==== Front PLoS One PLoS One plos plosone PLoS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0243126 PONE-D-20-27175 Research Article Medicine and Health Sciences Medical Conditions Infectious Diseases Viral Diseases Covid 19 Medicine and Health Sciences Health Care Health Care Facilities Hospitals Hospitalizations People and places Geographical locations South America Brazil Medicine and Health Sciences Epidemiology Pandemics Medicine and Health Sciences Epidemiology Medical Risk Factors Research and Analysis Methods Mathematical and Statistical Techniques Statistical Methods Multivariate Analysis Bivariate Analysis Physical Sciences Mathematics Statistics Statistical Methods Multivariate Analysis Bivariate Analysis Biology and Life Sciences Physiology Physiological Parameters Body Weight Obesity Medicine and Health Sciences Health Care Health Care Facilities Hospitals Intensive Care Units COVID-19 hospitalizations in Brazil’s Unified Health System (SUS) COVID-19 hospitalizations in Brazil’s Unified Health System (SUS)de Andrade Carla Lourenço Tavares ConceptualizationData curationFormal analysisMethodologySoftwareValidationWriting – original draftWriting – review & editing Pereira Claudia Cristina de Aguiar ConceptualizationData curationFormal analysisMethodologyValidationWriting – original draftWriting – review & editing Martins Mônica ConceptualizationData curationFormal analysisMethodologySupervisionWriting – original draftWriting – review & editing Lima Sheyla Maria Lemos ConceptualizationFormal analysisWriting – original draftWriting – review & editing https://orcid.org/0000-0002-9858-9276Portela Margareth Crisóstomo ConceptualizationData curationFormal analysisMethodologyProject administrationSupervisionValidationWriting – original draftWriting – review & editing* Departamento de Administração e Planejamento em Saúde, Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil Nunes Bruno Pereira Editor Universidade Federal de Pelotas, BRAZIL Competing Interests: The authors have declared that no competing interests exist. * E-mail: mportela@ensp.fiocruz.br 10 12 2020 2020 10 12 2020 15 12 e024312629 8 2020 16 11 2020 © 2020 de Andrade et al2020de Andrade et alThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Objective To study the profile of hospitalizations due to COVID-19 in the Unified Health System (SUS) in Brazil and to identify factors associated with in-hospital mortality related to the disease. Methods Cross-sectional study, based on secondary data on COVID-19 hospitalizations that occurred in the SUS between late February through June. Patients aged 18 years or older with primary or secondary diagnoses indicative of COVID-19 were included. Bivariate analyses were performed and generalized linear mixed models (GLMM) were estimated with random effects intercept. The modeling followed three steps, including: attributes of the patients; elements of the care process; and characteristics of the hospital and place of hospitalization. Results 89,405 hospitalizations were observed, of which 24.4% resulted in death. COVID-19 patients hospitalized in the SUS were predominantly male (56.5%) with a mean age of 58.9 years. The length of stay ranged from less than 24 hours to 114 days, with a mean of 6.9 (±6.5) days. Of the total number of hospitalizations, 22.6% reported ICU use. The odds on in-hospital death were 16.8% higher among men than among women and increased with age. Black individuals had a higher likelihood of death. The behavior of the Charlson and Elixhauser indices was consistent with the hypothesis of a higher risk of death among patients with comorbidities, and obesity had an independent effect on increasing this risk. Some states, such as Amazonas and Rio de Janeiro, had a higher risk of in-hospital death from COVID-19. The odds on in-hospital death were 72.1% higher in municipalities with at least 100,000 inhabitants, though being hospitalized in the municipality of residence was a protective factor. Conclusion There was broad variation in COVID-19 in-hospital mortality in the SUS, associated with demographic and clinical factors, social inequality, and differences in the structure of services and quality of health care. The author(s) received no specific funding for this work. Data AvailabilityAll relevant data are within the manuscript.OutbreaksCOVID-19Data Availability All relevant data are within the manuscript. ==== Body Introduction The Covid-19 pandemic caused by the SARS-CoV-2 virus has severely affected Brazil, which has become the country with the second highest number of cases and deaths in the world [1]. The first confirmed case of COVID-19 in Brazil and Latin America occurred on February 26, 2020 in the state of São Paulo. Less than a month later, the first death also occurred in São Paulo on March 17. Social distancing measures were first introduced in March in five states, namely Goiás, Rio de Janeiro, Santa Catarina, the Federal District and São Paulo [2]. Due to the rapid spread of the disease, all 26 states and the Federal District had already registered ten or more cases of the disease in early April, with a higher concentration of cases in Southeastern Brazil, especially in the states of São Paulo and Rio de Janeiro [3]. Given the rapid transmission of the virus, there was an abrupt and growing additional demand for hospitalizations worldwide, thus putting health care systems under strain in many countries [4]. According to the World Health Organization (WHO), 80% of patients with COVID-19 have mild and uncomplicated symptoms, 15% progress to hospitalization and 5% require admission to the intensive care unit (ICU) [5]. Brazil's Unified Health System (SUS) is the largest public and universal health system in the world, encompassing the entire country. About 75% of the Brazilian population does not have private health insurance and is exclusively dependent on the SUS [6, 7]. The system's underfunding and inadequate management, however, has undermined its structure, with broad variation in the quality of services provided across the country. The need to cope with COVID-19 has revealed weaknesses in the system, despite the increase in the number of general and intensive care hospital beds on offer and the construction of field hospitals. Several Brazilian states had to deal, to a greater or lesser extent, with a higher demand than the available capacity of the SUS to respond, which resulted, for example, in long queues for general and intensive care beds. In addition to the structure of the services supplied for COVID-19 cases and the measures implemented to control the pandemic, patient characteristics, such as age, sex, socioeconomic status and pre-existing conditions, interfere with the demand for hospitalization, the care provided and the outcomes [8, 9]. Worldwide, ICU mortality due to COVID-19 in hospitalizations of patients aged 18 years and older is higher than that normally seen in patients with other viral pneumonias [4]. As the pandemic progressed, reported death rates dropped from more than 50% to close to 40%. Comorbidities frequently referred in studies include obesity, hypertension, diabetes mellitus, cardiovascular, lung, chronic kidney and liver diseases, immunosuppression and cancers [4]. In Brazil, a cross-sectional observational study conducted with hospitalized COVID-19 patients identified a lower likelihood of death in young, female patients with fewer comorbidities, commensurable with what has been observed in other countries. Furthermore, it highlighted a higher risk of death among black and mixed race populations and in patients hospitalized in the Northern region compared to other regions of the country, which, for the authors, represents a specific manifestation of the disease in the Brazilian population. The authors suggest that the regional effect may be driven by the morbidity profile of patients in regions with lower levels of socioeconomic development [10]. Despite the acknowledged increase in international and national publications on COVID-19, there are still few studies that examine the risk factors and characteristics of hospitalizations for