The study analyzed the spatial distribution and factors associated with the risk for SARS, risk of death due COVID-19 and a proposal for a risk of death calculator. The risk factors associated with SARS were age, obesity and immunodepression. Our results are similar to the systematic reviews that assessed the most prevalent risk factors for the aggravation of COVID-1922–26.mHowever, none of these reviews included the environmental factor as a relevant aspect to the outcome of the disease, whether for the aggravation of or death by COVID-19. Some observational studies demonstrated a greater prevalence of mortality among vulnerable populations, but there is no solid evidence of this association27,28.
The second result of this study was the increased risk of mortality, with the descending order of importance: age, comorbidities, sex, and situation of vulnerability. Among the comorbidities most associated with a greater risk of mortality, in order from highest to lowest, were: renal disease, obesity, immunodepression, heart disease, respiratory sickness and diabetes.
The distribution of the density of confirmed COVID-19 cases in the area suggests that the virus spreads independent of the status of vulnerability of the region, being greater in more populated and vulnerable areas. A reflection on the vulnerability of specific groups is necessary, since the impact of the pandemic on the area needs to be contextualized, due to the diversity of urban social vulnerability scores that affect the largest Brazilian city.
In Paraisópolis, one of the largest favela of São Paulo, a higher density of confirmed COVID-19 cases was observed, as is also reported by other Brazilian studies showing a greater spread of the virus in situations of high vulnerability and population density29,30. However, we clearly see two areas with high socioeconomic levels that, in spite of low incidences, show a high chance of aggravation and death. This corroborates the study by Bermudi et al.30 that shows a high mortality rate in areas with the worst social conditions in São Paulo.
The findings of the study corroborate with the importance of control of chronic conditions and care for the elderly in health care services for mortality reduction31.There is also evidence for the need to prioritize such actions in regions that present higher vulnerability and adverse regional conditions. Considering the fact that the difficulties inherent in the region of residence affect health conditions, causing unfavorable outcomes for these populations, The Equity, one of the guidelines of the Unified Health System (SUS), should be implemented.
Despite of is plausible that poverty worsens health conditions32, this should not avoid controlling the risk factors that can undergo more immediate interventions, as well as strategies that provide isolation for the most vulnerable. SUS, free and universal, and the strong Primary HealthCare across the country are transformative for this reality33,34. In countries who does not have universal healthcare coverage, such as the USA, there has been a high mortality rate for COVID-1935.
In a prediction model for more serious forms of COVID-19 there is the recommendation for the inclusion of symptomology for medical support, resource planning and improved monitoring of COVID-19 patients13. The initial characteristics of the disease, including the number of symptoms, is predictive of its duration36,37. Our study identified cough, fever, sore throat, dyspnea, anosmia, loss of taste, diarrhea and fatigue as the main symptoms of COVID-19, considering the area above the ROC curve (AUC) significant, for both aggravated SARS and death.
The third result is the risk of death calculator, based on the symptoms, comorbidities and age, with good performance parameters. The model presented a better performance (AUC of 97.4%) when compared to the parameters of the calculator proposed by the American study (AUC of 85.3%) that used a population restricted to elderly people and a larger number of variables10.
Booth et al.39, also proposed a tool to predict death, considered molecular biomarkers in laboratory samples from PCR exams, but presented a limitation related to the cost and time to obtain the measurement of risk, including quality measures that are inferior to this study: AUC (93%), sensitivity (91%) and specificity (91%). Other studies focused only on the elderly or hospitalized cases13,39. However, broadening the discussion to include adults over 18 years old makes it stronger for general population use.
Thus, the proposed tool is a powerful response to support managers and professionals in health care service planning and the prioritization of more directed and assertive strategies and actions, such as monitoring positive cases with a higher prediction of death and directing and distributing resources.
We highlight the importance of this calculator as a tool to aid any health care service, including Primary Health Care Service. Professionals at Primary Health Care Services often lack consolidated directives that can help decision making. Based on the indicators and results obtained by the calculator, professionals can estimate the risk of death and anticipate preventative measures to avoid this outcome. Since it was performed using data collected and available from the Brazilian Ministry of Health information systems, it can be easily validated externally and even recalibrated for different national contexts. In other countries that use different systems, it is also possible to validate and calibrate, as data on age, sex, comorbidities and symptoms are accessible.
In addition to the risk factors highlighted by the results of this study, many countries are in ethical, political and economic crises, such as Brazil has been experiencing regarding a failure to respond, management policies against social distancing, a lack of federal coordination, negationism and neoliberal policies that can impact the outcomes of the pandemic. Thus, the response to the pandemic in these contexts needs to be questioned and larger studies are needed that incorporate these variables into the analyses, such as the need for interdisciplinary analyses40–42 considering clinical, demographic, socioeconomic and geospatial aspects, and incorporating the different aspects into a single model.
This study has several strengths, like the use of data from the Ministry of Health information systems on the records of patients with a confirmed COVID-19 diagnosis, including flu-like syndrome, GEoSES, IPVS encompassing a large number of participants, and a proposal for a risk of death calculator, a tool to aid any health care service. Despite the richness of the data, this study has limitations. First, the study used data that had not been collected with a scientific purpose; healthcare professionals collected the data with a high percentage of missing data, however it is still robust enough to support our results. During the period of the study, the principal variant in circulation changed—in January the P1 variant was introduced and the vaccination campaign began. However, upon performing the temporal validation, the measures reduced, but still showed excellent discrimination. The risk calculator shows great potential to be evaluated regarding its implementation.
In conclusion, the study contributed to the global effort in the fight against COVID-19, with evidence that brings together various contexts in the spread of the virus, as well as risk factors present in areas of greater vulnerability, including those with a high population density and poverty, such as favelas.