Statistical adjustments of sample representation in community-level estimates of COVID-19 transmission and immunity

社区层面 COVID-19 传播和免疫力估计中样本代表性的统计调整

• 批准号: 10600097
• 负责人: Yajuan Si
• 金额: $ 56.21万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10600097
• 关键词: 2019-nCoV; American; Antibodies; Area; Attention; Behavior; Black race; COVID testing; COVID-19 pandemic; COVID-19 patient; Calibration; Communities; Community Hospitals; Community Surveys; Data; Data Analyses; Data Collection; Data Set; Data Sources; Disease; Disparity; Electronic Health Record; Epidemic; Ethnic Origin; Foundations; Future; Geography; Government; Guidelines; Health; Health behavior and outcomes; Healthcare; Hospitals; Immunity; Incidence; Indiana; Individual; Inequality; Intervention; Investigation; Knowledge; Link; Locales; Measures; Methodology; Methods; Monitor; Outcome; Pathway interactions; Patients; Phase; Policies; Policy Maker; Population; Prevalence; Procedures; Proxy; Public Health; Race; Reporting; Reproducibility; Research; Rural; SARS-CoV-2 exposure; SARS-CoV-2 immunity; SARS-CoV-2 transmission; Sampling; Selection Bias; Sensitivity and Specificity; Seroprevalences; Statistical Data Interpretation; Statistical Methods; Stratification; Subgroup; System; Testing; Time; Vaccination; Vaccines; Variant; Viral; Virus Diseases; Vulnerable Populations; Work; acquired immunity; clinical predictive model; clinical predictors; comorbidity; coronavirus disease; data harmonization; data integration; demographics; future epidemic; health care service utilization; health disparity; member; pandemic disease; response; rural setting; screening; seropositive; sex; social; social determinants; social disparities; sociodemographics; socioeconomics; suburb; trend; vaccine access; viral transmission; web based interface; web interface

Abstract Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and vaccination rates in the community. The selection bias of these test data questions their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. Publicly available vaccination data are frequently cited as a proxy for population immunity, but this metric ignores the effects of naturally-acquired immunity. The health disparities concerning asymptomatic and symptomatic patients are not yet studied. The proposal develops a valid metric to estimate the true viral incidence and naturally/vaccine-acquired immunity prevalence in the community, examine the health disparities and social inequality, and monitor the epidemic over time as an operational surveillance system. The approach collects routine testing data on SARS-CoV-2 exposure and antibody seropositivity among patients in a hospital system and performs statistical adjustments of sample representation using multilevel regression and poststratification (MRP), which adjusts for measured differences between the sample and population and also yields stable small area estimates. The data collection and analysis procedure can provide information to entire communities with generalizability and focus on burdens within specific demographics, with close attention to vulnerable populations on disparities across health outcomes, social determinants, and behaviors. In particular, the research will yield group-specific estimates of disparities with respect to asymptomatic and symptomatic patients and how these discrepancies may impact the socio-demographically dependent spread of disease and its subsequent treatment. The MRP adjustment will be made publicly accessible via a web interface and promote broad investigations with integrated data sources toward a national study.

抽象的 在整个COVID-19-19大流行中，政府政策和医疗保健实施回应一直是 在社区中报告的阳性率和疫苗接种率的指导下。这些测试的选择偏差 数据质疑其有效性是社区中实际病毒事件的措施，并作为预测因素 临床负担。公开可用的暂停数据经常被认为是人口免疫的代理，但这是 公制忽略了自然获得免疫的影响。无症状的健康差异 有症状的患者尚未研究。该提案开发了有效的指标来估计真正的病毒事件 自然/疫苗可获得的免疫力在社区中患病率，检查健康分布和社会 不平等，并随着时间的流逝，作为操作监视系统的流行病。方法收集 关于医院系统中患者的SARS-COV-2暴露和抗体血清阳性的常规测试数据 并使用多级回归和术后进行样本表示的统计调整 （MRP），它可以调整样本和人口之间的差异，并且产生稳定的小 区域估计。数据收集和分析程序可以通过 普遍性和专注于特定人群中的伯伦斯（Burnens），密切关注脆弱的人群 关于健康结果，社会决定者和行为的差异。特别是，研究将产生 相对于无症状和有症状的患者的分布分布的特定估计 差异可能会影响疾病的社会人口统计学依赖性及其随后的治疗。 MRP调整将通过网络界面公开访问，并通过 全国研究的综合数据源。