Evidence map›Paper›PMID 34088289›Full record

ArticleBMC psychiatry2021

A distress-continuum, disorder-threshold model of depression: a mixed-methods, latent class analysis study of slum-dwelling young men in Bangladesh.

Syed Shabab Wahid, John Sandberg, Malabika Sarker, A S M Easir Arafat, Arifur Rahman Apu, Atonu Rabbani, Uriyoán Colón-Ramos, Brandon A Kohrt

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In one paragraph

Article in BMC psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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.

Syed Shabab WahidDepartment of Global Health, Milken Institute School of Public Health, George Washington University, 950 New Hampshire Ave NW #2, Washington, DC, 20052, USA.ORCID 0000-0003-0355-0537
John SandbergDepartment of Global Health, Milken Institute School of Public Health, George Washington University, 950 New Hampshire Ave NW #2, Washington, DC, 20052, USA.
Malabika SarkerBRAC James P Grant School of Public Health, BRAC University, 5th Floor, (Level-6), icddr,b Building 68 Shahid Tajuddin Ahmed Sharani, Mohakhali, Dhaka, 1212, Bangladesh. malabika@bracu.ac.bd.
A S M Easir ArafatBRAC James P Grant School of Public Health, BRAC University, 5th Floor, (Level-6), icddr,b Building 68 Shahid Tajuddin Ahmed Sharani, Mohakhali, Dhaka, 1212, Bangladesh.
Arifur Rahman ApuBRAC James P Grant School of Public Health, BRAC University, 5th Floor, (Level-6), icddr,b Building 68 Shahid Tajuddin Ahmed Sharani, Mohakhali, Dhaka, 1212, Bangladesh.
Atonu RabbaniBRAC James P Grant School of Public Health, BRAC University, 5th Floor, (Level-6), icddr,b Building 68 Shahid Tajuddin Ahmed Sharani, Mohakhali, Dhaka, 1212, Bangladesh.ORCID 0000-0002-3821-4106
Uriyoán Colón-RamosDepartment of Global Health, Milken Institute School of Public Health, George Washington University, 950 New Hampshire Ave NW #2, Washington, DC, 20052, USA.
Brandon A KohrtDepartment of Global Health, Milken Institute School of Public Health, George Washington University, 950 New Hampshire Ave NW #2, Washington, DC, 20052, USA.ORCID 0000-0002-3829-4820

Funding

RESHAPE Diversity SupplementR01MH120649 · NIMH · GEORGE WASHINGTON UNIVERSITY · PI KOHRT, BRANDON ALAN · 2019 to 2023
$3.5M
Reducing Barriers to Mental Health Task SharingK01MH104310 · NIMH · GEORGE WASHINGTON UNIVERSITY · PI KOHRT, BRANDON ALAN · 2014 to 2018
$908k
NIMH NIH HHS K01 MH104310NIMH NIH HHS R01 MH120649
6 · The paper itself

Abstract

backgroundBinary categorical approaches to diagnosing depression have been widely criticized due to clinical limitations and potential negative consequences. In place of such categorical models of depression, a 'staged model' has recently been proposed to classify populations into four tiers according to severity of symptoms: 'Wellness;' 'Distress;' 'Disorder;' and 'Refractory.' However, empirical approaches to deriving this model are limited, especially with populations in low- and middle-income countries.

methodsA mixed-methods study using latent class analysis (LCA) was conducted to empirically test non-binary models to determine the application of LCA to derive the 'staged model' of depression. The study population was 18 to 29-year-old men (n = 824) from an urban slum of Bangladesh, a low resource country in South Asia. Subsequently, qualitative interviews (n = 60) were conducted with members of each latent class to understand experiential differences among class members.

resultsThe LCA derived 3 latent classes: (1) Severely distressed (n = 211), (2) Distressed (n = 329), and (3) Wellness (n = 284). Across the classes, some symptoms followed a continuum of severity: 'levels of strain', 'difficulty making decisions', and 'inability to overcome difficulties.' However, more severe symptoms such as 'anhedonia', 'concentration issues', and 'inability to face problems' only emerged in the severely distressed class. Qualitatively, groups were distinguished by severity of tension, a local idiom of distress.

conclusionsThe results indicate that LCA can be a useful empirical tool to inform the 'staged model' of depression. In the findings, a subset of distress symptoms was continuously distributed, but other acute symptoms were only present in the class with the highest distress severity. This suggests a distress-continuum, disorder-threshold model of depression, wherein a constellation of impairing symptoms emerge together after exceeding a high level of distress, i.e., a tipping point of tension heralds a host of depression symptoms.

Indexed as

DepressionPoverty AreasAdolescentAdultAsiaBangladeshHumansLatent Class AnalysisMaleYoung AdultBangladeshClassificationDepressionLCALMICMixed methodsSlum

Identifiers

PMID34088289
PMCPMC8178879

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Registered trials

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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.