Nepal 177 ⦿ 57.9 ⦿ 0.9 ⦿ 2.1 ⦿ 1.2 ⦿
Pakistan 166 ⦿ 53.8 ⦿ 1.1 ⦿ 0.4 ▼ 0.6 ⦿
Papua New Guinea 105 ⦿ 9.9 ▼ 0.1 ▼ 0.4 ▼
Philippines 379 ▲ 48.6 ⦿ 0.8 ⦿ 4.6 ▲ 1.0 ⦿
Solomon Islands 0.2 ⦿ 2.1 ⦿ 1.4 ⦿
Sri Lanka 569 ▲ 45.6 ⦿ 1.2 ⦿ 2.1 ⦿ 4.0 ▲
Viet Nam 557 ▲ 43.0 ⦿ 0.8 ⦿ 1.1 ⦿ 2.6 ⦿
Source: Health spending, see Figure 6.1. Out-of-pocket spending, see Figure 6.8. Doctors per 1 000 population, see Figure 5.1. Nurses per
1 000 population, see Figure 5.2. Hospital beds per 1 000 population, see Figure 5.11.

 19
2 The health impact of COVID-19
COVID-19 has had a huge impact across the Asia-Pacific region, testing
the resilience of economies and health systems, and placing an immense
pressure on health workers operating at the front line. This chapter
analyses the direct impact of the pandemic on the health of the populations
by looking at COVID-19 cases and deaths as well as its indirect impact by
assessing the disruption to essential health services due to COVID-19. It
also looks at country responses to the pandemic based on the pandemic
situations and national capabilities and contexts. These analyses show that
COVID-19 has had an unequal impact in the region between high-, middle-
and low-income countries, in particular by amplifying inequities and
inequalities.

20 
The direct impact of COVID-19
The health impact of COVID-19 in Asia-Pacific countries has been tremendous. More than 144 million people
tested positive for COVID-19, and more than 1 million deaths have been registered from the virus from
1 January 2020 to 18 October 2022. Comparing worldwide, the health impact might appear less significant
than in other regions, as whilst the Asia-Pacific region makes up 37% of the global population, only 14% of
cases and 4% of deaths globally were reported from the region. However, as many infected people are
asymptomatic, and due to under-reporting, these figures might not reflect the true impact of COVID-19. This
is confirmed by an increasing number of studies that suggest that the real magnitude of infections have been
much larger than those officially reported in many regions (Byambasuren, 2021[1]; Ioannidis, 2021[2]).
In Asia-Pacific countries and territories, the average cumulative number of reported cases was 28 016 per
100 000 population in high-income countries, and 3 024 per 100 000 population in lower-middle- and low-
income countries from 1 January 2020 to 18 October 2022. The average cumulative number of reported
cases in upper-middle-income countries was much lower at 689 per 100 000 population mainly due to the
low prevalence of COVID-19 in China (Figure 2.1). Among countries in the Asia-Pacific region,
Brunei Darussalam – a country with a good surveillance system – reported the highest number of
confirmed cases per 100 000 population of more than 50 000 per 100 000 population, followed by two
OECD countries in the Asia-Pacific region, namely Korea, and Australia.
Figure 2.1. COVID-19 cumulative reported cases by country, from 1 January 2020 to 18 October
2022
Cumulative COVID-19 cases per 100 000 population
60 000
50 000
40 000
30 000
20 000
10 000
Note: Data are affected by countries’ capacity to detect COVID-19 infections – which was particularly limited in many countries at the onset of
the crisis – and by the testing strategies applied. Asia Pacific-H, Asia-Pacific high-income countries; Asia Pacific-UM, Asia-Pacific upper-middle-
income countries; Asia Pacific LM/L, Asia-Pacific lower-middle- and low-income countries. Population data refer to May 2020.
Source: WHO, https://covid19.who.int/data/ (accessed on 21 October 2022).
From 1 January 2020 to 18 October 2022, the average cumulative number of deaths per million population in
the Asia-Pacific region were 371, 48 and 247 in high-income, upper-middle-income, and lower-middle- and
low-income countries respectively, compared to 2 171 recorded deaths per million population across the
OECD. Some countries, such as Malaysia, exceeded the mark of 1 000 deaths per million population, whereas
China reported 4 deaths per million population (Figure 2.2). While most Asia-Pacific countries reported lower
death ratios compared to OECD countries, this does not necessarily imply that they were less affected, given
varying protocols, technical capacity and challenges in the attribution and reporting of the cause of death.

 21
Figure 2.2. COVID-19 cumulative reported deaths by country, from 1 January 2020 to 18 October 2022
Cumulative COVID-19 deaths per million population
2 500
2 000
1 500
1 000
500
Note: Data are affected by countries’ protocols and challenges in the attribution and reporting of cause of death. Asia Pacific-H, Asia-Pacific
high-income countries; Asia Pacific-UM, Asia-Pacific upper-middle-income countries; Asia Pacific LM/L, Asia-Pacific lower-middle- and low-
income countries. Population data refer to May 2020.
The number of new COVID-19 cases remained relatively low in 2020 in Asia and the Pacific. However, in
mid-2021 the number of new cases spiked in India, Indonesia and Japan. The emergence of the highly
contagious “Omicron variant” of concern contributed to the rapid increase in the number of cases in Australia
around Christmas 2021 peaking in January 2022. “Omicron” also contributed to the case numbers reaching
new heights in Japan, New Zealand and Korea in early and late March 2022, respectively. By comparison,
the increase in reported case numbers in India, Indonesia in the first quarter of 2022 was limited.
Figure 2.3. Newly reported weekly COVID-19 cases in Asia-Pacific, from 1 January 2020 to
18 October 2022
Weekly COVID-19 cases per 100 000 population
160
140
120
100
80
60
40
20
Note: Data are affected by countries’ capacity to detect COVID-19 infections – which was particularly limited in many countries at the onset of the crisis
– and by the testing strategies applied. Population data refer to May 2020. Week 2020-01: 3-9 January 2020; week 2022-41: 14-18 October 2022.

22 
The number of reported COVID-19 deaths in Asia-Pacific countries peaked in mid-May 2021, when about
35 000 deaths – almost 1.2 per million population – were recorded (Figure 2.4).
Figure 2.4. Weekly reported COVID-19 deaths in Asia-Pacific, from 1 January 2020 to 18 October
2022
Weekly COVID-19 deaths per million population
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Note: Data are affected by countries’ protocols and challenges in the attribution and reporting of cause of death. Population data refer to
May 2020. Week 2020-01: 3-9 January 2020; week 2022-41: 14-18 October 2022.
Looking at the differences across countries’ income groups, lower-middle- and low-income Asia-Pacific
countries (such as Cambodia and Pakistan) showed significantly lower weekly COVID-19 cases and
deaths compared to high-income Asia-Pacific countries (such as Brunei Darussalam) and OECD countries
from 1 January 2020 to 18 October 2022 (Figure 2.5). Higher testing capacities, different testing
requirements, surveillance systems and number of health care professionals to perform testing may be
among the reasons for the observed differences.
Among the upper-middle-income Asia-Pacific countries, the low number of cases reported in China, where
a dynamic zero COVID-19 approach is still enforced (as of October 2022), has had a significant impact on
the average rate.

 23
Figure 2.5. Weekly newly reported COVID-19 cases, Asia-Pacific countries by income level and
OECD countries, from 1 January 2020 to 18 October 2022
Asia Pacific - H Asia Pacific - UM Asia Pacific - LM/L OECD

Weekly COVID-19 cases per 100 000 population
2 000
1 800
1 600
1 400
1 200
1 000
800
600
400
200
0
Note: Data are affected by countries’ capacity to detect COVID-19 infections – which was particularly limited in many countries at the onset of
the crisis – and by the testing strategies applied. Population data refer to May 2020. Week 2020-01: 3-9 January 2020; week 2022-41: 14-18
October 2022.
The weekly death ratios in Asia-Pacific countries show a similar trend, with ratios generally lower compared
to the OECD average (Figure 2.6). Lower-middle- and low-income Asia-Pacific countries had generally
reported higher mortality ratios compared to high-income Asia-Pacific countries up until the end of 2021
when high-income Asia-Pacific countries started to report surging COVID-19 deaths.
Figure 2.6. Weekly reported COVID-19 deaths, Asia-Pacific countries by income level and
OECD countries, from 1 January 2020 to 18 October 2022
Asia Pacific - H Asia Pacific - UM Asia Pacific - LM/L OECD

Weekly COVID-19 deaths per million population
60
50
40
30
20
10
Note: Data are affected by countries’ protocols and challenges in the attribution and reporting of cause of death. Asia Pacific-H, Asia-Pacific
high-income countries; Asia Pacific-UM, Asia-Pacific upper-middle-income countries; Asia Pacific LM/L, Asia-Pacific lower-middle- and low-
income countries. Population data refer to May 2020. Week 2020-01: 3-9 January 2020; week 2022-41: 14-18 October 2022.

24 
Differences in the evolution of new COVID-19 infections and deaths across countries reflect variations in
containment and suppression/mitigation strategies and the timing of their implementation, as well as
differences in the capacity of health systems to treat COVID-19 patients and to adapt to ongoing
challenges. While case rates peaked in 2022, deaths peaked in 2021. Vaccination campaigns, along with
better disease management and strengthened health system capacity have had a major impact in reducing
case fatality rates and in decoupling case and death rates. Moreover, the differences of the characteristics
(e.g. transmissibility, virulence and severity) in the variant of concern and its effects have also contributed
to these trends. The death rates during the Delta variant dominant period were different to the death rates
during the Omicron variant dominant period. Still, factors beyond the immediate control of policy makers –
such as geographical characteristics, population demographics, and the prevalence of certain risk factors
such as comorbidities – made some countries more susceptible than others to high rates of infection and
mortality.
Vaccines have reduced the risk of severe illness and death from COVID-19
The rollout of COVID-19 vaccines in 2021 has been a milestone in global efforts to reduce COVID-19
hospitalisation, severe disease and death, and to protect health care systems. Although the vaccination
programme started slightly later than in the United States and European countries, Asia-Pacific countries
have steadily increased their vaccination rates, reaching 80% of total Asia-Pacific population vaccinated
with a second dose at the end of 2021. However, procurement of vaccines and implementation of mass
vaccination has been challenging. In most high-income Asia-Pacific countries, vaccines are mainly sourced
through national procurement or self-produced, while most low-income Asia-Pacific countries rely on
international support by COVAX and bilateral donations to secure necessary doses.
The vaccine deployment in Asia-Pacific countries has further faced challenges such as issues affecting
delivery strategies (e.g. human resource capacity, logistical issues, and cold chain management), and
issues related to demand generation (e.g. vaccine hesitancy). Asia-Pacific countries started the booster
vaccination programme in late 2021. The launch of the third dose/booster vaccination programme was
initially delayed compared to OECD countries. However, the roll out of booster vaccination programmes in
the Asia-Pacific region was quick and by early 2022 the average number of people with a booster dose in
high- and upper-middle-income countries in the Asia-Pacific region exceeded the OECD average
(Figure 2.7). As of the end of September 2022 across the region, the percentage of the population who
received a booster dose amounts to almost 84% in high-income countries, whereas that of lower-middle-
and low-income countries is slightly below 20%. This proves that there is significant inequality and inequity
when it comes to vaccine access between high- and low-income countries in the Asia-Pacific region.

 25
Figure 2.7. Booster vaccination progress, Asia-Pacific countries by income level and
OECD countries
OECD Asia Pacific - H Asia Pacific - UM Asia Pacific - LM/L

Booster shots administered per 100 population
90
80
70
60
50
40
30
20
10
0
Q3-2021 Q4-2021 Q1-2022 Q2-2022 Q3-2022
Note: Asia Pacific-H, Asia-Pacific high-income countries; Asia Pacific-UM, Asia-Pacific upper-middle-income countries; Asia Pacific LM/L, Asia-
Pacific lower-middle- and low-income countries.
Source: https://ourworldindata.org/coronavirus (accessed on 29 July 2022).
A high number of excess deaths was estimated for India and Indonesia
Whilst reported COVID-19 deaths are a critical measure to monitor the health impact of the pandemic,
international comparability of this indicator is limited due to differences in recording, registration and coding
practices across countries. Moreover, factors such as the low availability of diagnostic tests at the start of
the pandemic are likely to have impacted accurate attribution of the causes of death. Therefore, the
reported count of COVID-19 deaths is likely underestimated to varying degrees across countries.
An analysis of mortality from all causes – and particularly excess mortality, a measure of the total number
of deaths over and above what would have normally been expected based on death rates in previous years
at a given time of the year – provides a measure of overall mortality that is less affected by the factors
mentioned above.
In only two Asia-Pacific countries, Indonesia and India, does the number of cumulative excess deaths until
the end of 2021 exceed the OECD average, reaching 1 871 and 1 709 excess deaths per 1 million
population, respectively. Australia and New Zealand reported the highest number of negative excess
deaths (Figure 2.8).

26 
Figure 2.8. Cumulative excess mortality by country, from 1 January 2020 to 31 December 2021
Excess deaths per million population
2 000
1 500
1 000
500
- 500
Source: WHO, https://www.who.int/data/sets/global-excess-deaths-associated-with-COVID-19-modelled-estimates (accessed on 4 October 2022).
In 2020 and 2021, the overall number of excess deaths in the Asia-Pacific region was more than six times
higher than the reported number of cumulative COVID-19 deaths. The lowest number of excess deaths in
the Asia-Pacific region was recorded during the initial phase of the pandemic (negative 40 000 excess deaths
in April 2020), while the highest rate of excess deaths was recorded in May 2021 (135 000 excess deaths).
Lower-middle- and low-income Asia-Pacific countries show a significant gap between excess deaths and
COVID-19 reported deaths, with excess deaths approximately 8 times higher than COVID-19 deaths
(Figure 2.9). This gap is mainly driven by India and Indonesia (Figure 2.10).
Figure 2.9. Comparison of cumulative excess mortality to reported COVID-19 deaths, Asia-Pacific
countries by income level and OECD countries, from 1 January 2020 to 31 December 2021
Excess deaths COVID-19 deaths
Deaths per million population
3 000
2 500
2 000
1 500
1 000
500
- 500
OECD Asia Pacific - H Asia Pacific - UM Asia Pacific - LM/L
Source: WHO, https://www.who.int/data/sets/global-excess-deaths-associated-with-COVID-19-modelled-estimates (accessed on 4 October
2022); WHO, https://covid19.who.int/data/ (accessed on 21 October 2022).

 27
Figure 2.10. Comparison of cumulative excess mortality to cumulative reported COVID-19 deaths,
by country, from 1 January 2020 to 31 December 2021

Deaths per million population
4 000
3 500
3 000
2 500
2 000
1 500
1 000
500
0
- 500
-1 000
When comparing monthly reported COVID-19 deaths and excess deaths, the number of excess deaths
exceeds that of COVID-19 deaths except for the early pandemic from February until May 2020
(Figure 2.11). In May 2021, the peak in excess deaths observed is mainly due to India.
Figure 2.11. Comparison of monthly COVID-19 deaths to monthly excess deaths in Asia-Pacific,
from 1 January 2020 to 31 December 2021

Total number of deaths
1 600 000
1 400 000
1 200 000
1 000 000
800 000
600 000
400 000
200 000
0
- 200 000
- 400 000
- 600 000
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
2020 2021


28 
Life expectancy remained stable in all Asian Pacific countries between 2019 and in 2020,
while it decreased by almost one year in lower-middle- and low-income countries
between 2020 and 2021
Life expectancy at birth has remained stable – on average – for all country income groups in Asia-Pacific
between 2019 and 2020 despite the COVID-19 pandemic (Figure 2.12), while 80% of OECD member
countries reported a decrease. However, between 2020 and 2021, life expectancy has decreased by
almost one year in lower-middle- and low-income countries, while it has decreased by 0.6 years in upper-
middle-income countries and has remained stable in high-income countries. Even if interpreting these
trends in life expectancy is not entirely straightforward, evidence suggests that the life expectancy gap by
income level increased during the pandemic (Schwandt H, 2022[3]).
Figure 2.12. Comparison of life expectancy at birth in 2010, 2019, 2020, and 2021, Asia-Pacific
countries by income level
2010 2019 2020 2021

Years
85
82.8 82.9 83.0
81.2
80
75.1 75.3 74.7

75 73.4
70.6 70.5
69.6
70 67.8
65
60
Asia Pacific - H Asia Pacific - UM Asia Pacific - LM/L
Source: United Nations World Population Prospects (accessed on 29 September 2022).
COVID-19 has disproportionately hit vulnerable populations
While COVID-19 poses a threat to the entire population, not all population groups are similarly at risk of
adverse health outcomes of COVID-19. Vulnerable groups include those at a higher risk of susceptibility
to contracting, transmitting and recovering from the virus, such as essential workers in health and long-
term care settings with repeated exposure to the virus, and high-risk population for infections. Further,
biological based factors such as age and pre-existing health conditions increase the risk of severe health
outcomes. While age remains the largest risk factor for severe illness or death, people of all ages with
certain underlying health conditions – including obesity, cancer, hypertension, diabetes, and chronic
obstructive pulmonary disorder – face an elevated risk (ECDC, 2022[4]). Smoking and harmful alcohol use
also increases the likelihood of developing severe illness, experiencing worse health outcomes or dying
from COVID-19. The risks of adverse health outcome of COVID-19 are not equally distributed when it
comes to the vulnerable groups like refugees, migrants, indigenous peoples, ethnic minorities, people living
in slums or informal settlements or experiencing homelessness, persons with disabilities, remote or rural
locations, gender and sexual minorities, and people living in closed facilities.

 29
The vast majority of deaths from COVID-19 have occurred in older populations. Statistics from four
OECD countries in the Asia-Pacific region show that death rates among older age groups are higher
compared to that of all ages in all countries. For example, Korea has 497 deaths per million among all
aged population, whereas the over aged 60 group showed 1953 deaths (4 times), and over 80 aged group
marked 7 479 deaths (15 times).
Comparing within countries, death rates among people over 80 were significantly higher than in the general
population (Figure 2.13). Even for Japan, where the gap in death rates between the total population and
the senior population is the lowest, the population over 60 has a 2.7 times and that aged over 80 has a
6.5 times higher death ratio compared to the whole population.
Further, there are additional factors that create inequities and influence the level of vulnerability of specific
groups and the access they have to health and social services. This was demonstrated during the
pandemic, where the risks of adverse health outcome of COVID-19 were not equally distributed. Many
social determinants of health – income, employment, housing, physical environment, gender, disability,
indigeneity, social inclusion, education, food security and working conditions – influence COVID-19
outcomes. This includes but are not limited to refugees, migrants, indigenous peoples, ethnic minorities,
people living in slums or informal settlements or experiencing homelessness, persons with disabilities,
remote or rural locations, gender and sexual minorities, and people living in closed facilities.
Figure 2.13. Reported COVID-19 deaths by population age groups (through August 2022), selected
Asia-Pacific countries
All ages People aged 60 and over People aged 80 and over
COVID-19 deaths per million population
10 000
9 000
8 000
7 000
6 000
5 000
4 000
3 000
2 000
1 000
0
Australia Japan Korea New Zealand Philippines
Note: Data for Australia include all COVID-19 deaths (both doctor and coroner certified) that occurred and were registered by 30 June 2022.
Data for Japan are from 30 April 2020 to 9 August 2022. Data for New Zealand and Korea are up to 17 August 2022. Data for the Philippines
are up to July 2021.
Source: Australia: https://www.abs.gov.au/statistics/health/causes-death/provisional-mortality-statistics/jan-apr-2022; Japan:
https://covid19.mhlw.go.jp/en/; New Zealand: https://www.health.govt.nz/covid-19-novel-coronavirus/covid-19-data-and-statistics/covid-19-case-
demographics; Korea: http://ncov.mohw.go.kr/en/bdBoardList.do?brdId=16&brdGubun=161&dataGubun=&ncvContSeq=&contSeq=&board_id=;
Philippines: Department of Health, Government of the Philippines.

30 
The mental health impact of the pandemic has been enormous
The COVID-19 crisis has had a significant negative impact on population mental health. Social isolation
due to restricted mobility during the pandemic was a major driver of the increased prevalence of common
mental disorders. Such conditions and disorders, and their associated vulnerabilities may also have a
linkage with the consumption of alcohol, tobacco or illicit drugs during a pandemic. Loneliness, fear of
infection, personal suffering, grief after bereavement, and financial worries were also contributing factors
(WHO, 2022[5]; Loades et al., 2020[6]). Younger age, female gender and pre-existing health conditions were
often reported risk factors.
It is estimated that the global prevalence of anxiety and depression increased by more than 25% in the
first year of the pandemic, with young people and women particularly affected (WHO, 2022[5]). Figure 2.14
compares the prevalence of depression before and during the COVID-19 pandemic for a few countries
that have data. For example, in Australia, the prevalence of depression increased by more than
17 percentage points. Unfortunately, at a time when so many people required support, the pandemic also
disrupted the provision of mental health and social services. According to a global rapid assessment
conducted from June to August 2020, essential psychosocial support was lacking in many places, with
community-based activities and services for vulnerable groups particularly affected. On the other hand,
telemedicine was the most frequently reported strategy to overcome these service disruptions (WHO,
2020[7]).
As the full impact of the pandemic on mental health and well-being is likely to take a number of years to
fully emerge, there is a need for monitoring and measuring long-term health impacts of the pandemic. As
an example, a series of surveys in Australia has collected information from the same group of individuals
from just prior to COVID-19 and then 11 times since COVID-19 started to assess changes over time in life
satisfaction/well-being; psychological distress and mental health; loneliness; social cohesion; and financial
stress (Australian National University. Center for Social Research and Methods, 2022 [8]).
Figure 2.14. National estimates of prevalence of depression or symptoms of depression amongst

adults, pre-COVID-19 and in 2020, selected Asia-Pacific countries
Pre-COVID 2020
Prevalence of depression or symptoms of depression
40%
35%
30%
25%
20%
15%
10%
5%
0%
Australia Japan Korea
Note: Pre-COVID refers to 2017-18 for Australia, 2014 for Japan, 2011 for Korea. The survey instruments used to measure depression differ
between countries and between time points within countries (e.g. Australia), and therefore are not directly comparable, and some surveys may
have small sample sizes or not use nationally representative samples.
Source: Korean 2011 data: Park et al. (2012[9]), “A nationwide survey on the prevalence and risk factors of late life depression in South Korea”,
https://doi.org/10.1016/j.jad.2011.12.038; other data: OECD (2021[10]), “Tackling the mental health impact of the COVID-19 crisis: An integrated,
whole-of-society response”, https://doi.org/10.1787/0ccafa0b-en.

 31
Disruptions of health services during the pandemic
In the early stages of the pandemic, non-COVID-19 outpatient care and hospital care was suspended or
slowed down to reduce the risk of transmission and release capacity to avoid the risk of critical care being
overwhelmed by COVID-19 patients or having the health care workforce infected and affected and not
having staff capacity. This led to major disruptions in the normal flow of patients through the health care
system. Prevention activities, primary care and chronic care for patients with non-communicable diseases
(NCDs) were paused, disrupted and transformed, to divert resources to urgent pandemic activities and to
protect staff and patients from infection, notably through the use of telemedicine and other digital tools. As
many interventions were postponed during the peak cycles of the pandemic, waiting times for elective
surgery and cancer care increased significantly in many countries.
According to the WHO’s Pulse survey on continuity of essential health services during the COVID-19
pandemic, COVID-19 continues to challenge health systems and disrupt essential health services in Asia-
Pacific. On average, one-quarter of 66 essential (tracer) services used to assess continuity of care
(e.g. elective surgeries and procedures; antenatal care; cancer treatments) were disrupted during the
pandemic (Figure 2.15). The average level of disruption reported in the Asia-Pacific is, however, half of
the level observed in the world at 45%. These findings should be interpreted with caution given the various
response rates across WHO regions. As an example, in the WHO Western Pacific region out of the
35 countries that received the 3rd round pulse survey only four countries submitted a complete survey.
Data availability was a factor here, and re-running the survey 12 months later might have improved
response rates.
Figure 2.15. Percentage and level of disruption for 66 tracer services by country, fourth quarter
2021
5-25% 26-50% More than 50%

% of tracer services reporting disruptions
70%
60%
50%
40%
30%
Average: 24%
20%
10%
0%
Source: WHO PULSE survey (Round 3), 2022.
Outreach services and primary care services were reported to be the most disrupted across Asia-Pacific
reporting countries (Figure 2.16).

32 
Figure 2.16. Level of disruption to service availability, fourth quarter 2021
5-25% 26-50% More than 50%
Hospital inpatient services NZL LKA VNM IDN
Appointments with specialists NPL NZL LKA VNM LAO
Health post and home visits by CHWs IDN LAO LKA
Outreach services NPL IDN LAO LKA VNM
Palliative services SLB
Rehabilitation services SLB
Emergency surgeries SLB
24-hour emergency room/unit services VNM IDN
Ambulance services LAO VNM SLB NPL
Elective surgeries NZL LKA SLB
Prescription renewals for chronic medications NPL VNM LAO IDN
Unscheduled primary care clinic services NPL NZL SLB VNM IDN LKA
Routine schedules primary care services NPL NZL SLB VNM LKA IDN
0 1 2 3 4 5 6 7
Number of countries reporting disruption
Source: WHO PULSE survey (Round 3) 2022.
Emergency services in Lao PDR and Nepal and primary health care service in Nepal showed an increase
of disruption over time during the pandemic (Figure 2.17).
Figure 2.17. Comparison of the disruption to service availability, first quarter 2021 and fourth
quarter 2021
5-25% 26-50% More than 50%

Number of countries reporting disruptions
6
5
LKA LKA
4
IDN IDN NPL NPL LKA LKA
3
NPL NPL LAO LAO BGD BGD
2
BGD BGD BGD BGD LAO LAO
1
LAO LAO LKA LKA NPL NPL
0
Q1 2021 Q4 2021 Q1 2021 Q4 2021 Q1 2021 Q4 2021
Routine PHC
Emergency services Elective surgeries
Note: Only countries reporting some or zero level of disruption in both rounds of the PULSE survey were included in this chart.
Source: WHO PULSE surveys, 2021 and 2022.

 33
At service type level, a decrease in the level of disruption between the third quarter of 2020 and the fourth
quarter of 2021 was observed (Figure 2.18).
Figure 2.18. Comparison of the disruption to selected tracer services, second quarter 2020 and
fourth quarter 2021
5-25% 26-50% More than 50%

Number of countries
9
8
KHM KHM BRN BRN
7
BGD IDN IDN MNG BGD LKA
6
IDN LAO BGD IDN IDN KHM
5
LAO VNM KHM MYS LAO LAO
4
NPL BGD MNG BGD NPL IDN
3
NZL NPL LKA KHM LKA BGD
2
LKA NZL LAO LAO MNG MNG
1
VNM LKA MYS LKA KHM NPL
0
Immunisation - round 1 Immunisation - round 3 Mental, neurological, Mental, neurological, Nutrition - round 1 Nutrition - round 3
substance use - round 1 substance use - round 3
Source: WHO PULSE surveys (rounds 1 and 3), 2021 and 2022.
Disruption is due to a mix of supply and demand side factors. The most commonly reported factor on the
supply side was the cancellation of elective services and the redeployment of staff to provide COVID-19
relief, unavailability of services due to closings, and interruptions in the supply of medical equipment and
health products. On the demand side, decreased care seeking decisions due to a fear of infections are the
most common cause of disruption of essential services (Table 2.1).
Table 2.1. Reported cause of disruption by type of service, Q4 2021

Type of service Indonesia Lao PDR Nepal New Zealand Sri Lanka Viet Nam
Routine Intentional service Decreased Decreased Decreased Decreased
schedules PHC delivery care-seeking care-seeking care-seeking care-seeking
care clinic modifications
services
Unscheduled Decreased Decreased Decreased Intentional Unintended
PHC services care-seeking care-seeking care-seeking service delivery disruptions due to
modifications lack of health care
resources
Prescription Intentional service Decreased Decreased Intentional service
renewals for delivery care-seeking care-seeking delivery
chronic modifications modifications
medications
24-hour Unintended Unintended
emergency disruptions due to disruptions due to
room/unit lack of health care lack of health care
services resources resources
Ambulance Unintended Decreased Unintended
services disruptions due to care-seeking disruptions due to
lack of health care lack of health care
resources resources

34 
Type of service Indonesia Lao PDR Nepal New Zealand Sri Lanka Viet Nam
Elective Unintended Decreased
surgeries disruptions due to care-seeking
lack of health care
resources
Health post and Intentional service Decreased Decreased
home visits by delivery care-seeking care-seeking
CHWs modifications
Outreach Unintended Decreased Decreased Decreased Unintended
services disruptions due to care-seeking care-seeking care-seeking disruptions due to
lack of health care lack of health care
resources resources
Hospital inpatient Decreased Unintended Decreased Decreased
services care-seeking disruptions due to care-seeking care-seeking
lack of health care
resources
Appointment with Decreased Decreased Decreased Decreased
specialists care-seeking care-seeking care-seeking care-seeking
Source: WHO PULSE survey (round 3), 2022.
Generally decreased routine vaccination rates
Routine vaccination is a backbone of individual and public health, and a prerequisite for resilient health
systems. The COVID-19 pandemic, however, has challenged the continuation of routine vaccination
programmes, and induced disruptions in infancy and childhood vaccination programmes covering children
aged 9 weeks to 6 years in Asia-Pacific countries, due to patients’ fear of infection, restrictions on
movement/travel, and limited access to health care (Harris et al., 2021[11]).
In about one-third of countries in the Asia Pacific, childhood vaccination rates decreased in 2020 (Figure 2.19).
Between March and April 2020, vaccination coverage decreased by 23% for measles and 22% for bacille
Calmette-Guérin (BCG) (GAVI, 2020[12]). In Pakistan, for example, all mass vaccination programmes were
suspended between April and June 2020 and 40 million children missed their polio vaccination during this
period (Haqqi et al., 2020[13]). In Korea, however, vaccination rate increased 1% for Hep B and BCG for infant
and measles and pneumococcus for children in 2020, compared to the rate in 2019.
Figure 2.19. DPT vaccination rate decreased in about one-third of countries in Asia Pacific in 2020
2019 2020 2021

% of children vaccinated
100
90
80
70
60
50
40
30
20
10
0
Source: WHO/UNICEF estimates of national immunisation coverage (WUENIC) 2022.

 35
The HPV vaccination rate decreased in Australia, Fiji and Malaysia in 2020 (Figure 2.20), to then increase
to reach the 2019 levels in 2021.
Figure 2.20. HPV vaccination rate decreased in some countries in 2020

2019 2020 2021
% of girls aged 15
100
90
80
70
60
50
40
30
20
10
80 72 82 88 89 90 56 49 57 96 83 85
0
Australia Brunei Darussalam Fiji Malaysia
The vaccination rate of influenza among the elderly increased between 2019 and 2020 in Australia, Japan
and New Zealand, whereas it decreased in Korea (Figure 2.21).
A study found that the major reasons for vaccination reluctancy among Asian general populations were
doubts about the safety and efficacy of the vaccine. Many people did not regard themselves to be
vulnerable to the flu and regarded vaccination as unnecessary. Looking at an individual country, a Chinese
study on parent-reported vaccination behaviours showed that children and adolescents from larger families
whose parents had lower levels of education were less likely to improve prevention behaviours (Hou et al.,
2021[14]). As socio-economic status also affects vaccination behaviours, improvements in health literacy
and promotion of vaccine safety might be a key to achieve higher flu vaccine coverage.
Figure 2.21. Influenza vaccination rate among the elderly generally increased in 2020
2019 2020 2021
% of adults aged 65 or more vaccinated
100
90 85.8
80.7
80 73
69
70 66
62.2 61.9 62
60 56.2
50
50
40
30
20
10
0
Australia Japan Korea New Zealand
Source: OECD Health Statistics 2022, https://doi.org/10.1787/health-data-en.

36 
Cancer screening was disrupted in some countries in the Asia-Pacific region
During the early period of the pandemic, cancer screening was substantially disrupted (Fujisawa, 2022[15]).
Cancer screenings were halted in countries including Australia, Japan, New Zealand and Singapore. In
Japan, local governments and health care providers suspended cancer screening programmes in
accordance with the recommendation issued by the Ministry of Health, Labour and Welfare based on the
first declaration of a state of emergency on 7 April 2020. Countries also faced additional indirect challenges
in relation to cancer screening. In Australia, for instance, the drop in cancer incidence could be explained
by the need to confirm any diagnosis with pathological tests in laboratories that are already under pressure
from CVOID-19 testing (IJzerman et al, 2020[16]).
The challenges in providing and accessing cancer screening resulted in lower breast cancer screening
uptake during the initial phase of the pandemic, and the screening rate for 2020 was also lower than the
rate for 2019 (Figure 2.22). In Australia, screening for breast cancer among women aged 50-69 fell by 20%
between January and September 2020, compared to the same period in 2018, with the decline particularly
pronounced between March and May 2020, when breast screening services were paused (Australian
Institute of Health and Welfare, 2021[17]). A decline was also observed at the early stage of the pandemic
in 2020 in other countries including Japan (Toyoda et al., 2021[18]). In New Zealand, mammography
screening rates continued to decrease in 2021. Similar trends are reported for screening of cervical cancer.
A decline in cervical cancer screening was seen in Australia (Australia Government Department of Health,
2020[19]) and Japan (Japan Times, 2020[20]). The number of Cervical Screening Tests conducted was
expected to be lower in 2020 than in 2019, irrespective of the COVID-19 pandemic and subsequent
restrictions. This is largely due to the programme changing from 2-yearly Pap tests to 5-yearly Cervical
Screening Tests from December 2017, as most screening people were due for their first HPV test 2 years
after their last Pap test (during the years 2018 and 2019), with screening in 2020 mainly comprised of
people overdue for their first HPV test and those newly-screening (Australian Institute of Health and
Welfare, 2021[17]).

 37
Figure 2.22. Cancer screening rates decreased across countries
Breast cancer screening
2019 2020 2021

% of women aged 50-69
80
70 71 67
63 63
60 55
50
40
20
0
Australia Korea New Zealand
Cervical cancer screening
2019 2020 2021

80
71 69 68
62
60 57
40
20
0
Korea New Zealand
Note: Programme data.

Source: OECD Health Statistics 2022, https://doi.org/10.1787/health-data-en.
Colorectal cancer screening was also suspended in several countries, including New Zealand and
Singapore during early stage of outbreak in 2020 (Chiu et al., 2021[21]; OECD, 2021[22]). In Korea, beside
breast, cervical and colorectal cancer, the uptake for gastric, liver and lung cancer screenings also declined
in 2020 compared to 2019 (Kim, 2021[23]). Another Korean study also found that metropolitan areas faced
a larger decrease in stomach, colorectal, breast and cervical cancer screening compared to rural areas.
Consequently, newly diagnosed cancer cases declined in countries with available data including Australia,
Japan, Korea, and Hong Kong (China).
Hong Kong (China)’s public laboratory faced a drastic decrease in the number of pathologic specimens
received (Vardhanabhuti and Ng, 2021[24]). As a result, the reduction in the diagnosis of malignant lesions
was observed compared to the expected number from past three years. Large declines were observed
especially for colorectal (–10.0%) and prostate (–19.7%) cancers.
A Japanese study reveals the number of newly diagnosed cancer in 2020 was 5.8% lower compared to
the previous year. Especially, May 2020 when the country was under the state of emergency showed the
most significant decrease of 22%. Gastric cancer saw the most substantial decrease of 39.1% compared
to the last four years.

38 
Delayed cancer screening is expected to increase the future burden of cancer. An Australian study
estimated that a one-year pause in screening reduces 5-year breast cancer survival from 91.4% to 89.5%
(Feletto et al., 2020[25]). In New Zealand, cancer registrations – as recorded in a nationally-mandated
register of all new diagnoses of primary malignant cancers diagnosed – declined by 40% during its national
lockdown in 2020, compared to the previous year (Gurney et al., 2021[26]). While it took few months to go
back to its pre-COVID level, the cumulative of cancer registrations finally surpassed that of 2019 in
September 2020.
After the initial phase of the pandemic, cancer screening uptake started increasing across countries,
although to a varying degree. In Australia, for example, screening uptake between mid-July and mid-
September 2020 exceeded the corresponding period in 2018 (Cancer Australia, 2020[27]). To increase the
uptake of cancer screening, Japan ran public awareness campaigns.
Delayed and missed care for chronic conditions has been associated with worse health
outcomes
Patients with chronic health conditions have high health care needs and are at risk of complications if their
conditions are not well managed. Evidence shows how delays or missing regular care for a range of chronic
conditions, such as diabetes, exacerbates health complications and leads to severe health consequences.
In Korea, chronic respiratory disease such as COPD and asthma all saw a substantial decrease in
hospitalisations during COVID-19 (Huh et al., 2021[28]). The cumulative incidence of admissions was 58%
(COPD) and 48% (asthma) of the average rate during the four preceding years.
Early evidence shows that rates of diabetes-related complications have increased in several countries
during the pandemic due to decreased access to diabetes care and services (Khader, Jabeen and Namoju,
2020[29]; Ghosal et al., 2020[30]). For example, in Indonesia, 69.8% of patients with diabetes experienced
difficulties in managing their diabetes during the pandemic (Kshanti et al., 2021[31]). The difficulties included
attending diabetes consultation (30.1%), access to diabetes medication (12.4%), and checking blood
glucose levels (9.5%). Complications related to diabetes occurred in 24.6% of patients, with those who
had diabetes management difficulties 1.4 times more likely to have diabetes complications than those who
did not. In addition, a study on central India found that glycaemic control deviated during the lockdown
period, with a 0.51% increase in mean haemoglobin (HBA1c) for diabetes patients immediately after
lockdown, which may lead to a considerable increase in the annual incidence of complications associated
with diabetes (Khare and Jindal, 2021[32]).
In Japan, the severity of myocardial infarction also increased (Yasuda et al., 2021[33]).
Countries responded differently to the pandemic based on the pandemic

situations and national capabilities and contexts
Governments within the Asia-Pacific region and beyond put together substantial response packages to
combat COVID-19. The health sector was an early recipient of these additional resources. Amongst Asia
and Pacific countries with comparable data, central government budgetary commitments to health system
responses to COVID-19 between April 2020 and mid-November 2021 ranged from around 5% of GDP in
Nepal to around 0.1% in Lao PDR (Figure 2.23). However, for some countries this may not represent the
full picture of mobilisation of resources in response to COVID-19 as funds granted by international
agencies, foreign government or NGOs are not included.

