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. 2022 Sep 17:53:101652.
doi: 10.1016/j.eclinm.2022.101652. eCollection 2022 Nov.

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

Affiliations

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

David Chipanta et al. EClinicalMedicine. .

Abstract

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).

Keywords: 90–90–90 target; 95–95–95 target; Concentration Index; Decomposition approach; Equity; HIV care cascade; Socioeconomic inequalities.

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Conflict of interest statement

David Chipanta works for UNAIDS All authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Distribution of awareness of HIV positive status, access to ART, and HIV viral load suppression by wealth quintile and country (percentage, 95% CI) (PHIA 2015-2018). Q1 – (Poorest wealth quintile), Q2, Q3, Q4, Q5 (Richest wealth quintile) indicate quintiles of wealth distribution in each country. The light blue bars represent HIV-positive status awareness (First 90), the dark blue bars represent access to ART (Second 90), and the darker blue bars represent HIV viral load suppression (Third 90).
Figure 2
Figure 2
Concentration curves for awareness of HIV positive status, access to ART and viral suppression by countries (PHIA 2015-2018). Blue____ Awareness of HIV positive status; red_____Access to ART green____HIV viral load suppression ___Line of equality.

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