Association between Health and Wealth among Kenyan Adults with Hypertension
Bibliographic record
Abstract
BACKGROUND: This paper examines the relationship between hypertension and wealth in a national sample of Kenyan adults. METHODS: Data from 27,552 individuals from the Demographic and Health Survey Program (DHS) for Kenya were analyzed. Wealth index, a cumulative measure of household standard of living, was the outcome. The final analysis was stratified by gender with covariates added in blocks (demographics, economic, and cultural) to investigate the independent association of hypertension with wealth index. RESULTS: Approximately 7.6% of those with hypertension had a wealth index above the median. For women and men, hypertension was significantly associated with higher wealth index (women ß=0.26; CI=0.19; 0.34; men ß=0.36; CI=0.19; 0.53). After adjusting for age, rural location, children, employment, education, ethnicity, and religion, hypertension maintained statistical significance with wealth index for both women and men (women ß=0.06; CI=0.01; 0.11; men ß=0.20; CI=0.08; 0.31). CONCLUSIONS: As Kenya as a nation undergoes health care reform while also experiencing a high burden of hypertension, the results presented here provide preliminary evidence that may be used in support for decision makers for the wealth effects of health interventions. Additional work is needed to understand the longitudinal relationship between hypertension and wealth at the national level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".