COVID-19 in the population considering geographic variations [11]. Therefore, the following question arises: How does the profile of patients and hospitalizations observed in the international literature compare with hospitalizations that occurred in Brazil’s public health system in different regions of the country? The scope of this paper is to understand the profile of COVID-19 hospital admissions in the Unified Health System (SUS) and to identify associated factors with the occurrence of in-hospital deaths related to the disease, considering patient characteristics and the care offered, with a focus on regional variations. Methods Study design This is a cross-sectional, observational study based on secondary data on COVID-19 hospitalizations that occurred in the Unified Health System (SUS), which were available on the DATASUS website on August 4, 2020 [12], considering the first four months of the pandemic in Brazil, namely between the end of February and the last week in June. The SUS Hospital Information System (SIH) was the data source. Although this system may raise some coverage and quality concerns, as administrative data often do, it is the main source of information on hospital production nationwide and has been employed in other scientific studies. During a pandemic in which evidence still needs to be acquired, and Brazil has unquestionable importance in terms of the number of cases and deaths, the role it can play is not negligible. There is no other data source able to provide the information it can offer within a relatively short timeframe. In addition to demographic data (age, sex), it includes diagnostic data, type of admission (elective/emergency) and type of care (surgical/clinical), length of stay (LOS), use of intensive care (ICU), outcome at discharge and the amount reimbursed for the hospitalization. In 2016, it was expanded to accept up to nine secondary diagnosis registrations, potentially providing a better picture of the morbidity and severity case profile. In addition to secondary diagnoses, variables indicating their pre-existence (presence on admission) or that they were acquired during the process of care in the hospital designate, respectively, that they are patient attributes or results of performance and quality of care problems. Race/color is the only variable available in the SIH dataset that is responsive to socioeconomic conditions. The data used were extracted from the reduced type (RD) files for each state and Federal District, freely available on the DATASUS portal [12]. It is likely that hospitalizations in the months under scrutiny are underreported due to the specificities of the SIH data transmission process and its main purpose, which is reimbursement. Study sample Initially, we excluded hospitalizations of patients under 18 years of age. The selection of COVID-19 hospitalizations began with the following variables: procedure performed (which designates the type of treatment performed on the patient, be it clinical or surgical, and serves as the basis for hospitalization payment); primary and secondary diagnoses. We considered the patients whose hospital record indicated an association with COVID-19 in any of these variables as cases under scrutiny. Thus, all hospitalizations with the primary diagnosis or one of the secondary diagnoses identified with the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) code B34.2 –coronavirus infection of unspecified location–were included. This diagnostic category was defined in the technical guidelines of the SIH in the context of the pandemic [13]. In line with these guidelines, we also included hospitalizations coded “03.03.01.022–3—TREATMENT OF INFECTION BY THE NEW CORONAVIRUS—COVID 19” in the procedure variable [14]. This code was recently created to take effect from April 14, 2020 onwards. In hospitalizations prior to April, the procedure used was “03.03.01.019–3—TREATMENT OF OTHER DISEASES CAUSED BY VIRUSES (ICD-10: B25-B34).” Given its lack of specificity and relation to a wide range of core diagnoses, it was not considered an additional inclusion criterion beyond the B34.2, except for a few records containing the B97.2—Coronavirus, as the cause of classified diseases in other chapters as the main diagnosis. Data analysis The study focused on the analysis of patient sociodemographic and clinical characteristics, the care process and contextual variables related to the hospitalization, and their effects on the likelihood of in-hospital death. Descriptive and bivariate analyses were performed to characterize the population studied and to test the relationships between the independent variables (attributes of the patients, the care process and the hospital, place of residence and geographic location) with the dependent 'in-hospital death' variable. The scope of the information available in the dataset shaped the range of operationalized variables and the scope of the analyses. To account for the correlation among observations occurring in each hospital, resulting from the process of care and case-mix, we used the generalized linear mixed model (GLMM) with random effects intercept. This model was used to assess the different factors associated (independent variables) with the in-hospital death of COVID-19 patients (response with binary distribution). Thus, the modeling occurred, with the insertion of different blocks of variables, in three stages: (i) patient attributes–variables that express the patient risk profile and social inequality (race/color); (ii) elements of the care process; and (iii) characteristics of the hospital, place of residence and place of hospitalization. For the first stage, the case severity profile was based on demographic variables (sex and age) and comorbidities. The 'sex' variable is binary as informed in the SIH, and we considered female as the reference category. Age was treated as a categorical variable (18–39, 40–49, 50–59, 60–69, 70–79, 80–89 and ≥ 90 years). Comorbidities were contemplated in different ways: (i) calculating the Charlson Comorbidity Index (CCI) [15]; (ii) identifying the presence of comorbidities as proposed by the Elixhauser Comorbidity Index (ECI) for administrative databases [16]; (iii) considering specific comorbidities–obesity, arterial hypertension and diabetes [17]–due to their frequency in the population and their relevance in the COVID-19 literature, although they are already part of the previous comorbidity parameters used. CCI and ECI were chosen because they are widely used measures in models for predicting death [17] and have previously been applied to Brazilian patients [18]; the calculation used the ICD-10 coding algorithm for each clinical condition developed by Quan et al. [19]. Additionally, we considered whether COVID-19 was designated as a primary (reference category) or secondary diagnosis. In the case of the 'race/color' variable, we started out by using the five categories provided by SIH (white, black, mixed race, yellow and indigenous) and, after examination, we chose to consider the categories black, mixed race and others, as reference. Race/color is used as a proxy for social vulnerability and inequalities in socioeconomic conditions, health, access, use and effectiveness of care. In the second stage, two variables about the care process were added to the previous model: ICU use and LOS. The number of days spent in the intensive care unit (ICU) was transformed into a dichotomous variable (yes/no). The LOS was also categorized considering the distribution of deaths. Therefore, the following categories were established: 1 day or less; 2–7 days; 8–22 days and ≥ 23 days; hospitalizations in which the LOS was 0, were considered to be less than 24 hours and included in the first category. In the third and last stage of the modeling process, we inserted hierarchical level variables, namely the hospital, the place of residence and the geographic location where the hospitalization occurred. At the hospital level, the legal nature was also considered, and they were categorized as municipal, state and federal public hospitals, private for profit and private non-profit