Abstract

Introduction

The first case of COVID-19 in Brazil was officially confirmed on February 26, 2020, in the state of São Paulo. Shortly more than a month later, all 27 states of the country had already reported at least ten cases of the disease. Spread of the virus followed the country’s air traffic flows, first reaching the most populous state capitals, connected to São Paulo and Rio de Janeiro and later the cities in the metropolitan areas and gradually reaching the countryside ¹1. Candido DS, Claro IM, Jesus JG, Souza WM, Moreira FRR, Dellicour S, et al. Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science 2020; 369:1255-60.^,22. Coelho FC, Lana RM, Cruz OG, Codeco CT, Villela D, Bastos LS, et al. Assessing the potential impact of COVID-19 in Brazil: mobility, morbidity and the burden on the Health Care System. medRxiv 2020; 26 mar. http://medrxiv.org/lookup/doi/10.1101/2020.03.19.20039131v2.
http://medrxiv.org/lookup/doi/10.1101/20... . In the first six months of the pandemic in Brazil (as of August 22, 2020), according to the Coronavirus Panel (https://covid.saude.gov.br/), there were officially 5,323,630 confirmed COVID-19 cases and 155,900 deaths in the country. These records include cases (asymptomatic, mild, and severe) and deaths from SARS-CoV-2 confirmed by different laboratory tests (RT-PCR, rapid tests, serological tests), beside occasional clinical imaging diagnoses ³3. Secretaria de Vigilância em Saúde, Ministério da Saúde. Guia de vigilância epidemiológica emergência de saúde pública de importância nacional pela doença pelo coronavírus 2019: vigilância de síndromes respiratórias agudas COVID-19. Brasília: Ministério da Saúde; 2020..

Since the beginning of the COVID-19 pandemic, there was a major movement towards provision of data and information that could assist contingency plans and mitigation strategies, among other measures. In addition, a major global movement combined efforts by various research groups, institutions, and countries in the search for vaccines against the disease ⁴4. Domingues CMAS. Desafios para a realização da campanha de vacinação contra a COVID-19 no Brasil. Cad Saúde Pública 2021; 37:e00344620.. As of February 23, 2021, more than 250 vaccines - 182 in preclinical development, 73 in clinical development, and of these, 16 in phase III - were being tested ⁵5. World Health Organization. Draft landscape and tracker of COVID-19 candidate vaccines. https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines (acessado em 24/Fev/2021).
https://www.who.int/publications/m/item/... . Of this total, five had already been approved for use in some countries ⁶6. World Health Organization. Coronavirus disease (COVID-19): vaccines. https://www.who.int/news-room/q-a-detail/coronavirus-disease-(covid-19)-vaccines (acessado em 27/Jan/2021).
https://www.who.int/news-room/q-a-detail... , two vaccines were approved for emergency use in Brazil ⁷7. Agência Nacional de Vigilância Sanitária. Uso emergencial: confira votos, relatório e apresentações da reunião. https://www.gov.br/anvisa/pt-br/assuntos/noticias-anvisa/2021/confira-materiais-da-reuniao-extraordinaria-da-dicol (acessado em 16/Fev/2021).
https://www.gov.br/anvisa/pt-br/assuntos... , and one was approved with definitive registration ⁸8. Vacina da Pfizer é a 1ª contra a Covid a obter registro definitivo no Brasil. G1 2021; 23 fev. https://g1.globo.com/bemestar/vacina/noticia/2021/02/23/anvisa-concede-registro-definitivo-a-vacina-da-pfizer.ghtml.
https://g1.globo.com/bemestar/vacina/not... . Even before these vaccines were released, it was known that distribution of doses according to priority groups would be necessary due to the low availability of doses for the global demand ⁹9. World Health Organization. Coronavirus disease (COVID-19): vaccine access and allocation. https://www.who.int/news-room/q-a-detail/coronavirus-disease-(covid-19)-vaccine-access-and-allocation (acessado em 16/Fev/2021).
https://www.who.int/news-room/q-a-detail... . Specifically for Brazil, a continental-sized country with 210 million inhabitants, the definition of priority groups is essential for any campaign of this magnitude.

The Brazilian National Immunization Program (PNI in Portuguese), known internationally for its capacity to serve a country with continental dimensions and major socioeconomic diversity, with a broad vaccination calendar, is an important arm of the Brazilian Unified National Health System (SUS) (Brazilian Health Informatics Department. http://sipni.datasus.gov.br/si-pni-web/faces/apresentacaoSite.jsf, accessed on 16/Feb/2021) and is responsible for the National Plan for Operationalization of COVID-19 Vaccination ¹⁰10. Ministério da Saúde. Plano Nacional de operacionalização da vacinação contra a COVID-19. https://www.gov.br/saude/pt-br/media/pdf/2020/dezembro/16/plano_vacinacao_versao_eletronica-1.pdf (acessado em 15/Jan/2021).
https://www.gov.br/saude/pt-br/media/pdf... . The activities of the PNI include the annual influenza (H1N1) vaccination campaign, which is organized according to priority groups, as is COVID-19 (http://sipni.datasus.gov.br/si-pni-web/faces/apresentacaoSite.jsf).