 39
Figure 2.23. Government financial commitments to health from April 2020 to November 2021
% of GDP
6
Note: Reported as a percentage of the 2020 GDP. The Asian Development Bank Policy Database displays the monetary amounts announced
or estimated by the 68 members of the Asian Development Bank, two institutions, and nine other economies (i.e. a total of 79 entries) to fight
the COVID-19 pandemic.
Source: Asian Development Bank COVID-19 Policy Database (accessed on 27 September 2022).
In addition to allocating funds for maintaining essential health services improving resilience, governments
invested in digital health and in access to medicines and supplies (Figure 2.24).
Figure 2.24. Countries reporting investments for long-term health system recovery and/or
resilience for future health pandemics in selected areas, Q4 2021
Number of countries reporting investments

7
6
AUS AUS AUS AUS
5
IDN IDN IDN IDN IDN
4
LAO LAO LAO LAO LAO LAO LAO
3
NZL NZL NZL NZL NZL NZL NZL
2
LKA LKA LKA LKA LKA LKA LKA
1
BGD BGD BGD BGD BGD BGD BGD
0
Government allocated Government allocated Investment made in Invested made in Investment made to Investment made in Investement made to
funds for maintaining funds for health new facility digital health strenghten health access to medicines support infodemic
essential health system recovery infrastructure workforce and supplies management
services during the and/or health service
pandemic resilience and
preparedness
Source: WHO PULSE survey (round 3), 2022.

40 
The rapid development of remote consultations has offset at least partly the reduction of
in-person encounters with GPs and specialists
Examples of regulatory and policy interventions in the Asia-Pacific region include India’s Telemedicine
Practice Guidelines 2020 and interim guidance on use of telemedicine by Indonesia permitting doctors and
dentists to provide telemedicine services through mobile apps and Information and Communication
Technologies (ICT) systems. This latter was further supported by the Indonesian Medical council issuing
a regulation regarding the Clinical Authority and Medical Practice through Telemedicine during COVID-19.
Australia expanded the range of telehealth services subsidised on a fee-for-service basis to enable GPs,
specialists and other providers to maintain care for patients and temporarily doubled the incentive fee
payable for GPs to see certain categories of patients without any upfront cost. Furthermore, there were
two additional temporary incentive payments established to provide further incentive for GPs to see
patients at risk of COVID-19 without any upfront cost. Some telehealth items that were temporarily added
have now been permanently included in the MBS schedule (Australian Government - Department of Health
and Aged Care, 2022[34]). In Australia, telemedicine was also utilised to maintain health care support for
diabetes patients, with 80.8% of patients having a telehealth consultation during the pandemic 1 (Imai et al.,
2022[35]; Olson et al., 2021[36]). Six-monthly HbA1c testing and HbA1c levels had no significant difference
between those patients who had telehealth services and those who had face-to-face consultations. In the
quarter ending September 2020, 13.3% of all Medicare Benefits Schedule services processed,
15.5 million, were telehealth consultations, indicating the importance of such services in maintaining
access to care throughout the pandemic (AIHW, 2021[37]). Remote consultations were also widely used to
provide mental health related services and between 16 March 2020 and 27 September 2020, 2.5 million
Medicare-subsidised mental health related services were delivered via telehealth nationally, which
accounts for more than a third of all mental health related services delivered in that period (AIHW, 2022[38]).
The positive impact of telehealth was particularly pronounced in antenatal care, where a 10% drop in in-
person care between January and September 2020, compared to 2019, was almost entirely offset by an
uptake of 91 000 telehealth services (AIHW, 2021[39]). Australia further implemented e-prescribing from
May 2020 onwards, allowing health care providers to send electronics prescription to individuals via SMS
or email (Australian Digital Health Agency, n.d.[40]). Moreover, pharmacists were allowed to dispense
essential medicines without a prescription from a physician in the event that it was not practicable for a
patient to receive a new prescription to allow continuity of care to a patient that had been previously
prescribed the medicine (Australian Government. Department of Health and Aged Care, 2022[41]).
While in New Zealand telehealth services were used even before the pandemic, after March 2020 some
restrictions were relaxed, which, for instance, enabled the provision of telehealth services also to patients
who had not consulted a provider in-person before (OECD, forthcoming[42]). In New Zealand, remote
technologies were further used to facilitate repeated medication prescriptions (Al-Busaidi IS, 2020[43]).
Japan and Korea also temporarily allowed the use of telehealth services to ensure access and continuity
of care during the pandemic and made the legal and policy changes required to do so (OECD,
forthcoming[42]). In Korea as in many other countries, such legislative or regulatory changes were
introduced for a limited amount of time only to respond to the unprecedented effects of the COVID-19
pandemic, even though some countries are considering a permanent integration of telehealth into their
health care systems (OECD, forthcoming[42]).
While the widespread use of telehealth during the pandemic is remarkable, there is an urgent need for
more evidence about quality and cost-effectiveness of telehealth services in improving outcomes for those
living with chronic diseases, which is still rather limited (Al-Busaidi IS, 2020[43]). At the same time, many
obstacles to care still exist, including equal access to technology and new digital tools, and appropriate
digital health literacy (Hinchman et al., 2020[44]).

 41
An expanded role of community health workers and general practitioners
Community health workers (CHWs) in the South-East Asian countries responded to the COVID-19
pandemic by expanding their routine work in different ways (Bezbaruah et al., 2021[45]). In Bangladesh,
community health volunteers (CHVs) in refugee camps performed multiple roles such as visiting the camps’
residents, providing COVID-19 information on hygiene and symptoms, and looking for suspicious cases
and advising on patient care (Rahman and Yeasmine, 2020[46]). They also provided mental health support
where necessary, while maintaining their routine health care support. India’s most crowded slum’s
COVID-19 response was achieved by relying on CHWs who knew the local area and gained trust by the
community (Singh, 2020[47]; Shaikh, 2020[48]). CHWs in slums provided necessary information on
COVID-19 and delivered essential groceries and medicine, while supporting screening with thermal
scanner and pulse oximeter.
Thailand utilised pre-existing village health volunteers (VHV) to address the nation-wide COVID-19
pandemic. VHVs were given new roles in the primary health care system, supported local epidemiological
surveillance, helped to distribute the necessary medications for patients with chronic disease, and
promoted the COVID-19 prevention scheme.
General practices in Australia and New Zealand, which experienced several disasters between 2009 and
2016, undertook a range of critical roles in providing responsive health care. These included providing
primary health care in alternative health care facilities, adapting existing health facilities for the purposes
of providing disaster health care, and maintaining care continuity for management of chronic diseases. As
such, primary health care is key for effective health emergency management both for absorbing and
recovering from a shock.
Singapore’s Public Health Preparedness Clinics, which are pre-existing community-based facilities,
provided increased access to primary care during the COVID-19 pandemic (Lim and Wong, 2020[49]).
These facilities helped to distribute PPEs, provide patients with necessary care, and thereby helped reduce
local transmission (Sim et al., 2021[50]).
Conclusions
COVID-19 has had a huge impact across the Asia-Pacific region, testing the resilience of economies and
health systems, and placing an immense pressure on health workers operating at the front line. However,
COVID-19 has had an unequal impact in the region between high-, middle- and low-income countries, in
particular by amplifying inequities and inequalities.
In terms of the overall health impact, India and Indonesia were the most affected, based on data on
COVID-19 reported deaths. In contrast, most countries situated in South-East Asia as well as Pacific
Islands countries, have been less adversely affected to date. Variation in population density, the rural-
urban composition, the degree of international visitors, as well as demographic characteristics, among
others, may well explain these observed differences in death rates. Differences in the timing, use and
intensity of public health and social measures, in particular restrictions on movement, the speed and
effectiveness in which they were implemented, and testing and contact tracing infrastructure have also
played a role (International Monetary Fund, 2020[51]).
As of September 2022, the percentage of the population who received a booster shot amounts to
almost 84% in high-income countries, whereas that of lower-income countries is slightly below 20%. This
confirms that there is significant Inequity when it comes to vaccine access between high- and low-income
countries in the Asia-Pacific region.

42 
A WHO rapid situation assessment survey also illustrates that essential services have been severely
disrupted since the COVID-19 pandemic began. This could lead to a substantial number of additional
deaths and years of life lost, in particular in low- and middle-income countries.
While the widespread use of telehealth during the pandemic is remarkable, there is an urgent need for
more evidence about the cost-effectiveness of telehealth in improving outcomes for those living with
chronic diseases.
COVID-19 has had major effects on countries’ economies, social and health systems. It is critical to ensure
that economic pressures do not divert already limited resources away from essential health services in
low- and middle-income countries.
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 43
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imputed COVID-19 cases: Systematic review”, PLoS ONE, Vol. 16/4,
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Australia utilising MBS data: Skin, breast and colorectal cancers, and telehealth services,
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service screening perspectives from the Asia-Pacific region”, Preventive Medicine, Vol. 151, p. 106622,
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future outlook in Australia”, Public Health Research & Practice, Vol. 30/4,
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Fujisawa, R. (2022), “Impact of the COVID-19 pandemic on cancer care in OECD countries”, OECD Health [15]
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Situation-Report-14-20200728-1.pdf.
Ghosal, S. et al. (2020), “Increase in the risk of type 2 diabetes during lockdown for the COVID19 pandemic [30]
in India: A cohort analysis”, Diabetes & Metabolic Syndrome: Clinical Research & Reviews, Vol. 14/5,
pp. 949-952, https://doi.org/10.1016/J.DSX.2020.06.020.
Gurney, J. et al. (2021), “The impact of the COVID-19 pandemic on cancer diagnosis and service access in [26]
New Zealand–a country pursuing COVID-19 elimination”, The Lancet Regional Health - Western Pacific,
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Hinchman, A. et al. (2020), “Global health is local health: A multidisciplinary perspective of COVID-19”, [44]
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Hou, Z. et al. (2021), “The Influence of the COVID-19 Epidemic on Prevention and Vaccination Behaviors [14]
Among Chinese Children and Adolescents: Cross-sectional Online Survey Study”, JMIR public health
and surveillance, Vol. 7/5, https://doi.org/10.2196/26372.
Huh, K. et al. (2021), “Decrease in hospital admissions for respiratory diseases during the COVID-19 [28]
pandemic: a nationwide claims study”, Thorax, Vol. 76/9, pp. 939-941,
https://doi.org/10.1136/THORAXJNL-2020-216526.

44 
IJzerman et al (2020), Is a delayed cancer diagnosis a consequence of COVID-19?, [16]

https://pursuit.unimelb.edu.au/articles/is-a-delayed-cancer-diagnosis-a-consequence-of-covid-19
(accessed on 20 August 2021).
Imai, C. et al. (2022), “Telehealth use in patients with type 2 diabetes in Australian general practice during [35]
the COVID-19 pandemic: a retrospective cohort study”, BJGP Open, p. BJGPO.2021.0200,
https://doi.org/10.3399/BJGPO.2021.0200.
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pandemic : a multispeed recovery in Asia.
Ioannidis, J. (2021), “Infection fatality rate of COVID-19 inferred from seroprevalence data”, Bulletin of the [2]
World Health Organization, Vol. 99, pp. 19-33, https://doi.org/10.2471/BLT.20.265892.
Japan Times (2020), Japan Cancer Society urges people to get screened after testing dived 30% in 2020, [20]
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(accessed on 20 August 2021).
Khader, M., T. Jabeen and R. Namoju (2020), “A cross sectional study reveals severe disruption in [29]
glycemic control in people with diabetes during and after lockdown in India”, Diabetes & Metabolic
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levels in patients with diabetes: Experience from Central India”, Primary Care Diabetes,
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 45
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Note
1
These statistics were compiled using data supplied from practice management systems of approximately
800 general practices, so isn’t comparable with statistics derived from Medicare service data.

46 
3 Health status

 47
Life expectancy at birth and survival rate to age 65

Life expectancy at birth had continued to increase remarkably in Asia-Pacific up until 2019, reflecting sharp
reductions in mortality rates at all ages, particularly amongst infants and children (see indicators “Infant mortality”
and “Under age 5 mortality” in Chapter 3). These gains in longevity can be attributed to several factors, including
rising living standards, better nutrition and improved drinking water and sanitation facilities (see indicator “Water
and sanitation” in Chapter 4). Improved lifestyles, better education and enhanced access to health care also
play an important role (National Institute on Ageing, National Institutes of Health and WHO, 2011 [1]). The large
decline in under age 5 mortality, which reflects important commitment and investment at local, national, and
global levels over several decades, is another major drive of the increase of life expectancy (Dicker et al.,
2018[2]).
Life expectancy at birth across low- and lower-middle-income Asia-Pacific countries reached 70.6 years on
average in 2019, a gain of almost 3 years since 2010, whereas it reached 75.1 years in upper-middle-income
Asia-Pacific countries and territories, a gain of almost 2 years since 2010, similar to the trend observed across
OECD countries gained (Figure 3.1). Nonetheless, a very large regional divide persists as, on average, a
newborn in Hong Kong (China) is expected to live approximately 20 years more than a newborn in Papua New
Guinea. Hong Kong (China), Japan, Macau (China), Singapore, Korea, Australia and New Zealand reported a
life expectancy of more than 80 years in 2019. In contrast, Papua New Guinea, Myanmar, Pakistan, Fiji, Lao
PDR, India and Nepal had a life expectancy at birth of less than 70 years.
During the COVID-19 pandemic, life expectancy has decreased by one year in lower-middle- and low-income
Asia-Pacific countries from 2019 to 2021 (Figure 3.2), while it decreased by 0.4 years un upper-middle-income
countries and slightly increased in high-income countries during the same period. In Indonesia, life expectancy
at birth decreased by four years from 2019 to 2021, whereas it decreased by 2.5 years in India and the
Philippines.
Women have greater percentage of cohort surviving to age 65 (Figure 3.3), regardless of the income level of
the country. On average, 79.2% and 84.5% of a cohort of female newborns would survive to age 65 in low- and
lower-middle-, and upper-middle-income Asia-Pacific countries and territories, respectively, while only 69.3%
and 74.6% of male newborns will survive to age 65 in low- and lower-middle-, and upper-middle-income Asia-
Pacific countries and territories, respectively. In Macau (China), Korea, Hong Kong (China), Japan and
Singapore more than 94% of female newborns will survive to age 65, whereas in Papua New Guinea, Mongolia,
Myanmar, and Fiji, less than 2 out of 3 male newborns will survive to age 65. Many reasons contribute to this
gender difference, such as biological differences resulting in slower ageing of immune systems and the later
onset of cardiovascular diseases such as heart attacks and strokes amongst females (UNESCAP, 2017[3]).
Besides life expectancy, another indicator of the population health status is the healthy life expectancy. Higher
healthy life expectancy is generally associated with higher life expectancy, and therefore it is longer – on average
– for females. On one side, females born in 2019 in Japan, Singapore and Korea are expected to live around
75 years of good health, whereas on the other side, males from the same cohort in Papua New Guinea, Solomon
Islands, Pakistan, Mongolia, Fiji, Myanmar, Lao PDR and Cambodia have a healthy life expectancy of less than
60 years (Figure 3.4).
The difference of healthy life years for females born in 2019 between low- and lower-middle-, and upper-middle-
income countries and territories across Asia-Pacific is of four years, with 62.9 and 67.1 healthy life years,
respectively. This difference is increased to five years when comparing upper-middle-income to high-income
countries and territories, which exhibit an average of 72.3 healthy life years for females. Gender gaps amount
to 2.8; 3.0; and 2.0 healthy life years for low- and lower-middle-, upper-middle-, and high-income countries and
territories across Asia-Pacific, respectively. Men born in 2019 in high-income countries and territories across
Asia-Pacific are expected to have ten more years of healthy life than those born in low- and lower-middle-income
countries and territories, with an average of 70.2 and 60.2 healthy life years, respectively.

48 
Definition and comparability

Life expectancy at a specific age is the number of additional years that a person of that age can expect to
live if current mortality levels observed for higher ages continue for the rest of that person’s life. Thus, life
expectancy at birth is the number of years that today’s newborns would live on average if current age-specific
mortality rates were to continue throughout the lifespan of the newborn cohort.
Age-specific mortality rates are used to construct life tables from which life expectancies are derived. The
methodologies that countries and territories use to calculate life expectancy can vary somewhat, and these
can lead to differences of fractions of a year. Some countries and territories base their life expectancies on
estimates derived from censuses and surveys, and not on accurate registration of deaths.
Survival to age 65 refers to the percentage of a cohort of newborns that would survive to age 65, if subject
to current age-specific mortality rates.
Healthy life expectancy at birth measures the number of years in full health that a newborn can expect.
References
Dicker, D. et al. (2018), “Global, regional, and national age-sex-specific mortality and life expectancy, 1950– [2]
2017: a systematic analysis for the Global Burden of Disease Study 2017”, The Lancet, Vol. 392/10159,
pp. 1684-1735, https://doi.org/10.1016/S0140-6736(18)31891-9.
National Institute on Ageing, National Institutes of Health and WHO (2011), Global Health and Ageing. [1]
UNESCAP (2017), Inequality in Asia and the Pacific in the era of the 2030 agenda for sustainable [3]
development.

 49
Figure 3.1. Life expectancy at birth, 2000 and 2019

2010 2019
Years
100
84.4
85.0
85.3
83.8
82.9
82.1
83.3
90
82.8
81.0
76.9
75.8
76.0
79.0
74.7
74.1
75.1
71.8
73.2
72.8
71.9
70.7
71.7
70.4
70.6
69.7
68.1
67.9
69.6
80
66.8
66.6
65.5
70
60
50
Source: OECD Health Statistics 2022; United Nations World Population Prospects 2022.
StatLink 2 https://stat.link/d3g4lw
Figure 3.2. Changes in life expectancy at birth, 2019 and 2021

Years
90
85 Increase
80 Little change
75
Decrease
70
65
60
Source: OECD Health Statistics 2022; United Nations World Population Prospects 2022.
StatLink 2 https://stat.link/zpt2l1
Figure 3.3. Survival to age 65 (% of cohort), Figure 3.4. Healthy life expectancy at birth by
by sex, 2020 sex, 2019
Females Males Females Males
Papua New Guinea 61 69 Pakistan 57 57

Pakistan 69 73 Papua New Guinea 56 58
Fiji 63 74 Solomon Islands 57 59
Lao PDR 67 75 India 60 60
Myanmar 62 75 Fiji 59 61
India 69 75 Lao PDR 59 62
Cambodia 69 78 Nepal 61 62
Nepal 73 79 Myanmar 59 63
Asia Pacific-LM/L 69 79 Asia Pacific-LM/L 60 63
Philippines 65 80 Cambodia 60 63
Mongolia 60 80 Indonesia 62 64
Indonesia 72 80 Mongolia 57 64
Bangladesh 75 80 Philippines 60 64
Solomon Islands 74 81 Bangladesh 64 64
DPRK 71 83 Brunei Darussalam 65 66
Asia Pacific-UM 75 85 DPRK 63 67
Brunei Darussalam 79 85 Malaysia 65 67
Viet Nam 72 87 Asia Pacific-UM 64 67
Malaysia 77 87 Viet Nam 62 68
Thailand 75 88 Sri Lanka 65 69
China 84 89 China 67 70
Sri Lanka 79 91 Thailand 66 71
OECD 85 92 New Zealand 70 71
New Zealand 89 92 OECD 69 71
Asia Pacific-H 88 93 Australia
Australia 90 94 70 72
Singapore 90 94 Asia Pacific-H 70 72
Japan 90 95 Korea 71 75
Hong Kong (China) 90 95 Singapore 72 75
Korea 88 95 Japan 73 75
Macau (China) 91 96
0 25 50 75 100
Years
0 25 50 75 100
% of cohort
Source: WHO GHO 2022.
Source: The World Bank World Development Indicators Online.
StatLink 2 https://stat.link/codsbx
StatLink 2 https://stat.link/xhgk2s

50 
Neonatal mortality
Neonatal mortality, deaths in children within 28 days of birth, encompasses the effect of socio-economic and
environmental factors on newborns and mothers, and the capacities and responsiveness of national health
systems.
Indicators such as the education of the mother, quality of antenatal and childbirth care, preterm birth and
birthweight, Early Essential Newborn Care (EENC), and feeding practices are important determinants of
neonatal mortality (see section “Family planning” in Chapter 4). Early Essential Newborn Care (EENC) is
evidence-based, cost-effective, and comprises feasible interventions provided during childbirth and in the
postnatal period. The First Embrace is the core of EENC, defined as a life-saving practice that promotes skin-
to-skin contact immediately after birth between mother and child for no less than 90 minutes. Other EENC
interventions include: (1) ensuring the presence of a birth companion; (2) adopting a position of choice;
(3) providing adequate food and fluids; (4) using evidence-based criteria for episiotomy, and other procedures;
(5) eliminating harmful or unnecessary practices such as fundal pressure, forced pushing, and enema;
(6) administering oxytocin within one minute of birth. EENC has been introduced and scaled up across countries
and territories in Asia-Pacific (WHO, 2022[1]).
For instance, in India, three causes accounted for three out of every four neonatal deaths in 2015: prematurity and
low birthweight; neonatal infections; and birth asphyxia and birth trauma. However, even if neonatal infections and
birth asphyxia and birth trauma have steadily decreased since 2000, neonatal mortality due to prematurity and low
birthweight increased, rising from 342 000 deaths in 2000 to around 370 000 in 2015 (Fadel et al., 2017[2]).
Congenital anomalies and other conditions arising during pregnancy are also listed as primary causes of mortality
during the first four weeks of life. Undernutrition continues to be amongst the leading causes of death in both mothers
and newborns [see section “Child malnutrition (including undernutrition and overweight)” in Chapter 4]. In the Asia-
Pacific region, 72% of the deaths in the first year of life occurred during the neonatal period in 2020 (IGME, 2021[3]).
Sustainable Developing Goals set a target of reducing neonatal mortality to 12 deaths or less per 1 000 live
births by 2030. In 2020, the average amongst lower-middle- and low-income countries and territories in Asia-
Pacific was 15.8 deaths per 1 000 live births, almost halving the rate observed in 2000 but still above the SDG
target (Figure 3.5). Upper-middle-income Asia-Pacific countries almost reached the SDG target already in 2000
reporting a rate – on average – of 12.2 deaths per 1 000 live births, which then decreased to 6.2 in 2020. High-
income Asia-Pacific countries and territories reported neonatal mortality rates similar to those of the OECD, with
an average of 2.1 deaths per 1 000 live births in 2020.
In general, high-income countries and territories in Asia-Pacific experienced lower neonatal mortality rates than
lower-middle- and low-income countries and territories in the region. Singapore, Japan, Hong Kong (China),
Macau (China) and Korea reported two deaths or less per 1 000 live births in 2020, whereas neonatal mortality
rates were higher than 20 per 1 000 live births in Myanmar, Lao PDR, Papua New Guinea and India, and higher
than 40 per 1 000 live births in Pakistan.
Between 2000 and 2020, the neonatal mortality rate has fallen in almost all Asia-Pacific countries and territories
(Figure 3.5). The rate in 2020 was one-third of the rate in 2000 in DPRK and Mongolia, while in China the rate
reported in 2020 was one-sixth of the one reported in 2000. Both Brunei Darussalam and Fiji reported an
increase in neonatal mortality rates between 2000 and 2020.
Amongst the main determinants of neonatal mortality rates across countries and territories, we find income
status, geographical location, and mother education. For instance, in Pakistan, neonatal mortality is almost three
times higher in the poorest households compared to richest ones, and 50% higher when mothers have no formal
education rather than secondary or tertiary education. Geographical location is another determinant of
differences reported in neonatal mortality in the region, though relatively less impactful in comparison to
households’ income. For example, neonatal mortality rate in rural areas of Lao PDR and Pakistan was one-third
higher than the rate reported for urban areas, and a quarter higher in the case of Mongolia (Figure 3.6).
Neonatal mortality rates recede through cost-effective and appropriate interventions. These include neonatal
resuscitation training, prevention, and management of neonatal sepsis, reducing mortality from prematurity, and
prioritising the roles of breastfeeding and antenatal corticosteroids (Conroy, Morrissey and Wolman, 2014[4]).
Reductions in neonatal mortality will require not only the aforementioned strategies, but also ensuring that all
segments of the population benefit from these (Gordillo-Tobar, Quinlan-Davidson and Lantei Mills, 2017[5]).

 51

Neonatal mortality rate is defined as the number of children who die during their first 28 days of life, expressed
per 1 000 live births.
Mortality data are estimated using the UN IGME model, except for Hong Kong (China) and Macau (China),
for which data are gathered from local sources.
References
Conroy, N., B. Morrissey and Y. Wolman (2014), “Reducing Neonatal Mortality in Resource-poor Settings: [4]
What works?”, Journal of Neonatal Biology, Vol. 03/03, https://doi.org/10.4172/2167-0897.1000139.
Fadel, S. et al. (2017), “Changes in cause-specific neonatal and 1–59-month child mortality in India from [2]
2000 to 2015: a nationally representative survey”, The Lancet, Vol. 390/10106, pp. 1972-1980,
https://doi.org/10.1016/S0140-6736(17)32162-1.
Gordillo-Tobar, A., M. Quinlan-Davidson and S. Lantei Mills (2017), “Maternal and Child Health: The World [5]
Bank Group’s Response to Sustainable Development Goal 3: Target 3.1 & 3.2”.
IGME, U. (2021), Levels and trends in child mortality, United Nations Inter-agency Group for Child Mortality [3]
Estimation, https://cdn.who.int/media/docs/default-source/mca-documents/rmncah/unicef-2021-child-
mortality-report.pdf.
WHO (2022), Scaling up Early Essential Newborn Care, World Health Organization Western Pacific [1]
Regional Office, https://www.who.int/westernpacific/activities/scaling-up-early-essential-newborn-care.

52 
Figure 3.5. Neonatal mortality rates, 2000 and 2020
2000 2020
Neonatal deaths per 1 000 live births
60
50
40.4
40
30
22.3
21.7
21.5
20.3
17.5
16.9
15.8
20
12.6
13.2
11.7
11.6
10.0
8.9
7.9
7.8
6.2
6.1
4.9
10
4.6
4.0
3.5
2.6
2.6
2.4
2.1
1.5
1.4
1.1
0.8
0.8
0
Source: UN Inter-agency Group for Child Mortality Estimation (IGME) 2021; Hong Kong annual digest of statistics 2021; Macau yearbook of
Statistics, 2021.
StatLink 2 https://stat.link/ncg50i
Figure 3.6. Neonatal mortality rates by socio-economic characteristic, selected countries and
territories, nearest year
No education Secondary or tertiary education Rural Urban
Neonatal deaths per 1 000 live births Neonatal deaths per 1 000 live births
70 70
60 60
50 50
40 40
30 30
20 20
10 10
0 0
Lowest income quintile (poorest) Highest income quintile (richest)

Neonatal deaths per 1 000 live births
70
60
50
40
30
20
10
0
Bangladesh (2019) Lao PDR (2017) Mongolia (2018) Nepal (2019) Pakistan (2017-18)
Source: DHS and MICS surveys, various years.

StatLink 2 https://stat.link/na9wrz

 53
Infant mortality
Infant mortality reflects the effect of social, economic, and environmental factors on infants and mothers, as well
as the effectiveness of national health systems.
Pneumonia, diarrhoea, and malaria continue to be amongst the leading causes of death in infants. Cost-effective
and simple interventions as those comprised in the EENC are key to reduce infant mortality (see section “Neonatal
mortality”). Factors such as the health of the mother, quality of antenatal and childbirth care, preterm birth and
birth weight, immediate newborn care and infant feeding practices are important determinants of infant mortality.
Infant mortality can be reduced through cost-effective and appropriate interventions -akin to the EENC
interventions for newborns. These interventions include proper infant nutrition; provision of supportive health
services such as home visits and health check-ups; immunisation and controlling the influence of environmental
factors such as air pollution; and access to safely managed water and sanitation services. Management and
treatment of neonatal infections, pneumonia, diarrhoea, and malaria is also critical (UNICEF, 2013[1]).
In 2020, amongst lower-middle- and low-income Asia-Pacific countries and territories, the infant mortality rate
was 24.1 deaths per 1 000 live births, less than half the rate observed in 2000 (Figure 3.7). Upper-middle-
income Asia-Pacific countries and territories reported a rate of 10.8 deaths per 1 000 live births, down from 19.1
in 2000. Geographically, infant mortality was lower in eastern Asian countries and territories, and higher in South
and Southeast Asia. Hong Kong (China), Japan, Singapore, Macau (China) and Korea had less than three
deaths per 1 000 live births in 2020, whereas in Pakistan more than five children per 100 live births die before
reaching their first birthday.
Infant mortality rates have fallen dramatically in the Asia-Pacific since 2000, with many countries and territories
experiencing significant declines (Figure 3.7). In China, DPRK, Mongolia and Cambodia, rates have declined in
2020 to one-third or less of the value reported in 2000, whereas rates in Fiji and Brunei Darussalam have
increased in recent years.
Across countries and territories, important inequities persist in infant mortality rates largely related to income
status and mother’s education level (Figure 3.8). In Pakistan, Lao PDR and Nepal infant mortality rates are two
to three times higher in poorest households compared to richest ones. Similarly, in Lao PDR children born to
mothers with no education had a seven-fold higher risk of dying before their first birthday compared to children
whose mothers had achieved secondary or higher education. Geographical location (urban or rural) is another
determinant of infant mortality in the region, though relatively less important in comparison to household income
or mother’s education level – except for the Lao PDR, where infant mortality in rural areas is more than twice as
high as in urban settings (Figure 3.8). Reductions in infant mortality will require not only improving quality of
care, but also ensuring that all segments of the population benefit from better access to care.

The infant mortality rate is defined as the number of children who die before reaching their first birthday each
year, expressed per 1 000 live births.
Some countries and territories base their infant mortality rates on estimates derived from censuses, surveys,
and sample registration systems, and not on accurate and complete registration of births and deaths.
Differences amongst countries and territories in registering practices for premature infants may also add
slightly to international variations in rates. Infant mortality rates are generated by either applying a statistical
model or transforming under age 5 mortality rates based on model life tables.
Mortality data are estimated using the UN IGME model, except for Hong Kong (China) and Macau (China),
for which data are gathered from local sources.
References
UNICEF (2013), Sustainable Development starts with Safe, Healthy and Well-educated Children, [1]
http://www.unicef.org/parmo/files/Post_2015_UNICEF_Key_Messages.pdf.

54 
Figure 3.7. Infant mortality rates, 2000 and 2020 (or nearest year)
2000 2020
Infant deaths per 1 000 live births
90
75
54.2
60
35.3
45
35.2
35.0
27.0
24.3
23.6
24.1
23.0
22.0
20.9
30
19.5
16.7
16.6
13.2
10.8
11.6
9.6
7.4
7.4
15
5.5
5.9
3.9
3.7
3.3
3.1
2.6
2.2
1.8
1.8
1.5
0
Source: UN Inter-agency Group for Child Mortality Estimation (IGME) 2021; Hong Kong annual digest of statistics 2021; Macau yearbook of
Statistics, 2021.
StatLink 2 https://stat.link/sr6okn
Figure 3.8. Infant mortality rates by socio-economic characteristic, selected countries and
territories, nearest year
Lowest education Highest education Rural Urban
Infant deaths per 1 000 live births Infant deaths per 1 000 live births
100 100
90 90
80 80
70 70
60 60
50 50
40 40
30 30
20 20
10 10
0 0

Infant deaths per 1 000 live births
100
90
80
70
60
50
40
30
20
10
0

StatLink 2 https://stat.link/cpqnka

 55
Under age 5 mortality

The under age 5 mortality rate is an indicator of child health as well as the overall development and well-being
of a population. As part of their Sustainable Development Goals, the United Nations has set a target of reducing
under age 5 mortality to at least as low as 25 per 1 000 live births by 2030 (United Nations, 2015[1]).
The main causes of death amongst children under age 5 are those occurring in the newborn period (31.6%),
lower respiratory infections (13.9%), and diarrhoea (9.1%). Communicable and infectious diseases are
continuously some of the leading causes of under age 5 mortality, contribute to about 49% of deaths in children
belonging to this age group (Perin et al., 2022[2]; IGME, 2021[3]). Malnutrition, as the underlying cause of some
of these childhood diseases, is an impediment to the progress towards achieving the SDGs. In view of the
importance of improving nutrition to promote heath and development, in 2012 the World Health Assembly
endorsed a “Comprehensive implementation plan on maternal, infant and young child nutrition”, which specified
a set of six global nutrition targets. The UN General Assembly has also proclaimed the UN Decade of Action on
Nutrition (2016-25). Oral rehydration therapy is a cheap and effective means to offset the debilitating effects of
diarrhoea (WHO/UNICEF, 2006[4]), and countries and territories could also implement relatively inexpensive
public health interventions including immunisation, and provide clean water and sanitation (see indicator “Water
and sanitation” in Chapter 4 and “Childhood vaccination” in Chapter 7).
In 2020, 5 million children died worldwide before their fifth birthday and almost one out of ten of these deaths
(0.4 million) occurred in the Eastern and South-Eastern Asia regions (IGME, 2021[3]). The average under age 5
mortality rate across lower-middle- and low-, and upper-middle-income Asia-Pacific countries and territories was
29.4 and 13.0 deaths per 1 000 live births respectively (Figure 3.8). Hong Kong (China), Singapore, Japan,
Korea and Australia achieved very low rates of four or less deaths per 1 000 live births, below the average
across OECD countries. Mortality rates in Pakistan, Lao PDR, Papua New Guinea, and Myanmar were high at
more than 40 deaths per 1 000 live births Due to its population, India alone accounted for more than 15%
(0.78 million) of total under age five deaths in the world.
Whilst under age five mortality has significantly declined in lower-middle- and low-income Asia-Pacific countries
and territories, progress varies amongst countries and territories. In China, Cambodia and Mongolia, mortality
rate in 2020 was less than one-quarter of the rate reported in 2000 (Figure 3.9). Evidence (WHO, 2015[5])
suggests that reductions in Cambodia are associated with better coverage of effective preventive and curative
interventions such as essential immunisations, malaria prevention and treatment, vitamin A supplementation,
birth spacing, early and exclusive breastfeeding and improvements in socio-economic conditions. In order to
achieve the SDG target, countries and territories need to accelerate their efforts, for example by scaling effective
preventive and curative interventions, targeting the main causes of post-neonatal deaths, namely pneumonia,
diarrhoea, malaria and undernutrition, and reaching the most vulnerable newborn babies and children (UNICEF,
2013[6]). In addition, focused efforts need to be undertaken to improve neonatal survival as more than
three-quarters of under age 5 deaths occur in the neonatal period.
As is the case for infant mortality (see indicator “Infant mortality” in Chapter 3), inequalities in under age five
mortality rates are widely prevalent (Figure 3.10). Across countries and territories, under age five mortality rates
consistently vary based on household income and mother’s education level, and to a certain extent by
geographical location. For example, in Lao PDR under age five mortality was more than five times higher
amongst children whose mother had no education compared to those whose mother had at least completed
secondary education. In Pakistan, Lao PDR and Nepal disparities in under age five mortality according to
household income were also large with children in the poorest 20% of the population around three times more
likely to die before their fifth birthday than those in the richest 20%. Inequalities in mortality rates based on
geographic locations (rural or urban) were considerable in Lao PDR (Figure 3.10). Accelerating reductions in
under age 5 mortality will require identifying these populations and tailoring health interventions to effectively
address their needs.

