Overlapping substance using high-risk groups and infectious diseases: how dynamic modelling can evaluate risk and target HIV prevention

Abstract

It’s difficult to develop infectious disease interventions (such as for HIV) for overlapping high-risk populations such as people who inject drugs, men who have sex with men, and female sex workers, where there are multiple transmission routes. Recent advances in dynamic modelling of coinfection epidemics could disentangle the contributions of different risk groups and behaviours to help develop more efficient and effective prevention interventions.

Keywords: HIV; injecting drug use; mathematical modeling; prevention.

Figure 1. Model estimated percentage of HIV infections due to injecting risks (with remainder due to sexual risks), depending on the HCV-HIV coinfection prevalence amongst PWID in a given setting

Median is middle line, 25%/75% percentiles are limits of boxes and 10%/90% percentiles are whiskers. Figure reproduced from Vickerman et al.(10).

Figure 2. Overlapping risk populations and HIV epidemics in Tijuana, Mexico from 2003–2015

HIV estimates were pooled from multiple studies(14). PWID: people who inject drugs. FSW: female sex workers. MSM: men who have sex with men. MSM-IDU: men who have sex with men who inject drugs. IDU-client: Male clients of female sex workers who inject drugs. MSM-client: Male clients of female sex workers who also have sex with men.