According to the WHO, the focus for the definition of priority groups for COVID-19 vaccination in a context of community transmission should be, at the initial stage, direct to the reduction of morbidity and mortality, besides the maintenance of essential services and honoring the reciprocity principle (to protect individuals with high exposure to the virus while in service to the community) ⁹9. World Health Organization. Coronavirus disease (COVID-19): vaccine access and allocation. https://www.who.int/news-room/q-a-detail/coronavirus-disease-(covid-19)-vaccine-access-and-allocation (acessado em 16/Fev/2021).
https://www.who.int/news-room/q-a-detail... .

The main objective of the present study was to define risk groups for hospitalization and death from COVID-19. These analyses assisted the definition of priority groups for the National Plan for Operationalization of COVID-19 Vaccination drafted by the PNI for the campaign’s first stage. A study was performed with descriptive analyses of hospitalizations and deaths from severe acute respiratory illness associated with COVID-19 (SARI-COVID) reported in the first six months of the pandemic in Brazil. The current study is an update and extension of the analyses published previously in Niquini et al. ¹¹11. Niquini RP, Lana RM, Pacheco AG, Cruz OG, Coelho FC, Carvalho LM, et al. SRAG por COVID-19 no Brasil: descrição e comparação de características demográficas e comorbidades com SRAG por influenza e com a população geral. Cad Saúde Pública 2020; 36:e00149420., especially with the use of overrisk indicators for comparison of groups.

Methods

Data

Data on hospitalizations and deaths from SARI-COVID were obtained from SIVEP-Gripe (https://sivepgripe.saude.gov.br/sivepgripe), the official system for recording hospitalizations and deaths from severe acute respiratory illness (SARI) in the entire country. As part of the reporting protocol, every case identified as SARI has a biological sample collected for subsequent laboratory analysis. Since March 2020, testing of the samples also includes SARS-CoV-2. The study only included cases with recorded confirmation by RT-PCR, considered the gold standard laboratory diagnosis for SARS-CoV-2 infection.

We considered the records with date of first symptoms up to the 35^th Epidemiological Week (EW) of 2020 (August 29, 2020) to avoid loss of information from delays in keying in and updating the laboratory diagnosis. The SIVEP-Gripe variables considered for analysis of risk factors were the following: age group (aggregated every 5 years from 0 to 4 years up to 90 years and older), sex, presence of comorbidities (diabetes mellitus, chronic kidney disease, asthma, other chronic lung diseases, and chronic cardiovascular disease - CVD). Information on systemic hypertension (SH) was obtained from searches in the open text field that lists “other comorbidities”. Text searches used the strings “HAS” or “H.A.S.” or “HIPERTE” based on the stringr wrapper (https://CRAN.R-project.org/package=stringr) available in the R environment (http://www.r-project.org). Importantly, the obesity variable was excluded from the analyses, even though it is an important risk factor for COVID-19 ¹²12. Andrade CLT, Pereira CCA, Martins M, Lima SML, Portela MC. COVID-19 hospitalizations in Brazil's Unified Health System (SUS). PLoS One 2020; 15:e0243126.^,1313. Petrilli CM, Jones SA, Yang J, Rajagopalan H, O'Donnell L, Chernyak Y, et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 2020; 369:m1966., due to failures in completion of this information (besides that of other comorbidities) and the difficulty in defining obesity at the time of completion in the reporting. Assessment of obesity requires body mass index (BMI). In the reporting data, not only is the obesity variable poorly completed, but many reporting forms fail entirely to fill in BMI.

Demographic data on the Brazilian population in 2020 by age group and sex were obtained from projections performed by the Brazilian Institute of Geography and Statistics (IBGE) ¹⁴14. Instituto Brasileiro de Geografia e Estatística. Projeção da população do Brasil e Unidades da Federação por sexo e idade para o período 2000-2030. https://datasus.saude.gov.br/populacao-residente/ (acessado em 17/Out/2020).
https://datasus.saude.gov.br/populacao-r... .

Data on prevalence of comorbidities in the general population and stratified by age group for Brazil were obtained from the Brazilian National Health Survey (PNS in Portuguese) in 2013 ¹⁵15. Instituto Brasileiro de Geografia e Estatística. Pesquisa Nacional de Saúde 2013. Percepção do estado de saúde, estilos de vida e doenças crônicas. https://www.ibge.gov.br/estatisticas/sociais/saude/9160-pesquisa-nacional-de-saude.html (acessado em 17/Out/2020).
https://www.ibge.gov.br/estatisticas/soc... ^,1616. Malta DC, Stopa SR, Szwarcwald CL, Gomes NL, Silva Júnior JB, Reis AAC. A vigilância e o monitoramento das principais doenças crônicas não transmissíveis no Brasil - Pesquisa Nacional de Saúde, 2013. Rev Bras Epidemiol 2015; 18:3-16.. The target comorbidities were diabetes mellitus, chronic kidney disease (CKD), asthma (or asthmatic bronchitis), lung diseases (pulmonary emphysema, chronic bronchitis, or chronic obstructive pulmonary disease - COPD), and CVD.

CVD, as defined by the PNS, include systemic hypertension and heart diseases (myocardial infarction, angina, heart failure, and others). To produce a consistent definition, a compound variable was created in the SIVEP-Gripe database, called CVD, which includes systemic hypertension and chronic CVD. Age stratification for comorbidities considered three groups, 18-39 years, 40-59 years, and 60 years or older. It was not possible to calculate the prevalence in younger ages, since the PNS sample only includes individuals 18 years or older ¹⁵15. Instituto Brasileiro de Geografia e Estatística. Pesquisa Nacional de Saúde 2013. Percepção do estado de saúde, estilos de vida e doenças crônicas. https://www.ibge.gov.br/estatisticas/sociais/saude/9160-pesquisa-nacional-de-saude.html (acessado em 17/Out/2020).
https://www.ibge.gov.br/estatisticas/soc... ^,1616. Malta DC, Stopa SR, Szwarcwald CL, Gomes NL, Silva Júnior JB, Reis AAC. A vigilância e o monitoramento das principais doenças crônicas não transmissíveis no Brasil - Pesquisa Nacional de Saúde, 2013. Rev Bras Epidemiol 2015; 18:3-16.. An estimate was made of the number of persons with each comorbidity in 2020, assuming the prevalence measured by the PNS in 2013 ¹⁵15. Instituto Brasileiro de Geografia e Estatística. Pesquisa Nacional de Saúde 2013. Percepção do estado de saúde, estilos de vida e doenças crônicas. https://www.ibge.gov.br/estatisticas/sociais/saude/9160-pesquisa-nacional-de-saude.html (acessado em 17/Out/2020).
https://www.ibge.gov.br/estatisticas/soc... ^,1616. Malta DC, Stopa SR, Szwarcwald CL, Gomes NL, Silva Júnior JB, Reis AAC. A vigilância e o monitoramento das principais doenças crônicas não transmissíveis no Brasil - Pesquisa Nacional de Saúde, 2013. Rev Bras Epidemiol 2015; 18:3-16.. The population projection per age group is aggregated for 5-year periods as mentioned above, so that for the age group from 18-39 years, we added the projected values for the 20-24, 25-29, 30-34, and 35-39-year age groups, and for 18 and 19 years we used two-fifths of the IBGE projection for the age group from 15-19 years.

Data analysis

Incidence rate

Incidence rates were calculated for hospitalizations and deaths per 1,000 inhabitants for SARI-COVID by sex, age group, comorbidity, and comorbidity by age group. Incidence rate in the period consists of the ratio between total hospitalizations (or deaths) from SARI-COVID in the target group up to the 35^th EW in the entire country, divided by the group’s respective projection of the population in 2020 multiplied by 1,000.

Calculation of overrisk

An indicator commonly used in the comparison of risk between groups is relative risk. However, it was not possible to calculate this indicator with the available data, since the groups compared here do not consist of persons exposed versus unexposed to the target risk. We thus opted to use the term overrisk, defined here as the ratio between the risk of hospitalization or death in a specific group divided by the risk of the same event in the general population.

Formally, the overrisk (OvR) of a given group i, OvR _i , was defined as the ratio between the incidence rate of hospitalization or death in group i and the incidence rate of hospitalization or death in the general population. Let y _i be the number of hospitalizations (or deaths) in the target group i, and n _i the reference population for the group. Also let Y be the total hospitalizations (or deaths) in the general population and N this population’s size. The statistical model for calculation of overrisk assumes that the number of hospitalizations (or deaths) from SARI-COVID follows a binomial distribution in both the general population and the target groups. We thus have for each group i,

in which θ _i is the incidence rate of hospitalizations (or deaths) in group i, and J is the total number of groups.

For the general population, we assume that

in which θ ₀ is the incidence rate of hospitalizations (or deaths) in the general population. Therefore, the overrisk of group i is given by

Importantly, the groups consist of partitions of the population, that is Y=Σiyi, so the distribution defined above is an approximation. For large counts, the approximation error is small.

To exemplify overrisk, consider a hypothetical scenario in which 100 deaths are recorded in a specific group with 100,000 persons. Suppose that in the general population there are 102,000 deaths in 210 million inhabitants. In this case, OvR _i is calculated as: (100/100,000)/(102,000/210 million), giving an OvR _i of approximately 2.1. That is, the incidence rate in group i is about twice the incidence rate in the general population.

