The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study

BackgroundPrevious studies on the impact of social distancing on COVID-19 mortality in the United States have predominantly examined this relationship at the national level and have not separated COVID-19 deaths in nursing homes from total COVID-19 deaths. This approach may obscure differences in social distancing behaviors by county in addition to the actual effectiveness of social distancing in preventing COVID-19 deaths.ObjectiveThis study aimed to determine the influence of county-level social distancing behavior on COVID-19 mortality (deaths per 100,000 people) across US counties over the period of the implementation of stay-at-home orders in most US states (March-May 2020).MethodsUsing social distancing data from tracked mobile phones in all US counties, we estimated the relationship between social distancing (average proportion of mobile phone usage outside of home between March and May 2020) and COVID-19 mortality (when the state in which the county is located reported its first confirmed case of COVID-19 and up to May 31, 2020) with a mixed-effects negative binomial model while distinguishing COVID-19 deaths in nursing homes from total COVID-19 deaths and accounting for social distancing– and COVID-19–related factors (including the period between the report of the first confirmed case of COVID-19 and May 31, 2020; population density; social vulnerability; and hospital resource availability). Results from the mixed-effects negative binomial model were then used to generate marginal effects at the mean, which helped separate the influence of social distancing on COVID-19 deaths from other covariates while calculating COVID-19 deaths per 100,000 people.ResultsWe observed that a 1% increase in average mobile phone usage outside of home between March and May 2020 led to a significant increase in COVID-19 mortality by a factor of 1.18 (P<.001), while every 1% increase in the average proportion of mobile phone usage outside of home in February 2020 was found to significantly decrease COVID-19 mortality by a factor of 0.90 (P<.001).ConclusionsAs stay-at-home orders have been lifted in many US states, continued adherence to other social distancing measures, such as avoiding large gatherings and maintaining physical distance in public, are key to preventing additional COVID-19 deaths in counties across the country.     

Mask mandates save lives

We quantify the effect of statewide mask mandates in the United States in 2020. Our regression discontinuity design exploits county-level variation in COVID-19 outcomes across the border between states with and without mandates. State mask mandates reduced new weekly COVID-19 cases, hospital admissions, and deaths by 55, 11, and 0.7 per 100,000 inhabitants on average. The effect depends on political leaning with larger effects in Democratic-leaning counties. Our results imply that statewide mandates saved 87,000 lives through December 19, 2020, while a nationwide mandate could have saved 57,000 additional lives. This suggests that mask mandates can help counter pandemics, particularly if widely accepted.     

Social inequities in the distribution of COVID-19: An intra-categorical analysis of people with disabilities in the U.S.

BackgroundWhile recent reports suggest that people with disabilities (PwDs) are likely to be adversely impacted by COVID-19 and face multiple challenges, previous research has not examined if COVID-19 burdens are unequally distributed with respect to the disability characteristics of the U.S. population.ObjectiveThis article presents the first national scale study of the relationship between COVID-19 incidence and disability characteristics in the U.S. The objective is to determine whether COVID-19 incidence is significantly greater in counties containing higher percentages of socio-demographically disadvantaged PwDs, based on race, ethnicity, poverty status, age, and biological sex.MethodsThis study integrates county-level data on confirmed COVID-19 cases from the Johns Hopkins Center for Systems Science and Engineering database with multiple disability variables from the 2018 American Community Survey. Statistical analyses are based on bivariate correlations and multivariate generalized estimating equations that consider spatial clustering in the data.ResultsGreater COVID-19 incidence rate is significantly associated with: (1) higher percentages of PwDs who are Black, Asian, Hispanic, Native American, below poverty, under 18 years of age, and female; and (2) lower percentages of PwDs who are non-Hispanic White, above poverty, aged 65 or more years, and male, after controlling for spatial clustering.ConclusionsSocio-demographically disadvantaged PwDs are significantly overrepresented in counties with higher COVID-19 incidence compared to other PwDs. These findings represent an important starting point for more detailed investigation of the disproportionate impacts of COVID-19 on PwDs and highlight the urgent need for COVID-19 data collection systems to incorporate disability information.     

