Poverty and common mental disorders in low and middle income countries: A systematic review.

Abstract

In spite of high levels of poverty in low and middle income countries (LMIC), and the high burden posed by common mental disorders (CMD), it is only in the last two decades that research has emerged that empirically addresses the relationship between poverty and CMD in these countries. We conducted a systematic review of the epidemiological literature in LMIC, with the aim of examining this relationship. Of 115 studies that were reviewed, most reported positive associations between a range of poverty indicators and CMD. In community-based studies, 73% and 79% of studies reported positive associations between a variety of poverty measures and CMD, 19% and 15% reported null associations and 8% and 6% reported negative associations, using bivariate and multivariate analyses respectively. However, closer examination of specific poverty dimensions revealed a complex picture, in which there was substantial variation between these dimensions. While variables such as education, food insecurity, housing, social class, socio-economic status and financial stress exhibit a relatively consistent and strong association with CMD, others such as income, employment and particularly consumption are more equivocal. There are several measurement and population factors that may explain variation in the strength of the relationship between poverty and CMD. By presenting a systematic review of the literature, this paper attempts to shift the debate from questions about whether poverty is associated with CMD in LMIC, to questions about which particular dimensions of poverty carry the strongest (or weakest) association. The relatively consistent association between CMD and a variety of poverty dimensions in LMIC serves to strengthen the case for the inclusion of mental health on the agenda of development agencies and in international targets such as the millenium development goals.

Full text

Page 1

This article appeared in a journal published by Elsevier. The attached
copy is furnished to the author for internal non-commercial research
and education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
article (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
http://www.elsevier.com/copyright

Page 2

Author's personal copy
Poverty and common mental disorders in low and middle income countries:
A systematic reviewq
Crick Lunda,*, Alison Breena, Alan J Flishera, Ritsuko Kakumab, Joanne Corrigalla, John A Joskaa,
Leslie Swartzc, Vikram Pateld
aDepartment of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
bCentre for Addiction and Mental Health, University of Toronto, Toronto, Canada
cDepartment of Psychology, Stellenbosch University, Stellenbosch, South Africa
dCentre for Global Mental Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
a r t i c l e i n f o
Article history:
Available online 12 May 2010
Keywords:
Mental health
Poverty
Developing countries
Depression
Anxiety
Development
Systematic review
a b s t r a c t
In spite of high levels of poverty in low and middle income countries (LMIC), and the high burden posed
by common mental disorders (CMD), it is only in the last two decades that research has emerged that
empirically addresses the relationship between poverty and CMD in these countries. We conducted
a systematic review of the epidemiological literature in LMIC, with the aim of examining this relation-
ship. Of 115 studies that were reviewed, most reported positive associations between a range of poverty
indicators and CMD. In community-based studies, 73% and 79% of studies reported positive associations
between a variety of poverty measures and CMD, 19% and 15% reported null associations and 8% and 6%
reported negative associations, using bivariate and multivariate analyses respectively. However, closer
examination of specific poverty dimensions revealed a complex picture, in which there was substantial
variation between these dimensions. While variables such as education, food insecurity, housing, social
class, socio-economic status and financial stress exhibit a relatively consistent and strong association
with CMD, others such as income, employment and particularly consumption are more equivocal. There
are several measurement and population factors that may explain variation in the strength of the
relationship between poverty and CMD. By presenting a systematic review of the literature, this paper
attempts to shift the debate from questions about whether poverty is associated with CMD in LMIC, to
questions about which particular dimensions of poverty carry the strongest (or weakest) association. The
relatively consistent association between CMD and a variety of poverty dimensions in LMIC serves to
strengthen the case for the inclusion of mental health on the agenda of development agencies and in
international targets such as the millenium development goals.
? 2010 Elsevier Ltd. All rights reserved.
Introduction
Common mental disorders (CMD), which include depression,
anxietyandsomatoformdisorders,makeasignificantcontributionto
the burden of disease and disability in low and middle income
countries (LMIC) (Lopez, Mathers, Ezzati, Jamison, & Murray, 2006;
WHO, 2001). In spite of high levels of poverty in LMIC, and the high
burden posed by CMD in these countries, it is only in the last two
decades that research has emerged that empirically addresses the
relationship between poverty and CMD in these settings (Araya,
Lewis, Rojas, & Fritsch, 2003; Patel, Araya, de Lima, Ludermir, &
Todd,1999; Patel & Kleinman, 2003).
Recently, there has been debate in the literature regarding the
strength of this relationship. Narrative reviews of 5 epidemio-
logical studies from Brazil, Chile, India and Zimbabwe (Patel et al.,
1999) and of a further 11 studies from a range of LMIC (Patel &
Kleinman, 2003), suggest that CMD is strongly associated with
lower levels of education and socio-economic status, as well as
factors such as rapid social change, violence and insecurity,
particularlyamong women. However,
concluded that when using measures of poverty such as
otherreviewshave
qThis paper was produced as part of the work of the Mental Health and Poverty
Project, funded by the UK Department for International Development (DfID) for the
benefit of LMIC. VP is supported by a Wellcome Trust Senior Research Fellowship in
Tropical Medicine. JC is funded by the Western Cape Department of Health, South
Africa. Initial drafting of this review was conducted as part of the Municipal
Services Project, funded by the International Development Research Council. The
views expressed are not necessarily those of the funders. We would like to thank
Sara Cooper and Sarah Skeen for their assistance with the data extraction and
Stephen Stansfeld for commenting on an earlier draft of this paper. We declare that
we have no conflicts of interest.
* Corresponding author. Tel.: þ27 216850120; fax: þ27 216851223.
E-mail address: crick.lund@uct.ac.za (C. Lund).
Contents lists available at ScienceDirect
Social Science & Medicine
journal homepage: www.elsevier.com/locate/socscimed
0277-9536/$ e see front matter ? 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.socscimed.2010.04.027
Social Science & Medicine 71 (2010) 517e528

Page 3

Author's personal copy
consumption (defined as household per capita expenditure) or
level of education, there is no consistent association with indi-
cators of poor mental health (Das, Do, Friedman, McKenzie, &
Scott, 2007).
The debate regarding the consistency and strength of the
association between CMD and poverty is important, not only for
conceptual reasons. A clear association between mental ill-health
and poverty in LMIC would strengthen the case for the inclusion of
mental health on the agenda of development agencies and on
international targets such as the millenium development goals
(MDGs) (Miranda & Patel, 2005; Sachs & Sachs, 2007). On the other
hand, a weak association might suggest that interventions that
target the purported social determinants of CMD would exert
a limited effect (Das et al., 2007). In this instance, interventions
might be better directed towards protecting individuals and
households from adverse events (Das et al., 2007), as well as
secondary and tertiary prevention.
This debate has taken place against the backdrop of a relatively
well established field of study regarding poverty and mental
health in high income countries (HIC) (Saraceno & Barbui, 1997;
Saraceno, Levav, & Kohn, 2005). Unemployment (Weich & Lewis,
1998); adverse neighbourhood characteristics (Truong & Ma,
2006); low income, education, social class and socio-economic
status (SES) (Lorant et al., 2003); and more recently income
inequality (Pickett, James, & Wilkinson, 2006) have been shown
to be associated with negative mental health outcomes in these
countries. Theory regarding the mechanisms of this relationship is
broadly divided into the “social causation” hypothesis, in which
the conditions of poverty, such as stress, increased negative life
events, worse physical health, reduced access to health care and
stigma are thought to precipitate or maintain mental ill-health;
and the “social selection” or “social drift” hypothesis, in which
people living with mental illness are thought to drift into, or
remain in, conditions of poverty, as a result of increased health
expenditure, reduced income and lost employment (Dohrenwend
et al., 1992; Saraceno et al., 2005). It has been hypothesised that
the former theory may more readily apply to depression, whereas
the latter may be more appropriate for schizophrenia (Saraceno
et al., 2005).
In both HIC and LMIC, the definition of poverty appears to be
central to examining its association with mental health. Tradi-
tionally, “absolute” poverty refers to a fixed income level and
“relative” poverty refers to the level of income in relation to the
mean or median income of a population (Toye & Infanti, 2004). A
further distinction has been made between poverty and depri-
vation. Townsend argued that while deprivation refers to people’s
unmet needs for a number of basic commodities, poverty refers to
the lack of resources required to meet those needs (Townsend,
1979, 1987). The subsequent development of the term “multiple
deprivation” has come to refer to a range of indicators of social
and economic deprivation and exclusion in poverty studies
(Barnes, Wright, Noble, & Dawes, 2007; Toye & Infanti, 2004).
Attempts have also been made to develop composite deprivation
indices, such as the Index of Multiple Deprivation (IMD)
(Department of the Environment, 2000) and the Human Devel-
opment Index (HDI) (United Nations Development Programme,
2006).
In the light of the apparently contradictory findings from
existing literature in LMIC, and the complex relationship between
poverty and mental health, we carried out a systematic review of
the literature to further elucidate the relationship between
a variety of poverty indicators and CMD in LMIC. In particular, we
aimed to describe the strength and nature of any association, and
the type of poverty indicators most predictive of this relationship.
These may inform national and international policy interventions.
Methods
Search strategies
This review was part of a broader systematic review examining
the association between poverty and various mental illnesses. The
search strategies therefore reflect that of the broader review, from
which studies on CMD were subsequently selected. We searched
the MEDLINE, EconLit and PsycINFO databases, using Medical
Subject Heading (MeSH) terms (or equivalent terms for EconLit and
PsycINFO) in February 2008 and again in January 2009 for pub-
lished peer-review journal articles. Terms used to capture articles
relating to mental illness were “mental disorders” and all terms
included in MESH as sub-headings of mental disorders. Those for
capturing poverty-related studies included: “social class”, “social
environment”, “community networks”, “social support”, “violence”,
“poverty”,
“education”,
“educational
“unemployment”, “income”, “housing”, “health expenditures”,
“socioeconomic
factors”
and
“social
capturing studies carried out in LMIC included “developing coun-
tries”, and the names of all the individual countries classified as low
or middle income countries by the World Bank (World Bank, 2001).
Searches were conducted for studies published in all languages
between 1 January 1990 and 31 December 2008. This period was
selected because prior to 1990 there were few epidemiological
studies conducted in LMIC with sufficiently robust methodologies
to examine the association between CMD and poverty-related
variables.
Reference sections of key articles were reviewed and hand
searches were conducted to review tables of contents for Social
Science and Medicine; British Journal of Psychiatry; Social Psychiatry
and Psychiatric Epidemiology; and The Journal of Mental Health Policy
and Economics from 1 January 1990 to 31 December 2008.
status”,
“employment”,
conditions”.Thosefor
Types of studies and samples
Observational and intervention studies reporting epidemiolog-
ical data on measures of poverty and CMD and their relationship
among adults in one or more LMIC were eligible for review. The
studies had to include an internal comparison group of individuals
who represent one level of social or economic status in relation to
another. Comparison to reference datawas noteligible for inclusion
in this review.
Types of poverty measures
With the understanding that a range of poverty and deprivation
variables exert a complex and varied influence on mental health
outcomes, we elected to report on a range of these measures
separately, as they had been reported in the studies we reviewed,
rather than creating a composite poverty index. The following
indices were selected as exposure variables: education; income;
employment; housing and living environment (structural), which
included the physical condition of housing and living environments
and access to electricity, water and sanitation; housing and living
environment (overcrowding); financial stress; consumption; food
insecurity; social class and socio-economic status (SES). We
excluded studies that only examined exposures related to social
capital, violence and war.
Types of mental illness measures
Given the wide range of measures to assess CMD, we compiled
a list of conventionally used categories that would allow for
a meaningful comparison of studies examining the same outcomes.
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
518

