While the risk-specific incidence estimates from the National-Model were within the range of NHSS estimates over the period 2011 to 2018 (as mentioned in Model Verification), as expected from the design of the scenarios, the fit of incidence estimates from the Jurisdictional-Model varied by assumptions in jurisdictional-mixing and heterogeneity (Figure 2). Specifically, for years 2018 and 2019, risk-group specific incidence (Figure 2) and total incidence (Figure 3) estimated by the Jurisdictional-Model were sensitive to jurisdictional-mixing (comparing no-mixing scenarios S1, S5, S9 and S13, with Level-1-mixing scenarios S2, S6, S10, and S14, Level-2-mixing scenarios S3, S7, S11, and S15, and Level-3-mixing scenarios S4, S8, S12, and S16).
We did not attempt to calibrate behavioral data to improve the fit for each scenario as our objective is to test the sensitivity of jurisdictional-mixing and heterogeneity in care and demographics while keeping all else fixed. Further, the Jurisdictional-Model excluded some counties and states due to data suppression from small data. However, the magnitude of the estimates are close to the national ranges, providing verification that the Jurisdictional-Model, which simulated local HIV epidemics in 96 jurisdictions can collectively generate results close to the overall national estimates.
In baseline-intervention scenarios, incidence projections for the period 2018 to 2030 were sensitive to jurisdictional-mixing, both when assuming jurisdictional-homogeneity in care (S1 compared to S2, S3 and S4) and jurisdictional-heterogeneity in care (S9 compared to S10, S11, and S12) (Figure 2), but more so in the former than the latter as seen by the percent change in incidence (Table 2). Compared to S9, the aggregated national incidence in S10, S11, and S12 changed by 2–2%, 7–5%, and 8–9%, respectively, whereas, compared to S1, the aggregated national incidence in S2, S3, and S4 changed by 7–11%, 24–22%, and 24–26%, respectively, the range corresponding to years 2018 to 2030 (see ‘All’ risk-group ‘National’ in Table 2).
In EHE-plan-intervention scenarios, in 2018, the percent change in incidence in mixing compared to no-mixing were similar to that in baseline-intervention scenarios above, which is expected as they start at the same baseline in 2018. However, as incidence decreased over the period 2019 to 2030 from scale-up of care, the differences diminished (Figure 3, Table 3). Compared to S13, the aggregated national incidence in S14, S15, and S16 changed by 2% to -1%, 7% to -3%, and 8–1%, respectively, and compared to S5, the aggregated national incidence in S6, S7, and S8 changed by 7–4%, 24–6%, and 24–10%, respectively, the range corresponding to years 2018 and 2030 (see ‘All’ risk-group ‘National’ in Table 3). While the care metrics in S13 to S16 were estimated in the Jurisdictional-model during the simulation and thus varied by scenario and jurisdiction, the care metrics in S5 to S8 were extracted from the National-model and thus were constant across scenarios and jurisdictions. Therefore, diminishing differences in both sets of scenarios suggest that, while incidence is sensitive to jurisdictional-mixing when incidence was high, as incidence decreases, the sensitivity of mixing diminishes.
The differences in aggregated national incidence estimates between no-mixing and different levels of mixing assumptions observed in year 2018 (Figure 3, Tables 2 and 3) predominantly resulted from the non-EHE jurisdictions (see Figure 4, summarized in Tables 2 and 3). When assuming jurisdictional-homogeneity in care, compared to no-mixing S1, incidence in S2, S3, and S4 changed by 19–28%, 67–55%, and 60–60%, respectively, for non-EHE jurisdictions (see ‘All’ risk-group “Non-EHE” in Table 2), whereas, it changed by -3% to -4%, -10% to -8%, and -5% to -4%, respectively, for EHE jurisdictions (see ‘All’ risk-group “EHE” in Table 2), the range corresponding to years 2018 to 2030. Similarly, when assuming jurisdictional-heterogeneity in care, compared to no-mixing S9, incidence in S10, S11, and S12 changed by 5–7%, 18–15%, and 19–21%, respectively, for non-EHE jurisdictions (see ‘All’ risk-group “Non-EHE” in Table 2), whereas it changed by -1% to -1%, -2% to -2%, and 0–0%, respectively, for EHE jurisdictions (see ‘All’ risk-group “EHE” in Table 2).
In baseline year, 2018, though overall differences in incidence between mixing assumptions were minimal when assuming heterogeneity in care, i.e., differences between scenarios S9 to S12 (Table 2) were minimal and between S13 to S16 were minimal (Table 3), the differences at the individual jurisdictions varied over a wide range. Taking differences in incidence within each jurisdiction, compared to S13, incidence in S14, S15, and S16 changed by -8–30%, -31–109%, and -27–94%, respectively, the range corresponds to data across individual jurisdictions (see ‘All’ risk-group “National” in Table 4). Further, taking only EHE jurisdictions, compared to S13, incidence in S14, S15, and S16, changed by -8–11%, -31–39%, and -27–46%, respectively (see ‘All’ risk-group “EHE” Table 4 and Figure 5a). Considering only non-EHE jurisdictions, compared to Scenario 13, incidence in S14, S15, and S16, changed by -5–30%, -18–109%, and -11–94%, respectively (see ‘All’ risk-group “Non-EHE” in Table 4, and Figure 5b). Differences in risk-group specific incidences for S13 compared to S14, S15, and S16, for EHE and non-EHE jurisdictions had similar observations as above (Table 4, and Figures A2a and A2b for HM, A3a and A3b for HF, and A4a and A4b for MSM in Appendix). Scenarios S9 to S12 would have same observations as above for year 2018, as they start at the same baseline values as S13 to S16, respectively.
