Literature DB >> 32491091

Adults at high-risk of severe coronavirus disease-2019 (Covid-19) in Brazil.

Leandro F M Rezende1, Beatriz Thome1, Mariana Cabral Schveitzer1, Paulo Roberto Borges de Souza-Júnior2, Célia Landmann Szwarcwald2.   

Abstract

OBJECTIVE To estimate the proportion and total number of the general adult population who may be at higher risk of severe Covid-19 in Brazil. METHODS We included 51,770 participants from a nationally representative, household-based health survey (PNS) conducted in Brazil. We estimated the proportion and number of adults (≥ 18 years) at risk of severe Covid-19 by sex, educational level, race/ethnicity, and state based on the presence of one or more of the following risk factors: age ≥ 65 years or medical diagnosis of cardiovascular disease, diabetes, hypertension, chronic respiratory disease, cancer, stroke, chronic kidney disease and moderate to severe asthma, smoking status, and obesity. RESULTS Adults at risk of severe Covid-19 in Brazil varied from 34.0% (53 million) to 54.5% (86 million) nationwide. Less-educated adults present a 2-fold higher prevalence of risk factors compared to university graduated. We found no differences by sex and race/ethnicity. São Paulo, Rio de Janeiro, Minas Gerais, and Rio Grande do Sul were the most vulnerable states in absolute and relative terms of adults at risk. CONCLUSIONS Proportion and total number of adults at risk of severe Covid-19 are high in Brazil, with wide variation across states and adult subgroups. These findings should be considered while designing and implementing prevention measures in Brazil. We argue that these results support broad social isolation measures, particularly when testing capacity for SARS-CoV-2 is limited.

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Mesh:

Year:  2020        PMID: 32491091      PMCID: PMC7234208          DOI: 10.11606/s1518-8787.2020054002596

Source DB:  PubMed          Journal:  Rev Saude Publica        ISSN: 0034-8910            Impact factor:   2.106


INTRODUCTION

The World Health Organization (WHO) suggests that most people infected with the virus may develop mild or uncomplicated (80%) coronavirus disease 2019 (Covid-19), while the remaining 20% may develop its severe variation, requiring hospitalization (14%) or intensive care unit (6%)[1]. Established risk factors for severe disease among inpatients with Covid-19 in China included older age[2,3] and serious medical conditions such as cardiovascular disease[2], diabetes[2], chronic respiratory disease (in particular chronic obstructive pulmonary diseaseCOPD)[2], hypertension[2,4], cancer[2,5], and cerebrovascular disease[3,4]. Recent findings from United States (US) and Europe confirmed these risk factors and proposed new ones, such as chronic kidney disease, obesity, asthma and smoking[6]. The emergence of a highly transmissible pathogen[10] in a completely susceptible population has resulted in an exponential growth of new cases worldwide and a wide dissemination across the globe. As of April 12, 2020, the number of SARS-CoV-2 infections was above 1.8 million, reported in 185 countries/regions of the world[11]. High- and low-income regions are already facing overload of health facilities and facing scarcity of resources to fight the pandemic. In lower resource settings, countries have a short time to prepare prevention and management strategies, including the identification of high-risk populations and regions within countries. Herein, we propose a calculation of the proportion and total number of the general adult population who may be at higher risk for severe Covid-19, based on routinely collected data from a nationwide, household-based survey in Brazil. We argue that this method could be easily and rapidly applied within and across countries in order to craft tailored prevention strategies such as social isolation.

METHODS

We obtained data from the most recent representative, household-based survey conducted in Brazil, the National Health Survey (PNS, 2013 – Pesquisa Nacional de Saúde), carried out by the Ministry of Health in partnership with the Brazilian Institute of Geography and Statistics (IBGE). The PNS enrolled 62,202 adults who responded to a comprehensive questionnaire about several health-related issues. In this study, we included 51,770 participants who responded to the questionnaire about medical diagnosis and lifestyle risk factors, and had their weight and height measured. Further details about PNS have been described elsewhere[12].