56 

Under age 5 mortality is defined as the probability of a child born in a given year dying before reaching their
fifth birthday and is expressed per 1 000 live births.
Age-specific mortality rates are used to construct life tables from which under age 5 mortality is derived.
Some countries and territories base their estimates on censuses, surveys, and sample registration systems,
and not on accurate and complete registration of deaths.
Mortality data are estimated using the UN IGME model, except for Hong Kong (China), for which data are
gathered from local sources.
References
IGME, U. (2021), Levels and trends in child mortality, United Nations Inter-agency Group for Child Mortality [3]
Estimation, https://cdn.who.int/media/docs/default-source/mca-documents/rmncah/unicef-2021-child-
mortality-report.pdf.
Perin, J. et al. (2022), “Global, regional, and national causes of under-5 mortality in 2000–19: an updated [2]
systematic analysis with implications for the Sustainable Development Goals”, The Lancet Child &
Adolescent Health, Vol. 6/2, pp. 106-115, https://doi.org/10.1016/s2352-4642(21)00311-4.
UNICEF (2013), Sustainable Development starts with Safe, Healthy and Well-educated Children, [6]
http://www.unicef.org/parmo/files/Post_2015_UNICEF_Key_Messages.pdf.
United Nations (2015), Transforming our world: the 2030 Agenda for Sustainable Development, United [1]
Nations, https://sdgs.un.org/2030agenda.
WHO (2015), Success Factors for Women’s and Children’s Health: Cambodia, World Health Organization, [5]
https://apps.who.int/iris/handle/10665/254481.
WHO/UNICEF (2006), Oral rehydration salts: production of the new ORS, [4]

 57
Figure 3.9. Under age 5 mortality rates, 2000 and 2020 (or nearest year)
2000 2020
Under age 5 deaths per 1 000 live births
125
100
65.2
75
44.1
43.9
43.7
50
32.6
29.4
28.2
29.1
27.4
25.7
26.4
23.0
20.9
19.4
16.5
15.4
13.0
25
11.5
8.7
8.6
7.3
6.9
4.7
4.4
4.2
3.7
3.0
2.5
2.2
2.0
0
Source: UN Inter-agency Group for Child Mortality Estimation (IGME) 2021; Hong Kong annual digest of statistics 2021.
StatLink 2 https://stat.link/gpvsle
Figure 3.10. Under age 5 mortality rates by socio-economic and geographic factor, selected
countries and territories, nearest year

Under age 5 deaths per 1 000 live births Under age 5 deaths per 1 000 live births
100 100
75 75
50 50
25 25
0 0

Under age 5 deaths per 1 000 live births
100
75
50
25
0

StatLink 2 https://stat.link/2rjxzs

58 
Mortality from all causes

The burden from non-communicable diseases amongst adults – the most economically productive age group –
is rapidly increasing in Asia-Pacific. Increasing development in countries and territories is bringing an
“epidemiological transition”, whereby early deaths are replaced by late deaths, and communicable diseases by
non-communicable diseases (Omran AR, 2005[1]). The level of adult mortality, all-cause mortality for the
population and cause of death are important for identifying the country’s public health priorities and assessing
the effectiveness of a country’s health system.
There are wide disparities in mortality in the region. For males in 2019, age-standardised all-cause mortality
ranged from less than 400 per 100 000 population in Japan, Australia, and Singapore, to 1 400 or more per
100 000 in Mongolia and the Solomon Islands (Figure 3.11). Amongst females, age-standardised all-cause
mortality ranged from less than 250 per 100 000 population in Japan, Korea and Singapore, to over 900 per
100 000 population in the Solomon Islands, Papua New Guinea and Pakistan. All-cause mortality was higher
amongst men than women across countries and territories in 2019, while in Viet Nam, Korea, Mongolia and
Japan, rates for men were almost twice as high as those for females. Across lower-middle- and low-income
Asia-Pacific countries and territories, all-cause mortality, on average, was 1 058 per 100 000 population for adult
men and 749 per 100 000 population for adult women. Figures still much higher than the average mortality in
OECD member countries (511 per 100 000 population for men and 317 per 100 000 population for women), and
higher than the average mortality in upper-middle-income Asia-Pacific countries and territories (846 per
100 000 population for men and 565 per 100 000 population for women).
Age-standardised all-cause mortality for the entire population ranged from less than 320 per 100 000 population
in Japan, Korea and Singapore, to over 1 000 in Solomon Islands, Papua New Guinea, Mongolia, Fiji and
Pakistan (Figure 3.12). The average rate in lower-middle- and low-income Asia-Pacific countries and territories
was 877 per 100 000 population, more than twice that of the OECD. Nonetheless, mortality for the entire
population has declined in all reporting Asia-Pacific countries and territories (except for the Philippines) between
2000 and 2019, and the gap with OECD countries has narrowed.
The share of deaths due to non-communicable diseases is increasing in Asia-Pacific. Non-communicable
diseases such as cardiovascular diseases and cancers were the most common causes of death, being
responsible for over 82% and 81% of all deaths, on average, across high- and upper-middle-income Asia-Pacific
countries and territories, respectively (Figure 3.13; see also indicator “Mortality from cardiovascular diseases”
and indicator “Mortality from cancer” in Chapter 3). In OECD countries, the average was at 87% and the share
was also increasing. However, communicable diseases such as respiratory infections, diarrhoeal diseases, and
tuberculosis, along with maternal and perinatal conditions, also remained major causes of death amongst lower-
middle- and low-income countries and territories in Asia-Pacific accounting for 17% of all deaths.

Mortality rates are calculated by dividing annual numbers of deaths by mid-year population estimates. Rates
have been age-standardised to the World Standard Population to remove variations arising from differences
in age structures across countries and territories.
Complete vital registration systems do not exist in many developing countries and territories, and about
one-third of countries and territories in the region do not have recent data. Misclassification of causes of
death is also an issue. A general assessment of the coverage, completeness, and reliability of causes of
death data has been published by WHO (Mathers et al., 2005[2]).
The WHO Global Health Estimates (GHE) project draws on a wide range of data sources to quantify global and
regional effects of diseases, injuries, and risk factors on population health. WHO has also developed life tables
for all member states, based on a systematic review of all available evidence on mortality levels and trends.
The probability of dying between 15 and 60 years of age (adult mortality rate) derive from these life tables.
OECD averages are calculated as simple averages using WHO data for all 38 member countries, to improve
comparability with Asia-Pacific countries and territories by using the same standardisation process.

 59
References
Mathers, C. et al. (2005), “Counting the dead and what they died from: an assessment of the global status [2]
of cause of death data”, Bulletin of the World Health Organization, No. 83(3), World Health
Organization, https://apps.who.int/iris/handle/10665/269355.
Omran AR (2005), “The Epidemiologic Transition: A Theory of the Epidemiology of Population Change”, [1]
The Milbank Quarterly, Vol. 83/4, pp. 731-757.

60 
Figure 3.11. All-cause mortality for all population by sex, age-standardised, per 100 000 population,
2019
Females Males
Age-standardised rates per 100 000 population
1 600 1421 1400
1 400 1264 1259 1253
1 200 1106 1090 1081 10801058
969 969 963 959 887
1 000 1090 991 846
966 777 762 738 722
800 894 686 632
600 783 794 731 749 799 749 740 732 733 511 459
703 423 414 395 382 379
604 565 569 572
400 500 443 416
200 381 317 325
225 303 247 263 205
0
Note: OECD is a simple average calculated with data from WHO 2019 GHE.
Source: WHO 2019 Global Health Estimates.
StatLink 2 https://stat.link/h84b0u
Figure 3.12. All-cause mortality rates for all populations, age-standardised, 2000 and 2019
2000 2019
1 600
1 400
1 200
1245
1 000
1123
1062
1059
800
1025
989
930
893
892
877
844
840
600
810
751
734
699
697
674
630
400
573
556
499
356
404
320
318
317
312
283
200
0
StatLink 2 https://stat.link/umn02p
Figure 3.13. Proportions of age-standardised all-cause mortality rate by causes of deaths, 2019
Communicable, maternal, perinatal and nutritional conditions Noncommunicable diseases Injuries

100%
80%
60%
40%
20%
0%
StatLink 2 https://stat.link/kpovhy

 61
Mortality from cardiovascular disease

Cardiovascular disease (CVD) is the leading cause of death in Asia-Pacific, although highly preventable. CVD
was the cause of an estimated 9.85 million deaths in SEARO and WPRO and accounted for 45% of all NCD
deaths in 2019 in these regions.
CVD covers a range of diseases related to the circulatory system, including ischaemic heart disease (IHD) and
cerebrovascular disease (or stroke). Ischemic heart disease is caused by the accumulation of an atherosclerotic
plaque in the inner wall of a coronary artery, restricting blood flow to the heart. Cerebrovascular diseases refer
to a group of diseases that relate to problems with the blood vessels that supply the brain. Common types of
cerebrovascular disease include ischemic stroke, which develops when the brain’s blood supply is blocked or
interrupted, and haemorrhagic stroke, which occurs when blood leaks from blood vessels onto the subarachnoid
space or the surface of the brain. Together, IHD and stroke comprise 87.8% of all cardiovascular deaths in
WPRO and SEARO countries and territories combined (https://www.who.int/data/gho).
The majority of CVD is caused by risk factors that can be controlled, treated, or modified, such as high blood
pressure, high blood glucose, high blood cholesterol, obesity, lack of physical activity, tobacco use (see indicator
“Tobacco” in Chapter 4) and excessive alcohol consumption.
Age-standardised mortality from cardiovascular disease varied across countries and territories with a notably
high level, exceeding 545 deaths per 100 000 population in Solomon Islands in 2019 (Figure 3.14). This
contrasted with Lao PDR, Indonesia, Australia, Pakistan and Singapore where death rates were below 100 per
100 000 population. The large variation in mortality may be due to differences in the prevalence of risk factors
for CVD and access to high quality acute care (see indicator “In-hospital mortality following acute myocardial
infarction and stroke” in Chapter 7) across countries and territories. The average mortality rate from CVD in
lower-middle- and low-income Asia-Pacific countries and territories was more than twice the one in OECD
member countries (282 versus 122 deaths per 100 000 population). While most Asia-Pacific countries and
territories had decreased mortality from CVD, the rate increased in Solomon Islands, Korea, India, and
New Zealand from 2000 to 2019.
Success of reducing the mortality rates from CVD in OECD countries owes to a decline in smoking rates,
expanded health system’s capacity to control high cholesterol and blood pressure, and greater access to
effective care in the event of an acute episode such as a stroke or heart attack (OECD, 2015[1]). As an example,
in Japan population-based interventions such as salt reduction campaigns and an increased use of
antihypertensive drugs covered by the health insurance system were successful in controlling blood pressure,
resulting in the reduction of CVD mortality (Ikeda et al., 2011[2]).
The types of CVD that are fatal differ across countries and territories in the region. In Nepal, Viet Nam, Japan,
India, Cambodia, Bangladesh, China and Malaysia mortality from cerebrovascular disease was greater than IHD
(Figure 3.15). In all other Asia-Pacific countries and territories, the trend was similar to European and North
American countries and mortality from IHD was greater than for stroke (Ueshima et al., 2008[3]).
As the proportion of older people increases in Asia-Pacific (see indicator “Ageing” in Chapter 3), demand for
health care will increase and the complexity and type of care that CVD patients require will change. Increases
in total cholesterol and blood pressure, along with smoking, overweight/obesity, and high blood glucose (see
indicator “Diabetes” in Chapter 3) highlight the need for management of risk factors to control the CVD epidemic.
In addition to efforts to improve lifestyles, primary care needs to be strengthened and accessible, and quality of
acute care needs to improve through better emergency care and improved professional skills and training
capacity (OECD, 2015[1]).

62 

See indicator “Mortality from all causes” in Chapter 3 for definition, source, and methodology underlying
mortality rates.
References
Ikeda, N. et al. (2011), “What has made the population of Japan healthy?”, The Lancet, Vol. 378, pp. 1094- [2]
1105, https://doi.org/10.1016/S0140.
OECD (2015), Cardiovascular Disease and Diabetes: Policies for Better Health and Quality of Care, OECD [1]
Health Policy Studies, OECD Publishing, Paris, https://doi.org/10.1787/9789264233010-en.
Ueshima, H. et al. (2008), “Cardiovascular disease and risk factors in Asia: A selected review”, Circulation, [3]
Vol. 118/25, pp. 2702-2709, https://doi.org/10.1161/CIRCULATIONAHA.108.790048.

 63
Figure 3.14. Cardiovascular disease, estimated mortality rates, 2000 and 2019
2000 2019
850
750
650
550
450
350
250
150
282
546
537
390
363
362
345
332
331
309
301
289
276
240
239
237
236
228
224
210
203
139
122
106
95
97
73
50
68
64
-50
StatLink 2 https://stat.link/eigpuv
Figure 3.15. Proportions of cardiovascular disease deaths, 2019
Ischaemic heart disease Cerebrovascular disease Hypertensive heart disease Others
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
StatLink 2 https://stat.link/0n9wem

64 
Mortality from cancer

Cancer is the second leading cause of death after CVD in the Asia-Pacific region. Cancer was the cause of an
estimated 5 million deaths (or 24% of total NCD deaths) in Asia-Pacific in 2019.
There are more than 100 different types of cancers, with most named after the organ in which they start. Cancer
occurs when abnormal cells divide without control and are able to invade other tissues. While genetics are a risk
factor, only about 5% to 10% of all cancers are inherited. Modifiable risk factors such as smoking, obesity,
exercise, and excess sun exposure, as well as environmental exposures, explain as much as 90-95% of all
cancer cases (Islami et al., 2017[1]; Wilson et al., 2018[2]; Whiteman and Wilson, 2016[3]). Prevention, early
detection and treatment remain at the forefront in the battle to reduce the burden of cancer, and progress towards
fighting cancer needs to be monitored not only by mortality rates but also by survival estimates, taking account
of early detection of the disease and the effectiveness of early treatment (OECD, 2013[4]).
Myanmar had the highest cancer age-standardised mortality rate with almost 200 deaths per 100 000 population
in 2019 (Figure 3.16). Cancer deaths were less common in Sri Lanka, Fiji, Solomon Islands, Bangladesh, Papua
New Guinea, and Korea, with less than 90 deaths per 100 000 population.
The average rates of death in lower-middle- and low-, as well as in high-income countries and territories in Asia-
Pacific were lower than that of OECD countries -108 and 104, respectively, versus 114 deaths per
100 000 population in 2019-, whereas upper-middle-income countries and territories in the region had
comparatively higher rates at 115 deaths per 100 000 population in 2019. While cancer mortality had decreased
in most Asia-Pacific countries and territories and territories, New Zealand and DPRK reported an increase from
2000 to 2019 of 15.2% and 12.1%, respectively, while Sri Lanka, the Philippines, India, Cambodia and Fiji
reported increases from 2000 to 2019 of less than 5%.
Lung cancer was the leading type of cancer in across Asia-Pacific countries and territories (Figure 3.17),
accounting for 20.7%, 17.9%, and 13.5% of all cancer deaths – on average – in high, upper-middle, and lower-
middle- and low-income countries and territories in 2019, respectively. Liver cancer mortality is also high in
lower-middle- and low-income countries and territories in the region, accounting for 12.2% of all cancer deaths
on average in 2019. Myanmar had the highest cancer mortalities in the region; the large proportion of deaths
was due to liver cancer. Besides Myanmar, liver cancer deaths occurred frequently in Cambodia, Viet Nam and
Thailand. Incidence is expected to fall in coming decades, with increased immunisation for hepatitis B (see
indicator “Childhood vaccination” in Chapter 7).
Other main types of cancer deaths were stomach, colorectal and breast cancer. Mortality from stomach cancer
accounted for 5.8% and 9.1% of all cancer deaths in high-income and upper-middle-income countries and
territories respectively, linked to Helicobacter pylori infection, with a high prevalence in China, Myanmar,
Viet Nam, Indonesia and Nepal. The prevalence of colorectal cancer deaths was amongst the highest in the
region in Brunei Darussalam and Singapore. Breast cancer deaths, the most common cause amongst women,
were responsible for over 20% of all cancer deaths in DPRK, and the prevalence was also high in Papua New
Guinea, Korea and Mongolia.
As with cardiovascular disease, the ageing of the population will lead to many more cases of cancer in coming
decades, taxing underprepared health systems. Since the drugs and technologies for treating patients are
expensive, cancer control planning in the Asia-Pacific region might more effectively target smoking, physical
activity, and overweight/obesity. Early diagnosis is also a key to reducing mortality, so access to cancer
diagnosis and care needs to be promoted through public health interventions or wider health coverage (OECD,
2013[4]).

 65

mortality rates.
References
Islami, F. et al. (2017), “Cancer deaths and cases attributable to lifestyle factors and infections in China, [1]
2013”, Annals of Oncology, Vol. 28/10, pp. 2567-2574, https://doi.org/10.1093/annonc/mdx342.
OECD (2013), Cancer Care: Assuring Quality to Improve Survival, OECD Health Policy Studies, OECD [4]
Publishing, Paris, https://doi.org/10.1787/9789264181052-en.
Whiteman, D. and L. Wilson (2016), The fractions of cancer attributable to modifiable factors: A global [3]
review, Elsevier Ltd, https://doi.org/10.1016/j.canep.2016.06.013.
Wilson, L. et al. (2018), “How many cancer cases and deaths are potentially preventable? Estimates for [2]
Australia in 2013”, International Journal of Cancer, Vol. 142/4, pp. 691-701,
https://doi.org/10.1002/ijc.31088.

66 
Figure 3.16. All cancers, estimated mortality rates, 2000 and 2019
2000 2019
250
200
194
150
156
134
134
131
100
118
117
115
115
115
114
113
108
108
105
104
101
100
96
95
95
91
90
87
87
86
84
80
50
72
0
StatLink 2 https://stat.link/ne1roi
Figure 3.17. Proportions of cancer deaths, 2019
Others Breast Lung Liver Colorectal Stomach
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
StatLink 2 https://stat.link/f4ba5i

 67
Mortality from injuries

Injuries are a leading cause of death and disability for all age groups and took 2.1 million lives in 2019 in WPRO and
SEARO, accounting for 7.8% of all deaths in these regions (https://www.who.int/data/gho/data/themes/mortality-
and-global-health-estimates/ghe-leading-causes-of-death). Injuries can result from traffic collisions, drowning,
poisoning, falls or burns, and violence from assault, self-inflicted or acts or war. The magnitude of the problem varies
considerably across countries and territories by cause, age, sex, and income group. However, injury deaths, both
intentional and unintentional, are largely preventable events.
Age-standardised mortality from injuries was highest in Solomon Islands, Myanmar, and Fiji with greater than
70 deaths per 100 000 populations, while the rate was lowest in Singapore and Indonesia with less than
25 deaths per 100 000 population in 2019 (Figure 3.18). Upper-middle-income Asia-Pacific countries and
territories had almost twice the injury mortality rate than OECD countries (60 versus 32 deaths per
100 000 population).
Injury deaths have declined in all Asia-Pacific countries and territories between 2000 and 2019. A large decrease
in injury deaths observed in Sri Lanka was due to the end of armed conflict in 2009.
Deaths due to road traffic crashes represent 35.1% and 30.1% of all injuries-related deaths in upper-middle-, lower-
middle- and low-income Asia-Pacific countries and territories respectively in 2019. However, this figure should be
considered in the context of a corresponding global increase in the number of registered vehicles, suggesting that
interventions to improve global road safety have mitigated the expected rise in the number of deaths (WHO, 2015a).
With the support of Bloomberg Philanthropies, the WHO, the Global Road Safety Partnership and Johns Hopkins
University have been implementing the Bloomberg Philanthropies Global Road Safety Programme (BP-GRSP) in
ten countries and territories with high burden of fatal road traffic injuries, including China, Cambodia, India and
Viet Nam. Commencing in 2010, this five-year programme focuses on saving lives and preventing injuries by
scaling up enhanced enforcement of major risk factors like motorcycle helmet wearing, speed, alcohol or seatbelts,
pertinent to each country (Peden, 2010[1]). On 11 May 2011, the first ever Decade of Action for Road
Safety 2011-20 was launched with great enthusiasm and optimism across the world. Mandated by the United
Nations General Assembly, the Decade is a historic opportunity for countries and territories to stop and reverse the
trend which – without action – would lead to the loss of around 1.9 million lives on the roads each year by 2020
(http://www.who.int/roadsafety/decade_of_action/en/). This policy message was strengthened by SDG 3.6, which
targets halving the number of global deaths and injuries from road traffic accidents by 2030.
The main causes of injury deaths are different across countries and territories in the region (Figure 3.19). In
Thailand, Mongolia, Viet Nam, and Bangladesh, 44% or more of all injury deaths were due to road traffic crashes,
with Japan having one of the highest mortality rates for road traffic injuries amongst high-income countries and
territories at 40.6% of all injury deaths. In Singapore, Lao PDR and Indonesia, self-inflicted injuries were the leading
cause of injury mortality, accounting for over 50% of all injury deaths. Over 90% of people who had attempted or
committed suicide were diagnosed with psychiatric disorders such as severe depression, bipolar disorder and
schizophrenia (Turecki and Brent, 2016[2]), but mental disorders are still under-treated or ineffectively treated
(Hewlett and Moran, 2014[3]). Interpersonal violence is the main cause of injury deaths for men in the Philippines.

mortality rates.
Injury deaths where the intent is not determined are distributed proportionately to all causes below the group
level for injuries.
Estimates for road injury deaths drew on death registration data, reported road traffic deaths from official road
traffic surveillance systems and revised regression model for countries and territories without usable death
registration data (WHO, 2018[4]).

68 
References
Hewlett, E. and V. Moran (2014), Making Mental Health Count: The Social and Economic Costs of [3]
Neglecting Mental Health Care, OECD Health Policy Studies, OECD Publishing, Paris,
https://doi.org/10.1787/9789264208445-en.
Peden, M. (2010), “Road safety in 10 countries”, Injury Prevention, Vol. 16/6, p. 433, [1]
https://doi.org/10.1136/ip.2010.030155.
Turecki, G. and D. Brent (2016), “Suicide and suicidal behaviour”, The Lancet, Vol. 387/10024, pp. 1227- [2]
1239, https://doi.org/10.1016/s0140-6736(15)00234-2.
WHO (2018), Global status report on road safety 2018, World Health Organization, [4]

 69
Figure 3.18. Injuries, estimated mortality rates, 2000 and 2019
2000 2019
120
100
80
89
82
75
60
67
65
65
65
62
60
60
58
57
55
53
51
40
51
50
46
42
38
37
36
40
32
32
20
30
26
24
17
0
StatLink 2 https://stat.link/lv4s93
Figure 3.19. Proportions of injury deaths, 2019

Others Violence Self-inflicted Drownings Falls Road traffic
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
StatLink 2 https://stat.link/42bkf3

70 
Maternal mortality
Pregnancy and childbearing, whilst offering women opportunities for personal development and fulfilment, also
present inherent risks. Maternal mortality is an important indicator of a woman’s health and status. The
Sustainable Development Goals set a target of reducing the maternal mortality ratio to less than 70 deaths per
100 000 live births by 2030.
295 000 maternal deaths were estimated to have occurred worldwide in 2017, and a woman’s lifetime risk of
maternal death – the probability that a 15-year-old woman will die eventually from a maternal cause – is 0.53,
that is one woman in 190, which is approximately half the rate reported in 2000 (WHO, 2019[1]).
The leading causes of deaths are post-partum haemorrhage (PPH), infections, high blood pressure during
pregnancy, and unsafe abortion. Many of these deaths are preventable and occur in resource-poor settings
(WHO, 2019[1]). Fertility and maternal mortality have strong associations with economic development. Risk of
maternal death can be reduced through family planning, better access to high-quality antenatal, intrapartum,
and postnatal care by skilled health professionals.
Maternal mortality ratio (MMR) averaged around 140 deaths per 100 000 live births in lower-middle- and low-
income Asia-Pacific countries and territories in 2019, almost three times the upper-middle-income and more
than 15 times the high-income Asia-Pacific countries and territories average, respectively (Figure 3.20, left
panel). Estimates for 2019 show a small group of countries and territories – Singapore, Australia, Japan and
New Zealand – with very low ratios (less than 1 per 10 000 live births), whereas Solomon Islands, Nepal and
Papua New Guinea had high MMRs at 200 or more deaths per 100 000 live births. Almost 15% of the world’s
maternal deaths occurred in India and Pakistan alone.
Despite high ratios in certain countries and territories, significant reductions in maternal mortality have been
achieved in Asia-Pacific over the last 19 years (Figure 3.20, right panel). The MMR declined by 44% between
2000 and 2019 across lower-middle- and low-income Asia-Pacific countries and territories. Cambodia, Lao PDR
and Indonesia showed the largest reductions amongst countries and territories reporting ratios higher than the
low- and lower-middle-income countries and territories average in 2019. According to a study (WHO, 2015[2]),
Cambodia’s success is related to reduced fertility through wider use of contraceptives and increased coverage
of antenatal care and skilled birth attendance – achieved through increasing the number of midwives and
facilities providing Emergency Obstetric and Newborn Care. The national scale-up of the Early Essential
Newborn Care (EENC) programme – comprising simple and cost-effective interventions that benefit mothers
and newborns – is a key achievement of Cambodian Government with support from WHO.
Across Asia-Pacific countries and territories, maternal mortality is inversely related to the coverage of skilled
birth attendance (Figure 3.21). Papua New Guinea and Bangladesh reported that less than 60% of live births
are attended by skilled health professionals (see indicator “Pregnancy and birth” in Chapter 5) and present
relatively high MMRs -above 160 deaths per 100 000 live births-.
Higher coverage of antenatal care1 is associated with lower maternal mortality, indicating the effectiveness of
antenatal care across countries (Figure 3.22). Addressing disparities in the unmet need of family planning and
providing essential reproductive health services to underserved populations may also substantially reduce
maternal deaths in the region (UNESCAP, 2017[3]).
To improve quality of care, maternal death surveillance and response (MDSR) has been implemented in
countries and territories. MDSR is a continuous cycle of identification, notification and review of maternal deaths
followed by actions to prevent future death. Global survey of national MDSR system instigated in 2015 provides
baseline data on status of implementation. The implementation status of countries and territories in WPRO
(Cambodia, China, Fiji, Lao PDR, Malaysia, Mongolia and Papua New Guinea) can be found at:
http://www.who.int/maternal_child_adolescent/epidemiology/maternal-death-surveillance/en/.

 71

Maternal mortality is defined as the death of a woman while pregnant or during childbirth or within 42 days of
termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or
aggravated by the pregnancy or its management but not from unintentional or incidental causes (WHO,
2019[1]).
This includes direct deaths from obstetric complications of pregnancy, interventions, omissions, or incorrect
treatment. It also includes indirect deaths due to previously existing diseases, or diseases that developed
during pregnancy, where these were aggravated by the effects of pregnancy.
Maternal mortality is here measured using the maternal mortality ratio (MMR). It is the number of maternal
deaths during a given period per 100 000 live births during the same period.
There are difficulties in identifying maternal deaths precisely. Many countries and territories in the region do
not have accurate or complete vital registration systems, and so the MMR is derived from other sources
including censuses, household surveys, sibling histories, verbal autopsies, and statistical studies. Because
of this, estimates should be treated cautiously.
References
UNESCAP (2017), Inequality in Asia and the Pacific in the era of the 2030 agenda for sustainable [3]
development.
WHO (2019), Trends in maternal mortality 2000 to 2017, World Health Organization, [1]
WHO (2015), Success Factors for Women’s and Children’s Health: Cambodia, World Health Organization, [2]
Note
1
Evidence is based on at least four times, but latest WHO Recommendations are at least eight antenatal visits,
comprising pregnancy monitoring, managing problems such as anaemia, counselling and advice on preventive
care, diet, and delivery by or under the supervision of skilled health personnel.

72 
Figure 3.20. Estimated maternal mortality ratio, 2019 and percentage change since 2000
Estimated maternal mortality ratio Percentage change 2000-2019
327 Solomon Islands -7

217 Nepal -40
205 Papua New Guinea -29
191 Pakistan -40
182 Lao PDR -58
173 Myanmar -17
167 Indonesia -52
161 Bangladesh -45
161 Cambodia -58
140 Asia Pacific-LM/L -44
130 India -59
101 Fiji -27
71 Philippines -20
64 Thailand -37
50 Asia Pacific-UM -42
38 Mongolia -68
36 DPRK -72
28 Sri Lanka -52
24 Brunei Darussalam -17
20 Malaysia -50
15 Viet Nam -48
14 China -77
12 Korea -20
9 Asia Pacific-H -35
9 OECD -32
8 New Zealand -20
5 Japan -44
2 Australia -75
2 Singapore -82
400 300 200 100 0 -100 -75 -50 -25 0
Deaths per 100 000 live births % change over period
Note: OECD average is based on data from OECD Health Statistics 2022.
Source: OECD Health Statistics 2022; Bill and Melinda Gates Foundation.
StatLink 2 https://stat.link/nskejc
Figure 3.21. Skilled birth attendant coverage Figure 3.22. Antenatal care coverage and
and maternal mortality ratio, latest year maternal mortality ratio, latest year available
available
Skilled birth attendant coverage (%) Antenatal care coverage, at least four visits (%)
100
Asia-H KOR BRN
SGP LKA FJI SGP FJI PRK
OECD VNM 90 LKA
100 MNG AUS THA PHL
PRK IND IDN MNG IDN
CHN MYS
BRN 80
THA KHM VNM KHM
SLB 70
PHL CHN NPL
R² = 0.5586 SLB
60 LAO
80 MMR
Asia-LM/L PAK
Asia-UM NPL IND
50
PAK PNG
40
LAO MMR BGD
BGD
60 30
PNG
20 R² = 0.5029
10
40
0 50 100 150 200 250 300 0
Maternal mortality ratio 0 50 100 150 200 250 300
Maternal mortality ratio
Source: OECD Health Statistics 2022; WHO (2021); WHO Sources: WHO GHO 2021.
GHO 2021. StatLink 2 https://stat.link/pq3k6e
StatLink 2 https://stat.link/x3ukr0

 73
Tuberculosis
Tuberculosis (TB) is one of the leading causes of death from an infectious disease in Asia-Pacific. In 2020, there
were 5.8 million incident (new and relapsed) TB cases worldwide – a reduction from 7.1 million reported cases
in 2019 due to the COVID-19 pandemic indirect impact-, an estimated 1.3 million deaths amongst HIV-negative
people globally (WHO, 2021[1]). TB cases and deaths occur disproportionately amongst men, but the burden of
disease amongst women is also high as it remains amongst the top three killers for them in the world. Most
cases of TB are curable if diagnosed early and the appropriate treatment is provided, therefore curtailing onward
transmission of infection.
TB was declared a global health emergency by the WHO in 1993, and the WHO-co-ordinated Stop TB
Partnership set targets of halving TB prevalence and deaths by 2015 compared with a baseline of 1990. The
WHO’s End TB Strategy (post-2015) which followed the Stop TB Strategy aims at ending the global TB epidemic
by 2035, in line with the Sustainable Development Goals (Sharma, 2017[2]). In 2018, the UN General Assembly
High-Level Meeting on the fight against TB endorsed a political declaration to emphasise an importance of
accelerating progress towards End TB targets (UNGA, 2018[3]).
In Asia-Pacific, TB mortality rates were high in Nepal and Papua New Guinea with over 50 deaths of people
without HIV per 100 000 population (Figure 3.23, left panel).
South-East Asia accounted for 43% of the estimated TB cases globally in 2020, more than any other WHO
region. India (26.0% of TB cases globally), China (8.5%), Indonesia (8.4%), the Philippines (6.0%), Pakistan
(5.8%), and Bangladesh (3.6%) were amongst the most affected countries and territories in 2020 – keeping in
mind that these countries and territories also had important reductions in the reporting of cases due to the
COVID-19 pandemic – (WHO, 2021[1]). The case notification rate is particularly high in DPRK and Papua New
Guinea, at more than 300 cases per 100 000 population. An incidence rate higher than 500 cases per
100 000 population was estimated for the Philippines and DPRK, while for Australia and New Zealand less than
10 incident cases per 100 000 population were estimated (Figure 3.23, right panel).
High-quality TB services have expanded, and many cases are treated, reaching the treatment success rate for
new TB cases of more than 85% in many Asia-Pacific countries and territories in 2019 (Figure 3.24).
Nevertheless, Fiji reports a low treatment success rate at 30%. In countries and territories where TB
predominantly affects older people -such as Japan and Hong Kong (China)-, treatment success rate was lower
than 75%.
The Asia-Pacific region is rising to the challenges presented by TB. In a large part of the countries and territories,
case notification rates have declined from 2015 to 2020 (Figure 3.25). However, countries and territories like
Lao PDR, Thailand, Fiji, Indonesia, Bangladesh, Singapore, New Zealand, Australia and Brunei Darussalam are
showing upward trends, with the latter four belonging to the high-income economies group and experiencing low
base case notification rates. The region still faces important challenges in TB control, including providing
services to those in greatest need, especially the poor and vulnerable. HIV-TB co-infection, the emergence of
drug-resistant strains, a sizeable proportion of TB-affected population facing catastrophic costs due to TB,
funding gaps and the need for greater technical expertise all remain threats to progress (WHO, 2016[4]; WHO,
2019[5]). Concerning drug-resistant TB (MDR/RR-TB), the burden is high in China with 7.1% of new cases are
estimated to have MDR/RR-TB. This proportion is also high at 5.1% in Myanmar and Viet Nam, at above 4%.
Treatment of MDR/RR-TB can take up to two years and is far more costly than drug susceptible strains.

74 

Tuberculosis (TB) is a contagious disease, caused by the Mycobacterium tuberculosis bacteria. Tuberculosis
usually attacks the lungs but can also affect other parts of the body. It is spread through the air, when people
who have the disease cough, sneeze, talk or spit. Most infections in humans are latent and without symptoms,
with about one in ten latent infections eventually progressing to active disease. If left untreated, active TB
kills between 20% and 70% of its victims within ten years depending on severity.
The TB incidence rate is the number of new and relapse cases (newly occurring) of the disease estimated to
occur in a year, per 100 000 population. TB mortality does not include TB/HIV as per ICD-10. Case
notification rate is the total of new and relapse cases and cases with unknown previous TB treatment history
notified to the national programmes per 100 000 population.
References
Sharma, D. (2017), “New plan to end tuberculosis in south and southeast Asia”, The Lancet, [2]
Vol. 389/10075, p. 1183, https://doi.org/10.1016/S0140-6736(17)30817-6.
UNGA (2018), UN General Assembly High-Level Meeting on the fight against tuberculosis, [3]
https://www.who.int/news-room/events/un-general-assembly-high-level-meeting-on-ending-tb.
WHO (2021), Global Tuberculosis Report 2021, World Health Organization, [1]
WHO (2019), Global tuberculosis report 2019, https://apps.who.int/iris/handle/10665/329368. [5]
WHO (2016), Ending TB in the South-East Asia Region : regional strategic plan 2016-2020, WHO Regional [4]
Office for South-East Asia, https://apps.who.int/iris/handle/10665/250292.

