Objective To examine the relation between plasma HIV-1 RNA concentrations in the community and HIV incidence among injecting drug users. Design Prospective cohort study. Setting Inner city community in Vancouver, Canada. Participants Injecting drug users, with and without HIV, followed up every six months between 1 May 1996 and 30 June 2007. Main outcome measures Estimated community plasma HIV-1 RNA in the six months before each HIV negative participant’s follow-up visit. Associated HIV incidence. Results Among 622 injecting drug users with HIV, 12 435 measurements of plasma HIV-1 RNA were obtained. Among 1429 injecting drug users without HIV, there were 155 HIV seroconversions, resulting in an incidence density of 2.49 (95% confidence interval 2.09 to 2.88) per 100 person years. In a Cox model that adjusted for unsafe sexual behaviours and sharing used syringes, the estimated community plasma HIV-1 RNA concentration remained independently associated with the time to HIV seroconversion (hazard ratio 3.32 (1.82 to 6.08, P<0.001), per log₁₀ increase). When the follow-up period was limited to observations after 1 January 1988 (when the median plasma HIV RNA concentration was <20 000 copies/ml), the median viral load was no longer statistically associated with HIV incidence (1.70 (0.79 to 3.67, P=0.175), per log₁₀ increase). Conclusions A longitudinal measure of community plasma HIV-1 RNA concentration was correlated with the community HIV incidence rate and predicted HIV incidence independent of unsafe sexual behaviours and sharing used syringes. If these findings are confirmed, they could help to inform both HIV prevention and treatment interventions.

Since 1999, more than 700 000 people in the US have died of drug overdose, including more than 350 000 people who have died of opioid-involved overdose.^1-3 Based on current trends, it is projected that 700 000 more individuals will die of opioid-involved overdose in the next decade.⁴ The current criterion standard for treatment of opioid use disorder (OUD), as approved by the Food and Drug Administration, includes methadone, buprenorphine, and naltrexone.⁵ In a 2018 retrospective cohort study in Massachusetts,⁶ all-cause and opioid-related mortality rates were decreased by half among adults who received methadone or buprenorphine within 1 year after a nonfatal opioid-involved overdose, underscoring the strong protective benefits of these medications. However, few individuals at risk for opioid-related harms receive these medications in a timely fashion.⁷

Research from JAMA — Highly Active Antiretroviral Therapy and Survival in HIV-Infected Injection Drug Users

Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry.