Community predictors of COVID-19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data.
Community predictors of COVID-19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data.
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DOI：
10.1111/irv.12926
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发表时间：
2022-03
影响因子：
4.4
通讯作者：
Leibler JH
中科院分区：
文献类型：
--
作者：
Spangler KR;Patil P;Peng X;Levy JI;Lane KJ;Tieskens KF;Carnes F;Klevens RM;Erdman EA;Troppy TS;Fabian MP;Leibler JH
鉴于城市和县内存在巨大差异，COVID-19 大流行凸显了有针对性的地方干预措施的必要性。公开的病例数据通常会汇总到市或县一级，以保护患者隐私，但需要更精细的数据来识别可能随时间变化的社区级风险因素并采取行动。马萨诸塞州的个人 COVID-19 病例和死亡率数据被地理编码到居住地址，并汇总为两个时间段：“第一阶段”（2020 年 3 月至 6 月）和“第二阶段”（2020 年 9 月至 2021 年 2 月）。使用地址数据识别与长期护理机构、监狱或无家可归者收容所相关的机构案例并单独建模。人口普查区的社会人口统计和职业预测因素取自 2015-2019 年美国社区调查。我们使用混合效应负二项式回归来估计发病率比（IRR），并考虑城镇层面的空间自相关。在黑人和拉丁裔居民比例较高的人口普查区，病例发生率升高，第一阶段的关联性大于第二阶段。与基本工作人员比例相关的病例发生率在两个阶段也同样升高。死亡率 IRR 与病例 IRR 具有不同的模式，黑人和拉丁裔人口在不同阶段之间的下降幅度较小，而基本工作人员比例在不同阶段之间则有所增加。排除机构案例的死亡率模型与年龄、种族/族裔和基本工人地位之间产生了更强的关联。地理编码的家庭地址数据可以对社区疾病模式进行细致入微的分析，识别高风险亚组，并排除机构病例，以全面反映社区风险。
The COVID‐19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community‐level risk factors that can change over time. Individual COVID‐19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: “Phase 1” (March–June 2020) and “Phase 2” (September 2020 to February 2021). Institutional cases associated with long‐term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015–2019 American Community Survey. We used mixed‐effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town‐level spatial autocorrelation. Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status. Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high‐risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk.
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影响因子：
8.5
作者：
Bauer C;Zhang K;Lee M;Fisher-Hoch S;Guajardo E;McCormick J;de la Cerda I;Fernandez ME;Reininger B
通讯作者：
Reininger B
DOI：
10.15585/mmwr.mm6942e1
发表时间：
2020-10-23
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
Gold JAW;Rossen LM;Ahmad FB;Sutton P;Li Z;Salvatore PP;Coyle JP;DeCuir J;Baack BN;Durant TM;Dominguez KL;Henley SJ;Annor FB;Fuld J;Dee DL;Bhattarai A;Jackson BR
通讯作者：
Jackson BR
DOI：
10.1073/pnas.2020685118
发表时间：
2021-02-23
影响因子：
11.1
作者：
Perry BL;Aronson B;Pescosolido BA
通讯作者：
Pescosolido BA
影响因子：
13.8
作者：
Fisman, David N.;Bogoch, Isaac;Tuite, Ashleigh R.
通讯作者：
Tuite, Ashleigh R.
影响因子：
12.7
作者：
Krieger, N;Chen, JT;Subramanian, SV
通讯作者：
Subramanian, SV