 75
Figure 3.23. Estimate of the burden of disease caused by tuberculosis, 2020
Mortality rate Incidence rate (estimated) Case notification rate
29 Philippines 234
DPRK 348
52 Papua New Guinea 315
10 Mongolia 118
38 Myanmar 190
36 Indonesia 140
27 Asia Pacific-LM/L 153
22 Cambodia 174
21 Pakistan 124
56 Nepal 94
27 Bangladesh 140
37 India 118
11 Viet Nam 103
17 Thailand 123
25 Lao PDR 110
6 Malaysia 71
8 Asia Pacific-UM 71
5 Brunei Darussalam 72
7 Fiji 48
8 Solomon Islands 47
4 Sri Lanka 33
5 Macau (China) 56
2 China 43
2 Hong Kong (China) 49
4 Korea 46
1 Singapore 40
3 Asia Pacific-H 36
3 Japan 10
1 OECD 8
0 New Zealand 7
0 Australia 6
60 50 40 30 20 10 0 0 250 500 750 1000
Per 100 000 population Per 100 000 population
H represents lower and upper bounds.
Source: Global Tuberculosis Report 2021.
StatLink 2 https://stat.link/8u4k9o
Figure 3.24. Tuberculosis treatment success Figure 3.25. Change in tuberculosis case
for new and relapse TB cases, 2019 notification rate, 2015-20
Cambodia Lao PDR
Solomon Islands Brunei Darussalam
Bangladesh Thailand
China Australia
Pakistan Fiji
Viet Nam Indonesia
Lao PDR Asia Pacific-UM
Nepal Bangladesh
Myanmar New Zealand
Mongolia Singapore
Asia Pacific-LM/L Papua New Guinea
Philippines Viet Nam
Australia India
Thailand Macau (China)
Sri Lanka Asia Pacific-H
Macau (China) Malaysia
DPRK Asia Pacific-LM/L
India Philippines
New Zealand Hong Kong (China)
Indonesia DPRK
Korea Cambodia
Malaysia Pakistan
Singapore Mongolia
Asia Pacific-H Nepal
Brunei Darussalam China
Hong Kong (China) Sri Lanka
Asia Pacific-UM Myanmar
Papua New Guinea Japan
OECD Solomon Islands
Japan OECD
Fiji Korea
0 25 50 75 100 -50 -25 0 25 50 75
Treatment success (%) Change (%)
Source: Global Tuberculosis Report 2021. Source: Global Tuberculosis Report 2021.
StatLink 2 https://stat.link/pi4d0t StatLink 2 https://stat.link/8x20sr

76 
HIV/AIDS
Although the first cases of AIDS in Asia were reported mid-1980s, the more extensive spread of HIV began late
compared with the rest of the world, occurring in Cambodia, India, Myanmar and Thailand in the early 1990s
(Ruxrungtham, Brown and Phanuphak, 2004[1]; UNAIDS, 2013[2]). Asia is second only to sub-Saharan Africa as
the region with the greatest number of people with HIV. The UN set an SDG target to end the epidemic of AIDS
as a public threat by 2030.
In Asia-Pacific, the prevalence of HIV infection varied importantly, ranging from less than 0.1% of adults aged 15
to 49 in Bangladesh, Mongolia, New Zealand and Sri Lanka to 1% of adults aged 15 to 49 in Thailand in 2020
(Figure 3.26, left panel). Although HIV prevalence is low, the absolute number of people living with HIV was high
at more than 2.2 million in reporting countries and territories in 2021, because of Asia-Pacific’s large population
(Figure 3.26, right panel).
Expanded access to antiretroviral therapy (ART) has increased the survival rates of people living with HIV, but
about half of the people eligible for HIV treatment do not receive it worldwide (UNAIDS, 2018[3]). The estimated
ART coverage amongst persons living with HIV in 2021 was less than half in Pakistan, Indonesia, Bangladesh,
Mongolia, the Philippines and Fiji, whereas more than three-quarters had access to ART in Thailand, Cambodia
and New Zealand (Figure 3.27).
Over past years, many countries in Asia-Pacific responded to HIV/AIDS successfully and incidence rates have
declined. Bangladesh, Singapore and Sri Lanka had less than 0.01 new case of HIV infection per
1 000 uninfected population in 2021. However, almost 0.4 new cases of HIV infections per 1 000 uninfected
population were reported in Papua New Guinea in 2021 (Figure 3.28). Moreover, the Philippines more than
tripled the new cases of HIV infection between 2000 and 2018 (UNAIDS, 2019[4]).
Advances in HIV prevention and treatment could end AIDS as a public health threat in the region. Recent
evidence has emerged showing that antiretroviral drugs not only improve the health and prolong the lives of
people living with HIV, but also prevents HIV transmission. The rapid scale up antiretroviral therapy in
recent years in Asia and the Pacific provides unprecedented opportunity to successfully implement antiretroviral-
based interventions for prevention. The benefits of ART can be fully realised only if people living with HIV are
diagnosed and successfully linked to care. This will require targeted efforts and removing barriers especially
amongst key affected populations, as most of Asia’s epidemics occur amongst sex workers and their clients,
men who have sex with men, transgender persons, and injection drug users.

Human immunodeficiency virus (HIV) is a retrovirus that destroys or impairs the cells of the immune system.
As HIV infection progresses, a person becomes more susceptible to infections. The most advanced stage of
HIV infection is acquired immunodeficiency syndrome (AIDS). It can take 10-15 years for an HIV-infected
person to develop AIDS, although antiretroviral drugs can slow down the process.
The HIV prevalence amongst adults aged 15 to 49 is the number of persons aged 15 to 49 estimated to be
living with HIV divided by the total number of persons aged 15 to 49 at a particular time.
References
Ruxrungtham, K., T. Brown and P. Phanuphak (2004), “HIV/AIDS in Asia”, The Lancet, Vol. 364/9428, [1]
pp. 69-82, https://doi.org/10.1016/S0140-6736(04)16593-8.
UNAIDS (2019), Communities at the centre. Global AIDS update 2019. [4]
UNAIDS (2018), Miles to go. Global AIDS update 2018. [3]
UNAIDS (2013), HIV in Asia and the Pacific. [2]

 77
Figure 3.26. Estimated number of people living with HIV, 2021

Prevalence (adults aged 15 to 49) Living with HIV (all ages)
Thailand
1.0
Papua New Guinea
0.9
Myanmar
0.8
0.6 Cambodia
0.3 Indonesia
0.3 Lao PDR
0.3 Malaysia
0.3 Viet Nam
0.2 Fiji
0.2 India
0.2 Pakistan
0.2 Philippines
0.2 Singapore
0.1 Australia
0.1 Nepal
0.0 Bangladesh
0.0 Mongolia
0.0 New Zealand
0.0 Sri Lanka
1.5 1 0.5 0 0 100 200 300 400 500 600 700
% Thousands
StatLink 2 https://stat.link/48i7ud
Figure 3.27. Estimated antiretroviral therapy Figure 3.28. New HIV infections per 1 000
coverage amongst people living with HIV, uninfected population, 2021
2021
Thailand 86 Papua New Guinea

Cambodia 84 Myanmar
New Zealand 81 Philippines
Viet Nam 72 Fiji
Nepal 72 Malaysia
Myanmar 70 Indonesia
Sri Lanka 66 Thailand
Papua New Guinea 65 Cambodia
India 65 Viet Nam
Malaysia 55 India
Fiji 45 New Zealand
Philippines 41 Australia
Mongolia 38 Mongolia
Bangladesh 31 Sri Lanka
Indonesia 28 Singapore
Pakistan 14 Bangladesh
0 20 40 60 80 100 0 0.2 0.4 0.6
% Per 1 000 uninfected population
H represents lower and upper bounds. H represents lower and upper bounds.
Source: WHO GHO 2022. Source: WHO GHO 2022.
StatLink 2 https://stat.link/cj10ig StatLink 2 https://stat.link/u031z5

78 
Malaria
Malaria is a tropical disease caused by a parasite transmitted by the bites of infected female Anopheles
mosquitoes. After a period spent in the liver, malaria parasites multiply within red blood cells, causing symptoms
such as fever, headache, and vomiting. Malaria is preventable and curable and recently WHO recommended a
ground-breaking malaria vaccine for children at risk (WHO, 2021[1]). Still, if left untreated, malaria can become
life-threatening by disrupting the blood supply to vital organs.
As part of the SDG targets, the UN set a goal to end the epidemic of malaria by 2030. In 2021, WHO certified
China malaria free, a significant accomplishment for China and a major milestone for malaria elimination in the
Western Pacific region. Malaysia reported zero malaria cases and is part of the WHO E-2025 Initiative to
eliminate malaria by 2025, together with Korea and Vanuatu. Meanwhile, DPRK and Thailand were selected to
participate in the E-2025 initiative towards the elimination of malaria by 2025 (WHO, 2021[2]).
About 2.31 billion people are at high risk in Asia-Pacific. Malaria-endemic countries and territories in the region
are Papua New Guinea, Solomon Islands, Pakistan, India, Nepal, the Philippines, Indonesia, Myanmar, Lao
PDR, Cambodia, Thailand, DPRK, China, Viet Nam, Bangladesh, Korea and Malaysia. Malaria transmission is
intense in some areas of Papua New Guinea and the Solomon Islands; it is also intense in focal areas in the
Greater Mekong Sub-region, including forested areas of Cambodia, Lao PDR and Viet Nam, where malaria
disproportionately affects ethnic minorities and migrant workers. Malaria is also restricted in its distribution in
Malaysia and the Philippines. Mobile and indigenous populations as well as infants, young children and pregnant
women are especially vulnerable.
In 2020, South-East Asia accounted for 2% (5 million) of the estimated 241 million malaria cases globally.
Presumed and confirmed cases were concentrated in Papua New Guinea, Myanmar and Pakistan (Figure 3.29,
left panel). Death were estimated to be 9 000 in 2020, with the highest mortality rates in Papua New Guinea and
the Solomon Islands (Figure 3.29, right panel) (WHO, 2021[2]).
For a balanced understanding, changes in the number of malaria cases should be viewed in parallel with
changes in malaria incidence. The number of estimated cases per 1 000 population at risk showed a decline in
all reporting Asia-Pacific countries and territories from 2010 to 2020, except for Papua New Guinea
(Figure 3.30). After nearly four years of maintaining zero indigenous cases, and after intensive external
evaluations including field assessments, Sri Lanka was certified by WHO as malaria-free in September 2016.
The key interventions quoted for the successful reduction of malaria burden in Myanmar were placement of
village health volunteers strategically at rural, remote, hard to reach and conflict areas, good coverage of
insecticide-treated bed nets amongst at-risk population and improved access to artemisinin-based combination
treatment (Mu et al., 2016[3]; Linn et al., 2018[4]).
The number of malaria cases not treated increased to around three out of ten in Papua New Guinea and the
Philippines, whereas it decreased significantly to less than one in six in Nepal and Bangladesh from 2000 to
2020 (Figure 3.31). During the same period, the number of malaria cases not treated doubled to one in five in
Myanmar, while they decreased by two-thirds in Cambodia and went down to almost zero in Viet Nam.

Underreporting of malaria cases and deaths remain a major challenge in countries and territories with
inadequate and limited access to health services and weak surveillance systems. The number of deaths was
estimated by adjusting the number of reported malaria cases for completeness of reporting, the likelihood
that cases are parasite positive, and the extent of health service use.
Population at risk is defined as population living in areas where malaria transmission occurs.
For China, Korea, Sri Lanka, Malaysia, DPRK, and Thailand, it is assumed that all cases are identified and
treated. For the other countries and territories, the cases reported by the national malaria control programme
are adjusted for diagnosis and reporting completeness, and for care seeking behaviour to estimate the
proportion of malaria cases not treated.

 79
References
Linn, N. et al. (2018), “Are village health volunteers as good as basic health staffs in providing malaria [4]
care? A country wide analysis from Myanmar, 2015”, Malaria Journal, Vol. 17/1,
https://doi.org/10.1186/s12936-018-2384-4.
Mu, T. et al. (2016), “Malaria incidence in Myanmar 2005-2014: Steady but fragile progress towards [3]
elimination”, Malaria Journal, Vol. 15/1, https://doi.org/10.1186/s12936-016-1567-0.
WHO (2021), WHO recommends groundbreaking malaria vaccine for children at risk, [1]
https://www.who.int/news/item/06-10-2021-who-recommends-groundbreaking-malaria-vaccine-for-
children-at-risk.
WHO (2021), World Malaria Report 2021, https://www.who.int/teams/global-malaria- [2]

programme/reports/world-malaria-report-2021.

80 
Figure 3.29. Confirmed malaria cases and estimated mortality rates, 2020
Confirmed cases Estimated mortality rate
Papua New Guinea
0.75 0.60 Myanmar 33.11
0.24
0.37 Pakistan 0.21
0.25 Indonesia 0.53
0.19 India 0.57
0.08 Solomon Islands
18.24
0.02 Cambodia 0.36
0.01 Bangladesh 0.09
0.01 Philippines 0.15
0.00 China 0.00
0.00 DPRK 0.00
0.00 Korea 0.00
0.00 Lao PDR 0.18
0.00 Malaysia 0.00
0.00 Nepal 0.00
0.00 Sri Lanka 0.00
0.00 Thailand 0.02
0.00 Viet Nam 0.00
0.8 0.6 0.4 0.2 0 0 20 40 60 80
Million cases Per 100 000 population at risk

Source: World Malaria Report 2021.
StatLink 2 https://stat.link/mthsaw
Figure 3.30. Changes in malaria incidence Figure 3.31. Change in the proportion of
rate, 2010-20 malaria cases not treated, 2000-20
2010 2020 PNG

PHL
Solomon Islands 175
168 LAO
Papua New Guinea 148
164
Cambodia 35 MMR
6
NPL
India 3 18
BGD
Indonesia 39
Myanmar 67 SLB
3 increase
KHM
Pakistan 38 decrease
PAK
Lao PDR 2 13
IND no change
Philippines 11
04
Bangladesh IDN
VNM
Thailand 03
DPRK 01 0 5 10 15 20 25 30 35
%
Korea 00
Nepal 4 Source: World Malaria Report 2021.
0
Viet Nam 0
0 StatLink 2 https://stat.link/abzksd
0 50 100 150 200
Per 1 000 population at risk
Source: World Malaria Report 2021.

StatLink 2 https://stat.link/9z7ue3

 81
Diabetes
Diabetes is a chronic metabolic disease, characterised by high levels of glucose in the blood. It occurs either
because the pancreas stops producing the hormone insulin (type 1 diabetes, insulin-dependent diabetes,
genetic predisposition), which regulates blood sugar, or through a reduced ability to produce insulin (type 2
diabetes, non-insulin dependent, lifestyle related), or through reduced ability to respond to insulin (i.e. insulin
resistance). People with diabetes are at a greater risk of developing cardiovascular diseases such as heart
attack and stroke. They also have elevated risks for vision loss, foot and leg amputation due to damage to nerves
and blood vessels, and renal failure requiring dialysis or transplantation.
Diabetes is one of the most common non-communicable diseases globally, affecting 422 million people in 2014,
a prevalence of 9% and 7.9% amongst the male and female adult population (18 years or older) respectively
(NCD Risk Factor Collaboration, 2016[1]). In Asia-Pacific, about 227 million people live with type 2 diabetes and
about half of them are undiagnosed and unaware of developing long-term complications. In 2012, diabetes
caused 1.5 million deaths worldwide and an additional 2.2 million deaths were related to higher-than-optimal
blood glucose (WHO, 2016[2]).
Type 2 diabetes comprises 90% of people with diabetes around the world, and until recently, this type of diabetes
was seen only in adults, but it is now also occurring in children. For many people, the onset of type 2 diabetes
can be prevented or delayed through regular physical exercise and maintaining a healthy weight (see indicators
on “Child malnutrition (including undernutrition and overweight)” in Chapter 4) and a healthy diet. The cause of
type 1 diabetes is not fully understood yet – but we know there is a genetic predisposition and environmental
factors play a role as well.
Amongst the 27 Asia-Pacific countries and territories in this report, the prevalence of diabetes for women ranged
from 5% in Australia to 18.9% in Fiji of the adult population (Figure 3.32, right panel), while the prevalence for
males ranged from 5.5% in Viet Nam to 15.9% in Fiji (Figure 3.32, left panel). In all countries and territories in
this report (except Singapore), the prevalence of diabetes amongst males increased from 2000-14, whereas the
prevalence of diabetes amongst women increases in all countries and territories but Japan, Korea,
Brunei Darussalam, Hong Kong China and Singapore.
Amongst lower-middle- and low-income Asia-Pacific countries and territories, deaths attributable to high blood
glucose increased by 14% between 2000 and 2019 (Figure 3.33). More than 260 deaths per 100 000 population
were caused by high blood glucose in adults in Fiji in 2019. This mortality rate increased by 58% in Nepal
between 2000 and 2019 and increased by more than 40% in Pakistan and Sri Lanka.

Country data used in Figure 3.32 were downloaded from the NCD Risk Factor Collaboration website at:
http://ncdrisc.org/.
mortality rates.
References
NCD Risk Factor Collaboration (2016), “Worldwide trends in diabetes since 1980: a pooled analysis of 751 [1]
population-based studies with 4·4 million participants”, The Lancet, Vol. 387/10027, pp. 1513-1530,
https://doi.org/10.1016/s0140-6736(16)00618-8.
WHO (2016), Global report on diabetes, World Health Organization, [2]

82 
Figure 3.32. Diabetes prevalence amongst adults, 2010 and 2014

Prevalence male 2014 Prevalence male 2000 Prevalence female 2014 Prevalence female 2000
Fiji
Papua New Guinea
Solomon Islands
Pakistan
Mongolia
Nepal
Malaysia
Asia Pacific-UM
Bangladesh
China
Singapore
Korea
Hong Kong (China)
Asia Pacific-LM/L
Brunei Darussalam
India
Asia Pacific-H
Japan
Thailand
New Zealand
Lao PDR
OECD
Indonesia
Cambodia
Philippines
Sri Lanka
Myanmar
Australia
DPRK
Viet Nam
30 20 10 0 0 10 20 30
% %
H represents 95% uncertainty intervals.

Source: NCD Risk Factor Collaboration.
StatLink 2 https://stat.link/12u79b
Figure 3.33. Deaths attributable to high blood glucose for adults, estimated mortality rates, 2000
and 2019
2000 2019
300
261
250
200
150 137
107
100 76 75
59 54 51 51 47 40 39
50 36 34 31 27 20 20 19 15 15 13 13 9 9 8 6 2 2
0

StatLink 2 https://stat.link/c5gi9x

 83
Ageing
Population ageing is characterised by a rise in the share of the older people resulting from longer life expectancy
(see indicator “Life expectancy at birth and survival rate to age 65” in Chapter 3) and declining fertility rates. This
has been mainly due to better access to reproductive health care, primarily a wider use of contraceptives (see
indicator “Family planning” in Chapter 4). Population ageing reflects the success of health and development
policies over the last few decades.
The share of the population aged 65 years and over is expected to double in lower-middle- and low-income
Asia-Pacific countries and territories in the next decades to reach 13.4% in 2050. This is still lower than the high-
income and upper-middle-income countries and territories average in 2050 of 31.1% and 22.3%, respectively
(Figure 3.34). The share of older people will be particularly large in Korea and Hong Kong (China) where around
40% of the population will be aged 65 and over in 2050.
Globally, the speed of ageing in the region will be unprecedented. In 2050, seven Asia-Pacific countries and
territories will be qualified as “ageing society” (as compared to 14 countries and territories in 2021), eight as
“aged society” (four countries and territories in 2021) and 11 as “super-aged society” (only one country in 2020,
that is Japan). Only Papua New Guinea is expected to show a share of population over age 65 lower that 7%,
while 14 countries and territories fulfilled this criterion in 2020. The speed of ageing is particularly fast in Papua
New Guinea and Mongolia, where the share of the population over 65 is expected to increase by more than five
and four times, respectively, between 2021 and 2050. Many low- and middle-income countries and territories
are faced with much shorter timeframes to prepare for the challenges posed by the ageing of their populations.
The growth in the share of the population aged 80 years and over will be even more dramatic (Figure 3.34). On
average across lower-middle- and low-income Asia-Pacific countries and territories, the share of the population
aged 80 years and over is expected to almost triple between 2021 and 2050, to reach 2.9% of the population.
This proportion is expected to triple and quadruple in high-income and upper-middle-income countries and
territories to reach 12.1% and 7.2% during the same period, respectively. The proportion of the population
aged 80 years and over is expected to grow by six times in Singapore and Brunei Darussalam over the next
decades.
The pressure of population ageing will depend on the health status of people as they become older, highlighting
that the health and well-being of older people are strongly related to circumstances across their life course. As
the number of older people increases, there is likely to be a greater demand for health care that meets the need
of older people in the Asia-Pacific region in coming decades. All countries and territories in the region will
urgently need to address drastic changes in demographic structures and subsequent changes in health care
needs, especially the shifting disease burden to NDCs. Health promotion and disease prevention activities will
increasingly need to address cognitive and functional decline, including frailty and falls. The health and well-
being of older adults are determined by a complex interplay of factors that accumulate across a person’s lifetime
including political, social, economic, and environmental conditions that are largely outside the health sector.
Therefore, health systems will need to be reoriented to become more responsive to older people’s changing
needs, including by investing in integrated and person-centred service delivery, supported by health financing
arrangements and a health workforce with the right skills and ways of working, and integrated health and non-
health services (e.g. welfare, social, education). The development of long-term care systems as seen in
OECD countries may also be worth noting. Increasingly, there is a need to foster innovative home- and
community-based long-term care pathways tailored to older people’s specific and diverse needs.
Over the next few decades, the increase in the population aged 65 years or more will outpace the increase in
the economically active population aged 15-64 across countries and territories in Asia-Pacific (Figure 3.35). In
2050, the ratio of people aged 15-64 to people aged over 65 years will be two-fifths of the 2021 value in upper-
middle-income Asia-Pacific countries and territories (2.6 in 2050 vs 6.5 in 2021), whereas it will be slightly less
than half the 2021 value in high (3.6 vs 7.9) and lower-middle- and low-income (5.1 vs 10.6) Asia-Pacific
countries and territories. In Macau (China), Japan, India, Singapore, Thailand and China, there will be two or
less persons aged 15-64 for each person aged over 65 years in 2050. This underscores the importance of the
society reform to encourage social participation of older people. Older adults contribute to society in a variety of
ways including through paid and unpaid work, caregiver for family members, passing down knowledge and
traditions to the younger generations.

84 
These dramatic demographic changes will affect the financing of not only health systems but also social
protection systems, and the economy. Moreover, older age often exacerbates pre-existing inequities based on
income, education, gender, and urban/rural residence, highlighting the importance of equity-focused policy
making in future (OECD, 2017[1]). Population ageing does not only call for equity-focused, gender-responsive
and human rights-based action within the health sector but also require collaboration across sectors to address
the underlying determinants of health of older people, including housing, transport, and the built environment.

Population projections are based on the most recent “medium-variant” projections from the United Nations
(United Nations, 2022[2]).
In this report, we qualify a country as “ageing society” if the share of people aged 65 years or more is between
7% and 14% of the total population, as “aged society” if this share is between 15% and 20% and as “super-
aged society” if this share is 21% or higher.
References
OECD (2017), Preventing Ageing Unequally, https://dx.doi.org/10.1787/9789264279087-en. [1]
United Nations (2022), World Population Prospects 2022: Methodology of the United Nations population [2]
estimates and projections, United Nations,
https://population.un.org/wpp/Publications/Files/WPP2022_Methodology.pdf.

 85
Figure 3.34. Share of the population aged over 65 and 80 years, 2021 and 2050
2021 2050
65+. 80+ 65+ 80+
3.1 0.3 Papua New Guinea 0.9 7.3

3.5 0.5 Solomon Islands 0.9 6.4
4.2 0.5 Pakistan 0.9 6.4
4.4 0.6 Lao PDR 1.5 10.1
5.7 0.7 Mongolia 3.0 13.4
5.5 0.7 Philippines 1.9 10.8
5.3 0.7 Cambodia 2.9 12.9
4.4 0.7 Fiji 1.5 10.2
5.8 0.7 Brunei Darussalam 4.5 20.8
6.0 0.8 Bangladesh 3.3 15.4
6.6 0.8 Nepal 1.9 10.7
5.8 0.9 Asia Pacific-LM/L 2.9 13.4
6.3 0.9 Myanmar 2.2 13.6
6.8 1.0 Indonesia 2.8 15.0
6.8 1.1 India 3.3 15.0
7.3 1.1 Malaysia 4.1 17.4
8.8 1.6 Viet Nam 5.0 20.0
11.2 1.7 Asia Pacific-UM 7.2 22.3
10.2 1.8 Sri Lanka 6.1 21.5
14.1 2.3 DPRK 6.5 21.8
11.4 2.3 Macau (China) 11.0 28.2
13.1 2.3 China 10.3 30.1
12.3 2.6 Singapore 13.5 34.2
14.5 3.3 Thailand 12.9 31.6
15.9 3.8 New Zealand 9.5 24.3
16.7 4.0 Asia Pacific-H 12.1 31.1
16.3 4.2 Australia 8.7 23.8
16.6 4.2 Korea 15.9 39.4
18.1 4.8 OECD 10.2 27.7
19.6 5.4 Hong Kong (China) 17.7 40.6
29.8 10.2 Japan 15.6 37.5
50 % 40 30 20 10 0 0 10 20 30 40 % 50
Source: UN World Population Prospects, 2022.

StatLink 2 https://stat.link/6o7nri
Figure 3.35. Ratio of people aged 15-64 to people aged over 65 years, 2021 and 2050
2021 2050
Ratio
25 20.1
20
16.5
14.8
14.3
13.9
12.4
15
12.0
11.8
11.6
11.4
10.7
10.6
10.3
10.0
10.3
9.9
9.7
9.6
9.1
10
7.9
7.8
6.8
6.6
6.5
6.5
6.1
5.9
5.9
6.1
5.3
5.2
5.1
5.1
4.9
4.8
4.8
4.5
4.4
4.3
4.3
4.1
3.9
3.8
3.7
3.6
3.5
3.1
5
2.9
3.1
2.8
2.6
2.6
2.5
2.2
2.1
2.0
1.6
1.9
1.8
1.4
1.3
1.3
Source: UN World Population Prospects, 2022.

StatLink 2 https://stat.link/52pomw

86 
4 Determinants of health

 87
Family planning
The UN Sustainable Development Goals set a target of ensuring universal access to reproductive health care
services by 2030, including for family planning, information and education, and the integration of reproductive
health into national strategies and programmes. Providing family planning services is one of the most cost-
effective public health interventions, contributing to significant reductions in maternal mortality and morbidity as
well as overall socio-economic development (UNFPA, 2019[1]).
Reproductive health requires having access to effective methods of contraception and appropriate health care
through pregnancy and childbirth, to allow women and their partners to make decisions on fertility and provide
parents with the best chance of having a healthy baby. Women who have access to contraception can protect
themselves from unwanted pregnancy. Spacing births can also have positive benefits on both the reproductive
health of the mother and the overall health and well-being of the child.
Modern contraceptive methods are more effective than traditional ones (WHO/Johns Hopkins Bloomberg School
of Public Health, 2018[2]). The prevalence of modern methods use varies across countries and territories in Asia-
Pacific. It was high on average across high-income and upper-middle-income countries and territories (59.2%
and 62.0%, respectively). In a few of these countries and territories including China (80.5%), New Zealand
(74.7%), Thailand (71.3%), and DPRK (68.8%), at least two-thirds of married or in-union women of reproductive
age reported using modern contraceptive methods (Figure 4.1).The average prevalence was low in lower-
middle- and low-income countries and territories (47%). In the Solomon Islands, Pakistan, and
Papua New Guinea, less than one out of three married or in-union women reported using any modern method.
Based on population sizes, fertility rates, social welfare policies and regulations and service availability,
differences in demand for family planning satisfied with modern methods exist in all reporting Asia-Pacific
countries. In Nepal, demand satisfied is 34 percentage points higher amongst women with lowest education
than amongst women with highest education, with a similar pattern observed in other reporting countries.
(Figure 4.2). In Mongolia, demand satisfied is 13 percentage points higher amongst women living in rural areas
than amongst those living in urban areas (72% versus 59%), while the proportion of women living in urban areas
reporting demand for family planning satisfied is slightly higher than the proportion of women living in rural areas
in Bangladesh (79% versus 77%), Pakistan (59% versus 56%) and Viet Nam (74% versus 71%). Based on
income levels, the demand satisfied is 15 percentage points higher amongst women from households in the
lowest income quintile than amongst women in the highest quintile in Mongolia (75% versus 60%), while the
proportion of women in the highest income quintile reporting demand for family planning satisfied is higher than
the proportion of women in the lowest income quintile in Pakistan (59% versus 54%) and Viet Nam (77% versus
75%) (Figure 4.2). Evidence suggests that demand for family planning not satisfied is high amongst adolescents
and youth in Asia-Pacific countries and territories where the average age of marriage is low and gender
inequality is high (UNESCAP, 2018[3]).

Contraceptive prevalence is the percentage of women who are currently using, or whose sexual partner is
currently using, at least one method of contraception, regardless of the method used. It is usually reported
as a percentage of married or in-union women aged 15-49.
Women with a demand for family planning satisfied are those who are fecund and sexually active, are using
a method of contraception, and report wanting more children. It is reported as a percentage of married or in-
union women aged 15-49.
Information on contraceptive use and demand satisfied for family planning is generally collected through
nationally representative household surveys.

88 
References
UNESCAP (2018), Inequality in Asia and the Pacific in the era of the 2030 Agenda for Sustainable [3]
Development, United Nations Economic and Social Commission for Asia and the Pacific, Bangkok,
http://www.unescap.org/publications/inequality-asia-and-pacific-era-2030-agenda-sustainable-
development.
UNFPA (2019), State of World Population 2019: Unfinished business, United Nations Population Fund, [1]
New York, https://www.unfpa.org/sites/default/files/pub-
pdf/UNFPA_PUB_2019_EN_State_of_World_Population.pdf.
WHO/Johns Hopkins Bloomberg School of Public Health (2018), Family planning: a global handbook for [2]
providers: evidence-based guidance developed through worldwide collaboration,

 89
Figure 4.1. Contraceptive prevalence, married or in-union women, latest available estimate
Any method Any modern method
%
100
75
50
25
Source: UN World Contraceptive Use 2021; DHS and MICS surveys, various years; and Bureau of Health, Macau (China), 2014.
StatLink 2 https://stat.link/o96k83
Figure 4.2. Demand for family planning satisfied by modern methods by socio-economic
characteristics, selected countries, latest year available

% %
100 100
90 90
80 80
70 70
60 60
50 50
40 40
30 30
20 20
10 10
0 0

%
100
90
80
70
60
50
40
30
20
10
0
Bangladesh Mongolia (2018) Lao PDR (2017) DPRK (2017) Nepal (2019) Thailand (2019) Viet Nam (2020- Cambodia Pakistan (2017-
(2019) 21) (2021-22) 18)
Note: Lowest education may refer to no education.

StatLink 2 https://stat.link/fs9vi7

90 
Infant and young child feeding

Optimal feeding practices of infants can increase their chances of survival. They play an important role for
healthy growth and development, decrease rates of stunting and obesity and stimulate intellectual development
(UNICEF, 2019[1]).
Breastfeeding is an unequalled way of providing nutrition for infants. Breast milk gives infants the nutrients they
need for healthy development, including the antibodies that help protect them from common childhood illnesses
such as diarrhoea and pneumonia, the two primary causes of under-five child mortality worldwide. Breastfeeding
is also linked with better health outcomes later in life. Adults who were breastfed as babies often have lower
blood pressure and lower cholesterol, as well as lower rates of overweight, obesity and type 2 diabetes (Horta,
Cesar and WHO, 2013[2]; Horta, Loret de Mola and Victora, 2015[3]; Victora et al., 2016[4]). Breastfeeding also
improves school attendance and is associated with higher income in adult life. More than 800 000 deaths
amongst children under five could be saved every year globally if all children 0-23 months were optimally
breastfed. Breastfeeding also benefits mothers through assisting in fertility control, reducing the risk of breast
and ovarian cancer later in life and lowering rates of obesity (UNICEF, 2019[1])
The WHO Baby-Friendly Hospital Initiative outlines detailed recommendations on protecting, promoting, and
supporting breastfeeding in facilities providing maternal and newborn services (WHO, 2017[5]). WHO and
UNICEF recommend early initiation of breastfeeding within 1 hour of birth, exclusive breastfeeding for the first
6 months of life, and introduction of nutritionally-adequate and safe complementary (solid) foods at 6 months
together with continued breastfeeding up to 2 years of age or beyond.
In 2012, the World Health Assembly endorsed a comprehensive implementation plan on maternal, infant, and young
child nutrition, which specified a set of six global nutrition targets and one of the targets aims to increase the rate of
exclusive breastfeeding in the first six months up to at least 50% by 2025. Globally, this target has not been achieved
as 44% of children under six months being exclusively breastfed in 2021 (UNICEF, 2021[6]). However, in the Asia-
Pacific region, Sri Lanka, the Solomon Islands, DPRK, Cambodia, India, Bangladesh, Nepal, Papua New Guinea,
Mongolia, the Philippines, Myanmar and Indonesia have already achieved this target (Figure 4.3). The proportion of
infants exclusively breastfed for the first six months of life in lower-middle- and low-income Asia-Pacific countries
was two times the proportion reported in upper-middle-income countries. Policies and regulations on marketing of
breast-milk substitutes and workplace support to breastfeeding as well as breastfeeding counselling in health
facilities and societal beliefs favouring mixed feeding contribute to variations in exclusive breastfeeding rates across
countries (Local Burden of Disease Exclusive Breastfeeding Collaborators, 2021[7]).
However, several Asia-Pacific countries and territories are lagging as less than one in four infants was exclusively
breastfed in Thailand, China, and Viet Nam (Figure 4.3). Key factors contributing to inadequate breastfeeding
rates include unsupportive hospital and health care practices and policies; lack of adequate skilled support for
breastfeeding, specifically in health facilities and the community; aggressive marketing of breast milk substitutes
and inadequate maternity and paternity leave legislation and unsupportive workplace policies (UNICEF, 2019[1]).
Several countries and territories which increased exclusive breastfeeding practice have implemented these
policies. For example, the Bangladesh Breastmilk Substitutes (BMS) Act was developed in 2013 to ensure that
mothers and families get accurate and unbiased information, free of commercial pressure, to feed infants and
young children, and it also regulates the inappropriate marketing and distribution of BMS (Toolkits, 2019[8]). Since,
the rate of exclusive breastfeeding in Bangladesh increased from 55.3% in 2014 to 62.6% in 2019 (OECD/WHO,
2018[9]). Another example is Cambodia, where the government implemented several diverse activities starting in
2004, including the establishment of breastfeeding practices in hospitals and community-based volunteers
advocating the benefit of breastfeeding to expecting and new mothers. Consequently, the exclusive breastfeeding
rates for babies under six months in Cambodia rose from 7% in 2000 to 65% in 2014. However, after considerable
progress, there was a subsequent decline to 51% in 2020/21. This latter example demonstrates that it remains
difficult to achieve sustained improvements in exclusive breastfeeding practice even if countries see initial
improvements – therefore sustained and broad-based support is essential.
In Nepal, Bangladesh, Mongolia, Lao PDR and Pakistan, the rate of exclusive breastfeeding was higher amongst
women living in households in the poorest income quintile as compared to women living in the richest households
(Figure 4.5). Across countries and territories in Asia-Pacific, a higher level of education was not always associated
with a higher rate of exclusive breastfeeding. While in Bangladesh and Mongolia women with the highest education
level were much more likely to follow exclusive breastfeeding recommendations than those with the lowest
education, the opposite trend was observed in countries and territories such as Lao PDR and Pakistan. In Mongolia,
women living in rural areas are almost 50% more likely to breastfeed as compared to women living in urban areas.

 91
After the first six months of life, an infant needs additional nutritionally adequate and safe complementary foods,
while continuing breastfeeding. Appropriate complementary foods were introduced to around half of the children
between 6-8 months in India, whereas complementary foods were introduced to more than nine out of ten infants
in Sri Lanka, Thailand and Viet Nam (Figure 4.4).
Considering persisting high levels of childhood malnutrition (see indicator “Child malnutrition and overweight” in
Chapter 4), infant feeding practices must be further improved (UNICEF, 2019[1]).

Exclusive breastfeeding is defined as no other food or drink, not even water, other than breast milk (including
milk expressed or from a wet nurse) for the first six months of life, with the exception of oral rehydration salts,
drops and syrups (vitamins, minerals and medicines) (UNICEF, 2019[1]). Thereafter, to meet their evolving
nutritional requirements, infants should receive adequate and safe complementary foods (complementary
feeding) while continued breastfeeding up to two years of age or beyond.
The usual sources of information on the infant feeding practices are household surveys. They also measure
other indicators of infant feeding practices such as minimal meal frequency, minimal diet diversity and
minimum acceptable diet.
References
Horta, B., V. Cesar and WHO (2013), Long-term effect of breastfeeding: a systemic review, World Health [2]
Organization, Geneva, https://apps.who.int/iris/handle/10665/79198.
Horta, B., C. Loret de Mola and C. Victora (2015), “Long-term consequences of breastfeeding on [3]
cholesterol, obesity, systolic blood pressure and type 2 diabetes: a systematic review and meta-
analysis”, Acta Paediatrica, Vol. 104, pp. 30-37, https://doi.org/10.1111/apa.13133.
Local Burden of Disease Exclusive Breastfeeding Collaborators (2021), “Mapping inequalities in exclusive [7]
breastfeeding in low- and middle-income countries, 2000–2018”, Nature Human Behaviour, Vol. 5/8,
pp. 1027-1045, https://doi.org/10.1038/s41562-021-01108-6.
OECD/WHO (2018), Health at a Glance: Asia/Pacific 2018: Measuring Progress towards Universal Health [9]
Coverage, OECD Publishing, Paris, https://doi.org/10.1787/health_glance_ap-2018-en.
Toolkits (2019), Bangladesh Breastmilk Substitutes (BMS) Act: Protecting, promoting, and supporting [8]
breastfeeding by ending the unethical marketing of BMS,
https://toolkits.knowledgesuccess.org/toolkits/breastfeeding-advocacy-toolkit/bangladesh-breastmilk-
substitutes-bms-act-protecting-promoting-and-supporting-breastfeeding-ending.
UNICEF (2021), United Nations Children’s Fund, The State of the World’s Children 2021: On My Mind – [6]
Promoting, protecting and caring for children’s mental health, UNICEF, New York,
https://www.unicef.org/media/114636/file/SOWC-2021-full-report-English.pdf.
UNICEF (2019), The State of the World’s Children. Children, Food and Nutrition: Growing well in a [1]
changing world., United Nations International Children’s Emergency Fund, New York,
http://www.unicef.org/media/63016/file/SOWC-2019.pdf.
Victora, C. et al. (2016), “Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect”, [4]
The Lancet, Vol. 387/10017, pp. 475-490, https://doi.org/10.1016/s0140-6736(15)01024-7.
WHO (2017), Guideline: protecting, promoting and supporting breastfeeding in facilities providing maternity [5]
and newborn services, World Health Organization, https://apps.who.int/iris/handle/10665/259386.