Socioeconomic Disparities in Colorectal Cancer Mortality in the United States, 1990–2007

United States colorectal cancer mortality rates have declined; however, disparities by socioeconomic status and race/ethnicity persist. The objective of this study was to describe the temporal association between colorectal cancer mortality and socioeconomic status by sex and race/ethnicity. Cancer mortality rates in the United States from 1990 to 2007, which were generated by the National Center for Health Statistics, and county-level socioeconomic status, which was estimated as the proportion of county residents living below the national poverty line based on 1990 US Census Bureau data, were obtained from the Surveillance, Epidemiology, and End Results program. The Kunst–Mackenbach relative index of inequality, which considers data across all poverty levels when comparing risks in the poorest (≥20 %) and richest counties (<10 %), was calculated as the measure of association. The study found that colorectal cancer mortality rates were significantly lower in the poorest counties than the richest counties during 1990–1992 among non-Hispanic whites, non-Hispanic black women and non-Hispanic API men. Over time though the tendency was for the poorest counties to have higher mortality rates. By 2003–2007 colorectal cancer mortality rates were significantly higher in the poorest than the richest counties among all sex-race/ethnicity groups. This disparity was most noticeable and appeared to be increasing most among Hispanic men. This suggests that socioeconomic disparities in colorectal cancer mortality were apparent after stratifying by sex and race/ethnicity and reversed over time. Further studies into the causes of these disparities would provide a basis for targeted cancer control interventions and allocation of public health resources.     

Assessment of Coronavirus Disease 2019 Infection and Mortality Rates Among Nursing Homes With Different Proportions of Black Residents

ObjectiveCoronavirus disease 2019 (COVID-19) has disproportionately impacted nursing homes (NHs) with large shares of Black residents. We examined the associations between the proportion of Black residents in NHs and COVID-19 infections and deaths, accounting for structural bias (operationalized as county-level factors) and stratifying by urbanicity/rurality.DesignThis was a cross-sectional observational cohort study using publicly available data from the LTCfocus, Centers for Disease Control and Prevention Long-Term Care Facility COVID-19 Module, and the NYTimes county-level COVID-19 database. Four multivariable linear regression models omitting and including facility characteristics, COVID-19 burden, and county-level fixed effects were estimated.Setting and ParticipantsIn total, 11,587 US NHs that reported data on COVID-19 to the Centers for Disease Control and Prevention and had data in LTCfocus and NYTimes from January 20, 2020 through July 19, 2020.MeasuresProportion of Black residents in NHs (exposure); COVID-19 infections and deaths (main outcomes).ResultsThe proportion of Black residents in NHs were as follows: none= 3639 (31.4%), <20% = 1020 (8.8%), 20%-49.9% = 1586 (13.7%), ≥50% = 681 (5.9%), not reported = 4661 (40.2%). NHs with any Black residents showed significantly more COVID-19 infections and deaths than NHs with no Black residents. There were 13.6 percentage points more infections and 3.5 percentage points more deaths in NHs with ≥50% Black residents than in NHs with no Black residents (P < .001). Although facility characteristics explained some of the differences found in multivariable analyses, county-level factors and rurality explained more of the differences.Conclusions and ImplicationsIt is likely that attributes of place, such as resources, services, and providers, important to equitable care and health outcomes are not readily available to counties where NHs have greater proportions of Black residents. Structural bias may underlie these inequities. It is imperative that support be provided to NHs that serve greater proportions of Black residents while considering the rurality of the NH setting.     

