https://orcid.org/0000-0003-4459-9345
Figures
Abstract
The aim of this article is to assess the odds ratio of hospitalization and mortality due to COVID-19 in people with obesity using data from residents of Espírito Santo, Brazil. An observational, quantitative, cross-sectional study was carried out from the database available on the official channel of the State Health Secretariat of Espírito Santo. Crude odds ratio estimates (ORs) referring to the association between variables were calculated, as well as adjusted odds ratios (adjusted odds ratios—OR adj.) and their respective 95% confidence intervals (CI 95%). The results indicate that men, non-white, no education or with lower education level and age over 40 years old were more likely to be hospitalized and died of COVID-19. People with obesity are at risk of hospitalization and death due to COVID-19 54% and 113% higher than people who do not have obesity. People with obesity had a higher chance of hospitalization when they were over 40 years old, had breathing difficulty, and the comorbidities diabetes (2.18 higher) and kidney disease (4.10 higher). The odds ratio of death for people with obesity over 60 years old was 12.51 higher, and those who were hospitalized was 17.9 higher compared to those who were not hospitalized.
Citation: Reis ECd, Rodrigues P, Jesus TRd, de Freitas Monteiro EL, Virtuoso Junior JS, Bianchi L (2022) Risk of hospitalization and mortality due to COVID-19 in people with obesity: An analysis of data from a Brazilian state. PLoS ONE 17(3): e0263723. https://doi.org/10.1371/journal.pone.0263723
Editor: Jennifer A. Hirst, University of Oxford, UNITED KINGDOM
Received: April 4, 2021; Accepted: January 25, 2022; Published: March 4, 2022
Copyright: © 2022 Reis et al. This 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.
Data Availability: All relevant data from this study are third party data and are available from the database: https://coronavirus.es.gov.br/painel-covid-19-es. The authors confirm that they did not have any special access privileges.
Funding: The project received financial support from the Pan American Health Organization (PAHO).
Competing interests: NO authors have competing interests.
Introduction
The hospitalization and mortality rates due to COVID-19 have varied considerably due to several aspects, such as age group, current comorbidities, socioeconomic conditions, among other characteristics [1–6]. Regarding comorbidities, chronic diseases such as diabetes, coronary heart -disease and obesity have been associated with the worst prognosis for the disease [7–11].
When the first studies on risk factors for the severity of the disease began to be published, obesity was identified as one of those in which the risk of hospitalization and death increased, which throughout the pandemic period was confirmed by different systematic reviews [12–14]. However, few published studies that investigate the role of obesity as a risk factor for the severity of COVID-19 were carried out in Brazil. Carneiro et al (2021) investigated the relationship between overweight and obesity with the COVID-19 mortality rate in Brazilian states [15]. The authors found a positive and significant correlation between the variables. Souza et al (2021) in a study carried out with information on notified cases of the disease, noticed that individuals with heart disease, diabetes and declining age present a worse health outcome; in addition, they identified that socioeconomic conditions would also be associated with a worse outcome. Thus, they conclude that COVID-19 affects different population groups differently and unequally [16].
Thus, considering the high prevalence of people with overweight and obesity in Brazil, added to the still out-of-control pandemic context, it is important to know the factors related to hospitalization and death in people with obesity, to establish protection mechanisms for this population.
In this context, this cross-sectional study aims to assess associated odds ratio of hospitalization and mortality due to COVID-19 in people with obesity based on data from Espírito Santo residents, Brazil.
Method
This is an observational, quantitative, cross-sectional study, conducted from the database available on the official channel of the Health Department of Espírito Santo Government, “COVID-19 Panel”, for the dissemination of coronavirus cases in state level (https://coronavirus.es.gov.br/painel-COVID-19-es). The COVID-19 Panel is a system developed by government and powered by the eSUS/Health Surveillance System (eSUS/VS), which records all suspected and/or confirmed cases of COVID-19 in the state of Espírito Santo (ESPÍRITO SANTO, 2020) from notification forms filled out by health professionals from health units throughout the state.
This study included all patients confirmed by COVID-19 in Espírito Santo, until September 10th, 2020, which corresponded to 118,138 cases, according to Fig 1. The confirmation and notification of cases followed the criteria of Technical Note COVID-19 No. 29/2020 –GEVS/SESA/ES, elaborated by Health Department of Espírito Santo: 1. Case confirmed by laboratory diagnosis: the positive result Reverse Transcription—Polymerase Chain Reaction (RT-PCR) in real time per validated protocol; or the positive validated serological test (rapid test). 2. Case confirmed by clinical-epidemiological diagnosis: suspected case with a history of close or home contact with a laboratory confirmed case for COVID-19 [17]. Individuals with a confirmed diagnosis for COVID-19 and who had the evolution of the case closed (cure or death by COVID-19) were selected for this study. Therefore, all those who were still undergoing treatment for the disease, without information or who died of other causes, were excluded.
