Abstract

Introduction

The world has witnessed the expansion of a pandemic with an infectious cause called COVID-19, whose etiological agent is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first reports were in December 2019 in Wuhan, Hubei Province, China, as a set of acute respiratory diseases, subsequently with global spread ¹1. World Health Organization. WHO announces COVID-19 outbreak a pandemic. http://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19/news/news/2020/3/who-announces-covid-19-outbreak-a-pandemic (acessado em 25/Jul/2020).
http://www.euro.who.int/en/health-topics... . As of July 25, 2020, the World Health Organization (WHO) had reported a total of 15,538,736 confirmed cases and 634,325 deaths from the disease in 216 countries. United States, Brazil, India, Russia, South Africa, and Peru are the countries with the most reported cases thus far (World Health Organization. https://covid19.who.int/, accessed on 10/May/2020).

SARS-CoV-2 is transmitted mainly by contact with respiratory droplets from patients, and the disease mainly affects the respiratory, cardiovascular, gastrointestinal, and neurological systems. Clinical presentation ranges from asymptomatic to more severe forms with important involvement of the respiratory system. Symptoms consist mainly of fever, dry cough, and shortness of breath with the possibility of complications, mainly pneumonia, acute respiratory distress syndrome (ARDS), and death ²2. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020; 395:507-13.^,33. Xu X-W, Wu X-X, Jiang X-G, Xu K-J, Ying L-J, Ma C-L, et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ 2020; 368:m606.^,44. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020; 323:1061-9.. The disease is complex and with limited evidence on the best form of treatment ⁶6. Badawi A, Ryoo SG. Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis. Int J Infect Dis 2020; 49:129-33..

The presence of multiple chronic health problems appears to correlate with the pathogenesis of COVID-19, which had also been observed in previous coronavirus epidemics (MERS ⁶6. Badawi A, Ryoo SG. Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis. Int J Infect Dis 2020; 49:129-33. and SARS ⁷7. Yu C-M, Wong RS-M, Wu EB, Kong S-L, Wong J, Yip GW-K, et al. Cardiovascular complications of severe acute respiratory syndrome. Postgrad Med J 2006; 82:140-4.). Although the clinical evolution is not entirely clear, studies have shown a direct and important correlation between the patient’s age and disease burden (number and severity of conditions) and increased risk of unfavorable clinical outcomes, such as hospitalization, need for intensive care (ICU), and death ⁸8. Abate S, Checkol Y, Mantedafro B, Basu B. Prevalence and risk factors of mortality among hospitalized patients with COVID-19: a systematic review and meta-analysis. Bull World Health Organ 2020; [Epub ahead of print].. These factors associated with low lymphocyte count and high levels of lactic dehydrogenase at hospital admission were important and independent risk factors for unfavorable clinical progression in these patients ⁹9. Ji D, Zhang D, Xu J, Chen Z, Yang T, Zhao P, et al. Prediction for progression risk in patients with COVID-19 pneumonia: the CALL Score. Clin Infect Dis 2020; 71:1393-9..

Approximately 72% of patients admitted to ICU for COVID-19 presented preexisting chronic conditions, compared to 37% of those who did not require intensive care ³3. Xu X-W, Wu X-X, Jiang X-G, Xu K-J, Ying L-J, Ma C-L, et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ 2020; 368:m606.. A meta-analysis of eight studies and data from more than 46,000 Chinese patients showed that hypertension (17%), diabetes (8%), cardiovascular diseases (5%), and chronic respiratory diseases (2%) were the most frequent morbidities and with increased risk of developing a more serious course of SARS-CoV-2 infection ¹⁰10. Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis 2020; 94:91-5.. A complementary meta-analysis found that patients with preexisting cardiovascular conditions presented higher risk of severe forms of COVID-19 ¹¹11. Li B, Yang J, Zhao F, Zhi L, Wang X, Liu L, et al. Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China. Clin Res Cardiol 2020; 109:531-8..

