Urbanization and Socioeconomic Disparities in Hypertension among Older Adult Women in Sudan
Bibliographic record
Abstract
BACKGROUND: Evidence from the developed world associates higher prevalence of hypertension with lower socioeconomic status (SES). However, patterns of association are not as clear in Africa and other developing countries, with varying levels of socioeconomic development and epidemiological transition. Using wealth and education as indicators, we investigated association between SES and hypertension among older adult women in Sudan and examined whether urbanicity mediates the relationship. METHODS: The sample included women aged 50 years and over participating in the nationally representative population-based second Sudan Health Household Survey (SHHS) conducted in 2010. Principal components analysis was used to assign each household with a wealth score based on assets owned. The score was categorized into quintiles from lowest (poorest) to highest (richest). FINDINGS: The sample included a total of 5218 women, median and mean age 55 and 59 years, respectively, with the majority not have any schooling (81.6%). The overall prevalence of reported hypertension was found to be 10.5%. After adjustment for age, marital status, work status and urban/rural location, better wealth and higher education were independently and positively associated with hypertension prevalence rates. However, when stratified by urbanicity, the relationship between wealth and hypertension lost its significance for women in urban areas but maintained it in rural areas, increasing significantly and consistently with each increase in quintile index (adjusted odds ratio, aOR1 = 1.95 95% CI = 1.08-3.52; aOR2 = 5.25, 95% CI = 3.01-9.15; aOR3 = 8.27, 95% CI = 4.78-14.3; and aOR4 = and 11.4, 95% CI = 6.45-20.0; respectively). By contrast, education played a greater role in increasing the odds of hypertension among women in urban locations but not in rural locations (aOR = 2.14, 95% CI = 1.25-7.90 vs. aOR = 0.79, 95% CI = 0.27-2.30, respectively). CONCLUSIONS: Our findings of a socioeconomic gradient in the prevalence of hypertension among women, mediated by urbanization, call for targeted interventions from early stages of economic development in Sudan and similar settings of transitioning countries.
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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 | 0.001 | 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".