Association of Household Wealth Index, Educational Status, and Social Capital with Hypertension Awareness, Treatment, and Control in South Asia
Bibliographic record
Abstract
OBJECTIVE: Hypertension control rates are low in South Asia. To determine association of measures of socioeconomic status (wealth, education, and social capital) with hypertension awareness, treatment, and control among urban and rural subjects in these countries we performed the present study. METHODS: We enrolled 33,423 subjects aged 35-70 years (women 56%, rural 53%, low-education status 51%, low household wealth 25%, low-social capital 33%) in 150 communities in India, Pakistan, and Bangladesh during 2003-2009. Prevalence of hypertension and its awareness, treatment, and control status and their association with wealth, education, and social capital were determined. RESULTS: Age-, sex-, and location-adjusted prevalence of hypertension in men was 31.5% (23.9-40.2%) and women was 32.6% (24.9-41.5%) with variations in prevalence across study sites (urban 30-56%, rural 11-43%). Prevalence was significantly greater in urban locations, older subjects, and participants with more wealth, greater education, and lower social capital index. Hypertension awareness was in 40.4% (urban 45.9, rural 32.5), treatment in 31.9% (urban 37.6, rural 23.6), and control in 12.9% (urban 15.4, rural 9.3). Control was lower in men and younger subjects. Hypertension awareness, treatment, and control were significantly lower, respectively, in lowest vs. highest wealth index tertile (26.2 vs. 50.6%, 16.9 vs. 44.0%, and 6.9 vs. 17.3%, P < 0.001) and lowest vs. highest educational status tertile (31.2 vs. 48.4%, 21.8 vs. 42.1%, and 7.8 vs. 19.2%, P < 0.001) while insignificant differences were observed in lowest vs. highest social capital index (38.2 vs. 36.1%, 35.1 vs. 27.8%, and 12.5 vs. 9.1%). CONCLUSIONS: This study shows low hypertension awareness, treatment, and control in South Asia. Lower wealth and educational status are important in low hypertension awareness, treatment, and control.
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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 | 0.000 | 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 | 0.000 |
| 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".