Social determinants of health such as male sex and advanced age appear to correlate with mortality in hospitalized COVID-19 patients ⁸8. Abate S, Checkol Y, Mantedafro B, Basu B. Prevalence and risk factors of mortality among hospitalized patients with COVID-19: a systematic review and meta-analysis. Bull World Health Organ 2020; [Epub ahead of print].. This same systematic review showed that death was twice as likely in patients with any preexisting condition compared to those without such diseases. A study of 72,314 cases by the Chinese Center for Disease Control and Prevention showed a high case-fatality rate in patients with preexisting conditions: cardiovascular disease (10.5%), diabetes (7.3%), chronic respiratory disease (6.3%), hypertension (6%), and cancer (5.6%) ¹²12. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72,314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020; 323:1239-42.. A study of patients in China and Italy found that the presence of any of the above-mentioned self-reported morbidities was associated with 2.4 times higher risk of mortality ¹³13. Lippi G, Mattiuzzi C, Sanchis-Gomar F, Henry BM. Clinical and demographic characteristics of patients dying from COVID-19 in Italy versus China. J Med Virol 2020; 92:1759-60..

In Brazil, the first case of COVID-19 was reported in the city of São Paulo on February 25, 2020. Brazil has a high transmission rate and is the country of Latin America with the highest number of confirmed cases and deaths ¹⁴14. The Lancet. COVID-19 in Brazil: "so what?". Lancet 2020; 395:1461.. Global health authorities are concerned about the impact of the COVID-19 pandemic in middle and low-income countries due to weaknesses in the health systems, reduced availability of intensive care beds, limited number of mechanical ventilators, and prevalence of morbidities/infection ⁸8. Abate S, Checkol Y, Mantedafro B, Basu B. Prevalence and risk factors of mortality among hospitalized patients with COVID-19: a systematic review and meta-analysis. Bull World Health Organ 2020; [Epub ahead of print].^,1515. Kirby T. South America prepares for the impact of COVID-19. Lancet Respir Med 2020; 8:551-2..

Within this context, the accelerated aging process in Brazil has played out in a scenario of the high magnitude and impact of chronic and infectious diseases, besides serious socioeconomic iniquities 17. Thus, to characterize the contingent of persons at risk of severe COVID-19 can support preventive measures (when a vaccine is available, for example) and increase the intensity of nonpharmacological strategies for heightened protection of individuals at high risk ¹⁶16. Lima-Costa MF, Andrade FB, Souza PRB, Neri AL, Duarte YAO, Castro-Costa E, et al. The Brazilian Longitudinal Study of Aging (ELSI-Brazil): objectives and design. Am J Epidemiol 2018; 187:1345-53.. The current study thus aimed to measure the occurrence of multimorbidity and estimate the number of individuals in the Brazilian population 50 years or older at risk for severe COVID-19.

Methods

This was a cross-sectional nationwide study, using the results from the baseline of the Brazilian Longitudinal Study of Aging (ELSI-Brazil), conducted in 2015-2016 in 70 municipalities (counties) in all five of Brazil’s major geographic regions. The designed sample is representative of the Brazilian population 50 years or older and consisted of 9,412 individuals, representing a total of 42,407,714 persons in this age bracket in the country (study population). The sample’s composition used geographic stratification by three-stage clusters: municipalities, which were allocated in four strata according to the size of the resident population, census tract, and household. More details on the study’s methodology can be consulted in a previous publication ¹⁶16. Lima-Costa MF, Andrade FB, Souza PRB, Neri AL, Duarte YAO, Castro-Costa E, et al. The Brazilian Longitudinal Study of Aging (ELSI-Brazil): objectives and design. Am J Epidemiol 2018; 187:1345-53..