The study variables were derived from the eSUS/VS System notification forms, considering the following patient data: age group, gender, race/color, education level, signs and symptoms (defined by the database the options: fever, breathing difficulty, cough, running nose, sore throat, diarrhea and headache), comorbidities (defined by the database the options: lung disease, cardiovascular disease, kidney disease, diabetes, smoking, obesity), hospitalization (yes / no) and evolution (cure / death by COVID-19). This study was based on the STROBE guidelines for reporting observational studies [18].
Considering the study design, cross-sectional, the study population was observed only once and information regarding the outcome and exposure was collected at the same time. Thus, this study is a Fig 1 of the population and the associations which were noticed here do not have a cause-effect relationship. To quantify the noticed associations, crude odds ratio estimates (odds ratio—OR) were presented for the association among exposure variables and the outcome, as well as adjusted odds ratios (adjusted odds ratio—OR adj.) and their respective 95% confidence intervals (95%CI).
All analyzes were performed using R.4.0.3 software.
The study was carried out in accordance with the ethical principles of Resolution 466/2012 of the National Health Council; the approval of the work required by the Research Ethics Committee was not necessary, due to the use of secondary data, with free access and without identification of the subjects.
Results
Since the beginning of the pandemic until September 10th, 2020, 118,138 cases were confirmed by COVID-19 in the state of Espírito Santo. People who had not yet evolved (cure or death by COVID-19) described in the database were excluded, and data from 59.698 people were analyzed, and of these, 3025 were people with obesity (Fig 1).
Tables 1 and 2 show the characteristics of people with COVID-19 considered in this study by hospitalized group and mortality.
Most people who were hospitalized because of COVID-19 were over 60 years old (59.8%), male (41.8%), black or brown (58.0%) and had full high school (23.6%).
Regarding signs and symptoms, 68.5% had cough, 68.5% fever and 29.5% headache. The most frequent comorbidities among hospitalized patients were cardiovascular disease (55.6%), diabetes (30.9%) and obesity (10.3%).
Among people who died of COVID-19, most people were male (57.8%), were black or brown (54.3%), over 60 years old (76.7%), and had incomplete elementary school (4 to 8 years of schooling) (17.8%). Regarding signs and symptoms, cough and fever were the most frequent symptoms, 65.7% and 62.2% respectively. The most common registered comorbidities were cardiovascular disease (60.0%) and diabetes (34.1%), followed by obesity (10.9%).
According to the results presented in Table 3, fixing the other variables, it was identified that women are 37% less likely to be hospitalized by COVID-19 when compared to men. Black individuals are 21% more likely to be hospitalized by COVID-19 than white individuals.
Individuals from 40 to 59 years old and 60 years old or more had a chance of hospitalization by COVID-19 2.05 and 6.11 times, respectively, the chance of people from 18 to 39 years old.
Regarding education level, no education people and those with incomplete elementary school have a chance of hospitalization by COVID-19 respectively, 1.85 and 1.90 times the chance of people with a university diploma to be hospitalized.
Obesity, fever and breathing difficulty are characteristics associated with the chance of hospitalization by COVID-19. When compared to people who do not have these characteristics, the chance of hospitalization is, respectively, 1.62, 1.46 and 5.80 times in symptomatic cases. Running nose, sore throat, diarrhea and headache presented values indicating a "protective effect" for hospitalization by COVID-19, that is, in cases which individuals had these symptoms, there was a reduction in the chance of hospitalization by 45%, 48%, 32% and 60% compared to individuals who did not have these symptoms.
Individuals who have heart and kidney diseases, diabetes and smoke have increased chances of hospitalization by COVID-19, respectively, 1.38, 2.56, 1.71 and 1.97 times the chance of those who do not have these conditions of health.
The data in Table 4 only consider people with obesity, fixing the other variables, and indicate that women have 40% less chance of being hospitalized by COVID-19 when compared to men; black individuals are 61% more likely to be hospitalized by COVID-19 than white individuals to be hospitalized; people from 40 to 59 years old and 60 years old or more reflect a chance of hospitalization by COVID-19 1,98 and 4,23 times, respectively, the chance of people from 18 to 39 years old to be hospitalized. Education level does not impact the chance of hospitalization by COVID-19 in this scenario.