Risk Factors for Severe Covid-19

We included risk factors for severe Covid-19 based on currently available information from clinical studies and expertise[2], and for which exposure data were available in the PNS[12]. Age and medical diagnosis of cardiovascular disease, diabetes, hypertension, chronic respiratory disease, cancer, stroke, chronic kidney disease and asthma were assessed. We also obtained time (in years) since cancer diagnosis and treatment/medication use for chronic kidney disease (e.g. dialysis) and asthma to match definitions from the literature (e.g. moderate to severe asthma). Information about age, smoking status and measured body mass index (BMI) were also obtained/estimated. Prevalence of one or more risk factors for severe Covid-19 was estimated using two criteria (Table 1). Criterion 1 included first identified and established risk factors for severe Covid-19 such as age ≥ 65 years or medical diagnosis of cardiovascular disease, diabetes, hypertension, chronic respiratory disease, cancer or stroke. Although ≥ 60 years have been used to define older adults in Brazil, herein we considered ≥ 65 years to match the definition of risk factors for Covid-19 obtained from the literature and allow comparisons with other publications[2]. Criterion 2 additionally included diagnosis of chronic kidney disease and moderate to severe asthma, smoking status (current smokers) and obesity (BMI ≥ 30 kg/m[2]). Criterion 2 was used to provide a higher sensitivity for the proportion of adults at risk of severe illness. Denominator for both criteria 1 (n = 52,511) and 2 (n = 51,770) included all participants with complete questionnaires. We also estimated the sum of all risk factors for severe illness (0, 1, 2, 3 + risk factors).
Table 1

Definition of risk factors for severe Covid-19 according to two different proposed criteria.

Risk factorsDefinitionPresence of risk factor for severe Covid-19

Criterion 1Criterion 2
Agein years≥ 65 years≥ 65 years
Cardiovascular diseaseHas a doctor ever diagnosed you with a heart disease such as infarction, angina, heart failure or other?YesYes
DiabetesHas a doctor ever diagnosed you with diabetes?YesYes
HypertensionHas a doctor ever diagnosed you with hypertension (high blood pressure)?YesYes
Chronic respiratory diseaseHas a doctor already diagnosed you with any lung disease such as pulmonary emphysema, chronic bronchitis, or COPD (Obstructive Pulmonary Disease Chronic)?YesYes
CancerHas any doctor ever diagnosed you with cancer (excluding skin cancer)?YesYes
How many years ago since your cancer diagnosis?< 5 years< 5 years
StrokeHas any doctor ever diagnosed you with stroke?YesYes
ObesityMeasured body mass indexNo≥ 30 kg/m2
SmokingCurrent smokerNoYes (daily or less than daily)
Chronic kidney diseaseHas any doctor ever diagnosed you with chronic kidney disease?NoYes
What do you currently do or have done because of the chronic kidney disease?NoHemodialysis, peritoneal dialysis, took medication, underwent a kidney transplant
Moderate to severe asthmaHas any doctor ever diagnosed you with asthma (or asthmatic bronchitis)?NoYes
What do you currently do because of asthma?NoUse of inhalers, aerosol or tablets

Sociodemographic Covariates

Information on covariates including sex, race/ethnicity, educational level, and Brazilian state (26 states and the Federative District) were obtained to describe the proportion of adults at risk of severe Covid-19 by population strata. We also retrieved the total projected number of the Brazilian adult population (≥ 18 years) in 2020 by sex and state from the IBGE[13].

Statistical Analysis

We estimated the prevalence and 95% confidence intervals of adults at risk for severe Covid-19 (Criterion 1 and Criterion 2) by sex, education, race/ethnicity and Brazilian state. We performed sensitivity analyses for prevalence by considering two other definitions for older adults (≥ 60 years and ≥ 70 years). In order to obtain the total number of adults at risk of severe illness, we applied the prevalence to the number of adult’s population (≥ 18 years) by sex and state. The sample design was considered for all analyses using the survey prefix command (svy) in Stata version 15.0.