To measure the uncertainty of the estimates of OvR _i , from the Bayesian perspective, we attributed uniform a priori distributions for θ _i and θ ₀ so that the a posteriori distribution for each parameter was a beta distribution with known parameters. Samples from the a posteriori distribution of OvR _i are obtained via Monte Carlo simulation with a sampling size of 100,000 for the distribution of the OvR probability in each group i. This sample allows obtaining the 95% credibility interval (95%CI) for OvR _i , which is important for assessing whether the overrisk is significant, that is, when the lower limit of the credibility interval is greater than 1. Applying this method to the above-mentioned hypothetical example gives a 95%CI of 1.7-2.5.

Analogously, one defines stratified overrisk or SOvR to calculate the overrisk of a comorbidity in a specific stratum, for example, an age group. The hospitalizations (or deaths) are limited to a specific age group, and the denominator for the SOvR of group i is the incidence rate for the age group (ag) in question, that is,

in which θ _i,ag and θ _0,ag represent the incidence rates in group i and the general population for the target age group. The distribution of SOvR _i (ag) for group i is derived analogously to the distribution of overrisk for group i.

To measure the uncertainty of the estimates of stratified overrisk, one assumes that the number of hospitalizations (or deaths) from SARI-COVID also follows a binomial distribution for age groups or groups with comorbidities.

Thus, the estimated OvR _i for comorbidity c and age group ag, called OvR _c,ag , uses the incidence rate of hospitalizations or deaths in individuals with a given comorbidity and age group as the numerator and compares them to the incidence rate in the general population (denominator). The SOvR _c,ag (ag) uses the same numerator as OvR _c,ag , but the denominator is the incidence rate in the population of age group ag. Therefore, the interpretation of SOvR _c,ag relates to the overrisk associated with a specific comorbidity in a specific age group, while OvR _c,ag takes into account both the effect of the comorbidity and the joint effect of the age group itself.

Results

In Brazil, the total number of hospitalizations with positive RT-PCR diagnosis for SARS-CoV-2 with date of the first symptoms up to EW 35 was 292,089. During this same period, there were 102,562 deaths from SARI-COVID. These values correspond to a incidence rate of 1.4 hospitalizations and 0.5 deaths per 1,000 inhabitants during the period.

The incidence rates for hospitalizations (1.5 per 1,000 inhabitants) and deaths (0.6 per 1,000 inhabitants) were higher for males than for females. This translates as an increased overrisk of hospitalization in males of 1.115 (95%CI: 1.108; 1.122) and a decreased overrisk for females, OvR _s = 0.836 (0.831; 0.842), respectively. For deaths, the difference between the sexes is even greater, with OvR _s = 1.173 (1.161; 1.184) for males and OvR _s = 0.831 (95%CI: 0.822; 0.841) for females.

Figure 1a presents the incidence rates of hospitalizations and deaths from SARI-COVID per 1,000 inhabitants by age group. Figure 1b presents the OvR_ag by age group, in which the reference is the incidence rate of hospitalizations or deaths (Figure 1a) per 1,000 inhabitants in the general population. The resulting values indicate relative protection in age groups 0-4 years (OvR _ag = 0.150) up to 40-44 years (OvR _ag = 0.904), with OvR _ag for hospitalization reaching the minimum level between 10 and 14 years (OvR _ag = 0.033). Starting with the 45-49-year age group (OvR _ag = 1.133), there is a gradual increase in overrisk of hospitalization, reaching OvR _ag = 8.466 in individuals 90 years or older. In relation to deaths, there is strong relative protection in persons up to 24 years of age (OvR _ag < 0.050 for all the age groups), but which loses force starting at 25-29 years (OvR _ag = 0.144) up to 50-54 years (OvR _ag = 0.948). Starting at age group 55-59 years, with OvR _ag = 1.452, there is a gradual increase until reaching OvR _ag = 18.330 over 90 years of age.

Table 1 shows the prevalence and total number of persons with comorbidities by age group in Brazil, estimated by the PNS ¹²12. Andrade CLT, Pereira CCA, Martins M, Lima SML, Portela MC. COVID-19 hospitalizations in Brazil's Unified Health System (SUS). PLoS One 2020; 15:e0243126.. The prevalence of comorbidities, except for asthma, increases with age. CVD stand out with higher prevalence than other comorbidities in all age groups. The same table also shows the absolute number and incidence rates of hospitalizations and deaths from SARI-COVID per 1,000 inhabitants, stratified by comorbidity and age group.