Page 4

Author's personal copy
These categories included: depression; anxiety; common mental
disorders (defined as anxiety, depression and somatoform disor-
ders, usually measured by a screening tool designed to detect all
three of these disorders, such as the Self-Reporting Questionnaire
(SRQ-20) (Harding et al.,1980) or the General Health Questionnaire
(GHQ) (Goldberg & Hillier, 1979)); posttraumatic stress disorder
(PTSD) and postnatal depression. We excluded psychosis; demen-
tias; child and adolescent mental disorders; conversion disorders;
body dysmorphic disorders; personality disorders; eating disor-
ders; suicide; self harm; substance use disorders; intellectual
disability; epilepsy and developmental disorders.
Identification of studies
Using the above search methods, we identified 2988 articles
(Fig. 1). The first two authors (CL and AB) independently reviewed
the abstracts of these articles, using a hierarchical set of exclusion
criteria: “not mental health”, “not poverty”, “not relationship
between mental health and poverty”, “not epidemiological”, “not
low or middle income country”. Inter-rater reliability between the
two reviewers was moderate (Cohen’s kappa ¼ 0.75 for 359 articles
published in 2008). Once compared, the first two authors discussed
discordant results to reach agreement, and 389 articles were
included. Having obtained the full articles of these studies, we
excluded a further 76 articles as they did not provide statistical
analysis of the relationship between mental health and poverty.
Data extraction
Data from the 313 articles to be included were extracted into
a spreadsheet, which included 5 dimensions: (1) study character-
istics: author, year, country, rural versus urban, single versus multi-
centre, single versus two-stage design, setting (community-based,
clinic-based, hospital-based, registers), main purpose of the study,
design, sample size, sampling procedure, participation rate, dura-
tion of the study (if a cohort study), exclusion criteria, age, age unit,
ethnicity, % female, adult or child and adolescent; (2) poverty
measures (as listed above) and instruments used; (3) mental illness
measures (as listed above) and instruments used; (4) analysis:
proportion by poverty measure, proportion by mental health
measure, crude odds ratios (OR) (95% confidence intervals) for
poverty and mental health indicators, adjusted OR (95% confidence
intervals) for poverty and mental health indicators, variables
adjusted for, and interactions tested; and (5) quality assessment.
Quality assessment
Qualityassessments of all of the eligible studies werecarried out
independently by two reviewers (a data extractor and the first
author (CL)). We evaluated studies for methodological quality and
appropriateness for inclusion, without consideration of their
results, based on a set of pre-determined criteria derived from the
SIGN50 guidelines (http://www.sign.ac.uk/guidelines/fulltext/50/
annexc.html). The criteria for assessing the quality of the studies
are set out in Table 1. Studies rated as “?” were excluded from the
review, leaving 249 articles. Of these,118 articles that did not report
adult CMD as an outcome were removed from the analysis for this
paper.
Data analysis
Recognizing that the results of a single studycan be published in
multiple publications and that a single article can include results of
more than one study, care was taken to ensure that the unit of
analysis was the study rather than the article to avoid over/under-
counting studies in the analysis. We stratified the included studies
by poverty indicator, by study design, by setting and by those that
conducted bivariate and multivariate analysis. To explore specific
hypotheses we grouped studies by instrumentation, for example
comparing the strength of the poverty-CMD association between
screening instruments and structured diagnostic tools. Using these
stratifications, we calculated proportions of studies that demon-
strated positive, null and negative associations between poverty
and mental health variables. Our analysis focused on four main
issues: (1) definitions and measures of poverty; (2) definitions and
measures of CMD; (3) the consistency of the association between
poverty and CMD; and (4) possible causal mechanisms.
Given the heterogeneity of the studies’ design, measurement
and analysis it was not possible to pool the data to generate
summary estimates. Not all studies reported odds ratios, and those
that did used a variety of methods for measuring both the inde-
pendent and dependent variables. For example, in the case of
education studies, educational level was measured by literacy
versus no-literacy, tertiles (e.g., primary, secondary, tertiary),
quartiles (e.g., no education, primary, secondary, tertiary), quintiles
(no education, Grades 1e4, 5e7, 8e12, 13 and over) and years of
education (as a numerical variable). Pooling odds ratios from these
varying methods, while statistically possible, would have limited
validity.
Results
Overview of studies
A total of 131 published articles representing 115 studies from
33 countries were included in the final analysis (Table 2). Among
these, 18 studies were published in multiple articles and 3 articles
reported on multiple studies within a single article. Details of all
included studies are set out in Appendices.
Most of the studies were published in English, with 6 Portu-
guese, 5 Spanish and 1 Hebrew study. Most of the studies (77%) had
as their primary purpose the reporting of the prevalence or inci-
dence of CMD and their socio-economic correlates. The number of
eligible studies published per year increased steadily over the
review period, with 108 (82%) studies published between 2000 and
2008 (Fig. 2).
MEDLINE, PsycINFO, EconLit
Hand searches
n=2,988
Not mental health (n= 974)
Not poverty (n= 1046)
Not relationship between mental
health and poverty (n= 263)
Not epidemiological (n= 71)
Not LMIC (n= 180)
Abstracts Accepted
n=389
Full article review
n=313
Poor study quality (n=64)
Not adult CMD (n=118)
Final articles included in adult CMD
analysis
n=131
Abstract reviews
(author 1)
n=422
Abstract reviews
(author 2)
n=397
No statistical analysis of
poverty-CMD relationship
(n=76)
Fig. 1. Flow chart of literature search.
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
519

