Taking the inner route: spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil

• Published in: Epidemiology and infection Vol. 148; p. e118
• Main Authors: Fortaleza, C. M. C. B., Guimarães, R. B., de Almeida, G. B., Pronunciate, M., Ferreira, C. P.
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01-01-2020
• Subjects: Brazil - epidemiology; Cities - epidemiology; Classification; Communicable Disease Control; Confidence intervals; Coronavirus Infections - epidemiology; Coronavirus Infections - mortality; Coronavirus Infections - prevention & control; Coronaviruses; COVID-19; Demographics; Demography; Disease transmission; Ecological studies; Humans; Incidence; Mathematical models; Metropolitan areas; Mortality; Municipalities; Original Paper; Pandemics - prevention & control; Pneumonia, Viral - epidemiology; Pneumonia, Viral - mortality; Pneumonia, Viral - prevention & control; Population; Prediction models; Regression Analysis; Severe acute respiratory syndrome coronavirus 2; Social distancing; Urban areas; Urban Population; Vulnerable Populations; Brazil; Cities; United States > US; COVID-19; epidemiology; ecologic study; virus infection
• ISSN: 0950-2688; 1469-4409
• 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.