92 
Figure 4.3. Infants exclusively breastfed, first Figure 4.5. Infants exclusively breastfed in
6 months of life, latest available year the first 6 months of life, by selected
socio-economic and geographic factors
Sri Lanka (2016) 81
Solomon Islands (2015) 76
Lowest education Highest education
DPRK (2017) 71
India (2020-21) 64 %
Bangladesh (2019) 63 100
Nepal (2019) 62
80
Papua New Guinea (2017) 60
Mongolia (2020) 58
60
Asia Pacific-LM/L 57
Philippines (2018) 55
40
Myanmar (2016) 51
Cambodia (2020-21) 51 20
Indonesia (2017) 51
Pakistan (2018) 48 0
Lao, PDR (2017) 44
Malaysia (2016) 40
Fiji (2004) 40
Asia Pacific-UM 29
Viet Nam (2014) 24
China (2013) 21
%
Thailand (2019) 14
100
0 20 40 60 80 100
%
80
Source: UNICEF State of the world children 2021; India NHFS
2020-21. 60
StatLink 2 https://stat.link/d9q6x3
40
Figure 4.4. Infants aged 6-8 months with 20
solid, semi-solid and soft-foods, selected 0

countries and territories, latest year available
%
100
97 91 88 88
87 86 85
84 83
80 79 79 Rural Urban
80 78 76
75 %
100
60 65
80
40
60
20 40
0 20
* DHS surveys measure introduction of any solid and semi-solid

foods.
Source: UNICEF World Children Report 2022, DHS and MICS StatLink 2 https://stat.link/ix1m4d
surveys, various years.
StatLink 2 https://stat.link/qot5xn

 93
Child malnutrition (including undernutrition and overweight)

National development is largely dependent on healthy and well-nourished people, but many children are not always
able to access sufficient, safe and nutritious food and a balanced diet that meets their needs for optimal growth
and development for an active and healthy life (UNICEF, 2019[1]). Malnutrition amongst children in low-and middle-
income countries and territories encompass both undernutrition and a growing problem with overweight and
obesity. Many countries and territories are facing a double burden of malnutrition – characterised by the
coexistence of undernutrition along with overweight, obesity or diet-related non-communicable diseases (NCDs) –
which poses a real and growing health challenge. A double burden of malnutrition exists at the population,
household and individual levels in all countries. This includes overweight mothers and stunted children and children
who are both stunted and overweight. For example, one in two overweight children are stunted in Bangladesh, and
6% of stunted children have overweight mothers in Myanmar (WHO, 2020[2]). In order to simultaneously and
synergistically address these challenges, the United Nations declared the Decade of Action on Nutrition in 2016
until 2025 and proposed actions such as strengthening sustainable, resilient food systems for healthy diets,
assuring safe and supportive environments for nutrition at all ages, promoting nutrition-related education, and
strengthening nutrition governance and promoting accountability (WHO, 2017[3]). This will contribute to achieving
target 2.2 of the Sustainable Development Goals: “By 2030, end all forms of malnutrition, including achieving, by
2025, the internationally agreed targets on stunting and wasting in children under 5 years of age”.
Undernutrition is an important determinant of poor health amongst young children and is estimated to explain
around 45% of all under 5 child deaths worldwide (Development Initiatives, 2018[4]). To reduce under age 5
mortality, countries and territories need to not only implement effective preventive and curative interventions for
newborns, children, and their mothers during and after pregnancy (see indicator “Infant and child health” in
Chapter 5) but also to promote optimal feeding practice (see indicator “Infant feeding” in Chapter 4).
Child undernutrition is also associated with poorer cognitive and educational outcomes in later childhood and
adolescence and has important education and economic consequences at the individual, household, and
community levels. Overweight in childhood is related to early cardiovascular, gastrointestinal, musculoskeletal,
and orthopaedic problems. It is also a major predictor of obesity in adulthood, which is a risk factor for the leading
causes of poor health and early death. Hence, preventing overweight has direct benefits for children’s health
and well-being, in childhood and continuing into adulthood (UNICEF, 2019[1]).
In 2012, the World Health Assembly endorsed a comprehensive implementation plan on maternal, infant and
young child nutrition, which specified a set of six Global Nutrition Targets by 2025 and they include targets in
stunting, wasting and overweight (WHO, 2014[5]). In 2015, the UN SDG also set targets referring to stunting,
wasting and overweight amongst children.
High levels of stunting in a country are associated with poor socio-economic conditions and increased risk of
frequent and early exposure to adverse conditions such as illness and/or inappropriate feeding practices.
Wasting may also be the result of a chronic unfavourable condition, like unsafe water and poor or lacking sanitary
facilities. Recurrent events of wasting can increase the risk of stunting, and stunting increases the risk of
overweight and obesity later in life (UNICEF, 2019[1]).
In Asia-Pacific, many countries and territories had a high prevalence of stunting amongst children under
age 5. Stunting prevalence was high at around 50% in Papua New Guinea, and more than one in three children
were stunted in Pakistan and India. On the other hand, stunting prevalence was below 5% in Australia, Korea,
Singapore and China (Figure 4.6). In the past few years, Mongolia had made a substantial progress and became
the first country in the Asia-Pacific region to have achieved the Global Nutrition Target to reduce by 40% the
number of children under 5 years who are stunted. However, most South-East Asia countries are unlikely to
achieve the global target or national targets set for stunting and wasting (WHO, 2020[2]).
Countries and territories with high stunting prevalence had a high under age 5 mortality rate (Figure 4.7), also
reflecting the fact that about 45% of under age 5 deaths were attributable to undernutrition (Development
Initiatives, 2018[4]).
As to wasting, if there is no severe food shortage or an infectious disease (such as diarrhoea) that has caused
children to lose weight, the prevalence is usually below 5% even in low-income countries and territories
(https://www.who.int/nutgrowthdb/about/introduction/en/index2.html), but it was higher than 10% in India, Sri
Lanka, Papua New Guinea, Nepal, and Indonesia. So far, Australia, Mongolia, Korea, China, Japan, DPRK,
Brunei Darussalam, and Singapore have attained the Global Nutrition Target of reducing and maintaining
childhood wasting to less than 5% (Figure 4.6).

94 
In 2018, almost 20 million overweight or obese children under age 5 lived in Asia (UNICEF, 2019[1]), and a high
prevalence of overweight (5.2%) was reported for Pacific Island countries (UNICEF/WHO/WB, 2021[6]). However,
the prevalence of childhood overweight varied across Asia-Pacific countries and territories. More than one child out
of ten was overweight in Australia, Papua New Guinea and Mongolia, whereas less than 2% of children under age 5
were overweight in Myanmar, Japan, and India (Figure 4.8). Nepal, Pakistan and Thailand reduced under 5
overweight rates since 2012, so they meet the Global Nutrition Target 2025 of no increase in childhood overweight
prevalence (WHO, 2020[7]). A low prevalence of overweight, however, did not always mean a proper nutrition intake
amongst children. For instance, a study in Nepal showed that children under age 2 were getting a quarter of their
energy intake from non-nutritive snacks and beverages such as biscuits or instant noodles (UNICEF, 2019[1]).

Stunting (low height-for-age) reflects failure to reach linear growth potential as a result of long-term
suboptimal health and/or nutritional conditions. According to the WHO definition, stunting is a height-for-age
lower than 2 standard deviations below the WHO Child Growth Standards median. Wasting (low weight-for-
height) usually indicates recent and severe weight loss, because a person has not had enough food to eat
and/or has had an infectious disease, such as diarrhoea, which has caused them to lose weight. According
to the WHO definition, wasting is weight-for-height lower that 2 standard deviations below the WHO Child
Growth Standards median.
According to the WHO definition, child overweight is weight-for-height greater than 2 standard deviations
above WHO Child Growth Standards median.
References
Development Initiatives (2018), Global Nutrition Report 2018: Shining a light to spur action on nutrition, [4]
Development Initiatives, Britsol, http://www.globalnutritionreport.org/reports/global-nutrition-report-2018/.
UNICEF (2019), The State of the World’s Children. Children, Food and Nutrition: Growing well in a [1]
changing world., United Nations International Children’s Emergency Fund, New York,
http://www.unicef.org/media/63016/file/SOWC-2019.pdf.
UNICEF/WHO/WB (2021), Joint child malnutrition estimates, https://www.who.int/news/item/06-05-2021- [6]

the-unicef-who-wb-joint-child-malnutrition-estimates-group-released-new-data-for-2021.
WHO (2020), Prevalence of overweight among children under 5 years of age (% weight-for-height >+2 SD), [7]
https://www.who.int/data/gho/data/indicators/indicator-details/GHO/children-aged-5-years-overweight-(-
weight-for-height-2-sd).
WHO (2020), The double burden of malnutrition: priority actions on ending childhood obesity, World Health [2]
Organization, Regional Office for South-East Asia., https://apps.who.int/iris/handle/10665/336266.
WHO (2017), Work programme of the United Nations Decade of Action on Nutrition (2016-2025), World [3]
WHO (2014), Global Nutrition Targets 2025: Childhood overweight policy brief, World Health Organization, [5]

 95
Figure 4.6. Prevalence of stunting and wasting amongst children under age 5, latest year available
Stunting Wasting
%
50
40
30
20
10
Source: WHO GHO 2022; UNICEF 2021; DHS, MICS, and NHFS surveys, various years.
StatLink 2 https://stat.link/f4sdpz
Figure 4.7. Under-5 mortality and stunting Figure 4.8. Prevalence of overweight amongst
prevalence, latest year available children under age 5, latest year available
Stunting prevalence (%) Australia (2017-2018) 22.0
60
Papua New Guinea (2009-2011) 13.7
Mongolia (2018) 11.5
R² = 0.6223 Asia Pacific-H 10.5
50
Thailand (2015-2016) 9.8
PHL China (2013) 9.1
Brunei Darussalam (2009) 8.3
40
Indonesia (2013) 8.2
KHM Korea (2008-2011) 7.3
PAK SLB MNG
30 BRN Asia Pacific-UM 6.1
CHN KOR
LAO Viet Nam (2017) 6.0
LKA VNM
PNG Malaysia (2016) 5.9
MMR Fiji (2004) 5.1
20 NPL
MYS Solomon Island (2015) 4.9
IND SGP Asia Pacific-LM/L 4.5
10 Philippines (2018) 4.0
BGD
THA JPN Lao, PDR (2017) 3.5
IDN FJI
PRK Nepal (2016-17) 2.5
AUS Pakistan (2017-18) 2.4
0
0 10 20 30 40 50 60 70 Bangladesh (2019) 2.4
Under age 5 mortality (per 1 000 live births)
DPRK (2017) 2.3
Cambodia (2014) 2.2
Source: DHS and MICS surveys, various years; WHO GHO 2022;
Sri Lanka (2016) 2.0
UNICEF 2020; UN IGME; Childinfo 2019. India (2015-2016) 1.5
StatLink 2 https://stat.link/wr8jn0 Japan (2010) 1.5
Myanmar (2015-2016) 1.2
0 5 10 15 20 25
%
Source: UNICEF database; DHS and MICS surveys, various

years.
StatLink 2 https://stat.link/ah4gxb

96 
Water and sanitation

Safe water and adequate sanitation are vital to individual health, livelihood, and well-being. Yet, more than one
out of four people in the world, around 2 billion people, do not have access to basic sanitation services. A lack
of access to basic sanitation can lead to transmission of different diseases such as diarrhoea, cholera, and
hepatitis A -, and adds to the burden of malnutrition. Better access to water and sanitation could prevent the
deaths of 297 000 children under age 5 annually (WHO, 2019[1]). Improving access to water and sanitation
contributes not only to better health but also leads to great social and economic benefits, whether through higher
educational participation, improved living standards, lower health care costs or a more productive labour force.
Consequently, the United Nations has set a target of achieving universal and equitable access to safe and
affordable drinking water for all, as well as achieving access to adequate and equitable sanitation and hygiene
for all and end open defecation by 2030. Furthermore, UNICEF’s strategy for Water, Sanitation and Hygiene
(WASH) 2016-30 seeks to ensure that every child lives in a clean and safe environment, gains access to basic
sanitation and safe drinking water in early childhood development centres, school, health centres and in
humanitarian situations (UNICEF, 2018[2]).
In 2020, while more than nine in ten people in Asia-Pacific high-income countries and territories had access to
basic sanitation, in lower-middle and low-income countries and territories only two out of three people living in
rural areas and about four in five people living in urban areas had access to basic sanitation for adequate excreta
disposal (Figure 4.8, left panel). Access was low in rural areas at around 15% in Papua New Guinea and 20%
in the Solomon Islands, where open defecation was still common amongst most of the population. In urban
areas, only about half of the population had access to basic sanitation in Papua New Guinea and Bangladesh
in 2020.
Over recent years, the proportion of the population with access to basic sanitation facilities has grown in most
Asia-Pacific countries and territories, and faster improvement was observed in rural areas (Figure 4.8, right
panel). The progress was particularly rapid in rural areas in Nepal, India, Cambodia and Indonesia, where the
proportion of population with access to basic sanitation increased by more than 30 percentage points between
2010 and 2020. In urban areas, Nepal and Cambodia reported a large increase of more than 20 percentage
points in the proportion of population with access to basic sanitation during the same period. On the contrary,
Papua New Guinea and Myanmar reported a decrease in the percentage of the population having access to
basic sanitation in urban areas from 2010 to 2020.
In almost all Asia-Pacific countries and territories in 2020, more than nine out of ten people had access to basic
drinking water in urban areas, while access was limited in rural areas in some countries and territories. In
Papua New Guinea, slightly more than one in three people had access to basic drinking water in rural areas.
Access to basic water sources was also low in rural areas in the Solomon Islands (59%) and Mongolia (61%)
(Figure 4.9, left panel).
During the period of 2010-20, access to basic drinking water improved in most Asia-Pacific countries and
territories, and the progress was generally faster in lower-middle- and low-income countries and territories than
in upper-middle-income countries and territories. In urban areas, access to basic drinking water increased by
more than 10 percentage points in Myanmar and Lao PDR, while decreased by more than 1 percentage point
in Pakistan, Nepal and DPRK. In rural areas, Myanmar, Lao PDR and Mongolia reported an increase in the
population living in rural areas having access to basic drinking water of more than 15 percentage points, whereas
Solomon Islands reported the largest decrease of almost 10 percentage points from 2010 to 2020 (Figure 4.9,
right panel). In recent years, many countries and territories in the region, including Bangladesh, Mongolia, the
Philippines, and Viet Nam established water safety plans, allowing millions to access safer drinking water. Tax-
based public subsidies, well-designed water tariffs and strategic use of aid flows to the water sector can assist
in ensuring that poor and vulnerable groups have access to sustainable and affordable water services (WHO,
2018[3]).

 97

People that use improved sources of drinking water that required no more than 30 minutes per trip to collect
water are classified as having at least basic drinking water services. An improved drinking-water source is
constructed so that it is protected from outside contact, especially from faecal matter. Improved sources
include piped water, public taps, boreholes, and protected dug wells or springs (UNICEF/WHO, 2019[4]).
People that use an improved sanitation facility that was not shared with other households are classified as
having at least basic sanitation services. Improved sanitation facilities hygienically separate excreta from
human contact, using flushing to piped sewer systems, septic tanks, or pit latrines, along with improved pit
latrines or composting toilets (UNICEF/WHO, 2019[4]).
The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation (JMP) database includes
nationally representative household surveys and censuses that ask questions on water and sanitation, mostly
conducted in developing countries. Generally, developed countries supply administrative data.
Australia, Japan, New Zealand, Korea, and Singapore report a coverage of 100% for basic sanitation and
basic drinking water. Therefore, these countries are not shown in Figure 4.18 and Figure 4.19.
References
UNICEF (2018), WASH strategy, http://www.unicef.org/wash/3942_91538.html. [2]
UNICEF/WHO (2019), Progress on household drinking water, sanitation and hygiene 2000-2017. Special [4]
Focus on INequalities, United Nations International Children’s Emergency Fund and World Health
Organization, New York,
https://www.unicef.org/media/55276/file/Progress%20on%20drinking%20water,%20sanitation%20and%
20hygiene%202019%20.pdf.
WHO (2019), Sanitation, World Health Organization, https://www.who.int/news-room/fact- [1]

sheets/detail/sanitation.
WHO (2018), Guidelines on Sanitation and Health, World Health Organization, [3]

98 
Figure 4.8. Access to basic sanitation, 2020 and change between 2010-20
Urban Rural Urban Rural
2020 Change between 2010-20
Papua New Guinea
Solomon Islands
Mongolia
Bangladesh
Pakistan
Cambodia
Asia Pacific-LM/L
India
Lao, PDR
Myanmar
DPRK
Nepal
Indonesia
Philippines
Viet Nam
China
Sri Lanka
Asia Pacific-UM
Thailand
Fiji
New Zealand
Malaysia
Singapore
100 75 50 25 0 -10 0 10 20 30 40
% % point change
Source: WHO/UNICEF JMP database 2021.

StatLink 2 https://stat.link/nqm1rb
Figure 4.9. Access to basic drinking water, 2020 and change between 2010-20
Urban Rural Urban Rural
2020 Change between 2010-20
Papua New Guinea
Solomon Islands
Mongolia
Cambodia
Myanmar
Lao, PDR
Asia Pacific-LM/L
Indonesia
Pakistan
DPRK
India
Fiji
China
Malaysia
Nepal
Sri Lanka
Philippines
Asia Pacific-UM
Viet Nam
Bangladesh
Australia
New Zealand
Thailand
Brunei Darussalam
Singapore
100 75 50 25 0 -15 -10 -5 0 5 10 15 20 25

% % point change
Source: WHO/UNICEF JMP database 2021.

StatLink 2 https://stat.link/trn2y1

 99
Tobacco
Tobacco use is the leading global cause of preventable deaths and kills more than 8 million people each year,
of whom more than 7 million are from direct tobacco use and around 1.2 million are non-smokers exposed to
second-hand smoke. It is estimated that worldwide there were almost 1 billion current tobacco smokers
aged 15 years and above in 2020, 847 million of which were men. Amongst children between ages 13 and 15,
an estimated 24 million were smokers. Although global tobacco use has fallen over the past two decades, the
progress is still off track for achieving the target set by governments to cut tobacco use by 30% between 2010
and 2025 as part of the global efforts to reduce mortality from the four main non-communicable diseases
(cardiovascular diseases, cancer, chronic lung diseases and diabetes) (WHO, 2021[1]). The UN SDGs call for
strengthening the implementation of the World Health Organization Framework Convention on Tobacco Control
in all countries and territories, as appropriate.
Tobacco use is a major risk factor for six of the eight leading causes of premature mortality – ischemic heart
disease, cerebrovascular disease, lower respiratory infections, chronic obstructive pulmonary disease,
tuberculosis and cancer of the trachea, bronchus, and lung. Moreover, smoking in pregnancy can lead to low
birthweight and illness amongst infants (NCD Alliance, 2010[2]). Children who smoke in early adolescence also
increase their risk of cardiovascular diseases, respiratory illnesses, and cancer, and they are more likely to
experiment with alcohol and other drugs (CDC, 2021[3]). Smoking is also a risk factor for dementia. New studies
have shown that 14% of Alzheimer’s cases worldwide may be attributed to smoking (McKenzie, Batti and Tursan
d’Espaignet, 2014[4]; Livingston et al., 2017[5]). Recently, tobacco smoking is also found to be associated with
higher risks of developing severe symptoms and mortality amongst COVID-19 patients (WHO, 2020[6]; Vardavas
and Nikitara, 2020[7]). Smoking is harmful not only for smokers but also bystanders.
As of 2020, comprehensive smoke-free legislation was in place for almost 1.8 million people in 67 countries and
territories, covering only 23% of the world’s population. In Asia-Pacific, Australia, Brunei Darussalam, Cambodia,
Lao PDR, Nepal, New Zealand, Pakistan, Papua New Guinea and Thailand have comprehensive smoke-free
policies. Evidence shows that countries and territories with comprehensive smoke-free policies have decreased
the number of smokers and reduced mortality from smoking-related illnesses (WHO, 2021[1]).
The economic and social costs of tobacco use are also high, with families deprived of breadwinners who die
prematurely from tobacco-related diseases, large public health costs for treatment of tobacco-related diseases,
and lower workforce productivity (WHO, 2019[8]). Smoking rates in low-income countries are about half the rate
of rates in high-income countries (WHO, 2021[1]).
More than two in five men aged 15 and above in middle- and low-income Asia-Pacific countries and territories
reported current use of tobacco in 2020, as compared to one in four in high-income countries and territories
(Figure 4.11, left panel). The proportion of current tobacco users varied greatly across countries and territories.
This proportion amongst men was highest in Indonesia at 71.4%, and Myanmar, the Solomon Islands,
Papua New Guinea, Lao PDR, Bangladesh, and Mongolia, had over half of the adult males using tobacco
currently. New Zealand and Australia, however, reported the lowest prevalence, with around 15% of adult males
using tobacco currently. India has reduced smoking rates recently through implementation of multiple tobacco
control measures, including an innovative text message-based smoking cessation programme (WHO, 2019[8]).
However, India has a high prevalence of daily smokeless tobacco use amongst adults at 18.2% in 2018 (Global
Adult Tobacco Survey, https://www.who.int/tobacco/surveillance/survey/gats/GATS_India_2016-
17_FactSheet.pdf), and one in four adult men use smokeless tobacco daily.
There are large male-female disparities and 7.8%, 4.1% and 10.2% of women aged 15 and above report using
tobacco currently in high-, upper-middle-, and lower-middle- and low-income Asia-Pacific countries and
territories respectively (Figure 4.11, right panel). The rates were highest amongst female tobacco smokers in
Papua New Guinea (25.1%), Myanmar (19.7%), and the Solomon Islands (19.2%).
Tobacco use in adolescence has both immediate and long-term health consequences. Amongst youth aged 13 to
15 years, two in five males used tobacco in Papua New Guinea, and around one in four females used tobacco in
Papua New Guinea and Solomon Islands (Figure 4.12). In all reporting countries and territories, except for Nepal
and Fiji, the prevalence of tobacco use amongst females was higher for adolescents than adults. On the contrary,
the prevalence amongst males was higher for adults than for adolescents in all reporting countries and territories.

100 
Increasing tobacco prices through higher taxes is an effective intervention to reduce tobacco use, by discouraging
youth from initiating tobacco use and encouraging tobacco users to reduce their consumption or quit (WHO, 2019[8])
Higher taxes also assist in generating additional government revenue. However, only New Zealand, Sri Lanka and
Thailand have total taxes that account for over 75% of the tobacco retail price in 2020 (WHO, 2021[1]). In Thailand,
increased tax revenue has been used to support smoking cessation programmes (WHO, 2019[8]). As a measure
of the comparative cost that current tobacco users in Asia-Pacific incur, in Nepal, Papua New Guinea and Sri
Lanka, around one fifth of the GDP per capita is required to purchase 2000 cigarettes of the most sold brand, while
this figure is of less than 2% of the GDP per capita in Japan, Korea and Singapore (Figure 4.13).
In Asia-Pacific, health warnings against tobacco use, including labels on tobacco product packaging and anti-
tobacco mass media campaigns to build public awareness, could be used more to reduce tobacco use. Australia,
Pakistan, Singapore and Thailand report that pictorial warning labels have effectively impacted smoking-related
behaviour. To increase the effectiveness of health warnings, Australia, New Zealand, Singapore (starting in
2020) and Thailand have mandated plain packaging of tobacco products (WHO, 2019[8]).

Current tobacco use prevalence among adults is defined as the percentage of the population aged 15 years
and over who reported consuming one or more tobacco products, smoked or smokeless, on a daily or non-
daily basis.
Current tobacco use amongst youth is defined as the percentage of young people aged 13 to 15 years who
consumed any tobacco product at least once during the last 30 days prior to the survey.
References
CDC (2021), Health Effects of Cigarette Smoking, [3]

https://www.cdc.gov/tobacco/data_statistics/fact_sheets/health_effects/effects_cig_smoking/.
Livingston, G. et al. (2017), “Dementia prevention, intervention, and care”, The Lancet, Vol. 390/10113, [5]
pp. 2673-2734, https://doi.org/10.1016/S0140-6736(17)31363-6.
McKenzie, J., L. Batti and E. Tursan d’Espaignet (2014), WHO Tobacco Summaries: Tobacco and [4]
Dementia, World Health Organization, Geneva, https://www.who.int/publications-detail-redirect/WHO-
NMH-PND-CIC-TKS-14.1.
NCD Alliance (2010), Tobacco: a major risk factor for Non-communicable Diseases, [2]
https://ncdalliance.org/sites/default/files/rfiles/NCDA_Tobacco_and_Health.pdf.
Vardavas, C. and K. Nikitara (2020), “COVID-19 and smoking: A systematic review of the evidence”, [7]
International Society for the Prevention of Tobacco Induced Diseases, pp. 1-4,
https://doi.org/10.18332/tid/119324.
WHO (2021), WHO report on the global tobacco epidemic 2021: addressing new and emerging products, [1]
World Health Organization, https://apps.who.int/iris/handle/10665/343287.
WHO (2020), WHO statement: Tobacco use and COVID-19, http://www.who.int/news-room/detail/11-05- [6]
2020-who-statement-tobacco-use-and-covid-19.
WHO (2019), WHO report on the global tobacco epidemic, 2019: offer help to quit tobacco use, World [8]

 101
Figure 4.11. Age-standardised prevalence estimates for current tobacco use amongst persons
aged 15 and above, by sex, 2020
Males Females
New Zealand
Australia
Asia Pacific-H
OECD
Singapore
Brunei Darussalam
Japan
Pakistan
DPRK
Fiji
Korea
Cambodia
Philippines
India
Thailand
Sri Lanka
Asia Pacific-UM
Malaysia
Viet Nam
Nepal
Asia Pacific-LM/L
China
Mongolia
Bangladesh
Lao PDR
Papua New Guinea
Solomon Islands
Myanmar
Indonesia
80 60 40 20 0 0 10 20 30
% %
Source: WHO global report on trends in tobacco use 2021.

StatLink 2 https://stat.link/u2xgm8
StatLink 2 https://stat.link/03w4e2
Figure 4.12. Prevalence of current tobacco
use amongst youth aged 13 to 15, by sex,
latest available year Figure 4.13. Percentage of GDP per capita to
purchase 2000 cigarettes of the most sold
Females Males brand, latest available year
Papua New Guinea (2016) Nepal 21.9
Papua New Guinea 21.6
Malaysia (2017) Sri Lanka 19.0
Fiji 17.2
Solomon Islands (2011) India 13.8
Asia Pacific-LM/L 9.2
Myanmar (2016) Asia Pacific-UM 6.5
Philippines 6.0
Philippines (2015) Myanmar 5.8
Bangladesh 5.8
Indonesia 5.2
Thailand (2015) New Zealand 5.0
Malaysia 4.1
Mongolia (2019) Australia 3.9
Cambodia 3.3
Lao, PDR (2016) Lao, PDR 2.9
Thailand 2.7
Brunei Darussalam (2019) Viet Nam 2.6
Asia Pacific-H 2.6
Bangladesh (2014) OECD 2.3
China 2.1
Sri Lanka (2016) Mongolia 2.0
Singapore 1.7
Fiji (2016) Korea 1.2
Japan 1.2
Nepal (2015) 0 5 10 15 20 25
% GDP per capita
Cambodia (2016)
0 10 20 30 40 50 Source: WHO report on the global tobacco epidemic 2021.

% StatLink 2 https://stat.link/6odu48
Note: Youth aged 13 to 17 for Malaysia.

102 
5 Health care resources and

utilisation

 103
Doctors and nurses

Access to high-quality health services critically depends on the size, skill-mix, competency, geographic
distribution and productivity of the health workforce. Health workers are the cornerstone of health care systems.
The number of doctors per 1 000 population varies widely across Asia-Pacific countries and territories, but it is
generally lower than the OECD average (Figure 5.1). Across lower-middle- and low-income Asia-Pacific
countries and territories, there are 1.1 doctors 1 000population, whereas a higher number of doctors – 1.6 per
1 000 population – is reported in upper-middle-income countries and territories. Mongolia, Australia and DPRK
have the highest number of doctors per capita, with 3.9, 3.8, and 3.7 doctors per 1 000 population, respectively;
slightly higher than the OECD average of 3.6. In contrast, Papua New Guinea, Cambodia, and the
Solomon Islands, have the lowest number of physicians at or below 1 per 5 000 population.
The foundation for a strong and effective health workforce, able to respond to the 21st century priorities, requires
matching effectively the supply and skills of health workers to population needs, now and in the future (WHO,
2016[1]). To this aim, the specialisation-mix and distribution of doctors may be improved in Asia-Pacific. In Japan,
for example, the number of medical facilities with surgical and paediatric departments is on decline, while
shortages of doctors in emergency departments, obstetrics and gynaecology, internal medicine and anaesthesia
have been identified (Sakamoto, Rahman and Nomura, 2018[2]). Furthermore, an uneven geographical
distribution of health workers is a serious concern. The majority of health workers tend to be concentrated in
urban areas, leaving a shortage of health workers in remote and rural areas that results in poor availability of
health services particularly for vulnerable populations (Liu and Zhu, 2018[3]).
There is a large variation also in the number of nurses across countries and territories in Asia-Pacific
(Figure 5.2). The number of nurses is highest in high-income countries such as Australia, Japan and
New Zealand, with more than 10 nurses per 1 000 population. The supply is much lower in several low-income
countries and territories, including Papua New Guinea, Pakistan and Bangladesh, where there is 1 nurse or less
per 2000 population. On average, less than two nurses per 1 000 population work in lower-middle and low-
income Asia-Pacific countries. Furthermore, nurses are not well distributed geographically within countries and
territories such as Indonesia and the Philippines (Dayrit et al., 2018[4]; Harimurti, Prawira and Hort, 2017[5]), and
several other countries and territories in the region face the same issue (WHO, 2020[6]).
In some countries and territories, national human resources for health planning needs to take account of
migration trends in order to secure the necessary number of health professionals domestically. For example,
around 69 000 Indian-trained physicians worked in the United States, United Kingdom, Canada and Australia in
2017, and nearly 56 000 Indian-trained nurses work in the same four countries (Walton-Roberts and Rajan,
2020[7]), despite a domestic density of half of the Asia-Pacific average for doctors and less than half for nurses.
On the other hand, the Philippines is also the biggest supplier of nurses and a major exporter of doctors (Dayrit
et al., 2018[4]), but the density of these health professionals is at about the Asia-Pacific average.
As seen in OECD countries, nurses outnumber doctors, and there are 1.7 and 2.1 nurses per doctor in lower-
middle-, low-income-, and upper-middle-income Asia-Pacific countries, respectively (Figure 5.3). However,
there are some exceptions. Due to very few numbers of doctors, the Solomon Islands have 11 nurses per doctor.
On the other hand, doctors outnumber nurses in Pakistan and Bangladesh, whereas the same number of nurses
and doctors is reported in Myanmar and Mongolia.
Countries and territories in Asia-Pacific need to respond to the changing demand for health services and hence
the health professional skill-mix in the context of rapidly ageing populations (see indicator “Ageing” in Chapter 3).
The WHO global strategic directions (WHO, 2016[1]) provide the framework for strengthening health workforce
services to help countries and territories achieve universal health coverage. In addition, target 3.C of the
Sustainable Development Goals calls for “substantially increase the recruitment, development, training and
retention of the health workforce in developing countries, especially in least developed countries and small island
developing States”.
OECD countries, already experiencing population ageing, have developed formal systems to care for people
with limitations on activities of daily living, and long-term care workers, typically nurses and personal carers,
provide care and/or assistance to these people at home or in institutions (Muir, 2017[8]).

104 

Doctors include generalist medical doctors (including family and primary care doctors) and specialist medical
doctors.
For Asia-Pacific non-OECD countries and territories, “Nurses” refers to the number of nursing personnel,
including professional nurses, auxiliary nurses, enrolled nurses and related occupations such as dental
nurses and primary care nurses. For OECD countries, “Nurses” refers to practising nurses that provide
services directly to patients. This number includes professional nurses, associate professional nurses and
foreign nurses licensed to practice and actively practising in the country.
Data are based on head counts.
References
Dayrit, M. et al. (2018), The Philippines Health System Review, World Health Organization, Regional Office [4]
for South-East Asia, https://apps.who.int/iris/handle/10665/274579.
Harimurti, P., J. Prawira and K. Hort (2017), The Republic of Indonesia Health System Review, Health [5]
Systems in Transition, WHO Regional Office for South-East Asia,
Liu, X. and A. Zhu (2018), Attraction and Retention of Rural Primary Health-care Workers in the Asia Pacific [3]
Region, http://apps.who.int/iris/.
Muir, T. (2017), “Measuring social protection for long-term care”, OECD Health Working Papers, No. 93, [8]
OECD Publishing, Paris, https://doi.org/10.1787/a411500a-en.
Sakamoto, A., M. Rahman and S. Nomura (2018), Japan Health System Review, Health Systems in [2]
Transition, World Health Organization, Regional Office for South-East Asia,
Walton-Roberts, M. and S. Rajan (2020), “Global Demand for Medical Professionals Drives Indians Abroad [7]
Despite Acute Domestic Health-Care Worker Shortages”, Migration Information Source,
https://www.migrationpolicy.org/article/global-demand-medical-professionals-drives-indians-abroad.
WHO (2020), Health workforce country profiles, https://www.who.int/westernpacific/health-topics/health- [6]

workforce/country-profiles.
WHO (2016), Global strategy on human resources for health: workforce 2030, World Health Organization, [1]

 105
Figure 5.1. Doctors per 1 000 population, Figure 5.2. Nurses per 1 000 population,
latest year available latest year available
Mongolia (2018) 3.9 Australia (2019) 12.2
Australia (2019) 3.8 Japan (2018) 11.8
DPRK (2017) 3.7 New Zealand (2020) 10.6
OECD 3.6 OECD 9.6
New Zealand (2020) 3.4 Asia Pacific-H 8.1
Macau (China) (2020) 2.6 Korea (2019) 7.9
Asia Pacific-H 2.6 Singapore (2017) 6.2
Japan (2018) 2.5 Hong Kong (China) (2020) 6.2
Singapore (2019) 2.5 Brunei Darussalam (2018) 5.8
Korea (2019) 2.5 Philippines (2019) 4.6
Malaysia (2020) 2.3 DPRK (2017) 4.1
China (2019) 2.2 Mongolia (2018) 3.9
Hong Kong (China) (2020) 2.0 Macau (China) (2020) 3.8
Brunei Darussalam (2017) 1.6 Fiji (2019) 3.5
Asia Pacific-UM 1.6 Malaysia (2019) 3.4
Sri Lanka (2020) 1.2 Asia Pacific-UM 3.3
Pakistan (2019) 1.1 China (2019) 3.1
Asia Pacific-LM/L 1.1 Thailand (2019) 3.1
Thailand (2020) 1.0 Indonesia (2020) 2.3
Fiji (2015) 0.9 Solomon Islands (2018) 2.1
Nepal (2020) 0.9 Nepal (2020) 2.1
Viet Nam (2016) 0.8 Sri Lanka (2020) 2.1
Philippines (2020) 0.8 Asia Pacific-LM/L 1.8
Myanmar (2019) 0.7 India (2020) 1.7
India (2020) 0.7 Viet Nam (2016) 1.1
Bangladesh (2020) 0.7 Lao PDR (2020) 1.0
Indonesia (2020) 0.6 Myanmar (2019) 0.8
Lao PDR (2020) 0.4 Cambodia (2019) 0.6
Solomon Islands (2016) 0.2 Papua New Guinea (2019) 0.4
Cambodia (2014) 0.2 Bangladesh (2020) 0.4
Papua New Guinea (2019) 0.1 Pakistan (2019) 0.4
0 1 2 3 4 5 0 3 6 9 12 15
Note: Denominator for Hong Kong (China) is based on mid-year

population; for Macau (China) on end of year population. Note: Denominator for Hong Kong (China) is based on mid-year
Source: OECD Health Statistics 2022; WHO GHO, 2022; National population; for Macau (China) on end of year population.
Data Sources (see Annex A). Source: OECD Health Statistics 2022; WHO GHO, 2022; National
Data Sources (see Annex A).
StatLink 2 https://stat.link/4mzthk
StatLink 2 https://stat.link/c7vszw
Figure 5.3. Ratio of nurses to doctors, latest year available

Ratio
12
11.0
10
8
6.3 6.0
6 4.8 4.7
4.1
3.6
4 3.2 3.2 3.2 3.1 3.1 3.0 3.0
2.7 2.7 2.5 2.5
2.1 1.9 1.7
1.7 1.5 1.4 1.4 1.4
2 1.1 1.0 1.0
0.6 0.4
Note: Denominator for Hong Kong (China) is based on mid-year population; for Macau (China) on end of year population.
Source: OECD Health Statistics 2022; WHO GHO, 2022; National Data Sources (see Annex A).
StatLink 2 https://stat.link/y3etcn

106 
Consultations with doctors

Consultations with doctors are an important measure of overall access to health services, since most diseases
can be managed effectively in primary care without hospitalisation, and a doctor consultation often precedes a
hospital admission.
Generally, the annual number of doctor consultations per person in Asia-Pacific is lower than the OECD average
of 6.8, but there are some cross-country variations (Figure 5.4). The doctor consultation rate ranges from above
ten per person in Korea and Japan to less than one per person in Bangladesh and Cambodia. In general,
consultation rates tend to be highest in the high-income countries and territories in the region (except Singapore)
and significantly lower in low-income countries and territories, suggesting that income levels have some impact
on populations’ health care-seeking behaviours. It should be noted that in low-income countries and territories
most primary contacts are with medical assistants, clinical officers, or nurses, and not with doctors.
Mainly reflecting the limited supply of doctors (see indicator “Doctors and nurses” in Chapter 5), the number of
consultations per doctor is – in many Asia-Pacific countries and territories – higher that the OECD average at
2 122 per year (Figure 5.5). Doctors had more than 5 000 consultations on average in Korea, Sri Lanka, Thailand
and Japan in a year, while a doctor in Brunei Darussalam, Malaysia, New Zealand and Bangladesh, generally
delivers less than 1 300 consultations per year.
The number of consultations per doctor should not be taken as a measure of productivity as consultations can
vary in length and effectiveness, and doctors also undertake work devoted to inpatients, administration, and
research. This measure is also subject to comparability limitations such as the exclusion of doctors working in
the private sector or the inclusion of other health professionals providing primary care in some countries and
territories (see box below on “Definition and comparability”).
There is a close relationship between doctor consultation rates – a proxy for access to services – and healthy
life expectancy at birth, with consultation rates being highest in countries and territories reporting the highest
healthy life expectancy (Figure 5.6). This simple correlation, however, does not necessarily imply causality since
overall living standards may influence both consultation rates and life expectancy. There are also country
examples such as Mongolia (Singapore) where healthy life expectancy is much lower (higher) than expected
based on consultation rates, indicating that other factors, such as geographical accessibility and income level,
affect life expectancy.

