Dinh Tri, Ross Jordan, James Samantha, Klein Kristin, Chandran A Uma, Larios Oscar, Strong David, Conly John M
Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, Alberta, Canada.
Calgary Zone Public Health Surveillance, Alberta Health Services, Calgary, Alberta, Canada.
J Assoc Med Microbiol Infect Dis Can. 2024 Dec 19;9(4):274-283. doi: 10.3138/jammi-2024-0011. eCollection 2024 Dec.
With initial waves of COVID-19, many public health systems assumed each COVID-19 positive hospitalization was a direct cause from COVID-19 infection. Since January 2022, Alberta Health Services Communicable Disease Control Hospitalization Team (CDC-HT) implemented an admission criteria algorithm to adjudicate COVID-19 as a direct, contributing, or unrelated cause for all COVID-19 admissions in Alberta.
This quality improvement initiative sought to improve the admission algorithm's precision in reporting COVID-19 admissions. Patient hospitalization records from January-February 2022 with a positive COVID-19 test in the last 30 days were proportionally sampled in a geographically stratified manner across Alberta health zones. 261 patient records were sampled and determination of COVID-19 attribution by CDC-HT algorithm was compared to adjudication by a panel of infectious diseases physicians with extensive COVID-19 clinical experience.
Of 261 sampled COVID-19 admissions, blinded physician adjudication determined 39.9% were direct-cause, 17.2% contributing-cause, and 37.6% unrelated-cause. Within the same cohort the CDC-HT admission algorithm adjudicated 42.9% direct-cause, 24.5% contributing-cause, and 30.3% unrelated-cause. Cohen's kappa was 0.475, providing only moderate agreement. The majority of discrepancy was from over-attribution of unrelated hospitalizations as contributing cause. Implementation of this algorithm in Alberta throughout 2022 showed a fluctuating proportion of direct plus contributing COVID-19 hospitalizations as low as 40%.
There was misattribution bias in COVID-19 hospitalization determination using the admission algorithm. The findings from this analysis led to improvements in the algorithm to improve precision. Public health jurisdictions should review their COVID-19 hospitalization reporting approaches to ensure validity and consideration of incidental cases.
在新冠疫情初期，许多公共卫生系统认为每一例新冠病毒检测呈阳性的住院病例都是由新冠病毒感染直接导致的。自2022年1月起，艾伯塔省卫生服务传染病控制住院治疗团队（疾病预防控制中心住院治疗团队）实施了一种入院标准算法，以判定新冠病毒是艾伯塔省所有新冠病毒检测呈阳性住院病例的直接病因、促成病因还是无关病因。
这项质量改进计划旨在提高入院算法在报告新冠病毒检测呈阳性住院病例方面的准确性。对2022年1月至2月期间过去30天内新冠病毒检测呈阳性的患者住院记录，按比例在艾伯塔省各卫生区域进行地理分层抽样。共抽取了261份患者记录，并将疾病预防控制中心住院治疗团队算法对新冠病毒归因的判定结果与一组具有丰富新冠临床经验的传染病医生的判定结果进行比较。
在261例抽样的新冠病毒检测呈阳性住院病例中，不知情的医生判定39.9%为直接病因，17.2%为促成病因，37.6%为无关病因。在同一队列中，疾病预防控制中心住院治疗团队的入院算法判定42.9%为直接病因，24.5%为促成病因，30.3%为无关病因。科恩kappa系数为0.475，仅显示出中等程度的一致性。大多数差异来自将无关住院病例过度归因于促成病因。2022年全年在艾伯塔省实施该算法后，直接加促成的新冠病毒检测呈阳性住院病例比例波动较大，低至40%。
使用入院算法判定新冠病毒检测呈阳性住院病例存在归因偏差。该分析结果促使对算法进行改进以提高准确性。公共卫生辖区应审查其新冠病毒检测呈阳性住院病例报告方法，以确保有效性并考虑偶发病例。
