key: cord-0973916-qbk0ezo3 authors: Mattey‐Mora, Paola P.; Begle, Connor A.; Owusu, Candice K.; Chen, Chen; Parker, Maria A. title: Hospitalised versus outpatient COVID‐19 patients' background characteristics and comorbidities: A systematic review and meta‐analysis date: 2021-10-21 journal: Rev Med Virol DOI: 10.1002/rmv.2306 sha: 1db67cdbc832cd103d5c0988be711f8e4805c3eb doc_id: 973916 cord_uid: qbk0ezo3 This study aimed to systematically assess COVID‐19 patient background characteristics and pre‐existing comorbidities associated with hospitalisation status. The meta‐analysis included cross‐sectional, cohort, and case‐series studies with information on hospitalisation versus outpatient status for COVID‐19 patients, with background characteristics and pre‐existing comorbidities. A total of 1,002,006 patients from 40 studies were identified. Significantly higher odds of hospitalisation were observed in Black individuals (OR = 1.33, 95% CI: 1.04–1.70), males (OR = 1.59, 95% CI: 1.43–1.76), and persons with current/past smoking (OR = 1.59, 95% CI: 1.34–1.88). Additionally, individuals with pre‐existing comorbidities were more likely to be hospitalised [asthma (OR = 1.22, 95% CI: 1.02–1.45), COPD (OR = 3.68, 95% CI: 2.97–4.55), congestive heart failure (OR = 6.80, 95% CI: 4.97–9.31), coronary heart disease (OR = 4.40, 95% CI: 3.15–6.16), diabetes (OR = 3.90, 95% CI: 3.29–4.63), hypertension (OR = 3.89, 95% CI: 3.34–4.54), obesity (OR = 1.98, 95% CI: 1.59–2.46) and renal chronic disease (OR = 5.84, 95% CI: 4.51–7.56)]. High heterogeneity and low publication bias among all factors were found. Age was not included due to the large variability in the estimates reported. In this systematic review/meta‐analysis for patients with COVID‐19, Black patients, males, persons who smoke, and those with pre‐existing comorbidities were more likely to be hospitalised than their counterparts. Findings provide evidence of populations with higher odds of hospitalisation for COVID‐19. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a novel beta coronavirus that can be manifested as a mild to severe respiratory infection in humans along with asymptomatic transmission. 1 Emergence of the SARS-CoV-2 in late 2019, which became referred to as coronavirus disease-2019 or COVID-19, originated in Wuhan, China, and proceeded to escalate into a global pandemic, leaving more than 154 million people infected and 3.23 million people deceased. 2 Transmission of COVID-19 has been identified to be primarily facilitated through close contact air droplets and physical contact in addition to aerosol exposure in enclosed spaces. 3 Of particular concern is the number of infected individuals who require hospitalisation as debilitating and potentially deadly clinical complications such as acute respiratory distress and respiratory failure are more prevalent among hospitalised individuals. 4 Recorded hospitalisation rates due to COVID-19 are prone to significant variation week-to-week, which may be characterised by certain time intervals with larger peaks in hospitalisations. 5 The burden placed on local hospital systems due to increased rates of patients requiring admittance for COVID-19 complications is a pressing matter as this creates an additional burden on the available capacity for hospitals to treat new patients. 6 Furthermore, the burden placed upon the healthcare systems and the exposure to healthcare workers due to rates of hospitalisations and ICU transfers is not identical across the world, and identifying methods for minimising this burden are key. 7, 8 Several characteristics of healthcare systems have been identified that may be at play when examining burden of COVID-19 hospitalisations on individual healthcare systems, such as timing of outbreaks and more isolated, rural locations of health centres. 9 Prior evidence has suggested that patients admitted due to COVID-19 may experience longer durations of hospitalisation, increased reliance on oxygen therapy or invasive mechanical ventilation, and more intensive care unit (ICU) need compared to seasonal influenza patients. 10 In addition, COVID-19 hospitalisation has been identified to vary significantly depending on several predictors such as background characteristics and the presence of certain pre-existing comorbidities. 11 The range of factors that have been identified among COVID-19 patients requiring hospitalisation is extensive; background characteristics have been primarily based on differences in age, ethnicity/race, smoking history, and sex. Prior research conducted on hospitalised COVID-19 patients has shown that individuals requiring admission are typically of older age, from an ethnic or racial minority group, and male. 12 For example, research shows an independent predictor of COVID-19 hospitalisation includes age 65 years or older. 13 A variety of factors ranging from immune system strength to other chronic health conditions that are more prevalent among older individuals may increase the susceptibility to infection and subsequent hospitalisation. 14 Higher infection rates and need for hospitalisation among specific ethnic and racial groups, such as Hispanic and Black individuals, has been attributed to socio-economic differences, including living situations, employment status, and associations with pre-existing conditions. 