Table 2 shows the estimated overrisk of hospitalization and death stratified by comorbidity and age group. Two indicators are presented, differentiated by the denominator used. OvR _c,ag measures the overrisk of hospitalization or death in relation to the general population. Using this indicator, the comorbidity with the highest overrisk among those studied was diabetes (Table 2). Overrisk of hospitalization is 5.39 (5.34-5.43) times higher on average for adults with diabetes compared to the overall average for the Brazilian adult population. When comparing age groups, the overrisk in persons with diabetes is heavily influenced by age, varying from 2.82 (2.72-2.93) in adults under 40 years to 6.87 (6.81-6.93) in those over 60 years (Table 2). This difference is even more drastic when comparing overrisk of death in persons with diabetes. Diabetics over 60 years of age present overrisk 10 times higher than the population average. Persons with chronic kidney disease had overrisk 4.1 times higher of hospitalization and 6.9 times higher of death compared to the general population. The effect of age on overrisk is more evident for kidney diseases than for diabetes. In young adults with chronic kidney disease, the overrisk is 1.3 times that of the population average, and in the elderly this overrisk increases 14 times. Persons with CVD showed mean overrisk 2.2 times that of the mean level in the population. Age appears again as a factor that increases overrisk, since individuals 40-59 years of age have an overrisk 1.4 times the general average, and those over 60 years have an overrisk of 3.5 times. Meanwhile, asthma only constituted overrisk when associated with age over 60 years (Table 2).

The second indicator in Table 2, SOvR _c,ag (ag), compares overrisk for those with comorbidity and a specific age group in relation to the total population of that age group. This allows isolating the effect of comorbidity from the known effect of age. Among young adults, the presence of comorbidity greatly increases the overrisk, especially for diabetes [SOvR _c,ag (18-39 years) = 7.8], chronic kidney disease [SOvR _c,ag (18-39 years) = 3.55], and CVD [SOvR _c,ag (18-39 years) = 1.75]. Asthma alone did not appear as an overrisk factor for young adults. From 40 to 59 years, diabetes is associated with overrisk of 3.0 compared to the mean risk for the population in this group, and chronic kidney disease has an overrisk of 1.96. Asthma did not appear as a risk factor in this age group.

The OvR _ag for the age groups used in Table 2 are 0.360, 1.278, and 3.680 for hospitalization in the age groups 18-39, 40-59, and 60 years or older and 0.120, 0.807, and 5.431 for death, respectively.

Discussion

In this article we identify risk groups for COVID-19 in the first six months of the pandemic in Brazil to assist the definition of priority groups for the National Plan for Operationalization of COVID-19 Vaccination prepared by the PNI. Factors such as male sex, older age groups, and comorbidities are associated with overrisk of hospitalization and death from SARI-COVID, as found in other studies ¹²12. Andrade CLT, Pereira CCA, Martins M, Lima SML, Portela MC. COVID-19 hospitalizations in Brazil's Unified Health System (SUS). PLoS One 2020; 15:e0243126.^,1313. Petrilli CM, Jones SA, Yang J, Rajagopalan H, O'Donnell L, Chernyak Y, et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 2020; 369:m1966.^,1717. Ortiz-Prado E, Simbaña-Rivera K, Barreno LG, Diaz AM, Barreto A, Moyano C, et al. Epidemiological, socio-demographic and clinical features of the early phase of the COVID-19 epidemic in Ecuador. PLoS Negl Trop Dis 2021; 15:e0008958.^,1818. Harrison SL, Fazio-Eynullayeva E, Lane DA, Underhill P, Lip GYH. Comorbidities associated with mortality in 31,461 adults with COVID-19 in the United States: a federated electronic medical record analysis. PLoS Med 2020; 17:e1003321..

Male sex showed 33% and 41% overrisk of hospitalization and death compared to female sex, respectively. Although the proportions of cases did not differ significantly between the sexes, men showed higher odds of developing serious cases and evolving to death, due to biological differences such as immune response mechanisms and behavioral and sociocultural factors related to alcohol and tobacco consumption, work/occupation, and adherence to protective measures ¹⁹19. Peckham H, Gruijter NM, Raine C, Radziszewska A, Ciurtin C, Wedderburn LR, et al. Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission. Nat Commun 2020; 11:6317.^,2020. Abate BB, Kassie AM, Kassaw MW, Aragie TG, Masresha SA. Sex difference in coronavirus disease (COVID-19): a systematic review and meta-analysis. BMJ Open 2020; 10:e040129..