hospitals. For the place of residence, a variable indicating patient displacement to seek care was created, considering whether the municipality of residence and the location of the hospital were the same (dichotomous variable yes/no). Lastly, we used categories for each state (including the Federal District) to account for geographic effects in the occurrence of hospitalization; and municipal population size, which, in previous analysis, was more adequate than the municipal human development index (Municipal HDI). The predictive capacity of the different models was assessed based on the “c” statistic, and data analysis was performed using the SAS statistical package. Results The selection criteria for hospitalizations yielded 89,405 records, among which 13 corresponded to hospitalizations that started and ended in 2020 before the month of March. Altogether, 21,807 (24.4%) hospitalizations resulted in death. The COVID-19 patients hospitalized in the SUS were predominantly male (56.5%), aged between 18 and 114 years old, mean and standard deviation of 58.9 (± 16.8) and median of 60 years. The LOS ranged from 24 hours or less to 114 days, with an average of 6.9 (± 6.5) days and a median of 5 days. Altogether, hospitalizations represented R$ 332.10 million (Brazilian reais) in expenditures for the SUS, which varied between 40.40 and 111,914.50 reais per patient, with an average of R$ 3,714.50 (± 6,119.20) and first, second and third quartiles were 1,500.00, 1,610.40 and 2,087.20 Brazilian reais, respectively. Of the total number of hospitalizations, 22.6% registered ICU use, which accounted for 66.4% of the total amount paid for all COVID-19 hospitalizations. In these admissions, the average and median hospital length of stay was 10.3 (± 8.5) days and 8 days, respectively, and the amount paid for average and median hospital stay was 11,083.10 (± 9,674.30) and 8,036.40 reais, respectively. Considering ICU alone, the average LOS was 7.6 (± 6.8) days, with a median of 6 days. The analyses presented here considered the association between the occurrence of in-hospital death and three “blocks” of variables: sociodemographic and clinical attributes of patients; aspects related to the care process; and data from the macro context of the hospital organization and geographic location of the hospital. Tables 1–3 show descriptive statistics and bivariate analyses of these variables, when death occurred or not. 10.1371/journal.pone.0243126.t001Table 1 Bivariate analyses of sociodemographic and clinical variables and in-hospital mortality. COVID-19 hospitalizations in the Unified Health System in Brazil (N = 89,405). February to June 2020. Variables N % In-hospital death χ2 (p-value) Yes No N % N % Sex < 0.0001     Male 50,520 56.5 12,867 25.5 37,653 74.5     Female 38,885 43.5 8,940 23.0 29,945 77.0 Age (years) < 0.0001     18–39 13,028 14.6 1,113 8.5 11,915 91.5     40–49 13,556 15.2 1,730 12.8 11,826 87.2     50–59 17,724 19.8 3,234 18.2 14,490 81.8     60–69 19,098 21.4 5,363 28.1 13,735 71.9     70–79 15,593 17.4 5,615 36.0 9,978 64.0     80–89 8,483 9.5 3,775 44.5 4,708 55.5     ≥ 90 1,923 2.1 977 50.8 946 49.2 Ethnic group < 0.0001     White 21,260 23.8 4,635 21.8 16,625 78.2     Black 5,507 6.2 1,754 31.9 3,753 68.1     Mixed race 33,542 37.5 8,741 26.1 24,801 73.9     Yellow 3,071 3.4 595 19.4 2,476 80.6     Indigenous 152 0.2 44 28.9 108 71.1     Unspecified 25,873 28.9 6,038 23.3 19,835 76.7 Charlson Index < 0.0001     0 86,131 96.3 20,560 23.9 65,571 76.1     1 2,552 2.9 898 35.2 1,654 64.8 .     ≥ 2 722 0.8 349 48.3 373 51.7 Elixhauser comorbidities < 0.0001     Yes 5,574 6.2 1,887 33.9 3,687 66.1     No 83,831 93.8 19,920 23.8 63,911 76.2 Obesity < 0.0001     Yes 655 0.7 224 34.2 431 65.8     No 88,750 99.3 21,583 24.3 67,167 75.7 Hypertension < 0.0001     Yes 3,794 4.2 1,273 33.6 2,521 66.4     No 85,611 95.8 20,534 24.0 65,077 76.0 Diabetes < 0.0001     Yes 1,302 1.5 424 32.6 878 67.4     No 88,103 98.5 21,383 24.3 66,720 75.7 COVID-19 as secondary diagnosis 0.9408     Yes 2,777 3.1 679 24.5 2,098 75.5     No 86,628 96.9 21,128 24.4 65,500 75.6 Source: Ministry of Health–The SUS Inpatient Care Information System 10.1371/journal.pone.0243126.t002Table 2 Bivariate analyses of variables related to the inpatient healthcare process and in-hospital mortality. COVID-19 hospitalizations in the Unified Health System in Brazil (N = 89,405). February to June 2020. Variables N % In-hospital death χ2 (p-value) Yes No N % N % Length of stay (days) < 0.0001     0–1 10,558 11.8 3,399 32.2 7,159 67.8     2–7 49,842 55.7 9,993 20.0 39,849 80.0     8–22 25,975 29.1 7,429 28.6 18,546 71.4     ≥ 23 3,030 3.4 986 32.5 2,044 67.5 ICU use < 0.0001     Yes 20,204 22.6 11,247 55.7 8,957 44.3     No 69,201 77.4 10,560 15.3 58,641 84.7 Source: Ministry of Health–The SUS Inpatient Care Information System 10.1371/journal.pone.0243126.t003Table 3 Bivariate analyses of macro context variables and in-hospital mortality. COVID-19 hospitalizations in the Unified Health System in Brazil (N = 89,405). February to June 2020. Variables N % In-hospital death χ2 (Valor de p) Yes No N % N % Hospital ownership < 0.0001     Public–Municipality 37,539 42.0 7,544 20.1 29,995 79.9     Pubic–State 28,728 32.1 8,903 31.0 19,825 69.0     Public–Federal 958 1.1 313 32.7 645 67.3     Private 4,119 4.6 862 20.9 3,257 79.1     Philanthropic 18,061 20.2 4,185 23.2 13,876 76.8 State < 0.0001     Rondônia 957 1.1 127 13.3 830 86.7     Acre 64 0.1 28 43.8 36 56.3     Amazonas 4,682 5.2 1,628 34.8 3,054 65.2     Roraima 677 0.8 243 35.9 434 64.1     Pará 3,398 3.8 647 19.0 2,751 81.0     Amapá 237 0.3 106 44.7 131 55.3     Tocantins 265 0.3 55 20.8 210 79.2     Maranhão 5,946 6.6 1,644 27.6 4,302 72.4     Piauí 1,060 1.2 115 10.8 945 89.2     Ceará 6,141 6.9 1,472 24.0 4,669 76.0     Rio Grande do Norte 917 1.0 238 26.0 679 74.0     Paraíba 848 0.9 166 19.6 682 80.4     Pernambuco 8,524 9.5 2,370 27.8 6,154 72.2     Alagoas 804 0.9 246 30.6 558 69.4     Sergipe 271 0.3 44 16.2 227 83.8     Bahia 2,853 3.2 780 27.3 2,073 72.7     Minas Gerais 2,987 3.3 500 16.7 2,487 83.3     Espírito Santo 1,932 2.2 559 28.9 1,373 71.1     Rio de Janeiro 10,589 11.8 3,401 32.1 7,188 67.9     São Paulo 28,396 31.8 6,235 22.0 22,161 78.0     Paraná 1,753 2.0 291 16.6 1,462 83.4     Santa Catarina 988 1.1 147 14.9 841 85.1     Rio Grande do Sul 2,173 2.4 325 15.0 1,848 85.0     Mato Grosso do Sul 81 0.1 7 8.6 74 91.4     Mato Grosso 384 0.4 76 19.8 308 80.2     Goiás 902 1.0 124 13.7 778 86.3     Distrito Federal 1,576 1.8 233 14.8 1,343 85.2 Population size < 0.0001     ≤ 50.000 hab. 8,671 9.7 878 10.1 7,793 89.9     50.001–100.000 hab. 7,639 8.5 1,573 20.6 6,066 79.4     100.001–500.000 hab. 23,442 26.2 5,785 24.7 17,657 75.3     > 500.000 hab. 49,653 55.5 13,571 27.3 36,082 72.7 Residence/hospitalization < 0.0001     Same municipality 68,069 76.1 15,565 22.9 52,504 77.1     Different municipalities 21,336 23.9 6,242 29.3 15,094 70.7 Source: Ministry of Health–The SUS Inpatient Care Information System In Table 1, we observe higher hospital mortality for men and an increasing gradient of the likelihood of dying as age (age group) increases. Among individuals aged 18 to 39 years, 8.5% of hospitalizations resulted in death. For individuals between 70 and 79, 80 and 89, and 90 years old and above, the proportion of hospitalizations that resulted in death increased to 36.0%, 44.5% and 50.8%, respectively. Mixed race individuals are the majority in the 'race/color' variable, but the high percentage of hospitalizations with unspecified 'race/color' data (28.9%) draws special attention. Among the cases with complete information on the variable, there is a higher occurrence of deaths among blacks (31.9%), followed by indigenous people (28.9%) and mixed race people (26.1%). The percentage of in-hospital deaths with unspecified 'race/color' data is slightly higher than that observed among whites and lower than that observed among mixed race people. It is also worth noting that only 152 admissions of indigenous individuals were observed, comprising 0.2% of the total. The clinical variables expose low levels