15 Furthermore, male patients have also been shown to have higher risk for hospitalisation in multiple samples. 16 Observations of COVID-19 hospitalisation in association with pre-existing comorbidities has been well-documented in existing COVID-19 research. Prior systematic reviews have identified chronic conditions including cardiovascular conditions, diabetes mellitus, respiratory diseases, and kidney diseases, as critical chronic conditions associated with hospitalisation for COVID-19. 17 Additionally, elevated body mass index (BMI) has been associated to hospitalisation; individuals with BMI over 25 kg/m 2 (overweight) and over 30 kg/m 2 (obese) have been shown to require hospitalisation from COVID-19 at higher odds in comparison to individuals at healthy weights. 18 Considering the preliminary results found in the literature for COVID-19 patients, a robust systematic review and meta-analysis was conducted to determine the background characteristics and pre-existing comorbidities associated with hospitalisation for COVID-19 patients. This will help identify the most vulnerable populations for severe COVID-19 infections that would require hospitalisation. This study focused on the clinical outcome (hospitalisation vs outpatient) of COVID-19 in association with background characteristics (i.e., ethnicity/race, sex, and smoking status) and preexisting comorbidities (i.e., diabetes mellitus, cardiovascular diseases, respiratory diseases, obesity, hypertension, and renal chronic disease). We conducted a broad literature search in three datasets: (1) PubMed, (2) Web of Science, and (3) Cochrane Library, that comprised preprint and published papers from December 2019 to December 2020. The search was not geographically limited, and included papers written in English, Spanish, or Chinese. Additionally, we searched the reference lists of relevant systematic reviews and meta-analyses to identify potential supplemental studies (one paper was included from this search). This systematic review and meta-analysis followed the Preferred Reporting for Systematic Review and Meta-Analysis (PRISMA) consensus statement. 19 The protocol of this study is registered in PROSPERO (CRD42021235460). (according to World Health Organisation guidance), which examined patient demographic factors such as ethnicity/race, sex, smoking status, and pre-existing comorbidities including cardiovascular diseases (CVD) (coronary heart disease and congestive heart failure), diabetes mellitus, hypertension, obesity, renal chronic disease, and respiratory diseases (asthma and chronic obstructive pulmonary disease (COPD)) were included in this meta-analysis. Age was not included in this analysis due to the variability in the estimates reported. For external validity and relevance to the general population, inclusion criteria encompassed studies conducted in patients aged 18 years or older. Studies in subpopulation specific samples were excluded (e.g., children, adolescents, pregnant women, nursing homes, institutionalised individuals, HIV-only, COPD-only, cardiomyopathic-only, renal-only, hepatic-only). Service Centre for Reviews and Dissemination. NIH quality assessment tools include specific scales for observational, case-series and experimental studies. The scales were applied based on the study design. 20 For observational designs, papers with scores of 0-4 were graded as poor, 5-8 were graded as fair, and more than 9 were graded as good. For case-series and reports, papers with scores of 0-3 were graded as poor, 4-6 were graded as fair, and more than 7 were graded as good. The scores of each study were compared, and any discrepancies were assessed by a third investigator (CC). After the quality assessment, the two investigators proceeded with the data extraction, comparing between the investigators for validity and accuracy. Any discrepancies were again assessed by a third investigator (CC). Data extraction included the following information from each study: title, study design, publication stage, study period, location, first author, publication year, total positive cases, and total number for hospitalised patients and outpatients. For each condition we collected the number of events for hospitalised and outpatients. Crude odds ratios were included when available. The PRISMA checklist of the mansucript is available in the supplementary material. The association of background characteristics and pre-existing comorbidities with hospitalisation was assessed. Exposure variables were demographics (i.e., ethnicity/race, sex, smoking) and pre-existing comorbidities extracted from the papers. Each ethnicity/race category was coded as a non-mutually exclusive binary outcome due to the variability in definitions among the included studies (e.g., White vs Non-White). Our outcome variable was hospitalisation status (hospitalised vs outpatients). We estimated the odds ratios (ORs) with 95% confidence interval (95% CI) for COVID-19 hospitalised patients to one of the potential factors compared to COVID-19 outpatients from the extracted raw data or reported crude ORs. A random effect model was utilised to calculate pooled odds ratios when the test of heterogeneity (I 2 statistic) was moderate (50%-74%) or high (≥75%) in the pooled estimates. 