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

Elsevier, eClinicalMedicine, Volume 53, November 2022
Chipanta D., Amo-Agyei S., Giovenco D., Estill J., Keiser O.

Background: 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). Methods: 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. Findings: 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 (p< 0·05) in Tanzania for PLHIV, to CIX = 0·378 (p<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 (p<0·05) for PLHIV in Eswatini, and CIX = -0·192 (p<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 (p<0·05) among PLHIV in Zambia to CIX=0·774 (p<0·1) among ALHIV in Cameroun and pro-poor in 4/12 countries (Tanzania, Zimbabwe, Lesotho and Eswatini), ranging from CIX = -0·072 (p<0·1) among PLHIV in Zimbabwe to CIX = -0·203 (p<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 (p< 0·1) among PLHIV in Uganda to CIX = 0·275 (p<0·01) among WLHIV in Ethiopia. Three countries (Tanzania CIX = 0·069 (p< 0·5), Uganda CIX = 0·077 (p< 0·1), and Zambia CIX = 0·116 (p< 0·1)) reported pro-rich and three countries (Côte d'Ivoire CIX = -0·125 (p< 0·1), Namibia CIX = -0·076 (p< 0·05), and Eswatini CIX = -0·050 (p< 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. Interpretation: 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. Funding: This study was supported by the Swiss National Science Foundation (grant 202660).