Poverty as Barrier to Access to Antiretroviral Therapy in Kenya Markus Haacker Harvard School of Public Health and University College London with Charles Birungi, UNAIDS and University College London Read full paper in June 2018 issue of African Journal of AIDS Research. IAEN Conference, Amsterdam July 21, 2018

Kenya context Paper in part is motivated by work on the Kenya HIV “investment case.” Strong government commitment to improving social equity and health outcomes. High degree of heterogeneity in social and economic indicators, and in state of HIV epidemic, across Kenya. Substantial work on refining HIV prevention policy to align with situation across the 47 counties.

Global context Paper also reflects concerns on state of global policy discourse on social barriers to treatment: UNAIDS Gap report (2014): Substantially discusses barriers to treatment access for only 1 of 12 adult populations left behind (people displaced or affected by conflict). 2015 UNAIDS-Lancet commission discusses socio-economic factors almost exclusively with regard to HIV prevention. Global AIDS Update (2016) discusses sub-national data only with regard to HIV prevention and populations at high risk of HIV infection.

Motives for considering subnational and socio-economic differences in treatment access in policy design Marginal gains from extending treatment access in underserved populations plausibly greater than in populations where most pressing needs are met. In particular in transition to test-and-treat (where much of increase in treatment headcount may be driven by early access. But this does not necessarily apply to cost-effectiveness. Aligning HIV policies with national health and social policy objectives, thus building support for HIV program.

Drawbacks of and limitations to engaging with socio-economic barriers to treatment access Programmatic value of clear indicators of coverage and progress in scaling-up. Avoid getting bogged down in wider health system reform issues and socio-economic barriers in treatment access overall. Empirical analysis of determinants of treatment coverage difficult, requires consistent data on people receiving treatment and corresponding number of PLWH. Most evidence indirect, based on patient data (except for regional roll-out of South African treatment program).

Methods Simple cross-sectional analysis across 47 Kenyan counties. Fairly small sample for meaningful econometric analysis. Regionally aggregated data weak indicators for socio-economic barriers. High degree of heterogeneity (economic aspects, state of epidemic, treatment access, etc.) Dependent variable ART coverage, regressors include HIV prevalence, health capacities, income, poverty , education, urbanisation. Use instruments for HIV prevalence to address measurement error and reverse causality issues: Male circumcision, proximity to Lake Victoria.

Summary of data

Determinants of treatment access

Treatment access and poverty (reduced form)

Interpretation Poverty comes out as most important determinant of treatment access. (Consistently significant, together with an indicator for health sector capacities which in turn is correlated with poverty.) While treatment coverage has increased between 2012 and 2015, the gap has not changed in relative terms, and widened absolutely. Kenya example shows that poverty-related gaps are potentially important in designing effective policies on treatment access Alignment with other social and economic development objectives? Effects of increasing treatment coverage different across counties?

Next steps? This is useful: Replicate/update analysis, apply to other countries, benefitting from increased availability of sub-national data. Weak power of cross-sectional regional data: Develop analysis on population surveys capturing HIV status and treatment access. Think through policy implications More differentiated interpretation of data on treatment access? Identifying social barriers to access, differentiate strategies on expanding access accordingly (modes of service delivery, priority-setting?).