of information about secondary diagnoses, resulting in a significant underreporting of clinical conditions relevant to the patients' prognosis. Approximately 78.3% of hospitalizations did not have any secondary diagnoses. Nevertheless, the results indicate a higher occurrence of deaths among patients with greater severity according to the Charlson and Elixhauser comorbidity indices or patients with diseases such as hypertension, diabetes and obesity, which show a pattern consistent with expectations. Table 1 also features a variable created to detect any differences in mortality between patients with COVID-19 as the primary diagnosis or as a secondary diagnosis. Hospitalizations with the COVID-19 diagnosis registered in one of the secondary diagnoses corresponded to 3.1% of the sample, and in this case, the bivariate analyses did not reveal any differences with respect to the occurrence of death. In Table 2, the percentage of hospitalizations of less than 24 hours or of one day (32.2%), as well as the high mortality of this category (11.8%), are salient statistics. Approximately two thirds of the total hospitalizations lasted up to 7 days (29.1%), between 8 and 22 days (3.4%) and 23 days or more (3.4%). Based on the bivariate analyses, there appears to be a greater concentration of deaths in the extreme categories of LOS, with a lower occurrence of deaths (20.0%) among those with LOS between 2 and 7 days. The proportion of deaths among those who used the ICU was high (55.7%) compared to those who did not (15.3%). It is worth highlighting the difference between the LOS of those who did not die and those who died during hospitalization. In the first group, the average LOS was 6.7 (± 6.2) days, and the median was 5 days. Among the patients who died, the average LOS was 7.6 (± 7.2) days, and the median was 6 days. Table 3 shows that most hospitalizations for COVID-19 occurred in municipal public hospitals (42%), followed by state public hospitals (32.1%) and philanthropic hospitals (20.2%). The share of private hospitals contracted by SUS was 4.6%, while that of federal hospitals was 1.1%. The distribution of hospitalizations by state presents, to a certain extent, a representative picture of the period analyzed, in which some states in the Southeastern, Northeastern and Northern regions were most affected by the epidemic. Just under a third (31.8%) of the hospitalizations analyzed occurred in São Paulo, the largest and wealthiest Brazilian state. São Paulo was followed by Rio de Janeiro (11.8%), Pernambuco (9.5%), Ceará (6.9%), Maranhão (6.7%) and Amazonas (5.2%). Among these states, the occurrence of in-hospital deaths was especially high in Amazonas (34.1%) and Rio de Janeiro (32.1%), with figures also higher than the national average (24.4%) in Pernambuco (27.8%) and Maranhão (27.6%). The Amazonian states of Acre, Roraima and Amapá corresponded to 0.1%, 0.8% and 0.3%, respectively, of hospitalizations in the country, but they are states with relatively small populations, which stood out for the high proportions of observed in-hospital deaths—43.8%, 35.9% and 44.7%, respectively. The states of Alagoas (30.6%), Espírito Santo (28.9%), Bahia (27.3%) and Rio Grande do Norte (26, 8%) also had in-hospital death percentages higher than the national average. Table 3 also shows that 81.7% of admissions for COVID-19 in the first four months of the epidemic in the country occurred in municipalities with more than 100 thousand inhabitants, with more deaths observed in these municipalities than in other smaller ones. More than ¾ of hospitalizations were carried out in hospitals located in the same municipality of residence of the patient, with a higher occurrence of deaths when the patient had to travel to receive hospital care. Table 4 presents the three regression models that explain the occurrence of in-hospital death, considering the progressive inclusion of the “blocks” of variables already mentioned. The first line of the table provides the variance of random intercepts related to hospital units for each model. In general terms, we observe that some patterns of mortality among categories of variables change from the descriptive analyses to the multivariate models, given the control for confounding factors. There is also consistency in the results from adding “blocks” of variables from one model to the next, although, strictly speaking, the 'race/color' mixed race variable loses statistical significance between the second and third models. 10.1371/journal.pone.0243126.t004Table 4 Logistic regression models with the factors associated with the variation in in-hospital mortality in COVID-19 hospitalizations in the Unified Health System (N = 89,405). Brazil, February to June 2020. Variable Model 1 Model 2 Model 3 Estimate Standard Error OR 95%CI Estimate Standard Error OR 95%CI Estimate Standard Error OR 95%CI σ2^ 1.254 0.064 - - - 1.038 0.056 - - - 0.870 0.051 - - - Intercept -3.113 0.049 - - - -4.035 0.054 - - - -4.839 0.102 - - - Sex     Male 0.181 0.018 1.199 1.157 1.242 0.155 0.020 1.168 1.124 1.213 0.155 0.020 1.168 1.124 1.214     Female - - 1.000 - - - - 1.000 - - - - 1.000 - - Ethnic group     Black 0.173 0.042 1.189 1.094 1.291 0.149 0.046 1.160 1.061 1.269 0.136 0.046 1.145 1.047 1.253     Mixed race 0.052 0.025 1.053 1.002 1.106 0.068 0.027 1.070 1.014 1.129 0.040 0.027 1.041 0.986 1.098     Other - - 1.000 - - - - 1.000 - - - - 1.000 - - Age (years)     18–39 - - 1.000 - - - - 1.000 - - - - 1.000 - -     40–49 0.411 0.043 1.508 1.387 1.640 0.432 0.046 1.541 1.409 1.685 0.436 0.046 1.547 1.414 1.692     50–59 0.841 0.039 2.318 2.147 2.502 0.854 0.042 2.349 2.164 2.550 0.859 0.042 2.360 2.174 2.562     60–69 1.396 0.037 4.037 3.751 4.345 1.416 0.040 4.120 3.806 4.459 1.422 0.040 4.144 3.829 4.486     70–79 1.805 0.038 6.077 5.641 6.547 1.866 0.041 6.464 5.965 7.004 1.873 0.041 6.507 6.004 7.052     80–89 2.233 0.041 9.325 8.597 10.115 2.380 0.045 10.802 9.895 11.792 2.391 0.045 10.923 10.004 11.927     ≥ 90 2.544 0.061 12.734 11.288 14.364 2.825 0.066 16.868 14.835 19.180 2.836 0.066 17.049 14.991 19.391 Charlson Index     0 - - 1.000 - - - - 1.000 - - - - 1.000 - -     1 0.330 0.072 1.391 1.208 1.602 0.322 0.079 1.379 1.182 1.609 0.317 0.079 1.372 1.176 1.601     ≥ 2 0.641 0.094 1.898 1.579 2.280 0.635 0.104 1.888 1.540 2.314 0.632 0.104 1.881 1.536 2.305 Elixhauser comorbidities     Yes 0.278 0.073 1.321 1.145 1.523 0.359 0.080 1.431 1.224 1.674 0.345 0.080 1.411 1.207 1.651     No - - 1.000 - - - - 1.000 - - - - 1.000 - - Obesity     Yes 0.616 0.100 1.851 1.521 2.252 0.448 0.112 1.565 1.258 1.947 0.447 0.111 1.563 1.256 1.944     No - - 1.000 - - - - 1.000 - - - - 1.000 - - Hypertension     Yes -0.120 0.072 0.887 0.770 1.021 -0.157 0.079 0.855 0.732 0.998 -0.157 0.079 0.854 0.732 0.997     No - - 1.000 - - - - 1.000 - - - - 1.000 - - Diabetes     Yes -0.233 0.093 0.792 0.660 0.950 -0.296 0.102 0.744 0.609 0.909 -0.289 0.102 0.749 0.613 0.915     No - - 1.000 - - - - 1.000 - - - - 1.000 - - COVID-19 as secondary diagnosis     Yes 0.153 0.058 1.165 1.040 1.305 0.149 0.063 1.161 1.026 1.314 0.139 0.063 1.149 1.015 1.301     No - - 1.000 - - - - 1.000 - - - - 1.000 - - Length of stay (days)     0–1 - - - - - 1.263 0.034 3.537 3.308 3.780 1.276 0.034 3.582 3.350 3.829     2–7 - - - - - 0.238 0.023 1.269 1.214 1.327 0.244 0.023 1.276 1.221 1.335     8–22 - - - - - - - 1.000 - - - - 1.000 - -     ≥ 23 - - - - - -0.418 0.049 0.658 0.598 0.725 -0.423 0.049 0.655 0.595 0.722 ICU use     Yes - - - - - 2.431 0.027 11.374 10.784 11.997 2.415 0.027 11.192 10.609 11.806     No - - - - - - - 1.000 - - - - 1.000 - - Hospital ownership     Public—State - - - - - - - - - - 0.479 0.085 1.615 1.366 1.909     Public—Federal - - - - - - - - - - 0.430 0.234 1.538 0.972 2.434     Private - - - - - - - - - - -0.073 0.173 0.930 0.662 1.306     Philanthropic - - - - - - - - - - 0.247 0.080 1.281 1.095 1.498     Public—Municipality - - - - - - - - - - - - 1.000 - - State     Acre - - - - - - - - - - 1.690 0.581 5.418 1.736 16.909     Amazonas - - - - - - - - - - 1.123 0.189 3.073 2.121 4.453     Pará - - - - - - - - - - 0.666 0.156 1.947 1.434 