Impact of interventions targeting unhealthy alcohol use in Kenya on HIV transmission and AIDS-related deaths

Abstract

Background: HIV remains a major cause of preventable morbidity and mortality in Kenya. The effects of behaviors that accompany unhealthy alcohol consumption are a pervasive risk factor for HIV transmission and progression. Our objective was to estimate the portion of HIV infections attributable to unhealthy alcohol use and to evaluate the impact of hypothetical interventions directed at unhealthy alcohol use on HIV infections and deaths.

Methods: We estimated outcomes over a time horizon of 20 years using a computer simulation of the Kenyan population. This computer simulation integrates a compartmental model of HIV transmission with a mechanistic model of HIV progression that was previously validated in sub-Saharan Africa. Integration of the transmission and progression models allows simultaneous consideration of alcohol's effects on HIV transmission and progression (e.g., lowering antiretroviral adherence may increase transmission risk by elevating viral load, and may simultaneously increase progression by increasing the likelihood of AIDS). The simulation considers important aspects of heterogeneous sexual mixing patterns, including assortativeness of partners by age and activity level, age-discordant relationships, and high activity subgroups. Outcomes included number of new HIV infections, number of AIDS deaths, and infectivity (number of new infections per infected person per year).

Results: Our model estimated that the effects of behaviors accompanying unhealthy alcohol consumption are responsible for 13.0% of new HIV infections in Kenya. An alcohol intervention with effectiveness similar to that observed in a published randomized controlled trial of a cognitive-behavioral therapy-based intervention in Kenya (45% reduction in unhealthy alcohol consumption) could prevent nearly half of these infections, reducing their number by 69,858 and reducing AIDS deaths by 17,824 over 20 years. Estimates were sensitive to assumptions with respect to the magnitude of alcohol's underlying effects on condom use, antiretroviral therapy adherence, and sexually transmitted infection prevalence.

Conclusions: A substantial number of new HIV infections in Kenya are attributable to unhealthy alcohol use. An alcohol intervention with the effectiveness observed in a published randomized controlled trial has the potential to reduce infections over 20 years by nearly 5% and avert nearly 18,000 deaths related to HIV.

Keywords: HIV Prevention; HIV/AIDS; Sub-Saharan Africa; Unhealthy Alcohol Use.

Fig 1

Schematic of HIV computer simulation. The transmission module and the progression module pass information back and forth, so that interventions that directly impact transmission risk may indirectly impact progression risk, and interventions that directly impact progression risk may indirectly impact transmission risk. Additionally, there are important simulation features that are not depicted in the diagram. The probability of transmission is higher from men to women, and lower from women to men. Additionally, mixing may be asymmetric by age (greater from older men to younger women; lower from younger men to older women). Although similarly sized cubes are used to designate different states of the simulation, this is not meant to suggest that the number of individuals in each state is similar: the proportion may vary from state to state, and may also vary between corresponding states of men and women.

Fig 2

Calibration of East Africa HIV simulation model. (A) Model calibration of HIV prevalence over time. The dotted line shows model projections and the solid line shows the HIV middle estimate from published literature (WHO, 2008). The long dashed line shows a high estimate and the short dashed line shows a low estimate from the literature (WHO, 2008). (B) Model calibration of people living with HIV (PLWHIV) over time. The dotted line shows model projections and the solid line shows the middle estimated number of PLWHIV from published literature (WHO, 2008). The long dashed line shows a high estimate and the short dashed line shows a low estimate from the literature (WHO, 2008). (C) Model calibration of AIDS-related deaths over time. The dotted line shows model projections and the solid line shows the middle estimated AIDS-related deaths from published literature (WHO, 2008). The long dashed line shows a high estimate and the short dashed line shows a low estimate from the literature (WHO, 2008).