Page 5

Author's personal copy
Poverty measures
There was variation in the manner in which each poverty vari-
able was defined. For example in the case of income, some reported
individual income, some household income and some developed
aggregates per household member. For SES, some aggregated
education, income and residential area, others created ill-defined
categories of “high, medium and low”. We elected to report SES as it
was defined by the authors of the studies, but evidence of
measurement bias counted against the study in the quality
assessment (described above). For example if a study reported SES
categories of “high, medium and low” but did not provide any
indication of how these categories were derived or what measures
were used to assess SES, the study was marked as having a risk of
measurement bias.
Mental illness measures
Various tools were used to assess CMD as a whole, such as the
General Health Questionnaire (GHQ) (including a mixture of short
and full versions), Self-Reporting Questionnaire (SRQ-20) and the
Revised Clinical Interview Schedule (CIS-R). Other screening tools
were used to assess depression, such as the Edinburgh Postnatal
Depression Scale (EPDS), Beck Depression Inventory (BDI) and the
Centre for Epidemiologic Studies-Depression scale (CES-D). Struc-
tured diagnostic tools were used, such as the Composite Interna-
tional Diagnostic Interview (CIDI) to detect depressive or anxiety
disorders according to DSM-IV, ICD-10 or DSM III-R criteria. Several
studies used clinical interviews, frequently as a second-stage
assessment. Overall, 18% used a two-stage assessment design.
Poverty-CMD associations
Most studies reported positive associations between a range of
poverty indicators and CMD (Odds ratios (OR) with 95%CI > 1, or
p < 0.05). In community-based studies, 73% and 79% of studies
reported positive associations between a variety of poverty
measures and CMD, 19% and 15% reported null associations and 8%
and 6% reported negative associations, using bivariate and multi-
variate analyses respectively (Table 3). In facility-based studies
(clinic and hospital-based studies), trends were similar, with 76%
and 69% of studies reporting positive associations, 22% and 31%
reported null associations and 1% and 0% reported negative asso-
ciations, using bivariate and multivariate analyses respectively.
Among those studies that did report positive associations, poverty
measures were associated not only with increased prevalence of
CMD, but also with increased severity, longer course and worse
outcome. However, closer examination of specific poverty dimen-
sions revealed a complex picture, in which there was substantial
variation between poverty indicators.
Income
Community-based studies that employed bivariate analyses
showed a relatively consistent positive association between low
income and CMD (77% of studies). However, when other variables
Table 2
Study characteristics.
VariableNumber of studiesa
%
Setting
Community-based
Clinic-based
Hospital-based
Registries, prisons or military
76
25
10
67
22
9
34
Location
Rural
Urban
Both
13
54
48
11
47
42
Disorderb
Anxiety
Depression
CMD
26
71
30
Poverty indicatorb
Income
Education
Employment
SES
Social class
Financial stress
Housing/living environment (structural)
Housing/living environment (overcrowding)
Food insecurity
Consumption
34
90
44
13
6
8
11
8
7
4
Study design
Case control
Cohort
Cross-sectional
65
11
98
10
85
Sampling procedure
Consecutive
Random
Selective
Otherc
23
70
12
10
20
61
10
9
Quality
þþ
þ
Sample sizesa
Minimum value
First quartile (25th percentile)
Median (50th percentile)
Third quartile (75th percentile)
Maximum value
25
90
22
78
49
303
718
1350
35014
aExcept in the case of sample sizes, where number of participants are given.
bPercentages are not given for disorders and poverty indicators as some studies
examined more than one category.
c9 of the 10 “Other” studies sampled all people in the population. One stated that
the sample was representative but did not describe the sampling method.
Table 1
Study quality assessment criteria.
Study design
All study
designs
Presentation of an appropriate research question, risk for bias
due to selection, confounding and/or measurement, and
reporting of confidence intervals.
Comparable cases and controls, same exclusion criteria,
participation rate, similarities at baseline, clear case-control
definitions, clear establishment of controls, blindness to
exposure, reliability of exposure measure, identification of
potential confounders and use of sensitivity analysis.
Participation rate, blindness to exposure, reliability of
exposure measure, identification of potential confounders
and use of sensitivity analysis.
Comparable baseline, response rate, outcome present at
baseline, losses to follow-up, impact of losses to follow-up,
clearly defined outcome, blind outcome assessment,
acknowledgement of impact of non-blind assessment, reliable
exposure assessment, validity of outcome assessment and
reliability of exposure measure.
Case control
studies
Cross-sectional
studies
Cohort studies
Overall ratings
þþ
All or almost all of the above criteria were fulfilled, and those
criteria that were not fulfilled were thought unlikely to alter
the conclusions of the study.
Some of the above criteria were fulfilled, and those criteria
that were not fulfilled were thought unlikely to alter the
conclusions of the study.
Few or no criteria were fulfilled, and the conclusions of the
study were thought likely or very likely to alter with their
inclusion.
þ
?
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
520

Page 6

Author's personal copy
were controlled for in multivariate analyses, the positive associa-
tion dropped to 62% of studies. For example, in Santiago, Chile,
income was not associated with CMD after adjusting for age, sex,
physical disease, working status, social support, education, income
decrease and quality of housing. Only recent income decrease (OR:
2.14, 95% CI: 1.70e2.70), lower level of education (OR: 2.44, 95% CI:
1.50e3.97) and poorerhousing quality (OR: 1.53, 95% CI: 1.05e2.23)
showed independent and statistically significant associations with
an increased prevalence of CMD after adjusting for other explana-
tory variables (Araya et al., 2003). In the only study which reported
a negative association, this association was reported when
comparing depression scores between 2nd and 4th highest income
quartiles of $500e1490 and >$3000 (OR: 1.7, 95% CI: 1.1e2.7) in
South Korea (Cho et al., 2007).
Only 2 community-based cohort studies examined income, and
both reported positive associations between low income and CMD,
over 12-month periods in India (Patel, Kirkwood, Pednekar, Weiss,
& Mabey, 2006) and Taiwan (Seplaki, Goldman, Weinstein, & Lin,
2006). In the one cohort study that reported both bivariate and
multivariate analyses, the strength of the association was reduced
from OR: 0.23 (95% CI: 0.1e0.7) to OR: 0.41 (95% CI: 0.1e1.3) (the
latter significant for trend: p ¼ 0.04) when adjusting for socio-
economic, reproductive and physical health risk factors (Patel,
Kirkwood, et al., 2006).
In facility-based studies, trends were similar: while bivariate
analyses showed a relatively consistent positive association
between low income and CMD (78% of studies), in multivariate
analyses the association was more equivocal (only 50% of studies
showed this association).
Education
Of the 53 community-based studies that examined education,
most (66% of bivariate and 67% of multivariate analyses) found
that less education was associated with higher rates of CMD. A
small proportion of studies reported a null association (25% of
bivariate and 30% of multivariate analyses), while 9% of bivariate
analyses noted that less education was associated with less CMD.
These latter findings appear to be at least partially a factor of study
quality. In the 15 high quality (þþ) studies using bivariate analysis,
only one study showed a negative association and 2 showed null
associations. Of the 12 multivariate high quality studies, 11
reported a significant association between low education level and
CMD, after controlling for a range of social, economic and demo-
graphic variables.
Similarly, all 3 of the community-based cohort studies that
explored educational status and CMD in bivariate analysis found
that less education was associated with higher rates of CMD.
Women with more education had lower risk of postpartum
depression in Turkey (p ¼ 0.001) (Gulseren et al., 2006) and
CMD in India (OR: 0.11, 95% CI: 0.0e0.9) (Patel, Kirkwood, et al.,
2006). However, in Pakistan, the association was with the
husbands’ educational level (OR: 1.7, 95% CI: 1.3e2.2), but not
the educational level of the women themselves (Rahman &
Creed, 2007).
In the 33 facility-based studies that examined education,
findings were similar. Of the 2 facility-based cohort studies, one
demonstrated a similar association between higher educational
level and reduced CMD after 12 months (OR: 0.8, 95% CI:
0.7e0.9), after controlling for sex, age and site of recruitment in
Zimbabwe (Todd et al., 1999). The other study reported a posi-
tive association in bivariate analysis in the rural area (p ¼ 0.04)
but not the urban area (p ¼ 0.26), and a null association in
multivariate analysis (OR: 1.98, 95% CI: 0.7e5.6) after controlling
for psychosocial, pregnancy and delivery related factors in
Lebanon (Chaaya et al., 2002).
Unlike income, the consistency of the association between
education and CMD was not attenuated in multivariate analysis:
a similar proportion of studies that carried out bivariate and
multivariate analyses reported positive, null and negative associa-
tions. This was the case in both community and facility-based
settings.
Employment status
Just over half of the community-based studies reported a posi-
tive association between employment status (unemployment and
under-employment) and CMD (60% of bivariate and 59% of multi-
variate analyses). Many of these studies did not specifically
compare employed and unemployed groups e for example exam-
ining differences between several employed occupational cate-
gories (Adelekan, Ndom, Ekpo, & Oluboka, 1999) or comparing
“blue collar” and “white collar” workers (Liu et al.,1998). When we
analysed only the 16 studies that compared employed and unem-
ployed groups, 11 studies showed a positive association between
unemployment and CMD, 4 studies found null associations and one
study found a negative association. Two of the null associations
werefromsamples of Taiwanese earthquake victims and their PTSD
outcomes, which limits the generalisability of these findings (Kuo,
Wu, Ma, Chiu, & Chou, 2007; Lai, Chang, Connor, Lee, & Davidson,
raeyybdeh s i lbups e idutsforebmuN
0
5
01
51
02
52
8002700260025002400230022002100200029991899179916991599149913991299119910991
raeY
Number of studies
Fig. 2. Number of studies published by year.
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
521

