Healthcare seeking for chronic illness among adult slum dwellers in Bangladesh: A descriptive cross-sectional study in two urban settings
Bibliographic record
Abstract
INTRODUCTION: Accompanying rapid urbanization in Bangladesh are inequities in health and healthcare which are most visibly manifested in slums or low-income settlements. This study examines socioeconomic, demographic and geographic patterns of self-reported chronic illness and healthcare seeking among adult slum dwellers in Bangladesh. Understanding these patterns is critical in designing more equitable urban health systems and in enabling the country's goal of Universal Health Coverage by 2030. METHODS: This descriptive cross-sectional study compares survey data from slum settlements located in two urban sites in Bangladesh, Tongi and Sylhet. Reported chronic illness symptoms and associated healthcare-seeking strategies are compared, and the catastrophic impact of household healthcare expenditures are assessed. RESULTS: Significant differences in healthcare-seeking for chronic illness were apparent both within and between slum settlements related to sex, wealth score (PPI), and location. Women were more likely to use private clinics than men. Compared to poorer residents, those from wealthier households sought care to a greater extent in private clinics, while poorer households relied more on drug shops and public hospitals. Chronic symptoms also differed. A greater prevalence of musculoskeletal, respiratory, digestive and neurological symptoms was reported among those with lower PPIs. In both slum sites, reliance on the private healthcare market was widespread, but greater in industrialized Tongi. Tongi also experienced a higher probability of catastrophic expenditure than Sylhet. CONCLUSIONS: Study results point to the value of understanding context-specific health-seeking patterns for chronic illness when designing delivery strategies to address the growing burden of NCDs in slum environments. Slums are complex social and geographic entities and cannot be generalized. Priority attention should be focused on developing chronic care services that meet the needs of the working poor in terms of proximity, opening hours, quality, and cost.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".