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.

摘要 在整个COVID-19大流行期间，政府政策和医疗保健实施应对措施 以社区报告的阳性率和疫苗接种率为指导。这些测验的选择偏倚 数据质疑其作为社区实际病毒发病率的衡量标准和 临床负担公众可获得的疫苗接种数据经常被引用作为人群免疫力的代表， 度量忽略了自然获得性免疫的影响。关于无症状和 尚未对有症状的患者进行研究。该提案开发了一个有效的指标来估计真实的病毒发病率 和自然/疫苗获得性免疫流行的社区，审查健康差距和社会 这是一个有效的监测系统，可以长期监测这一流行病。方法收集 医院系统患者SARS-CoV-2暴露和抗体血清阳性的常规检测数据 并使用多级回归和后分层对样本表示进行统计调整 (MRP)，它调整了样本和总体之间的测量差异， 面积估计。数据收集和分析程序可以向整个社区提供信息， 普遍性和侧重于具体人口统计范围内的负担，并密切关注弱势群体 健康结果、社会决定因素和行为之间的差异。特别是，这项研究将产生 无症状和有症状患者的组特异性差异估计，以及这些差异如何 这种差异可能影响到依赖社会人口统计的疾病传播及其随后的治疗。 MRP调整将通过网络界面公开访问，并促进广泛的调查， 整合数据源进行全国性研究。