Explaining the Varying Patterns of COVID-19 Deaths Across the United States: 2-Stage Time Series Clustering Framework

BackgroundSocially vulnerable communities are at increased risk for adverse health outcomes during a pandemic. Although this association has been established for H1N1, Middle East respiratory syndrome (MERS), and COVID-19 outbreaks, understanding the factors influencing the outbreak pattern for different communities remains limited.ObjectiveOur 3 objectives are to determine how many distinct clusters of time series there are for COVID-19 deaths in 3108 contiguous counties in the United States, how the clusters are geographically distributed, and what factors influence the probability of cluster membership.MethodsWe proposed a 2-stage data analytic framework that can account for different levels of temporal aggregation for the pandemic outcomes and community-level predictors. Specifically, we used time-series clustering to identify clusters with similar outcome patterns for the 3108 contiguous US counties. Multinomial logistic regression was used to explain the relationship between community-level predictors and cluster assignment. We analyzed county-level confirmed COVID-19 deaths from Sunday, March 1, 2020, to Saturday, February 27, 2021.ResultsFour distinct patterns of deaths were observed across the contiguous US counties. The multinomial regression model correctly classified 1904 (61.25%) of the counties’ outbreak patterns/clusters.ConclusionsOur results provide evidence that county-level patterns of COVID-19 deaths are different and can be explained in part by social and political predictors.     

Vital Signs: Changes in Firearm Homicide and Suicide Rates — United States, 2019–2020

IntroductionThe majority of homicides (79%) and suicides (53%) in the United States involved a firearm in 2020. High firearm homicide and suicide rates and corresponding inequities by race and ethnicity and poverty level represent important public health concerns. This study examined changes in firearm homicide and firearm suicide rates coinciding with the emergence of the COVID-19 pandemic in 2020.MethodsNational vital statistics and population data were integrated with urbanization and poverty measures at the county level. Population-based firearm homicide and suicide rates were examined by age, sex, race and ethnicity, geographic area, level of urbanization, and level of poverty.ResultsFrom 2019 to 2020, the overall firearm homicide rate increased 34.6%, from 4.6 to 6.1 per 100,000 persons. The largest increases occurred among non-Hispanic Black or African American males aged 10–44 years and non-Hispanic American Indian or Alaska Native (AI/AN) males aged 25–44 years. Rates of firearm homicide were lowest and increased least at the lowest poverty level and were higher and showed larger increases at higher poverty levels. The overall firearm suicide rate remained relatively unchanged from 2019 to 2020 (7.9 to 8.1); however, in some populations, including AI/AN males aged 10–44 years, rates did increase.Conclusions and Implications for Public Health PracticeDuring the COVID-19 pandemic, the firearm homicide rate in the United States reached its highest level since 1994, with substantial increases among several population subgroups. These increases have widened disparities in rates by race and ethnicity and poverty level. Several increases in firearm suicide rates were also observed. Implementation of comprehensive strategies employing proven approaches that address underlying economic, physical, and social conditions contributing to the risks for violence and suicide is urgently needed to reduce these rates and disparities.     

Reporting and Availability of COVID-19 Demographic Data by US Health Departments (April to October 2020): Observational Study

BackgroundThere is an urgent need for consistent collection of demographic data on COVID-19 morbidity and mortality and sharing it with the public in open and accessible ways. Due to the lack of consistency in data reporting during the initial spread of COVID-19, the Equitable Data Collection and Disclosure on COVID-19 Act was introduced into the Congress that mandates collection and reporting of demographic COVID-19 data on testing, treatments, and deaths by age, sex, race and ethnicity, primary language, socioeconomic status, disability, and county. To our knowledge, no studies have evaluated how COVID-19 demographic data have been collected before and after the introduction of this legislation.ObjectiveThis study aimed to evaluate differences in reporting and public availability of COVID-19 demographic data by US state health departments and Washington, District of Columbia (DC) before (pre-Act), immediately after (post-Act), and 6 months after (6-month follow-up) the introduction of the Equitable Data Collection and Disclosure on COVID-19 Act in the Congress on April 21, 2020.MethodsWe reviewed health department websites of all 50 US states and Washington, DC (N=51). We evaluated how each state reported age, sex, and race and ethnicity data for all confirmed COVID-19 cases and deaths and how they made this data available (ie, charts and tables only or combined with dashboards and machine-actionable downloadable formats) at the three timepoints.ResultsWe found statistically significant increases in the number of health departments reporting age-specific data for COVID-19 cases (P=.045) and resulting deaths (P=.002), sex-specific data for COVID-19 deaths (P=.003), and race- and ethnicity-specific data for confirmed cases (P=.003) and deaths (P=.005) post-Act and at the 6-month follow-up (P<.05 for all). The largest increases were race and ethnicity state data for confirmed cases (pre-Act: 18/51, 35%; post-Act: 31/51, 61%; 6-month follow-up: 46/51, 90%) and deaths due to COVID-19 (pre-Act: 13/51, 25%; post-Act: 25/51, 49%; and 6-month follow-up: 39/51, 76%). Although more health departments reported race and ethnicity data based on federal requirements (P<.001), over half (29/51, 56.9%) still did not report all racial and ethnic groups as per the Office of Management and Budget guidelines (pre-Act: 5/51, 10%; post-Act: 21/51, 41%; and 6-month follow-up: 27/51, 53%). The number of health departments that made COVID-19 data available for download significantly increased from 7 to 23 (P<.001) from our initial data collection (April 2020) to the 6-month follow-up, (October 2020).ConclusionsAlthough the increased demand for disaggregation has improved public reporting of demographics across health departments, an urgent need persists for the introduced legislation to be passed by the Congress for the US states to consistently collect and make characteristics of COVID-19 cases, deaths, and vaccinations available in order to allocate resources to mitigate disease spread.     

Neighborhood Disadvantage Measures and COVID-19 Cases in Boston, 2020

ObjectiveUnderstanding the pattern of population risk for coronavirus disease 2019 (COVID-19) is critically important for health systems and policy makers. The objective of this study was to describe the association between neighborhood factors and number of COVID-19 cases. We hypothesized an association between disadvantaged neighborhoods and clusters of COVID-19 cases.MethodsWe analyzed data on patients presenting to a large health care system in Boston during February 5–May 4, 2020. We used a bivariate local join-count procedure to determine colocation between census tracts with high rates of neighborhood demographic characteristics (eg, Hispanic race/ethnicity) and measures of disadvantage (eg, health insurance status) and COVID-19 cases. We used negative binomial models to assess independent associations between neighborhood factors and the incidence of COVID-19.ResultsA total of 9898 COVID-19 patients were in the cohort. The overall crude incidence in the study area was 32 cases per 10 000 population, and the adjusted incidence per census tract ranged from 2 to 405 per 10 000 population. We found significant colocation of several neighborhood factors and the top quintile of cases: percentage of population that was Hispanic, non-Hispanic Black, without health insurance, receiving Supplemental Nutrition Assistance Program benefits, and living in poverty. Factors associated with increased incidence of COVID-19 included percentage of population that is Hispanic (incidence rate ratio [IRR] = 1.25; 95% CI, 1.23-1.28) and percentage of households living in poverty (IRR = 1.25; 95% CI, 1.19-1.32).ConclusionsWe found a significant association between neighborhoods with high rates of disadvantage and COVID-19. Policy makers need to consider these health inequities when responding to the pandemic and planning for subsequent health needs.     

COVID-19 vaccination rates vary by community vulnerability: A county-level analysis

We used the COVID-19 Community Vulnerability Index and 7 theme scores to assess associations between vulnerability and county-level COVID-19 vaccination (n = 2415 counties) through May 25th, 2021. When comparing vaccination rates among quintiles of CCVI scores, Theme 3 (housing type, transportation, household composition, and disability) was associated with the largest disparity, with the least vulnerable counties (Q1) having 33% higher rates of vaccination among individuals aged 18+ (53.5% vs 40.2%) compared to counties with the highest vulnerability (Q5). Using generalized linear models with binomial distributions and log links, we found that a 10-point increase in the CCVI index, socioeconomic vulnerability, housing type and composition, and epidemiological factors were associated with at least a 1.0 percentage point decline in county-level vaccination. The association between community vulnerability and lower vaccination rates suggests the need for continued efforts for equitable COVID-19 vaccination across marginalized communities.     

A safety study evaluating non-COVID-19 mortality risk following COVID-19 vaccination

BackgroundThe safety of COVID-19 vaccines plays an important role in addressing vaccine hesitancy. We conducted a large cohort study to evaluate the risk of non-COVID-19 mortality after COVID-19 vaccination while adjusting for confounders including individual-level demographics, clinical risk factors, health care utilization, and community-level socioeconomic risk factors.MethodsThe retrospective cohort study consisted of members from seven Vaccine Safety Datalink sites from December 14, 2020 through August 31, 2021. We conducted three separate analyses for each of the three COVID-19 vaccines used in the US. Crude non-COVID-19 mortality rates were reported by vaccine type, age, sex, and race/ethnicity. The counting process model for survival analyses was used to analyze non-COVID-19 mortality where a new observation period began when the vaccination status changed upon receipt of the first dose and the second dose. We used calendar time as the basic time scale in survival analyses to implicitly adjust for season and other temporal trend factors. A propensity score approach was used to adjust for the potential imbalance in confounders between the vaccinated and comparison groups.ResultsFor each vaccine type and across age, sex, and race/ethnicity groups, crude non-COVID-19 mortality rates among COVID-19 vaccinees were lower than those among comparators. After adjusting for confounders with the propensity score approach, the adjusted hazard ratios (aHRs) were 0.46 (95% confidence interval [CI], 0.44–0.49) after dose 1 and 0.48 (95% CI, 0.46–0.50) after dose 2 of the BNT162b2 vaccine, 0.41 (95% CI, 0.39–0.44) after dose 1 and 0.38 (95% CI, 0.37–0.40) after dose 2 of the mRNA-1273 vaccine, and 0.55 (95% CI, 0.51–0.59) after receipt of Ad26.COV2.S.ConclusionWhile residual confounding bias remained after adjusting for several individual-level and community-level risk factors, no increased risk was found for non-COVID-19 mortality among recipients of three COVID-19 vaccines used in the US.     

Shifting US Patterns of COVID-19 Mortality by Race and Ethnicity From June–December 2020

ObjectivesThe COVID-19 pandemic has disproportionately affected racial and ethnic minorities in the United States and has been devastating for residents of nursing homes (NHs). However, evidence on racial and ethnic disparities in COVID-19–related mortality rates within NHs and how that has changed over time has been limited. This study examines the impact of a high proportion of minority residents in NHs on COVID-19–related mortality rates over a 30-week period.DesignLongitudinal study.Setting and ParticipantsCenters for Medicare & Medicaid Services Nursing Home COVID-19 Public Use File data from 50 states from June 1, 2020, to December 27, 2020.MethodsWe linked data from 11,718 NHs to (1) Nursing Home Compare data, (2) the Long-Term Care: Facts on Care in the U.S., and (3) US county-level data on COVID cases and deaths. Our primary independent variable was proportion of minority residents (blacks and Hispanics) in NHs and its association with mortality rate over time.ResultsDuring the first 6 weeks from June 1, 2020, NHs with a higher proportion of black residents reported more COVID-19 deaths per 1000 followed by NHs with a higher proportion of Hispanic residents. Between 7 and 12 weeks, NHs with a higher proportion of Hispanic residents reported more deaths per 1000, followed by NHs with a higher proportion of black residents. However, after 23 weeks (mid-November 2020), NHs serving a higher proportion of white residents reported more deaths per 1000 than NHs serving a high proportion of black and Hispanic residents.Conclusions and ImplicationsThe disparities in COVID-19–related mortality for nursing homes serving minority residents is evident for the first 12 weeks of our study period. Policy interventions and the equitable distribution of vaccine are required to mitigate the impact of systemic racial injustice on health outcomes of people of color residing in NHs.     

U.S. Nursing Home Quality Ratings Associated with COVID-19 Cases and Deaths

ObjectivesTo inform future policies and disaster preparedness plans in the vulnerable nursing home setting, we need greater insight into the relationship between nursing homes’ (NHs’) quality and the spread and severity of COVID-19 in NH facilities. We therefore extend current evidence on the relationships between NH quality and resident COVID-19 infection rates and deaths, taking into account NH structural characteristics and community characteristics.DesignCross-sectional study.Setting and Participants15,390 Medicaid- and Medicare-certified NHs.MethodsWe obtained and merged the following data sets: (1) COVID-19 weekly data reported by each nursing home to the Centers for Disease Control and Prevention’s National Healthcare Safety Network, (2) Centers for Medicare & Medicaid Services Five Star Quality Rating System, (3) county-level COVID-19 case counts, (4) county-level population data, and (5) county-level sociodemographic data.ResultsAmong 1-star NHs, there were an average of 13.19 cases and 2.42 deaths per 1000 residents per week between May 25 and December 20, 2020. Among 5-star NHs, there were an average of 9.99 cases and 1.83 deaths per 1000 residents per week. The rate of confirmed cases of COVID-19 was 31% higher among 1-star NHs compared with 5-star NHs [model 1: incidence rate ratio (IRR) 1.31, 95% confidence interval (CI) 1.23-1.39], and the rate of COVID-19 deaths was 30% higher (IRR 1.30, 95% CI 1.20, 1.41). These associations were only partially explained by differences in community spread of COVID-19, case mix, and the for-profit status and size of NHs.Conclusions and ImplicationsWe found that COVID-19 case and death rates were substantially higher among NHs with lower star ratings, suggesting that NHs with quality much below average are more susceptible to the spread of COVID-19. This relationship, particularly with regard to case rates, can be partially attributed to external factors: lower-rated NHs are often located in areas with greater COVID-19 community spread and serve more socioeconomically vulnerable residents than higher-rated NHs.     

Worldwide prevalence of fungal coinfections among COVID-19 patients: a comprehensive systematic review and meta-analysis

Microbial coinfections can increase the morbidity and mortality rates of viral respiratory diseases. Therefore, this study aimed to determine the pooled prevalence of fungal coinfections in coronavirus disease 2019 (COVID-19) patients. Web of Science, Medline, Scopus, and Embase were searched without language restrictions to identify the related research on COVID-19 patients with fungal coinfections from December 1, 2019, to December 30, 2020. A random-effects model was used for analysis. The sample size included 2,246 patients from 8 studies. The pooled prevalence of fungal coinfections was 12.60%. The frequency of fungal subtype coinfections was 3.71% for Aspergillus, 2.39% for Candida, and 0.39% for other. The World Health Organization’s Regional Office for Europe and Regional Office for Southeast Asia had the highest (23.28%) and lowest (4.53%) estimated prevalence of fungal coinfection, respectively. Our findings showed a high prevalence of fungal coinfections in COVID-19 cases, which is a likely contributor to mortality in COVID-19 patients. Early identification of fungal pathogens in the laboratory for COVID-19 patients can lead to timely treatment and prevention of further damage by this hidden infection.     

SARS-CoV-2 and Wisconsin Nursing Homes: Temporal Dynamics During the COVID-19 Pandemic

ObjectivesEvidence suggests that quality, location, and staffing levels may be associated with COVID-19 incidence in nursing homes. However, it is unknown if these relationships remain constant over time. We describe incidence rates of COVID-19 across Wisconsin nursing homes while examining factors associated with their trajectory during 5 months of the pandemic.DesignRetrospective cohort study.Setting/ParticipantsWisconsin nursing homes.MethodsPublicly available data from June 1, 2020, to October 31, 2020, were obtained. These included facility size, staffing, 5-star Medicare rating score, and components. Nursing home characteristics were compared using Pearson chi-square and Kruskal-Wallis tests. Multiple linear regressions were used to evaluate the effect of rurality on COVID-19.ResultsThere were a total of 2459 COVID-19 cases across 246 Wisconsin nursing homes. Number of beds (P < .001), average count of residents per day (P < .001), and governmental ownership (P = .014) were associated with a higher number of COVID-19 cases. Temporal analysis showed that the highest incidence rates of COVID-19 were observed in October 2020 (30.33 cases per 10,000 nursing home occupied-bed days, respectively). Urban nursing homes experienced higher incidence rates until September 2020; then incidence rates among rural nursing homes surged. In the first half of the study period, nursing homes with lower-quality scores (1-3 stars) had higher COVID-19 incidence rates. However, since August 2020, incidence was highest among nursing homes with higher-quality scores (4 or 5 stars). Multivariate analysis indicated that over time rural location was associated with increased incidence of COVID-19 (β = 0.05, P = .03).Conclusions and ImplicationsHigher COVID-19 incidence rates were first observed in large, urban nursing homes with low-quality rating. By October 2020, the disease had spread to rural and smaller nursing homes and those with higher-quality ratings, suggesting that community transmission of SARS-CoV-2 may have propelled its spread.     

Neither Race nor Ethnicity Impact the Mortality of Residents of Veterans Affairs Community Living Center With COVID-19

ObjectivesCOVID-19 disproportionately affected nursing home residents and people from racial and ethnic minorities in the United States. Nursing homes in the Veterans Affairs (VA) system, termed Community Living Centers (CLCs), belong to a national managed care system. In the period prior to the availability of vaccines, we examined whether residents from racial and ethnic minorities experienced disparities in COVID-19 related mortality.DesignRetrospective cohort study.Setting and ParticipantsResidents at 134 VA CLCs from April 14 to December 10, 2020.MethodsWe used the VA Corporate Data Warehouse to identify VA CLC residents with a positive SARS-CoV-2 polymerase chain reaction test during or 2 days prior to their admission and without a prior case of COVID-19. We assessed age, self-reported race/ethnicity, frailty, chronic medical conditions, Charlson comorbidity index, the annual quarter of the infection, and all-cause 30-day mortality. We estimated odds ratios and 95% confidence intervals of all-cause 30-day mortality using a mixed-effects multivariable logistic regression model.ResultsDuring the study period, 1133 CLC residents had an index positive SARS-CoV-2 test. Mortality at 30 days was 23% for White non-Hispanic residents, 15% for Black non-Hispanic residents, 10% for Hispanic residents, and 16% for other residents. Factors associated with increased 30-day mortality were age ≥70 years, Charlson comorbidity index ≥6, and a positive SARS-CoV-2 test between April 14 and June 30, 2020. Frailty, Black race, and Hispanic ethnicity were not independently associated with an increased risk of 30-day mortality.Conclusions and ImplicationsAmong a national cohort of VA CLC residents with COVID-19, neither Black race nor Hispanic ethnicity had a negative impact on survival. Further research is needed to determine factors within the VA health care system that mitigate the influence of systemic racism on COVID-19 outcomes in US nursing homes.     

Outcomes of COVID-19: Disparities by ethnicity

ObjectivesTo investigate the role of ethnicity in COVID-19 outcome disparities in a cohort in Kuwait.MethodsThis is a retrospective analysis of 405 individuals infected with SARS-CoV-2 in Kuwait. Outcomes such as symptoms severity and mortality were considered. Multivariate logistic regression models were used to report the odds ratios (OR) for ICU admission and dying from COVID-19.ResultsThe cohort included 290 Arabs and 115 South Asians. South Asians recorded significantly higher COVID-19 death rates compared to Arabs (33% vs. 7.6%, P value<0.001). When compared to Arabs, South Asians also had higher odds of being admitted to the ICU (OR = 6.28, 95% CI: 3.34–11.80, p < 0.001). South Asian patients showed 7.62 (95% CI: 3.62–16.02, p < 0.001) times the odds of dying from COVID-19.ConclusionCOVID-19 patients with South Asians ethnicity in Kuwait are more likely to have worse prognosis and outcome when compared to patients with Arab ethnicity. This suggest a possible role for ethnicity in COVID-19 outcome disparities and this role is likely to be multifactorial.     

Mortality from heart,respiratory, and kidney disease in coal mining areas of Appalachia

Purpose  The purpose of this study was to test whether population mortality rates from heart, respiratory and kidney disease were higher as a function of levels of Appalachian coal mining after control for other disease risk factors. Methods  The study investigated county-level, age-adjusted mortality rates for the years 2000–2004 for heart, respiratory and kidney disease in relation to tons of coal mined. Four groups of counties were compared: Appalachian counties with more than 4 million tons of coal mined from 2000 to 2004; Appalachian counties with mining at less than 4 million tons, non-Appalachian counties with coal mining, and other non-coal mining counties across the nation. Forms of chronic illness were contrasted with acute illness. Poisson regression models were analyzed separately for male and female mortality rates. Covariates included percent male population, college and high school education rates, poverty rates, race/ethnicity rates, primary care physician supply, rural-urban status, smoking rates and a Southern regional variable. Results  For both males and females, mortality rates in Appalachian counties with the highest level of coal mining were significantly higher relative to non-mining areas for chronic heart, respiratory and kidney disease, but were not higher for acute forms of illness. Higher rates of acute heart and respiratory mortality were found for non-Appalachian coal mining counties. Conclusions  Higher chronic heart, respiratory and kidney disease mortality in coal mining areas may partially reflect environmental exposure to particulate matter or toxic agents present in coal and released in its mining and processing. Differences between Appalachian and non-Appalachian areas may reflect different mining practices, population demographics, or mortality coding variability.     

Unemployment,SNAP Enrollment,and Food Insecurity Before and After California's COVID-19 Shutdown

ObjectivesTo examine whether the decrease in very low food security (VLFS) observed in California shortly after California's coronavirus disease (COVID-19) shutdown remained throughout Federal Fiscal Year (FFY) 2020. To investigate associations among unemployment, Supplemental Nutrition Assistance Program (SNAP) enrollment, and VLFS across FFY 2020.MethodsTelephone interview responses from mothers from randomly sampled households from low-income areas throughout California to the 6-item US Department of Agriculture Food Security Survey Module identified VLFS families. Logistic regression examined VLFS rates before vs after California's COVID-19 shutdown, with race/ethnicity, age, and education as covariates. Pearson correlations were calculated for unemployment, SNAP enrollment, and VLFS.ResultsMost (66.4%) of the 2,682 mothers were Latina. VLFS declined from 19.3% before to 14.5% after California's COVID-19 shutdown (adjusted odds ratio, 0.705; P = 0.002). The correlation for unemployment and SNAP household participation was 0.854 (P = 0.007), and for SNAP participation and VLFS was −0.869 (P = 0.005).Conclusions and ImplicationsPublicly-funded assistance programs may lower food insecurity, even during a time of increased economic hardship. Examining the specific factors responsible for the observed decline in VLFS has merit. Whether VLFS remains below the rate observed before California's COVID-19 shutdown is worthy of ongoing study.     

The Impact of Funding Inpatient Treatments for COVID-19 on Health Equity in the United States: A Distributional Cost-Effectiveness Analysis

ObjectivesWe conducted a distributional cost-effectiveness analysis (DCEA) to evaluate how Medicare funding of inpatient COVID-19 treatments affected health equity in the United States.MethodsA DCEA, based on an existing cost-effectiveness analysis model, was conducted from the perspective of a single US payer, Medicare. The US population was divided based on race and ethnicity (Hispanic, non-Hispanic black, and non-Hispanic white) and county-level social vulnerability index (5 quintile groups) into 15 equity-relevant subgroups. The baseline distribution of quality-adjusted life expectancy was estimated across the equity subgroups. Opportunity costs were estimated by converting total spend on COVID-19 inpatient treatments into health losses, expressed as quality-adjusted life-years (QALYs), using base-case assumptions of an opportunity cost threshold of $150 000 per QALY gained and an equal distribution of opportunity costs across equity-relevant subgroups.ResultsMore socially vulnerable populations received larger per capita health benefits due to higher COVID-19 incidence and baseline in-hospital mortality. The total direct medical cost of inpatient COVID-19 interventions in the United States in 2020 was estimated at $25.83 billion with an estimated net benefit of 735 569 QALYs after adjusting for opportunity costs. Funding inpatient COVID-19 treatment reduced the population-level burden of health inequality by 0.234%. Conclusions remained robust across scenario and sensitivity analyses.ConclusionsTo the best of our knowledge, this is the first DCEA to quantify the equity implications of funding COVID-19 treatments in the United States. Medicare funding of COVID-19 treatments in the United States could improve overall health while reducing existing health inequalities.