Socioeconomic Risk Markers of Leprosy

RESEARCH ARTICLE
Socioeconomic risk markers of leprosy in

high-burden countries: A systematic review
and meta-analysis
Julia Moreira Pescarini1*, Agostino Strina1,2, Joilda Silva Nery1,3, Lacita
Menezes Skalinski4,5, Kaio Vinicius Freitas de Andrade4,6, Maria Lucia F. Penna7,
Elizabeth B. Brickley2‡, Laura C. Rodrigues2‡, Mauricio Lima Barreto1,4‡, Gerson
Oliveira Penna8‡
1 Centro de Integração de Dados e Conhecimentos para Saúde (Cidacs), Fundação Oswaldo Cruz,
a1111111111
Salvador, Brazil, 2 Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical
a1111111111 Medicine, London, United Kingdom, 3 Universidade Federal do Vale do São Francisco (UNIVASF), Paulo
a1111111111 Afonso, Brazil, 4 Instituto de Saúde Coletiva, Universidade Federal da Bahia, Salvador, Brazil,
a1111111111 5 Universidade Estadual de Santa Cruz (UESC), Ilheus, Brazil, 6 Universidade Estadual de Feira de
a1111111111 Santana (UEFS), Feira de Santana, Brazil, 7 Universidade Federal Fluminense, Instituto de Saúde da
Comunidade, Niterói, Brazil, 8 Centro de Medicina Tropical, Universidade de Brasilia (UNB), Brasilia, Brazil
‡ These authors are joint senior authors on this work.

* juliapescarini@gmail.com
OPEN ACCESS
Citation: Pescarini JM, Strina A, Nery JS, Skalinski

LM, Andrade KVFd, Penna MLF, et al. (2018)
Abstract
Socioeconomic risk markers of leprosy in high-
Over 200,000 new cases of leprosy are detected each year, of which approximately 7% are
burden countries: A systematic review and meta-
analysis. PLoS Negl Trop Dis 12(7): e0006622. associated with grade-2 disabilities (G2Ds). For achieving leprosy elimination, one of the
https://doi.org/10.1371/journal.pntd.0006622 main challenges will be targeting higher risk groups within endemic communities. Neverthe-
Editor: Peter Steinmann, Swiss Tropical and Public less, the socioeconomic risk markers of leprosy remain poorly understood. To address this
Health Institute, SWITZERLAND gap we systematically reviewed MEDLINE/PubMed, Embase, LILACS and Web of Science
Received: April 18, 2018 for original articles investigating the social determinants of leprosy in countries with > 1000
cases/year in at least five years between 2006 and 2016. Cohort, case-control, cross-sec-
Accepted: June 19, 2018
tional, and ecological studies were eligible for inclusion; qualitative studies, case reports,
Published: July 9, 2018
and reviews were excluded. Out of 1,534 non-duplicate records, 96 full-text articles were
Copyright: © 2018 Pescarini et al. This is an open reviewed, and 39 met inclusion criteria. 17 were included in random-effects meta-analyses
access article distributed under the terms of the
for sex, occupation, food shortage, household contact, crowding, and lack of clean (i.e.,
Creative Commons Attribution License, which
permits unrestricted use, distribution, and treated) water. The majority of studies were conducted in Brazil, India, or Bangladesh while
reproduction in any medium, provided the original none were undertaken in low-income countries. Descriptive synthesis indicated that
author and source are credited. increased age, poor sanitary and socioeconomic conditions, lower level of education, and
Data Availability Statement: All relevant data are food-insecurity are risk markers for leprosy. Additionally, in pooled estimates, leprosy was
within the paper and its Supporting Information associated with being male (RR = 1.33, 95% CI = 1.06–1.67), performing manual labor (RR
files
= 2.15, 95% CI = 0.97–4.74), suffering from food shortage in the past (RR = 1.39, 95% CI =
Funding: This review is funded by Medical 1.05–1.85), being a household contact of a leprosy patient (RR = 3.40, 95% CI = 2.24–
Research Council (MRC) (MR/N017250/1 to L.C.
5.18), and living in a crowded household (5 per household) (RR = 1.38, 95% CI = 1.14–
R.) and CONFAP/ESRC/MRC/BBSRC/FAPDF 2015
– Doenças Negligenciadas (Processo FAP-DF 1.67). Lack of clean water did not appear to be a risk marker of leprosy (RR = 0.94, 95% CI
193.000.008/2016 to G.O.P). During the period of = 0.65–1.35). Additionally, ecological studies provided evidence that lower inequality, better
investigation, JMP was supported by Capes/
human development, increased healthcare coverage, and cash transfer programs are linked
Fiocruz – Plano Brasil Sem Miséria and EBB was
supported with funding from the Wellcome Trust with lower leprosy risks. These findings point to a consistent relationship between leprosy
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0006622 July 9, 2018 1 / 20

Socioeconomic risk markers of leprosy
and the UK’s Department for International and unfavorable economic circumstances and, thereby, underscore the pressing need of
Development (205377/Z/16/Z) as well as the leprosy control policies to target socially vulnerable groups in high-burden countries.
European Union’s Horizon 2020 research and
innovation program under ZikaPLAN grant
agreement No. 734584. The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript. All
authors had full access to the review data and
Author summary
share final responsibility for the decision to submit Many cases of leprosy still occur in low and middle-income countries, with a considerable
for publication. proportion of them leading to permanent nerve damage and visible physical deformities.
Competing interests: The authors have declared Disease elimination can be achieved with a better understanding of the sociodemographic
that no competing interests exist. characteristics of those most affected by the disease and by targeting those with greater
risk within endemic countries. To address this question, we reviewed all published studies
evaluating the social determinants of leprosy in countries endemic for leprosy. We found
39 studies, most of them conducted in Brazil (i.e., an upper-middle-income country),
India or Bangladesh (i.e., lower-middle income countries), and none in low-income coun-
tries. Our review found strong evidence that males, household contacts of leprosy patients,
individuals living in crowded households, and individuals who suffered food shortage in
the past are more affected by leprosy. Evidence also exists that increasing age, poor sani-
tary and socioeconomic conditions, lower levels of education, and food insecurity are
associated with a greater risk of leprosy. Our review underscores the importance of
improving living conditions and decreasing inequality in low and middle-income coun-
tries to achieve leprosy elimination.
Introduction
Leprosy, a chronic infectious disease caused by Mycobacterium leprae, remains endemic in 13
low and middle-income countries worldwide [1]. While effective and affordable multidrug
therapies have the potential to cure infections, failures in detection and treatment can lead to
the development of stigmatizing leprosy-associated grade-2 disabilities (G2Ds) [1, 2]. By recent
estimates, 7% of the more than 200,000 new cases of leprosy detected each year occur in indi-
viduals who have already developed G2Ds by the time of diagnosis. To reduce the incidence of
infection and prevent the onset of new G2Ds, the World Health Organization has advocated
for targeted detection and intervention among higher risk groups within endemic countries
[1, 3]. However, defining and intervening with the target groups at a subnational level remains
a challenge due to a lack of understanding regarding the epidemiological risk markers of
leprosy.
In recent years, there has been an increased recognition of the social determinants of health
and of the potential of social interventions to enhance disease treatment and control strategies
[4]. In the case of leprosy, existing evidence suggests that poor living conditions may be associ-
ated with increased risk, while the discrimination and fears associated with leprosy may lead to
treatment delays, G2Ds, and decreases in individual economic productivity, thereby perpetuat-
ing poverty [5]. Recognizing this bidirectional association, several countries have made efforts
to break the link between poverty and leprosy by incorporating poverty reduction efforts as a
major component in health policies promoting leprosy control [6]. To better inform these
health policies and to address residual gaps in knowledge related to the markers of leprosy
risk, this systematic review aims to collate and appraise the published evidence on the effect of
social, demographic, and economic factors and leprosy occurrence in high-burden settings.