A consequence of the differences in the jurisdictional-level incidence estimates is that the jurisdiction-level decisions inferred from the model would vary based on our mixing assumption. We summarize HIV-test intervals across jurisdictions into two cohorts: interval <2 years, and interval between 2-4 years (Table 5). When test interval was <2 years, compared to S13, test intervals in S14, S15, and S16, changed by -23–15%, -44–53%, and -45–48%, respectively, the range corresponding to the minimum and maximum changes over all years, risk-groups, and jurisdictions (see ‘All’ risk group “National” in Table 5). Representing these in test intervals, suppose S13 on average suggests testing every 1 year, S14, S15, and S16, would suggest testing every 0.8 to 1.2 years, 0.6 to 1.5 years, and 0.6 to 1.5 years, respectively. When test interval was 2-4 years, compared to S13, test intervals in S14, S15, and S16, changed by -14–15%, -33–60%, and -28–37%, respectively, the range is the minimum and maximum changes over all years, risk-groups, and jurisdictions (see ‘All’ risk group “National” in Table 5). Representing these in test intervals, suppose S13 on average suggests testing every 3 years, S14, S15, and S16, would suggest testing every 2.6 to 3.5 years, 2 to 4.8 years, and 2.2 to 4.1 years, respectively. These changes in testing intervals from changes in mixing assumptions were similar in both EHE and non-EHE jurisdictions.
The estimated levels of retention-in-care were similar across S13 to S16, and high (ranging from 93.5–100%), suggesting the need for highly effective retention-in-care programs to achieve the EHE targets.
For EHE jurisdictions, the cumulative reduction in incidence (over the period 2018 to 2030) in EHE scenarios compared to baseline scenarios were similar across jurisdictional-mixing and jurisdictional-heterogeneity assumptions (Table 6). However, for non-EHE jurisdictions, reduction in incidence were similar across jurisdictional-heterogeneity assumptions but different across jurisdictional-mixing assumptions. In non-EHE jurisdictions, while the expected incidence reduction in no-mixing assumption was 5% when assuming jurisdictional-homogeneity in care (and 9% when assuming jurisdictional-heterogeneity in care), the incidence reductions in level-1 mixing was 14% (and 15%), level-2 mixing was 24% (and 24%), and level-3 mixing was 23% (and 25%) (Table 6).
Compared to incidence in 2019, none of the EHE-plan-intervention scenarios (S5 to S8 or S9 to S12) could reduce incidence by 75% by 2025 or 90% by 2030, as aimed for in the EHE plan. When considering jurisdictional-heterogeneity (S9 to S12), aggregated incidence in EHE jurisdictions in 2018 were similar or higher than aggregated incidence in non-EHE jurisdictions. With intervening in EHE jurisdictions as per the EHE-plan, aggregated incidence in EHE jurisdictions significantly reduced over the period 2019 to 2025 (~43% in S16). Because of continuation of the baseline-intervention up to 2025 in non-EHE jurisdictions, its aggregated incidence change over the period 2019 to 2025 was minimal, its incidence surpassing that in the EHE jurisdictions by the end of 2024. Over the period 2019 to 2025, though non-EHE jurisdictions had some reductions in incidence in scenarios with mixing (~11% in S16) benefiting from the interventions in EHE, incidence in EHE jurisdictions increased because of the mixing, thus negating the overall benefits. As a result, by the end of 2025, the reduction in national aggregated reduction in incidence was 28% (in S16). The reduction in national aggregated incidence by 2030 compared to 2019 was about 58% (in S16). Note that the EHE plan is to first focus on only EHE jurisdictions for the first phase (2019 to 2025) and then non-EHE jurisdictions in second phase (2025 to 2030), i.e., scaling-up interventions over period 2019 to 2030 for EHE jurisdictions and period 2025 to 2030 for non-EHE jurisdictions. Instead, if we scale-up interventions over period 2019 to 2025 in all jurisdictions, both EHE and non-EHE, and keep it constant thereafter up to 2030, we would achieve a reduction in national incidence of about 52% by 2025 and 67% by 2030 (see Figure A5 in Appendix).
The corresponding changes in prevalence (number of people with HIV) estimates over the period 2017 to 2030 are presented in Figure 6 along with the national surveillance estimates (‘NHSS-National’) for years 2017-2019 and sum of the 96 jurisdictions for 2017 (‘NHSS-Sum of jurisdictions’). Simulated estimates of prevalence match close to the surveillance estimates for years 2017 to 2019, however, following from the changes in incidence over time (Figure 2), prevalence were most sensitive to jurisdictional-mixing in Scenarios S1 to S4 which assumed homogeneity in care and baseline interventions.