Socioeconomic Inequalities in the Risk Factors of Noncommunicable Diseases Among Women of Reproductive Age in Sub-saharan Africa: A Multi-Country Analysis of Survey Data
Bibliographic record
Abstract
Background: Understanding the socioeconomic discordance associated with the risk factors of non-communicable diseases (NCDs) can help direct effective interventions to end its persistent occurrence. We examined the prevalence of high blood pressure, overweight/obesity, alcohol consumption and tobacco use among women and compared across wealth quintiles in sub-Saharan Africa countries. Methods: This study included 454,080 women of reproductive age (15-49 years) from the current Demographic and Health Survey (DHS) conducted between 2008/09-2017 across 33 sub-Saharan Africa countries. The outcome variables were high blood pressure, overweight/obesity, alcohol consumption and tobacco use. The prevalence of the risk factors of NCDs and sample characteristics across different levels of wealth quintiles were examined. Furthermore, socioeconomic inequalities were measured using concentration index (CI) and Lorenz curve considering urban-rural differentials. Results: The prevalence of high blood pressure and overweight/obesity were 1.2%-17.3% and 6.7%-44.5% respectively with significant wealth quintile differences. More so, alcohol consumption prevalence was 4.1%-47.3% and tobacco use was 0.3%-9.9%. The overall prevalence of high blood pressure was 5.5%, overweight/obesity accounted for about 23.1%, alcohol consumption and tobacco users were 23.9% and 2.4% respectively. The socioeconomic inequalities in high blood pressure (CI = 0.1352, p< 0.001); overweight/obesity (CI = 0.2285, p< 0.001) and alcohol consumption (CI = 0.0278, p< 0.001) were significantly more in the higher socioeconomic group, compared to the lower socioeconomic group. In contrast, the prevalence of tobacco use (CI = -0.2551, p< 0.001) was significantly more in the lower socioeconomic group, compared to the higher socioeconomic group. The test for differences in rural vs urban concentration indices for high blood pressure, overweight/obesity, alcohol consumption, and tobacco use were statistically significant in all the health indicators (p<0.05). Conclusion: An effective intervention should incorporate a high-risk approach to terminate risk distribution by directing resources to key population women. To improve the benefit to risk ratio and enhance the cost effectiveness of preventive health programmes, it is paramount to understand the worth of equity-based strategies. Integrating equity elements to interventions is a key measure towards ensuring that policies and programmes meet their milestones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".