preprints.org > doi: 10.20944/preprints202004.0497.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 27 April 2020 / Approved: 28 April 2020 / Online: 28 April 2020 (10:23:16 CEST)

Objectives: The impact of COVID-19 in metropolitan areas has been extensively studied. The geographic spread to smaller cities is of great concern and may follow hierarchical influence of urban centers. With that in mind, we investigated factors that affect vulnerability of inner municipalities in São Paulo State, Brazil, an area with 24 million inhabitants. Methods: Surveillance data for confirmed COVID-19 cases and admissions for severe acute respiratory disease (SARD) up to April 18^th were recorded for each of 604 municipalities that lay outside São Paulo metropolitan area. Vulnerability was assessed in Multivariable models, including sociodemographic indexes, road distance to the State Capital and the municipalities classification proposed by the Brazilian Institute of Geography and Statistics. Municipalities of great regional relevance were used as reference category for that classification. The outcome of interest for Cox regression was having COVID-cases, with time counting from the first report in São Paulo State. For binomial negative regression models, the outcomes of interest were rates of confirmed COVID-19 cases and admissions for SARD.Results: A total of 198 (32.8%) municipalities had autochthonous COVID-19 cases. In Cox models, affected municipalities were likely to have greater population density (Hazard Ratio[HR] for each 100 inhabitants per square kilometer, 1.07; 95% Confidence Interval [CI], (1.05-1.10)), proportion of inhabitants in urban area (HR, 1.02; 95%CI, 1.00-1.04), higher human development index (HDI, HR for 1%, 1.06; 95%CI, 1.00-1.13) and Gini Index for Inequality of income (HR for 1%, 1.04, 95% CI, 1.00-1.07). On the other hand, distance from the Capital was protective (HR for each 100Km, 0.82; 95%CI, 0.74-0.90). The HR95%[95%CI] also varied negatively according to the categories of influence of major centers (0.41 [0.22-0.77], 0.16 [0.09-0.32], 0.07 [0.03-0.15]). The binomial negative regression models for COVID-19 incidence also detected positive association with population density (Incidence Rate Ratio[IRR], 1.13; 95%CI, 1.07-1.18) and proportion of urban population (IRR, 1.04; 95%CI, 1.01-1.05), protection for cities distant to the Capital (IRR=0.73; 95%CI, 0.68-0.81) and increasing negative association for categories of influence (0.19 [0.09-0.42], 0.07 [0.03-0.15] and 0.03 [0.02-0.08]). Similar findings were detected when we used SARD incidence as outcome.Conclusion: Municipalities with greater population, density and regional influence were more likely to be affected earlier and more intensely by COVID-19. Non-pharmacological measures should be strengthened in those areas of greater risk.

COVID-19; Epidemiology; Ecologic study