key: cord-0806387-h8449h99 authors: Magesh, Shruti; John, Daniel; Li, Wei Tse; Li, Yuxiang; Mattingly-app, Aidan; Jain, Sharad; Chang, Eric Y.; Ongkeko, Weg M. title: Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic-Review and Meta-analysis date: 2021-11-11 journal: JAMA Netw Open DOI: 10.1001/jamanetworkopen.2021.34147 sha: c0cbceeedefc2b457c0734aa1b8d4a13cb9a1b19 doc_id: 806387 cord_uid: h8449h99 IMPORTANCE: COVID-19 has disproportionately affected racial and ethnic minority groups, and race and ethnicity have been associated with disease severity. However, the association of socioeconomic determinants with racial disparities in COVID-19 outcomes remains unclear. OBJECTIVE: To evaluate the association of race and ethnicity with COVID-19 outcomes and to examine the association between race, ethnicity, COVID-19 outcomes, and socioeconomic determinants. DATA SOURCES: A systematic search of PubMed, medRxiv, bioRxiv, Embase, and the World Health Organization COVID-19 databases was performed for studies published from January 1, 2020, to January 6, 2021. STUDY SELECTION: Studies that reported data on associations between race and ethnicity and COVID-19 positivity, disease severity, and socioeconomic status were included and screened by 2 independent reviewers. Studies that did not have a satisfactory quality score were excluded. Overall, less than 1% (0.47%) of initially identified studies met selection criteria. DATA EXTRACTION AND SYNTHESIS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Associations were assessed using adjusted and unadjusted risk ratios (RRs) and odds ratios (ORs), combined prevalence, and metaregression. Data were pooled using a random-effects model. MAIN OUTCOMES AND MEASURES: The main measures were RRs, ORs, and combined prevalence values. RESULTS: A total of 4 318 929 patients from 68 studies were included in this meta-analysis. Overall, 370 933 patients (8.6%) were African American, 9082 (0.2%) were American Indian or Alaska Native, 101 793 (2.4%) were Asian American, 851 392 identified as Hispanic/Latino (19.7%), 7417 (0.2%) were Pacific Islander, 1 037 996 (24.0%) were White, and 269 040 (6.2%) identified as multiracial and another race or ethnicity. In age- and sex-adjusted analyses, African American individuals (RR, 3.54; 95% CI, 1.38-9.07; P = .008) and Hispanic individuals (RR, 4.68; 95% CI, 1.28-17.20; P = .02) were the most likely to test positive for COVID-19. Asian American individuals had the highest risk of intensive care unit admission (RR, 1.93; 95% CI, 1.60-2.34, P < .001). The area deprivation index was positively correlated with mortality rates in Asian American and Hispanic individuals (P < .001). Decreased access to clinical care was positively correlated with COVID-19 positivity in Hispanic individuals (P < .001) and African American individuals (P < .001). CONCLUSIONS AND RELEVANCE: In this study, members of racial and ethnic minority groups had higher risks of COVID-19 positivity and disease severity. Furthermore, socioeconomic determinants were strongly associated with COVID-19 outcomes in racial and ethnic minority populations. Meta-regression for measures of clinical care quality in the following cohorts: meta-regression for preventable hospital stays in correlation with Asian Americans who tested positive for COVID-19 in cohort studies; meta-regression for primary care physician availability in correlation with Asian Americans and Hispanics who tested positive for COVID-19 (cohort studies) and Whites who are deceased (cross-sectional studies); and meta-regression for the amount of uninsured individuals in correlation with African Americans who tested positive for COVID-19 (cohort studies) and Whites who are deceased (cross sectional studies). The following keywords were used to search by all fields, which includes full text, author name, journal name, and phrase, in each database: "COVID-19 AND race", "COVID-19 AND ethnicity", "COVID-19 AND Asian patients", "COVID-19 AND Black patients", "COVID-19 AND White patients", "COVID-19 AND Hispanic/Latino patients", "COVID-19 AND American Indian/Alaska Natives patients", "COVID-19 AND Pacific Islander patients", "COVID-19 AND multiracial patients", "income AND COVID-19"; "socioeconomic status AND COVID-19", and "employment AND COVID-19." We used both the keyword and Medical Subject Heading (MeSH) term for the following keywords to increase the scope of our systematic review and meta-analysis: "COVID-19 AND ethnicity (MeSH term: COVID-19 AND ethnic groups)", "COVID-19 AND race (MeSH term: COVID-19 AND race factors)", "socioeconomic status AND COVID-19 (MeSH term: COVID-19 AND social class)". MeSH terms provide controlled vocabulary for searches in databases, such as Pubmed. We chose to use both the MeSH term and the non-MeSH term for these particular keywords, as the non-MeSH term yielded significantly more results than the MeSH term. MeSH terms could not be used for the following keywords, as they were not available on the database: "COVID-19 AND Asian patients", "COVID-19 AND Black patients", "COVID-19 AND White patients", "COVID-19 AND Hispanic/Latino patients", "COVID-19 AND American Indian/Alaska Natives patients", "COVID-19 AND Pacific Islander patients", and "COVID-19 AND multiracial patients". MeSH terms were solely used for the following keywords: "income AND COVID-19" and "employment AND COVID-19". Our original keyword searches yielded 21,745 total results. Of these articles, 14,519 were unique (eFigure 1). We excluded studies based on Abstract if they met one of the following criteria: (1) The article is irrelevant for the study question or has insufficient data, (2) The article does not discuss an outcome that is of interest, (3) The article is published in a non-standard format and/or in a foreign language. Only studies with original clinical data were included. Following the Abstract review, we screened the full text of the remaining 287 articles. After subsequent fulltext screening using the same 3 exclusion criteria, a total