RESULTS

Participants characteristics and risk factors for severe illness are presented by age group (Table 2). Compared with younger participants, older adults (≥ 65 years) were less educated, more likely women, white and presented higher prevalence of risk factors for severe Covid-19, except for smoking. Prevalence of one or more risk factors for severe illness was 47.3% in younger vs 75.9% in older adults.
Table 2

Characteristics and risk factors for severe Covid-19 by age group in Brazil, PNS 2013

CharacteristicsAge groupsTotal

< 65 years≥ 65 years
Number of participants23.83827.93251.770
Mean age, years (se)39.7 (11.4)73.5 (14.1)44.3 (15.0)
Sex (%)   
Men45.442.945.0
Education (%)   
None or incomplete primary education15.167.022.2
Complete primary or incomplete secondary education27.214.025.4
Complete secondary education or incomplete undergraduate course42.710.338.3
University Graduate15.08.714.1
Race/ethnicity (%)   
White48.355.949.4
Non-white51.744.150.6
Risk factors for Severe Covid-19 (%)   
Cardiovascular disease3.413.04.7
Diabetes5.120.77.2
Chronic respiratory disease1.54.41.9
Hypertension18.855.323.7
Cancer0.62.20.8
Stroke1.06.11.7
Obesity (BMI ≥30 kg/m2)22.022.722.1
Smoking14.69.613.9
Chronic kidney disease0.72.00.9
Moderate to severe asthma1.51.71.5
Number of risk factors for severe Covid-19* (%)   
None52.724.148.8
130.935.131.5
212.025.213.8
3+4.415.65.9

SE: standard error

* Diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (< 5 years of diagnosis), stroke, obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets)

SE: standard error * Diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (< 5 years of diagnosis), stroke, obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets) Proportion and total number of adults at risk for severe Covid-19 in Brazil varied from 34.0% (53 million adults) to 54.5% (86 million adults) (Table 3). Overall, 46% of the sample presented no risk factor, 30.0% with one, 15.0% with two, and 9% with 3 or more risk factors for severe illness. Sensitivity analyses considering older adults ≥ 60 years and ≥ 70 years suggested that prevalence could vary from 36.7%–56.2% to 32.3%–53.3%, respectively (Table 4).
Table 3

Prevalence of one or more risk factor for severe Covid-19 among the Brazilian general adult population by risk criteria and sociodemographic characteristics, PNS 2013.

CharacteristicsPrevalence of one or more risk factors for severe Covid-19

Criterion 1 (n = 52,511)Criterion 2 (n = 51,770)


Prevalence (%)95%CIPrevalence (%)95%CI
Total34.033.2–34.754.453.6–55.2
Sex    
Men31.630.5–32.853.352.1–54.5
Women35.934.9–36.855.454.3–56.4
Education    
None or incomplete primary education66.364.7–67.980.278.9–81.4
Complete primary or incomplete secondary education30.529.2–31.955.053.5–56.5
Complete secondary education or incomplete undergraduate course20.419.4–21.442.240.9–43.6
University Graduate27.025.1–29.146.144.1–48.3
Race/ethnicity    
White34.933.8–36.055.053.9–56.2
Non-white33.121.1–34.053.952.8–54.9

Criterion 1: age ≥ 65 years or diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (< 5 years of diagnosis), or stroke

Criterion 2: additionally, obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under Hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets)

Table 4

Sensitivity analysis: prevalence of one or more risk factors for severe Covid-19 among the Brazilian general adult population by risk criteria, definitions of older age and sociodemographic characteristics in Brazil, PNS 2013.

CharacteristicsRisk factors for severe Covid-19

Criterion 1 (n = 52,511)Criterion 2 (n = 51,770)


Older age defined as ≥ 60 yearsOlder age defined as ≥ 70 yearsOlder age defined as ≥ 60 yearsOlder age defined as ≥ 70 years