Regarding the symptoms, breathing difficulty was a symptom associated with the chance of hospitalization due to COVID-19. When compared to people who do not have this symptom, the chance of hospitalization is, respectively, 3.20 times in symptomatic cases. Symptoms such as sore throat, diarrhea and headache presented values indicating a "protective effect" for hospitalization by COVID-19, that is, in cases which individuals had these symptoms, there was a reduction in the chance of hospitalization of 48%, 57% and 52% compared to individuals who did not have these symptoms.
Individuals who had kidney disease and diabetes have a chance of hospitalization by COVID-19, respectively, 5.32 and 2.04 times the chance of those who do not have these health problems.
Table 5 presents the logistic regression model for the association of sociodemographic factors and symptoms with death by COVID-19, and the data show that women are 34% less likely to evolve to death by COVID-19 when compared to men.
People from 40 to 59 years old or 60 years old or more have a chance of death by COVID-19 3.85 and 21.01 times, respectively, the chance of people from 18 to 39 years old of dying. Missing a university diploma is a risk factor for death by COVID-19, and the chance of death may range from 1.61 to 4.08 times, and the behavior of decreasing OR is almost linear as education level increases.
Obesity increased the chance of death by 2.08 times when compared to people who did not have this condition. Symptoms such as fever and breathing difficulty were associated with the chance of death by COVID-19. When compared to people who do not have these symptoms, the chance of dying is, respectively, 1.43 and 3.15 times in symptomatic cases. Running nose, sore throat, diarrhea and headache presented values that indicate a "protective effect" for death by COVID-19, that is, in cases which individuals had these symptoms, there was a reduction in the chance of death of 32%, 37%, 21% and 48% compared to individuals who did not have these symptoms.
Individuals who had lung, heart, kidney diseases and diabetes have a chance of dying by COVID-19, respectively, 1.34, 1.24, 2.90 and 1.70 times the chance of those who do not have these health problems.
The results presented in Table 6 only consider people with obesity and show that women are 32% less likely to evolve to death by COVID-19 when compared to men. People from 40 to 59 years old or 60 years old or more increase the chance of death by COVID-19 in 3.86 and 20.28 times, respectively, when compared to the chance of people from 18 to 39 years old to die. Missing a university diploma is a risk factor for death by COVID-19, the chance of death may range from 1.60 to 3.99 times.
Fever and breathing difficulty are symptoms associated with the chance of death by COVID-19. When compared to people who do not have these symptoms, the chance of dying is, respectively, 1.43 and 3.22 times in symptomatic cases. Running nose, sore throat, diarrhea and headache showed estimates that indicate a "protective effect" for death by COVID-19, that is, in cases which individuals had this symptom, there was a reduction in the chance of death in 33%, 37%, 21% and 47% compared to individuals who did not present these symptoms.
Individuals with kidney disease and diabetes have a chance of dying by COVID-19 of 5.32 and 2.04 times, respectively, the chance of individuals who do not have these comorbidities of dying. It is important to emphasize that the low prevalence of death in patients with kidney disease, may have resulted in its statistical significance.
Discussion
Data showed that men, non-white, no education or with low education level and declining age were more likely to be hospitalized and die of COVID-19 in the state of Espírito Santo.
The severity of the disease according to gender has also been assessed in other studies [19,20]. Previous research has shown that the X chromosome is known to keep the largest number of genes related to the immune system in the entire genome. Women, for presenting chromosome XX, are generally more responsive to infections [19]. In addition, studies show that in males there is a greater presence of receptors for SARS-CoV-2, the Angiotensin-Converting Enzyme 2 (ECA2), in their alveolar cells if compared to women [21].
Takahashi et al. (2020) while monitoring 98 patients with COVID-19 admitted to Yale Hospital from March 18th to May 9th, 2020, noticed significantly higher levels of pro-inflammatory chemokines and cytokines in male participants, such as IL-8, IL-18 and CCL5, and a significantly lower number of T cells, both in the total count and in the proportion of live cells, over the course of the disease, which contributed to the worsening of their clinical condition [22].
Other authors also evaluated the frequency of race/color in people with COVID-19 and identified differences. An analysis carried out in the United Kingdom noticed that hospitalization by COVID-19 was found in 32 out of 7714 (0.4%) black participants, 28 out of 10.614 (0.2%) Asian participants and 489 out of 400,438 (0.1%) white participants [23]. A similar result was noticed in a study conducted in Detroit, United States, in which 2.316 (63.7%) people diagnosed with COVID-19 and who were hospitalized, 55.7% were black/brown [24].