Methods
Search strategy and eligibility criteria
The protocol for the systematic review has been registered in the International Prospective
Register of Systematic Reviews (PROSPERO) as CRD42016051212 [7]. To identify studies
reporting associations between socioeconomic variables and leprosy outcomes in high-burden
countries, we searched MEDLINE, Embase, LILACS, and Web of Science up to 20th January
2017 using the strategy detailed in S1 Text and reviewed reference lists for additional relevant
articles. No language restrictions were applied to the search; however, full text review was lim-
ited to articles published in English, Spanish, Portuguese, and French. Studies were eligible for
inclusion if they: (i) were carried out in one of the 20 high-burden countries (i.e., defined as
officially reporting more than 1,000 cases per year in at least five consecutive or non-consecu-
tive years between 2006 and 2016 (Fig 1)[8, 9]; (ii) had a cohort, case-control, cross-sectional,
or ecological study design; (iii) measured associations between one or more socioeconomic
variables (i.e., age, sex, urban/rural residence, housing conditions/crowding, education/occu-
pation, and social deprivation) and diagnosed leprosy disease. Studies were excluded if they:
(i) had a qualitative or review design, (ii) exclusively used Phenolic Glycolipid I (PGL-1) posi-
tivity as a biomarker of leprosy exposure [10], (iii) lacked a clear description of the study popu-
lation, or (iv) exclusively analyzed sex and/or age as the sociodemographic variables.
Data extraction and analysis

Four reviewers (J.M.P, A.S., K.A., and L.M.S.) worked in duplicate to appraise records, evalu-
ate study quality using the Newcastle-Ottawa scale (NOS) for individual level studies [11], and
extract data using a standardized form (S1 Table). We used the NOS form for cohorts to evalu-
ate data quality for cross-sectional studies; however the quality score was limited to a maxi-
mum of 7 points as it was not possible to demonstrate that leprosy was not present at the start
Fig 1. Number of eligible studies in countries officially reporting more than 1,000 cases per year in at least five consecutive or non-consecutive years
between 2006 and 2016.
https://doi.org/10.1371/journal.pntd.0006622.g001

of the study and due to the lack of follow up. Specifically, the reviewers extracted data related
to the study protocol (i.e., geographic location, baseline survey dates, study design, study popu-
lation, number of participants, method of leprosy ascertainment, and number of leprosy cases)
and the measure of association (i.e., socioeconomic characteristics of leprosy cases and the
comparison group, effect sizes, and statistical adjustment for potential confounders). Discrep-
ancies were resolved by consensus. Individual level studies with data on different comparison
groups (i.e., both cohort and case-controls in the same study) were considered in only one
study, but data were extracted for all groups. Methods and results are reported following the
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines
(for checklist, see S2 Table) [12].
The studies included in this review were summarized in two groups defined by whether the
risk markers and leprosy outcomes were evaluated in individuals or at a population level.
When estimates for a given risk marker was reported in at least three individualized studies,
we estimated summary relative risks (RR) and its 95% Confidence Intervals (95% CI) by pool-
ing effect sizes using random-effects meta-analyses. As leprosy is a rare disease, odds ratios
and hazard ratios were assumed to approximate the same RR [13]. Studies conducted only
among household contacts of leprosy patients or those with insufficient information to calcu-
late the point estimates and its 95% CIs were not included in the meta-analysis. We assessed
heterogeneity in RR estimates using I2 statistics and Cochran’s Q test p-values. Data analysis
was performed in Stata, version 15.0, and R, version 3.4.0.
Results
The database search retrieved 1,534 independent records. After screening the abstracts, 96 full
texts were reviewed, and 34 were selected for inclusion in the systematic review. Five addi-
tional eligible studies were identified through the references of the selected papers (Fig 2).
Data were extracted from a total of 39 articles, comprising seven cohorts [14–20], seven case-
controls [21–27], 13 cross-sectional studies [28–40], and 13 ecological studies [30, 41–52]; one
record employing mixed methods (i.e., ecological and cross-sectional design) was listed as two
separate studies (see Table 1 for individual studies and Table 2 for ecological studies). Of the
individual studies, one cohort study assessed both the prevalence of leprosy in households con-
taining an index case (cross-sectional) and followed those household contacts without leprosy
prospectively [20]; a second study (case-control) considered two control groups, one proximal
and one randomly selected [32].
The included studies were conducted in eight out of the 20 high-burden countries (Brazil
[20, 23, 26, 32, 37, 39, 41–52], India [16, 18, 21, 28–31, 33, 35, 38], Bangladesh [19, 24, 25, 27,
36], Indonesia [17, 22], Egypt [34], Myanmar [15], Philippines [14] and Sri Lanka [40]—Fig
1). With the exception of Brazil, which is an upper-middle income country, all are classified as
lower-middle income countries. The studies were published between 1942 and 2016, with the
majority (N = 30) published after the year 2000. In the 31 studies that collected data from indi-
vidual participants, prevalence estimates ranged from 12/10,000 persons in India [29] to 511/
10,000 persons in Sri Lanka [40], while incidence estimates ranged from 0.49/1,000 person-
years in Indonesia [19] to 2.88/1,000 person-years in Brazil [17] (see Table 1). The quality
scores of the 27 individual level studies included varied across the study designs, with 11 stud-
ies receiving a score greater than or equal to seven (NB: NOS ranges from zero to nine). For
the cohort studies, scores ranged from five to nine, and weaknesses were related to potential
biases associated with loss to follow up. For the case-control studies, scores ranged from five to
eight, with one study having a potential selection bias in the control group. For the cross-sec-
tional studies, scores ranged from three to seven.

