Socioeconomic inequalities in uptake of HIV testing during antenatal care: evidence from Sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Measuring socioeconomic inequalities in healthcare usage represents a critical step towards promoting health equity, in alignment with the principles of universal health coverage and the United Nations' Sustainable Development Goals. In this study, we assessed the socioeconomic inequalities in HIV testing during antenatal care (ANC) in sub-Saharan Africa. METHODS: Sub-Saharan Africa was the focus of this study. Benin, Burundi, Cameroon, Ethiopia, Gambia, Guinea, Liberia, Malawi, Mali, Mauritania, Mozambique, Rwanda, Sierra Leone, Uganda, Zambia, and Zimbabwe were the countries included in the study. This study used current Demographic and Health Surveys data spanning from 2015 to 2022. A total of 70,028 women who tested for HIV as part of antenatal contacts formed the sample for analysis. We utilized the standard concentration index and curve to understand the socioeconomic inequalities in HIV testing during antenatal care among women. Additionally, a decomposition analysis of the concentration index was ran to ascertain the contributions of each factor to the inequality. RESULTS: Overall, 73.9% of women in sub-Saharan Africa tested for HIV during ANC. The countries with the highest proportions were Malawi, Rwanda, Zambia, and Zimbabwe. Mali Benin, Guinea, Mali, and Mauritania were the countries with the lowest proportions of HIV testing. Being among the richer [AOR 1.10, 95% CI: 1.02,1.18] and richest [AOR 1.41, 95% CI:1.30, 1.54] wealth quintiles increased the odds of HIV testing during ANC. The concentration value of 0.03 and the curve show that HIV testing is more concentrated among women in the highest wealth quintile. Hence, wealthy women are advantaged in terms of HIV testing. As the model's residual value is negative (-0.057), the model overestimates the level of inequality in the outcome variable (HIV during ANC), which means that the model's explanatory factors can account for higher concentration than is the case. CONCLUSION: We found that there is substantial wealth index-related inequalities in HIV testing, with women of the poorest wealth index disadvantaged in relation to the HIV testing. This emphasizes the necessity for sub-Saharan Africa public health programs to think about concentrating their limited resources on focused initiatives to grasp women from these socioeconomic circumstances. To increase women's access to HIV testing, maternal and child health programs in sub-Saharan Africa should attempt to minimize female illiteracy and poverty. Consequently, health education may be required to provide women with comprehensive HIV knowledge and decrease the number of lost opportunities for women to get tested for HIV. Given the link between knowledge of HIV and HIV testing, it is important to focus on community education and sensitization about HIV and the need to know one's status.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".