Page 7

Author's personal copy
2004). Overall, the trend suggests that unemployment is associated
with CMD, but that this may vary depending on local contextual
and measurement factors.
As with income, controlling for other variables such as age,
gender, education and physical health appears to diminish the
association between unemployment and CMD. Among multivariate
analyses, 12 of 17 studies reported a positive association, and the
remainder reported null associations.
In facility-based studies, the association was less evident. Only
11 of the 22 studies that conducted bivariateanalyses, and 2 of the 6
studies that conducted multivariate analyses showed a positive
association between reduced employment and CMD. In the latter
studies, the association was weakened when controlling for age,
sex, marital status, education, income, clinic, pregnancy-related
factors, relationship status and ethnicity.
Housing
In community-based studies that reported structural housing
conditions, 11 of the 12 studies showed a positive association
between worse housing conditions and CMD, using bivariate
analyses, and one study showed a negative association. The nega-
tive association was found in a study from Nigeria that classified
housing on the basis of “hard” versus “earth” floors, with those
living in houses with earth floors at decreased risk (Gureje, Kola, &
Afolabi, 2007). In multivariate analysis, 2 of the 3 studies showed
a positive association: in Nigeria low scores on the GHQ-12 were
associated with below average living conditions (OR: 3.3, 95% CI:
1.99e5.50) (Amoran,Lawoyin, & Oni, 2005); andin South Korealow
scores on the Korean Geriatric Depression Scale were associated
with rented accommodation (OR: 1.75, 95% CI: 1.18e2.60) (Kim,
Shin, Yoon, & Stewart, 2002). There was only one cohort study
Table 3
Proportions of studies showing positive, null and negative associations between poverty measures and CMD, by setting.
Poverty indicatorSettinga
AnalysisAssociation with CMD
PositiveNullNegative UnknownTotal
n
%
n
%
n
%
n
%
Lower incomeCommunity-basedBivariate
Multivariate
Bivariate
Multivariate
1777
62
78
50
5
4
2
2
23
31
22
50
0
1
0
0
0
8
0
0
0
0
0
0
0
0
0
0
22
138
7
2
Facility-based9
4
Lower education Community-based Bivariate
Multivariate
Bivariate
Multivariate
35
20
20
66
67
61
64
13 25
30
36
36
5
0
1
0
9
0
3
0
0
1
0
0
0
3
0
0
53
30
33
14
9
Facility-based12
95
UnemploymentCommunity-basedBivariate
Multivariate
Bivariate
Multivariate
12
13
11
60
59
50
33
7
8
9
4
35
36
41
67
1
0
1
0
5
0
5
0
0
1
1
0
0
5
5
0
20
22
22 Facility-based
26
Lower SESCommunity-based Bivariate
Multivariate
Bivariate
Multivariate
6
5
3
2
100
100
43
67
0
0
4
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
5
7
3
Facility-based 57
33
Lower social classCommunity-basedBivariate
Multivariate
Bivariate
Multivariate
4
4
1
0
100
100
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
4
1
0
Facility-based
0
Increased financial stressCommunity-based Bivariate
Multivariate
Bivariate
Multivariate
4
3
3
2
801
0
1
0
200
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
3
4
2
100
75
100
0
Facility-based25
0
Worse housing/living
environment (structural)
Community-basedBivariate
Multivariate
Bivariate
Multivariate
11 920
0
0
0
0
0
0
0
1
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
12
3
0
0
1003
0
0
Facility-based0
0
Worse housing/living
environment (overcrowding)
Community-basedBivariate
Multivariate
Bivariate
Multivariate
4
1
2
0
67
50
2
1
0
0
33
50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
2
2
0
Facility-based 100
00
Food insecurityCommunity-basedBivariate
Multivariate
Bivariate
Multivariate
2
1
4
3
67
50
0
0
0
0
0
0
0
0
1
1
0
0
33
50
0
0
0
0
0
0
0
0
0
0
3
2
4
3
Facility-based100
100
Reduced consumptionCommunity-based Bivariate
Multivariate
Bivariate
Multivariate
1
1
0
0
25 2
0
0
0
501
0
0
0
250
0
0
0
0
0
0
0
4
1
0
0
100 0
0
0
0
0
0
Facility-based0
0
Mean Community-basedBivariate
Multivariate
Bivariate
Multivariate
10 73
79
76
69
3
2
4
2
19
15
22
31
1
0
0
0
8
6
1
0
0
0
0
0
0
1
1
0
6
7
3
Facility-based
aFacility-based settings include clinics and hospitals.
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
522

Page 8

Author's personal copy
that examined structural housing, where lack of access to tap water
in the house was associated with higher 12-month incidence of
CMD among women in India (OR: 2.09, 95% CI: 1.0e4.2) (Patel,
Kirkwood, et al., 2006).
Regardingovercrowding and CMD, the trendwas towards worse
mental health in situations of overcrowding. Among the 6 studies
that examined overcrowding, 4 reported a positive association
between overcrowded housing and CMD based on bivariate anal-
yses, and 2 a null association. Both of the latter studies reported
PTSD outcomes: one was a sample of 314 villagers affected by the
2004 tsunami in Tamil Nadu, India (Kumar et al., 2007) and the
other was of mothers of under-fives in Kabul, Afghanistan (Seino,
Takano, Mashal, Hemat, & Nakamura, 2008). Of the 2 studies that
conducted multivariate analyses, 1 reported a positive association
in Mexico (OR: 3.69, 95% CI: 1.19e11.39) (Sabin, Lopes, Nackerud,
Kaiser, & Varese, 2003) and 1 a null association in Zimbabwe (OR:
1.66, 95% CI: 0.83e3.30) (Abas & Broadhead, 1997).
Only two facility-based studies reported structural and over-
crowding housing variables, neither of which conducted multi-
variate analyses. These studies reported positive associations
between the number of people per room in households and CMD in
India (p ¼ 0.04) (Patel, Pereira, et al., 1998) and living in a squatter
area and postnatal depression in Turkey (p ¼ 0.003) (Danaci, Dinc,
Deveci, Sen, & Icelli, 2002).
Socio-economic status (SES)
Five of the 6 studies reported a positive association between low
SES and CMD, based on bivariate analyses. The exceptional study
was among elderly Nigerians where SES was inversely associated
with lifetime depression (OR: 0.5, 95% CI: 0.3e0.8) measured using
the CIDI (Gureje et al., 2007). All 5 of the studies that reported
multivariate analyses showed a positive association between low
SES and CMD. Multivariate analyses therefore showed that people
from low SES groups in these countries are more likely to have
CMD, even after adjusting for a range of health and demographic
variables. There is also evidence that SES may be temporally linked
to CMD: a cohort study in Pakistan showed that women in the low
SES category are three times as likely to have postnatal depression
at 12-month follow-up than women in a higher SES category, after
adjusting for SRQ score, BDI score and life events (OR: 3.1, 95% CI:
1.2e8.4) (Rahman & Creed, 2007).
Among facility-based studies the findings for SES were more
equivocal. In bivariate analyses, 4 of the 8 studies reported null
associations and the remaining 4 reported positive associations. In
the 3 studies that conducted multivariate analyses, one reported an
inverse associationbetween SES
compulsive disorder in a small sample in Brazil (OR: 20.72, 95% CI:
1.42e303.32) (Ferrao et al., 2006); one reported an inverse asso-
ciation between SES and postpartum depression in Turkey (OR: 4.1
95% CI: 1.83e9.17) (Dindar & Erdogan, 2007) and one reported
a null association for CMD (OR: 1.68, 95% CI: 0.94e3.01) (Liu, Prince,
Blizard, & Mann, 2002). It should be noted that in the Turkey study
high SES was also associated with increased risk, compared to
middle SES (OR: 2.09, 95% CI: 1.03e4.23) (Dindar & Erdogan, 2007).
and refractory obsessive-
Social class
All 4 community-based studies showed positive bivariate
associations between low social class and CMD. Similarly, 3 of the 4
studies with multivariate analyses showed this association, while
one study reported null associations for mood disorders and
anxiety disorders, using the CIDI in Sao Paulo, Brazil (Andrade,
Walters, Gentil, & Laurenti, 2002).
Among facility-based studies, there was a similar trend. Only
one study examined this variable, and in bivariate analysis reported
a positive association between low social class and postpartum
depression in Pelotas, Brazil (OR: 3.89, 95% CI: 1.28e1.78) (Moraes
et al., 2006).
Food insecurity
Two of three community-based studies reported a positive
bivariate association between food insecurity and CMD. Food
insecurity was associated with PTSD among both those who had
(OR: 2.04, 95% CI: 1.45e2.88) and those who had not experienced
armed conflict (OR: 1.86, 95% CI: 1.07e3.23) in Afghanistan (Seino
et al., 2008); and hunger in the last 3 months was associated
with CMD in India (OR: 3.37, 95% CI: 1.3e8.8) (Patel, Kirkwood,
et al., 2006). In multivariate analyses, one study reported a posi-
tive association between not having food in the house for the next
meal and CMD in Agincourt, South Africa (OR: 2.59, 95% CI:
1.12e5.98), but a null association for a sample from Khayelitsha,
South Africa (OR: 0.66, 95% CI: 0.31e1.43) (Havenaar, Geerlings,
Vivian, Collinson, & Robertson, 2008). One study showed a nega-
tive association between lack of food and PTSD with both bivariate
and multivariate analyses among Guatemalan refugees living in
Mexico 20 years after civil conflict (Sabin et al., 2003).
Among facility-based studies, all 4 studies with bivariate anal-
yses and all 3 studies with multivariate analyses showed a positive
association between food insecurity and CMD. Food insecurity was
associated with the onset of an episode of depression and the
persistence of an existing episode in Harare, Zimbabwe (Patel et al.,
1997).
Three cohort studies examined food insecurity: one commu-
nity-based and two clinic-based, all of which demonstrated posi-
tive associations between CMD and food insecurity. In the
community-based study, 12-month incidence of CMD was associ-
ated with hunger in the past 3 months (OR: 3.37, 95% CI: 1.3e8.8)
(Patel, Kirkwood, et al., 2006). In the clinic-based studies, hunger
during the past month was associated with postpartum depression,
but only among mothers of female infants in Goa, India (RR: 3.0,
95% CI: 1.7e5.1) (Patel, Rodrigues, & DeSouza, 2002). The other
clinic-based study in Zimbabwe, demonstrated an association
between being unable to buy food and CMD at 6 months, but not at
12 months, after controlling for age, sex and site of recruitment
(Patel, Todd, et al., 1998; Todd et al., 1999).
Consumption
In a study examining community-based data in 5 countries,
mental health status was marginally worse for low consumption
households in 2 countries (Bosnia and Tonga), but was improved for
lowconsumptionhouseholds inone country (Mexico) and there was
no association in 2 countries (Indonesia and India) (Das et al., 2007).
Financial stress
Most of the community-based studies demonstrated a positive
association between financial stressand CMD: in 4 of 5 studies with
bivariate analyses and 3 studies with multivariate analyses. In one
study, financial stress was not associated with depression among
incarcerated offenders in Nigeria (Fatoye, Fatoye, Oyebanji, &
Ogunro, 2006).
In facility-based studies, 3 of the 4 studies with bivariate analyses,
and both of the studies with multivariate analyses reported a positive
association between financial stress and CMD. In one, the negative
impact of finance-related life events was related to major depressive
episodes among clinic attenders in Uganda, but this was not statisti-
cally significant (Muhwezi, Agren, Neema, Maganda, & Musisi, 2008).
Two cohort studies explored financial stress. Among women in
India, those expressing difficulty making ends meet were more
likely to have CMD at 12-months follow-up (OR: 2.39, 95% CI:
1.2e4.9), after adjusting for socio-economic, reproductive and
health risk factors (Patel, Kirkwood, et al., 2006). In Taiwan,
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
523

