Prevalence, Correlates, and Treatment Gap of Schizophrenia Among Adults in Bangladesh: Findings From a Nationwide Household Survey
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Schizophrenia contributes significantly to the global burden of disease; however, the data from low- and middle-income countries (LMICs) remain limited. Bangladesh, characterized by distinct sociodemographic and nutritional risk patterns, provides a critical context for understanding the epidemiology of schizophrenia in such settings. We hypothesized that the prevalence of schizophrenia would be higher among individuals residing in rural areas and those with lower levels of education and economic status. STUDY DESIGN: We conducted a cross-sectional, nationwide household (HH) survey as part of the Bangladesh National Mental Health Survey 2019. A stratified, multi-stage random sampling approach was used to recruit 7270 adults aged 18 years and older. Participants were initially screened using the 24-item Self-Reporting Questionnaire. Those screening positive underwent diagnostic assessment by trained psychiatrists using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. STUDY RESULTS: The weighted prevalence of schizophrenia among adults was 0.6% (95% confidence interval: 0.4%-0.9%). Multivariate analyses revealed that schizophrenia was significantly associated with unemployment, a family history of mental illness, a family history of suicidal behavior and being divorced or separated. The prevalence did not differ between age, sex, residence (urban vs. rural), income, or educational level. Among individuals diagnosed with schizophrenia, 39.3% had received treatment. CONCLUSIONS: This study provides the first nationwide HH survey data on schizophrenia in Bangladesh, identifying familial and marital disruptions as key correlates. These findings challenge commonly held assumptions about urbanicity and socioeconomic disadvantage as risk factors in LMIC contexts and highlight the urgent need to address the treatment gap.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".