Consultations with doctors are defined as contacts with physicians (both generalists and specialists, for more
details see indicator “Doctors and nurses” in Chapter 5). These may take place in doctors’ offices or clinics,
in hospital outpatient departments and at home.
Two main data sources are used to estimate consultation rates: administrative data and household health
surveys. In general, administrative data sources in non-OECD countries and territories of the Asia-Pacific
region only cover public sector physicians or physicians remunerated by the public sector, although
physicians in the private sector provide a large share of overall consultations in most of these countries and
territories. Moreover, outpatient visits recorded in administrative data can be also with non-physicians. The
alternative data source is household health surveys, but these tend to produce lower estimates owing to
incorrect recall and non-response rates. Administrative data have been used where available, but survey
data are used for Hong Kong (China), Singapore, Solomon Islands and Sri Lanka. Caution must be applied
in interpreting the data from different sources.
The annual number of consultations per doctor is estimated by dividing the number of total consultations in
a year by the number of doctors.

 107
Figure 5.4. Doctor consultations per capita, Figure 5.5. Estimated number of
latest year available consultations per doctor, latest year available
Korea (2020) 14.7 Korea (2019) 6989
Japan (2019) 12.4 Sri Lanka (2016) 6704

Thailand (2007) 6563
Macau (China) (2020) 7.3
Japan (2019) 5011
OECD 6.8
Fiji (2010) 4885
Australia (2021) 6.1 Singapore (2010) 4233
Sri Lanka (2016) 6.0 Cambodia (2011) 4181
China (2018) 6.0 Macau (China) (2020) 2790
Mongolia (2017) 5.7 China (2018) 2760

OECD33 2122
New Zealand (2017) 3.8
Mongolia (2017) 1994
Fiji (2010) 2.9
Australia (2019) 1908
Malaysia (2019) 2.3 Viet Nam (2010) 1879
Viet Nam (2010) 2.3 Bangladesh (2011) 1241
Thailand (2005) 2.1 New Zealand (2019) 1168
Singapore (2013) 1.7 Malaysia (2019) 1092

Brunei Darussalam (2020) 763
Papua New Guinea (2010) 1.6
0 2 000 4 000 6 000 8 000
Brunei Darussalam (2020) 1.6
Number of consultations
Solomon Islands (2006) 1.5
Source: OECD Health Statistics 2022; National Data Sources (see
Cambodia (2015) 0.6 Annex A).
Bangladesh (2010) 0.4 StatLink 2 https://stat.link/0djeif
0 5 10 15 20
Number of consultations
Source: OECD Health Statistics 2022; National Data Sources (see

Annex A).
StatLink 2 https://stat.link/ndqsx7
Figure 5.6. Doctor consultations per capita and healthy life expectancy at birth, latest year available
Healthy life expectancy at birth, years
76
74 JPN
SGP
72 KOR
AUS
70 NZL
CHN
68 THA
LKA
66 BRN MYS
BGD VNM
64
62
KHM R² = 0.2697
MNG
60
0 3 6 9 12 15
Doctor consultations per capita
Source: OECD Health Statistics 2022; WHO GHO 2020; National Data Sources (see Annex A).
StatLink 2 https://stat.link/tvwbx6

108 
Medical technologies
The need to prevent diseases, diagnose early and treat effectively under the Universal Health Coverage
mandate of the Sustainable Development Goals 3 calls for safe, effective, and appropriate medical care.
Medical technologies are crucial in the prevention, diagnosis and treatment of illness and diseases as well as
patient rehabilitation, but they also contribute to increases in health spending devices (WHO, 2017e). Computed
tomography (CT) scanners and magnetic resonance imaging (MRI) units help doctors diagnose a range of
conditions by producing images of internal organs and structures of the body. MRI exams do not expose patients
to ionising radiation, unlike conventional radiography and CT scanning. Mammography is used to diagnose
breast cancer, and radiation therapy units are used for cancer treatment. However, such equipment is expensive.
Data indicate that there are huge differences in availability of technologies across countries and territories, and
that the higher the country income level the higher the availability of medical equipment per million population
for all four selected medical equipment types.
Japan has by far the highest number of CT scanners per million population. More than 115 CT scanners are
available per million population in Japan, as opposed to less than one per million population in Bangladesh,
Pakistan, Papua New Guinea, Lao PDR and Myanmar (Figure 5.7). Also for MRI units, Japan reports 55 units
per million population, whereas Cambodia, Myanmar, Pakistan, the Philippines, Sri Lanka and Bangladesh,
report less than one unit per million population (Figure 5.8) Korea has the highest number of mammographs at
421.9 per million females aged 50-69, as opposed to Bangladesh, Pakistan, Myanmar, Sri Lanka and Papua
New Guinea, where less than 10 mammographs are available per million females aged 50-69 (Figure 5.9).
There is no general guideline or benchmark regarding the ideal number of CT scanners or MRI units per
population. However, if there are too few units, this may lead to access problems in terms of geographic proximity
or waiting times. If there are too many, this may result in an overuse of these costly diagnostic procedures, with
little if any benefits for patients. Although there is limited evidence on the use of medical technologies in the
Asia-Pacific region, data from OECD countries show that several countries with a high number of CT scanners
and MRIs, such the United States, also have a higher number of diagnostic exams per population, suggesting
some degree of overuse (OECD, 2017[1]).
The availability of treatment equipment is also much higher in high-income countries. Australia and Japan have
over 10 radiation therapy units per million population, whereas there is less than one unit per 10 million people
in Papua New Guinea, Cambodia, Bangladesh, Lao PDR, Indonesia, Pakistan, Nepal, Viet Nam, Myanmar, the
Philippines and India (Figure 5.10).
Clinical guidelines have been developed in some OECD countries to promote more rational use of diagnostic
technologies (OECD, 2017[1]). In the United Kingdom, the National Institute for Health and Clinical
Excellence (NICE) has issued a number of guidelines on the appropriate use of MRI and CT exams (NICE,
2020[2])e. In Australia, a “Choosing Wisely” campaign has developed clear guidelines for doctors and patients
to reduce the use of unnecessary diagnostic tests and procedures. The guidelines include, for instance, avoiding
imaging studies such as MRI, CT or X-rays for acute low back pain without specific indications (Choosing Wisely
Australia, 2020[3]). In Australia, clinicians may use Diagnostic Imaging Pathways (DIP), an evidence-based
clinical decision support tool and educational resource for diagnostic imaging. DIP guides the choice of the most
appropriate diagnostic examinations in the correct sequence in a wide range of clinical scenarios. The broad
objective is to reduce the number of unnecessary examinations that may expose patients to risk without benefits,
and increase the number of appropriate examinations resulting in cost-effective diagnosis (Government of
Western Australia, 2020[4]).

 109

The data cover equipment installed both in hospitals and the ambulatory sector and public and private sectors
in most countries and territories. However, there is only partial coverage for some countries and territories.
In Myanmar, data refer to equipment in the public sector. MRIs in Brunei Darussalam refer to those in the
private sector, and in Mongolia, radiation therapy units refer to those in the public sector. For Australia, the
number of medical technology equipment includes only those eligible for public reimbursement (about 60%
of total MRI units are eligible for reimbursement under Medicare, the universal public health system).
References
Choosing Wisely Australia (2020), Choosing Wisely - Recommendations, [3]

https://www.choosingwisely.org.au/recommendations.
Government of Western Australia (2020), Diagnostic Imaging Pathways, [4]

http://imagingpathways.health.wa.gov.au/.
NICE (2020), NICE guidance, National Institute for Health and Care Excellence, http://nice.org.uk. [2]
OECD (2017), Tackling Wasteful Spending on Health, OECD Publishing, Paris, [1]
https://doi.org/10.1787/9789264266414-en.

110 
Figure 5.7. Computed tomography scanners, Figure 5.8. MRI units, latest year available
latest year available
Japan 115.7 Japan 55.2

Australia 69.4 Korea 30.1
New Zealand 45.0
OECD 16.7
Korea 40.6
New Zealand 15.4
OECD 29.0
Singapore 8.9 Australia 14.8
Mongolia 8.1 Singapore 7.8
Brunei Darussalam 7.2 Malaysia 2.9
Malaysia 6.4
Brunei Darussalam 2.4
Thailand 6.0
Mongolia 1.4
Fiji 3.4
Sri Lanka 1.7 Fiji 1.1
Cambodia 1.2 Bangladesh 0.5
Philippines 1.1 Sri Lanka 0.4
Myanmar 0.8
Philippines 0.3
Lao PDR 0.7
Pakistan 0.2
Papua New Guinea 0.4
Pakistan 0.3 Myanmar 0.1
Bangladesh 0.3 Cambodia 0.1
0 50 100 150 0 20 40 60
Per million population Per million population
Source: OECD Health Statistics 2022; WHO Global atlas of Source: OECD Health Statistics 2022; WHO Global atlas of
medical devices 2022 (forthcoming). medical devices 2022 (forthcoming).
StatLink 2 https://stat.link/uypzv6 StatLink 2 https://stat.link/j1l6qv
Figure 5.9. Mammographs, latest year Figure 5.10. Radiation therapy equipment,
available latest year available
Korea 421.9 Australia 11.8

Japan 10.1
Japan 267.4 New Zealand 8.4
Australia 201.2 OECD 8.0
OECD 176.2
Korea 6.6
Singapore 5.1
New Zealand 162.4 Brunei Darussalam 4.6
Singapore 127.7 Malaysia 2.3
Thailand 2.0
Brunei Darussalam 91.9
Mongolia 1.8
Malaysia 86.7 China 1.3
Mongolia 33.3 Sri Lanka 1.1
India 0.7
Fiji 28.8
Philippines 0.6
Thailand 27.9 Myanmar 0.5
Philippines 13.1 Viet Nam 0.5
Nepal 0.4
Papua New Guinea 8.5 Pakistan 0.4
Sri Lanka 2.8 Indonesia 0.3
Myanmar 2.2 Lao PDR 0.3
Bangladesh 0.3
Pakistan 1.6 Cambodia 0.2
Bangladesh 1.1 Papua New Guinea 0.1
0 200 400 600 0 2 4 6 8 10 12 14

Per million females aged 50-69 Per million population
Source: OECD Health Statistics 2022; WHO Global atlas of Source: OECD Health Statistics 2022; WHO Global atlas of
medical devices 2022 (forthcoming). medical devices 2022 (forthcoming).
StatLink 2 https://stat.link/glemor StatLink 2 https://stat.link/y2zl93

 111
Hospital care
Hospitals in most countries and territories account for the largest part of health care expenditure. Capacity of
the hospital sector and access to hospital care are assessed in this report by the number of hospital beds and
hospital discharge rates. However, increasing the numbers of beds and overnight stays in hospitals does not
always bring positive outcomes as resources need to be used efficiently. Hence, the average length of
stay (ALOS) is also used to assess appropriate access to and use of hospital care, but caution is needed in its
interpretation. Although, all other things being equal, a shorter stay will reduce the cost per discharge and provide
care more efficiently by possibly shifting care from inpatient to less expensive post-acute settings, too short a
length of stay may reduce the comfort and hamper the recovery of the patient or increase hospital readmissions.
The number of hospital beds is 2.6 and 2.8 per 1 000 population on average across upper-middle and lower-
middle and low-income Asia-Pacific countries and territories, respectively; lower than the OECD average of 4.6
and the high-income Asia-Pacific countries and territories average of 5.4 (Figure 5.11). More than one bed per
100 population is available in DPRK, Korea and Japan, whereas the stock of beds is less than one per 1 000
population in India, Pakistan, Bangladesh and Cambodia. These large disparities reflect substantial differences
in the resources invested in hospital care across countries and territories.
Hospital discharge is at 121.3 and 130 per 1 000 population on average in upper-middle and lower-middle and
low-income Asia-Pacific countries and territories, respectively; close to the OECD average of 130.6
(Figure 5.12). The highest rates are in Sri Lanka and Mongolia, with over 275 discharges per 1 000 population
in a year, while in Bangladesh, Cambodia and Nepal, discharge rates are less than 50 per 1 000 population,
suggesting deferrals in accessing hospital services.
In general, countries and territories with more hospital beds tend to have higher discharge rates, and vice versa
(Figure 5.13). However, there are some notable exceptions. Korea and Japan, with the second and third highest
number of hospital beds per population, respectively, have relatively low discharge rates; while Sri Lanka, with
a close-to-average hospital beds availability for the region, has the highest discharge rate.
In Asia-Pacific, the variation across countries and territories in the number of days spent – on average – in
hospital is large (Figure 5.14). Lower-middle- and low-income countries and territories report the lowest ALOS
in Asia-Pacific at 4.9 days. The longest average length of stay is of more than 16 days in Japan, while the
shortest length of stay is 2.5 days in Lao PDR and Bangladesh. In Japan, “social admission”, in that some “acute
care” beds are devoted to long-term care for the elderly, partly explains the large number of beds and long ALOS
(Sakamoto, Rahman and Nomura, 2018[1]). A short ALOS, coupled with the high admission rates in Sri Lanka,
suggests that inpatient services may be partly substituting for outpatient and primary care.

All hospital beds include those for acute care and chronic/long-term care, in both the public and private
sectors. A discharge is defined as the release of a patient who has stayed at least one night in hospital. It
includes deaths in hospital following inpatient care but usually excludes same-day separations. The
discharge rates presented are not age-standardised, not considering differences in the age structure of the
population across countries and territories.
The figures reported for ALOS refer to the number of days that patients spend overnight in an acute-care
inpatient institution. ALOS is generally measured by dividing the total number of days stayed by all patients
in acute-care inpatient institutions during a year by the number of admissions or discharges. There are
considerable variations in how countries and territories define acute care, and what they include or exclude
in reported statistics. For the most part, reported ALOS data in the developing countries and territories of the
Asia-Pacific region cover only public sector institutions.
References
Sakamoto, A., M. Rahman and S. Nomura (2018), Japan Health System Review, Health Systems in [1]
Transition, World Health Organization, Regional Office for South-East Asia,

112 
Figure 5.11. Hospital beds per 1 000 Figure 5.12. Hospital discharges per 1 000
population, latest year available population, latest year available
DPRK (2010) 14.3 Sri Lanka (2019) 345
Korea (2020) 12.7
Japan (2020) 12.6 Mongolia (2017) 276
Mongolia (2017) 8.0 Hong Kong (China) (2020) 252
Asia Pacific-H 5.4
China (2020) 5.0 China (2019) 182
OECD 4.6 Australia (2019) 174
Hong Kong (China) (2020) 4.1 Korea (2020) 154
Australia (2016) 3.8 New Zealand (2019) 146
Brunei Darussalam (2020) 2.9 Asia Pacific-H 144
Asia Pacific-LM/L 2.8 Thailand (2018) 144
New Zealand (2021) 2.7
Viet Nam (2014) 2.6 Japan (2017) 134
Asia Pacific-UM 2.6 OECD 131
Macau (China) (2020) 2.5 Asia Pacific-LM/L 130
Thailand (2018) 2.1
Fiji (2017) 2.0 Asia Pacific-UM 121
Singapore (2020) 2.0 Singapore (2018) 104
Lao PDR (2012) 1.5 Brunei Darussalam (2020) 100
Solomon Islands (2012) 1.4
Malaysia (2019) 1.3 Macau (China) (2019) 92
Nepal (2016) 1.2 Malaysia (2019) 86
Myanmar (2017) 1.0
Indonesia (2017) 1.0 Fiji (2017) 73
Philippines (2014) 1.0 Myanmar (2016) 52
Cambodia (2016) 0.9 Nepal (2019/20) 42
Bangladesh (2019) 0.9
Pakistan (2017) 0.6 Cambodia (2011) 41
India (2017) 0.5 Bangladesh (2011) 24
0 4 8 12 16 0 50 100 150 200 250 300 350 400
Source: OECD Health Statistics 2022; WHO GHO 2020, Source: OECD Health Statistics 2022; National sources (see
Hong Kong annual statistic digest 2021, National sources (see Annex A).
Annex A). StatLink 2 https://stat.link/ldaf3b
StatLink 2 https://stat.link/65m3na
Figure 5.13. Hospital beds per 1 000 Figure 5.14. Average length of stays for acute
population and hospital discharges per 1 000 care in hospitals, latest year available
population, latest year available
Japan (2020) 16.4
China (2018) 9.3
Hospital discharges, per 1 000 population Brunei Darussalam (2020) 8.0
400 Mongolia (2011) 7.9
Korea (2020) 7.8
350 Macau (China) (2020) 7.8
LKA Asia Pacific-H 7.4
India (2014) 6.8
300 Viet Nam (2003) 6.7
MNG OECD 6.5
250 HKG Fiji (2011) 6.0
Papua New Guinea (2008) 6.0
Asia Pacific-UM 5.8
200 Myanmar (2016) 5.1
AUS CHN Cambodia (2011) 5.0
Singapore (2013) 5.0
150 THA NZL KOR
Philippines (2012)
OECD JPN 4.9
SGP
100 MYS BRN New Zealand (2019) 4.7
MAC Australia (2019) 4.7
FJI R² = 0.188 Hong Kong (China) (2010) 4.7
50 MMR
KHM Indonesia (2011) 4.4
NPL Malaysia (2019) 4.1
BGD Thailand (2009) 4.0
0
0 2 4 6 8 10 12 14 Nepal (2019/20)
Hospital beds, per 1 000 population 3.0
Bangladesh (2017) 2.5
Lao PDR (2012) 2.5
0 5 10 15 20
Source: OECD Health Statistics 2022; WHO GHO 2022. Days
StatLink 2 https://stat.link/sjpe57 Source: OECD Health Statistics 2022; National data sources (see
Annex A).
StatLink 2 https://stat.link/08h6bc

 113
Pregnancy and birth

Antenatal care, delivery attended by skilled health professionals and access to health facilities for delivery are
important for the health of both mothers and their babies as they reduce the risk of birth complications and
infections (see indicators on “Infant feeding” in Chapter 4). WHO currently recommends a minimum of eight
antenatal contacts (WHO, 2016[1]), and antenatal care coverage has been monitored to ensure universal access
to sexual and reproductive health care services, including for family planning, information and education, and
the integration of reproductive health into national strategies and programmes by 2030 (Sustainable
Development Goal 3.7). Receiving antenatal care at least four times increases the likelihood of receiving
effective maternal health interventions during the antenatal period. This is one of the indicators in the Global
Strategy for Women’s, Children’s and Adolescents’ Health (2016-2030) Monitoring Framework, and one of the
tracer indicators of health services for the universal health coverage (SDG indicator 3.8.1)
In Asia-Pacific, seven in ten pregnant women  on average  received the recommended four visits in lower-middle-
and low-income countries and territories, but access to antenatal care varies across countries and territories
(Figure 5.15, left panel). Malaysia and the Korea have nearly complete coverage of four antenatal visits. At the
other end, in Bangladesh and Papua New Guinea the coverage of four antenatal care visits is less than 50%.
The majority of births (99%) in high and upper-middle-income Asia-Pacific are attended by a skilled health
professional. This contrasts with lower-middle and low-income countries, where 81.5% of births are attended by
a skilled health professional (Figure 5.15, right panel). Skilled birth attendance is relatively low in Papua New
Guinea (56.4%), Bangladesh (59%) and Myanmar (60.2%), where home births supported by untrained
traditional birth attendants are more common.
In Asia-Pacific, delivery in health facilities varies across countries and territories (Figure 5.16). In Thailand,
Mongolia, Viet Nam and DPRK, almost all deliveries take place at a health facility. On the other hand, in
Bangladesh, most deliveries occur at home and less than 55% of births takes place in a health facility. Across
countries and territories, deliveries in health facilities are more common among mothers giving birth for the first
time, or those who have had at least four antenatal visits, as well as among mothers living in urban regions and
those with higher education and wealth.
Access to skilled birth attendants varies by socio-economic factors (Figure 5.17). Mongolia, Thailand and DPRK
have a high coverage of births attended by skilled health professionals among mothers with different education
and income levels, as well as living in different geographical locations. However, in other countries and territories,
the coverage of births attended by skilled health professionals is highly unequal among women of different income
and education levels. For example, in Lao PDR and Bangladesh, access differs almost three-fold between mothers
of the lowest education level and mothers of the highest education levels. Disparity by household income is largest
in Lao PDR and Bangladesh, again with almost three-fold difference between mothers living in household at the
highest and at the lowest income quintiles. In contrast, differences in access to skilled care at birth remain relatively
small between urban and rural areas across countries and territories (except in Lao PDR, Nepal, and Bangladesh).

The major source of information on care during pregnancy and birth are health interview surveys.
Demographic and Health Surveys (DHS), for example, are nationally representative household surveys that
provide data for a wide range of indicators in the areas of population, health, and nutrition. Standard DHS
Surveys have large sample sizes (usually between 5 000 and 30 000 households) and typically are
conducted every five years, to allow comparisons over time. Women who had a live birth in the five years
preceding the survey are asked questions about the birth, including how many antenatal care visits they had,
who provided assistance during delivery, and where the delivery took place.
References
WHO (2016), WHO recommendations on antenatal care for a positive pregnancy experience, World Health [1]

114 
Figure 5.15. Provision of care during pregnancy and birth, 2021 or latest year available
At least 4 antenatal visits during last pregnancy Births attended by skilled health personnel
Malaysia
Korea
Asia Pacific-H
DPRK
OECD
Brunei Darussalam
China
Sri Lanka
Asia Pacific-UM
Australia
Thailand
Fiji
Mongolia
Viet Nam
Philippines
Nepal
Indonesia
Cambodia
Asia Pacific-LM/L
Solomon Islands
Lao PDR
Myanmar
India
Pakistan
Papua New Guinea
Bangladesh
Japan
New Zealand
Singapore
100 80 60 40 20 0 0 20 40 60 80 100
% %
Note: Women included are aged 15-49.
Source: UNICEF 2022.
StatLink 2 https://stat.link/nacobx
Figure 5.16. Place of delivery, latest year Figure 5.17. Births attended by skilled health
available professionals, by socio-economic and
geographic factors, latest year available
Other/Missing Private facility Public facility Home
% Lowest education Highest education
100 Rural Urban
Lowest income Highest income
Solomon Islands (2015)

80
Lao PDR (2017)
Myanmar (2015-16)
60 Lao PDR (2017)
Mongolia (2018)
40 Thailand (2019)
Viet Nam (2020-2021)
20 Pakistan (2017-18)
Nepal (2019)
0 DPRK (2017)
Bangladesh (2019)
0 20 40 60 80 100
% of skilled birth attendance by socio-economic factors
Source: DHS and MICS surveys, various years. Source: DHS and MICS surveys, various years.
StatLink 2 https://stat.link/xyhpls StatLink 2 https://stat.link/8ydqjb

 115
Infant and child health

Basic care for infants and children includes promoting and supporting early and exclusive breastfeeding (see
indicators on “Infant feeding” in Chapter 4) and identifying conditions requiring additional care and counselling
on when to take an infant and young child to a health facility. There are several cost-effective preventive and
curative services for leading causes of childhood morbidity and mortality. These comprise vitamin A
supplementation, measles vaccination, oral rehydration therapy (ORT) and zinc supplementation for severe
diarrhoea, and antibiotic treatment for acute respiratory infection (ARI) (Bhutta et al., 2013[1]).
As a safe and effective vaccine is available for measles, its coverage has been used to monitor the progress
towards achieving the SDG target 3.2 to end preventable deaths of newborns and children under 5 years of age
by 2030. This vaccine is also considered a marker of access of children to health services.
Access to preventive care varies across Asia-Pacific as shown by children receiving two annual high-dose
vitamin A supplementations (Figure 5.18) and vaccination coverage (see indicator “Childhood vaccination” in
Chapter 7). Access to vitamin A supplementation is markedly low in the Philippines, Papua New Guinea, and
the Solomon Island with less than 40%, whereas Bangladesh, DPRK and Myanmar have nearly complete
coverage.
Less than one child in four with diarrhoea in the Philippines, Viet Nam, Mongolia and Lao PDR, and less than
one child in ten with diarrhoea in the Solomon Islands, Cambodia, Papua New Guinea and Myanmar, received
oral rehydration solution and zinc supplement (Figure 5.19). Furthermore, less than half of children with
diarrhoea received continued feeding and ORT in Pakistan, the Philippines, India and Papua New Guinea. The
coverage was as high as 71% in Mongolia, DPRK and Thailand (Figure 5.20).
Access to appropriate medical care for children with ARI can also be improved in many countries and territories
in the region. Although almost three-quarters of children with symptoms are taken to a health facility, only less
than two-thirds of them receive antibiotic treatment (Figure 5.21). There is a correlation between treatment
coverage for diarrhoea and ARI. Antibiotic treatment for ARI is particularly low in
Myanmar, the Philippines and Pakistan, where the treatment for diarrhoea is also low. This suggests a need to
expand access to care to treat leading causes of child mortality in these countries and territories.

Prevention and treatment coverage data are usually collected through household surveys. Accuracy of
survey reporting varies and is likely to be subject to recall bias. Seasonal influences related to the prevalence
of diarrhoeal disease and acute respiratory infection may also affect cross-national data comparisons.
Children aged 6-59 months who received vitamin A supplementation refers to full dose.
Children aged under 5 years with diarrhoea receiving continued feeding and ORT refers to those receiving
continued feeding and oral rehydration solution, gruel or increased fluids.
The prevalence of acute respiratory infection is estimated by asking mothers whether their children under
five had been ill with a cough accompanied by short, rapid breathing in the two weeks preceding a survey,
as these symptoms are compatible with ARI.
References
Bhutta, Z. et al. (2013), “Interventions to address deaths from childhood pneumonia and diarrhoea [1]
equitably: What works and at what cost?”, The Lancet, Vol. 381/9875, pp. 1417-1429,
https://doi.org/10.1016/S0140-6736(13)60648-0.

116 
Figure 5.18. Children aged 6-59 months who Figure 5.19. Children aged under 5 years with
received full dose vitamin A supplementation, diarrhoea receiving oral rehydration solution
latest year available and zinc supplements, latest year available
Bangladesh (2020) 97 DPRK (2017) 45
DPRK (2020) 93 Bangladesh (2019) 35
Myanmar (2019) 93 Nepal (2019) 29
Nepal (2020) 85 India (2019-2021) 24
Viet Nam (2011) 83 Viet Nam (2021) 21
Cambodia (2019) 72 Indonesia (2017) 18
Pakistan (2017/18) 64 Fiji (2021) 17
Indonesia (2012) 61 Philippines (2017) 17
Mongolia (2013) 61 Mongolia (2018) 13
Lao PDR (2020) 54 Lao PDR (2017) 13
India (2020) 54 Pakistan (2017 - 2018) 8
Solomon Islands (2015) 37 Myanmar (2015-2016) 7
Papua New Guinea (2020) 34 Papua New Guinea (2018) 5
Philippines (2020) 29 Cambodia (2014) 3
0 20 40 60 80 100 0 20 40 60 80 100
% %
Source: UNICEF 2012; DHS and MICS surveys, various years. Source: DHS and MICS surveys, various years.
StatLink 2 https://stat.link/r1me7i StatLink 2 https://stat.link/obis65
Figure 5.20. Children aged under 5 years with Figure 5.21. Care seeking and antibiotic
diarrhoea receiving continued feeding and treatment among children aged under 5 years
oral rehydration therapy, latest year available with acute respiratory infection, latest year
available
Mongolia (2018) 71
Taken to a health facility
DPRK (2017) 71 With antibiotic treatment
Thailand (2015-16) 71 Malaysia (2016) 92

DPRK (2017) 86
Sri Lanka (2016) 63
Nepal (2019) 82
Nepal (2019) 62 41
Thailand (2016/2015) 80
Lao PDR (2017) 61 70
Solomon Islands (2015) 79
Indonesia (2017) 61 76
Mongolia (2018/2013) 63
Myanmar (2016) 56 Indonesia (2017) 75
34
Solomon Islands (2015) 56 Viet Nam (2021) 73
69
Cambodia (2014) 52 Pakistan (2018/2017-18) 71
43
Viet Nam (2021) 51 Cambodia (2014) 69
66 81
Bangladesh (2019) 51 Philippines (2017/2013) 50
Fiji (2021) 50
Myanmar (2016) 43 58
India (2021) 56
India (2019-2021) 45 52
Sri Lanka (2016)
Philippines (2017) 45 46
Bangladesh (2019) 63
Pakistan (2018) 40
35 Lao PDR (2017) 45
0 20 40 60 80 100 0 20 40 60 80 100
% %
Source: UNICEF 2021; NHFS, DHS and MICS surveys, various Note: First year refers to children taken to a health facility, the
years. second year refers to those who received antibiotic treatment.
StatLink 2 https://stat.link/vhwqpk Source: UNICEF 2021, NHFS, DHS and MICS surveys, various years.
StatLink 2 https://stat.link/3l56sp

 117
Mental health care

For the first time, world leaders have recognised the promotion of mental health and well-being, and the
prevention and treatment of substance abuse, as health priorities within the global development agenda. The
inclusion of mental health and substance abuse in the Sustainable Development Agenda, which was adopted
at the United Nations General Assembly in September 2015, is likely to have a positive impact on communities
and countries and territories where millions of people will receive much needed help. A particular prevention
priority in the area of mental health concerns suicide, which accounted for an estimated 793 000 deaths in 2016
(WHO, 2018[1]). Target 3.2 of the Mental Health Action Plan 2013-20, calls for a 10% reduction in the rate of
suicide in countries by 2020. The UN Sustainable Development Goals include target 3.4 to address non-
communicable diseases and mental health with an indicator to reduce suicide mortality by a third by 2030.
In many parts of the Asia-Pacific region, appropriate care may not be available and access to mental health care
may be limited for people with mental health problems. Access to mental health care can be assessed by the
supply of professionals and the availability of psychiatric beds in different settings such as general hospitals,
mental health hospitals and community facilities.
Psychiatrists are generally responsible for the prevention, diagnosis and treatment of a variety of mental health
problems, including schizophrenia, depression, learning disabilities, alcoholism and drug addiction, eating
disorders and personality disorders. The number of psychiatrists is lower in all countries and territories in Asia-
Pacific, except New Zealand, than the OECD average of 18.1 per 100 000 population (Figure 5.22). Developed
OECD countries in the region such as New Zealand, Australia, Japan and Korea, report the highest number of
psychiatrists, whereas in middle- and low-income Asia-Pacific countries and territories there is fewer than one
psychiatrist on average per 100 000 population. This suggests that many countries and territories in the region
may underinvest in mental health care. As is the case for many other medical specialties (see indicator “Doctors
and nurses” in Chapter 5), psychiatrists are not distributed evenly across jurisdictions within each country and
territory. For example, in Australia, when considering time spent as a clinician, there were 11 clinical full-time
equivalent psychiatrists per 100 000 population, with rates ranging from 6.6 in the Northern Territory to 12.3 in
South Australia (Australian Institute of Health and Welfare, 2019[2]).
Mental health nurses play an important and increasing role in the delivery of mental health services in hospital,
primary care, or other settings, but in many Asia-Pacific countries and territories, their number is still very low
(Figure 5.23). Australia has the highest rate with almost 90 mental health nurses per 100 000 population,
followed by New Zealand with more than 70 nurses per 100 000 population. However, there are fewer than five
mental health nurses – on average – per 100 000 population in middle- and low-income Asia-Pacific countries
and territories, and less than one nurse per 100 000 population in Pakistan, Cambodia, Bangladesh, Nepal,
Myanmar and the Philippines, suggesting again the need for an appropriate supply of mental health care
workforce to guarantee access.
Some countries, such as Australia, have introduced programmes to improve access to mental health care by
extending the role of mental health nurses in primary care. Under the Mental Health Nurse Incentive Program
launched in 2007, mental health nurses in Australia work with general practitioners, psychiatrists and other
mental health professionals to treat people suffering from different mental health conditions. An evaluation of
this programme found that mental health nurses have the potential to make a significant contribution to enhance
access and quality of mental health care through flexible and innovative approaches (Australian Department of
Health and Ageing, 2012[3]).
For the last decade, WHO flagship programme for mental health is the “mental health Gap Action programme
(mhGAP)” (WHO, 2016[4]). The programme includes the scaling up of care for priority mental, neurological and
substance use conditions in non-specialised care settings, such as PHC. The programme has produced WHO-
Guidelines Review Committee (GRC) approved recommendations for the management of above mentioned
priority conditions. The programme also produced the mhGAP Intervention Guide, which is a practical tool for
non-specialist clinicians, and which comes with a relevant set of implementation tools as well as a further
simplified version for humanitarian and health emergency settings. Currently, mhGAP is implemented in
90 countries.

118 
There are 7.5 and 19.9 mental health beds per 100 000 population on average in lower-middle- and low-income,
and upper-middle-income Asia-Pacific countries and territories, respectively, with Bangladesh, Papua New
Guinea and Nepal reporting less than two psychiatric beds per 100 000 population (Figure 5.24). The large
majority of beds in middle- and low-income countries and territories are available in mental health hospitals.