Prevalence (%)95%CIPrevalence (%)95%CIPrevalence (%)95%CIPrevalence (%)95%CI
Total36.736.0–37.532.331.6–33.056.255.3–57.053.352.5–54.0
Sex        
Men34.533.3–35.630.028.9–31.154.953.7–56.152.251.0–53.4
Women38.637.6–39.534.233.3–35.157.256.1–58.254.253.2–55.2
Education        
None or incomplete primary72.070.4–73.462.260.5–63.883.482.3–84.677.576.2–78.8
Complete primary or incomplete secondary32.230.9–33.629.328.0–30.656.254.6–57.754.152.5–55.6
Complete secondary or incomplete university22.121.1–23.219.818.8–20.843.442.0–44.741.840.4–43.2
University Graduate30.028.0–32.125.623.7–27.548.145.9–50.245.143.0–47.2
Race/ethnicity        
White38.036.9–39.233.132.0–34.257.055.8–58.253.852.6–54.9
Non-white35.534.5–36.531.630.6–32.555.354.3–56.352.851.8–53.9

Criterion 1: age group or diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (< 5 years of diagnosis), or stroke; Criterion 2: additionally obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets).

Criterion 1: age ≥ 65 years or diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (< 5 years of diagnosis), or stroke Criterion 2: additionally, obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under Hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets) Criterion 1: age group or diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (< 5 years of diagnosis), or stroke; Criterion 2: additionally obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets). Proportion of adults at risk for severe Covid-19 was 2-fold higher in less educated participants compared with university graduated. We found no differences in prevalence estimates by sex and race/ethnicity (Table 3). Estimates varied widely across states, with higher prevalence in the South and Southeast regions of the country (Figure). The highest prevalence was 39.5%–58.4% in Rio Grande do Sul, followed by 36.0–55.8% in Rio de Janeiro and 35.6%–58.2% in São Paulo. The lowest prevalence was found in Amapá (23.4%–45.9%), followed by Roraima (25.0%–48.6%) and Amazonas (25.1%–48.7%). The highest number of adults at risk of severe illness was found in São Paulo (17-21 million), Minas Gerais (6–9 million) and Rio de Janeiro (5–7 million) (Table 5).
Figure

Adults at high-risk of severe Covid-19 in Brazil by state and risk criteria.

a Criterion 1 (C1): age ≥ 65 years or diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (<5 years of diagnosis), or stroke;

b Criterion 2 (C2): additionally, obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets).

Table 5

Prevalence of one or more risk factors for severe Covid-19 among the Brazilian general adult population by risk criteria and Brazilian states, PNS 2013.

Brazilian StatesAdult population (≥ 18 years)Prevalence of one or more risk factors for severe Covid-19, %

Criterion 1 (n = 52,511)Criterion 2 (n = 51,770)


Prevalence (%)95%CIN at riskPrevalence (%)95%CIN at risk
Brazil158,255,55434.033.2–34.753,806,88854.453.6–55.286,091,021
Brazilian States       
Rondônia1,296,21829.626.7–32.7383,68150.347.3–53.2651,998
Acre581,75428.125.3–31.0163,47350.046.8–53.2290,877
Amazonas2,769,20125.122.6–27.8695,06948.745.7–51.71,348,601
Roraima430,93925.022.3–27.9107,73548.645.0–52.2209,436
Pará5,971,47726.223.2–29.31,564,52745.241.8–48.72,699,108
Amapá570,29823.420.2–26.9133,45045.941.6–50.3261,767
Tocantins1,125,02333.129.0–37.6372,38352.248.7–55.7587,262
Maranhão4,873,27930.026.3–34.01,461,98448.543.9–53.02,363,540
Piauí2,383,42532.729.4–36.1779,38053.049.6–56.31,263,215
Ceará6,788,40333.831.0–36.72,294,48053.750.8–56.63,645,372
Rio Grande do Norte2,632,40333.230.2–36.3873,95852.949.7–56.11,392,541
Paraiba2,984,64733.430.6–36.3996,87249.046.0–51.91,462,477
Pernambuco7,035,04033.230.7–35.82,335,63353.450.8–55.93,756,711
Alagoas2,377,98331.728.6–35.0753,82153.549.7–57.31,272,221
Sergipe1,688,95530.828.0–33.8520,19850.046.7–53.2844,478
Bahia11,044,98630.326.8–34.13,346,63148.944.8–53.05,400,998
Minas Gerais16,425,18335.633.1–38.25,847,36555.152.0–58.29,050,276
Espírito Santo3,047,43931.527.6–35.6959,94348.143.6–52.71,465,818
Rio de Janeiro13,419,46436.033.8–38.14,831,00755.853.6–58.07,488,061
São Paulo35,414,77635.633.7–37.412,607,66058.256.2–60.220,611,400
Paraná8,736,01434.931.7–38.23,048,86957.153.3–60.94,988,264
Santa Catarina5,578,84234.130.2–38.21,902,38555.951.6–60.13,118,573
Rio Grande do Sul8,902,26339.536.8–42.33,516,39458.455.6–61.15,198,922
Mato Grosso do Sul2,045,88134.731.6–37.8709,92157.654.5–60.71,178,427
Mato Grosso2,543,64231.928.9–35.1811,42254.851.9–57.61,393,916
Goiás5,277,38334.431.5–37.41,815,42052.049.1–54.92,744,239
Distrito Federal2,310,63629.927.3–32.5690,88049.246.3–52.11,136,833

