Ending the HIV Epidemic Among Persons Who Inject Drugs: A Cost-Effectiveness Analysis in Six US Cities

A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States

BACKGROUND

Fatal drug overdoses and serious injection-related infections are rising in the US. Multiple concurrent infections in people who inject drugs (PWID) exacerbate poor health outcomes, but little is known about how the synergy among infections compounds clinical outcomes and costs. Injection drug use (IDU) converges multiple epidemics into a syndemic in the US, including opioid use and HIV. Estimated rates of new injection-related infections in the US are limited due to widely varying estimates of the number of PWID in the US, and in the absence of clinical trials and nationally representative longitudinal observational studies of PWID, simulation models provide important insights to policymakers for informed decisions.

METHODS

We developed and validated a MultimorbiditY model to Reduce Infections Associated with Drug use (MYRIAD). This microsimulation model of drug use and associated infections (HIV, hepatitis C virus [HCV], and severe bacterial infections) uses inputs derived from published data to estimate national level trends in the US. We used Latin hypercube sampling to calibrate model output against published data from 2015 to 2019 for fatal opioid overdose rates. We internally validated the model for HIV and HCV incidence and bacterial infection hospitalization rates among PWID. We identified best fitting parameter sets that met pre-established goodness-of-fit targets using the Pearson's chi-square test. We externally validated the model by comparing model output to published fatal opioid overdose rates from 2020.

RESULTS

Out of 100 sample parameter sets for opioid use, the model produced 3 sets with well-fitting results to key calibration targets for fatal opioid overdose rates with Pearson's chi-square test ranging from 1.56E-5 to 2.65E-5, and 2 sets that met validation targets. The model produced well-fitting results within validation targets for HIV and HCV incidence and serious bacterial infection hospitalization rates. From 2015 to 2019, the model estimated 120,000 injection-related overdose deaths, 17,000 new HIV infections, and 144,000 new HCV infections among PWID.

CONCLUSIONS

This multimorbidity microsimulation model, populated with data from national surveillance data and published literature, accurately replicated fatal opioid overdose, incidence of HIV and HCV, and serious bacterial infections hospitalization rates. The MYRIAD model of IDU could be an important tool to assess clinical and economic outcomes related to IDU behavior and infections with serious morbidity and mortality for PWID.

Evaluating the sensitivity of jurisdictional heterogeneity and jurisdictional mixing in national level HIV prevention analyses: context of the U.S. ending the HIV epidemic plan

Background

The U.S. Ending the HIV epidemic (EHE) plan aims to reduce annual HIV incidence by 90% by 2030, by first focusing interventions on 57 regions (EHE jurisdictions) that contributed to more than 50% of annual HIV diagnoses. Mathematical models that project HIV incidence evaluate the impact of interventions and inform intervention decisions. However, current models are either national level, which do not consider jurisdictional heterogeneity, or independent jurisdiction-specific, which do not consider cross jurisdictional interactions. Data suggests that a significant proportion of persons have sexual partnerships outside their own jurisdiction. However, the sensitivity of these jurisdictional interactions on model outcomes and intervention decisions hasn’t been studied.

Methods

We developed an ordinary differential equations based compartmental model to generate national-level projections of HIV in the U.S., through dynamic simulations of 96 epidemiological sub-models representing 54 EHE and 42 non-EHE jurisdictions. A Bernoulli equation modeled HIV-transmissions using a mixing matrix to simulate sexual partnerships within and outside jurisdictions. To evaluate sensitivity of jurisdictional interactions on model outputs, we analyzed 16 scenarios, combinations of a) proportion of sexual partnerships mixing outside jurisdiction: no-mixing, low-level-mixing-within-state, high-level-mixing-within-state, or high-level-mixing-within-and-outside-state; b) jurisdictional heterogeneity in care and demographics: homogenous or heterogeneous; and c) intervention assumptions for 2019–2030: baseline or EHE-plan (diagnose, treat, and prevent).

Results

Change in incidence in mixing compared to no-mixing scenarios varied by EHE and non-EHE jurisdictions and aggregation-level. When assuming jurisdictional heterogeneity and baseline-intervention, the change in aggregated incidence ranged from − 2 to 0% for EHE and 5 to 21% for non-EHE, but within each jurisdiction it ranged from − 31 to 46% for EHE and − 18 to 109% for non-EHE. Thus, incidence estimates were sensitive to jurisdictional mixing more at the jurisdictional level. As a result, jurisdiction-specific HIV-testing intervals inferred from the model to achieve the EHE-plan were also sensitive, e.g., when no-mixing scenarios suggested testing every 1 year (or 3 years), the three mixing-levels suggested testing every 0.8 to 1.2 years, 0.6 to 1.5 years, and 0.6 to 1.5 years, respectively (or 2.6 to 3.5 years, 2 to 4.8 years, and 2.2 to 4.1 years, respectively). Similar patterns were observed when assuming jurisdictional homogeneity, however, change in incidence in mixing compared to no-mixing scenarios were high even in aggregated incidence.

Conclusions

Accounting jurisdictional mixing and heterogeneity could improve model-based analyses.