Page 9

Author's personal copy
difficulty meeting expenses was associated with increased score of
approximately 1.2 on the CES-D score for depression in the after-
math of an earthquake (Ordinary least squares regression coeffi-
cient: 1.2, 95% CI: 0.4e2.0, p < 0.01), after adjusting for health,
earthquake experience,social
demographic characteristics with or without significant interaction
terms (Seplaki et al., 2006).
andeconomic characteristics,
Discussion
Summary of main results
This review presents findings on a complex body of epidemio-
logical literature with heterogeneous methods, instrumentation,
study settings and populations, published between 1990 and 2008.
Despite the heterogeneity, the literature shows a relatively
consistent trend inwhich CMD is associated with a range of poverty
dimensions in LMIC. Among community-based studies that con-
ducted multivariate analysis, 79% reported positive associations
between a variety of poverty measures and CMD, and 6% reported
negative associations. This finding is consistent with previous
narrative reviews (Patel et al., 1999; Patel & Kleinman, 2003)
although it reports on a wider range of studies, poverty measures
and countries, over a longer time period.
However, there are important differences in the consistency and
strength of the association between poverty and CMD, across
poverty indicators. While variables such as education, food inse-
curity, housing, social class, SES and financial stress exhibit a rela-
tively consistent and strong association with CMD, others such as
income, employment and particularly consumption are more
equivocal. This latter finding may provide some explanation for
studies that have questioned the association between poverty and
mental ill-health in LMIC (Das et al., 2007).
For example, in the case of income, while most studies showed
a significant association between low income and CMD, we were
not able to draw clear conclusions regarding this trend, particularly
as many of the studies that do show associations do not use
multivariate analyses. It may be the case that relative poverty,
sudden changes in income level, adverse life events and the stress
associated with low income, rather than absolute poverty, are
stronger predictors of mental health status, particularly when other
variables are controlled (Myer, Stein, Grimsrud, Seedat, & Williams,
2008; Patel & Kleinman, 2003). Nevertheless, caution should be
exercised in the interpretation of null associations that are found
after multivariate analyses. The weakening of an association may
indicate that variables such as education, housing, food insecurity
and SES are mediators of the relationship between income and
CMD, and that low income may exert its effect on CMD through
these other dimensions of deprivation.
Measurement factors
The reasons for differences in the consistency of the association
between CMD and poverty are an important point of discussion.
These may be divided into measurement and population factors. In
relation to measurement of CMD, studies that assess CMD as
a whole through a screening tool (such as the SRQ-20, GHQ and
EPDS), appear to show more consistent associations than studies
that use more complex, multi-faceted diagnostic instruments to
assess individual disorders, suchas the CIDI. Forexample all 6 of the
studies using the SRQ-20 showed a positive relationship between
CMD (including depression) and lower education in bivariate
analysis (De Lima et al.,1999; Faria, Facchini, Fassa, & Tomasi,1999;
Hackett, Sagdeo, & Creed, 2007; Ludermir, 2000; Marín-León, de
Oliveira, de Azevedo Barros, Dalgalarrondo, & Botega, 2007;
Reichenheim & Harpham, 1991) whereas only 2 of the 5 studies
that used the CIDI to assess the relationship between depression
and education reported a positive association (Cho et al., 2007;
Vorcaro, Lima-Costa, Barreto, & Uchoa, 2001) and 3 reported
a null association (Gureje et al., 2007; Karam et al., 2006; Robison
et al., 2003). This may indicate that the association of CMD with
poverty may be stronger for CMD as a whole than for individual
diagnostic categories such as anxiety or depression. Broader
measures may assess “psychological distress” whereas more
specific diagnostic tools may detect more “biological” forms of
disorder, which are less likely to be socially determined. Further-
more, broader measures contain items which reflect somatic
concerns and it is possible that poor physical health status
combined with pooraccess tohealth caremaylead tohigher scores.
These hypotheses are supported when comparing outcomes for
psychological distress with diagnostic categories, within the same
sample in a study. For example, in the South African Stress and
Health Survey a strong association was found between SES and
psychological distress (measured by the Kessler-10) after adjusting
for demographic characteristics, social constructs and life events
(OR 2.11, 95% CI: 1.36e3.29) (Myer et al., 2008), but no significant
association was reported for 12-month or lifetime depression or
anxiety disorders (Stein et al., 2008; Williams et al., 2008).
In addition, therewas considerable heterogeneity in the manner
in which poverty variables were measured. For example income
included both household and personal income, and annual and
monthly income; different cut-offs and income categories were
employed; and it is difficult to compare findings between 1990 and
2008 when inflation impacts on the value of the income. Similar
inconsistencies were evident for other poverty variables. These
variations make it difficult to compare the findings from different
studies and draw consistent conclusions regarding the relationship
between these poverty variables and CMD.
Population factors
Aside from measurement factors, a number of population
factors may explain the variability in associations. Given the broad
nature of this review, it is not possible to analyse their specific role
in detail, but it is important toat least note their potential influence.
These may be divided into proximal and distal factors. Among
proximal factors, studies in this review indicate that women are
more likely than men to have CMD in LMIC and that women living
in poverty are particularly vulnerable (Harpham, Snoxell, Grant, &
Rodriguez, 2005). Further analysis is required of the role of
gender in weakening or strengthening the associations reported
here. Secondly, physical health lies in the causal pathway between
poverty and CMD and is likely to influence variability in the rela-
tionship (Adewuya, Ola, Aloba, Mapayi, & Okeniyi, 2006; Harpham,
Huttly, De Silva, & Abramsky, 2005; Patel, DeSouza, et al., 2003;
Rahman, Iqbal, Bunn, Lovel, & Harrington, 2004). Thirdly, indi-
vidual genetic andpsychological factors are crucial in mediating the
extent to which poverty-related variables, such as sudden loss of
income, are translated into symptoms of mental disorder, such as
depressed mood, poor concentration, insomnia and fatigue asso-
ciated with major depression (Patel, Flisher, & Cohen, 2006).
Further research is required regarding the intermediate steps
between socioeconomic risk factors and specific mental health
outcomes.
Among more distal factors, firstly, access to health care is likely
to influence the relationship between poverty and CMD (WHO
Commission on Social Determinants of Health, 2005). Inadequate
access to health care is likely to adversely affect mental health
status, given evidence of a high degree of comorbidity between
physical and mental disorders (Prince et al., 2007), and increased
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
524

