Socioeconomic inequalities in the 90-90-90 target, among people living with HIV in 12 sub-Saharan African countries - Implications for achieving the 95-95-95 target - Analysis of population-based surveys.

Abstract

Inequalities undermine efforts to end AIDS by 2030. We examined socioeconomic inequalities in the 90-90-90 target among people living with HIV (PLHIV) -men (MLHIV), women (WLHIV) and adolescents (ALHIV). We analysed the available Population HIV Impact Assessment (PHIA) survey data for each of the 12 sub-Saharan African countries, collected between 2015 and 2018 to estimate the attainment of each step of the 90-90-90 target by wealth quintiles. We constructed concentration curves, computed concentration indices (CIX) -a negative (positive) CIX indicated pro-poor (pro-rich) inequalities- and identified factors associated with, and contributing to inequality. Socioeconomic inequalities in achieving the 90-90-90 target components among PLHIV were noted in 11 of the 12 countries surveyed: not in Rwanda. Awareness of HIV positive status was pro-rich in 5/12 countries (Côte d'Ivoire, Tanzania, Uganda, Malawi, and Zambia) ranging from CIX=0·085 (<i>p<</i> 0·05) in Tanzania for PLHIV, to CIX = 0·378 (<i>p<</i>0·1) in Côte d'Ivoire for ALHIV. It was pro-poor in 5/12 countries (Côte d'Ivoire, Ethiopia, Malawi, Namibia and Eswatini), ranging from CIX = -0·076 (<i>p<</i>0·05) for PLHIV in Eswatini, and CIX = -0·192 (<i>p<</i>0·05) for WLHIV in Ethiopia. Inequalities in accessing ART were pro-rich in 5/12 countries (Cameroun, Tanzania, Uganda, Malawi and Zambia) ranging from CIX=0·101 (<i>p<</i>0·05) among PLHIV in Zambia to CIX=0·774 (<i>p<</i>0·1) among ALHIV in Cameroun and pro-poor in 4/12 countries (Tanzania, Zimbabwe, Lesotho and Eswatini), ranging from CIX = -0·072 (<i>p<</i>0·1) among PLHIV in Zimbabwe to CIX = -0·203 (<i>p<</i>0·05) among WLHIV in Tanzania. Inequalities in HIV viral load suppression were pro-rich in 3/12 countries (Ethiopia, Uganda, and Lesotho), ranging from CIX = 0·089 (<i>p<</i> 0·1) among PLHIV in Uganda to CIX = 0·275 (<i>p<</i>0·01) among WLHIV in Ethiopia. Three countries (Tanzania CIX = 0·069 (<i>p<</i> 0·5), Uganda CIX = 0·077 (<i>p<</i> 0·1), and Zambia CIX = 0·116 (<i>p<</i> 0·1)) reported pro-rich and three countries (Côte d'Ivoire CIX = -0·125 (<i>p<</i> 0·1), Namibia CIX = -0·076 (<i>p<</i> 0·05), and Eswatini CIX = -0·050 (<i>p<</i> 0·05) pro-poor inequalities for the cumulative CIX for HIV viral load suppression. The decomposition analysis showed that age, rural-urban residence, education, and wealth were associated with and contributed the most to inequalities observed in achieving the 90-90-90 target. Some PLHIV in 11 of 12 countries were not receiving life-saving HIV testing, treatment, or achieving HIV viral load suppression due to socioeconomic inequalities. Socioeconomic factors were associated with and explained the inequalities observed in the 90-90-90 target among PLHIV. Governments should scale up equitable 95-95-95 target interventions, prioritizing the reduction of age, rural-urban, education and wealth-related inequalities. Research is needed to understand interventions to reduce socioeconomic inequities in achieving the 95-95-95 target. This study was supported by the Swiss National Science Foundation (grant 202660).