Fig 2. Flowchart for selection of studies.

Sex and age

Sex and/or age were investigated and/or adjusted for in 17 studies, including five cohorts [14,
16–18, 32], four case-controls [23, 24, 26, 27], and eight cross-sectional studies [29, 32–36, 38,
40]. Six out of 17 studies considered sex as a confounder in adjusted models, seven out of 13
considered age in the adjusted model, and five included both [20, 23, 26, 27, 33]. Fourteen
studies analyzed the sex or age of the exposed and unexposed populations directly, one cross-
sectional study examined the sex and age of family head [32], one cohort study evaluated the
sex and age of the both the index patient and their contact [20], and one case-control study
included sex and age only for adjustment without providing point estimates [26]. Out of 16
studies that investigated the association of leprosy with sex, four reported a higher prevalence

Table 1. Observational studies conducted at the individual level of the association of leprosy with socioeconomic risk markers in high-burden countries.
Ref Author (year) Country NOS Study period Type of study Age Total size Leprosy Frequency Prevalence/
cases measure incidence in the
studied area
[14] Doull Philippines 7 1936–37 (Talisay), Cohort/Pop. All ages 21,791 402 I 1/1,000 PYR
(1942) 1933 (Cordova) (Talisay); 1/1,000
PYR (Cordova)
[28] Nigam India 6 1974–1975 Cross-sectional/ All ages 3,362 18 P 5/1,000
(1977) Pop.
[29] Bhavsar India 3 1976–1978 Cross-sectional/ Children/ 21,412 26 P 12/10,000
(1980) Pop. Adolescents (5–19
years old)
[15] Dominguez Myanmar 6 1964–76 Cohort/ Pop. All ages 52,026 1,367 I NA
(1980)
[30] Sommerfelt India 4 1982 Cross-sectional/ All ages 7,428 131 P 18/1,000
(1985) Pop.
[31] Chaturvedi India 4 1979–1983 Cross-sectional All ages 63,321 691 P 11/1,000
(1988) Pop.
[21] George India 8 1983–1984 Case-control/HB All ages 288 72 - NA
(1990)
[32] Andrade Brazil 7 1988 Cross-sectional/ All ages 926 137 P NA
(1994) Pop.
[16] Ranade India 9 1952–1886 Cohort/Contacts Unspecified 6,284 331 I 5/1,000 PYR (24/
(1995) 1,000 )
[33] Kumar (2001) India 7 1999–2000 Cross-sectional/ All ages 17,161 95 P 6/1,000
Pop.
[22] Bakker (2002) Indonesia 6 June/July 2000 (1st Case-control/ Over 6 years old 192 96 P 195/10,000
survey) and Nov Contacts
2000 (2nd survey)
[34] Hegazy Egypt 5 1999–2001 Cross-sectional/ All ages 9,643 24 P 25/10,000
(2002) Pop.
Pop.
[17] Bakker (2006) Indonesia 7 2000–2004 (6 Cohort/ Pop. All ages 4,903 44 I 3/1,000 PYR
surveys)
[23] Kerr-Pontes Brazil 5 2002 Case-control/ Adults (>18 years 1,083 226 - NA
(2006) Pop. old)
[36] Moet Bangladesh 5 2002–2003 Cross-sectional/ Over 5 years old 21,870 159 P 7/1,000
(2006) Contacts
[18] Kumar (2007) India 5 1999–2005 Cohort/ Pop. All ages 42,113 77 I 6/10,000 PYR
[19] Fischer Bangladesh 7 1989–2003 Cohort/ Pop. Unspecified 1,500,000 11,060 I 1/1,000 PYR
(2008)
[37] Durães Brazil 4 2004–2007 Cross-sectional/ All ages 1,040 211 P NA
(2010) Contacts
[24] Feenstra Bangladesh 8 2009 Case-control/ Over 5 years old 289 90 - NA
(2011) Pop.
[20] Sales Brazil 8 1987 to 2007 Cohort and All ages 6,158 319 (133 I 3/ PYR
(2011) cross-sectional/ new)
Contacts
[25] Feenstra Bangladesh 8 2009 Case-control/ Over 5 years old 289 90 - NA
(2013) Pop.
HB
[39] Moura Brazil 3 2006 Cross-sectional/ All ages 637 15 P 2/100
(2013) Contacts
(Continued)

Table 1. (Continued)
Ref Author (year) Country NOS Study period Type of study Age Total size Leprosy Frequency Prevalence/
cases measure incidence in the
studied area
[26] Murto Brazil 5 2009–2010 Case-control/HB Adults (>15 years 680 340 - NA
(2013) old)
[27] Wagenaar Bangladesh 7 2013 Case-control/ Adults (18–50 152 52 - NA
(2015) Pop. years old)
[40] Dabrera Sri Lanka 4 2012 Cross-sectional/ All ages 753 39 P 511/10,000
(2016) Pop.
Pop.: Population based; HB: Hospital-based; I: incidence; P: prevalence; PYR: person-years at risk; NA: not applicable.

Prevalence in the survey that preceded the study.