Age is a strong and well-established risk factor for hospitalization and death from COVID-19 ¹²12. Andrade CLT, Pereira CCA, Martins M, Lima SML, Portela MC. COVID-19 hospitalizations in Brazil's Unified Health System (SUS). PLoS One 2020; 15:e0243126.^,2121. Xiong D, Zhang L, Watson GL, Sundin P, Bufford T, Zoller JA, et al. Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California. Epidemics 2020; 33:100418.^,2222. Ranzani OT, Bastos LSL, Gelli JGM, Marchesi JF, Baião F, Hamacher S, et al. Characterisation of the first 250 000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. Lancet Respir Med 2021; 9:407-18.. Younger age groups (up to 40-44 years) were associated with relative protection from hospitalization or death during the first months of the pandemic, compared to the mean risk in the general population. Starting at 45-49 years there is an overrisk, reaching eight-fold in the age group 90 years and older (OvR _ag = 8.466). In relation to overrisk of death, younger age groups present strong relative protection up to 24 years, with OvR _ag close to zero. Starting at 55-59 years there is increased overrisk, reaching nearly 18 times in the age group 90 years and older. Importantly, the increase is not exponential in terms of the absolute numbers and proportions of cases, since there is a concentration in the age groups from 15-69 years. This may be related to the greater exposure of these age groups, since they represent the active workforce and are subject to greater flexibilization of social distancing measures ²¹21. Xiong D, Zhang L, Watson GL, Sundin P, Bufford T, Zoller JA, et al. Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California. Epidemics 2020; 33:100418..

Ranzani et al. ²²22. Ranzani OT, Bastos LSL, Gelli JGM, Marchesi JF, Baião F, Hamacher S, et al. Characterisation of the first 250 000 hospital admissions for COVID-19 in Brazil: a retrospective analysis of nationwide data. Lancet Respir Med 2021; 9:407-18. showed that a large proportion (84%) of patients hospitalized with COVID-19 in Brazil presented at least one comorbidity, but the authors did not report specific information on the comorbidities. Among the comorbidities assessed in the current study, chronic kidney disease, diabetes, other lung diseases, and CVD showed higher risk of hospitalization and death. Presence of chronic kidney disease or diabetes and age over 60 appeared as an even stronger risk factor, reaching 10 to 14 times greater overrisk of death than in the general population. Other studies show these diseases as important risk factors for infection ¹⁸18. Harrison SL, Fazio-Eynullayeva E, Lane DA, Underhill P, Lip GYH. Comorbidities associated with mortality in 31,461 adults with COVID-19 in the United States: a federated electronic medical record analysis. PLoS Med 2020; 17:e1003321., hospitalization ¹³13. Petrilli CM, Jones SA, Yang J, Rajagopalan H, O'Donnell L, Chernyak Y, et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 2020; 369:m1966., and death from COVID-19 ²³23. Ssentongo P, Ssentongo AE, Heilbrunn ES, Ba DM, Chinchilli VM. Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: a systematic review and meta-analysis. PLoS One 2020; 15:e0238215..

In general, all the comorbidities are associated with higher specific overrisk by age group in younger ages, with a downward gradient in older age groups, a pattern that is inverted when considering the overrisk in relation to the general population, both for hospitalization and death. This phenomenon occurs because when we compare to the general population, the overrisk is combined with the age group’s specific risk, which naturally has a large and preponderant impact. For example, for CVD there is no evidence of overrisk in the young population, 18-39 years of age, compared to the general population. However, there is overrisk of hospitalization in young persons with CVD, 1.7 times greater than in persons in the same age group. The overrisk of death is 2.8 times.

There is no evidence of overrisk for asthma, although it presents an overrisk of hospitalization and death when compared to the general population at older ages. This overrisk is only attributed to age, since the overrisk of hospitalization (and death) in persons over 60 years with asthma is 1.7 (2.2) times greater than the general population, while the overrisk of hospitalization (and death) in persons over 60 years is 3.7 (5.4) times greater. The relative protective effect of asthma has been reported in other studies, and some hypotheses have been raised, such as prompt adoption of protective measures ²⁴24. Mendes NF, Jara CP, Mansour E, Araújo EP, Velloso LA. Asthma and COVID-19: a systematic review. Allergy Asthma Clin Immunol 2021; 17:5.; aspects related to type-2 immune response that cause the accumulation of eosinophils ²⁴24. Mendes NF, Jara CP, Mansour E, Araújo EP, Velloso LA. Asthma and COVID-19: a systematic review. Allergy Asthma Clin Immunol 2021; 17:5.^,2525. Wakabayashi M, Pawankar R, Narazaki H, Ueda T, Itabashi T. Coronavirus disease 2019 and asthma, allergic rhinitis: molecular mechanisms and host-environmental interactions. Curr Opin Allergy Clin Immunol 2021; 21:1-7.^,2626. Lee SC, Son KJ, Han CH, Jung JY, Park SC. Impact of comorbid asthma on severity of coronavirus disease (COVID-19). Sci Rep 2020; 10:21805.^,2727. Carli G, Cecchi L, Stebbing J, Parronchi P, Farsi A. Is asthma protective against COVID-19? Allergy 2020. [Epub ahead of print].; treatment commonly indicated for asthma ²⁸28. Kumar K, Hinks TSC, Singanayagam A. Treatment of COVID-19-exacerbated asthma: should systemic corticosteroids be used? Am J Physiol Lung Cell Mol Physiol 2020; 318:L1244-L1247.^,2929. Morais-Almeida M, Bousquet J. COVID-19 and asthma: to have or not to have T2 inflammation makes a difference? Pulmonology 2020; 26:261-3.^,3030. Global Initiative for Asthma. Recommendations for inhaled asthma controller medications. https://ginasthma.org/recommendations-for-inhaled-asthma-controller-medications/ (acessado em 23/Fev/2021).
https://ginasthma.org/recommendations-fo... ; and poor description of comorbidities for COVID-19 ²⁴24. Mendes NF, Jara CP, Mansour E, Araújo EP, Velloso LA. Asthma and COVID-19: a systematic review. Allergy Asthma Clin Immunol 2021; 17:5.. However, more in-depth studies are still needed to establish the causes of this apparent protective effect of asthma in COVID-19.

