What lies behind gender inequalities in HIV/AIDS in sub-Saharan African countries: evidence from Kenya, Lesotho and Tanzania
Bibliographic record
Abstract
Within sub-Saharan Africa, women are disproportionately at risk for acquiring and having human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS). It is important to clarify whether gender inequalities in HIV prevalence in this region are explained by differences in the distributions of HIV risk factors, differences in the effects of these risk factors or some combination of both. We used an extension of the Blinder-Oaxaca decomposition approach to explain gender inequalities in HIV/AIDS in Kenya, Lesotho and Tanzania using data from the demographic and health and AIDS indicator surveys. After adjusting for covariates using Poisson regression models, female gender was associated with a higher prevalence of HIV/AIDS in Kenya [prevalence ratio (PR) = 1.73, 95% confidence interval (CI) = 1.33, 2.23 in 2003] and Lesotho (PR = 1.39, 95% CI = 1.20, 1.62 in 2004/05), but not in Tanzania. Decomposition analyses demonstrated two distinct patterns over time. In Tanzania, the gender inequality in HIV/AIDS was explained by differences in the distributions of HIV risk factors between men and women. In contrast, in Kenya and Lesotho, this inequality was partly explained by differences in the effects across men and women of measured HIV/AIDS risk factors, including socio-demographic characteristics (age and marital status) and sexual behaviours (age at first sex); these results imply that gender inequalities in HIV/AIDS would persist in Kenya and Lesotho even if men and women had similar distributions of HIV risk factors. The production of gender inequalities may vary across countries, with inequalities attributable to the unequal distribution of risk factors among men and women in some countries and the differential effect of these factors between groups in others. These different patterns have important implications for policies to reduce gender inequalities in HIV/AIDS.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".