Retrospective observational study of the robustness of provider network structures to the systemic shock of COVID-19: a county level analysis of COVID-19 outcomes

回顾性观察研究:评估医疗服务提供者网络结构应对新冠肺炎系统性冲击的稳健性——基于县级新冠肺炎疫情结果的分析

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Abstract

OBJECTIVE: To evaluate whether certain healthcare provider network structures are more robust to systemic shocks such as those presented by the current COVID-19 pandemic. DESIGN: 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. SETTING: US county level COVID-19 population outcomes by 3 September 2020. PARTICIPANTS: Healthcare provider patient sharing network statistics were measured at the county level (with n=2541-2573 counties, depending on the network measure used). PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 mortality rate at the population level, COVID-19 mortality rate at the case level and the COVID-19 positive case rate. RESULTS: 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. CONCLUSIONS: 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.

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