Page 10

Author's personal copy
risk for physical health problems among people living in condi-
tions of poverty (Inandi et al., 2002; Patel, 2001; Patel, Flisher,
et al.,2006;Sale&Gadanya,
Commission on Social Determinants of Health, 2005). Secondly,
the presence or absence of violence, including civil conflict, crime
and domestic violence, is also likely to influence the extent to
which poor communities are vulnerable to CMD (Cardozo et al.,
2004; Ceballo, Ramirez, Castillo, Caballero, & Lozoff, 2004;
Harpham, Snoxell, etal., 2005;
Vythilingum, & Stein, 2004; Stein, Seedat, & Emsley, 2002; Ward,
Flisher, Zissis, Muller, & Lombard, 2001; WHO, 2002). Thirdly,
income inequality may be a factor which is likely to influence the
poverty-CMD relationship. For example, settings like Chile and
Brazil, with high levels of inequality show strong associations
between poverty variables and mental health (Araya, Rojas,
Fritsch, Acuna,&Lewis, 2001;
Conversely, settings with relatively homogenous socioeconomic
strata, such as Ethiopia (Kebede & Alem, 1999a, 1999b) and
Nigeria, (Gureje et al., 2007) show relatively weak associations.
Additionally, perceptions or experience of deprivation, and
comparison of one’s own status with that of others may inform
mental healthstatus, particularly
Kleinman, 2003). Cifuentes et al. (2008) found that the Gini
index was positively associated with major depressive episodes,
but only in high HDI countries (Cifuentes et al., 2008). Fourthly,
urbanisation may be a factor which may explain variability. Pop-
ulations undergoing rapid urbanisation may be hypothesised as
being more vulnerable to mental disorder (Harpham & Blue,
1997),throughfactors such
housing, crime, unemployment, breakdown of traditional family
structures, hazardous environmental conditions, and insufficient
access to clean water, sanitation, education, health and other basic
services (Blue & Harpham, 1996; Flisher & Chalton, 2001; Gillis,
Welman, Koch, & Joyi, 1991; Harpham, 1994; Harpham &
Molyneux,2001; Ludermir
Academies Press, 2003). Fifthly, poverty-related variables may be
context-specific and dependent on the social and cultural
meaning attributed to them (Sabin et al., 2003). Finally, macro-
economic contextual factors are likely to have a substantial impact
on all of the poverty indicators chosen for this study. Situations
such as the 1999e2002 economic crisis in Argentina, the
economic crisis in Zimbabwe, the 1997 Asian financial crisis are all
likely to have informed the poverty e mental health relationship
in these countries during these periods.
2009;WHO,2001; WHO
Seedat, Nyamai,Njenga,
Ludermir&Lewis, 2001).
fordepression (Patel&
asovercrowding,inadequate
& Harpham,1998;National
Mechanisms of the relationship
Given that the vast majority of studies are cross-sectional, it is
difficult to draw clear conclusions regarding the direction of the
poverty-CMD relationship. The findings of this review are consis-
tent with the conceptualisation of poverty and mental health as
interacting in a “vicious cycle” (Patel, 2001). In this model, people
living in poverty are at increased risk of developing CMD, through
social exclusion, high stressors, reduced social capital, malnutrition,
obstetric risks and increased risk of violence and trauma, all of
which increase the risk for higher prevalence of mental disorders,
inadequate care and a more severe course of the condition
(Saraceno & Barbui, 1997). Conversely, people with CMD may be
more likely to drift into poverty, due to increased health expendi-
ture, reduced productivity, lost employment, school dropout,
reduced social support and stigma associated with these conditions
(Patel & Kleinman, 2003).
Based on the evidence from the limited number of cohort
studies in this review, there appears to be some support for the first
causal pathway, namely that some poverty conditions (particularly
low education, food insecurity and financial stress) may lead to
CMD. There is high consistency across studies examining the
associations (with the exception of income, employment and
household consumption) and many findings from the cross-
sectional and case-control studies are supported by longitudinal
studies. Furthermore, many of the associations in cohort studies are
strong (odds ratios greater than 2) and in one instance (education)
there is evidence of a doseeresponse relationship (Araya et al.,
2003). This finding is consistent with findings from other studies
in high income countries, which show that CMD is more likely to fit
with social causation theory whereas conditions such as schizo-
phrenia are more likely to apply to social selection or social drift
theory (Dohrenwend et al., 1992; Saraceno & Barbui, 1997).
However, the evidence for poverty causing CMD does not preclude
the possibility that CMD may cause poverty. The absence of
evidence from cohort studies that explored the impact of CMD on
poverty status does not imply that there is evidence of the absence
of this causal pathway.
Limitations
There are several limitations to this review. Firstly, the review
focused only on published peer-review journal articles and
a systematic review of grey literature was not undertaken.
Secondly, the studies in this review are taken from only 33
countries, and there are limits to which these findings can be
generalised to other LMIC. Thirdly, by focusing on variation
between poverty predictors, we were not able, within the limited
space available, to explore variation between outcomes, such as
those between depression and anxiety. Fourthly, publication bias
may limit the conclusions that can be drawn from this review, as
studies that show a positive association may be more likely to be
published than those that show a null association (Dwan et al.,
2008). Fifthly, in the case of diffuse multiple associations, aggre-
gate variables tend to yield higher associations than more specific
variables. It was not possible to disaggregate these due to the
nature of the studies we included, but the effect of possibly over-
stating the strength of the associations in some instances, needs to
be acknowledged. Sixthly, we were not able to explore the impact
of macro-economic factors on the relationship between poverty
and CMD in these countries. There are also several limitations to
the field currently, namely that the vast majority of studies are
cross-sectional, no intervention studies met the inclusion criteria
for this review, and most studies focus on risk rather than
resilience.
Future research
Four major areas can be identified for future research. Firstly,
epidemiological studies need to disaggregate patterns of comor-
bidity between different disorders, including between mental
disorders and physical health, and to explore in more detail the
interrelationship between biological, personal, social and economic
factors as they impact on mental health in poor communities. This
should include the adoption of a multiple deprivation rather than
absolute poverty approach, by which indicators of deprivation are
sequentially added to regression models not to see if the associa-
tion is nullified, but rather to see how much each changes the
strength of the association. Secondly, longitudinal epidemiological
studies are required to provide further clarity on the direction of
causality in the relationship between CMD and poverty. A critical
research question in this context is what factors provide protection
of people living in circumstances of multiple deprivation from
developing CMD. These studies are complex and expensive to
mount,butare essentialifmore targetedpolicy-directed
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
525

Page 11

Author's personal copy
interventions are to be developed. Thirdly, qualitative studies are
essential to understand complex local realities and lived experi-
ences. These are likely to yield important differences within and
between different developing country contexts and support the
development of theory. Fourthly, interventions that aim to break
the vicious cycle of poverty and mental ill-health need to be
developed and evaluated. These may be aimed at either “end” of
the cycle: by reducing the impact of the poverty related risk factors
that increase the prevalence or severity of CMD, (Ahmed, Rana,
Chowdhury, & Bhuiya, 2002; Case, 2004; McKenzie, Patel, &
Araya, 2004) or by reducing the likelihood that people with
mental disorders will slide into poverty, for example through