21 Due to high heterogeneity between studies for all the background characteristics and pre-existing comorbidities, randomeffects models were used for the meta-analysis of hospitalisation. In addition, subgroup and sensitivity analyses were conducted to look into heterogeneity. These analyses were determined a priori by (1) study design (i.e., case series, cohort, cross-sectional) and (2) publication stage (i.e., published, pre-print), as these could have been potential causes of heterogeneity in the results. Publication bias and small study effect were assessed with the Egger's regression test and the Harbord's modified test which accounted for the heterogeneity and binary outcomes. 22, 23 To reduce publication bias, we included both published studies and literature published in medRxiv. All statistical analyses were conducted with Stata, Version 15 24 with an alpha at the 0.05 level. 26, 35, 38, 53, 55, 58, 63 (Table S1 ). Table 3 and Figures S6, S7 , S8, S9, S10, S11, S12, and S13). After conducting the subgroup analyses by study design and printing stage, we found that the association for Black patients in case-series and cohort studies remained significant but was not the case for cross-sectional studies ( Figure S14 ). For asthma, the association remained positive in cohort studies, but we found no significant association in case-series, and a significant inverse association in cross-sectional studies. It is worth noting that only 2 cross-sectional studies included asthma ( Figure S15 ). All other associations did not change. We found a potential small-study effect bias in the analysis for White race and Hispanic ethnicity, for both the Egger's and Harbord's test (p < 0.01 respectively). In addition, publication bias was 76 Cytokines are utilised in the body to assist with intercellular communication and inflammatory responses, 76 which may have significant impact on clinical severity of SARS-CoV-2 infection and an increased risk of hospitalisation. This connection is key to consider, as risk of comorbidities, and particularly, cardiovascular diseases, which have been linked to higher activity involving innate immune system functions. 76 Significant interplay can also be observed in prior studies, as pre-existing chronic conditions such as obesity may additionally have strong associations with other prognostic factors for worse COVID-19 outcomes such as CVD, respiratory disease, diabetes, and renal disease. 77 Due to the heterogeneity and variability among the factors in the included studies, one of the limitations of this study is the estimation of unadjusted odds ratios when analysing the potential factors. Our pooled estimates account for the crude odds ratios, as it was not possible to account for potential confounders. There was also a high level of heterogeneity between the included studies, which might reduce the strength the precision of the pooled point estimates even after conducting the subgroup analysis. Several factors could account for this heterogeneity, including sample size, location, time during the pandemic, and individual variability which could have increased the variation of estimates for hospitalisation. 78 Furthermore, some of the investigated factors had smaller samples size in some of the strata. In addition, discrepancies were found between the coefficients and confidence intervals of both tests for bias due to unknown potential sources of bias and confounding that were not considered in this study. 79 Lastly, we expect the COVID-19 pandemic to continue to evolve and expect our findings to only generalise to 2020 (e.g., due to advancement in vaccination and/or newer and more effective treatments). To our knowledge, this is the first meta-analysis to report the pooled estimates for factors and hospitalisation status, which helps to provide stronger support for findings from individual studies that examined background characteristics and pre-existing comorbidities associated with COVID-19 hospitalisation. Black individuals, males, and persons with former and current smoking had a higher odds of COVID-19 hospitalisation. Persons with chronic conditions were significantly associated with COVID-19 hospitalisation as well. As vaccinations are slowly underway around the world, social restrictions by local governments are reduced, new strains emerged, and the burden on the healthcare systems remain, it is vital to identify the most vulnerable populations that will require hospitalisation due to COVID-19. Characteristics of SARS-CoV-2 and COVID-19 Johns Hopkins University & Medicine. COVID-19 Map. 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The authors whose names are listed in this document, certify that they have no affiliation with or involvement in any organisation or entity with financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Every person who meets the authorship criteria are listed as authors, and all authors certify their participation in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Additionally, the authors certify that this material has not been published and not will be The data that support the findings of this study are available from the corresponding author upon reasonable request.