High CD4 counts associated with better economic outcomes for HIV-positive adults and their HIV-negative household members in the SEARCH Trial

Abstract

Background: Country decisions to scale-up "test and treat" approaches for HIV depend on consideration of both the health and economic consequences of such investments. Evidence about economic impacts of expanded antiretroviral therapy (ART) provision is particularly relevant for decisions regarding foreign assistance levels for HIV/AIDS programs. We used baseline data from the Sustainable East Africa Research in Community Health (SEARCH) cluster randomized controlled trial in Kenya and Uganda to examine the association between HIV status, CD4+ T-cell counts, viral suppression, and multiple indicators of economic well-being.

Methods and findings: Socio-economic surveys were conducted in households with HIV-positive and HIV-negative adults sampled after a census of 32 communities participating in the SEARCH trial (NCT01864603). Data were obtained for 11,500 individuals from 5,884 households in study communities. Participants were stratified based on their own HIV status as well as CD4 counts and viral suppression status if they were HIV-positive. HIV-negative participants residing in households with no HIV-positive adults were considered separately from HIV-negative participants residing in households with ≥1 HIV-positive adult. Generalized estimating equation models were used to examine the relationship between HIV status, CD4 counts, ART, viral suppression, and outcomes of employment, self-reported illness, lost time from usual activities due to illness, healthcare utilization, health expenditures, and hospitalizations. In all models, HIV-negative participants in households with no HIV-positive persons were the reference group. There was no significant difference in the probability of being employed between HIV-positive participants with CD4>500 and the reference group of HIV-negative participants residing in households with no HIV-positive adults (marginal effect, ME, 1.49 percentage points; 95% confidence interval, CI, -1.09, 4.08). However, HIV-positive participants with CD4 351-500 were less likely to be employed than the reference group (ME -4.50, 95% CI -7.99, -1.01), as were HIV-positive participants with CD4 ≤350 (ME -7.41, 95% CI -10.96, -3.85). Similarly, there was no significant difference in employment likelihood between HIV-negative participants who resided in households with a CD4>500 HIV-positive person and the reference group (ME -1.78, 95% CI -5.16, 1.59). HIV-negative participants residing with an HIV-positive person with CD4 351-500, however, were less likely to be employed than the reference group (ME -7.03, 95% CI -11.49, -2.57), as were people residing with a household member with CD4 ≤350 (ME -6.28, 95% CI -10.76, -1.80). HIV-positive participants in all CD4 categories were more likely to have lost time from usual activities due to illness and have incurred healthcare expenditures. Those with CD4>500 had better economic outcomes than those with CD4 351-500, even among those not virally suppressed (p = 0.004) and not on ART (p = 0.01).

Conclusions: Data from a large population-representative sample of households in east Africa showed a strong association between the health of HIV-positive persons and economic outcomes. The findings suggest there may be economic benefits associated with maintaining high CD4 counts, both for HIV-positive persons and their HIV-negative household members. The association of high CD4 counts with improved outcomes is consistent with the hypothesis that early ART initiation can avert declines in employment and other economic outcomes. Prospective longitudinal evaluation is needed to assess the causal impact of early ART initiation on economic functioning of households.

Fig 1. Local polynomial graphs of employment and healthcare outcomes and CD4 counts among HIV-positive adults.

Fig 2. Association between CD4 count and socio-economic outcomes, based on ART status and viral load.

Abbreviations: ART, antiretroviral therapy; CI, confidence interval; DVL, detectable viral load, HIV+, HIV-positive; HIV-, HIV-negative; HH, household; ME, marginal effect. Notes: Marginal effects, in percentage points, with 95% confidence intervals around them, were calculated after fitting GEE regression models for working age adults (18–65 years old), with binomial distribution, logistic link, and exchangeable correlation at the household level. Reference group were HIV-negative participants in households that did not have any HIV-positive adults. Models controlled for education level, age, age-squared, gender, marital status, wealth index, number of children in household, number of household members, and community indicators.