Department of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Center for Advancing Population Science, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
BMJ Open. 2022 May 30;12(5):e059420. doi: 10.1136/bmjopen-2021-059420.
To evaluate whether certain healthcare provider network structures are more robust to systemic shocks such as those presented by the current COVID-19 pandemic.
Using multivariable regression analysis, we measure the effect that provider network structure, derived from Medicare patient sharing data, has on county level COVID-19 outcomes (across mortality and case rates). Our adjusted analysis includes county level socioeconomic and demographic controls, state fixed effects, and uses lagged network measures in order to address concerns of reverse causality.
US county level COVID-19 population outcomes by 3 September 2020.
Healthcare provider patient sharing network statistics were measured at the county level (with n=2541-2573 counties, depending on the network measure used).
COVID-19 mortality rate at the population level, COVID-19 mortality rate at the case level and the COVID-19 positive case rate.
We find that provider network structures where primary care physicians (PCPs) are relatively central, or that have greater betweenness or eigenvector centralisation, are associated with lower county level COVID-19 death rates. For the adjusted analysis, our results show that increasing either the relative centrality of PCPs (p value<0.05), or the network centralisation (p value<0.05 or p value<0.01), by 1 SD is associated with a COVID-19 death reduction of 1.0-1.8 per 100 000 individuals (or a death rate reduction of 2.7%-5.0%). We also find some suggestive evidence of an association between provider network structure and COVID-19 case rates.
Provider network structures with greater relative centrality for PCPs when compared with other providers appear more robust to the systemic shock of COVID-19, as do network structures with greater betweenness and eigenvector centralisation. These findings suggest that how we organise our health systems may affect our ability to respond to systemic shocks such as the COVID-19 pandemic.
评估某些医疗服务提供者网络结构在面对当前 COVID-19 大流行等系统性冲击时是否更具稳健性。
我们使用多变量回归分析,衡量源自医疗保险患者共享数据的提供者网络结构对县一级 COVID-19 结果(死亡率和病例率)的影响。我们的调整分析包括县一级的社会经济和人口统计学控制、州固定效应,并使用滞后网络措施来解决反向因果关系的问题。
截至 2020 年 9 月 3 日的美国县一级 COVID-19 人口结果。
医疗服务提供者患者共享网络统计数据在县一级进行测量(使用的网络措施不同,县的数量为 2541-2573 个)。
人口层面的 COVID-19 死亡率、病例层面的 COVID-19 死亡率和 COVID-19 阳性病例率。
我们发现,初级保健医生(PCP)相对集中的提供者网络结构,或者具有更高的中间中心性或特征向量集中化的网络结构,与较低的县一级 COVID-19 死亡率相关。对于调整后的分析,我们的结果表明,PCP 的相对集中性(p 值<0.05)或网络集中化(p 值<0.05 或 p 值<0.01)每增加 1 个标准差,与 COVID-19 死亡人数减少 1.0-1.8 人/每 10 万人(或死亡率降低 2.7%-5.0%)相关。我们还发现提供者网络结构与 COVID-19 病例率之间存在关联的一些迹象。
与其他提供者相比,PCP 相对集中的提供者网络结构在面对 COVID-19 这样的系统性冲击时似乎更具稳健性,中间中心性和特征向量集中化较高的网络结构也是如此。这些发现表明,我们组织医疗体系的方式可能会影响我们应对 COVID-19 等系统性冲击的能力。