Psychiatrists have post-graduate training in psychiatry and may also have additional training in a psychiatric
specialty, such as neuropsychiatry or child psychiatry. Psychiatrists can prescribe medication, which
psychologists cannot do in most countries and territories. Data include psychiatrists, neuropsychiatrists and
child psychiatrists, but psychologists are excluded. Mental health nurses usually have formal training in
nursing at a university level.
Data are based on head counts.
References
Australian Department of Health and Ageing (2012), Evaluation of the Mental Health Nurse Incentive [3]
Program Final Report.
Australian Institute of Health and Welfare (2019), Mental health services - in brief 2019. [2]
WHO (2018), Mental health atlas 2017, World Health Organization, [1]
WHO (2016), mhGAP intervention guide for mental, neurological and substance use disorders in non- [4]
Specialized health settings : mental health gap action programme (mhGAP), World Health Organization,

 119
Figure 5.22. Psychiatrists, per 100 000 Figure 5.23. Nurses working in mental health
population, 2020 or latest year available sector, per 100 000 population, 2020 or latest
year available
New Zealand 20.0 Australia 87.9

OECD 18.1 New Zealand 71.6
Australia 18.0 OECD 53.2
Japan 13.0 Asia Pacific-H 42.1
Asia Pacific-H 11.2 Singapore 21.9
Korea 8.0 Brunei Darussalam 15.0
Singapore 4.3 Korea 14.0
Brunei Darussalam 3.7 China 5.7
Mongolia 3.1 Thailand 5.5
China 2.6 Mongolia 5.3
Asia Pacific-UM 1.3 Fiji 5.2
Malaysia 1.2
Asia Pacific-UM 4.8
Viet Nam 1.0
Sri Lanka 2.9
Thailand 0.9
Viet Nam 2.9
Nepal 0.6
Malaysia 2.9
Indonesia 2.3
Sri Lanka 0.6
Fiji 0.6
Indonesia 0.4 Solomon Islands 1.3
Solomon Islands 0.3 Papua New Guinea 1.2
Cambodia 0.3 Philippines 0.8
Philippines 0.2 Myanmar 0.6
Myanmar 0.2 Nepal 0.4
Bangladesh 0.2 Bangladesh 0.4
Papua New Guinea 0.1 Cambodia 0.2
Pakistan 0.1 Pakistan 0.1
0 5 10 15 20 25 0 20 40 60 80 100
Source: OECD Health Statistics 2022; WHO Mental Health Atlas Source: OECD Health Statistics 2022; WHO Mental Health Atlas
2020. 2020.
StatLink 2 https://stat.link/wkbvh5 StatLink 2 https://stat.link/70if6y
Figure 5.24. Mental health beds, per 100 000 population, 2020 or latest year available
In general hospitals In mental hospitals In community residential facilities

Per 100 000 population
250
200
150
100
50
Source: OECD Health Statistics 2022; WHO Mental Health Atlas 2020.
StatLink 2 https://stat.link/q957hc

120 
Access to health care

People should be able to access health services when they need to, irrespective of their gender, economic status,
education, and place of residence. The United Nations 2030 Agenda for Sustainable Development aims to leave no
one behind, and it is said explicitly in SDG 10 “to reduce inequality within and among countries”. SDG 3 is a call to
ensure healthy lives and promote well-being for all at all ages, which implies tackling inequalities in health. However,
differences in access to health care for women aged 15-49 either due to financial issues or distance to health facility
are commonplace across countries in Asia-Pacific. Additionally, an extra layer of restrictions on access to health
care for indigenous women in Asia-Pacific seems to exist as well, with indigenous women experiencing more health
vulnerabilities when compared to non-Indigenous women, including continuous challenges and barriers to access
quality and equitable health care services (Thummapol, Park and Barton, 2018[1]).
Women aged 15-49 report problems in access to care due to financial reasons, and the proportion of women
with no education reporting problems in accessing care due to financial reasons is consistently higher than the
proportion of women with secondary or higher education. Differences in access to care for financial reasons are
also reported for women living in rural areas vis-à-vis urban areas, and for women from households in the lowest
income quintile compared to women from households in the highest income quintile. Differences in access to
care by social determinant are larger in countries such as Papua New Guinea, and Cambodia, while differences
are narrower in Indonesia, India and Pakistan. In India, women aged 15-49 from households in the lowest
income quintile have 3.6 times more difficulties in access to care due to financial reasons when compared to
those from households in the highest income quintile (see Figure 5.25).
Distance to providers represent another barrier in access to health care for women aged 15-49 in Asia-Pacific
countries. Women either with higher education levels, from households in the higher income quintile, or living in
urban areas report less problems in access care than those with lower education, from households in the lower
income quintile, or living in rural areas. Differences are larger for countries such as Papua New Guinea, Nepal
and Pakistan, while for Indonesia, India and Bangladesh, differences in access to care due to distance to
providers by social determinant are comparatively narrower. In Myanmar, women aged 15-49 from households
in the lowest income quintile have 3.9 times more difficulties in access to care due to distance to providers
compared to those from households in the highest income quintile, while difficulties in access to care for those
with lower education are 3.8 times higher than for those with the highest education (see Figure 5.26).
Inequalities in access to health care are also reported in OECD countries. A quarter of individuals aged 18 or
older report unmet need (defined as forgoing or delaying care) because limited availability or affordability of
services compromise access. People may also forgo care because of fear or mistrust of health service providers.
Strategies to reduce unmet need, particularly for the less well-off, need to tackle both financial and non-financial
barriers to access (OECD, 2019[2]).

Indicators consider women aged 15-49. By accessing health care, the indicators refer to any type of health
care when the respondent is sick, and these are not limited to reproductive health care.
In the DHS survey, problems in accessing care due to financial reasons consider respondents who indicated
that the issue was “getting money needed for treatment”, while for distance the indicated issue was related to
“distance to health facility”. When referring to “lowest education”, this could also mean “no formal education”.
References
OECD (2019), Health for Everyone?: Social Inequalities in Health and Health Systems, OECD Health Policy [2]
Studies, OECD Publishing, Paris, https://doi.org/10.1787/3c8385d0-en.
Thummapol, O., T. Park and S. Barton (2018), “Exploring health services accessibility by indigenous [1]
women in Asia and identifying actions to improve it: a scoping review”, Ethnicity & Health,
Vol. 25/7, pp. 940-959, https://doi.org/10.1080/13557858.2018.1470607.

 121
Figure 5.25. Women aged 15-49 who reported Figure 5.26. Women aged 15-49 who reported
problems in accessing care due to financial problems in accessing care due to distance,
reasons, by socio-economic characteristics, by socio-economic characteristics, latest
latest year available year available
Lowest education Highest education Lowest education Highest education
% %
100 100
80 80
60 60
40 40
20 20
0
0

%
%
100
100
80
80
60
60
40
40
20 20
0 0
Rural Urban Rural Urban
% %
100 100
80
80
60
60
40
20 40
0 20
Source: DHS surveys, various years. Source: DHS surveys, various years.
StatLink 2 https://stat.link/ng8sfq StatLink 2 https://stat.link/jrzcln

122 
6 Health expenditure and financing

 123
Health expenditure per capita and in relation to GDP

Across Asia-Pacific countries, per capita health spending continued to rise prior to the COVID-19 pandemic.
Low- and lower-middle-income Asia-Pacific countries per capita health spending increased by 65% between
2010 and 2019, while in upper middle countries it grew by 76% during the same period; spending in high-income
countries also grew, but more modestly at 33%. Despite this, huge differences in per capita health care spending
remained in Asia-Pacific countries in 2019 (Figure 6.1), ranging from only 105 international dollars (USD PPPs)
in Bangladesh to 5 294 international dollars (USD PPPs) in Australia. For comparison, average OECD current
health spending per capita in 2019 was around 15 times that of the low-income countries in Asia-Pacific (4 353
versus 286 USD PPPs).
How much countries spend on health care as a share of GDP over time can be ascribed to both changes in
health spending and economic performance. The health care sector continued to expand faster than the overall
economy in Asia-Pacific, resulting in an increasing share of the economy devoted to health. On average,
between 2010 and 2019, the growth rate in per capita health spending in real terms was 4.7% per year; higher
than the 3.6% observed for gross domestic product (GDP) (Figure 6.2). All countries above the diagonal line in
Figure 6.2 reported that health expenditure has grown faster than the economy. This means that the share of
health care expenditure in all GDP expenditure has continued to increase. For both health spending and overall
economic activity, growth in China was the strongest in the region – more than twice the average rate. By
contrast, Brunei Darussalam was the only country to report a decrease in both per capita health spending and
GDP in real terms between 2010 and 2019.
Health expenditure accounted for 3.9% of GDP in low- and lower-middle-income countries in 2019, unchanged
from 2010. Health expenditure accounted for 4.2% and 7.5% of GDP in upper-middle-income and high-income
Asia-Pacific countries respectively in 2019, an increase of 0.6 and 1 percentage point compared to 2010. In
2019, the share of GDP varied from a low of 2.2% in Brunei Darussalam up to 10.7% in Japan (Figure 6.3).
Generally, the richer a country is, the greater the share of their income devoted to health care. The percentage
of GDP spent on health across OECD countries is – on average – more than twice that of the Asia-Pacific low-
and middle-income countries (8.7% versus 4%) and 1 percentage point higher than that in high-income
countries. Between 2010 and 2019, the share of health in relation to GDP declined by more than 3 percentage
points in Solomon Islands, whereas it increased in Myanmar, China, Korea, Australia and Japan 1 by more than
1 percentage point (Figure 6.3).
Although health systems remain a highly labour-intensive sector, capital has become an increasingly important
factor of production of health services over recent decades, as reflected, for example, by the growing importance
of diagnostic and therapeutic equipment or the expansion of information and communications technology in
health care. However, capital investments in health tend to be more susceptible to economic cycles than current
spending on health care. As a proportion of GDP, Philippines, China and Australia were the highest spenders
on capital investment in 2019 with more than 0.5% of their GDP going on construction, equipment and
technology in the health sector (Figure 6.4), whereas less than 0.1% of GDP was spent in capital investment in
health in Brunei Darussalam in 2019.

Current health expenditure is defined by the sum of expenditure for all the core health care functions – that
is total health care services, medical goods dispensed to outpatients, prevention and public health services,
and health administration and health financing. Expenditure on these functions is included as long as it is
final consumption for residents in the country or abroad. For this reason, imports for final use are included
and exports for final use are excluded.
Economy-wide Purchasing Power Parities (PPPs) are used as the most available conversion rates. These
are based on a broad basket of goods and services, chosen to be representative of all economic activity. The
use of economy-wide PPPs means that the resulting variations in health expenditure across countries
reported in international dollars (USD PPPs) reflect not only variations in the volume of health services, but
also any variations in the prices of health goods and services relative to prices in the rest of the economy.

124 
To make useful comparisons of real growth rates over time, it is necessary to deflate (i.e. remove inflation
from) nominal health expenditure using a suitable price index, and also to divide by the population, to derive
real spending per capita. Due to the limited availability of reliable health price indices, an economy-wide
(GDP) price index is used in this publication.
The annual average growth rate was computed using a geometric growth rate formula:
((9√2019 value ⁄ 2010 value)-1)*100
Gross fixed capital formation in the health sector is measured by the total value of the fixed assets that health
providers have acquired during the accounting period (less the value of the disposals of assets) and that are
used repeatedly or continuously for more than one year in the production of health services. The breakdown
by assets includes infrastructure (e.g. hospitals, clinics), machinery and equipment (including diagnostic and
surgical machinery, ambulances, and ICT equipment), as well as software and databases. Gross fixed capital
formation is reported by many countries under the System of Health Accounts.
Note
1
A break in series for Japan in 2011 contributes to this result.

 125
Figure 6.1. Health expenditure per capita, Figure 6.2. Annual average growth rate in per
2019 capita health expenditure and GDP, real
terms, 2010-19
Government/Compulsory Voluntary/Out-of-pocket Health expenditure (%)
10
Australia CHN
Japan
New Zealand
OECD
Singapore 8
Asia Pacific-H SLB
Korea
Brunei Darussalam KOR
Malaysia MYS MNG
China 6
SGP KHM
Asia Pacific-UM LKA
Thailand PAK FJI BGD
Sri Lanka IND
Viet Nam THA IDN PHL LAO
4
Fiji NPL
JPN
Mongolia PNG
Philippines
Indonesia AUS
Cambodia 2
Asia Pacific-LM/L NZL
Myanmar
Lao PDR
India 0
Nepal
Pakistan BRN
Bangladesh
Papua New Guinea
-2
0 1000 2000 3000 4000 5000 6000 -2 0 2 4 6 8 10
USD PPP
GDP (%)
Source: WHO Global Health Expenditure Database; OECD Health Source: WHO Global Health Expenditure Database.
Statistics 2022. StatLink 2 https://stat.link/xg2ij9
StatLink 2 https://stat.link/3m76kn
Figure 6.3. Change in health expenditure as a Figure 6.4. Gross fixed capital formation in
share of GDP, 2010-19 the health care sector as a share of GDP,
2019
JPN Philippines
AUS
NZL China
KOR
AP-H Australia¹
CHN Papua New Guinea
VNM Increase
MMR Mongolia
AP-UM Little change
SGP Korea
LKA Decrease Nepal
MYS
FJI Singapore¹
THA Pakistan¹
PAK
PNG Sri Lanka¹
OECD
KHM Myanmar
PHL NPL Fiji
AP-LM/L Thailand
MNG Indonesia
IDN
BRN Malaysia
IND
LAO Lao PDR
BGD
Brunei Darussalam
0 2 4 6 8 10 12 0.0 0.2 0.4 0.6
% %
Source: WHO Global Health Expenditure Database; OECD Health 1. Data refer to 2018.
Statistics 2022. Source: WHO Global Health Expenditure Database.
StatLink 2 https://stat.link/0uj5lq StatLink 2 https://stat.link/t9akf2

126 
Financing of health care from government and compulsory health

insurance schemes
Health care can be paid for through a variety of financing arrangements. In some countries, health care might
be predominantly financed through government schemes by which individuals are automatically entitled to health
care based on their residency. In other cases, compulsory health insurance schemes (through either public or
private entities) linked to the payment of social contributions or health insurance premiums finance the bulk of
health spending. In addition to these, a varying proportion of health care spending consists of households’ out-
of-pocket payments – either as standalone payments or as part of co-payment arrangements – as well as various
forms of voluntary payment schemes such as voluntary health insurance.
Generally, the higher the income level of a country, the higher the share of health care spending financed through
government and compulsory health insurance schemes. This overall pattern of health care financing can be
seen across Asia-Pacific countries: 74.1% in high-income countries versus 44.9% in low- and lower-
middle-income countries (Figure 6.5). In New Zealand, Japan and Brunei Darussalam more than 75% of all
health expenditure was paid for through government schemes and compulsory health insurance in 2019. The
same pattern was observed in two low-income countries, Solomon Islands and Papua New Guinea. By contrast,
in Myanmar and Bangladesh less than 25% of health spending was covered by such schemes. Between 2010
and 2019, the share of health expenditure financed by government and compulsory health insurance schemes
increased by more than 10 percentage points in Pakistan, Indonesia, Singapore and Lao PDR, whereas it
decreased by more than 10 percentage points in Viet Nam.
Figure 6.6 highlights the change in government and compulsory health insurance schemes spending as a share
of GDP between 2010-19. On average, there was a slight increase in upper-middle- and high-income countries
in Asia-Pacific from 2.2% to 2.6% and 4.7% to 5.5% of GDP respectively, whereas the share for low- and lower-
middle-income countries remained unchanged at 1.7% of GDP over the same period. Japan1 reported an
increase of around 1.5 percentage points in the period in study.
Governments provide a multitude of goods and services out of their overall budgets. Hence, setting priorities for
health in budget allocations is a choice by governments and society as health care is competing with many other
sectors such as education, defence and poverty alleviation programmes. A number of factors including, among
others, general government revenues, nondiscretionary obligations such as debt servicing, and the capacity of
health ministers to influence the overall budgetary allocation to the health sector determines the size of public
funds allocated to health. Relative budget priorities may also shift from year to year because of political decision-
making and economic effects. In 2019, health spending by government schemes and compulsory insurance
stood at around 7.2% of total government expenditure across low- and lower-middle-income countries, whereas
it represented 10.1% of total government expenditure in upper-middle-income countries in Asia-Pacific
(Figure 6.7). In Japan, Australia, New Zealand and Singapore more than 15% of public spending was dedicated
to health care. On the other hand, less than 5% of government expenditure was allocated to health care in India,
Nepal, Myanmar and Bangladesh. The level of public spending on health care is also linked to the capacity of
spending by government as measured by the share of government spending in GDP. Government spending
accounted for around one fourth of GDP in low- and middle-income countries, whereas it represented one-third
of GDP in high-income Asia-Pacific countries in 2019.

 127

Health care financing can be analysed from the point of view of financing schemes (financing arrangements
through which health services are paid for and obtained by people, e.g. social health insurance), financing
agents (organisations managing the financing schemes, e.g. social insurance agency), and types of revenues
(e.g. social insurance contributions). Here “financing” is used in the sense of financing schemes as defined
in the System of Health Accounts (OECD/WHO/Eurostat, 2011[1]) and includes government schemes,
compulsory health insurance as well as voluntary health insurance and private funds such as households’
out-of-pocket payments, NGOs and private corporations. Out-of-pocket payments are expenditures borne
directly by patients and include cost-sharing arrangements and any informal payments to health care
providers, but excludes prepayment to any insurance schemes.
Relating spending from government and compulsory insurance schemes to total government expenditure
can lead to an overestimation of the share of government and compulsory insurance schemes spending in
total government spending in countries where private insurers provide compulsory insurance.
References
OECD/WHO/Eurostat (2011), A System of Health Accounts: 2011 Edition, OECD Publishing, Paris, [1]
https://doi.org/10.1787/9789264116016-en.
Note
1
A break in series in 2011 contributes to this result.

128 
Figure 6.5. Health expenditure by government scheme and compulsory insurance scheme as a
share of health expenditure, 2010 and 2019
% of health expenditure
100
90 Increase
80
70 Little change
60 Decrease
50
40
30
20
10
0
Source: WHO Global Health Expenditure Database; OECD Health Statistics 2022.
StatLink 2 https://stat.link/0i3cgl
Figure 6.6. Health expenditure by government scheme and compulsory insurance scheme as a
share of GDP, 2010 and 2019
% of GDP
10
9 Increase
8
Little change
7
6 Decrease
5
4
3
2
1
0
StatLink 2 https://stat.link/pxribn
Figure 6.7. Health expenditure by government and compulsory health insurance schemes as a
share of total government expenditure, 2010 and 2019
% of total government expenditure
30
Increase
25
Little change
20
Decrease
15
10
5
0
StatLink 2 https://stat.link/h4i618

 129
Financing of health care from households’ out-of-pocket

payments and voluntary payment schemes
For each dollar spent on health, more than 60 cents continued to be financed “out-of-pocket” in Cambodia,
Bangladesh and Myanmar in 2019. On average, the share of health spending paid out-of-pocket has fallen in
countries of all income groups in Asia-Pacific between 2010 and 2019: by around 2 percentage points to 40.4%
in low- and lower-middle-income countries, by 5 percentage points to 22.9% in upper-middle-income countries
and by around 4 percentage points to 17.9% in high-income countries (Figure 6.8). However, the pattern is quite
diverse across the countries in the region and could also be attributed to increasing unmet needs because of
access barriers and/or financial constraints. While two-thirds of the Asia-Pacific reporting countries showed a
decrease in the share of out-of-pocket spending, including more than 10 percentage points for Pakistan, India,
Singapore and Indonesia, Cambodia reported a growth of more than 10 percentage points over the same period.
Research (Wang, Torres and Travis, 2018[1]) suggest that the main driver of households’ out-of-pocket expenditure
is medicines, composing more than 60% of total out-of-pocket spending in countries of the WHO South-East Asia
Region. In Bangladesh and India, this percentage could be as high as 80%. Furthermore, the share of OOP
spending on medicines was even higher among the poorer population, suggesting a disproportionally higher
financial burden. In line with these findings, WHO and The World Bank has reported that the WHO South-East Asia
and Western Pacific regions had the highest percentage of the population in the world facing catastrophic health
spending – defined as out of pocket health spending exceeding the 10% of income – in 2017, pushing more people
below the poverty line (WHO/World Bank, 2019[2]). Figure 6.9 shows that health expenditure by other voluntary
payment schemes (e.g. PHI, spending by NGOs) represented – on average – around 10% of current expenditure
on health in countries of all income groups in Asia-Pacific. This share increased by more than 5 percentage points
to 14.5% in upper-middle-income countries, whereas it increased by 1 percentage point to 8.1% in high-income
countries, and slightly decreased to 8.9% in low- and lower-middle-income Asia-Pacific countries from 2010 to
2019. Less than 5% of current health expenditure was from voluntary payment schemes in Mongolia, Japan,
Bangladesh and Lao PDR in 2019, while it represented 15% or more in Thailand, Indonesia, Fiji and Nepal in the
same year. Fiji reported an increase of 12.4 percentage points between 2010 and 2019, whereas Viet Nam and
Thailand reported an increase of more than 7 percentage points during the same period.

Health care financing can be analysed from the point of view of financing schemes (financing arrangements
through which health services are paid for and obtained by people, e.g. social health insurance), financing
agents (organisations managing the financing schemes, e.g. social insurance agency), and types of revenues
(e.g. social insurance contributions). Here “financing” is used in the sense of financing schemes as defined
in the System of Health Accounts (OECD/WHO/Eurostat, 2011[3]) and includes government schemes,
compulsory health insurance as well as voluntary health insurance and private funds such as households’
out-of-pocket payments, NGOs and private corporations. Out-of-pocket payments are expenditures borne
directly by patients and include cost-sharing arrangements and any informal payments to health care
providers, but excludes prepayment to any insurance schemes.
References
OECD/WHO/Eurostat (2011), A System of Health Accounts: 2011 Edition, OECD Publishing, Paris, [3]
https://doi.org/10.1787/9789264116016-en.
Wang, H., L. Torres and P. Travis (2018), “Financial protection analysis in eight countries in the WHO [1]
South-east Asia region”, Bulletin of the World Health Organization, Vol. 96/9,
https://doi.org/10.2471/BLT.18.209858.
WHO/World Bank (2019), Global Monitoring Report on Financial Protection in Health 2019, World Health [2]

130 
Figure 6.8. Health expenditure by households’ out-of-pocket as a share of health expenditure, 2010
and 2019
90
80 Increase
70 Little change
60
Decrease
50
40
30
20
10
0
StatLink 2 https://stat.link/u6q290
Figure 6.9. Health expenditure by voluntary health care payment schemes as a share of health
expenditure, 2010 and 2019
25
Increase
20
Little change
15 Decrease
10
StatLink 2 https://stat.link/8sydpg

 131
Health expenditure by type of service

Factors such as how care is organised and prioritised across providers, what the population needs are, and the
various input costs, all affect how health spending is distributed across different services. Curative and
rehabilitative care services comprise the greatest share – typically accounting for around 64% of all health
spending across Asia-Pacific reporting countries (Figure 6.10). Medical goods (mostly retail pharmaceuticals)
take up a further 15%, followed by a growing share on preventive care, which in 2019 averaged around 8% of
health spending. Administration and overall governance of the health system, together with ancillary services
and long-term care covered the remainder. Across OECD countries, long-term care and medical goods
accounted for a higher share of health care spending as compared to Asia-Pacific reporting countries.
The structure of spending across the various types of care can vary considerably by country. More than
three-quarters of health spending in Viet Nam, China, Cambodia and Malaysia can be accounted for by curative
and rehabilitative care services. At the other end of the scale, Nepal saw curative and rehabilitative services
account for less than half of all spending.
Spending on medical goods comprises the second largest category. As such, medical goods accounted for more
than a fourth of all health spending in Nepal, India and the Philippines. Of note that spending on pharmaceuticals
consumed in the hospital settings is not included -theoretically – in these figures.
Around one fourth of the total spending can be attributed to preventive care in Fiji, whereas preventive care
accounts for only 3% of spending in Sri Lanka, and around this level in Australia, Japan and Korea.
When restricting the analysis to spending by government schemes and compulsory insurance schemes, curative
and rehabilitative care services comprise the greatest share – typically accounting for 68% of all health spending
across Asia-Pacific reporting countries (Figure 6.11). Preventive care takes up a further 10%. Administration
and overall governance of the health system covered 16% of the remainder spending. Across OECD countries,
long-term care and pharmaceuticals accounted for a higher share of government health care spending as
compared to Asia-Pacific reporting countries. The low share of pharmaceuticals spending in government health
spending at 4% flags the limitations of the benefit baskets in most Asia Pacific countries.
The structure of government and compulsory insurance spending across the various types of care can vary
considerably by country. Around 90% of health spending in Sri Lanka can be attributed to curative and
rehabilitative care services. At the other end of the scale, Lao PDR and Nepal saw curative and rehabilitative
services account for half or less of all government spending. In Lao PDR, Cambodia and Nepal, the higher share
of government spending was attributed to administration and other services.
Around 30% of government total spending is attributed to preventive care in Fiji, whereas preventive care
accounts for 2% of government spending in Pakistan.

The System of Health Accounts defines the boundaries of the health care system from a functional
perspective, with health care functions referring to the different types of health care services and goods.
Current health expenditure comprises personal health care (curative care, rehabilitative care, long-term care,
ancillary services and medical goods) and collective services (prevention and public health services as well
as administration – referring to governance and administration of the overall health system rather than at the
health provider level).
The category of “medical goods” refers to retail pharmaceuticals, delivered to patients via pharmacies and
other retail outlets. Pharmaceuticals are also consumed in other care settings – primarily the hospital inpatient
sector – where by convention the pharmaceuticals used are considered as an input to the overall service
treatment and not separately accounted.

132 
Figure 6.10. Health expenditure by type of service, 2019

Curative and rehabilitative care Long-term care (health) Ancillary services
Medical goods Preventive care Administration and other health care services
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Source: WHO Global Health Expenditure Database.

StatLink 2 https://stat.link/sitjf1
Figure 6.11. Domestic general government health expenditure by type of service, 2019
Curative and rehabilitative care Long-term care (health) Ancillary services
Medical goods Preventive care Administration and other health care services
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Sri Lanka Viet Nam China Pakistan Malaysia Thailand Indonesia India Myanmar Fiji Cambodia Japan Korea Lao PDR Nepal
(2018)
Source: WHO Global Health Expenditure Database.

StatLink 2 https://stat.link/srh7dq

 133
7 Quality of care

134 
Childhood vaccination
Childhood vaccination continues to be one of the most cost-effective health policy interventions, preventing 4 to
5 million deaths every year (WHO, 2019[1]). Nevertheless, the global coverage for three doses of diphtheria-
tetanus-pertussis (DTP3) vaccine dropped from 86% in 2019 to 81% in 2021 and an estimated 25 million children
under the age of 1 year did not receive basic vaccines, the highest number since 2009 (WHO, 2020[2]).
All countries and territories in Asia-Pacific have established vaccination programmes including a minimum
number of routine vaccines (i.e. against polio, diphtheria, tetanus, pertussis, measles); additional vaccines
(i.e. against pneumococcus, rotavirus, Japanese encephalitis and human papilloma virus) are included at
national or subnational level based on local morbidity, mortality and cost-effectiveness analysis.
Health systems providing high quality of care deliver effective, safe and people-centred health care. These
national and subnational vaccination programmes are effective and safe in reducing disease burden of the
population, and the level of adherence to the guidelines on childhood vaccination also reflects importantly the
quality of care provided in countries, as well as availability, accessibility and affordability of vaccination services.
Diphtheria tetanus toxoid and pertussis, measles and hepatitis B vaccines are taken here as examples as they
represent, in timing and frequency of vaccination, the full spectrum of organisational challenges related to routine
vaccination for children. Pertussis, known as whooping cough, is a respiratory infection caused by
bacteria. Immunisation is the most effective way of preventing infection. Three doses of pertussis vaccine,
together with diphtheria and tetanus toxoid reduces the risk of severe pertussis among infants. WHO
recommends the administration of the first dose as early as 6 weeks of age, subsequent doses given at least
4 weeks apart, and the third dose of the primary series should be completed by 6 months of age, if possible
(WHO, 2020[3]). Measles is a highly contagious viral disease. The measles vaccine is not only safe and effective,
but also inexpensive. Although vaccination has substantially reduced global measles deaths and the estimated
number of deaths decreased by 62% between 2000 and 2019, measles is still common in many developing
countries and measles incidence has increased globally including in Asia since 2016 (Patel et al., 2020[4]). WHO
recommends measles immunisation to all susceptible children, adolescents and adults if not contraindicated.
Two doses of measles vaccine, either alone, or combined with rubella, and/or mumps, should be the standard
for national childhood immunisation programmes (WHO, 2020[5]). Vaccination for hepatitis B is considered
effective in preventing infection and its chronic consequences, such as cirrhosis and liver cancer. Yet, in 2019,
hepatitis B resulted in 820 000 deaths, mostly from cirrhosis and hepatocellular carcinoma. Globally, WHO
Western Pacific is the region with most infections in the world, and 116 million people are chronically infected
(WHO, 2022[6]). Hepatitis B vaccination is recommended for all children, and at least three doses of hepatitis B
vaccine should be the standard for national immunisation programmes (WHO, 2021[7]).
Reviews of the evidence supporting the efficacy of vaccines included in routine immunisation programmes have
concluded that they are safe and highly effective against mortality and morbidity caused by the diseases
concerned. Hence, high coverage of these programmes illustrates effective delivery of high-quality health care.
The COVID-19 pandemic, however, impeded access to childhood vaccinations in many countries as these
services had been scaled down or closed, or people were concerned about risks of COVID-19 infection (WHO,
2020[2]). Consequently, vaccination rates have not returned to the pre-pandemic period in about half of the
countries in the Asia-Pacific region (see Chapter 2 “The health impact of COVID-19”).
In 2021, the overall vaccination of children against pertussis (provided through combined vaccines containing
diphtheria and tetanus), measles and hepatitis B was high in most Asia-Pacific countries. In most high and
upper-middle-income Asia-Pacific countries, almost all children aged around one year received the
recommended measles, DTP3 and hepatitis B vaccination, meeting the WHO’s minimum threshold of 95% to
avoid vaccine-preventable diseases outbreaks. On the contrary, the average vaccination rate in lower-middle
and low-income Asia-Pacific countries for these diseases was around 75%, which is insufficient to ensure
interruption of disease transmission and protection of the whole population (Figures 7.1, 7.2 and 7.3). The
average rate for these countries in 2021 was about 10 percentage points lower than the average rate in 2019
(European Commission, 2017[8]; OECD/WHO, 2020[9]), suggesting a substantial negative impact of COVID-19
pandemic on vaccination programmes.

 135
Vaccination coverage rates for DTP3, measles and hepatitis B were similar for each Asia-Pacific country. Brunei
Darussalam and China had the highest rate in Asia-Pacific at 99% against all of them. However, in Papua New
Guinea, Myanmar and DPRK, less than one in two children were vaccinated with all three (Figures 7.1, 7.2
and 7.3).

Childhood vaccination policies differ slightly across countries. Thus, these indicators are based on the actual
policy in a given country. Some countries administer combination vaccines (e.g. MR for measles and rubella)
while others administer the vaccinations separately.
References
European Commission (2017), Cancer Screening in Report on the implementation of the Council [8]
Recommendation on cancer screening,
https://ec.europa.eu/health/sites/health/files/major_chronic_diseases/docs/2017_cancerscreening_2ndr
eportimplementation_en.pdf.
OECD/WHO (2020), Health at a Glance: Asia/Pacific 2020: Measuring Progress Towards Universal Health [9]
Coverage, OECD Publishing, Paris, https://doi.org/10.1787/26b007cd-en.
Patel, M. et al. (2020), “Progress Toward Regional Measles Elimination — Worldwide, 2000–2019”, [4]
MMWR. Morbidity and Mortality Weekly Report, Vol. 69/45, pp. 1700-1705,
https://doi.org/10.15585/mmwr.mm6945a6.
WHO (2022), Hepatitis B, World Health Organization, https://www.who.int/news-room/fact- [6]

sheets/detail/hepatitis-b.
WHO (2021), WHO recommendations for routine immunization - summary tables, World Health [7]
Organization, https://cdn.who.int/media/docs/default-
source/immunization/immunization_schedules/immunization-routine-table1.pdf.
WHO (2020), Measles, World Health Organization, [5]

https://www.who.int/immunization/diseases/measles/en/.
WHO (2020), Pertussis, World Health Organization, https://www.who.int/health-topics/pertussis#tab=tab_2. [3]
WHO (2020), Vaccines work at all ages, everywhere, World Health Organization, http://www.who.int/news- [2]
room/commentaries/detail/vaccines-work-at-all-ages-everywhere.
WHO (2019), Immunization, World Health Organization, https://www.who.int/news-room/facts-in- [1]

pictures/detail/immunization.

136 
Figure 7.1. Vaccination coverage for diphtheria, tetanus toxoid and pertussis-containing vaccine,
third dose (DTP3), 2021
100
90
80
70
60
50
40
30
20
99
99
99
98
98
98
97
96
96
96
96
95
95
95
92
91
90
87
87
85
83
83
75
75
67
57
41
37
31
10
0

StatLink 2 https://stat.link/9b0loa
Figure 7.2. Vaccination coverage for measles-containing vaccine, first dose (MCV1), 2021
100
90
80
70
60
50
40
30
20
99
99
98
98
97
97
97
96
96
96
96
95
95
93
91
90
89
89
86
84
81
74
73
72
67
57
44
42
38
10
0

StatLink 2 https://stat.link/vspokj
Figure 7.3. Vaccination coverage for hepatitis B-containing vaccine, third dose (HepB3), 2021
100
90
80
70
60
50
40
30
20
99
99
99
98
98
97
97
96
96
95
95
95
94
92
92
91
90
87
87
85
83
83
75
75
67
57
41
37
31
10
0

StatLink 2 https://stat.link/18j25l

 137
In-hospital mortality following acute myocardial infarction and

stroke
Cardiovascular disease is the major cause of death in Asia-Pacific, accounting for 35% of the total deaths in the
region in 2019 (Zhao, 2021[1]). Ischaemic heart diseases and stroke were the two major causes of death in Asia-
Pacific in 2019, accounting for 25.4% of total deaths in South-East Asia and 34.5% of all deaths in the Western
Pacific region (Institute for Health Metrics and Evaluation, 2022[9]; indicator “Mortality from cardiovascular
diseases” in Chapter 3). Additionally, both are associated with significant health, social and other non-financial
costs, because of the persistent disabilities suffered by many survivors.
Quality, notably effectiveness of treatment following acute myocardial infarction (AMI) and stroke has improved
significantly over the past decades. Until the 1990s, treatment focused on prevention of complications and
rehabilitation but since then great improvements in AMI survival rates were achieved with thrombolysis (Gil et al.,
1999[2]). Effectiveness of treatment for ischaemic stroke has also improved dramatically over the last decade,
through early identification of suspected ischaemic stroke patients and timely acute reperfusion therapy.
Countries can further improve quality of stroke care through timely transportation of patients, evidence‑based
medical interventions and access to high-quality specialised facilities such as stroke units (OECD, 2015[3]). Due
to COVID-19, however, access to high-quality care was hampered in some cases. In Hong Kong (China), for
instance, there was an increase in the delayed access to high-quality care among patients suffering from AMIs
during the early period of the pandemic because of hospitals following additional precautionary measures to
prevent infection and/or patients fearing infection (Tam et al., 2020[4]).
For both AMI and stroke, the case-fatality rate is a useful measure of acute care quality, reflecting notably the
effectiveness of medical interventions, including early thrombolysis or treatment with aspirin when appropriate,
and catheterisation, as well as co-ordinated and timely transport of patients. For AMI, age-standardised in-
hospital case-fatality rates within 30 days of admission were low in Australia (3.2%) and New Zealand (4.3%)
and high in Singapore (10.7%) in 2019 (Figure 7.4). The case-fatality rate generally decreased over the past
decade and the cross-country difference decreased over time. Beyond the quality of care provided in hospitals,
differences in hospital transfers, average length of stay, emergency retrieval times and average severity of AMI
and stroke may influence 30-day case-fatality.
For ischemic stroke, the lowest case-fatality rates were reported in Japan (3.0%) and Korea (3.5%), while
New Zealand reported the highest rate of 6.5% (Figure 7.5). Fatality rates for haemorrhagic stroke are
significantly higher than for ischemic stroke for all countries, and countries that achieve better survival for one
type of stroke also tend to do well for the other. The lowest case-fatality rates for haemorrhagic stroke were
reported in Korea (15.5%), with New Zealand reporting the highest rate of 20.9% (Figure 7.6). Given the initial
steps of care for stroke patients are similar, this suggests that system-based factors play a role in explaining the
differences across countries. Low rates in Japan are due in part to recent efforts dedicated to improving the
treatment of stroke patients through systematic blood pressure monitoring, major material investment in
hospitals and establishment of stroke units (OECD, 2015[5]).
National measures for AMIs and stroke are affected by within-country variations in performance at the hospital
level. Reducing this variation is key to providing equitable care and reducing overall mortality rates. Although
monitoring and reporting of hospital-level performance is becoming increasingly important in Asia-Pacific, only
Korea is regularly reporting hospital-level performance (OECD, 2019[6]). Multiple factors contribute to variations
in outcomes of acute care, including hospital structure, processes of care and organisational culture. Recent
research points to higher total numbers of hospital patients as being significantly related to higher performance;
this may support national movements towards concentration of care services (Lalloué et al., 2019[7]).

138 

The in-hospital case-fatality rate following AMI, ischemic and haemorrhagic stroke is defined as the number
of people who die within 30 days of being admitted (including same day admissions) to hospital. Ideally, rates
would be based on individual patients, however not all countries and territories have the ability to track
patients in and out of hospital, across hospitals or even within the same hospital because they do not currently
use a unique patient identifier. Therefore, this indicator is based on unique hospital admissions and restricted
to mortality within the same hospital, and hence, differences in practices in discharging and transferring
patients may influence the findings.
Standardised rates adjust for differences in age (45+ years) of the OECD population with AMI or stroke, and
facilitate more meaningful international comparisons.
References
Gil, M. et al. (1999), “Relationship of Therapeutic Improvements and 28-Day Case Fatality in Patients [2]
Hospitalized With Acute Myocardial Infarction Between 1978 and 1993 in the REGICOR Study, Gerona,
Spain”, Circulation, Vol. 99/13, pp. 1767–1773, https://doi.org/10.1161/01.CIR.99.13.1767.
Lalloué, B. et al. (2019), “Does size matter? The impact of caseload and expertise concentration on AMI 30- [7]
day mortality—A comparison across 10 OECD countries”, Health Policy, Vol. 123/5, pp. 441-448,
https://doi.org/10.1016/j.healthpol.2019.03.007.
OECD (2019), Health at a Glance 2019: OECD Indicators, OECD Publishing, Paris, [6]
https://doi.org/10.1787/4dd50c09-en.
OECD (2015), Cardiovascular Disease and Diabetes: Policies for Better Health and Quality of Care, OECD [3]
Health Policy Studies, OECD Publishing, Paris, https://doi.org/10.1787/9789264233010-en.
OECD (2015), OECD Reviews of Health Care Quality: Japan 2015: Raising Standards, OECD Reviews of [5]
Health Care Quality, OECD Publishing, Paris, https://doi.org/10.1787/9789264225817-en.
Tam, C. et al. (2020), “Impact of Coronavirus Disease 2019 (COVID-19) Outbreak on ST-Segment– [4]
Elevation Myocardial Infarction Care in Hong Kong, China”, Circulation: Cardiovascular Quality and
Outcomes, Vol. 13/4, https://doi.org/10.1161/circoutcomes.120.006631.
Zhao, D. (2021), “Epidemiological Features of Cardiovascular Disease in Asia”, JACC: Asia, Vol. 1/1, pp. 1- [1]
13, https://doi.org/10.1016/j.jacasi.2021.04.007.