secondary prevention (Patel, Chisholm, et al., 2003). It would be
helpful to measure variables as continuous, rather than categorical,
or at least with sufficient categories to allow rigorous evaluation of
doseeresponse relationships.
Conclusion
The epidemiological literature of the last 19 years indicates that
the social and economic conditions of poverty are linked with
CMD in LMIC. The mechanisms by which the cycle of poverty and
CMD is maintained are complex and multi-dimensional. By pre-
senting a systematic review of the literature, this paper has
attempted to shift the debate from questions about whether
poverty is associated with CMD in LMIC, to questions about which
particular dimensions of poverty carry the strongest (or weakest)
association.
For policy makers, a major conclusion from this review is that
efforts to address the burden of CMD in LMIC will be limited if
they only target individual-level interventions (Truong & Ma,
2006). The relatively consistent association between CMD and
poverty in LMIC serves to strengthen the case for the inclusion of
mental health on the agenda of development agencies and on
international targets such as the MDGs (Miranda & Patel, 2005;
Sachs & Sachs, 2007). More specifically, the findings of this
review call for increasing precision in the targeting and evaluation
of interventions. For example strategies for housing improvements
in urban settings should include assessments of mental health
consequences of such interventions, with a view to reducing
health service burden and costs, and improving mental health of
communities.
This implies that development policies aimed at economic
growth, measured for example by increased gross domestic
product (GDP), are unlikely to carry mental health benefits for
populations if the result is to increase food insecurity and adverse
living circumstances, and worsen educational outcomes within
a given society. Crucially, it is interventions that promote security,
education and social, welfare and health safety nets that are more
likely to protect the mental health of populations, and allow for the
full development of human potential.
Appendix. Supplementary data
Supplementary data associated with this article can be found in
online version at doi:10.1016/j.socscimed.2010.04.027.
References
Abas, M. A., & Broadhead, J. C. (1997). Depression and anxiety among women in an
urban setting in Zimbabwe. Psychological Medicine, 27, 59e71.
Adelekan, M. L., Ndom, R. J., Ekpo, M., & Oluboka, O. (1999). Epidemiology of
childhood behavioural disorders in Ilorin, Nigeriaefindings from parental
reports. West African Journal of Medicine, 18, 39e48, [erratum appears in West
African Journal of Medicine 1999 ApreJun;18(2):152].
Adewuya, A. O., Ola, B. A., Aloba, O. O., Mapayi, B. M., & Okeniyi, J. A. O.
(2006). Impact of postnatal depression on infants’ growth in Nigeria. In
African Psychiatric Association and Allied Professionals meeting, Addis Ababa,
Ethiopia.
Ahmed, S. M., Rana, A. K. M. M., Chowdhury, M., & Bhuiya, A. (2002). Measuring
perceived health outcomes in non-western culture: does SF-36 have a place?
Journal of Health, Population and Nutrition, 20, 334e342.
Amoran, O. E., Lawoyin, T. O., & Oni, O. O. (2005). Risk factors associated with
mental illness in Oyo State, Nigeria: a community based study. Annals of General
Psychiatry, 4.
Andrade, L., Walters, E. E., Gentil, V., & Laurenti, R. (2002). Prevalence of ICD-10
mental disorders in a catchment area in the city of Sao Paulo, Brazil. Social
Psychiatry & Psychiatric Epidemiology, 37, 316e325.
Araya, R., Lewis, G., Rojas, G., & Fritsch, R. (2003). Education and income: which is
more important for mental health? Journal of Epidemiology and Community
Health, 57, 501e505.
Araya, R., Rojas, G., Fritsch, R., Acuna, J., & Lewis, G. (2001). Common mental
disorders in Santiago, Chile: prevalence and socio-demographic correlates.
British Journal of Psychiatry, 178, 228e233.
Barnes, H., Wright, G., Noble, M., & Dawes, A. (2007). The South African index of
multiple deprivation for children: Census 2001. Cape Town: HSRC Press.
Blue, I., & Harpham, T. (1996). Urbanization and mental health in developing
countries. Current Issues in Public Health, 2, 181e185.
Cardozo,B.L.,Bilukha,O.O.,Crawford,C.A.G.,Shaikh,I.,Wolfe,M.I.,Gerber,M.L.,etal.
(2004). Mental health, social functioning, and disability in Postwar Afghanistan.
JAMA: The Journal Of The American Medical Association, 292, 575e584.
Case, A. (2004). Does money protect health status? Evidence from South African
pensions. In D. Wise (Ed.), Perspectives on the economics of Aging (pp. 287e305).
Chicago: University of Chicago Press.
Ceballo, R., Ramirez, C., Castillo, M., Caballero, G. A., & Lozoff, B. (2004). Domestic
violence and women’s mental health in Chile. Psychology of Women Quarterly,
28, 298e308.
Chaaya, M., Campbell, O. M., El, K. F., Shaar, D., Harb, H., & Kaddour, A. (2002).
Postpartum depression: prevalence and determinants in Lebanon. Archives of
Women’s Mental Health, 5, 65e72.
Cho, M. J., Kim, J. K., Jeon, H. J., Suh, T., Chung, I. W., Hong, J. P., et al. (2007). Lifetime
and 12-month prevalence of DSM-IV psychiatric disorders among Korean
adults. Journal of Nervous & Mental Disease, 195, 203e210.
Cifuentes, M., Sembajwe, G., Tak, S., Gore, R., Kriebel, D., & Punnett, L. (2008). The
association of major depressive episodes with income inequality and the
human development index. Social Science & Medicine, 67, 529e539.
Danaci, A. E., Dinc, G., Deveci, A., Sen, F. S., & Icelli, I. (2002). Postnatal depression in
turkey: epidemiological and cultural aspects. Social Psychiatry & Psychiatric
Epidemiology, 37, 125e129.
Das, J., Do, Q. T., Friedman, J., McKenzie, D., & Scott, K. (2007). Mental health and
poverty in developing countries: revisiting the relationship. Social Science &
Medicine, 65, 467e480.
De Lima, M. S., Hotopf, M., Mari, J. J., Beria, J. U., De Bastos, A. B., & Mann, A.
(1999). Psychiatric disorder and the use of benzodiazepines: an example of
the inverse care law from Brazil. Social Psychiatry & Psychiatric Epidemiology,
34, 316e322.
Department of the Environment, T. a. t. R.D. (2000). Measuring multiple deprivation
at the small area level the indices of deprivation 2000. London: DETR.
Dindar, I., & Erdogan, S. (2007). Screening of Turkish women for postpartum
depression within the first postpartum year: the risk profile of a community
sample. Public Health Nursing, 24, 176e183.
Dohrenwend, B. P., Levav, I., Shrout, P. E., Schwartz, S., Naveh, G., Link, B. G., et al.
(1992). Socioeconomic status and psychiatric disorders: the causation-selection
issue. Science, 255, 946e952.
Dwan, K., Altman, D. G., Arnaiz, J. A., Bloom, J., Chan, A. W., Cronin, E., et al. (2008).
Systematic review of the empirical evidence of study publication bias and
outcome reporting bias. PloS ONE, 3. doi:10.1371/journal.pone.0003081.
Faria, N. M., Facchini, L. A., Fassa, A. G., & Tomasi, E. (1999). A cross-sectional study
about mental health of farm-workers from Serra Gaucha (Brazil). Revista de
Saude Publica, 33, 391e400, (in Portuguese).
Fatoye, F. O., Fatoye, G. K., Oyebanji, A. O., & Ogunro, A. S. (2006). Psychological
characteristics as correlates of emotional burden in incarcerated offenders in
Nigeria. East African Medical Journal, 83, 545e552.
Ferrao, Y. A., Shavitt, R. G., Bedin, N. R., de Mathis, M. E., Carlos, L. A., Fontenelle, L. F.,
et al. (2006). Clinical features associated to refractory obsessive-compulsive
disorder. Journal of Affective Disorders, 94, 199e209.
Flisher, A., & Chalton, D. (2001). Urbanisation and adolescent risk behaviour. South
African Medical Journal, 91, 243e249.
Gillis, L. S., Welman, M., Koch, A., & Joyi, M. (1991). Psychological distress and
depression in urbanising elderly black persons. South African Medical Journal,
79, 490e495.
Goldberg, D., & Hillier, V. F. (1979). A scaled version of the General Health Ques-
tionnaire. Psychological Medicine, 9, 139e145.
Gulseren, L., Erol, A., Gulseren, S., Kuey, L., Kilic, B., & Ergor, G. (2006). From ante-
partum to postpartum: a prospective study on the prevalence of peripartum
depression in a semiurban Turkish community. Journal of Reproductive Medicine,
51, 955e960.
Gureje, O., Kola, L., & Afolabi, E. (2007). Epidemiology of major depressive disorder
in elderly Nigerians in the Ibadan study of ageing: a community-based survey
[see comment]. Lancet, 370, 957e964.
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
526