N at risk: number of adults (≥18 years) at risk of severe Covid-19

Criterion 1: age ≥ 65 years or diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (< 5 years of diagnosis), or stroke; Criterion 2: additionally obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under Hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets)

Adults at high-risk of severe Covid-19 in Brazil by state and risk criteria.

a Criterion 1 (C1): age ≥ 65 years or diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (<5 years of diagnosis), or stroke; b Criterion 2 (C2): additionally, obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets). N at risk: number of adults (≥18 years) at risk of severe Covid-19 Criterion 1: age ≥ 65 years or diagnosis of cardiovascular disease, diabetes, chronic respiratory disease, hypertension, cancer (< 5 years of diagnosis), or stroke; Criterion 2: additionally obesity (BMI ≥ 30 kg/m2), current smoking, chronic kidney disease (diagnosis and under Hemodialysis, peritoneal dialysis, taking medication or did a kidney transplant), moderate to severe asthma (diagnosis and taking inhalers, aerosol or tablets)

DISCUSSION

In this study, we estimated that a third (53 million) to over a half (86 million) of Brazilian adults present at least one risk factor for severe Covid-19. Our findings point to high prevalence of serious medical conditions in younger, but mostly, among older adults. Less educated adults present 2-fold higher prevalence of risk factors compared with university graduated. São Paulo, Rio de Janeiro, Minas Gerais and Rio Grande do Sul were the most vulnerable states in absolute and relative terms of adults at high-risk. Contrasts between South and Southeast vs North and Northeast regions might be due to different age structure, prevalence of health condition and/or access to medical diagnosis and care. Estimating the proportion of the population at risk for severe Covid-19 within and across countries is key to improve prevention measures. However, to our knowledge, these estimates are still sparse worldwide. In the US, it was estimated that four in ten (37.6%) adults ≥ 18 years may be at high-risk of severe Covid-19[14]. During the pandemic, time is limited and hence the use of existing health information to support countries’ response is imperative. These findings and methods to identify high-risk settings may be useful to plan and manage prevention strategies in Brazil and other low- to middle-income settings with routinely collected data from population-based surveys, but limited testing capacity for SARS-CoV-2. The understanding of risk factors for severe Covid-19 has so far supported the implementation of prevention strategies. It is interesting to note that non-communicable diseases such as cardiovascular disease, cancer, respiratory diseases, and diabetes, which accounts for most of deaths globally[15], play a role on worsening the impact of the Covid-19 pandemic. Since isolation of infected cases and contact tracing alone will not likely suffice to control the pandemic[16], countries have largely implemented social isolation measures. The combination of different interventions such as case isolation, social distancing of the entire population, household quarantine, school closure and, ultimately, complete lockdown is predicted to have significant impact on transmission[17]. Protecting the groups that are most at risk[18], such as older adults and people with comorbidities, by widely and temporarily refraining from engaging in social contact, remains imperative. As knowledge on the clinical course of Covid-19 advances, the understanding of risk factors for severe disease will be improved, and so will the estimates of most-at-risk populations. Our results have some limitations. Prevalence of risk factors for severe Covid-19 is likely underestimated due to self-reported medical diagnosis of comorbidities and smoking status. Underlying diseases have been associated with poorer prognosis among inpatients with Covid-19, but some people may have lower risk due to well-controlled blood pressure and serum glucose, for instance, which may have overestimated the proportion and number of adults at risk. Undiagnosed, asymptomatic diseases such as diabetes and hypertension are concerns, especially in low-income settings. This may partially explain differences of adults at risk between Brazilian states. Estimates considered the same weight for all risk factors assessed, which may not be applicable. Furthermore, other known risk factors for severe Covid-19 such as living in a nursing home or long-term care facility, and immunosuppression could not be captured in our study. Lastly, risk factors information date from 2013, the most recent representative, household-based health survey of Brazilian adults. The proportion of older adults has increased in Brazil in the past seven years, as well as the prevalence of obesity and other non-communicable diseases[19], which may have underestimated our estimates. On the other hand, the prevalence of tobacco smoking has decreased, which may have overestimated the adults at risk of severe Covid-19. In conclusion, proportion and total number of adults at risk of severe Covid-19 is high in Brazil, with wide variation across states and adult subgroups. These findings should be considered while designing and implementing prevention measures. We argue that these results support broad social isolation measures, particularly while testing capacity for SARS-CoV-2 is limited.
  10 in total

