Evaluating the sensitivity of jurisdictional-heterogeneity and jurisdictional-mixing in national-level HIV prevention analyses: Context of the U.S. Ending the HIV Epidemic goal.

Abstract 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 jurisdictions (county or state) (EHE-jurisdictions) that contributed to more than 50% of annual HIV diagnoses. Mathematical models that simulate future HIV incidence projections help 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 significant proportion of persons have sexual-partnerships with persons outside their own jurisdiction. However, the sensitivity of these jurisdictional interactions on model outcomes and intervention decisions have not been studied. Methods: We developed a compartmental simulation of HIV in the U.S., through composition of 57 EHE and 46 non-EHE jurisdictions, with populations interacting across jurisdictions through sexual partnerships. To evaluate sensitivity of jurisdictional interactions on model outputs, we analyzed 16 scenarios, combinations of 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; jurisdictional-heterogeneity in care and demographics: homogenous or heterogeneous; and 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, while the change in aggregated incidence ranged from -2% to 0% for EHE and 5% to 21% for non-EHE, 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 for jurisdictional-mixing and jurisdictional-heterogeneity could help improve model-based analyses.