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.
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.
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DOI：
10.1186/s12874-022-01756-w
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发表时间：
2022-11-26
影响因子：
4
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中科院分区：
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美国结束艾滋病毒流行 (EHE) 计划的目标是到 2030 年将年度艾滋病毒发病率降低 90%，首先将干预措施集中在 57 个地区（EHE 管辖区），这些地区占年度艾滋病毒诊断的 50% 以上。预测艾滋病毒发病率的数学模型可以评估干预措施的影响并为干预决策提供信息。然而，当前的模型要么是国家层面的，不考虑管辖区异质性，要么是独立管辖区特定的，不考虑跨管辖区的相互作用。数据表明，很大一部分人在自己的管辖范围之外有性伙伴关系。然而，这些管辖权互动对模型结果和干预决策的敏感性尚未得到研究。我们开发了一个基于常微分方程的区室模型，通过对代表 54 个 EHE 和 42 个非 EHE 管辖区的 96 个流行病学子模型进行动态模拟，生成美国国家层面的 HIV 预测。伯努利方程使用混合矩阵模拟艾滋病毒传播，以模拟司法管辖区内外的性伙伴关系。为了评估管辖权互动对模型输出的敏感性，我们分析了 16 种情景的组合，a) 管辖范围外性伴侣混合的比例：无混合、州内低水平混合、州内高水平混合或州内外高水平混合； b) 护理和人口统计方面的辖区异质性：同质或异质； c) 2019-2030 年干预假设：基线或 EHE 计划（诊断、治疗和预防）。与非混合场景相比，混合发生率的变化因 EHE 和非 EHE 管辖区以及聚合级别而异。当假设辖区异质性和基线干预时，EHE的总发病率变化范围为- 2％至0％，非EHE为5％至21％，但在每个辖区内，EHE的总发病率变化范围为- 31％至46％，非EHE的总发病率变化范围为 - 18％至109％。因此，发病率估计对管辖范围内的管辖混合更加敏感。因此，从模型中推断出的实现 EHE 计划的特定管辖区的 HIV 检测间隔也很敏感，例如，当无混合场景建议每 1 年（或 3 年）进行一次检测时，三种混合水平建议分别每 0.8 至 1.2 年、0.6 至 1.5 年和 0.6 至 1.5 年进行检测（或分别为2.6至3.5年、2至4.8年和2.2至4.1年）。当假设管辖权同质性时，观察到类似的模式，但是，即使在聚合发生率中，与非混合情景相比，混合发生率的变化也很高。会计管辖区混合和异质性可以改善基于模型的分析。在线版本包含可在 10.1186/s12874-022-01756-w 获取的补充材料。
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. 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). 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. Accounting jurisdictional mixing and heterogeneity could improve model-based analyses. The online version contains supplementary material available at 10.1186/s12874-022-01756-w.
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DOI：
10.15585/mmwr.mm7047a3
发表时间：
2021-11-26
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
Baugher AR;Trujillo L;Kanny D;Freeman JQ;Hickey T;Sionean C;Respress E;Chapin-Bardales J;Marcus R;Finlayson T;Wejnert C;National HIV Behavioral Surveillance Study Group
通讯作者：
National HIV Behavioral Surveillance Study Group
影响因子：
3.1
作者：
Gesink D;Wang S;Guimond T;Kimura L;Connell J;Salway T;Gilbert M;Mishra S;Tan D;Burchell AN;Brennan DJ;Logie CH;Grace D
通讯作者：
Grace D
影响因子：
39.2
作者：
Fojo AT;Schnure M;Kasaie P;Dowdy DW;Shah M
通讯作者：
Shah M
影响因子：
6.4
作者：
Krebs, Emanuel;Zang, Xiao;Nosyk, Bohdan
通讯作者：
Nosyk, Bohdan
DOI：
10.1016/s2352-3018(21)00147-8
发表时间：
2021-09
期刊：
The lancet. HIV
影响因子：
--
作者：
Quan AML;Mah C;Krebs E;Zang X;Chen S;Althoff K;Armstrong W;Behrends CN;Dombrowski JC;Enns E;Feaster DJ;Gebo KA;Goedel WC;Golden M;Marshall BDL;Mehta SH;Pandya A;Schackman BR;Strathdee SA;Sullivan P;Tookes H;Nosyk B;Localized HIV Economic Modeling Study Group
通讯作者：
Localized HIV Economic Modeling Study Group