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2.  Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China.

Authors:  Dawei Wang; Bo Hu; Chang Hu; Fangfang Zhu; Xing Liu; Jing Zhang; Binbin Wang; Hui Xiang; Zhenshun Cheng; Yong Xiong; Yan Zhao; Yirong Li; Xinghuan Wang; Zhiyong Peng
Journal:  JAMA       Date:  2020-03-17       Impact factor: 56.272

3.  Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy.

Authors:  Giacomo Grasselli; Alberto Zangrillo; Alberto Zanella; Massimo Antonelli; Luca Cabrini; Antonio Castelli; Danilo Cereda; Antonio Coluccello; Giuseppe Foti; Roberto Fumagalli; Giorgio Iotti; Nicola Latronico; Luca Lorini; Stefano Merler; Giuseppe Natalini; Alessandra Piatti; Marco Vito Ranieri; Anna Mara Scandroglio; Enrico Storti; Maurizio Cecconi; Antonio Pesenti
Journal:  JAMA       Date:  2020-04-28       Impact factor: 56.272

4.  [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China].

Authors: 
Journal:  Zhonghua Liu Xing Bing Xue Za Zhi       Date:  2020-02-10

5.  Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017.

Authors: 
Journal:  Lancet       Date:  2018-11-08       Impact factor: 79.321

6.  Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study.

Authors:  Rong-Hui Du; Li-Rong Liang; Cheng-Qing Yang; Wen Wang; Tan-Ze Cao; Ming Li; Guang-Yun Guo; Juan Du; Chun-Lan Zheng; Qi Zhu; Ming Hu; Xu-Yan Li; Peng Peng; Huan-Zhong Shi
Journal:  Eur Respir J       Date:  2020-05-07       Impact factor: 16.671

7.  Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with Coronavirus Disease 2019 - United States, February 12-March 28, 2020.

Authors: 
Journal:  MMWR Morb Mortal Wkly Rep       Date:  2020-04-03       Impact factor: 17.586

8.  ACE-2 expression in the small airway epithelia of smokers and COPD patients: implications for COVID-19.

Authors:  Janice M Leung; Chen X Yang; Anthony Tam; Tawimas Shaipanich; Tillie-Louise Hackett; Gurpreet K Singhera; Delbert R Dorscheid; Don D Sin
Journal:  Eur Respir J       Date:  2020-05-14       Impact factor: 16.671

9.  Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China.

Authors:  Wenhua Liang; Weijie Guan; Ruchong Chen; Wei Wang; Jianfu Li; Ke Xu; Caichen Li; Qing Ai; Weixiang Lu; Hengrui Liang; Shiyue Li; Jianxing He
Journal:  Lancet Oncol       Date:  2020-02-14       Impact factor: 41.316

10.  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.

Authors:  Zunyou Wu; Jennifer M McGoogan
Journal:  JAMA       Date:  2020-04-07       Impact factor: 56.272

  10 in total
  20 in total

Review 1.  Reviving the mutual impact of SARS-COV-2 and obesity on patients: From morbidity to mortality.