Analyzes by Baqui et al (2020) with 11.321 Brazilian patients diagnosed with COVID-19 showed that, after age, the most important factor for hospital mortality was being brown or, to a lesser extent, black compared to white race [25].
Racial differences in the frequency of aggravation of COVID-19 can be multifactorial and are still unclear. These data may reflect differences in working conditions and health determinants they are submitted to, as well as being related to potential biological factors [23,26,27]. However, black/brown Brazilians have, on average, less economic security, live in favorable conditions to contagion, are less likely to be able to work remotely and constitute a substantial proportion of health workers, making them the most vulnerable to COVID– 19 [28].
In our study, no education people or those with lower education level had a higher chance of hospitalization and death, which can be explained by less access to information and health services, possibly having the incomes affected during the pandemic and living in inadequate hygienic and sanitary conditions [29].
In the investigation of 45,161 questionnaires carried out nationwide, by Oswaldo Cruz Foundation (Fiocruz), it was highlighted that the groups that least adhered to the social distance initiatives to control COVID-19 were composed by men (31.7%), from 30 to 49 years old (36.4%), with low education level (33.0%) and who kept working during the pandemic (81.3%) [30].
Regarding signs and symptoms, cough, headache and fever were the most ordinary identified ones in our study. Fever and breathing difficulty increased the chances of hospitalization and death, while running nose, sore throat, diarrhea and headache were shown to be protective effects. In addition, the fact of being hospitalized increased the chances of death in almost 20 times. In Wuhan, China, the most ordinary symptoms at the beginning of the disease in 138 hospitalized people were fever (136 [98.6%]), fatigue (96 [69.6%]), dry cough (82 [59.4%]), myalgia (48 [34.8%]) and dyspnea (43 [31.2%]). The less ordinary symptoms were headache, dizziness, abdominal pain, diarrhea, nausea and vomiting [31].
For 278 positive patients for COVID-19 in New York, the presence of gastrointestinal symptoms was associated with a longer duration of the disease, however, with a tendency for a lower rate of admission to the Intensive Care Unit and lower mortality [32].
In our analyses have also shown that obesity, heart, kidney and lung diseases, diabetes and smoking increased the chances of hospitalization. Obesity represented 4.5% of the total diagnoses of COVID-19, among comorbidities, it was the third risk factor that most increased the chances of hospitalization and the second related to the increase of the chances of death.
Vardavas and Nikitara (2020) evidenced in their systematic review that smoking patients were more likely to worsen COVID-19 than non-smokers [33]. Smoking is related to a higher expression of SARS-CoV-2 receptors, which can be the reason for the highest prevalence of more severe symptoms in this subgroup of patients [34]. In the study by Azar et al [35], comorbidities such as congestive heart failure or type 2 diabetes were associated with a greater chance of hospitalization compared to those who did not have these conditions.
Bello-Chavolla et al. [36] when evaluating the confirmed and negative cases of COVID-19 and their demographic and health characteristics in the General Directorate of Epidemiology of the Ministry of Health of Mexico found that 51,633 individuals tested positive for SARS-CoV-2. When assessing age, there was a reduced chance of positivity for SARS-CoV-2 in patients <40 years old. However, in stratified models, it was found that for patients with diabetes, positivity for SARS-CoV-2 was associated with obesity, male gender and age <40 years old. Patients with obesity who had COVID-19 confirmed had an almost five-time increase in the risk of mortality (OR = 4.989; 95% CI = 4.444–5.600). In addition, they also had higher rates of Intensive Care Unit admission (5.0% vs. 3.3%) and were more likely to be intubated (5.2% vs. 3.3%) [36].
In our study, the highest chances of hospitalization and death for people with obesity were related to age over 60 years old, followed by the age group from 40 to 59 years old, who had breathing difficulties, diabetes and who had been hospitalized. The analysis by Klang et al. (2020), in New York City, with data from 3,406 patients, 572 patients under 50 years old and 2.834 over 50 years old have shown that in the youngest age group, 60 (10.5%) patients died, and the analysis univariate demonstrated that, for the youngest group, BMI ≥ 40 kg / m² was significantly associated with death (p <0.001) [37].
