A multilevel analysis of the determinants of HIV testing among men in Sub-Saharan Africa: Evidence from Demographic and Health Surveys across 10 African countries
Bibliographic record
Abstract
Sub-Saharan Africa, the epicenter of the HIV epidemic, has seen significant reductions in new infections over the last decade. Although most new infections have been reported among women, particularly adolescent girls, men are still disadvantaged in accessing HIV testing, care, and treatment services. Globally, men have relatively poorer HIV testing, care, and treatment indices when compared with women. Gender norms and the associated concept of masculinity, strength, and stereotypes have been highlighted as hindering men's acceptance of HIV counseling and testing. Therefore, men's suboptimal uptake of HIV testing services will continue limiting efforts to achieve HIV epidemic control. Thus, this study aimed to identify individual, neighborhood, and country-level determinants of sub-optimal HIV testing among men in Sub-Saharan African countries. We analyzed demographic and health datasets from surveys conducted between 2016 and 2020 in Sub-Saharan African Countries. We conducted multivariable multilevel regression analysis on 52,641 men aged 15-49 years resident in 4,587 clusters across 10 countries. The primary outcome variable was ever tested for HIV. HIV testing services uptake among men in these ten Sub-Saharan African countries was 35.1%, with a high of 65.5% in Rwanda to a low of 10.2% in Guinea. HIV testing services uptake was more likely in men with increasing age, some form of formal education, in employment, ever married, and residents in relatively wealthier households. We also found that men who possessed health insurance, had some form of weekly media exposure, and had accessed the internet were more likely to have ever received an HIV test. Unlike those noted to be less likely to have ever received an HIV test if they had discriminatory attitudes towards HIV, comprehensive HIV knowledge, recent sexual activity, and risky sexual behavior were positive predictors of HIV testing services uptake among men. Furthermore, men in communities with high rurality and illiteracy were less likely to receive an HIV test. Individual and community-level factors influence the uptake of HIV testing among Sub-Saharan African men. There was evidence of geographical clustering in HIV testing uptake among men at the community level, with about two-thirds of the variability attributable to community-level factors. Therefore, HIV testing programs will need to design interventions that ensure equal access to HIV testing services informed by neighborhood socioeconomic conditions, peculiarities, and contexts.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".