The current study’s outcome variable was the simultaneous occurrence of two or more risk conditions for COVID-19. In addition to multimorbidity (≥ 2 conditions), we also analyzed the occurrence of 1 preexisting condition. The following conditions were selected ¹⁷17. Clark A, Jit M, Warren-Gash C, Guthrie B, Wang HH, Mercer SW, et al. How many are at increased risk of severe COVID-19 disease? Rapid global, regional and national estimates for 2020. medRxiv 2020; 22 abr. https://www.medrxiv.org/content/10.1101/2020.04.18.20064774v1.
https://www.medrxiv.org/content/10.1101/... : cardiovascular diseases (hypertension, stroke, acute myocardial infarction, angina, and heart failure), chronic kidney disease, chronic neurological disease (Alzheimer’s disease and Parkinson’s disease), chronic respiratory disease (emphysema, chronic obstructive pulmonary disease, bronchitis - measured together with the same question), diabetes, arthritis, asthma, cancer, depression, and obesity. With the exception of obesity, which was characterized by the objective measurement of weight and height, the other conditions were obtained from the interviewee’s own answer to the following question: “Has a doctor ever told you that you have?”. The lack of information on conditions was treated as absence of the problems. Obesity was calculated according to body mass index (BMI), obtained by dividing weight in kilograms by height in meters squared (both as the mean of two measurements), and categorized according to the following cutoff points: BMI ≥ 30kg/m² and BMI ≥ 27kg/m² for individuals under 60 years and ≥ 60 years of age, respectively. Classification of BMI according to age used the criteria recommended by the WHO ¹³13. Lippi G, Mattiuzzi C, Sanchis-Gomar F, Henry BM. Clinical and demographic characteristics of patients dying from COVID-19 in Italy versus China. J Med Virol 2020; 92:1759-60.^,1818. World Health Organization. Obesity: preventing and managing the global epidemic. http://www.who.int/entity/nutrition/publications/obesity/WHO_TRS_894/en/index.html (acessado em 14/Mai/2020).
http://www.who.int/entity/nutrition/publ... .

We also used the indicators for severity of health status: bad/very bad self-rated health, frailty ¹⁹19. Andrade JM, Duarte YAO, Alves LC, Andrade FCD, Souza Junior PRB, Lima-Costa MF, et al. Perfil da fragilidade em adultos mais velhos brasileiros: ELSI-Brasil. Rev Saúde Pública 2018; 52 Suppl 2:17s., and report of some difficulty in performing basic activities of daily living (BADL) ¹⁶16. Lima-Costa MF, Andrade FB, Souza PRB, Neri AL, Duarte YAO, Castro-Costa E, et al. The Brazilian Longitudinal Study of Aging (ELSI-Brazil): objectives and design. Am J Epidemiol 2018; 187:1345-53.. The target BADL were: crossing the room or walking from one room to another, getting dressed, bathing, eating, lying down or getting out of bed, and using the bathroom.

The independent variables were sex (female, male), major geographic region of Brazil (North, Northeast, Central, Southeast, and South), age (in complete years: 50-59, 60-69, 70-79, ≥ 80), and schooling (none, 1-4, 5-8, ≥ 9 years).

The analyses were performed in Stata SE 15.0 (https://www.stata.com) and included calculation of the prevalence (%) and estimated absolute number of persons in the population. We estimated the prevalence rates for 1 and ≥ 2 conditions according to age bracket, region, and sex. The estimates of occurrence and population projections for bad/very bad self-rated health, frailty, and incapacity for basic activities of daily living were stratified by schooling. Statistical significance was assessed with Pearson’s chi-square test. Sampling parameters and individual weights were considered in all the analyses.

ELSI-Brazil was approved by the Ethics Research Committee of the René Rachou Institute, Oswaldo Cruz Foundation (case review n. 886,754). All participants signed a free and informed consent form before starting the interview, and the study met all the regulatory and legal requirements.

Results

Half of the study population was female (53.9%), 50 to 69 years of age (47.6%), and lived in the Southeast region (47.2%). Of the total, 13.3% had no schooling and 26.9% had ≥ 9 years of schooling.

Approximately 34 million Brazilians ≥ 50 years of age have ≥ 1 risk condition for severe COVID-19. Percentagewise, the occurrence was similar between regions, with higher estimated absolute numbers in the Southeast (≈16.3 million) and Northeast (≈8 million). Persons under 60 years presented lower prevalence of morbidities, but they represented a larger number of individuals in absolute terms. Half of the study population (52%) had multimorbidity at risk for severe COVID-19 (22,068,747 persons), proportionally higher in the Central, Southeast, and South of Brazil. The Southeast (≈11 million) and Northeast (≈4.5 million) still presented the highest estimated absolute numbers. There was an increase in the prevalence of multimorbidity with advancing age, independently of geographic region (Table 1).

Among women, the most prevalent risk condition were cardiovascular diseases, obesity, arthritis, and depression. Having at least one risk condition for severe COVID-19 was more prevalent among women in the Southeast (89.5%) and lower in the Northeast (84.1%). The South showed the highest prevalence of cardiovascular diseases (63.3%). The Southeast recorded higher prevalence of obesity (48.6%) and depression (36.1%), and the North showed higher prevalence of arthritis (37.7%) in women (Figure 1a). Prevalence of one or more risk conditions for severe COVID-19 was 86.4% for women and 74.3% for men. Having one or more risk conditions for severe COVID-19 was more prevalent in women in the Southeast (89.5%) and less prevalent in the Central (81.6%).