In the research by Ong et al. (2020), in Singapore, in patients under 60 years old it was verified that BMI ≥25kg / m² was significantly associated with pneumonia on chest X-ray at admission (p = 0.017), requiring low oxygen supplementation flow (OR = 6.32; 95% CI = 1.23–32.34) and mechanical ventilation (OR = 1.16; 95% CI = 1.00–1.34). BMI ≥25kg / m² was also associated with significantly higher serum levels of lactate dehydrogenase (p = 0.011), which were associated with the severity of the disease [38].
The mechanisms involving the role of obesity in the pathogenesis of COVID-19 are not yet well defined, but individuals with obesity generally have a decreased immune response to infectious pathogens, which can also affect the lung parenchyma, increasing the risk of inflammatory lung diseases [39]. In addition, as it is characterized as a low-grade inflammation, in obesity mononuclear cells increase the transcription of pro-inflammatory cytokines, which increases the secretion of these cytokines [40].
Evidence suggests that adipose tissue is a pro-immunogenic and richly vascularized organ, with the ability to increase the pro-inflammatory response to viral infection. Thus, it can potentiate and prolong viral shedding in an environment that is already inflamed with the local amplification of cytokines, which can hinder the patient’s recovery [41].
Zhang and colleagues in a logistic regression model also identified the factors that address the mechanisms underlying obesity predisposing COVID-19 patients to death. Through which the index related to inflammation, PCR, heart damage (hs-cTnI and NT-proBNP) and increased clotting activity (D-dimer) are characterized as significantly associated with adverse clinical outcomes in patients with high BMI. In addition, the decrease in lymphocytes and eosinophils or in total globulin levels was also correlated with the poor prognosis in these patients [42].
Abdominal obesity can restrict ventilation, preventing diaphragm excursion, as it reduces the compliance of the lung, chest wall and the entire respiratory system, resulting in decreased blood oxygen saturation and breathing functional capability [41,43].
Therefore, the data analyzed here confirm those found by other authors and show that obesity can be considered a risk factor for hospitalization and death by COVID-19, especially when in addition to obesity, other conditions such as age over 40 years old are present (more severe for those over 60 years old), the presence of comorbidities such as diabetes and kidney disease.
Study limitations
This study has some limitations that deserve to be highlighted. Among them are the limitations of a cross-sectional study that analyzed data from a specific time and did not assess other aspects related to the illness of these individuals over time. Another important limitation is that this study is based on data from the state health department, which are obtained through the records in the Health Units, and although the notification forms have a lot of mandatory registration information, one cannot be sure about the recording quality of these data.
References
- 1. Borges GM, Crespo CD. Aspectos demográficos e socioeconômicos dos adultos brasileiros e a COVID-19: uma análise dos grupos de risco a partir da Pesquisa Nacional de Saúde, 2013. Cad Saúde Pública [Internet]. 2020 [citado 1° de novembro de 2020];36(10):e00141020. Disponível em: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2020001005011&tlng=pt pmid:33111839
- 2. Codeço CT, Villela D, Coelho FC, Bastos LS, Carvalho LM, Gomes MFC, et al. Estimativa de risco de espalhamento da COVID-19 no Brasil e avaliação da vulnerabilidade socioeconômica nas microrregiões brasileiras. Análise do risco de disseminação do novo Coronavírus (COVID-19) / RP [Internet]. 2020;3(FIOCRUZ/PROCC). Disponível em: http://bibliotecadigital.fgv.br/dspace/handle/10438/28942.
- 3. CDC COVID-19 Response Team. Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with Coronavirus Disease 2019—United States, February 12-March 28, 2020. MMWR Morb Mortal Wkly Rep. 3 de abril de 2020;69(13):382–6. pmid:32240123
- 4. Li X, Xu S, Yu M, Wang K, Tao Y, Zhou Y, et al. Risk factors for severity and mortality in adult COVID-19 in patients in Wuhan. J Allergy Clin Immunol [Internet]. julho de 2020 [citado 1° de novembro de 2020];146(1):110–8. Disponível em: https://linkinghub.elsevier.com/retrieve/pii/S0091674920304954 pmid:32294485
- 5. Rezende LFM, Thome B, Schveitzer MC, de Souza-Júnior PRB, Szwarcwald CL. Adults at high-risk of severe coronavirus disease-2019 (Covid-19) in Brazil. Rev Saúde Pública [Internet]. 20 de maio de 2020 [citado 18 de junho de 2020];54:50. Disponível em: http://www.scielosp.org/scielo.php?script=sci_arttext&pid=S0034-89102020000100239&lang=pt pmid:32491091
- 6.
