Department of Infectious Diseases, Botucatu School of Medicine, São Paulo State University (UNESP), City of Botucatu, São Paulo State, Brazil.
Department of Geography, Faculty of Science and Technology, São Paulo State University (UNESP), City of Presidente Prudente, São Paulo State, Brazil.
Epidemiol Infect. 2020 Jun 19;148:e118. doi: 10.1017/S095026882000134X.
Even though the impact of COVID-19 in metropolitan areas has been extensively studied, the geographic spread to smaller cities is also of great concern. We conducted an ecological study aimed at identifying predictors of early introduction, incidence rates of COVID-19 and mortality (up to 8 May 2020) among 604 municipalities in inner São Paulo State, Brazil. Socio-demographic indexes, road distance to the state capital and a classification of regional relevance were included in predictive models for time to COVID-19 introduction (Cox regression), incidence and mortality rates (zero-inflated binomial negative regression). In multivariable analyses, greater demographic density and higher classification of regional relevance were associated with both early introduction and increased rates of COVID-19 incidence and mortality. Other predictive factors varied, but distance from the State Capital (São Paulo City) was negatively associated with time-to-introduction and with incidence rates of COVID-19. Our results reinforce the hypothesis of two patterns of geographical spread of SARS-Cov-2 infection: one that is spatial (from the metropolitan area into the inner state) and another which is hierarchical (from urban centres of regional relevance to smaller and less connected municipalities). Those findings may apply to other settings, especially in developing and highly heterogeneous countries, and point to a potential benefit from strengthening non-pharmaceutical control strategies in areas of greater risk.
尽管 COVID-19 在大都市地区的影响已经得到了广泛研究,但该病毒向较小城市的地理传播也引起了极大关注。我们开展了一项生态学研究,旨在确定巴西圣保罗州内陆 604 个城市 COVID-19 早期引入、发病率和死亡率(截至 2020 年 5 月 8 日)的预测因素。预测模型中纳入了社会人口学指标、到州首府的道路距离以及区域相关性分类,用于 COVID-19 引入时间(Cox 回归)、发病率和死亡率(零膨胀二项负回归)的预测。在多变量分析中,较高的人口密度和较高的区域相关性分类与 COVID-19 的早期引入以及发病率和死亡率的增加相关。其他预测因素各不相同,但距州首府(圣保罗市)的距离与引入时间以及 COVID-19 的发病率呈负相关。我们的研究结果强化了 SARS-Cov-2 感染地理传播的两种模式假说:一种是空间模式(从大都市区到内陆州),另一种是层级模式(从区域相关性较强的城市中心到较小且联系较少的城市)。这些发现可能适用于其他环境,尤其是在发展中国家和高度异质的国家,这表明在风险较高的地区加强非药物控制策略可能会带来益处。