Denominator not specified.
https://doi.org/10.1371/journal.pntd.0006622.t001
of leprosy among males [14, 16, 17, 29], of which only one provided adjusted estimates. One
study reported that contacts of male patients had higher leprosy incidence [20], and the others
did not report differences between males and females. Eleven studies were included in the
meta-analysis of the association between male sex and leprosy. The crude overall RR for male
sex was 1.33 (95% CI: 1.06, 1.67), with a substantial heterogeneity between the studies (I2 =
64.2%) (Fig 3). The effect decreased along the study years. The association between age and
leprosy was assessed in 13 studies, of which six found a positive association with increasing age
[18, 24, 32, 34, 36].
Education and occupation

The association between education and leprosy was evaluated in one cohort [20], three case-
controls [23, 24, 26], and four cross-sectional studies [32–34, 40]. Different categorizations for
Table 2. Ecological studies of the association of leprosy with socioeconomic risk markers in high-burden countries.
Ref Author (year) Country Study period Unit of analysis Nº of study units Leprosy cases Frequency Prevalence/ incidence in the studied
measure area
[30] Sommerfelt (1985) India 1978 and Grouped 12 131 P 18/1,000
1982 villages
[41] Kerr-Pontes (2004) Brazil 1991–1999 Municipality 165 NR I 1-15/10,000 (by municipality)
[42] Lana (2009) Brazil 2003–2006 Municipality 853 NR I NR
[43] Imbiriba (2009) Brazil 1998–2004 Census tracts 1,536 4,104 I 4/10,000
[44] Queiroz (2010) Brazil 1995–2006 Census tracts 170 808 I 0-32/10,000 (by census tract)
[45] Cury (2012) Brazil 1998–2007 Census tracts 432 379 I 10/100,000
[46] Barreto (2014) Brazil 2004–2010 Census tracts 114 499 I 25-97/1000 (by census tracts)
[47] Cabral-Miranda Brazil 2005–2011 Municipality 417 1,674 I 1(2005) to 0.5/10,000 (2011)
(2014)
[48] Freitas (2014) Brazil 2009–2011 Municipality 5,565 NR I 9/100,000
[49] Nery (2014) Brazil 2004–2011 Municipality 1,358 200,966 I 75/100,000 (2004) to 46 /100,000 (2011)
[50] Duarte-Cunha (2015) Brazil 1998–2006 Neighbourhood 40 2,572 I 4/10,000
[51] Nobre (2015) Brazil 2001–2013 Municipality 167 3,927 I 8 (2001) to 9/100,000 (2013)
[52] Castro (2016) Brazil 2010 States 27 NR I 22/100,000
P: Prevalence; I: incidence; NR: not reported.

Yearly average new case detection rate in the study period.

Fig 3. Association between leprosy and socioeconomic markers. Pooled estimates using random-effects meta-analyses are calculated by
subgroups of socioeconomic variable. Error bars show the point RR with their 95% CIs on the log scale for each study. Diamonds show the
combined point estimate. I2 statistic and Q-test p-value are reported.
education included family literacy [26], having formal education [33] and level of schooling
[20, 23, 24, 32, 34, 40]. Three out of eight studies pointed to a higher number of leprosy cases
among less educated individuals [23, 32, 33], and the associations remained significant after

controlling for confounders (Table 3). In the study by Sales and colleagues, the educational
level of the index patient was negatively associated with other prevalent leprosy cases within
the family, but not among incident cases [20]. Andrade and colleagues (1994) suggested that a
lower level of education was associated with higher leprosy incidence among neighbours, but
not among other random groups [32]. Occupation status was analyzed in two case-controls
studies [23, 27] and two cross-sectional studies [33, 40], most commonly by separating manual
workers (e.g., factory, construction, or agriculture workers), from non-manual workers (e.g.,
traders or office workers) [23, 27, 33, 40]; unemployment as risk factor was also studied [40].
In the four studies included in the meta-analysis for occupation, there was a positive, but not
statistically significant, association between leprosy and manual labor (RR = 2.15, 95%
CI = 0.97–4.74; I2 = 92.6%) (Fig 3).
Social deprivation and food security

The relationship between income and leprosy was assessed in one cohort [20], four case-con-
trols [23, 24, 26, 27], and four cross-sectional studies [28, 29, 31, 34] using per capita household
income [20, 26–29, 31] or socioeconomic position defined by self-assessment [27], assets score
[24] or social score [34]). Three studies reported statistically significant associations between
poverty and leprosy in univariate analysis [20, 27, 29], but the associations attenuated after
adjusting for potential mediators, such as age, sex or occupation. Poverty measures differed
among the studies, making a meta-analysis not appropriate; however, the direction of the asso-
ciation was consistent across studies, providing evidence of an inverse association between
socioeconomic position and leprosy risk.
Factors related to food insecurity, an established correlate of poverty [53], were studied as a
risk factor for leprosy in three case-control studies, two of which were carried out in Bangladesh
[24, 27] and one in Brazil [23]. Food shortage in the past year was assessed twice [24, 27], ever
food-shortage three times [23, 24, 27], and food expenditure, score of food insecurity (House-
hold Food Insecurity Access Scale, HFIAS), Dietary Diversity Score (DDS), and household food
stocks were evaluated once each [27]. Low food diversity and low stocks of food were not associ-
ated with increased number of leprosy cases, while food expenditure and HFIAS were negatively
associated with leprosy [27]. In the meta-analysis, ever food-shortage was significantly associ-
ated with higher leprosy risks (RR = 1.39, 95% CI = 1.05–1.85; I2 = 29.3%) (Fig 3).
Contact with leprosy patients

Sharing a household with a current leprosy case was strongly associated with risk of developing
the disease in all nine studies that investigated this factor (five cohorts [14–18], three case-con-
trols [21, 25, 26], and one cross-sectional study [40]). One study conducted by Feenstra and
colleagues, which used a score of social interaction with a leprosy patient (i.e., in the house-
hold, within the neighborhood, and outside the neighborhood), found that contacts in the
household and within the neighborhood shared similar risks of leprosy [25]. The meta-analysis
of the other eight studies estimated a crude RR of 3.40 (95% CI = 2.24–5.18) associated with
household sharing, with a substantial heterogeneity (I2 = 95.9%) (Fig 3). Six studies also evalu-
ated the association between being a household or familial contact of a leprosy patient as
opposed to any other type of contact, and all found that household or familial contacts had
higher risk of leprosy than general contacts [16, 20, 22, 36, 37, 39].
Living conditions and water supply

Household conditions were assessed in six studies, including three case-control and three
cross-sectional studies, as house ownership [27], habitation type (i.e., private accommodation)