Wolff D, Nee S, Hickey NS, Marschollek M. Risk factors for Covid-19 severity and fatality: a structured literature review. INFECTION. 2020.
- 7. Abdi A, Jalilian M, Sarbarzeh PA, Vlaisavljevic Z. Diabetes and COVID-19: A systematic review on the current evidences. Diabetes Res Clin Pract [Internet]. agosto de 2020 [citado 1° de novembro de 2020];166:108347. Disponível em: https://linkinghub.elsevier.com/retrieve/pii/S0168822720305994 pmid:32711003
- 8. 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 [Internet]. maio de 2020 [citado 1° de dezembro de 2020];109(5):531–8. Disponível em: http://link.springer.com/10.1007/s00392-020-01626-9 pmid:32161990
- 9. Rod JE, Oviedo-Trespalacios O, Cortes-Ramirez J. A brief-review of the risk factors for covid-19 severity. Rev Saúde Pública [Internet]. 10 de julho de 2020 [citado 1° de agosto de 2021];54:60. Disponível em: http://www.revistas.usp.br/rsp/article/view/172287 pmid:32491116
- 10. Soares R de CM, Mattos LR, Raposo LM. Risk Factors for Hospitalization and Mortality due to COVID-19 in Espírito Santo State, Brazil. Am J Trop Med Hyg [Internet]. 2 de setembro de 2020 [citado 1° de novembro de 2020];103(3):1184–90. Disponível em: http://www.ajtmh.org/content/journals/10.4269/ajtmh.20-0483 pmid:32682453
- 11. Zhou Y, Yang Q, Chi J, Dong B, Lv W, Shen L, et al. Comorbidities and the risk of severe or fatal outcomes associated with coronavirus disease 2019: A systematic review and meta-analysis. Int J Infect Dis IJID Off Publ Int Soc Infect Dis. outubro de 2020;99:47–56.
- 12.
Chang T-H, Chou C-C, Chang L-Y. Effect of obesity and body mass index on coronavirus disease 2019 severity: A systematic review and meta-analysis. Obes Rev.
- 13. Noor FM, Islam MM. Prevalence and Associated Risk Factors of Mortality Among COVID-19 Patients: A Meta-Analysis. J Community Health [Internet]. 2020;((Noor F.M.) M.Sc. Department of Statistics, University of Dhaka, Bangladesh). Disponível em: https://www.embase.com/search/results?subaction=viewrecord&id=L632849282&from=export pmid:32918645
- 14. Pranata R, Lim MA, Yonas E, Vania R, Lukito AA, Siswanto BB, et al. Body mass index and outcome in patients with COVID-19: A dose–response meta-analysis. Diabetes Metab [Internet]. 1° de março de 2021 [citado 27 de maio de 2021];47(2):101178. Disponível em: https://www.sciencedirect.com/science/article/pii/S1262363620300975 pmid:32738402
- 15.
Carneiro RAVD, Hillesheim D, Hallal ALC. Correlation of overweight condition and obesity with mortality by COVID-19 in Brazil’s state capitals. Arch Endocrinol Metab [Internet]. 21 de abril de 2021 [citado 21 de junho de 2021]; https://www.scielo.br/j/aem/a/frXg9MHFpCQvDf6Qn6MXK5w/?lang=en.
- 16. de Souza FSH, Hojo-Souza NS, de O Batista BD, da Silva CM, Guidoni DL. On the analysis of mortality risk factors for hospitalized COVID-19 patients: A data-driven study using the major Brazilian database. PLOS ONE [Internet]. 18 de março de 2021 [citado 21 de junho de 2021];16(3):e0248580. Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248580 pmid:33735272
- 17.
Espírito Santo. NOTA TÉCNICA COVID-19 N° 29/2020 –GEVS/SESA/ES. Definição de Casos Operacionais e Critérios de Coleta [Internet]. 2021. https://coronavirus.es.gov.br/Media/Coronavirus/NotasTecnicas/NOTA%20T%C3%89CNICA%20COVID.19%20N.%2029.20%20Defini%C3%A7%C3%A3o%20de%20caso%20e%20crit%C3%A9rios%20de%20coleta.pdf.
