Velayati Arash, Dahale Devesh, Dahlin Arielle, Hamilton Caleb, Provost Lloyd P, Erwin Paul
Health Systems Engineering (Mr Dahale), Southeast Health Medical Center (Drs Velayati and Dahlin), and Alabama College of Osteopathic Medicine (Drs Velayati and Hamilton), Southeast Health, Dothan, Alabama; Associates in Process Improvement, Austin, Texas (Mr Provost); and School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama (Dr Erwin).
J Public Health Manag Pract. 2021;27(3):305-309. doi: 10.1097/PHH.0000000000001366.
To understand county-level variation in case fatality rates of COVID-19, a statewide analysis of COVID-19 incidence and fatality data was performed, using publicly available incidence and case fatality rate data of COVID-19 for all 67 Alabama counties and mapped with health disparities at the county level. A specific adaptation of the Shewhart p-chart, called a funnel chart, was used to compare case fatality rates. Important differences in case fatality rates across the counties did not appear to be reflective of differences in testing or incidence rates. Instead, a higher prevalence of comorbidities and vulnerabilities was observed in high fatality rate counties, while showing no differences in access to acute care. Funnel charts reliably identify counties with unexpected high and low COVID-19 case fatality rates. Social determinants of health are strongly associated with such differences. These data may assist in public health decisions including vaccination strategies, especially in southern states with similar demographics.
为了解阿拉巴马州各县新冠病毒病病死率的差异，我们利用该州67个县公开的新冠病毒病发病率和病死率数据，对新冠病毒病发病率和死亡数据进行了全州范围的分析，并绘制了县级健康差异地图。我们使用了一种特殊的休哈特p控制图（称为漏斗图）来比较病死率。各县病死率的重要差异似乎并非反映在检测或发病率的差异上。相反，在病死率高的县观察到合并症和脆弱性的患病率更高，而在获得急性护理方面没有差异。漏斗图能够可靠地识别出新冠病毒病病死率意外高和低的县。健康的社会决定因素与这些差异密切相关。这些数据可能有助于公共卫生决策，包括疫苗接种策略，尤其是在人口结构相似的南部各州。
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