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. 2020 Jun 19:148:e118.
doi: 10.1017/S095026882000134X.

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

Affiliations

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

C M C B Fortaleza et al. Epidemiol Infect. .

Abstract

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.

Keywords: COVID-19; ecologic study; epidemiology; virus infection.

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Conflict of interest statement

All authors state that they have no conflict of interest regarding this study. A preprint version was submitted to a repository (preprints.org).

Figures

Fig. 1.
Fig. 1.
Map of São Paulo State, Brazil, highlighting different classifications of municipalities, according to their regional influence and connectiveness. The São Paulo City metropolitan area (grey area) was excluded from our analysis.
Fig. 2.
Fig. 2.
Cox regression graphics for time until introduction of COVID-19 in municipalities from inner São Paulo State, Brazil (based on surveillance data up to 8 May 2020).

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