Factors associated with mortality in HIV-infected people in rural and urban South Africa - Kennedy N. Otwombe; Max Petzold; Tebogo Modisenyane; Neil A. Martinson; Tobias Chirwa (Taylor & Francis Group, 2014)
Background: Factors associated with mortality in HIV-infected people in sub-Saharan Africa are widely reported. However rural-urban disparities and their association with all-cause mortality remain unclear. Furthermore, commonly used classical Cox regression ignores unmeasured variables and frailty. Objective: To incorporate frailty in assessing factors associated with mortality in HIV-infected people in rural and urban South Africa. Design: Using data from a prospective cohort following 6,690 HIV-infected participants from Soweto (urban) and Mpumalanga (rural) enrolled from 2003 to 2010; covariates of mortality were assessed by the integrated nested Laplace approximation method. Results: We enrolled 2,221 (33%) rural and 4,469 (67%) urban participants of whom 1,555 (70%) and 3,480 (78%) were females respectively. Median age (IQR) was 36.4 (31.0-44.1) in rural and 32.7 (28.2-38.1) in the urban participants. The mortality rate per 100 person-years was 11 (9.7-12.5) and 4 (3.6-4.5) in the rural and urban participants, respectively. Compared to those not on HAART, rural participants had a reduced risk of mortality if on HAART for 6-12 (HR: 0.20, 95% CI: 0.10-0.39) and >12 months (HR: 0.10, 95% CI: 0.05-0.18). Relative to those not on HAART, urban participants had a lower risk if on HAART >12 months (HR: 0.35, 95% CI: 0.27-0.46).The frailty variance was significant and >1 in rural participants indicating more heterogeneity. Similarly it was significant but <1 in the urban participants indicating less heterogeneity. Conclusion: The frailty model findings suggest an elevated risk of mortality in rural participants relative to the urban participants potentially due to unmeasured variables that could be biological, socio-economic, or healthcare related. Use of robust methods that optimise data and account for unmeasured variables could be helpful in assessing the effect of unknown risk factors thus improving patient management and care in South Africa and elsewhere.