Authors:  Tapan Behl; Sachin Kumar; Sukhbir Singh; Saurabh Bhatia; Ali Albarrati; Mohammed Albratty; Abdulkarim M Meraya; Asim Najmi; Simona Bungau
Journal:  Biomed Pharmacother       Date:  2022-05-24       Impact factor: 7.419

2.  Does aeroallergen sensitivity and allergic rhinitis in children cause milder COVID-19 infection?

Authors:  Emine Vezir; Mina Hizal; Burcu Cura Yayla; Kubra Aykac; Arzu Yilmaz; Gamze Kaya; Pembe Derin Oygar; Yasemin Ozsurekci; Mehmet Ceyhan
Journal:  Allergy Asthma Proc       Date:  2021-11-01       Impact factor: 2.587

3.  Asthma and allergic diseases are not risk factors for hospitalization in children with coronavirus disease 2019.

Authors:  Burcin Beken; Gokcen Kartal Ozturk; Fatma Deniz Aygun; Cigdem Aydogmus; Himmet Haluk Akar
Journal:  Ann Allergy Asthma Immunol       Date:  2021-01-23       Impact factor: 6.347

4.  [Transitions between diagnostic states in people with COVID-19 in Colombia].

Authors:  Luis Carlos Manrique Ruiz; Guberney Muñetón Santa; Osmar Leandro Loaiza Quintero
Journal:  Rev Panam Salud Publica       Date:  2020-12-17

Review 5.  Hemoglobinopathy and pediatrics in the time of COVID-19.

Authors:  Thiago de Souza Vilela; Josefina Aparecida Pellegrini Braga; Sandra Regina Loggetto
Journal:  Hematol Transfus Cell Ther       Date:  2020-12-02

6.  Diabetes and Covid-19 among hospitalized patients in Saudi Arabia: a single-centre retrospective study.

Authors:  Abdullah M Alguwaihes; Mohammed E Al-Sofiani; Maram Megdad; Sakhar S Albader; Mohammad H Alsari; Ali Alelayan; Saad H Alzahrani; Shaun Sabico; Nasser M Al-Daghri; Anwar A Jammah
Journal:  Cardiovasc Diabetol       Date:  2020-12-05       Impact factor: 9.951

7.  Mathematical modeling of the transmission of SARS-CoV-2-Evaluating the impact of isolation in São Paulo State (Brazil) and lockdown in Spain associated with protective measures on the epidemic of CoViD-19.

Authors:  Hyun Mo Yang; Luis Pedro Lombardi Junior; Fábio Fernandes Morato Castro; Ariana Campos Yang
Journal:  PLoS One       Date:  2021-06-15       Impact factor: 3.240

8.  Choosing a hospital assistance ship to fight the covid-19 pandemic.

Authors:  Igor Pinheiro de Araújo Costa; Sérgio Mitihiro do Nascimento Maêda; Luiz Frederico Horácio de Souza de Barros Teixeira; Carlos Francisco Simões Gomes; Marcos Dos Santos
Journal:  Rev Saude Publica       Date:  2020-08-10       Impact factor: 2.106

9.  Impact of the COVID-19 pandemic on drug treatment of patients with peripheral arterial disease: an observational cross-sectional study.

Authors:  Heloisa Amaral Braghieri; Marília de Almeida Correia; Juliana Ferreira de Carvalho; Paulo Longano; Nelson Wolosker; Gabriel Grizzo Cucato; Raphael Mendes Ritti-Dias; Hélcio Kanegusuku
Journal:  J Vasc Bras       Date:  2021-06-17

10.  Impact of COVID-19 Pandemic on Sexual Minority Populations in Brazil: An Analysis of Social/Racial Disparities in Maintaining Social Distancing and a Description of Sexual Behavior.

Authors:  Thiago S Torres; Brenda Hoagland; Daniel R B Bezerra; Alex Garner; Emilia M Jalil; Lara E Coelho; Marcos Benedetti; Cristina Pimenta; Beatriz Grinsztejn; Valdilea G Veloso
Journal:  AIDS Behav       Date:  2021-01
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