Table 3. Adjusted point estimates of the association of leprosy with socioeconomic risk markers in high-burden countries in individualized studies.
Ref Year Marker Exposed group Unexposed group Type Measure Adjusted E for:
Sex Age Leprosy Work or Others
patient education
contact
[32]A 1994 Education Less than High School High School ORadj 2.54 (1.06, □ ■ □ □ ■
6.09)
[32]B 1994 Education Less than High School High School ORadj 1.78 (0.79, □ ■ □ □ ■
4.00)
[33] 2001 Education No formal education Formal education ORadj 1.79 (1.11, ■ ■ □ ■ ■
2.86)
[23] 2006 Education Lower level of education High level of education ORadj 1.87 (1.29, ■ ■ □ □ ■
2.74)
[20]D 2011 Education <4 years of formal >10 years of formal ORadj 0.82 (0.49, ■ ■ ■ ■ ■
education education 1.36)
[20]D 2011 Education <4 years of formal >10 years of formal ORadj 0.60 (0.34, ■ ■ ■ ■ ■
[20]C 2011 Education <4 years of formal >10 years of formal ORadj 1.43 (0.96, ■ ■ ■ ■ ■
[20]C 2011 Education <4 years of formal >10 years of formal ORadj 2.72 (1.54, ■ ■ ■ ■ ■
[33] 2001 Work type Housewives/students/ Manual workers ORadj 0.53 (0.28, ■ ■ □ ■ ■
others 1.02)
[27] 2015 Work type Business Laborer ORadj 0.66 (0.13, ■ ■ □ ■ ■
3.25)
Social deprivation and food
security
[23] 2006 Food availability Ever experienced food Never experienced food ORadj 1.54 (1.45, ■ ■ □ ■ ■
shortage shortage 1.63)
[24] 2011 Food availability Food shortage in the past No recent food shortage ORadj 1.79 (1.06, □ ■ □ □ □
year 3.02)
[27] 2015 Food availability Household food stock Household food stock ORadj 0.66 (0.29, ■ ■ □ ■ ■
present absent 1.50)
[27] 2015 Malnutrition Low diversity of food— Higher diversity of food ORadj 0.83 (0.58, ■ ■ □ ■ ■
Dietary Diversity Dietary Diversity 1.18)
Score 9 Score > 9
Contact with leprosy patients
[25] 2013 Contact Household contact Social contacts outside ORadj 1.09 (1.01, □ ■ □ □ ■
the neighbourhood 1.19)
[25] 2013 Contact Social contacts within the Social contacts outside ORadj 1.07 (1.03, □ ■ □ □ ■
neighbourhood the neighbourhood 1.11)
[36] 2006 Physical proximity Share the same roof and Neighbors of next-door ORadj 2.44 (1.44, □ ■ ■ □
(among contacts) kitchen with a leprosy neighbors or social 4.12)
patient contacts
[20]C 2011 Physical proximity Household contact Nonhousehold contact ORadj 1.33 (1.02, ■ ■ □ ■ ■
(among contacts) 1.73)
Living conditions and water supply
[32]B 1994 Household Ground/cement floor Carpet/wood/ceramic ORadj 0.87 (0.49, □ ■ □ ■ ■
construction floor 1.55)
[32]B 1994 House ownership Non-private House/flat ORadj 3.95 (1.79, □ ■ □ ■ ■
accommodation 8.72)
[27] 2015 House ownership Landowner Landless ORadj 0.34 (0.14, ■ ■ □ ■ ■
0.81)
[32]A 1994 Household size Rooms in the Rooms in the ORadj 0.76 (0.38, □ ■ □ ■ ■
household 2 household > 2 1.53)
(Continued)

Ref Year Marker Exposed group Unexposed group Type Measure Adjusted E for:
Sex Age Leprosy Work or Others
patient education
contact
[32]B 1994 Household size Rooms in the Rooms in the ORadj 0.69 (0.45, □ ■ □ ■ ■
household 2 household > 2 1.06)
[27] 2015 Household size Household size (per m2) ORadj 0.76 (0.55, ■ ■ □ ■ ■
1.04)
[32]B 1994 Clean water No tap water Tap water ORadj 0.37 (0.15, □ ■ □ ■ ■
0.91)
[23] 2006 Clean water Regular bath in open No regular bath in open ORadj 1.77 (1.12, ■ ■ □ ■ ■
waters in the past 10 years waters in the past 10 2.81)
years
[35] 2003 Sanitation Sanitary facility in the Household without a ORadj 1.39 (1.03, □ □ □ ■ ■
household toilet 1.89)
[33] 2001 Household Clean household Dirty/very dirty ORadj 0.49 (0.33, ■ ■ □ ■ ■
cleaniness household 0.75)
[35] 2003 Household Clean household and Dirty household and ORadj 0.56 (0.36, ■ ■ □ ■ ■
cleaniness surroundings surroundings 0.86)
[23] 2006 Household Low frequency of High frequency of ORadj 1.81 (1.30, ■ ■ □ ■ ■
cleaniness changing bed linen changing bed linen 2.52)
[17] 2006 Crowding Residents in the household Residents in the HRadj 3.12 (1.34, □ □ □ □ ■
8 household <8 7.27)
[20]C 2011 Crowding Residents in the household Residents in the ORadj 0.71 (0.53, ■ ■ ■ ■ ■
[20]D 2011 Crowding Residents in the household Residents in the ORadj 1.19 (0.79, ■ ■ ■ ■ ■
Other sociodemographic indicators
[19] 2008 Health and social Distance to health clinics RRadj 1.01 (0.98, □ □ □ □ ■
assistance (per 1 km) 1.03)
[27] 2015 Religion Hindu Muslims ORadj 1.41 (0.52, ■ ■ □ ■ ■
3.88)
[26] 2013 Migration Migrated in the past 5 year Did not migrate in the ORadj 1.51 (1.0, ■ ■ ■ ■ ■
past 5 years 2.28)
A
Households with leprosy patient compared with neighbor households.
B
Households with leprosy patient compared with random household outside the neighborhood.
C
Cross-sectional study assessing prevalence of leprosy inside the household with index leprosy case.
D
Cohort study assessing the incidence.
E
■ Presence or □ Absence
[32], house size (i.e., in square meters and number of rooms) [24, 27, 32], and building or floor
material [23, 31–33]. Neither owning the house [27], residing in private accommodation [32],
nor house size [27] were significantly associated with leprosy after adjusting for factors such as
education, work and household food stocks [27, 32]. Only one of the four studies looking at
building materials found an association in univariate analysis between poorer building mate-
rial (i.e., floor or house walls made of materials different than cement/bricks) and leprosy [31].
Crowding was measured as the number of residents in the household in four studies [17, 20,
32, 40] and residents per room in three studies [23, 24, 34]. Although only one individual
study found evidence that crowding was significantly associated with higher leprosy risks [17],
the pooled RR provides evidence that crowding, (i.e., five individuals living in the same
household or four individuals sharing the same bedroom) may be a significant risk marker