 139
Figure 7.4. In-hospital case-fatality rates within 30 days after admission for acute myocardial
infarction, patients 45 years old and over, 2009 and 2019 (or nearest years)
2009 2019
Age-sex standardised rate per 100 admissions aged 45 years and over
15
10
10.7
9.7
8.9
5 7.4
6.6
4.3
0 3.2
Source: OECD Health Statistics 2022.

StatLink 2 https://stat.link/tq8an4
Figure 7.5. In-hospital case-fatality rates within 30 days after admission for ischemic stroke,
patients 45 years old and over, 2009 and 2019 (or nearest years)
2009 2019
15
10
5 7.7
6.5
4.7 4.9 5.4
3.0 3.5
0

StatLink 2 https://stat.link/8wjep4
Figure 7.6. In-hospital case-fatality rates within 30 days after admission for haemorrhagic stroke,
patients 45 years old and over, 2009 and 2019 (or nearest years)
2009 2019
35
30
25
20
15 19.9 20.9 21.5
17.3 18.4
10 15.5
5
0

StatLink 2 https://stat.link/50comx

140 
Screening, survival and mortality for breast cancer

The burden of breast cancer among women is significant in the Asia-Pacific region, where it is the cancer with
the highest incidence and mortality rates in South-East Asia and the highest incidence and second highest
mortality rates in Western Pacific region. In 2020, according to estimates based on pre-pandemic trend,
approximately 948 000 women were expected to be newly diagnosed with breast cancer and over 316 000 died
of the disease in the region (International Agency for Research on Cancer (IARC), 2022[17]; see indicator
“Mortality from cancer” in Chapter 3). Several factors are known to increase the risk of breast cancer, such as
increasing age, genetic predisposition, oestrogen replacement therapy and lifestyle factors including obesity,
physical inactivity, nutrition habits and alcohol consumption (World Cancer Research Fund/American Institute
for Cancer Research, 2018[1]; González-Jiménez et al., 2014[2]).
In many Asia-Pacific countries, the incidence of breast cancer has increased over recent decades. Over recent
decades, age-standardised annual incidence rates per 100 000 women have risen quickly in China, India, Japan
and Korea (IARC, 2022[3]) and the rates reached over 75 per 100 000 women in Japan and Singapore and about
65 per 100 000 women in Fiji and Korea in 2020. Incidence rates were already high (over 90 per 100 000 women)
in Australia and New Zealand, where they have increased more slowly in recent years (IARC, 2022[4]).
In the 1990s, Australia, Japan and New Zealand introduced national breast cancer screening programmes to
effectively detect the disease early and reduce mortality (OECD, 2013[5]; IARC, 2016[6]). This has contributed to
higher proportions of women being diagnosed at an early stage, and in those countries, over 50% of women
with breast cancer were diagnosed at an early stage of disease during 2010-14 (OECD, 2021[7]). Korea and
Singapore also introduced a national screening programme around 2000, while China introduced screening
programmes at the community level in the late 2000s (IARC, 2016[6]). In 2015, Indonesia rolled out its screening
programme nationally and the roll-out of breast cancer programmes is ongoing in Brunei Darussalam and
Viet Nam (Wahidin, 2018[8]; Pham et al., 2019[9]; Ministry of Health Brunei Darussalam, 2020[10]). Most of these
countries monitor effective implementation of breast cancer screening programme. Prior to the pandemic,
mammography rate was high at above 70% in New Zealand and Korea while low at just over 10% in Brunei
Darussalam. However, in 2020, the COVID-19 pandemic disrupted breast cancer screening programmes in
countries in Asia-Pacific (Figure 7.7; see Chapter 2 “The health impact of COVID-19”).
Cancer survival is one of the key measures of the effectiveness of health care systems in managing cancer,
reflecting both early detection and the effectiveness of treatment. The wide range in age-standardised five-year net
survival in Asia-Pacific countries and territories (Figure 7.8; Allemani et al., 2018[31]) suggests that the quality of
breast cancer care varies widely in the region. For women diagnosed during 2010-14, age-standardised five-year
net survival was highest in high-income countries such as Australia and Japan (89.5% and 89.4%, respectively),
whereas in Malaysia, India and Thailand, the probability that breast cancer patients survive their cancer for at least
five years was less than 70%. In most Asia-Pacific countries and territories, five-year net survival for women with
breast cancer has improved in recent years, reflecting overall improvement in the quality of cancer care. China,
India, Korea and Thailand in particular have seen a large improvement in five-year net survival since 2000-04.
In 2020, mortality rates from breast cancer, reflecting effectiveness in early detection and treatment, and underlying
trends in incidence, prevalence and survival, varied over ten-fold between countries and territories in the Asia-Pacific
region. The rate was lowest in Mongolia at 3.9 per 100 000 women and the highest in Fiji at 41.0 per 100 000 women.
The average age-standardised mortality rate was higher in upper-middle, lower-middle- and low-income countries
than in high-income countries (Figure 7.9), although the pattern of incidence rates in the region was opposite.

For mammography rate, target population and frequency of screening differ across countries, so data need
to be interpreted with care.
Five-year net survival refers to the cumulative probability that cancer patients survive for at least 5 years after
diagnosis, after controlling for the risk of death from other causes. Five-year net survival for patients
diagnosed during 2000-04 is based on a cohort approach, since all patients have been followed up for at
least 5 years. For patients diagnosed during 2010-14, a period approach is used, which allows estimation of
5-year survival although 5 years of follow-up are not available for all patients. Cancer survival estimates are
age-standardised with the International Cancer Survival Standard (ICSS) weights (Allemani et al., 2018[11]).
See indicator “Mortality from cancer” in Chapter 3 for the definition of cancer mortality rates.

 141
References
Allemani, C. et al. (2018), “Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): [11]
analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322
population-based registries in 71 countries”, The Lancet, Vol. 391/10125, pp. 1023-1075,
https://doi.org/10.1016/S0140-6736(17)33326-3.
González-Jiménez, E. et al. (2014), “Breastfeeding and the prevention of breast cancer: a retrospective [2]
review of clinical histories”, Journal of Clinical Nursing, Vol. 23/17-18, pp. 2397-2403,
https://doi.org/10.1111/jocn.12368.
IARC (2022), Cancer Over Time, International Agency for Research on Cancer, Lyon, [3]
https://gco.iarc.fr/overtime/en.
IARC (2022), Cancer Today, International Agency for Research on Cancer, Lyon, https://gco.iarc.fr/today/. [4]
IARC (2016), Breast Cancer Screening, IARC Handbooks of CAncer Prevention, No. 15, International [6]
Agency for Research on Cancer, Lyon, https://publications.iarc.fr/Book-And-Report-Series/Iarc-
Handbooks-Of-Cancer-Prevention/Breast-Cancer-Screening-2016.
Ministry of Health Brunei Darussalam (2020), Breast Cancer Screening, [10]

http://ppkk.gov.bn/SitePages/Breast-Cancer.aspx.
OECD (2021), Health at a Glance 2021: OECD Indicators, OECD Publishing, Paris, [7]
https://doi.org/10.1787/ae3016b9-en.
OECD (2013), Cancer Care: Assuring Quality to Improve Survival, OECD Health Policy Studies, OECD [5]
Publishing, Paris, https://doi.org/10.1787/9789264181052-en.
Pg Suhaimi, A. et al. (2020), “Predictors of non-communicable diseases screening behaviours among adult [14]
population in Brunei Darussalam: a retrospective study”, Journal of Public Health, Vol. 29/6, pp. 1303-
1312, https://doi.org/10.1007/s10389-020-01240-z.
Pham, T. et al. (2019), “Cancers in Vietnam—Burden and Control Efforts: A Narrative Scoping Review”, [9]
Cancer Control, Vol. 26/1, https://doi.org/10.1177/1073274819863802.
Wahidin, M. (2018), “Overview of Ten Years (2007-2016) Cervical and Breast Cancer Screening Program [8]
in Indonesia”, Journal of Global Oncology, Vol. 4/Supplement 2, https://doi.org/10.1200/jgo.18.21100.
World Cancer Research Fund/American Institute for Cancer Research (2018), Diet, nutrition, physical [1]
activity and breast cancer, World Cancer Research Fund/American Institute for Cancer Research,
https://www.wcrf.org/sites/default/files/Breast-cancer-report.pdf.
Yeung, M. et al. (2019), “Hong Kong female’s breast cancer awareness measure: Cross-sectional survey”, [13]
World Journal of Clinical Oncology, Vol. 10/2, pp. 98-109, https://doi.org/10.5306/wjco.v10.i2.98.
Zhang, M. et al. (2021), “Breast Cancer Screening Rates Among Women Aged 20 Years and Above — [12]
China, 2015”, China CDC Weekly, Vol. 3/13, pp. 267-273, https://doi.org/10.46234/ccdcw2021.078.

142 
Figure 7.7. Mammography screening in women aged 50-69 within the past two years, 2020 (or
nearest year)
2019 2020
80
71 67 70
63 62
60 55 55
50 47 45
39
40
27 26
20 11
0
New Zealand ¹ Korea ¹ OECD36/28 Australia ¹ Asia Pacific-H Japan ² Singapore ¹ Hong Kong China ² Brunei
(China) ² Darussalam ²
1. Programme data. 2. Survey data.

Source: OECD Health Statistics 2022, Zhang et al. (2021[12]), “Breast Cancer Screening Rates Among Women Aged 20 Years and Above —
China, 2015”, https://doi.org/10.46234/ccdcw2021.078; Yeung et al. (2019[13]), “Hong Kong female’s breast cancer awareness measure: Cross-
sectional survey”, https://doi.org/10.5306/wjco.v10.i2.98 and Pg Suhaimi et al. (2020[14]), “Predictors of non-communicable diseases screening
behaviours among adult population in Brunei Darussalam: a retrospective study”, https://doi.org/10.1007/s10389-020-01240-z.
StatLink 2 https://stat.link/cao2f1
Figure 7.8. Breast cancer five-year net survival, women diagnosed during 2000-04 and 2010-14
Confidence interval 2010-14 2000-04 2010-14
Age-standardised five-year net survival (%)
100
90 89 88 87 86 84
80 83 83 80 76 72 69 66 65
60
40
20
0
Note: For all countries, 95% confidence intervals for women diagnosed during 2010-14 are represented by grey areas. For Hong Kong (China),
Mongolia and Malaysia the estimate in light blue is for 2005-09. 1. Data represent coverage of less than 100% of the national population.
2. Survival estimates are considered less reliable. See Allemani et al. (2018) for more information.
Source: CONCORD programme, London School of Hygiene and Tropical Medicine.
StatLink 2 https://stat.link/oqtixz
Figure 7.9. Breast cancer mortality, 2020

Age-standardised rate per 100 000 population
45
40
27.7
35
41.0
30
20.7
19.3
19.0
18.9
18.8
17.8
25
15.8
15.3
14.1
13.8
13.8
13.3
13.3
12.7
12.5
12.0
11.7
20
11.0
10.3
10.0
10.0
9.9
9.6
9.3
15
7.6
6.4
3.9
10
5
0
Source: IARC Global Cancer Observatory 2022.

StatLink 2 https://stat.link/0ohzq4

 143
Vaccination, survival and mortality for cervical cancer

According to estimates based on the pre-pandemic trend, about 337 000 women in Asia-Pacific countries and
territories were expected newly diagnosed with cervical cancer in 2020 (IARC, 2022[19]; see indicator on “Cancer
incidence” in Chapter 3), although invasive cervical cancer is preventable if pre-cancerous or pre-invasive
changes are detected and treated before progression occurs. WHO recommends human papilloma virus (HPV)
vaccination for girls aged 9-14 years (WHO, 2017[1]) since vaccination against the main types of HPV
responsible for cervical cancer is expected to effectively reduce incidence.
An increasing number of countries and territories in Asia-Pacific have national HPV vaccination programmes,
but the target populations vary, based on epidemiological and other evidence such as cost-effectiveness that is
specific to each country. In 2021, HPV vaccination coverage ranges widely between 1% of girls in the target age
group in Singapore and almost 90% in Brunei Darussalam (Figure 7.10). A growing number of countries and
territories in the region have also started implementing population-based cervical cancer screening programmes,
and HPV test or Pap smear test is available through screening programmes in Australia, Brunei Darussalam,
China, Fiji, Japan, Korea, Mongolia, New Zealand, Singapore, Sri Lanka, Thailand and Viet Nam (WHO, 2020[2]).
Following these preventive services, cervical cancer incidence has decreased in Australia, New Zealand, Korea,
Singapore and Thailand. On the contrary, it increased significantly in Japan and China to a smaller extent. In
Asia-Pacific region, the incidence rate is lowest in Australia and New Zealand (both 5.6 new cases per
100 000 women) while the rate is almost 30 new cases per 100 000 women in Fiji and Papua New Guinea,
followed by Solomon Islands (IARC, 2022[3]).
HPV vaccination and cervical cancer screening participation was sometimes adversely affected by the
COVID-19 pandemic, as were childhood vaccination programmes and breast cancer screening (see indicator
on “Childhood vaccination” and “Screening, survival and mortality for breast cancer”). Data are available only
for a few countries in Asia-Pacific. Although HPV vaccination rate continued to increase in Brunei Darussalam
in 2020, it decreased in Australia, New Zealand and Malaysia. The decline was particularly large in Malaysia
(13 percentage points from 2019). Cervical cancer screening rates also decreased in at least some countries in
the region during the pandemic (see Chapter 2 “The health impact of COVID-19”).
During 2010-14, age-standardised five-year net survival for cervical cancer, reflecting effectiveness in early
detection and treatment, ranged from 53.9% in Thailand to 77.3% in Korea (Figure 7.11). In most countries and
territories in Asia-Pacific, net survival for cervical cancer were stable between 2000-04 and 2010-14 periods.
The variation across countries and territories in the region has decreased over time as net survival for China
and India improved significantly from 53 to 68% and 45 to 59%, respectively over the decade, converging
towards the best performers.
Cervical cancer mortality rates vary almost 14-fold across countries in Asia-Pacific (Figure 7.12). High-income
Asia-Pacific countries had low mortality rates in 2020, but the rates were high at around 20 deaths per 100 000
women in Fiji, Papua New Guinea and Solomon Islands where incidence rates for cervical cancer are also high.
Trends in cervical cancer mortality rates reflect coverage of HPV vaccination, effectiveness in early detection
and treatment, and underlying trends in incidence, prevalence and survival. The mortality rates for cervical
cancer have declined in Australia, New Zealand and Korea, but the mortality rate is slowly increasing in Japan
(IARC, 2022[4]) where HPV vaccination was put on pause between 2013 and 2021 (Ministry of Health, Labour
and Welfare, 2022[5]).

See the indicator “Screening, survival and mortality for breast cancer” for the definition of net survival. See
the indicator “Mortality from cancer” in Chapter 3 for the definition of cancer mortality rates.

144 
References
Allemani, C. et al. (2018), “Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): [6]
analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322
population-based registries in 71 countries”, The Lancet, Vol. 391/10125, pp. 1023-1075,
https://doi.org/10.1016/S0140-6736(17)33326-3.
Ministry of Health, Labour and Welfare (2022), HPV vaccine ni kansuru Q&A, [5]
https://www.who.int/teams/noncommunicable-diseases/surveillance/data/cancer-profiles.
WHO (2020), Cancer Country Profiles, World Health Organization, [2]

https://www.who.int/teams/noncommunicable-diseases/surveillance/data/cancer-profiles.
WHO (2017), Human papillomavirus vaccines, World Health Organization, [1]

https://www.who.int/teams/immunization-vaccines-and-biologicals/diseases/human-papillomavirus-
vaccines-(HPV).

 145
Figure 7.10. Vaccination coverage for human papillomavirus vaccine, complete schedule, females
by age 15 (15HPVc), 2018-20
2018 2019 2020
% of girls aged 15
100
90
80
70
60
50
40
30
20
10
86 88 89 82 96 83 54 59 74 77 75 72 58 66 62 58 58 58 47 64 53 56 1 1
0
Brunei Malaysia Asia Pacific-H Australia New Zealand OECD27/27/19 Korea Fiji Singapore
Darussalam 5/5/3

StatLink 2 https://stat.link/73xywm
Figure 7.11. Cervical cancer: Age-standardised five-year net survival, 2000-04 and 2010-14
100
77 71 69 68 67 66
80 60 59 57 54
60
66 66 63
40
20
0
Note: For all countries, 95% confidence intervals for women diagnosed during 2010-14 are represented by grey areas. For Hong Kong (China)
and Malaysia the estimate in light blue is for 2005-09. 1. Data represent coverage of less than 100% of the national population. 2. Survival
estimates are considered less reliable. See Allemani et al. (2018[6]) for more information.
StatLink 2 https://stat.link/ewla1h
Figure 7.12. Cervical cancer mortality, 2020

Age-standardised rate per 100 000
25
20.7
19.1
16.4
20
14.4
14.4
11.6
11.4
11.1
15
10.1
8.8
8.3
7.9
7.4
6.7
6.7
10
6.5
5.8
5.7
5.3
4.9
4.0
3.4
3.3
3.0
2.9
2.9
2.0
1.8
1.5
5
0
Source: IARC Global Cancer Observatory 2022.

StatLink 2 https://stat.link/q34cl1

146 
Survival for other cancers

In Asia-Pacific countries and territories, according to estimates based on pre-pandemic trends, almost 462 000
people were expected newly diagnosed with oesophageal cancer and over 415 000 people died of it in 2020.
Among all cancers, oesophageal cancer has the sixth highest incidence rates and fifth highest mortality rates in
the region (IARC, 2022[1]). The risk is higher among men, and among people who smoke and drink alcohol.
Age-standardised five-year net survival for oesophageal cancer, reflecting effectiveness in early detection and
treatment, has improved in most countries in the region since the early 2000s. For adults diagnosed during
2010-14, the highest five-year net survival was in Japan (36.0%) and Korea (31.3%) and the lowest was in India
(4.1%) and Thailand (7.1%) (Figure 7.13). Countries with population-based gastric screening programmes, such
as Korea and Japan, have experienced substantial improvements over the past few decades, and now have the
highest levels of oesophageal cancer survival worldwide. Prior to the COVID-19 pandemic, mortality rates due
to oesophageal cancer had also decreased in Australia, Japan, Korea, New Zealand and Singapore (IARC,
2022[2]). However, the pandemic may reverse these trends in some cases because emerging evidence points
to an increased severity of oesophageal cancer among patients diagnosed during the early period of the
pandemic (Okuyama et al., 2022[3]; Miyawaki et al., 2022[4]).
In 2020, over 37 000 people were expected newly diagnosed with melanoma of the skin and almost 12 000
people died of it in Asia-Pacific (IARC, 2022[1]). In 2020, incidence rates vary widely, from below 0.2 per
100 000 population in Viet Nam and Nepal and to over 30 per 100 000 population in Australia and New Zealand
(IARC, 2022[1]). Melanoma of the skin is mainly caused by exposure to ultraviolet radiation, and people with a
low level of skin pigmentation, a family history of the disease or poor immune function are at higher risk.
Age-standardised five-year net survival for melanoma of the skin ranges from 30% in Thailand to over 90% in
Australia and New Zealand (Figure 7.14). In countries with high incidence rates, such as Australia and
New Zealand, public health efforts have focused on raising awareness of the importance of recognition of the
early symptoms of melanoma, helping to achieve the highest levels of survival. In some countries such as
Singapore and Korea, a less favourable distribution of histologic sub-types – such as a higher proportion of
nodular and acral lentiginous melanomas, which have a poorer prognosis – may also help to explain relatively
low survival estimates (CONCORD Working Group, 2022[5]). Health policies targeting specific populations could
help improve awareness, early diagnosis and access to treatment.
In recent years, net survival from melanoma of the skin has increased in most countries. Together with public
health efforts, the introduction of immunotherapies and targeted treatments for metastatic melanoma has led to
unprecedented clinical benefit and may have contributed to improving short-term survival (Di Carlo et al.,
2020[6]). During the pandemic, dermatology units around the world rapidly adopted telemedicine and this may
improve access to and outcomes of care if the quality of telemedicine is assured.
Leukaemia is the most common cancer among children aged 0-14 and up to 86 000 children in Asia-Pacific
region were expected newly diagnosed in 2020 (IARC, 2022[1]). The causes of leukaemia are not well known,
but some known risk factors include inherited factors, such as Down syndrome and a family history of leukaemia,
and non-inherited factors, such as exposure to ionising radiation. There are different types of leukaemia but
about three-quarters of cases among children are acute lymphoblastic leukaemia (ALL). The prognosis for
leukaemia depends on various factors including age, initial white blood cell count, gender, initial reaction to
induction treatment and type of leukaemia. Children with acute leukaemia who are free of the disease for
five years are considered to have been cured, as remission after five years is rare.
Age-standardised five-year net survival for ALL among children was on average 88.5% during 2010-14 in high-
income countries in Asia-Pacific and 68.6% in upper-middle-income countries in the region (Figure 7.15) and net
survival improved over the period across countries, mainly due to progress in chemotherapy and stem cell
transplantation technology. Mortality due to leukaemia among children decreased across countries where data
are available (IARC, 2022[2]). However, countries have not benefited equally from progress in medical
technologies; while survival estimate is high at 91% in New Zealand and Australia, it is low at 58% in China.

 147

Net survival is defined in the indicator “Incidence, survival and mortality for breast cancer”.
References
CONCORD Working Group (2022), “Does the morphology of cutaneous melanoma help explain the [5]
international differences in survival? Results from 1,578,482 adults diagnosed during 2000‐2014 in 59
countries ( <scp>CONCORD</scp> ‐3)”, British Journal of Dermatology,
https://doi.org/10.1111/bjd.21274.
Di Carlo, V. et al. (2020), “Trends in short-term survival from distant-stage cutaneous melanoma in the [6]
United States, 2001-2013 (CONCORD-3)”, JNCI Cancer Spectrum, Vol. 4/6,
https://doi.org/10.1093/jncics/pkaa078.
Miyawaki, Y. et al. (2022), “Impact of the coronavirus disease 2019 pandemic on first-visit patients with [4]
oesophageal cancer in the first infection wave in Saitama prefecture near Tokyo: a single-centre
retrospective study”, Japanese Journal of Clinical Oncology, Vol. 52/5, pp. 456-465,
https://doi.org/10.1093/jjco/hyac002.
Okuyama, A. et al. (2022), “Impact of the COVID-19 pandemic on the diagnosis of cancer in Japan: an [3]
observational study of hospital-based cancer registries data”, The Lancet Oncology, Vol. 23, p. S22,
https://doi.org/10.1016/s1470-2045(22)00421-1.

148 
Figure 7.13. Oesophageal cancer five-year net survival, patients diagnosed during 2000-04 and 2010-14
100
80
60
36 31 30
40 24 24
17 16 15 15 14
20 7 4
0
Note: For all countries, 95% confidence intervals for patients diagnosed during 2010-14 are represented by grey areas. For Malaysia the estimate
in light blue is for 2005-09. 1. Data represent coverage of less than 100% of the national population. 2. 2010-14 survival estimate for Malaysia
and 2000-04 survival estimate for India are not age-standardised. 3. Survival estimate for 2000-04 is considered less reliable.
StatLink 2 https://stat.link/w0eh4c
Figure 7.14. Melanoma five-year net survival, patients diagnosed during 2000-04 and 2010-14
100
80 93 92 60
83 50
60 75 69 40
62 60
40
20 30
0
1. Data represent coverage of less than 100% of the national population. 2. Survival estimates are not age-standardised. 3. Survival estimates
are considered less reliable.
StatLink 2 https://stat.link/qb2eyx
Figure 7.15. Childhood leukaemia five-year net survival, children diagnosed during 2000-04 and 2010-14
100
91 91 89 89 88
80 84 84 82
76
69 66
60 58
40
20
0
Note: Malaysia the estimate in light blue is for 2005-09 and for India, the estimate in dark blue is for 2005-09. 1. Data represent coverage of less
than 100% of the national population. 2. Survival estimates are considered less reliable. 3. Survival estimates are not age-standardised.
StatLink 2 https://stat.link/b0z2gt

 149
Annex A. National data sources
Bangladesh
Bangladesh Health Bulletin, 2019,

https://old.dghs.gov.bd/images/docs/Publicaations/Health%20Bulletin%202019%20Print%20Version%20
(2)-Final.pdf
Brunei Darussalam
Ministry of Health, Health Information Booklet 2017 (2019 update),

http://www.moh.gov.bn/Downloadables/Health%20Information%20Bookler%202017%20(revised%20as
%20of%20January%202019).pdf
Cambodia
Ministry of Health, Health Strategic Plan 2016-20,

http://hismohcambodia.org/public/fileupload/carousel/HSP3-(2016-2020).pdf
China
National Bureau of Statistics of China, China Statistical Yearbook 2019,

http://www.stats.gov.cn/tjsj/ndsj/2019/indexeh.htm
Hong Kong (China)
Hong Kong, China Annual Digest of Statistics 2019,

http://www.censtatd.gov.hk/hkstat/sub/sp140.jsp?productCode=B1010003
Department of Health, Health Statistics,
https://www.dh.gov.hk/english/statistics/statistics_hs/statistics_hs.html
Macau (China)
Statistics and Census Service, Macao Yearbook of Statistics 2019,

https://www.dsec.gov.mo/en-US/Home/Publication/YearbookOfStatistics
Malaysia
Ministry of Health, Malaysia Health Facts 2021,

https://www.moh.gov.my/moh/resources/Penerbitan/Penerbitan%20Utama/HEALTH%20FACTS/Health_
Facts_2021.pdf

150 
Myanmar
Annual public health statistics, 2020,

http://mohs.gov.mm/
Nepal
Ministry of Health, Annual Report, 2020-21,

https://dohs.gov.np/annual-report-fy-2077-78-2019-20/
Singapore
Ministry of Health, Singapore Health Facts,

http://www.moh.gov.sg/content/moh_web/home/statistics/Health_Facts_Singapore.html
Sri Lanka
Ministry of Health, Annual Health Statistics,

http://www.health.gov.lk/moh_final/english/public/elfinder/files/publications/AHB/2020/Final%20AHS%20
2018.pdf.

 151
Annex B. Additional information on demographic

and economic context
Table A B.1. Total mid-year population, thousands, 1980 to 2025

1980 1990 2000 2010 2020 2025
Australia 14 706 17 048 19 018 22 019 25 670 26 958
Bangladesh 83 930 107 148 129 193 148 391 167 421 176 422
Brunei Darussalam 188 262 334 396 442 459
Cambodia 6 199 8 911 12 119 14 364 16 397 17 294
China 982 372 1 153 704 1 264 099 1 348 191 1 424 930 1 424 382
Democratic People’s Republic of Korea 17 974 20 800 23 367 24 686 25 867 26 320
Fiji 645 780 833 905 920 950
Hong Kong (China) 4 979 5 839 6 731 7 132 7 501 7 500
India 696 828 870 452 1 059 634 1 240 614 1 396 387 1 454 607
Indonesia 148 177 182 160 214 072 244 016 271 858 282 004
Japan 117 624 123 686 126 804 128 105 125 245 121 960
Korea 38 171 44 120 46 789 48 813 51 845 51 690
Lao People’s Democratic Republic 3 298 4 314 5 431 6 323 7 319 7 838
Macau (China) 245 350 432 557 676 722
Malaysia 13 216 17 517 22 945 28 718 33 200 35 028
Mongolia 1 698 2 161 2 451 2 703 3 294 3 538
Myanmar 33 466 40 100 45 538 49 391 53 423 55 337
Nepal 15 600 19 617 24 560 27 162 29 349 31 577
New Zealand 3 147 3 397 3 855 4 346 5 061 5 311
Pakistan 80 624 115 414 154 370 194 454 227 197 249 949
Papua New Guinea 3 105 3 865 5 508 7 583 9 750 10 701
Philippines 48 420 61 559 77 958 94 637 112 191 120 864
Singapore 2 401 3 022 4 054 5 164 5 910 6 090
Solomon Islands 234 324 430 540 691 773
Sri Lanka 14 944 17 204 18 776 20 669 21 715 22 000
Thailand 45 738 55 228 63 067 68 270 71 476 71 953
Viet Nam 52 968 66 913 79 001 87 411 96 649 100 104
Note: 2025 figures are based on medium variant estimates.

Source: UNDESA, World Population Prospects: The 2022 Revision.

152 
Table A B.2. Share of the population aged 65 and over, 1980 to 2025
1980 1990 2000 2010 2020 2025
Australia 9.6 11.1 12.4 13.6 16.2 17.9
Bangladesh 3.4 3.5 3.8 4.4 5.6 6.7
Brunei Darussalam 2.8 2.6 2.9 3.4 5.5 7.4
Cambodia 3.0 3.0 3.1 3.6 5.3 6.8
China 4.4 5.3 6.9 8.6 12.6 14.9
Democratic People’s Republic of Korea 3.4 4.4 6.4 9.5 11.1 13.0
Fiji 2.6 2.8 3.2 4.2 5.5 6.5
Hong Kong (China) 6.4 8.6 11.2 13.3 18.8 23.3
India 4.0 4.1 4.5 5.1 6.7 7.6
Indonesia 3.7 4.0 5.0 5.9 6.7 7.5
Japan 9.3 12.4 17.8 23.6 29.6 30.4
Korea 3.8 4.9 7.1 11.0 15.8 20.3
Lao People’s Democratic Republic 3.3 3.4 3.5 3.9 4.3 4.9
Macau (China) 7.5 6.6 7.3 7.1 11.7 15.1
Malaysia 3.3 3.7 4.1 5.1 7.0 8.4
Mongolia 4.8 3.8 3.4 3.8 4.3 5.4
Myanmar 4.1 4.4 4.9 5.2 6.5 7.5
Nepal 3.4 3.6 3.8 4.7 6.0 6.3
New Zealand 9.8 11.1 11.7 13.0 15.6 17.6
Pakistan 3.4 3.5 3.5 3.7 4.2 4.5
Papua New Guinea 1.1 1.8 2.3 2.6 3.0 3.5
Philippines 3.3 3.3 3.8 4.3 5.2 6.0
Singapore 4.9 5.6 6.3 7.2 13.2 18.1
Solomon Islands 3.2 3.2 3.3 3.4 3.5 3.6
Sri Lanka 4.9 6.1 7.1 7.6 10.8 12.7
Thailand 3.4 4.3 6.1 8.8 13.9 17.5
Viet Nam 5.5 5.6 6.2 6.5 8.4 10.4
Note: 2025 figures are based on medium variant estimates.


 153
Table A B.3. Crude birth rate, per 1 000 population, 1980-85 to 2015-20
1980-85 1990-95 2000-05 2010-15 2015-20
Australia 15.6 14.7 12.8 13.3 12.9
Bangladesh 42.2 33.0 26.0 20.2 18.4
Brunei Darussalam 30.7 28.3 19.2 16.7 15.0
Cambodia 50.6 38.0 26.5 24.5 22.7
China 21.6 17.9 12.5 12.6 11.9
Democratic People’s Republic of Korea 21.7 20.7 16.8 14.0 13.9
Fiji 33.1 28.1 24.0 20.7 21.5
Hong Kong (China) 15.3 12.4 8.4 10.5 11.1
India 35.5 30.0 25.3 20.0 18.0
Indonesia 31.7 24.4 22.0 20.2 18.2
Japan 12.8 9.8 8.9 8.4 7.5
Korea 20.1 16.0 10.5 8.9 7.4
Lao People’s Democratic Republic 42.9 41.5 29.7 25.5 23.8
Macau (China) 21.2 15.1 7.5 11.3 11.0
Malaysia 31.1 27.2 19.4 17.2 16.8
Mongolia 38.2 27.5 18.9 26.0 24.4
Myanmar 34.4 25.7 24.3 18.7 17.7
Nepal 41.2 37.2 29.7 20.9 20.0
New Zealand 15.8 16.6 14.2 13.7 12.6
Pakistan 42.1 38.2 30.3 29.7 28.5
Papua New Guinea 38.3 34.5 33 28.8 27.2
Philippines 35.7 31.9 28.8 24.1 20.6
Singapore 17.0 17.6 11.3 9.3 8.8
Solomon Islands 42.4 38.8 35.1 30.8 32.7
Sri Lanka 25.8 19.8 18.6 16.4 16.0
Thailand 24.2 18.2 13.6 11.3 10.5
Viet Nam 31.4 26.7 16.9 17.4 16.9

154 
Table A B.4. Fertility rate, live births per woman aged 15-49, 1980-85 to 2015-20
1980-85 1990-95 2000-05 2010-15 2015-20
Australia 1.9 1.9 1.8 1.9 1.8
Bangladesh 6.0 4.1 2.9 2.2 2.1
Brunei Darussalam 3.8 3.1 2.0 1.9 1.8
Cambodia 6.4 5.1 3.4 2.7 2.5
China 2.6 2.0 1.6 1.6 1.7
Democratic People’s Republic of Korea 2.8 2.3 2.0 2.0 1.9
Fiji 3.8 3.4 3.0 2.6 2.8
Hong Kong (China) 1.7 1.2 1.0 1.2 1.3
India 4.7 3.8 3.1 2.4 2.2
Indonesia 4.1 2.9 2.5 2.5 2.3
Japan 1.8 1.5 1.3 1.4 1.4
Korea 2.2 1.7 1.2 1.2 1.1
Lao People’s Democratic Republic 6.4 5.9 3.9 2.9 2.7
Macau (China) 2.1 1.4 0.8 1.2 1.2
Malaysia 4.0 3.4 2.5 2.1 2.0
Mongolia 5.8 3.3 2.1 2.8 2.9
Myanmar 4.7 3.2 2.9 2.3 2.2
Nepal 5.6 5.0 3.6 2.3 1.9
New Zealand 2.0 2.1 1.9 2.0 1.9
Pakistan 6.4 5.7 4.2 3.7 3.6
Papua New Guinea 5.5 4.7 4.4 3.8 3.6
Philippines 4.9 4.1 3.7 3.1 2.6
Singapore 1.7 1.7 1.3 1.2 1.2
Solomon Islands 6.4 5.5 4.6 4.1 4.4
Sri Lanka 3.2 2.4 2.3 2.1 2.2
Thailand 2.9 2.0 1.6 1.5 1.5
Viet Nam 4.6 3.2 1.9 2.0 2.1

Health at a Glance: Asia/Pacific 2022
MEASURING PROGRESS TOWARDS UNIVERSAL HEALTH COVERAGE
This seventh edition of Health at a Glance Asia/Pacific presents a set of key indicators of health status,
the determinants of health, health‑care resources and utilisation, health‑care expenditure and financing,
and quality of care across 27 Asia‑Pacific countries and territories. It also provides a series of dashboards
to compare performance across countries and territories, and a thematic analysis on the health impact
of COVID‑19. Drawing on a wide range of data sources, it builds on the format used in previous editions
of Health at a Glance, and gives readers a better understanding of the factors that affect the health
of populations and the performance of health systems in these countries and territories. Each of the indicators
is presented in a user‑friendly format, consisting of charts illustrating variations across countries
and territories, and over time, brief descriptive analyses highlighting the major findings conveyed by the data,
and a methodological box on the definition of the indicators and any limitations in data comparability. An annex
provides additional information on the demographic and economic context in which health systems operate.
PRINT ISBN 978-92-64-42245-2

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MEASURING PROGRESS TOWARDS UNIVERSAL

Please cite this publication as:

ISBN 978-92-64-42245-2 (print)

Health at a Glance: Asia/Pacific

Corrigenda to publications may be found on line at: www.oecd.org/about/publishing/corrigenda.htm.

Lessons learned across Asia-Pacific during the pandemic

How to prepare for the future

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Poonam Khetrapal Singh Zsuzsanna Jakab, Stefano Scarpetta,

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

2 The health impact of COVID-19 19

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

5 Health care resources and utilisation 102

6 Health expenditure and financing 122

7 Quality of care 133

Annex A. National data sources 149

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Follow OECD Publications on:

Look for the 12 at the bottom of the tables or graphs in

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Structure of the publication

Health at a Glance: Asia-Pacific 2022 is divided into seven chapters:

Asia-Pacific countries and territories

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Selection and presentation of indicators

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Acronyms and abbreviations

AIDS Acquired immunodeficiency syndrome

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Life expectancy decreased by 1 year during the COVID-19 pandemic and

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

1 Country and territory dashboards

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Table 1.1. Dashboard on health status

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Table 1.2. Dashboard on risk factors for health

Note: F, females; M, males.

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Table 1.3. Dashboard on quality of care

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Health care resources

Table 1.4. Dashboard on health care resources

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

2 The health impact of COVID-19

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

The direct impact of COVID-19

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

Asia Pacific - H Asia Pacific - UM Asia Pacific - LM/L OECD

Asia Pacific - H Asia Pacific - UM Asia Pacific - LM/L OECD

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022

OECD Asia Pacific - H Asia Pacific - UM Asia Pacific - LM/L

HEALTH AT A GLANCE: ASIA/PACIFIC 2022 © OECD/WHO 2022