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指标很重要:改进养老院中 COVID-19 疫情的比较。

The Metrics Matter: Improving Comparisons of COVID-19 Outbreaks in Nursing Homes.

机构信息

Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina - Chapel Hill, Chapel Hill, NC, USA; Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham VA Health Care System, Durham, NC, USA.

Department of Public Health Sciences, University of Chicago, Chicago, IL, USA; Center for Health and the Social Sciences, University of Chicago, Chicago, IL, USA.

出版信息

J Am Med Dir Assoc. 2021 May;22(5):955-959.e3. doi: 10.1016/j.jamda.2021.03.001. Epub 2021 Mar 10.

Abstract

OBJECTIVES

In the United States, nursing facility residents comprise fewer than 1% of the population but more than 40% of deaths due to Coronavirus Disease 2019 (COVID-19). Mitigating the enormous risk of COVID-19 to nursing home residents requires adequate data. The widely used Centers for Medicare & Medicaid Services (CMS) COVID-19 Nursing Home Dataset contains 2 derived statistics: Total Resident Confirmed COVID-19 Cases per 1000 Residents and Total Resident COVID-19 Deaths per 1000 Residents. These metrics provide a misleading picture, as facilities report cumulative counts of cases and deaths over different time periods but use a point-in-time measure as proxy for number of residents (number of occupied beds in a week), resulting in inflated statistics. We propose an alternative statistic to better illustrate the burden of COVID-19 cases and deaths across nursing facilities.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

Using the CMS Nursing Home Compare and COVID-19 Nursing Home Datasets, we examined facilities with star ratings and COVID-19 data passing quality assurance checks for each reporting period from May 31 to August 16, 2020 (n = 11,115).

METHODS

We derived an alternative measure of the number of COVID-19 cases per 1000 residents using the net change in weekly census. For each measure, we compared predicted number of cases/deaths by overall star rating using negative binomial regression with constant dispersion, controlling for county-level cases per capita and nursing home characteristics.

RESULTS

The average number of cases per 1000 estimated residents using our method is lower compared with the metric using occupied beds as proxy for number of residents (44.8 compared with 66.6). We find similar results when examining number of COVID-19 deaths per 1000 residents.

CONCLUSIONS AND IMPLICATIONS

Future research should estimate the number of residents served in nursing facilities when comparing COVID-19 cases/deaths in nursing facilities. Identifying appropriate metrics for facility-level comparisons is critical to protecting nursing home residents as the pandemic continues.

摘要

目的

在美国,护理院居民占总人口的比例不到 1%,但却占 2019 年冠状病毒病(COVID-19)死亡人数的 40%以上。减轻 COVID-19 对养老院居民的巨大风险需要充足的数据。美国医疗保险和医疗补助服务中心(CMS)广泛使用的 COVID-19 养老院数据集包含 2 个衍生统计数据:每 1000 名居民中确诊的 COVID-19 病例总数和每 1000 名居民中 COVID-19 死亡病例总数。这些指标提供了一个误导性的画面,因为各机构报告的病例和死亡人数是在不同时间段内累计的,但使用一个时点指标作为居民人数(一周内占用的床位数)的代理,导致统计数据膨胀。我们提出了一种替代统计方法,可以更好地说明 COVID-19 在各护理院的负担。

设计

回顾性队列研究。

地点和参与者

使用 CMS 养老院比较和 COVID-19 养老院数据集,我们检查了在每个报告期(2020 年 5 月 31 日至 8 月 16 日)都通过了星级评定和 COVID-19 数据质量保证检查的机构(n=11115)。

方法

我们使用每周普查的净变化推导出每 1000 名居民 COVID-19 病例数的替代衡量标准。对于每种衡量标准,我们使用带有固定分散的负二项回归,根据县人均病例数和养老院特征,比较了整体星级评定预测的病例/死亡数。

结果

与使用占用床位作为居民人数代理的指标相比(44.8 比 66.6),我们使用该方法估计的每 1000 名估计居民的病例数较低。当检查每 1000 名居民的 COVID-19 死亡人数时,我们得到了类似的结果。

结论和意义

未来的研究在比较护理院的 COVID-19 病例/死亡时,应估计在护理院服务的居民人数。确定设施级比较的适当指标对于在大流行持续期间保护养老院居民至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b74/7945871/73f4fd72e51b/gr1_lrg.jpg

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