of 68 studies were included for data analysis. Study and patient characteristics were collected, including the study type, location, mean and median age, total number of patients in the study, and medical comorbidities. Specifically, we extracted data for the following medical comorbidities and conditions which we observed to be commonly reported across various studies: smoking status (both former and current smokers), median body mass index (BMI), BMI over 40, cardiovascular disease (including other heart conditions such as coronary artery disease), hypertension, chronic obstructive pulmonary disease (COPD), diabetes mellitus or diabetes, and occurence of malignancy or cancer. For the purposes of this analysis, we considered Hispanics and Latinos as a single cohort. The studies included did not differentiate between various Asian populations, so many Asian populations were considered as a single cohort. Following initial data review, we extracted the zip code, geographic location and/or congressional district from each study included in our meta-analysis in order to identify socioeconomic variables for subsequent analyses. In instances where congressional district information was not provided, we determined this information based on the zip code or geographic location of the study. From this extracted information, we obtained the following data for various measures of socioeconomic disparity from external websites for each study: (1) County median income and the percentage of each race in the district where the study was conducted was taken from the US Census Bureau's website at the congressional district level. ( Excluded due to insufficient data: adjusted (eTable 3, eTable 4). Studies in the unadjusted model that did not include information for one of these variables were excluded from the adjustment analysis of that particular variable. Methods to estimate missing data, such as multiple imputation, were not used as the studies were conducted separately (not a randomized trial) and the number of known outcomes would not be sufficient for accurate imputation. No more than two individual measures were adjusted at once in order to minimize the effects of overfitting (see the composite measures mentioned below). Additionally, fitting was only calculated if the predictor variable(s) had at least 2 more outcomes than the variables being adjusted for. The mixed-effects models were fitted to the median value(s) of the variable(s) being adjusted for in order to reduce the effects of outliers. We calculated a combined measure for both Comorbidities and Clinical Care using a unit-weighted composite function, as several variables were required to appropriately adjust for these factors. The Comorbidity measure was composed using the following comorbidities that were available in the study group: ever smoker, BMI, cardiovascular disease, hypertension, COPD, diabetes, and cancer. The following variables were used to compose an estimate for the quality of Clinical care: Percent of the population under 65 that are uninsured, ratio of the population to primary care physicians, and the rate of hospital stays for ambulatory-care sensitive conditions per 100,000 Medicare enrollees (preventable hospital stays). In order to test for the similarity of variables used for the combined measures, only composed variables with a Chronbach's alpha score > 0.7 were used for adjustment (eTable 3, eTable 4). The clinical-care measure for Hispanic/Latino COVID-19 positive RR/OR was the only unit-weighted composite variable which yielded an alpha score < 0.7. Thus, RR/OR adjustment was not implemented for this cohort. 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Random effects models were used to assess summary proportions and account for study heterogeneity, as studies included in the meta-analysis contained diverse patient populations (eFigures 5-7).Random-effects models do not condition on the true outcomes, but instead the studies in the meta-analysis are assumed to be a random sample of the large population of studies. This is ideal for the purposes of this study, as our study population is a hypothetical population of an infinitely possible subset of study populations that may have been sampled or will be sampled in the future. In a random-effects model, the true outcomes in the studied population are assumed to be normally distributed, with µ representing the average true outcome, and representing the variance of the true outcomes: ∼ ( , 2 ). The random-effects model may also be represented as a linear combination of the average true outcome and uniformly distributed variables: = + + , where ∼ (0, 2 )and ∼ (0, ) ( is the sampling variance associated with the observed outcomes).Logit transformations were applied to all proportional data, and the Cochran's Q test and the I 2 index were used to quantify study heterogeneity (eTable 6). Meta-regression analysis was conducted to assess correlations between study effect size and socioeconomic variables. These models were used to further examine the correlations between race/ethnicity and COVID-19 outcomes. Publication bias was assessed using the Egger's test for publication bias. Leave-onesensitivity analysis was conducted to examine the impact of dominating studies or outliers on results.The relative risk ratio (RR) (with 95% confidence interval) and the odds ratio (OR) (with 95% confidence interval) describe the risk (or odds) of COVID-19 severity in different racial and ethnic groups relative to Whites. RR/OR measures were adjusted by fitting a mixed-effects model with Restricted Maximum Likelihood (REML) estimation. 8 different models were used to test for the effect of confounding variables on risk outcomes: Sex-Adjusted ( Figure 1 , Figure 2 ); Age-Adjusted (eTable 3, eTable 4); Sex & Age-adjusted ( Figure 1, Figure 2 ); ADI-adjusted (eTable 3, eTable 4); Income-adjusted (eTable 3, eTable 4); Clinical Care-adjusted (eTable 3, eTable 4); Urban Oppurtunity Index (UOI)-adjusted (eTable 3, eTable 4); Comorbidities-