- 18. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. abril de 2008;61(4):344–9. pmid:18313558
- 19. 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 [Internet]. outubro de 2020 [citado 20 de agosto de 2021];10(10):e040129. Disponível em: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2020-040129 pmid:33028563
- 20. Dowd JB, Andriano L, Brazel DM, Rotondi V, Block P, Ding X, et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proc Natl Acad Sci [Internet]. 5 de maio de 2020 [citado 20 de agosto de 2021];117(18):9696–8. Disponível em: http://www.pnas.org/lookup/doi/10.1073/pnas.2004911117 pmid:32300018
- 21. Zhang H, Penninger JM, Li Y, Zhong N, Slutsky AS. Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target. Intensive Care Med [Internet]. abril de 2020 [citado 1° de janeiro de 2021];46(4):586–90. Disponível em: http://link.springer.com/10.1007/s00134-020-05985-9 pmid:32125455
- 22. Yale IMPACT Research Team, Takahashi T, Ellingson MK, Wong P, Israelow B, Lucas C, et al. Sex differences in immune responses that underlie COVID-19 disease outcomes. Nature [Internet]. 10 de dezembro de 2020 [citado 20 de agosto de 2021];588(7837):315–20. Disponível em: http://www.nature.com/articles/s41586-020-2700-3 pmid:32846427
- 23. Patel AP, Paranjpe MD, Kathiresan NP, Rivas MA, Khera AV. Race, socioeconomic deprivation, and hospitalization for COVID-19 in English participants of a national biobank. Int J Equity Health [Internet]. 6 de julho de 2020 [citado 2 de novembro de 2020];19. Disponível em: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336098/.
- 24. Miller J, Fadel RA, Tang A, Perrotta G, Herc E, Soman S, et al. The Impact of Sociodemographic Factors, Comorbidities, and Physiologic Responses on 30-Day Mortality in Coronavirus Disease 2019 (COVID-19) Patients in Metropolitan Detroit. Clin Infect Dis [Internet]. 1° de junho de 2021 [citado 20 de agosto de 2021];72(11):e704–10. Disponível em: https://academic.oup.com/cid/article/72/11/e704/5908303 pmid:32945856
- 25. Baqui P, Bica I, Marra V, Ercole A, van der Schaar M. Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study. Lancet Glob Health [Internet]. agosto de 2020 [citado 1° de janeiro de 2021];8(8):e1018–26. Disponível em: https://linkinghub.elsevier.com/retrieve/pii/S2214109X20302850 pmid:32622400
- 26. Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and Mortality among Black Patients and White Patients with Covid-19. N Engl J Med. 25 de junho de 2020;382(26):2534–43. pmid:32459916
- 27. Webb Hooper M, Nápoles AM, Pérez-Stable EJ. COVID-19 and Racial/Ethnic Disparities. JAMA [Internet]. 23 de junho de 2020 [citado 18 de agosto de 2021];323(24):2466. Disponível em: https://jamanetwork.com/journals/jama/fullarticle/2766098 pmid:32391864
- 28. Tavares FF, Betti G. The pandemic of poverty, vulnerability, and COVID-19: Evidence from a fuzzy multidimensional analysis of deprivations in Brazil. World Dev [Internet]. março de 2021 [citado 1° de janeiro de 2021];139:105307. Disponível em: https://linkinghub.elsevier.com/retrieve/pii/S0305750X20304344.
- 29. Webster RK, Brooks SK, Smith LE, Woodland L, Wessely S, Rubin GJ. How to improve adherence with quarantine: rapid review of the evidence. Public Health [Internet]. maio de 2020 [citado 1° de janeiro de 2021];182:163–9. Disponível em: https://linkinghub.elsevier.com/retrieve/pii/S0033350620300718 pmid:32334182
- 30. Szwarcwald CL, de Souza Júnior PRB, Malta DC, de A Barros MB, de AFM Magalhães M, Xavier DR, et al. Adesão às medidas de restrição de contato físico e disseminação da COVID-19 no Brasil. Epidemiol E Serviços Saúde [Internet]. 2020 [citado 1° de janeiro de 2021];29(5):e2020432. Disponível em: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2237-96222020000500305&tlng=pt.
- 31. 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 [Internet]. 17 de março de 2020 [citado 1° de janeiro de 2021];323(11):1061. Disponível em: https://jamanetwork.com/journals/jama/fullarticle/2761044.
- 32. Nobel YR, Phipps M, Zucker J, Lebwohl B, Wang TC, Sobieszczyk ME, et al. Gastrointestinal Symptoms and Coronavirus Disease 2019: A Case-Control Study From the United States. Gastroenterology [Internet]. julho de 2020 [citado 1° de janeiro de 2021];159(1):373–375.e2. Disponível em: https://linkinghub.elsevier.com/retrieve/pii/S001650852030490X pmid:32294477
- 33. Vardavas C, Nikitara K. COVID-19 and smoking: A systematic review of the evidence. Tob Induc Dis [Internet]. 20 de março de 2020 [citado 20 de agosto de 2021];18(March). Disponível em: http://www.journalssystem.com/tid/COVID-19-and-smoking-A-systematic-review-of-the-evidence,119324,0,2.html.