2.644     Amapá - - - - - - - - - - 2.346 0.536 10.441 3.649 29.871     Maranhão - - - - - - - - - - 0.433 0.146 1.542 1.158 2.052     Ceará - - - - - - - - - - 0.681 0.131 1.976 1.528 2.556     Rio Grande do Norte - - - - - - - - - - 0.970 0.231 2.639 1.677 4.152     Paraíba - - - - - - - - - - 0.676 0.301 1.966 1.090 3.545     Pernambuco - - - - - - - - - - 0.526 0.126 1.693 1.322 2.167     Alagoas - - - - - - - - - - 0.962 0.268 2.618 1.547 4.429     Bahia - - - - - - - - - - 0.330 0.166 1.392 1.006 1.925     Rio de Janeiro - - - - - - - - - - 0.827 0.124 2.286 1.794 2.912     São Paulo - - - - - - - - - - 0.214 0.089 1.239 1.041 1.475     Other - - - - - - - - - - - - 1.000 - - Population size     < 100.000 hab. - - - - - - - - - - - - 1.000 - -     ≥ 100.000 hab. - - - - - - - - - - 0.543 0.069 1.721 1.503 1.972 Residence/hospitalization     Same municipality - - - - - - - - - - -0.099 0.026 0.906 0.861 0.952     Different municipalities - - - - - - - - - - - - 1.000 - - -2 Res Log Pseudo-Likelihood 447143.5 467927.5 468080.5 C statistics 0.6942 0.8017 0.8179 Source: Ministry of Health–The SUS Inpatient Care Information System Considering model 3, it is possible to observe that the odds of in-hospital death among men were 16.8% higher than among women. The patients’ age groups were very important predictors of the likelihood of death. Compared to patients between 18 and 39 years old, patients aged 40 to 49 years were 54.7% more likely to die in the hospital, while for patients aged 90 years or more this increase reached 1,604.9%. Furthermore, blacks had higher odds of death during the hospital stay (OR = 1.14; 95% CI 1.05–1.25), compared to the reference category including whites, yellows, indigenous and individuals without a record of the variable. The adjusted risk of in-hospital death for mixed race people was not statistically significant at the 5% level, but “borderline.” Despite the substantial underreporting of clinical conditions, the behavior of the Charlson and Elixhauser indices was consistent with the hypothesis of higher likelihood of death among patients with comorbidities. The odds of death were 37.2% and 88.1% (model 3) higher among patients with Charlson index scores equal to 1 and ≥2, respectively, compared to those with a score equal to zero, controlling for other variables. For those with Elixhauser comorbidities, the adjusted odds of dying were 41.1% higher than for those without such comorbidities. Presence of hypertension (OR = 0.85; 95% CI 0.73–0.99) and diabetes (OR = 0.75; 95% CI 0.61–0.91) had protective effects, regardless of the inclusion of the other comorbidity measures. Obesity was statistically associated with higher odds of in-hospital death (56.3% higher among obese people, compared to non-obese people). Finally, patients who had COVID-19 as a secondary diagnosis, liable, in some cases, to have acquired the infection in the hospital itself, had 14.9% higher odds of dying in the hospital than those for whom the disease was registered as the main diagnosis. Some changes were observed in the second regression model when compared to what was observed in the bivariate analyses of LOS and occurrence of death (Table 2). Controlling for other variables, the higher odds of in-hospital death (OR = 3.58; 95% CI 3.35–3.83) for those whose stay was up to 1 day continued to be apparent. Compared to patients with LOS between 8 and 22 days, those with LOS between 2 and 7 days were more likely to die (OR = 1.28; 95% CI 1.22–1.34), while patients with a hospital stay of at least 23 days were less likely to die (OR = 0.66; 95% CI 0.60–0.72). ICU use was a relevant predictor of higher likelihood of in-hospital death (OR = 11.19; 95% CI 10.61–11.81). Regarding the third model, which included contextual variables related to the organizational aspect of the hospital and its geographic location, the results point to a greater likelihood of in-hospital deaths in state public hospitals and philanthropic hospitals, compared to municipal public hospitals, in addition to a group of Brazilian states where in-hospital mortality due to COVID-19 was more critical in the period under scrutiny. The hospitalized patients in the states of Amazonas, Rio Grande do Norte, Alagoas, Rio de Janeiro, Ceará, Paraíba, Pará, Pernambuco and Maranhão, were at least 50% more likely to die than in other states in the reference category (predominantly states in the Southern and Midwestern regions), controlling for other variables. The highest odds ratios were, however, observed for Acre (OR = 5.42; 95% CI 1.74–16.91) and Amapá (OR = 10.44; 95% CI 3.65–29.87), which, due to the small number of hospitalizations, had estimates with very broad confidence intervals. Finally, the odds of death during hospitalization were 72.1% higher in municipalities with at least 100 thousand inhabitants and being admitted to a hospital in the same municipality of residence remained a protective factor for the outcome variable considered. Based on the statistics related to the goodness of fit of the models, the three blocks of variables in the models constituted explanatory factors for the variation in in-hospital deaths. The attributes of the patients themselves allowed for a reasonable predictive capacity of the model (c = 0.69), which significantly increased with the inclusion of variables related to the care process (c = 0.80). Then, a more modest improvement was observed with the inclusion of the variables related to the hospital's organizational context and geographic area (c = 0.82). Discussion The study provides a comprehensive overview of COVID-19 hospitalizations that occurred in the SUS, including 89,405 hospitalizations, among which 24.4% resulted in death. By focusing on the exploration of explanatory factors for the occurrence of death during hospitalizations due to COVID-19, it contributes with relevant findings to the international debate, confirming knowledge that has been consolidated, raising questions and exposing specificities of the Brazilian context. Sociodemographic factors and the presence of comorbidities have been identified as associated with COVID-19 hospitalization and death [20, 21]. Similar to what was described in other studies, males, older age group gradually higher, black race/color, Charlson score, presence of Elixhauser comorbidity and obesity presented higher adjusted odds of death [7, 8, 22–24]. This study ratifies the association of a higher risk of COVID-19 in-hospital mortality with being black, as reported in other studies [10, 25, 26]. It is more ambiguous, however, with regard to the risk differentiation of mixed race people, contrasting with the study published by Baqui et al, which even attributed higher risk to mixed race people than to blacks [10]. In Brazil, color/racial differences are correlated with socioeconomic conditions, and blacks and mixed race people are in general more vulnerable than whites. In the specific context of COVID-19, they are still likely to expose themselves more often to the virus [27]. Nevertheless, in the country as a whole, the mixed race color may express a wide spectrum of ethnic mix, which could result in some imprecision and blur effects. It is noteworthy that some studies have looked for explanations for the higher mortality among blacks beyond the socioeconomic aspects, accounting for pathophysiological mechanisms. One relationship of interest is that among being black, COVID-19 and the risk of venous thrombosis [28]. The low level of comorbidity reporting (21,7% of the hospitalizations) in our data is a weakness in the study. It corresponds to just over a quarter of the proportion of patients with at least one chronic disease among those admitted to hospital in a study from New York [20]. It probably reflects some negligence in relation to clinical information in administrative data, but also a negative culture of underreporting, aggravated by the stressful conditions for COVID-19 patient attendance. In spite of the problem, the Charlson and Elixhauser indices, as expected, were shown to have positive associations with the in-hospital mortality risk, and obesity was shown