Prevalence of multimorbidity was higher in women (59.4%), most prevalent in the South (67%), while among men the prevalence of multimorbidity was 43.5%, with the highest prevalence in the Southeast (47.3%) (Figure 1). In men, the most prevalent conditions were cardiovascular diseases, obesity, diabetes, and arthritis. The highest prevalence rates of cardiovascular diseases (54.1%) and obesity (36%) were in the Central, and diabetes (16.7%) and arthritis (15.4%) were more prevalent in men in the North (Figure 1b).

The most prevalent conditions related to severe COVID-19 among Brazilians ≥ 50 years of age were cardiovascular diseases (56%), obesity (39%), arthritis (21%), and depression (18.5%), with little variation between regions of the country. When stratified by age, the most prevalent conditions in all ages were cardiovascular diseases and obesity (Figure 2). In relation to gender, women presented higher prevalence rates of individual conditions when compared to men, except for cancer (Figure 2).

The largest differences in prevalence rates between the sexes were for arthritis and depression. Women presented prevalence rates for arthritis of 25% and 31.1% in the lowest and highest age brackets, respectively, while for men the prevalence rates were 9% and 16.4%, respectively. Mean prevalence of depression was 25.2% in women, compared to 10.6% in men (Figure 2).

Prevalence of multiple risk conditions for severe COVID-19 plus indicators of precarious health status was proportionally similar between regions of the country, with higher absolute numbers in the Northeast and Southeast. A total of 2,412,355, 3,656,104, and 4,774,649 individuals presented multiple risk conditions for severe COVID-19 plus frailty, bad self-rated health, and incapacity for BADL, respectively (Table 2).

Associations according to schooling showed that lower schooling correlated with higher prevalence rates and estimated absolute numbers. For example, 18.4% of persons with no schooling presented multimorbidity plus incapacity for BADL, compared to 6.4% for individuals with ≥ 9 years of schooling. All the differences according to schooling were statistically significant (Table 3).

Discussion

Our results reveal the magnitude of occurrence of multiple conditions associated with the risk of developing clinically severe forms of COVID-19 among Brazilians in the process of aging. According to estimates, at least 34 million Brazilians ≥ 50 years of age presented some risk condition, which highlights the considerable contingent of persons at risk of severe COVID-19, thus representing more than the total population of other South American countries except Colombia and Argentina. Only 40 countries of the world have larger total populations than Brazil’s estimated contingent at risk of severe COVID-19. Meanwhile, multimorbidity affected half of the study population and was higher in the country’s southernmost regions, although the Northeast and Southeast presented the largest absolute numbers of individuals with ≥ 2 preexisting conditions. Cardiovascular diseases and obesity were the most frequent conditions in both women and men. The severity of health status (functional incapacities, frailty, or bad/very bad self-rated health) associated with multimorbidity was also frequent in the sample, both in relative terms (> 6%) and in the estimated number in the population (> 2.4 million).

Multimorbidity is a public health problem in Brazil due to its magnitude, complex clinical management, and impact for society and the health systems. Added to this is the scarcity of scientific evidence, especially from randomized clinical trials ²⁰20. Academy of Medical Sciences. Multimorbidity: a priority for global health research. London: Academy of Medical Sciences; 2018.^,2121. Smith SM, Wallace E, O'Dowd T, Fortin M. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev 2016; 3:CD006560.. This area still needs progress in the form of epidemiological measurement in the country, but the occurrence is obviously high, mainly in the elderly ²²22. Carvalho JN, Roncalli AG, Cancela MC, Souza DLB. Prevalence of multimorbidity in the Brazilian adult population according to socioeconomic and demographic characteristics. PLoS One 2017; 12:e0174322.^,2323. Nunes BP, Batista SRR, Andrade FB, Souza Junior PRB, Lima-Costa MF, Facchini LA. Multimorbidity: the Brazilian Longitudinal Study of Aging (ELSI-Brazil). Rev Saúde Pública 2018; 52 Suppl 2:10s.. This combination of different chronic conditions tends to create inflammatory processes, increasing the susceptibility to different problems, including acute infectious diseases ²⁴24. Lopardo GD, Fridman D, Raimondo E, Albornoz H, Lopardo A, Bagnulo H, et al. Incidence rate of community-acquired pneumonia in adults: a population-based prospective active surveillance study in three cities in South America. BMJ Open 2018; 8:e019439.. A study in the Brazilian city of Manaus in 2015 found a higher dengue rate in the previous year in persons with multimorbidity ²⁵25. Araujo MEA, Silva MT, Galvão TF, Nunes BP, Pereira MG. Prevalence and patterns of multimorbidity in Amazon Region of Brazil and associated determinants: a cross-sectional study. BMJ Open 2018; 8:e023398..