for leprosy (RR = 1.32, 95% CI = 1.13–1.53; I2 = 0.0%) (Fig 3). Of note, Kerr-Pontes and col-
leagues did not find an association between bed sharing and higher risk of leprosy [23].
Water and sanitation were investigated in one case-control [23] and in five cross-sectional
studies [26, 29, 32, 34, 35]. Specifically, household access to clean water was assessed in three
studies [23, 32, 34], waste collection in one [26], sanitation (sewage system or the presence of a
sanitary facility in the house) in three studies, [23, 29, 35] and socio-sanitary score based on
type of water supply and crowding in one [29]. Of the three studies investigating access to
clean water, only the report by Andrade and colleagues found an association between clean
water and a lower incidence of leprosy in adjusted estimates, when comparing households
with leprosy with a random household, but not with a neighbouring household [32]. The pres-
ence of waste collection services [26] and good sanitary conditions score were associated with
a lower prevalence of leprosy [29]. Cleanliness habits (e.g., sweeping the house, high frequency
of changing bed linen) [23, 32] and household cleanliness (i.e., living in a dirty household or
surroundings) [33, 35] were assessed in four studies, of which three found a negative associa-
tion between cleanliness and leprosy [23, 33, 35]. Pooled statistics were calculated for lack of
clean water in the household in three studies, including one with two comparisons group
(RR = 0.94; 95% CI = 0.65,1.35; I2 = 62.5%) (Fig 3) and provided no evidence that clean water
correlates with lower leprosy incidence.
Other sociodemographic indicators

The studies at the individual level investigated a range of other sociodemographic factors,
including ethnic background, marital status, religion, urbanization, and migration status, but
the overall evidence was limited. For example, in the one case-control study that examined eth-
nicity and marriage as correlates of leprosy, the authors report no difference between white
and black/brown or unmarried and married individuals [23]. The relationship between reli-
gion and leprosy was evaluated in three studies, one held in Bangladesh [27] and two in India
[31, 33], with higher leprosy prevalence among Muslims reported in one [31]. In addition, of
the three studies evaluating urbanicity and leprosy [29, 30, 38], two found that individuals liv-
ing in urban (versus rural areas) [38] or in rural villages (versus the rural surrounding areas)
have lower leprosy prevalence [30]. The distance from the household to health clinics, which
can also be a measure of urbanization in mixed rural/urban areas, was evaluated by Fisher and
colleagues (2008) in Bangladesh, but no relationship was found between leprosy detection rate
and proximity to a clinic [19]. Recent migration (i.e., in the past 5 years) was evaluated once
and was positively associated with leprosy [26].
Ecological trends
Ecological studies provide an important line of evidence on the relationship between socioeco-
nomic and demographic factors and leprosy (Tables 2 and 4). Associations of leprosy with
increased urbanization [41, 45, 47–50], illiteracy/lower education [30, 41, 48–51] and unem-
ployment [49–51] were consistently reported at the ecological level. Regions with a higher per-
centage of households with access to clean water [41, 50, 52], waste collection services [50, 51],
or sanitation (i.e., a sewage system or a sanitary facility) [48, 50–52] reported a lower number
of leprosy cases in the all but one of the studies [44, 48, 50, 52]. The mean number of individu-
als per household or per room was considered in seven studies [41, 46–50, 52], five of which
found it positively associated with leprosy [46–49, 52]. Socioeconomic deprivation was mea-
sured as the percentage of people living in poverty or extreme poverty (i.e., according to a pre-
defined threshold) [30, 41, 49–51], scores indicating poverty, socioeconomic groups, and
social status (including deprivation) [43–45]. Half of these studies found a correlation between

Table 4. Adjusted point estimates of the association of leprosy with socioeconomic risk markers in high burden countries in ecological studies.
Ref Year Marker Exposed group Unexposed group Type Measure
[41] 2004 Education Children not going to school (per %) βadj1 0.02 (0.00,
0.05)
[41] 2004 Education Mean years of study among aged 25yrs (per βadj1 1.35 (0.62,
year) 2.08)
[48] 2014 Education Illiteracy rate 24% Illiteracy rate < 8% RRadj 2.15 (1.83,
2.53)
[49] 2014 Education Illiteracy rate 20.42% Illiteracy rate < 20.42% RRadj 1.12 (1.07,
1.18)
[51] 2015 Education Illiteracy rate (per %) ORadj 1.10 (0.98,
1.24)
[49] 2014 Unemployment Unemployment rate 7.47% Unemployment rate < 7.47% RRadj 1.20 (1.16,
1.23)
[51] 2015 Unemployment Unemployment rate (per %) ORadj 1.03 (0.93,
1.14)
Social deprivation and food security
[49] 2014 Income Poor 27.42% Poor < 27.42% RRadj 1.13 (1.08,
1.18)
[51] 2015 Income Per capita household income (per BRL) ORadj 0.99 (0.98,
1.01)
[51] 2015 Income Poor (<USD 70/month) (per %) ORadj 0.94 (0.86,
1.03)
[43] 2009 Economic and social Low life conditions (index) Fair life conditions (index) ORadj 4.43 (3.14,
indices/scores 6.24)
[51] 2015 Malnutrition Malnutrition in children <1 year old (per %) ORadj 0.95 (0.62,
1.48)
Living conditions
[50] 2015 Clean water Households with water supply (per %) RRadj 10.00 (2.32,
50.00)
[48] 2014 Sanitation Households without adequate sanitation 16% Households without adequate RRadj 1.34 (1.47,
sanitation < 6% 1.81)
[51] 2015 Sanitation Households with adequate sanitation (per %) ORadj 1.01 (0.98,
1.05)
[51] 2015 Waste collection Households without adequate trash collection (per ORadj 0.97 (0.92,
%) 1.02)
[47] 2014 Crowding Mean residents in the household (per unit) RRadj 0.43 (p = 0.04)
[49] 2014 Crowding Residents in the household 3.6 Residents in the household <3.6 RRadj 1.04 (1.01,
1.08)
[48] 2014 Crowding Residents per room 0.65 Residents per room < 0.51 RRadj 1.41 (1.26,
1.58)
Social and health indicators
[49] 2014 Health and social Coverage of Family Health Program > 95.06% Coverage of Family health RRadj 1.12 (1.08,
assistance Program 72.02% 1.17)
[48] 2014 Health and social Coverage of Family Health Program 80% Coverage of Family health Program < 50% RRadj 1.29 (1.17,
assistance 1.41)
[50] 2015 Health and social Number of health campaigns for leprosy detection RRadj 1.02 (0.96,
assistance (per unit) 1.08)
[50] 2015 Health and social Number of reference units assisted by leprosy RRadj 1.69 (1.10,
assistance control programme (per unit) 2.62)
[51] 2015 Health and social Vaccination coverage (per %) ORadj 1.02 (0.95,
assistance 1.09)
[49] 2014 Health and social Coverage of cash transfer program 48.11% Coverage of cash transfer program 27.75% RRadj 0.79 (0.74,
assistance 0.83)
(Continued)