to increase that risk, irrespective of other factors [29]. The protective effects of hypertension and diabetes in the multivariate models seem paradoxical and inconsistent with some reports in the literature [17, 30], but may also reflect the control for the Charlson and Elixhauser indices. In fact, in a review of the relationship between hypertension and the use of angiotensin-converting enzyme (ACE) inhibitors with COVID-19 outcomes, the authors argue that there is no evidence to support the hypothesis that hypertension or inhibitors of the renin-angiotensin system contribute to unfavorable outcomes in viral infections [31]. The median LOS of 5 days is consistent with data in the United States [17], but differs substantively from data in Lombardy, the Italian region most affected by the pandemic in the first months of 2020, with a median of 28 days of hospitalization [32]. Findings of this study indicate how the characteristics of the patients affect the relationship between LOS and in-hospital mortality. Controlling for confounding variables, the occurrence of shorter hospitalizations is significantly associated with the occurrence of death. Especially, the high risk of death in the first 24 hours of hospitalization, may reflect problems patients had to access timely inpatient care, as well as the quality of care immediately received. Among the hospitalizations that used the ICU, the adjusted odds ratio of death was extreme (OR 11.74), probably reflecting the severity of the cases, but also some synergy with the quality of care. In a meta-analysis that included 24 studies from three continents, a combined mortality rate of 41.6% of patients admitted to the ICU was observed, a value well below the 55.7% found in this study [4]. In the context of the pandemic, the SUS hospital network has been crucial for responding to the demands for acute care that emerged. However, numerous problems related to health service structural conditions and performance have arisen, including the insufficient number of hospital beds and staff to perform specialized care in the ICU [8, 33, 34]. There was, consequently, a broad variation in healthcare effectiveness. In Northern Brazil, at the beginning of the COVID-19 outbreak, states such as Acre, Roraima, Amazonas, Pará and Amapá featured municipalities with exceptionally low or no capacity whatsoever to treat severe cases of the disease [33], which is reflected in the high adjusted odds of death, especially in Amapá, Acre and Amazonas. In Northeastern Brazil, Rio Grande do Norte was the state with the highest odds of in-hospital death, with only three municipalities with minimal capacity to deal with severe cases of the infection at the beginning of the pandemic. Despite the greater availability of hospital beds in Southern and Southeastern Brazil, São Paulo and Rio de Janeiro were hard hit by the pandemic and Rio de Janeiro, in particular, had comparatively very high odds of in-hospital mortality, besides other serious outcomes. There was poor interaction between the state and municipalities severely affected, such as in the capital, the management of the pandemic was chaotic, and the strategy of implementing field hospitals failed partially, with some never completed and others delivered too late. Our study has limitations, the main issue being the source of information used. The SIH only covers the SUS hospital network, which makes it impossible to carry out a more comprehensive analysis, including healthcare received by those privately insured. It is likely that differences in COVID-19 in-hospital mortality arose reflecting inequities in supply and access to critical resources in specific states of the country [35]. In addition to this, the data flow from providers to the system, and the subsequent consolidation of the information, is slower than desirable to monitor the care provided in a pandemic context that requires swift decisions. Issues regarding the sufficiency and quality of the information recorded should also be stressed, notably the high underreporting of comorbidities and the 'race/color' variable. Furthermore, it was not possible to include cases treated in the emergency wards, and data on the evolution of cases (such as vital signs), and on the care process (professionals involved, use of invasive mechanical ventilation and laboratory tests, including tests for the detection of COVID-19) are absent from this source, which precludes more detailed analysis. Moreover, the study does not cover deaths that occurred outside hospitals, which constitutes an important statistic to fully grasp the scale of the pandemic morbidity and mortality scenario. Despite the limitations mentioned, the study has the merit of examining in-hospital mortality with national coverage of the COVID-19 patients who were admitted to hospitals and received care from the SUS, thereby enabling the assessment of the effects of individual and contextual risk factors. Although the source of information, design and statistical modeling limit comparability, the findings broadly corroborate those highlighted by Baqui et al. (2020) regarding regional and racial/ethnic variation in the Brazilian context [10]. In addition, although there is a vast and growing literature on COVID-19, there are still few attempts to address the issue with the strategies we used, focusing on the profile and outcomes of COVID-19 hospitalizations nationwide and painstakingly assess the effects of the groups of variables [10, 17]. In the Brazilian context, the socioeconomic gradient emerges, even with the limits of the data on race/color and geographic location to trace the multiple facets of the inequalities in society [36]. The broad disparities in the performance of the health system among states also becomes apparent. This is related, in part, not only to the structure and prior organization of the services available, but also to the insufficient regional/local capacity to coordinate actions to deal with COVID-19, in the absence of national coordination able to mitigate the major regional differences in an immense and diverse country. It is of paramount importance to emphasize that this study addresses hospitalizations in the initial months of the pandemic, reflecting the major crisis faced by some capitals, especially in the North, Northeast and Southeast of Brazil, with a high caseload and insufficient healthcare capacity. Covid-19 clinical management, predominantly for severe cases, has subsequently evolved. It is to be expected that the same analyses in subsequent months might provide another overview of how the country has been affected. With the results provided, we hope to contribute to the improvement of the care delivered and to define strategies to face future developments in the progress of the pandemic until the population has access to an effective vaccine. It is important to remember that the pandemic has evolved dynamically throughout the country. The authors are grateful for support from PrInt Fiocruz-CAPES Program. CCAP, MM e MCP are recipients of productivity fellowships from the Brazilian National Council of Scientific and Technological Development (CNPq). 10.1371/journal.pone.0243126.r001 Decision Letter 0 Nunes Bruno Pereira Academic Editor © 2020 Bruno Pereira Nunes2020Bruno Pereira NunesThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Submission Version0 30 Sep 2020 PONE-D-20-27175 COVID-19 hospitalizations in Brazil’s Unified Health System (SUS) PLOS ONE Dear Dr. Portela, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. 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(Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: It's a large study covering Unified Health System in the COVID-19 pandemic. But I feel it's lack of novelty and failed to provide new information regarding risk factors of in-hospital mortality in patients with COVID-19. You mentioned that 78.3% of hospitalizations did not have any secondary diagnoses, which made me concern that there might be misdocumentation affecting the validity of the data. Also, it might be better to consider length of stay and ICU use as outcomes. Reviewer #2: The theme chosen for the study was of great relevance. The Title and absctract were adequate and consistent. The introduction presented fundamental characteristics in its composition. As an example, I point out the description of the unique health system, peculiar to Brazil and necessary for the understanding of the study. I advise that the text between lines 77 to 99 could be summarized. The objectives were clear. 