COVID-19 in Brazilian cities: Impact of social determinants, coverage and quality of primary health care

Background Brazil, as many other countries, have been heavily affected by COVID-19. This study aimed to analyze the impact of Primary health care and the family health strategy (FHS) coverage, the scores of the National Program for Improving Primary Care Access and Quality (PMAQ), and socioeconomic and social indicators in the number of COVID-19 cases in Brazilian largest cities. Methods This is an ecological study, carried out through the analysis of secondary data on the population of all Brazilian main cities, based on the analysis of a 26-week epidemiological epidemic week series by COVID-19. Statistical analysis was performed using Generalized Linear Models with an Autoregressive work correlation matrix. Results It was shown that greater PHC coverage and greater FHS coverage together with an above average PMAQ score are associated with slower dissemination and lower burden of COVID-19. Conclusion It is evident that cities with less social inequality and restrictions of social protection combined with social development have a milder pandemic scenario. It is necessary to act quickly on these conditions for COVID-19 dissemination by timely actions with high capillarity. Expanding access to PHC and social support strategies for the vulnerable are essential.


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Reviewer #2: (No Response)
Response: All data used for this manuscript is in the public domain. In the Methods section you will find all this information, as well as links to access the full material.

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Review Comments to the Author
Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #2: Methods -1. The COVID-19 reporting systems where data were collected were not detailed. Are they mild and serious cases? Were the systems used e sus ve (mild) and sivepripe (severe)?
Response: Thank you for your comments. In the present study, no differentiation was made between severe and mild cases. Cases diagnosed by COVID-19 were used, according to data extracted from the Covid-19 Panel (https://covid.saude.gov.br/) database, fed by the Health Surveillance Department and made available by the SUS Informatics Department. This information can be found in the methods section of the manuscript.
2. The standardization of the incidence coefficient would be important, due to the difference in the impact of the disease in the elderly. The proportion of elderly people (> = 60 years) varies from 6.9% in Boa Vista to 20.4% in Porto Alegre, according to data from DATASUS in 2020.

Response:
We agree with the reviewer that the higher proportion of elderly people in the capitals of the southern region of the country make them more likely to have COVID-19 cases. However, this difference in reported proportion does not match official data from the Brazilian Institute of Geography and Statistics, the official body for this information (https://censo2010.ibge.gov.br/sinopse/index.php?uf=43&dados=26#topo_piramide). In any case, the objective of the research is not to estimate the differences in the load of COVID-19 between the capitals, where standardization would be of great importance, as the age composition would impact the compared estimates.
The objective was to estimate the effects of socioeconomic conditions and health system organization prior to the pandemic on the occurrence of COVID-19 cases. In this facet, the effect of age composition is reduced in the inferences, as the comparison is made with levels of independent variables, such as coverage of the PHC and FHS, which do not suffer a direct effect from the age composition. Furthermore, they assume that the primary health care system must be organized according to the local population profile. Thus, the inferential analysis does not compare the Brazilian capitals, but analyzes them as a single group, being stratified by factors such as healthcare coverage.
Response: Thank you for your comments. The following sentences were added: To date, only criteria for confirmation of cases (laboratory and clinical-epidemiological) were used as a form of diagnosis of Covid-19, using immunological tests, rapid test or classical serology for the detection of antibodies.
Results. 4. Figure 1 shows the image per city per 100,000 inhabitants per city. What is considered each circle. Which cities are shown.
Response: Figure 1 shows a general graph of the incidence in each city analyzed (circles) in order to show the exponential evolutionary profile in all of them. 5. table 1: I do not understand some results. In situations of PHC coverage below 50%, PMAQ score low demonstrated "nine times more cases" per 100,000 inhabitants COVID-19 cases than those with a score medium or high (B=9.08; p<0.001). In the PHC 50-74% stratum, cities with intermediate FHS coverage and PMAQ scores high showed almost "twice less" (B=2,36) COVID-19 incidence rates than cities with lower ratings.
Response: The first interpretation is correct. This is equivalent to stating that in situations of low PHC coverage, having better quality of care had an impact on the dissemination of COVID-19. On the other hand, the second statement is based on the inverse interpretation of the model's coefficient relative to the worst-case coverage and quality of care. This inversion of interpretation may have led to confusion.
6. For the interpretation of Gini and HDI, it is more appropriate to observe the interaction of these factors and not the main effect. The Gini-HDI interaction shows a negative relationship (B=-72.46; p<0.001), which may suggest a mitigating effect in cities with high HDI and low Gini.