The current analysis used available data from the SIVEP-Gripe notification system (https://sivepgripe.saude.gov.br/sivepgripe), thus taking advantage of the existing infrastructure and data flows in the surveillance of respiratory syndromes in the country. The importance of SIVEP-Gripe during the pandemic was clearly demonstrated by the follow-up done with the system for monitoring SARI cases, InfoGripe (http://info.gripe.fiocruz.br), which proved capable of identifying the impact of the novel coronavirus on the hospital network even before laboratory confirmation of the first cases ³¹31. Bastos LS, Niquini RP, Lana RM, Villela DAM, Cruz OG, Coelho FC, et al. COVID-19 e hospitalizações por SRAG no Brasil: uma comparação até a 12ª semana epidemiológica de 2020. Cad Saúde Pública 2020; 36:e00070120..

This study has some limitations that are inherent to data collected for surveillance purposes. First, there is a potential bias in the completion and keying-in of the reporting forms without direct case-by-case follow-up in the hospital network, as would occur in a clinical trial. This is exemplified by failure to complete fields in the five comorbidities analyzed in this article, varying from 8.8% in CVD to 25.9% in asthma, where these are limited to the comorbidities listed explicitly as individual variables in the SIVEP-Gripe database. In the way the forms are constructed, the fields for comorbidities that are not filled out probably indicate their absence, but there may also be cases of persons with comorbidities but without adequate recording, which would mean an underestimation of the calculated overrisk. Besides, these fields are completed with information provided by the patient or accompanying person, potentially leading to under-notification. Still, this is the same procedure used in the PNS, thereby favoring the data’s comparability. An important difference that may minimize failures in the PNS is that data completion is performed after standardization and training of the study’s interviewers on the questions and/or data collection.

Although we assume that the definitions of diseases in the PNS and SIVEP-Gripe are equivalent, it is possible that they are not. For example, the PNS uses the term chronic kidney failure, while SIVEP-Gripe uses chronic kidney disease. As informed in the Methods section, CVD was created from chronic CVD and information from the open field on hypertension, but this may not be sufficient for perfect mutual consistency of the variables, although we do not believe that eventual differences are sufficient to invalidate the comparisons.

Another important limitation is the underestimation of uncertainty calculated for overrisk, since the populations are estimates from projections by the IBGE or estimates by the PNS, and these uncertainties were ignored when calculating the distribution of overrisk. Another potential bias is related to the PNS data used in the analyses. These data refer to the year 2013 and thus may be outdated for representing the profile of diseases in the general population in 2020.