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Please note that Supporting Information files do not need this step. 10.1371/journal.pone.0243126.r002 Author response to Decision Letter 0 Submission Version1 5 Nov 2020 EDITOR’S AND REVIEWERS’ COMMENTS AND AUTHOR’S ANSWERS EDITOR Comment: Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Two reviewers have assessed the manuscript and provide discordant decisions. The main reasons for reject the paper is some concerns about study validity. Please, the new version should include specific information about study validity to be the manuscript considered to publication. Answer: The concerns with the validity of the study aroused especially from the underreporting of comorbidities, but there were also the indications, by one of the two reviewers, of “lack of novelty and failed to provide new information regarding risk factors of in-hospital mortality”. Our first point is that administrative data may be really important economic and prompt sources in observational studies in the Health Services Research field. The SUS Hospital Information System (SIH), as other administrative data worldwide, have been broadly employed in scientific studies, despite the inherent limitations. The SIH is the main source of information on hospital production in Brazil. It includes all hospitalizations in the Brazilian public system, responsible for covering 75% of the Brazilian population in the whole country. The anonymized SIH database is freely accessible in DATASUS website, and, to the best of our knowledge, our study is the first to use them to picture COVID-19 pandemic in Brazil. Even having some flow problems and not being available in real time, the data is mostly available in relatively short time. In the course of a pandemic in which evidence still needs to be accumulated, and Brazil has unquestionable importance as, at this point, the third country in number of cases and second in number of deaths, the role it can play is not negligible. At the same time, we are honest and explicitly recognize in the text the weaknesses of the data, arguing that they are supplanted by the contributions the study brings out. Despite the efforts made in the last years to expand the number of fields for secondary diagnoses from one to nine in the SIH, they are not fulfilled. We truly have reasons to believe, based in previous studies and the experience as researchers, that there is a bad culture of clinical data underreporting in the country. If the study were based on the medical records in the hospitals, we would still have a lot of missing data. Hospitals with research activities often keep separate data for patients included in studies. The alternative of only be confident on primary data, however, would be unpractical and would neglect the capacity of secondary data provide useful information. In our data, 78.3% of hospitalizations did not have a secondary diagnosis. It is likely that the COVID-19 scenario had even contributed for worse underreporting of comorbidities. In fact, data misrecording is expected in urgency contexts as the pandemic. Despite the problem, the death predictive capacity of model 1, the one which accounts for the clinical predictors of inpatient mortality, was 0.69, borderline to the range that is considered adequate (0.70-0.80). We developed the study from the perspective of health services researchers and not clinicians. The level of hospital mortality is seen as an outcome that result not only from clinical aspects, but also the quality of healthcare. It was not our goal to necessarily provide new information on clinical risk factors of in-hospital mortality, but the available evidence was employed and tested in the modeling process. The focus was to compare COVID-19 hospital mortality adjusted by severity, expressing variations in quality of care – effectiveness and access inequalities – between hospital ownership and geographic areas. Even considering the papers available, more evidence on care delivered in different countries seems important yet. The high volume of inpatients analyzed, severity adjusted strategies, focus on quality of care, and comparing geographical areas inequalities are the strong points. Comment: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf Answer: This was done. Comment: We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service. Whilst you may use any professional scientific editing service of your choice, PLOS has partnered with both American Journal Experts (AJE) and Editage to provide discounted services to PLOS authors. Both organizations have experience helping authors meet PLOS guidelines and can provide language editing, translation, manuscript formatting, and figure formatting to ensure your manuscript meets our submission guidelines. To take advantage of our partnership with AJE, visit the AJE website (http://learn.aje.com/plos/) for a 15% discount off AJE services. To take advantage of our partnership with Editage, visit the Editage website (www.editage.com) and enter referral code PLOSEDIT for a 15% discount off Editage services. If the PLOS editorial team finds any language issues in text that either AJE or Editage has edited, the service provider will re-edit the text for free. Upon resubmission, please provide the following: • The name of the colleague or the details of the professional service that edited your manuscript • A copy of your manuscript showing your changes by either highlighting them or using track changes (uploaded as a *supporting information* file) • A clean copy of the edited manuscript (uploaded as the new *manuscript* file) Answer: This was done. The English reviewer was Derrick Guy Phillips (Tel.: 55-21-99182-0989) – Union-registered translator since 1978 and Sworn Public Translator certified by the Board of Trade in Rio de Janeiro since March 2010. / M.A. – Master of Arts Degree ‘summa cum laude’ in Modern Languages & Philosophy (French, Spanish, Portuguese & Philosophy) from the University of St Andrews. Scotland (September 1969 through June 1973). / Post-Graduate Diploma in “International Marketing for Language Graduates” from the University of Central London, England (September 1977 through June 1978) – Group Award and Special Mention. / Translator and Director of Feedback Traduções Ltda. (Translation Agency), since 1978. Comment: If you are reporting a retrospective study of medical records or archived samples, please ensure that you have discussed whether all data were fully anonymized before you accessed them. Answer: The data obtained were already anonymized. Comment: Please note that according to our submission guidelines (http://journals.plos.org/plosone/s/submission-guidelines), outmoded terms and potentially stigmatizing labels should be changed to more current, acceptable terminology. For example: “Pardo” should be changed to “mixed race” (as appropriate). Answer: This was done. Comment: In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability. Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized. Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access. We will update your Data Availability statement to reflect the information you provide in your cover letter. Answer: OK. The data is open access, as described in the manuscript. We are making the data used in the study available. Anyway, we can make the data we employed in the study available following instructions of the journal. REVIEWERS 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: No Reviewer #2: Yes Answer: As we have already indicated in this letter and in the manuscript, we acknowledge that the study has some limitations. However, we are also convinced that it provides a good overview of COVID-19 hospitalizations in the Brazilian Unified Health System in the first four months of the pandemic in Brazil and highlights the huge variation in in-hospital mortality, associated with social and healthcare quality inequities throughout the country. We maintain that our research is sound and the data support all conclusions provided. The study is replicable. ________________________________________ 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes Answer: We believe that we used the best statistical approach available for the research question at hand. We were interested in analyzing factors associated with in-hospital mortality due to Covid-19 and applied variables commonly used in the health service research literature to address this type of question. Furthermore, we were careful to use appropriate modelling to account for the violation of the assumption of independence among observations in the same hospital. ________________________________________ 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes Answer: OK. ________________________________________ 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: No Reviewer #2: Yes Answer: We are providing a new version of the manuscript revised by an English native speaker and professional translator. ________________________________________ 5. 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 #1: It's a large study covering Unified Health System in the COVID-19 pandemic. But I feel it's lack of novelty and failed to provide new information regarding risk factors of in-hospital mortality in patients with COVID-19. You mentioned that 78.3% of hospitalizations did not have any secondary diagnoses, which made me concern that there might be misdocumentation affecting the validity of the data. Also, it might be better to consider length of stay and ICU use as outcomes. Answer: To the best of our knowledge, this is the first study using data from the SUS Hospital Information System, an administrative database which covers, nationally, all hospitalizations that take place in the SUS, that provides healthcare to 75% of the Brazilian population. We were able to analyze 89,405 hospitalizations across the country in the first four months of the COVID-19 pandemic in Brazil. Considering the importance of Brazil in the global COVID-19 pandemic scene, we would probably be making a bigger mistake if we neglected information which can be extracted from the database, and would have no better source, especially if swiftness and economic criteria were applied. Despite the underreporting of comorbidities, the strong gradient observed for age in all models, which has even increased along the inclusion of the blocks of variables, seems to indicate age as a more reliable indicator to predict in-hospital death in the context. Such analyses are needed to shine a light on in-hospital mortality and associated factors. Although much has been discussed in the media and in the academic output about comorbidities such as hypertension, obesity and diabetes, we found some novel results such as: the surprisingly higher chance of death when the length of stay was short (less than 24 hours), probably reflecting access barriers; higher odds ratio adjusted by severity in some areas, indicating effectiveness variability; the in-hospital mortality risk related to transference to another city for healthcare; specific differences among hospitals with different kinds of ownership. Also, it is true that the important race gradient (higher mortality among blacks, followed by mixed race people) had already been shown in other studies, but it is likely to reflect marked social inequities that need to be exposed and addressed by policy makers. Accumulating evidence and understanding how COVID-19 outcomes are affected by other factors beyond clinical factors is the overriding goal. With regard to the comment about the inclusion of length of stay (LOS) and ICU use as explanatory variables, we justify our choice underlining our health services researchers’ perspective. Both variables used are healthcare processing variables. ICU use ends up reflecting, to some extent, clinical severity, beyond the comorbidity data itself. LOS, in turn, seems to reflect healthcare access and quality. Our study design and analyses were not oriented towards detecting causality relations in which cause precedes effect. By including both variables, we sought to identify simply how the risk of in-hospital mortality varies vis-à-vis distinct standards of hospital stay duration and ICU use or not. A data quality gap is to be expected in all sources of information, even medical records. The emergency room, ICU and wards were operating in crisis mode and under dramatic conditions. Based on the abovementioned points, we believe that all possible information should be obtained and examined to draw a more complete picture of the care provided, in various contexts and countries. Reviewer #2: The theme chosen for the study was of great relevance. The Title and abstract were adequate and consistent. The introduction presented fundamental characteristics in its composition. As an example, I point out the description of the unique health system, peculiar to Brazil and necessary for the understanding of the study. I advise that the text between lines 77 to 99 could be summarized. The objectives were clear. As a suggestion, I would include the last sentence after the objectives (lines 116 to 118) as a strong point of the work and not at the end of this paragraph. The methods have been well described. I advise you to describe the Charlson and Elixhauser indices in this topic and not in the results. The results reported are consistent and very important topics. We advise you to keep the discussion more concise, valuing the most important results. Answer: Thank you for your careful review of our manuscript and comments about the adequacy of our title, abstract, objectives and results. We think that we attended all your recommendations. We summarized the text between lines 77 and 99 of the previous version to nine lines (73-81) in the new version. We also accepted your suggestion transferring the last sentence after the objectives to the end of the Discussion section, as a strong point of the work. In fact, the descriptions and references of the Charlson and Elixhauser indices were already in the Methods section. We have highlighted them in the file with track changes, and added a few more explanations to make the text clearer. No explanations about them were left in the results. We have worked on the Discussion and made it shorter by emphasizing the results that we considered most important. Attachment Submitted filename: Response to reviewers.docx Click here for additional data file. 10.1371/journal.pone.0243126.r003 Decision Letter 1 Nunes Bruno Pereira Academic Editor © 2020 Bruno Pereira Nunes2020Bruno Pereira NunesThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Submission Version1 17 Nov 2020 COVID-19 hospitalizations in Brazil’s Unified Health System (SUS) PONE-D-20-27175R1 Dear Dr. Portela, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Bruno Pereira Nunes, Ph.D. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: 10.1371/journal.pone.0243126.r004 Acceptance letter Nunes Bruno Pereira Academic Editor © 2020 Bruno Pereira Nunes2020Bruno Pereira NunesThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 23 Nov 2020 PONE-D-20-27175R1 COVID-19 hospitalizations in Brazil’s Unified Health System (SUS) Dear Dr. Portela: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Bruno Pereira Nunes Academic Editor PLOS ONE ==== Refs References 1 Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde . Boletim Epidemiológico Especial. Doença pelo Coronavírus COVID-19 . Semana Epidemiológica 32 (02 a 08/08) . 2020 ; no 26 Available in: https://www.saude.gov.br/images/pdf/2020/August/12/Boletim-epidemiologico-COVID-26.pdf [accessed on 20/08/2020]. 2 Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde . Boletim Epidemiológico Especial COE-COVID19: Doença pelo Coronavírus COVID-19 . 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