Ref Year Marker Exposed group Unexposed group Type Measure

[41] 2004 Inequality and human Increased inequality (Theils L index) (per unit βadj1 1.67 (0.39,
development from 0 to 1) 2.94)
[49] 2014 Inequality and human Inequality (Gini index) 0.54 Inequality (Gini index) < 0.54 RRadj 1.07 (1.04,
development 1.11)
[48] 2014 Inequality and human Inequality (Gini index) 0.55 Inequality (Gini index) < 0.50 RRadj 1.26 (1.16,
development 1.37)
[47] 2014 Inequality and human Increased inequality (Gini index) (per unit from 0 RRadj 3.84 (p = 0.00)
development to 1)
Population and environment
[41] 2004 Urbanization Relative population growth between 1991 and 1999 βadj1 1.02 (1.01,
(per %) 1.04)
[48] 2014 Urbanization Living in metropolis (municipality with > 900,000 Living in small towns (municipality with up RRadj 1.92 (1.15,
inhabitants) to 20,000 inhabitants) 3.18)
[48] 2014 Urbanization Urbanization rate 65% Urbanization rate < 47% RRadj 2.53 (1.40,
1.67)
[49] 2014 Urbanization Urbanization rate 59.8% Urbanization rate < 59.8% RRadj 0.99 (0.93,
1.06)
[49] 2014 Urbanization Urban population (per %) RRadj 0.02 (p<0.01)
[47] 2014 Migration Residents born in the State (per %) RRadj - 0.04
(p = 0.00)
1
Linear regression.
having better living conditions and lower leprosy burden [43–45, 49]. Migration, evaluated as
the percentage of people born in other regions, was positively associated with leprosy [47].
Ecological studies also provided evidence of a correlation between malnutrition and leprosy
among children [30, 51].
Ecological evidence also suggests that, in general, indicators of social development and pol-
icy interventions were negatively associated with leprosy burden. Inequality was measured
using Gini Index or Theil’s L index in four studies [41, 47–49] and as income ratio between the
richest 20% and the poorest 20% (20–20 Income Ratio) in one study [48]. Human Develop-
ment Index (HDI) was assessed in another study [42]. Overall, the studies provided strong and
consistent evidence of an association between increased inequality and/or lower socioeco-
nomic development and higher leprosy risks [41, 42, 47–49]. On the other hand, the presence
of specific campaigns and health services for leprosy detection were associated with higher lep-
rosy incidence rates, potentially by enhancing the leprosy detection efficiency [50]. While
higher coverage of primary health care in Brazil was associated with higher leprosy new case
detection in two studies [48, 49], no associations with leprosy were found using other metrics
for health care access, including: the number of general public health services [41], number of
physicians per 1,000 inhabitants [41], vaccination coverage [51] and infant mortality rates
[41]. In Brazil, an analysis of the impact of a conditional cash transfer program showed that
increased coverage of the program benefits was associated with a reduction in leprosy new
case detection rates [49].
Discussion
This systematic review points to a consistent relationship between leprosy and unfavorable
socioeconomic circumstances. For individual level studies, meta-analyses provide evidence for
increased risks of leprosy in individuals who are male, share homes with leprosy cases, live in

crowded conditions, and have experienced food shortages in the past. In ecological level stud-
ies, point estimates for the associations between leprosy and sociodemographic risk markers of
crowding, sanitation, and poverty remained largely consistent with individual level studies and
across different geographic settings.
Overall, males had a greater risk of leprosy. However, the effect diminished in studies that
are more recent; the pattern is potentially attributable to higher detection of leprosy among
women over time and/or to change in exposure level of different risk markers in men and
women. In most studies, literacy and high levels of education were associated with lower lep-
rosy rates, although pooled estimates for education were not possible due to incomparable cat-
egories. Better education, in both sexes, can increase health knowledge and healthy behaviors,
foster access to better work conditions and resources and promote greater autonomy [54],
which could potentially reduce leprosy infection and transmission.
The type of work performed by an individual reflects their socioeconomic status and condi-
tions and can vary across time and both within and between countries, especially in large and
multicultural ones (e.g., India and Brazil). Pooled estimates between work and leprosy showed
high statistical heterogeneity across the different studies, which might suggest that performing
manual or agriculture work might correspond with different levels of poverty and living condi-
tions in the different study settings (e.g., India, Brazil, Bangladesh or Sri Lanka), resulting in
differences in the levels of exposure to M. leprae or chances of developing symptomatic disease.
Food shortage, an indicator of extreme poverty and undernourishment [27] also appeared to
be a risk marker of leprosy. Food-shortage was assessed in places where seasonality can influ-
ence work, income, food prices, consequently reducing dietary diversity [23, 24, 27]. More
studies are needed about other possible risk markers of poverty and education inequalities,
such as ethnicity [55, 56], which was assessed only once [23].
Person-to-person contact inside the household is one of the most likely sources for leprosy
transmission [57]; nevertheless, similarities of social, sanitary, and poverty conditions shared
by families and neighbors, which can contribute to leprosy transmission, are poorly taken into
account. The higher leprosy prevalence among crowded households in the meta-analysis sup-
port the hypothesis that crowding can both facilitate transmission and also be a general indica-
tor of poverty. Additionally, the association between religion and higher risk of leprosy in the
study of Chaturvedi (1988) was mainly attributed to increased household crowding in some
religious group [31], which also corroborates the idea that crowding may be associated with
infection and/or disease development.
Most studies characterized the study setting as rural or urban areas, but only ecological
studies showed consistent correlations between urbanization and higher leprosy rates. Studies
performed at the individual level, showed that household characteristics and basic socio-sani-
tary conditions were strongly related with leprosy burden. In 2015, only 58% of the global pop-
ulation had access to clean water and 68% to adequate sanitation, with marked inequalities
between rural/urban and rich/poor areas, including many high-burden countries for leprosy
[58]. The absence of association between lack of access to clean water and leprosy in the meta-
analysis might derive from high heterogeneity among the living conditions of those affected.
Migration from a relatively higher-burden setting is an important risk factor for infectious
diseases transmission and reactivation in lower-burden settings (e.g., as has been previously
demonstrated for tuberculosis) [59, 60]. This result differs from the two studies that evaluated
migration history as a potential risk factor for leprosy. Nevertheless, the origin of migrants or
the incidence/prevalence in their country or region of origin was not described.
The point estimates for the association between the socioeconomic or demographic charac-
teristics (i.e., crowding, sanitation, and poverty) and leprosy in both individualized and eco-
logical studies followed the same direction, suggesting no ecological fallacy and strengthening

