Evidence map›Paper›PMID 34670640›Full record

ArticleEpidemiology and psychiatric sciences2021

Inequalities in the prevalence of major depressive disorder in Brazilian slum populations: a cross-sectional analysis.

Charlie F M Pitcairn, Anthony A Laverty, Jasper J L Chan, Oyinlola Oyebode, Matías Mrejen, Julia M Pescarini, Daiane Borges Machado, Thomas V Hone

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Article in Epidemiology and psychiatric sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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  7. Slums and pandemics.Journal of development economics · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Charlie F M PitcairnSchool of Public Health, Imperial College London, London, UK.ORCID https://orcid.org/0000-0002-9641-7673
Anthony A LavertyDepartment of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK.
Jasper J L ChanSchool of Public Health, Imperial College London, London, UK.ORCID https://orcid.org/0000-0003-4251-3819
Oyinlola OyebodeWarwick Medical School, University of Warwick, Coventry, UK.
Matías MrejenSão Paulo School of Business Administration, Fundação Getulio Vargas, São Paulo, Brazil.
Julia M PescariniCentro de Integração de Dados e Conhecimentos para Saúde (Cidacs), Fundação Oswaldo Cruz, Salvador, Brazil.
Daiane Borges MachadoCenter of Data and Knowledge Integration for Health, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil.
Thomas V HoneDepartment of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsThe mental health of slum residents is under-researched globally, and depression is a significant source of worldwide morbidity. Brazil's large slum-dwelling population is often considered part of a general urban-poor demographic. This study aims to identify the prevalence and distribution of depression in Brazil and compare mental health inequalities between slum and non-slum populations.

methodsData were obtained from Brazil's 2019 National Health Survey. Slum residence was defined based on the UN-Habitat definition for slums and estimated from survey responses. Doctor-diagnosed depression, Patient Health Questionnaire (PHQ-9)-screened depression and presence of undiagnosed depression (PHQ-9-screened depression in the absence of a doctor's diagnosis) were analysed as primary outcomes, alongside depressive symptom severity as a secondary outcome. Prevalence estimates for all outcomes were calculated. Multivariable logistic regression models were used to investigate the association of socioeconomic characteristics, including slum residence, with primary outcomes. Depressive symptom severity was analysed using generalised ordinal logistic regression.

resultsNationally, the prevalence of doctor diagnosed, PHQ-9 screened and undiagnosed depression were 9.9% (95% confidence interval (CI): 9.5-10.3), 10.8% (95% CI: 10.4-11.2) and 6.9% (95% CI: 6.6-7.2), respectively. Slum residents exhibited lower levels of doctor-diagnosed depression than non-slum urban residents (8.6%; 95% CI: 7.9-9.3 v. 10.7%; 95% CI: 10.2-11.2), while reporting similar levels of PHQ-9-screened depression (11.3%; 95% CI: 10.4-12.1 v. 11.3%; 95% CI: 10.8-11.8). In adjusted regression models, slum residence was associated with a lower likelihood of doctor diagnosed (adjusted odds ratio (adjusted OR): 0.87; 95% CI: 0.77-0.97) and PHQ-9-screened depression (adjusted OR: 0.87; 95% CI: 0.78-0.97). Slum residents showed a greater likelihood of reporting less severe depressive symptoms. There were significant ethnic/racial disparities in the likelihood of reporting doctor-diagnosed depression. Black individuals were less likely to report doctor-diagnosed depression (adjusted OR: 0.66; 95% CI: 0.57-0.75) than white individuals. A similar pattern was observed in Mixed Black (adjusted OR: 0.72; 95% CI: 0.66-0.79) and other (adjusted OR: 0.63; 95% CI: 0.45-0.88) ethnic/racial groups. Slum residents self-reporting a diagnosis of one or more chronic non-communicable diseases had greater odds of exhibiting all three primary depression outcomes.

conclusionsSubstantial inequalities characterise the distribution of depression in Brazil including in slum settings. People living in slums may have lower diagnosed rates of depression than non-slum urban residents. Understanding the mechanisms behind the discrepancy in depression diagnosis between slum and non-slum populations is important to inform health policy in Brazil, including in addressing potential gaps in access to mental healthcare.

Indexed as

Major Depressive DisorderPoverty AreasBrazilCross-Sectional StudiesHumansPrevalenceBrazilDepressioninequalitiesLMICPHQ-9slumsurban health

Identifiers

PMID34670640
PMCPMC8546499

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.