LAUSR

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.