Although the knowledge is still incipient on the biological mechanism that increases the risk of infections among persons with multimorbidity, the mechanism appears to be associated with increased inflammation and the body’s decreased immune response capacity ²⁶26. Madjid M, Payam S-N, Solomon SD, Vardeny O. Potential effects of coronaviruses on the cardiovascular system: a review. JAMA Cardiol 2020; 5:831-40.^,2727. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol 2018; 15:505-22.^,2828. Friedman E, Shorey C. Inflammation in multimorbidity and disability: an integrative review. Health Psychol 2019; 38:791-801.. Nevertheless, it essential to understand this process in greater detail, considering the identification of differences according to morbidity patterns ²⁹29. Ferreira GD, Simões JA, Senaratna C, Pati S, Timm PF, Batista SR, et al. Physiological markers and multimorbidity: a systematic review. J Comorb 2018; 8:2235042X18806986..

Given the current lack of treatment and a vaccine for the prevention of COVID-19 and the epidemiological evidence on the greater severity of the novel coronavirus among persons with multimorbidity, the adoption of nonpharmacological interventions is crucial for the prevention of severe cases of the infection ³⁰30. Aquino E, Silveira IH, Pescarini JM, Aquino R, Souza-Filho JA, Rocha AS, et al. Medidas de distanciamento social no controle da pandemia de COVID-19: potenciais impactos e desafios no Brasil. Ciênc Saúde Colet 2020; 25 Suppl 1: 2423-46.^,3131. Garcia LP, Duarte E. Intervenções não farmacológicas para o enfrentamento à epidemia da COVID-19 no Brasil. Epidemiol Serv Saúde 2020; 29:e2020222.. Our findings revealed a huge contingent of persons at risk of severe COVID-19 in all regions of Brazil, despite the relative and absolute differences in the occurrence of risk conditions. Even when specifying for individuals with multimorbidity plus severe health status, the number is still high, emphasizing the need to protect the entire population and mainly persons in the process of aging and in situations of vulnerability. The Brazilian Unified National Health System (SUS) and primary healthcare, through coordination of care by the Family Health Strategy, will continue to play a relevant role in mitigating social iniquities in health through prevention of COVID-19 infection and management of chronic conditions and multimorbidity during and after the pandemic, especially protecting the poorer population ³⁰30. Aquino E, Silveira IH, Pescarini JM, Aquino R, Souza-Filho JA, Rocha AS, et al. Medidas de distanciamento social no controle da pandemia de COVID-19: potenciais impactos e desafios no Brasil. Ciênc Saúde Colet 2020; 25 Suppl 1: 2423-46.^,3131. Garcia LP, Duarte E. Intervenções não farmacológicas para o enfrentamento à epidemia da COVID-19 no Brasil. Epidemiol Serv Saúde 2020; 29:e2020222..

