Association between women’s household decision-making autonomy and health insurance enrollment in sub-saharan Africa
Bibliographic record
Abstract
BACKGROUND: Out of pocket payment for healthcare remains a barrier to accessing health care services in sub-Saharan Africa (SSA). Women's decision-making autonomy may be a strategy for healthcare access and utilization in the region. There is a dearth of evidence on the link between women's decision-making autonomy and health insurance enrollment. We, therefore, investigated the association between married women's household decision making autonomy and health insurance enrollment in SSA. METHODS: Demographic and Health Survey data of 29 countries in SSA conducted between 2010 and 2020 were analyzed. Both bivariate and multilevel logistic regression analyses were carried out to investigate the relationship between women's household decision-making autonomy and health insurance enrollment among married women. The results were presented as an adjusted odds ratio (AOR) and the 95% confidence interval (CI). RESULTS: The overall coverage of health insurance among married women was 21.3% (95% CI; 19.9-22.7%), with the highest and lowest coverage in Ghana (66.7%) and Burkina Faso (0.5%), respectively. The odds of health insurance enrollment was higher among women who had household decision-making autonomy (AOR = 1.33, 95% CI; 1.03-1.72) compared to women who had no household decision-making autonomy. Other covariates such as women's age, women's educational level, husband's educational level, wealth status, employment status, media exposure, and community socioeconomic status were found to be significantly associated with health insurance enrollment among married women. CONCLUSION: Health insurance coverage is commonly low among married women in SSA. Women's household decision-making autonomy was found to be significantly associated with health insurance enrollment. Health-related policies to improve health insurance coverage should emphasize socioeconomic empowerment of married women in SSA.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".