Exposure to air pollution and COVID-19 mortality in the United States: A nationwide cross-sectional study

AbstractObjectivesUnited States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States.DesignA nationwide, cross-sectional study using county-level data.Data sourcesCOVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) up to April 22, 2020 from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center.Main outcome measuresWe fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state’s issuance of stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses.ResultsWe found that an increase of only 1 μg/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% confidence interval [CI]: 2%, 15%). The results were statistically significant and robust to secondary and sensitivity analyses.ConclusionsA small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite the inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely.Summary BoxWhat is already known on this topicLong-term exposure to PM2.5 is linked to many of the comorbidities that have been associated with poor prognosis and death in COVID-19 patients, including cardiovascular and lung disease.PM2.5 exposure is associated with increased risk of severe outcomes in patients with certain infectious respiratory diseases, including influenza, pneumonia, and SARS.Air pollution exposure is known to cause inflammation and cellular damage, and evidence suggests that it may suppress early immune response to infection.What this study addsThis is the first nationwide study of the relationship between historical exposure to air pollution exposure and COVID-19 death rate, relying on data from more than 3,000 counties in the United States. The results suggest that long-term exposure to PM2.5 is associated with higher COVID-19 mortality rates, after adjustment for a wide range of socioeconomic, demographic, weather, behavioral, epidemic stage, and healthcare-related confounders.This study relies entirely on publicly available data and fully reproducible, public code to facilitate continued investigation of these relationships by the broader scientific community as the COVID-19 outbreak evolves and more data become available.A small increase in long-term PM2.5 exposure was associated with a substantial increase in the county’s COVID-19 mortality rate up to April 22, 2020.

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The Opioid Epidemic and Homicide in the United States

Journal of Research in Crime and Delinquency ◽

10.1177/0022427820986848 ◽

2021 ◽

pp. 002242782098684

Author(s):

Richard Rosenfeld ◽

Joel Wallman ◽

Randolph Roth

Keyword(s):

United States ◽

Death Rate ◽

The United States ◽

Opioid Epidemic ◽

Drug Markets ◽

Cross Sectional ◽

Homicide Rates ◽

Overdose Death ◽

Sectional Analysis ◽

The Relationship

Objectives: Evaluate the relationship between the opioid epidemic and homicide rates in the United States. Methods: A county-level cross-sectional analysis covering the period 1999 to 2015. The race-specific homicide rate and the race-specific opioid-related overdose death rate are regressed on demographic, social, and economic covariates. Results: The race-specific opioid-related overdose death rate is positively associated with race-specific homicide rates, net of controls. The results are generally robust across alternative samples and model specifications. Conclusions: We interpret the results as reflecting the violent dynamics of street drug markets, although more research is needed to draw definitive conclusions about the mechanisms linking opioid demand and homicide.

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Racial, Economic and Health Inequality and COVID-19 Infection in the United States

10.1101/2020.04.26.20079756 ◽

2020 ◽

Cited By ~ 11

Author(s):

Vida Abedi ◽

Oluwaseyi Olulana ◽

Venkatesh Avula ◽

Durgesh Chaudhary ◽

Ayesha Khan ◽

...

Keyword(s):

United States ◽

New York ◽

Health Care Access ◽

Urban Areas ◽

Death Rate ◽

The United States ◽

Preliminary Evidence ◽

Cross Sectional Study ◽

Cross Sectional ◽

The Impact

AbstractBackgroundThere is preliminary evidence of racial and social-economic disparities in the population infected by and dying from COVID-19. The goal of this study is to report the associations of COVID-19 with respect to race, health and economic inequality in the United States.MethodsWe performed a cross-sectional study of the associations between infection and mortality rate of COVID-19 and demographic, socioeconomic and mobility variables from 369 counties (total population: 102,178,117 [median: 73,447, IQR: 30,761-256,098]) from the seven most affected states (Michigan, New York, New Jersey, Pennsylvania, California, Louisiana, Massachusetts).FindingsThe risk factors for infection and mortality are different. Our analysis shows that counties with more diverse demographics, higher population, education, income levels, and lower disability rates were at a higher risk of COVID-19 infection. However, counties with higher disability and poverty rates had a higher death rate. African Americans were more vulnerable to COVID-19 than other ethnic groups (1,981 African American infected cases versus 658 Whites per million). Data on mobility changes corroborate the impact of social distancing.InterpretationThe observed inequality might be due to the workforce of essential services, poverty, and access to care. Counties in more urban areas are probably better equipped at providing care. The lower rate of infection, but a higher death rate in counties with higher poverty and disability could be due to lower levels of mobility, but a higher rate of comorbidities and health care access.

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Mask Wearing and Control of SARS-CoV-2 Transmission in the United States

10.1101/2020.08.23.20078964 ◽

2020 ◽

Cited By ~ 1

Author(s):

Benjamin Rader ◽

Laura F White ◽

Michael R Burns ◽

Jack Chen ◽

Joe Brilliant ◽

...

Keyword(s):

United States ◽

The United States ◽

Grocery Store ◽

Sensitivity Analyses ◽

Reproductive Number ◽

Positive Trend ◽

Respiratory Pathogens ◽

Transmission Control ◽

Cross Sectional ◽

Social Distancing

Introduction: Cloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, population level, empirical research remains limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake. Methods: Serial cross-sectional surveys were administered June 3 through July 27, 2020 via a web platform. Surveys queried individuals' likelihood to wear a face mask to the grocery store or with family and friends. Responses (N = 378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (Rt), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (Rt <1) for each state and week. Multiple sensitivity analyses were considered to corroborate findings across mask wearing definitions, Rt estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates. Results: We find an increasing trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (Rt <1). We also find that communities with high mask wearing and social distancing have the highest predicted probability of a controlled epidemic. These positive associations were maintained across sensitivity analyses. Following state mandates, mask wearing did not show significant statistical changes in uptake, however the positive trend of increased mask wearing over time was preserved. Conclusion: Widespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.

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On the Relationship between Development and Fertility: The Case of the United States

Comparative Population Studies ◽

10.12765/cpos-2015-13 ◽

2015 ◽

Vol 40 (4) ◽

Author(s):

Igor Ryabov

Keyword(s):

United States ◽

Human Development ◽

Negative Relationship ◽

The United States ◽

Median Income ◽

County Level ◽

Cross Sectional ◽

Development Indicators ◽

Rural Counties ◽

The Relationship

The present article addresses the question of whether there is a link between the spatial patterns of human development and period fertility in the United States at the county level. Using cross-sectional analyses of the relationship between Total Fertility Rate (TFR) and an array of human development indicators (pertaining to three components of the Human Development Index (HDI) – wealth, health, and education), this study sheds light on the relationship between fertility and human development. The analyses were conducted separately for urban, suburban and rural counties. According to the multivariate results, a negative association between selected human development indicators and TFR exists in suburban and rural counties, as well as in the United States as a whole. However, this is not the case for urban counties, where the results were inconclusive. Some indicators (e.g., median income per capita) were found to be positively, and some (e.g., the share of adults with at least bachelor’s degree) negatively, associated with TFR in urban counties. All in all, our results provide evidence of a negative relationship between human development indicators and period fertility in the United States at the county level, a finding which is consistent with the basic tenets of classic demographic transition theory.

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COVID-19 and excess mortality in the United States: A county-level analysis

PLoS Medicine ◽

10.1371/journal.pmed.1003571 ◽

2021 ◽

Vol 18 (5) ◽

pp. e1003571

Author(s):

Andrew C. Stokes ◽

Dielle J. Lundberg ