Studying the social determinants of COVID-19 in a data vacuum.
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ABSTRACT:
SUBMITTER: Choi KH
PROVIDER: S-EPMC8206942 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ImportanceAs the United States continues to accumulate COVID-19 cases and deaths, and disparities persist, defining the impact of risk factors for poor outcomes across patient groups is imperative.ObjectiveOur objective is to use real-world healthcare data to quantify the impact of demographic, clinical, and social determinants associated with adverse COVID-19 outcomes, to identify high-risk scenarios and dynamics of risk among racial and ethnic groups.DesignA retrospective cohort of COVID-19 patients diagnosed between March 1 and August 20, 2020. Fully adjusted logistical regression models for hospitalization, severe disease and mortality outcomes across 1-the entire cohort and 2-within self-reported race/ethnicity groups.SettingThree sites of the NewYork-Presbyterian health care system serving all boroughs of New York City. Data was obtained through automated data abstraction from electronic medical records.ParticipantsDuring the study timeframe, 110,498 individuals were tested for SARS-CoV-2 in the NewYork-Presbyterian health care system; 11,930 patients were confirmed for COVID-19 by RT-PCR or covid-19 clinical diagnosis.Main outcomes and measuresThe predictors of interest were patient race/ethnicity, and covariates included demographics, comorbidities, and census tract neighborhood socio-economic status. The outcomes of interest were COVID-19 hospitalization, severe disease, and death.ResultsOf confirmed COVID-19 patients, 4,895 were hospitalized, 1,070 developed severe disease and 1,654 suffered COVID-19 related death. Clinical factors had stronger impacts than social determinants and several showed race-group specificities, which varied among outcomes. The most significant factors in our all-patients models included: age over 80 (OR=5.78, p= 2.29×10 -24 ) and hypertension (OR=1.89, p=1.26×10 -10 ) having the highest impact on hospitalization, while Type 2 Diabetes was associated with all three outcomes (hospitalization: OR=1.48, p=1.39×10 -04 ; severe disease: OR=1.46, p=4.47×10 -09 ; mortality: OR=1.27, p=0.001). In race-specific models, COPD increased risk of hospitalization only in Non-Hispanics (NH)-Whites (OR=2.70, p=0.009). Obesity (BMI 30+) showed race-specific risk with severe disease NH-Whites (OR=1.48, p=0.038) and NH-Blacks (OR=1.77, p=0.025). For mortality, Cancer was the only risk factor in Hispanics (OR=1.97, p=0.043), and heart failure was only a risk in NH-Asians (OR=2.62, p=0.001).Conclusions and relevanceComorbidities were more influential on COVID-19 outcomes than social determinants, suggesting clinical factors are more predictive of adverse trajectory than social factors.Key pointsQUESTION: What is the impact of patient self-reported race, ethnicity, socioeconomic status, and clinical profile on COVID-19 hospitalizations, severity, and mortality?FINDINGS: In patients diagnosed with COVID-19, being over 50 years of age, having type 2 diabetes and hypertension were the most important risk factors for hospitalization and severe outcomes regardless of patient race or socioeconomic status.MEANING: In this large sample pf patients diagnosed with COVID-19 in New York City, we found that clinical comorbidity, more so than social determinants of health, was associated with important patient outcomes.
| S-EPMC8043490 | biostudies-literature
Project description:As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns.
| S-EPMC7556498 | biostudies-literature
Project description:Early reports indicate that the social determinants of health are implicated in COVID-19 incidence and outcomes. To inform the ongoing response to the pandemic, we conducted a rapid review of peer-reviewed studies to examine the social determinants of COVID-19. We searched Ovid MEDLINE, Embase, PsycINFO, CINAHL and Cochrane Central Register of Controlled Trials from December 1, 2019 to April 27, 2020. We also searched the bibliographies of included studies, COVID-19 evidence repositories and living evidence maps, and consulted with expert colleagues internationally. We included studies identified through these supplementary sources up to June 25, 2020. We included English-language peer-reviewed quantitative studies that used primary data to describe the social determinants of COVID-19 incidence, clinical presentation, health service use and outcomes in adults with a confirmed or presumptive diagnosis of COVID-19. Two reviewers extracted data and conducted quality assessment, confirmed by a third reviewer. Forty-two studies met inclusion criteria. The strongest evidence was from three large observational studies that found associations between race or ethnicity and socioeconomic deprivation and increased likelihood of COVID-19 incidence and subsequent hospitalization. Limited evidence was available on other key determinants, including occupation, educational attainment, housing status and food security. Assessing associations between sociodemographic factors and COVID-19 was limited by small samples, descriptive study designs, and the timeframe of our search. Systematic reviews of literature published subsequently are required to fully understand the magnitude of any effects and predictive utility of sociodemographic factors related to COVID-19 incidence and outcomes. PROSPERO: CRD4202017813.
| S-EPMC8011781 | biostudies-literature
Project description:IntroductionIncluding race as a biological construct in risk prediction models may guide clinical decisions in ways that cause harm and widen racial disparities. This study reports on using race versus social determinants of health (SDoH) in predicting the associations between cardiometabolic disease severity (assessed using cardiometabolic disease staging) and COVID-19 hospitalization.MethodsElectronic medical record data on patients with a positive COVID-19 polymerase chain reaction test in 2020 and a previous encounter in the electronic medical record where cardiometabolic disease staging clinical data (BMI, blood glucose, blood pressure, high-density lipoprotein cholesterol, and triglycerides) were available from 2017 to 2020, were analyzed in 2021. Associations between cardiometabolic disease staging and COVID-19 hospitalization adding race and SDoH (individual and neighborhood level [e.g., Social Vulnerability Index]) in different models were examined. Area under the curve was used to assess predictive performance.ResultsA total of 2,745 patients were included (mean age of 58 years, 59% female, 47% Black). In the cardiometabolic disease staging model, area under the curve was 0.767 vs 0.777 when race was included. Adding SDoH to the cardiometabolic model improved the area under the curve to 0.809 (p<0.001), whereas the addition of SDoH and race increased the area under the curve to 0.811. In race-stratified models, the area under the curve for non-Hispanic Blacks was 0.781, whereas the model for non-Hispanic Whites performed better with an area under the curve of 0.821.ConclusionsCardiometabolic disease staging was predictive of hospitalization after a positive COVID-19 test. Adding race did not markedly increase the predictive ability; however, adding SDoH to the model improved the area under the curve to ≥0.80. Future research should include SDoH with biological variables in prediction modeling to capture social experience of race.
| S-EPMC9212800 | biostudies-literature
Project description:BackgroundThere are several reports of health disparities related to COVID-19. Understanding social determinants of health (SDoH) could help develop mitigation strategies to prevent further COVID-19 spread. Our aim is to evaluate self-reported and census-based SDoH as a mediator of health disparities in COVID-19.MethodsWe conducted a cross-sectional ecological study and included all COVID-19 cases report by the COVID-19 Florida dashboard as the dependent variable. The independent variables were census-based median household income, population and household size, and self-reported SDoH using a validated survey. We calculated the incidence rate ratio (IRR) of COVID-19 by zip code using Poisson regression and structured equation modelling to evaluate the mediation effect of income and SDoH on COVID-19 cases.ResultsWe included 97,594 COVID-19 positive cases across 79 Miami-Dade ZIP codes with a median age of 43 years; females represented 50.7% of the cases. The highest IRR (4.44) were for ZIP code 33125 (income $21,106, 6% Black, 93% Hispanic), while the lowest IRR (0.86) was for ZIP code 33146 (median household incomes $96,609, 3% Black and 53% Hispanic). In structured equation models, the indirect coefficient of income in the relationship between race/ethnicity and COVID-19 were only significant for Blacks and not Hispanics.ConclusionsThis ecological analysis using ZIP code and aggregate individual-level SDoH shows that in Miami-Dade county, COVID infection is associated with economic disadvantage in a particular geographical area and not with racial/ethnic distribution.
| S-EPMC7673244 | biostudies-literature
Project description:BackgroundThe COVID-19 epidemic in the United States is widespread, with more than 200,000 deaths reported as of September 23, 2020. While ecological studies show higher burdens of COVID-19 mortality in areas with higher rates of poverty, little is known about social determinants of COVID-19 mortality at the individual level.Methods and findingsWe estimated the proportions of COVID-19 deaths by age, sex, race/ethnicity, and comorbid conditions using their reported univariate proportions among COVID-19 deaths and correlations among these variables in the general population from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). We used these proportions to randomly sample individuals from NHANES. We analyzed the distributions of COVID-19 deaths by race/ethnicity, income, education level, and veteran status. We analyzed the association of these characteristics with mortality by logistic regression. Summary demographics of deaths include mean age 71.6 years, 45.9% female, and 45.1% non-Hispanic white. We found that disproportionate deaths occurred among individuals with nonwhite race/ethnicity (54.8% of deaths, 95% CI 49.0%-59.6%, p < 0.001), individuals with income below the median (67.5%, 95% CI 63.4%-71.5%, p < 0.001), individuals with less than a high school level of education (25.6%, 95% CI 23.4% -27.9%, p < 0.001), and veterans (19.5%, 95% CI 15.8%-23.4%, p < 0.001). Except for veteran status, these characteristics are significantly associated with COVID-19 mortality in multiple logistic regression. Limitations include the lack of institutionalized people in the sample (e.g., nursing home residents and incarcerated persons), the need to use comorbidity data collected from outside the US, and the assumption of the same correlations among variables for the noninstitutionalized population and COVID-19 decedents.ConclusionsSubstantial inequalities in COVID-19 mortality are likely, with disproportionate burdens falling on those who are of racial/ethnic minorities, are poor, have less education, and are veterans. Healthcare systems must ensure adequate access to these groups. Public health measures should specifically reach these groups, and data on social determinants should be systematically collected from people with COVID-19.
| S-EPMC7799807 | biostudies-literature
Project description:Adherence to coronavirus disease 2019 (COVID-19) preventive guidelines (ACPG) is an important strategy to control the COVID-19 pandemic effectively. The present study aimed to identify and summarize the social determinants of ACPG among the general population. A comprehensive review was performed from December 2019 to February 2021 through searching electronic databases. Two independent reviewers assessed and selected relevant studies. Next, the characteristics and main findings of the included studies were summarized. Finally, the World Health Organization's conceptual framework of social determinants of health was used to synthesize the identified social determinants of ACPG. Forty-one of 453 retrieved articles met the inclusion criteria. The study results showed different patterns of ACPG among various communities. Furthermore, 84 social determinants were identified and categorized into structural and intermediary determinants. ACPG is a set of complex behaviors associated with different individual sociodemographic and behavioral characteristics; living and working conditions; COVID-19 knowledge, attitudes, and risk perceptions; exposure to sources and information level; leisure activities; social support; trust; social norms; psychosocial well-being; socio-economic position; and the socio-economic and political context. Interventions to promote ACPG among the general population should consider the identified social determinants of ACPG.
| S-EPMC8721272 | biostudies-literature
Project description:Racial/ethnic minorities have experienced higher COVID-19 infection rates than whites, but it is unclear how individual-level housing, occupational, behavioral, and socioeconomic conditions contribute to these disparities in a nationally representative sample. In this study, we assess the extent to which social determinants of health contribute to racial/ethnic differences in COVID-19 infection. Data are from the Understanding America Study's Understanding Coronavirus in America survey (UAS COVID-19 waves 7-29). UAS COVID-19 is one of the only nationally representative longitudinal data sources that collects information on household, work, and social behavioral context during the pandemic. We analyze onset of COVID-19 cases, defined as a positive test or a diagnosis of COVID-19 from a healthcare provider since the previous survey wave, over a year of follow-up (June 2020-July 2021). We consider educational attainment, economic resources, work arrangements, household size, and social distancing as key social factors that may be structured by racism. Cox hazard models indicate that Hispanic people have 48% higher risk of experiencing a COVID-19 infection than whites after adjustment for age, sex, local infection rate, and comorbidities, but we do not observe a higher risk of COVID-19 among Black respondents. Controlling for engagement in any large or small social gathering increases the hazard ratio for Hispanics by 9%, suggesting that had Hispanics had the same social engagement patterns as whites, they may have had even higher risk of COVID-19. Other social determinants-lower educational attainment, working away from home, and number of coresidents-all independently predict higher risk of COVID-19, but do not explain why Hispanic Americans have higher COVID-19 infection risk than whites.
| S-EPMC9162789 | biostudies-literature
Project description:Americans have significantly poorer health outcomes and shorter longevity than citizens of other industrialized nations. Poverty is a major driver of these poor health outcomes in the United States. Innovative anti-poverty policies may help reduce economic malaise thereby increasing the health and longevity of the most vulnerable Americans. However, there is no consensus framework for studying the health impacts of anti-poverty social policies. In this paper, we describe a case study in which leading global experts systematically: (1) developed a conceptual model that outlines the potential pathways through which a social policy influences health, (2) fits outcome measures to this conceptual model, and (3) estimates an optimal time frame for collection of the selected outcome measures. This systematic process, called the Delphi method, has the potential to produce estimates more quickly and with less bias than might be achieved through expert panel discussions alone. Our case study is a multi-component randomized-controlled trial (RCT) of a workforce policy called MyGoals for Healthy Aging.
| S-EPMC7246501 | biostudies-literature
Project description:The unpredictability and unknowns surrounding the ongoing coronavirus disease (COVID-19) pandemic have led to an unprecedented consequence taking a heavy toll on the lives and economies of all countries. There have been efforts to predict COVID-19 case counts (CCC) using epidemiological data and numerical tokens online, which may allow early preventive measures to slow the spread of the disease. In this paper, we use state-of-the-art natural language processing (NLP) algorithms to numerically encode COVID-19 related tweets originated from eight cities in the United States and predict city-specific CCC up to eight days in the future. A city-embedding is proposed to obtain a time series representation of daily tweets posted from a city, which is then used to predict case counts using a custom long-short term memory (LSTM) model. The universal sentence encoder yields the best normalized root mean squared error (NRMSE) 0.090 (0.039), averaged across all cities in predicting CCC six days in the future. The R 2 scores in predicting CCC are more than 0.70 and often over 0.8, which suggests a strong correlation between the actual and our model predicted CCC values. Our analyses show that the NRMSE and R 2 scores are consistently robust across different cities and different numbers of time steps in time series data. Results show that the LSTM model can learn the mapping between the NLP-encoded tweet semantics and the case counts, which infers that social media text can be directly mined to identify the future course of the pandemic.
| S-EPMC9490453 | biostudies-literature