- 34. Cai H. Sex difference and smoking predisposition in patients with COVID-19. Lancet Respir Med [Internet]. abril de 2020 [citado 4 de novembro de 2020];8(4):e20. Disponível em: https://linkinghub.elsevier.com/retrieve/pii/S221326002030117X pmid:32171067
- 35. Azar KMJ, Shen Z, Romanelli RJ, Lockhart SH, Smits K, Robinson S, et al. Disparities In Outcomes Among COVID-19 Patients In A Large Health Care System In California: Study estimates the COVID-19 infection fatality rate at the US county level. Health Aff (Millwood) [Internet]. 1o de julho de 2020 [citado 4 de novembro de 2020];39(7):1253–62. Disponível em: http://www.healthaffairs.org/doi/10.1377/hlthaff.2020.00598 pmid:32437224
- 36. Bello-Chavolla OY, Bahena-López JP, Antonio-Villa NE, Vargas-Vázquez A, González-Díaz A, Márquez-Salinas A, et al. Predicting Mortality Due to SARS-CoV-2: A Mechanistic Score Relating Obesity and Diabetes to COVID-19 Outcomes in Mexico. J Clin Endocrinol Metab [Internet]. 1° de agosto de 2020 [citado 4 de novembro de 2020];105(8):2752–61. Disponível em: https://academic.oup.com/jcem/article/105/8/2752/5849337 pmid:32474598
- 37. Klang E, Kassim G, Soffer S, Freeman R, Levin MA, Reich DL. Severe Obesity as an Independent Risk Factor for COVID‐19 Mortality in Hospitalized Patients Younger than 50. Obesity [Internet]. setembro de 2020 [citado 20 de agosto de 2021];28(9):1595–9. Disponível em: https://onlinelibrary.wiley.com/doi/10.1002/oby.22913 pmid:32445512
- 38. Ong SWX, Young BE, Leo Y-S, Lye DC. Association of Higher Body Mass Index With Severe Coronavirus Disease 2019 (COVID-19) in Younger Patients. Clin Infect Dis [Internet]. 19 de novembro de 2020 [citado 20 de agosto de 2021];71(16):2300–2. Disponível em: https://academic.oup.com/cid/article/71/16/2300/5831985 pmid:32382755
- 39. Apostolopoulos V, de Courten MPJ, Stojanovska L, Blatch GL, Tangalakis K, de Courten B. The complex immunological and inflammatory network of adipose tissue in obesity. Mol Nutr Food Res [Internet]. janeiro de 2016 [citado 20 de agosto de 2021];60(1):43–57. Disponível em: https://onlinelibrary.wiley.com/doi/10.1002/mnfr.201500272 pmid:26331761
- 40. Ghanim H, Aljada A, Hofmeyer D, Syed T, Mohanty P, Dandona P. Circulating Mononuclear Cells in the Obese Are in a Proinflammatory State. Circulation [Internet]. 21 de setembro de 2004 [citado 20 de agosto de 2021];110(12):1564–71. Disponível em: https://www.ahajournals.org/doi/10.1161/01.CIR.0000142055.53122.FA pmid:15364812
- 41. Ryan PM, Caplice NM. Is Adipose Tissue a Reservoir for Viral Spread, Immune Activation, and Cytokine Amplification in Coronavirus Disease 2019? Obesity [Internet]. julho de 2020 [citado 20 de agosto de 2021];28(7):1191–4. Disponível em: https://onlinelibrary.wiley.com/doi/10.1002/oby.22843 pmid:32314868
- 42. Zhang F, Xiong Y, Wei Y, Hu Y, Wang F, Li G, et al. Obesity predisposes to the risk of higher mortality in young COVID‐19 patients. J Med Virol [Internet]. novembro de 2020 [citado 20 de agosto de 2021];92(11):2536–42. Disponível em: https://onlinelibrary.wiley.com/doi/10.1002/jmv.26039 pmid:32437016
- 43. Parameswaran K, Todd DC, Soth M. Altered respiratory physiology in obesity. Can Respir J. junho de 2006;13(4):203–10. pmid:16779465