The results point to higher occurrence of the outcomes in groups with less schooling, corroborating the prevailing social iniquity and its impact on elderly Brazilians’ health ¹⁶16. Lima-Costa MF, Andrade FB, Souza PRB, Neri AL, Duarte YAO, Castro-Costa E, et al. The Brazilian Longitudinal Study of Aging (ELSI-Brazil): objectives and design. Am J Epidemiol 2018; 187:1345-53.. Health inequalities in Brazil can be seen in the differences in prevalence rates for chronic conditions and multimorbidity, and in access to and use of health services ²²22. Carvalho JN, Roncalli AG, Cancela MC, Souza DLB. Prevalence of multimorbidity in the Brazilian adult population according to socioeconomic and demographic characteristics. PLoS One 2017; 12:e0174322.^,3232. Chiavegatto Filho ADP, Wang Y-P, Malik AM, Takaoka J, Viana MC, Andrade LH. Determinants of the use of health care services: multilevel analysis in the Metropolitan Region of Sao Paulo. Rev Saúde Pública 2015; 49:15.^,3333. Barreto ML, Rasella D, Machado DB, Aquino R, Lima D, Garcia LP, et al. Monitoring and Evaluating Progress towards Universal Health Coverage in Brazil. PLoS Med 2014; 11:e1001692.. Multimorbidity is prevalent in women, elderly, persons with less schooling, and the unemployed ²²22. Carvalho JN, Roncalli AG, Cancela MC, Souza DLB. Prevalence of multimorbidity in the Brazilian adult population according to socioeconomic and demographic characteristics. PLoS One 2017; 12:e0174322.^,3434. Pathirana TI, Jackson CA. Socioeconomic status and multimorbidity: a systematic review and meta-analysis. Aust N Z J Public Health 2018; 42:186-94.. In addition, despite the strides in access to and use of health services in the Brazilian population, important social and geographic inequalities still persist. The use of health services correlates directly with individual factors such as higher schooling and having a private health plan ³⁵35. Travassos C, Oliveira EXG, Viacava F. Desigualdades geográficas e sociais no acesso aos serviços de saúde no Brasil: 1998 e 2003. Ciênc Saúde Colet 2006; 11:975-86.. Although present before the pandemic, health inequalities may increase with COVID-19, generating risks of different outcomes from acquiring the disease and aggravating the clinical status in individuals with the same level of morbidity.

The study has some limitations. Information on preexisting conditions was obtained by self-report (except for obesity), which may underestimate disease rates due to difficulties in access to diagnosis, especially for lower-income persons ²³23. Nunes BP, Batista SRR, Andrade FB, Souza Junior PRB, Lima-Costa MF, Facchini LA. Multimorbidity: the Brazilian Longitudinal Study of Aging (ELSI-Brazil). Rev Saúde Pública 2018; 52 Suppl 2:10s.. Another limitation is the use of conditions defined as posing risk for severe COVID-19. This topic is still subject to preliminary information, and it is possible that better scientific evidence will help us better select the conditions associated with severe COVID-19. In addition, we only selected the conditions related to the risk of severe COVID-19 that are available in the ELSI-Brazil study’s database ¹⁷17. Clark A, Jit M, Warren-Gash C, Guthrie B, Wang HH, Mercer SW, et al. How many are at increased risk of severe COVID-19 disease? Rapid global, regional and national estimates for 2020. medRxiv 2020; 22 abr. https://www.medrxiv.org/content/10.1101/2020.04.18.20064774v1.
https://www.medrxiv.org/content/10.1101/... .

This study is based on initial evidence of the effect of the presence of chronic diseases on the potential risk of SARS-CoV-2 infection, mainly on its negative clinical outcomes. Although the evidence is still incipient, the findings thus far consistently identify the importance of the relationship between chronic conditions and severe COVID-19 ⁸8. Abate S, Checkol Y, Mantedafro B, Basu B. Prevalence and risk factors of mortality among hospitalized patients with COVID-19: a systematic review and meta-analysis. Bull World Health Organ 2020; [Epub ahead of print].. Thus, the study of the epidemiology of multimorbidity related to severe COVID-19 in the Brazilian population, especially in the elderly, may represent an important strategy for the definition of strategies and tools for caring for the population with accumulated risks, from the demographic, socioeconomic, and health status point of view. The findings underline the importance of differential measures in a country with continental dimensions.

Acknowledgments

The baseline for the ELSI-Brazil Study was funded by the Ministry of Health (Department of Science and Technology of the Secretariat of Science, Technology, and Strategic Inputs - DECIT/SCTIE; case 404965/2012-1); Coordinating Division for Health of the Elderly, Department of Strategic Program Actions of the Healthcare Secretariat (COSAPI/DAPES/SAS; cases 20836, 22566, and 23700); and Ministry of Science, Technology, Innovation, and Communication. The current study ha not received any specific funding. Nunes BP receives funding from the National Council for Scientific and Technological Development (CNPq; grant 432474/2016-1) and the Rio Grande do Sul State Research Support Foundation (FAPERGS; grant 19/2551-0001231-4) related to research on the occurrence of multimorbidity.

References

Publication Dates

History