the association between these risk markers and leprosy. Nevertheless, it is important to men-
tion that few studies reported the potential for reverse causality in both cross-sectional and
ecological investigations (e.g., leprosy ! unemployment). Freitas and colleagues (2014) sug-
gested that higher detection rates of leprosy in municipalities with greater Family Health Pro-
gram coverage can also be attributed to preferential targeting of municipalities by their leprosy
rates [48]. Also, there is a possible link between leprosy-associated stigma and loss of employ-
ment, which could further worsen living conditions.
Some limitations of this systematic review include, first, the generalizability of the ecological
findings as only one investigation was conducted outside of Brazil. Second, the findings pre-
sented here originate from studies carried out only in lower middle- and upper-middle econo-
mies, as we could not locate any relevant study carried out in a low-income country; the
findings, although plausible, may be less applicable to low-income countries. Third, although
we included a large number of social, demographic, and environmental factors as potential
descriptors in the search strategy, some rare factors linked with leprosy burden might have
missed. We selected all high burden countries for leprosy since 2001, but endemic countries
facing civil war in the last 10 years might not have been included in WHO statistics or, by con-
sequence, in this review. Fourth, heterogeneity of social/cultural/economic structures between
countries and within large countries such as Brazil and India prevented us from combining
characteristics such as education in the meta-analysis. Fifth, although the majority of studies
were published in the 21st century, the high-burden countries have experienced substantial
economic growth in the past two decades, which has the potential to limit the generalizability
of the meta-analysis estimates. Also, economic growth occurred in the past two decades, in
which the majority of these studies have taken place could have contributed to higher hetero-
geneity in the effects between the studied social markers and leprosy. Despite these limitations,
this review aggregated sparse evidence from diverse study settings, showing consistent associa-
tions between social determinants and leprosy across studies. Future research should prioritize
investigations in low-income countries, address other markers of poverty (e.g., ethnicity, rural
to urban migrants), explore heterogeneity between and within countries, and investigate the
impact of recent poverty reduction programs.
Leprosy has been gradually included in the portfolio of diseases associated with poverty and
in countries, like Brazil, has been incorporated into social programs [61]. For instance, high
leprosy burden was accounted for in the prioritization of Brazilian municipalities in social pro-
tection programs, such as “Plano Brasil sem Miséria” [6]. Despite these advances, the options
for combining curative approaches with prevention efforts particularly designed to address
social determinants have not been fully considered in the context of leprosy control programs
in many countries. Social determinants of leprosy have been poorly studied to date and need
to be particularly addressed in those countries where leprosy incidence is still high and human
development remains low. In agreement with the WHO Global Leprosy Strategy 2016–2020,
which recommends the increase of inter-sectoral collaboration to further reduce the global
and local leprosy burden, this review provides additional evidence that elimination of leprosy
at the international level requires reduction of social inequalities, improving access of adequate
housing and sanitation conditions and targeting social vulnerable groups and communities.
In conclusion, this study underscores the many ways that poverty can create conditions
that perpetuate leprosy risk. In addition, these findings call attention to persistent gaps in
knowledge of the associations between leprosy and socioeconomic risk markers and highlight
a lack of studies conducted in low-income countries. Thus, political commitment must priori-
tize investments in not only the diagnosis of leprosy, but also in research on the social determi-
nants of this ancient disease, and in the integration of leprosy-specific programs into social
policies aiming to eradicate poverty.

Supporting information
S1 Text. Search strategy used to study the socioeconomic factors associated with leprosy
burden.
(DOCX)
S1 Table. Summary table of the 39 appraised records.
(PDF)
S2 Table. Checklist for the PRISMA guidelines.
(DOC)
Acknowledgments
We would like to acknowledge Martha Silvia Martinez-Silveira (Fiocruz-Bahia) and all other
colleagues from Fiocruz-Bahia, Cidacs and Instituto de Saude Coletiva (ISC/UFBA) who con-
tributed with valuable inputs during the development of this systematic review.
Author Contributions
Conceptualization: Julia Moreira Pescarini, Agostino Strina, Joilda Silva Nery, Laura C.
Rodrigues, Gerson Oliveira Penna.
Data curation: Julia Moreira Pescarini.
Formal analysis: Julia Moreira Pescarini.
Funding acquisition: Agostino Strina, Joilda Silva Nery, Laura C. Rodrigues, Mauricio Lima
Barreto, Gerson Oliveira Penna.
Investigation: Julia Moreira Pescarini, Agostino Strina, Joilda Silva Nery, Lacita Menezes Ska-
linski, Kaio Vinicius Freitas de Andrade.
Methodology: Julia Moreira Pescarini, Agostino Strina, Joilda Silva Nery, Elizabeth B.
Brickley.
Supervision: Elizabeth B. Brickley, Laura C. Rodrigues, Mauricio Lima Barreto, Gerson Oli-
veira Penna.
Validation: Lacita Menezes Skalinski, Kaio Vinicius Freitas de Andrade.
Visualization: Julia Moreira Pescarini.
Writing – original draft: Julia Moreira Pescarini, Agostino Strina, Joilda Silva Nery.
Writing – review & editing: Julia Moreira Pescarini, Agostino Strina, Joilda Silva Nery, Lacita
Menezes Skalinski, Kaio Vinicius Freitas de Andrade, Maria Lucia F. Penna, Elizabeth B.
Brickley, Laura C. Rodrigues, Mauricio Lima Barreto, Gerson Oliveira Penna.
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Socioeconomic Risk Markers of Leprosy

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RESEARCH ARTICLE

Socioeconomic risk markers of leprosy in

‡ These authors are joint senior authors on this work.

Citation: Pescarini JM, Strina A, Nery JS, Skalinski

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Data extraction and analysis

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Fig 2. Flowchart for selection of studies.

Sex and age

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Education and occupation

P: Prevalence; I: incidence; NR: not reported.

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Social deprivation and food security

Contact with leprosy patients

Living conditions and water supply

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Other sociodemographic indicators

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Ref Year Marker Exposed group Unexposed group Type Measure

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