The current study makes a key contribution to the elaboration of the national vaccination plan, but it should not be considered in isolation since there are other risk factors that were not considered, because they are absent from at least one of the available databases used in the analyses. Examples include liver cirrhosis ³²32. Ioannou GN, Liang PS, Locke E, Green P, Berry K, O'Hare AM, et al. Cirrhosis and SARS-CoV-2 infection in US Veterans: risk of infection, hospitalization, ventilation and mortality. Hepatology 2021; 74:322-35., sickle cell disease ³³33. Centers for Disease Control and Prevention. Evidence used to update the list of underlying medical conditions that increase a person's risk of severe illness from COVID-19. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/evidence-table.html (acessado em 24/Fev/2021).
https://www.cdc.gov/coronavirus/2019-nco... , Down syndrome ³⁴34. Clift AK, Coupland CAC, Keogh RH, Hemingway H, Hippisley-Cox J. COVID-19 mortality risk in down syndrome: results from a cohort study of 8 million adults. Ann Intern Med 2021; 174:572-6., and cancer ³⁵35. Ferrari BL, Ferreira CG, Menezes M, De Marchi P, Canedo J, Melo AC, et al. Determinants of COVID-19 mortality in patients with cancer from a community oncology practice in Brazil. JCO Glob Oncol 2021; 7:46-55.^,3636. Carreira H, Strongman H, Peppa M, McDonald HI, dos-Santos-Silva I, Stanway S, et al. Prevalence of COVID-19-related risk factors and risk of severe influenza outcomes in cancer survivors: a matched cohort study using linked English electronic health records data. Research Paper 2020; 29:100656.. There is also evidence of greater morbidity and mortality from diseases with airborne transmission in populations such as indigenous peoples ³⁷37. Cardoso AM, Resende PC, Paixao ES, Tavares FG, Farias YN, Barreto CTG, et al. Investigation of an outbreak of acute respiratory disease in an indigenous village in Brazil: Contribution of Influenza A(H1N1)pdm09 and human respiratory syncytial viruses. PLoS One 2019; 14:e0218925. the prison population ³⁸38. Sacchi FPC, Praça RM, Tatara MB, Simonsen V, Ferrazoli L, Croda MG, et al. Prisons as Reservoir for Community Transmission of Tuberculosis, Brazil. Emerg Infect Dis 2015; 21:452-5.. Further quantitative or qualitative studies are necessary to complement the definition of risk groups. Other elements besides individual risk should orient the definition of priority groups. COVID-19 vaccine is a common good, and its use should be guided by equity and health promotion values. Its distribution, especially in a context of limited doses, should consider principles of maximization of human welfare (reduction of mortality and suffering), reciprocity with those most dedicated to frontline care and cure (healthcare workers), equity (access by the most vulnerable), and legitimacy ⁹9. World Health Organization. Coronavirus disease (COVID-19): vaccine access and allocation. https://www.who.int/news-room/q-a-detail/coronavirus-disease-(covid-19)-vaccine-access-and-allocation (acessado em 16/Fev/2021).
https://www.who.int/news-room/q-a-detail... . These objectives can only be met through negotiation with society.

In addition, the COVID-19 epidemic has direct and indirect effects on population groups with comorbidities. The direct effect is increased risk of hospitalization and death from SARI-COVID in the presence of comorbidities, already well-documented. The indirect effect is reduction of care and treatment of these conditions due to reduced mobility and access to healthcare services, which may have resulted in the lack of control of these conditions. It is thus necessary to emphasize the impact of COVID-19 on noncommunicable diseases. The resumption of public health promotion policies for NCDs, including in response to the pandemic and to complement vaccination, must be included as an urgent and essential component to minimize serious cases and deaths from the association of COVID-19 with the underlying condition ³⁹39. World Health Organization. The impact of the COVID-19 pandemic on noncommunicable disease resources and services: results of a rapid assessment. https://apps.who.int/iris/handle/10665/334136 (acessado em 24/Fev/2021).
https://apps.who.int/iris/handle/10665/3... . Throughout the world, human and financial resources have been shifted to deal with the pandemic, which has also impacted the follow-up of these diseases ³⁹39. World Health Organization. The impact of the COVID-19 pandemic on noncommunicable disease resources and services: results of a rapid assessment. https://apps.who.int/iris/handle/10665/334136 (acessado em 24/Fev/2021).
https://apps.who.int/iris/handle/10665/3... by the family health teams in SUS.

In early 2020, when the COVID-19 pandemic was declared, the following question was raised: are we prepared to deal with this pandemic in Brazil? ⁴⁰40. Lana RM, Coelho FC, Gomes MFC, Cruz OG, Bastos LS, Villela DAM, et al. Emergência do novo coronavírus (SARS-CoV-2) e o papel de uma vigilância nacional em saúde oportuna e efetiva. Cad Saúde Pública 2020; 36:e00019620.. The underfinancing and dismantlement of the SUS have caused a huge reduction in human resources and infrastructure, directly impacting the data’s quality and certainly the success of the PNI in reaching the necessary vaccination coverage.

Despite the limitations discussed above, the current study provided support for the National Plan for Operationalization of COVID-19 Vaccination under the PNI through identification of population groups with overrisk of hospitalization and death from the disease. We used data that were collected routinely in the healthcare establishments, emphasizing the importance of the availability of quality data. The pandemic clearly revealed Brazil’s prevailing reality, which has been identified for years by researchers and healthcare workers: the need for a notification system that is well operated and completed by properly trained workers and the urgency of guaranteeing financing for the SUS ⁴⁰40. Lana RM, Coelho FC, Gomes MFC, Cruz OG, Bastos LS, Villela DAM, et al. Emergência do novo coronavírus (SARS-CoV-2) e o papel de uma vigilância nacional em saúde oportuna e efetiva. Cad Saúde Pública 2020; 36:e00019620..

Acknowledgments

The authors wish to acknowledge the Brazilian National Influenza Surveillance Network (LACENs, NICs, state and municipal surveillance departments, and the Influenza Working Group, Department of Immunization and Communicable Diseases, Health Surveillance Secretariat, Ministry of Health) for the partnership and Inova Fiocruz, CNPq (Ref. 441057/2020-9, Ref. 309569/2019-2, and 307489/2018-3), FAPERJ (E26/203.172/2) for the financing.

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