UGANDA MARTRYS UNIVERSITY

FACULTY OF HEALTH SCIENCES

BACHELOR OF SCIENCE PUBLIC HEALTH AND HEALTH PROMOTION

COURSE UNIT: HEALTH ECONOMICS:

LECTURER: DR. AERON NAMAASA

YEAR: THREE SEMESTER : ONE

STUDENT NAME : MAGOBA ALICE

REGISTRATION NUMBER : 2021-B452-20272

INDIVIDUAL COURSE WORK

QUESTION

Explain the following Concepts and how they can be determined .

1. QUALYS and DALYS

2. Life Expectancy
 1.(a) QUALYs

In health economics,QUALYs (Quality-Adjusted Life Years) is a measure used to assess

the value of medical interventions and treatments by considering both their efficacy and
impact on patients' quality of life. Qualys combine the length of life gained from a
treatment with the quality of that life, often measured in terms of the patient's ability to
perform daily activities, pain levels, and overall well-being. This metric allows health
economists and policymakers to compare the cost-effectiveness of different healthcare
interventions by quantifying the health benefits achieved relative to the resources
invested. By incorporating quality of life considerations alongside clinical outcomes,
Qualys provide a more comprehensive understanding of the value of healthcare
interventions, helping decision-makers allocate resources efficiently to maximize
population health outcomes within budget constraints.

Quality-Adjusted Life Years (QALYs) are typically determined by multiplying the number
of years lived with a particular health outcome by the quality of life experienced during
those years. The quality of life is measured on a scale from 0 to 1, where 0 represents
death and 1 represents perfect health. Here's the formula and calculation:

QALYs = Years of life x Quality of life

Like, if a person has a chronic condition that is reducing his quality of life to 50% of that
experienced by a fully healthy person. He is expected to live five years. There is a new
medicine that will help this person’s symptoms and improve his quality of life to 75%.
However, it will not affect the time he is expected to live. Overall, this person will gain
1.25 QALYs (5 years x 0.25) compared to if he was given no treatment at all.

In this case, without treatment, the person's quality of life is at 50% for five years,
resulting in a total of 2.5 QALYs (5 years x 0.5). With the new medicine, their quality of
life improves to 75%, resulting in a total of 3.75 QALYs (5 years x 0.75). The difference
between these two scenarios is indeed 1.25 QALYs.

1.(b) DALYs
Disability-adjusted life years (DALYs) are a measure of the overall disease burden in a
population, expressed as the number of healthy years lost due to illness, disability, or
premature death. DALYs are calculated by adding the years of life lost (YLL) due to
mortality and the years lived with disability (YLD) due to morbidity. DALYs can be used
to compare the health impact of different diseases and interventions, and to prioritize
health policies and resource allocation. DALYs are related to, but distinct from, quality-
adjusted life years (QALYs), which measure the health benefit of an intervention

DALYs, or disability-adjusted life years, are a measure of the overall disease burden in a
population. They are calculated by adding the years of life lost due to premature
mortality (YLLs) and the years lived with disability due to morbidity (YLDs).

The formula for DALYs is:

DALY=YLL+YLD

The formula for YLLs is:

YLL=N×L

where N is the number of deaths and L is the standard life expectancy at the age of
death.

The formula for YLDs is:

YLD=I×DW×L

where I is the number of incident cases, DW is the disability weight, and L is the average
duration of the case until remission or death.

Here is an example of how to calculate DALYs for a disease that causes 100 deaths and
500 cases of disability in a population. Assume that the standard life expectancy is 80
years, the average age of death is 50 years, the average duration of disability is 10 years,
and the disability weight is 0.5.

The YLLs for this disease are:

YLL=100(80−50) The YLDs for this disease are:

=100×30 YLDs=500×0.5×10

YLL=100×30=3000 YLD=500×0.5×10 =2500

YLL=3000 YLD=2500

The DALYs for this disease are:

DALY=3000+2500 = 5500

DALY=5500.

This means that the disease causes a loss of 5500 healthy years in the population.

3. Life expectancy

Life expectancy in health economics refers to the average number of years a person is
expected to live based on current mortality rates and other demographic factors. It
serves as a key indicator of population health and well-being, reflecting the overall
effectiveness of healthcare systems, public health interventions, and socioeconomic
conditions.

In health economics, life expectancy is used to assess the impact of various factors such
as medical treatments, lifestyle choices, and environmental conditions on population
health outcomes. Improvements in life expectancy are often associated with
advancements in medical technology, access to healthcare, disease prevention efforts,
and socioeconomic development.

Life expectancy estimates play a crucial role in health policy decision-making, resource
allocation, and planning for healthcare services and infrastructure. By understanding
trends in life expectancy, policymakers can identify areas for intervention and allocate
resources to address disparities and improve overall population health outcomes.

Life expectancy in health economics can be determined through statistical analysis of

mortality data collected from a population over a specified period. The process involves
calculating the average age at death for individuals within a given population,
considering factors such as age, gender, socioeconomic status, and geographic location.
Life tables, constructed using mortality rates and survivorship data, provide a structured
framework for estimating life expectancy.

Additionally, predictive modeling techniques can forecast life expectancy based on

historical trends and demographic variables. These models may incorporate factors like
healthcare access, disease prevalence, lifestyle behaviors, and environmental factors to
provide more accurate projections.

Life expectancy estimates are invaluable for health economists, policymakers, and
healthcare providers as they inform resource allocation, policy formulation, and
program planning. By understanding the expected lifespan of a population, stakeholders
can tailor interventions to address specific health needs and promote longer, healthier
lives.

Life expectancy can be calculated using various statistical methods, typically based on
historical data and demographic trends. One common method is the life table approach,
which involves analyzing age-specific mortality rates in a population.

Here's a simplified example of how you might calculate life expectancy using the life
table method:

1. Gather Data: Collect age-specific mortality rates for the population you're studying.
These rates typically come from vital statistics records, census data, or other
demographic sources.

2. Create a Life Table: Construct a life table that organizes the mortality rates by age
group, typically in 1-year intervals. The table will include columns for age (x), the
number of people alive at the beginning of each age interval (l[x]), the number of deaths
during each age interval (d[x]), and the probability of dying during each age interval
(q[x]).

3. Calculate Survival Probabilities: Use the mortality rates to calculate the probability of
surviving each age interval. This is done by subtracting the probability of dying from 1.
The survival probability for age x is denoted as ( p[x] ).

4. Calculate Life Expectancy: Life expectancy can be calculated by summing up the

expected remaining years of life for each age group. It's typically denoted as ( e(x) ) or
( T(x) ). One common method is to use the following formula:
 This formula sums the survival probabilities from age
x to the maximum age considered in the life table.

5. Interpretation: The resulting life expectancy represents the average number of years
a person at age x is expected to live, based on current mortality rates.

Here's a basic example to illustrate the concept:

Suppose we have the following age-specific mortality rates for a hypothetical

population:

- Age 0-1: 0.005 (per year)

- Age 1-2: 0.001 (per year)

- Age 2-3: 0.002 (per year)

Using this data, I can construct a life table and calculate life expectancy for various age
groups.

U
sing the formula mentioned earlier, we can calculate life expectancy for someone aged
0:

We sum up the survival probabilities indefinitely. In practice, we would limit the

calculation to a certain maximum age.