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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».