Page 12

Author's personal copy
Hackett, R. J., Sagdeo, D., & Creed, F. H. (2007). The physical and social associations
of common mental disorder in a tribal population in South India. Social
Psychiatry and Psychiatric Epidemiology, 42, 712e715.
Harding, T. W., De Aravango, V., Baltazar, J., Climent, C. E., Ibrahim, H. H. A., Ladrido-
Ignacio, L., et al. (1980). Mental disorders in primary health care: a study of
their frequency and diagnosis in four developing countries. Psychological
Medicine, 10, 231e241.
Harpham, T. (1994). Urbanization and mental health in developing countries:
a research role for social scientists, public health professionals and social
psychiatrists. Social Science & Medicine, 39, 233e245.
Harpham, T., & Blue, I. (1997). Linking health policy and social policy in urban
settings: the new development agenda. Transactions of the Royal Society of
Tropical Medicine and Hygiene, 91, 497e498.
Harpham, T., Huttly, S., De Silva, M. J., & Abramsky, T. (2005). Maternal mental health and
childnutritionalstatusinfourdevelopingcountries.BMJPublishingGroup.(Rep.No.59).
Harpham, T., & Molyneux, C. (2001). Urban health in developing countries: a review.
Progress in Development Studies, 1, 113e137.
Harpham, T., Snoxell, S., Grant, E., & Rodriguez, C. (2005). Common mental disorders
in a young urban population in Colombia. The British Journal Of Psychiatry: The
Journal Of Mental Science, 187, 161e167.
Havenaar, J. M., Geerlings, M. I., Vivian, L., Collinson, M., & Robertson, B. (2008).
Common mental health problems in historically disadvantaged urban and rural
communities in South Africa: prevalence and risk factors. Social Psychiatry &
Psychiatric Epidemiology, 43, 209e215.
Inandi, T., Elci, O. C., Ozturk, A., Egri, M., Polat, A., & Sahin, T. K. (2002). Risk factors
for depression in postnatal first year, in eastern Turkey. International Journal of
Epidemiology, 31, 1201e1207.
Karam, E. G., Mneimneh, Z. N., Karam, A. N., Fayyad, J. A., Nasser, S. C., Chatterji, S.,
et al. (2006). Prevalence and treatment of mental disorders in Lebanon:
a national epidemiological survey [see comment]. Lancet, 367, 1000e1006.
Kebede, D., & Alem, A. (1999a). Major mental disorders in Addis Ababa, Ethiopia. II.
Affective disorders. Acta Psychiatrica Scandinavica, Supplementum, 397, 18e23.
Kebede, D., & Alem, A. (1999b). Major mental disorders in Addis Ababa, Ethiopia. III.
Neurotic and somatoform disorders. Acta Psychiatrica Scandinavica, Supple-
mentum, 397, 24e29.
Kim, J. M., Shin, I. S., Yoon, J. S., & Stewart, R. (2002). Prevalence and correlates of
late-life depression compared between urban and rural populations in Korea.
International Journal of Geriatric Psychiatry, 17, 409e415.
Kumar, M. S., Murhekar, M. V., Hutin, Y., Subramanian, T., Ramachandran, V., &
Gupte, M. D. (2007). Prevalence of posttraumatic stress disorder in a coastal
fishing village in Tamil Nadu, India, after the December 2004 tsunami. American
Journal of Public Health, 97, 99e101.
Kuo, H. W., Wu, S. J., Ma, T. C., Chiu, M. C., & Chou, S. Y. (2007). Posttraumatic
symptoms were worst among quake victims with injuries following the Chi-Chi
quake in Taiwan. Journal of Psychosomatic Research, 62, 495e500.
Lai, T. J., Chang, C. M., Connor, K. M., Lee, L. C., & Davidson, J. R. (2004). Full and
partial PTSD among earthquake survivors in rural Taiwan. Journal of Psychiatric
Research, 38, 313e322.
Liu, C. K., Lai, C. L., Tai, C. T., Lin, R. T., Yen, Y. Y., & Howng, S. L. (1998). Incidence and
subtypes of dementia in southern Taiwan: impact of socio-demographic factors.
Neurology, 50, 1572e1579.
Liu, S. I., Prince, M., Blizard, B., & Mann, A. (2002). The prevalence of psychiatric
morbidity and its associated factors in general health care in Taiwan. Psycho-
logical Medicine, 32, 629e637.
Lopez, D. A., Mathers, D. C., Ezzati, M., Jamison, T. D., & Murray, J. L. C. (2006). Global
burden of disease and risk factors. New York: Oxford University Press/The World
Bank.
Lorant, V., Deliege, D., Eaton, W., Robert, A., Philippot, P., & Ansseau, M. (2003).
Socio-economic inequalities in depression: a meta-analysis. American Journal of
Epidemiology, 157, 98e112.
Ludermir, A. B. (2000). Productive employment, gender and mental health. Cader-
nos de Saude Publica, 16, 647e659, (in Portuguese).
Ludermir, A. B., & Harpham, T. (1998). Urbanization and mental health in Brazil:
social and economic dimensions. Health & Place, 4, 223e232.
Ludermir, A. B., & Lewis, G. (2001). Links between social class and common mental
disorders in Northeast Brazil. Social Psychiatry & Psychiatric Epidemiology, 36,
101e107.
Marín-León, L., de Oliveira, H. B., de Azevedo Barros, M. B., Dalgalarrondo, P., &
Botega, N. J. (2007). Social inequality and common mental disorders. Revista
Brasileira de Psiquiatria, 29.
McKenzie, K., Patel, V., & Araya, R. (2004). Learning from low income countries:
mental health. BMJ: British Medical Journal, 329, 1138e1140.
Miranda, J. J., & Patel, V. (2005). Achieving the millennium development goals: does
mental health play a role? PloS Medicine. [On-line]. Available: www.
plosmedicine.org.
Moraes, I. G., Pinheiro, R. T., Silva, R. A., Horta, B. L., Sousa, P. L., & Faria, A. D. (2006).
Prevalence of postpartum depression and associated factors [see comment].
Revista de Saude Publica, 40, 65e70, (in Portuguese).
Muhwezi, W. W., Agren, H., Neema, S., Maganda, A. K., & Musisi, S. (2008). Life
events associated with major depression in Ugandan primary healthcare (PHC)
patients: issues of cultural specificity. International Journal of Social Psychiatry,
54, 144e163.
Myer, L., Stein, D. J., Grimsrud, A., Seedat, S., & Williams, D. R. (2008). Social
determinants of psychological distress in a nationally-representative sample of
South African adults. Social Science & Medicine, 66, 1828e1840.
National Academies Press. (2003). Cities transformed: demographic change and its
implications in the developing world. http://books.nap.edu/catalog/10693.html
[On-line].
Patel, V. (2001). Poverty, inequality, and mental health in developing countries. In
D. A. Leon, & G. Walt (Eds.), Poverty, inequality and health: An international
perspective (pp. 247e262). Oxford: Oxford University Press.
Patel, V., Araya, R., de Lima, M., Ludermir, A., & Todd, C. (1999). Women, poverty and
common mental disorders in four restructuring societies. Social Science &
Medicine, 49, 1461.
Patel, V., Chisholm, D., Rabe-Hesketh, S., Dias-Saxena, F., Andrew, G., & Mann, A.
(2003). The efficacy and cost-effectiveness of a drug and psychological treat-
ment for common mental disorders in general health care in Goa, India:
a randomised controlled trial. Lancet, 361, 33e39.
Patel, V., DeSouza, N., & Rodrigues, M. (2003). Postnatal depression and infant
growth and development in low income countries: a cohort study from Goa,
India. Archives of Disease in Childhood, 88, 34e37.
Patel, V., Flisher, A., & Cohen, A. (2006). Social and cultural determinants of mental
health. In R. Murray, K. Kendler, P. McGuffin, S. Wessely, & D. Castle (Eds.),
Essentials of psychiatry (2 ed.). Cambridge: Cambridge University Press.
Patel, V., Kirkwood, B. R., Pednekar, S., Weiss, H., & Mabey, D. (2006). Risk factors for
common mental disorders in women. Population-based longitudinal study.
British Journal of Psychiatry, 189, 547e555.
Patel, V., & Kleinman, A. (2003). Poverty and common mental disorders in devel-
oping countries. Bulletin of the World Health Organization, 81, 609e615.
Patel, V., Pereira, J., Coutinho, L., Fernandes, R., Fernandes, J., & Mann, A. (1998a).
Poverty, psychological disorder and disability in primary care attenders in Goa,
India. British Journal of Psychiatry, 172, 533e536.
Patel, V., Rodrigues, M., & DeSouza, N. (2002). Gender, poverty, and postnatal
depression: a study of mothers in Goa, India [see comment]. American Journal of
Psychiatry, 159, 43e47.
Patel, V., Todd, C., Winston, M., Gwanzura, F., Simunyu, E., Acuda, W., et al. (1997).
Common mental disorders in primary care in Harare, Zimbabwe: associations
and risk factors. The British Journal of Psychiatry, 171, 60e64.
Patel, V., Todd, C., Winston, M., Gwanzura, F., Simunyu, E., Acuda, W., et al. (1998).
Outcome of common mental disorders in Harare, Zimbabwe. British Journal of
Psychiatry, 172, 53e57.
Pickett, K. E., James, O. W., & Wilkinson, R. G. (2006). Income inequality and the
prevalence of mental illness: a preliminary international analysis. Journal of
Epidemiology and Community Health, 60, 646e647.
Prince, M., Patel, V., Saxena, S., Maj, M., Maselko, J., Phillips, M. R., et al. (2007). No
health without mental health e a slogan with substance. Lancet, 370, 859e877.
Rahman, A., & Creed, F. (2007). Outcome of prenatal depression and risk
factors associated with persistence in the first postnatal year: prospective
study from Rawalpindi,Pakistan.
115e121.
Rahman, A., Iqbal, Z., Bunn, J., Lovel, H., & Harrington, R. (2004). Impact of maternal
depression on infant nutritional status and illness: a cohort study. Archives of
General Psychiatry, 61, 946e952.
Reichenheim, M. E., & Harpham, T. (1991). Maternal mental health in a squatter
settlement in Rio de Janeiro. The British Journal Of Psychiatry: The Journal Of
Mental Science, 159, 683e690.
Robison, J., Curry, L., Gruman, C., Covington, T., Gaztambide, S., & Blank, K. (2003).
Depression in later-life Puerto Rican primary care patients: the role of illness,
stress, social integration, and religiosity. International Psychogeriatrics, 15,
239e251.
Sabin, M., Lopes, C. B., Nackerud, L., Kaiser, R., & Varese, L. (2003). Factors associated
with poor mental health among Guatemalan refugees living in Mexico 20 years
after civil conflict [see comment]. JAMA, 290, 635e642.
Sachs, S. E., & Sachs, J. D. (2007). Mental health in the millennium development
goals: not ignored [comment]. PLoS Medicine/Public Library of Science, 4, e56,
author.
Sale, S., & Gadanya, M. (2009). Prevalence and factors associated with depression in
HIV/AIDS patients aged 15e25 years at Aminu Kano Teaching Hospital, Nigeria.
Journal of Child and Adolescent Mental Health, 20(2), 95e99.
Saraceno, B., & Barbui, C. (1997). Poverty and mental illness. Canadian Journal of
Psychiatry, 42, 285e290.
Saraceno, B., Levav, I., & Kohn, R. (2005). The public mental health significance of
research on socio-economic factors in schizophrenia and major depression.
World Psychiatry, 4, 181e185.
Seedat, S., Nyamai, C., Njenga, F., Vythilingum, B., & Stein, D. (2004). Trauma
exposure and post-traumatic stress symptoms in urban African schools.
Survey in Cape Town and Nairobi. British Journal of Psychiatry, 184,
169e175.
Seino, K., Takano, T., Mashal, T., Hemat, S., & Nakamura, K. (2008). Prevalence of and
factors influencing posttraumatic stress disorder among mothers of children
under five in Kabul, Afghanistan, after decades of armed conflicts. Health &
Quality of Life Outcomes, 6, 29.
Seplaki, C. L., Goldman, N., Weinstein, M., & Lin, Y. H. (2006). Before and after the
1999 Chi-Chi earthquake: traumatic events and depressive symptoms in an
older population. Social Science & Medicine, 62, 3121e3132.
Stein, D. J., Seedat, S., Herman, A., Moomal, H., Heeringa, S. G., Kessler, R. C., et al.
(2008). Lifetime prevalence of psychiatric disorders in South Africa. British
Journal of Psychiatry, 192, 112e117.
Stein, D., Seedat, S., & Emsley, R. (2002). Violence in the world and in South Africa:
what is a doctor to do? South African Medical Journal, 92, 789e790.
JournalofAffectiveDisorders, 100,
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
527

Page 13

Author's personal copy
Todd, C., Patel, V., Simunyu, E., Gwanzura, F., Acuda, W., Winston, M., et al. (1999).
The onset of common mental disorders in primary care attenders in Harare,
Zimbabwe. Psychological Medicine, 29, 97e104.
Townsend, P. (1979). Poverty in the United Kingdom. Harmondsworth, Middlesex:
Penguin Books.
Townsend, P. (1987). Deprivation. Journal of Social Policy, 16, 125e146.
Toye, M., & Infanti, J. (2004). Social inclusion and community economic development:
Literature review. Victoria, BC: The Canadian CED Network.
Truong,K.D.,&Ma,S.(2006).Asystematicreviewofrelationsbetweenneighborhoods
and mental health. Journal of Mental Health Policy and Economics, 9,137e154.
United Nations Development Programme. (2006). Human development report
2006. Beyond scarcity: Power, poverty and global water crisis. New York:
MacMillan.
Vorcaro, C. M., Lima-Costa, M. F., Barreto, S. M., & Uchoa, E. (2001). Unexpected high
prevalence of 1-month depression in a small Brazilian community: the Bambui
Study. Acta Psychiatrica Scandinavica, 104, 257e263.
Ward, C., Flisher, F., Zissis, C., Muller, M. L., & Lombard, C. (2001). Exposure to
violence and its relationship to psychopathology in adolescents. Injury
Prevention, 7, 297e301.
Weich, S., & Lewis, G. (1998). Poverty, unemployment, and common mental
disorders: population based cohort study. British Medical Journal, 317, 115e119.
WHO. (2001). World health report 2001, mental health: New understanding, new hope.
Geneva: WHO.
WHO. (2002). World report on violence and health. Geneva: WHO.
WHO Commission on Social Determinants of Health. (2005). Action on the social
determinants of health: Learning from previous experiences. Geneva: WHO.
Williams, D. R., Herman, A., Stein, D. J., Heeringa, S. G., Jackson, P. B.,
Moomal, H., et al. (2008). Twelve-month mental disorders in South Africa:
prevalence, service use and demographic correlates in the population-based
South African Stress and Health Study. Psychological Medicine, 38, 211e220.
World Bank. (2001). World development report 2000/2001-attacking poverty. New
York: Oxford University Press for the World Bank.
C. Lund et al. / Social Science & Medicine 71 (2010) 517e528
528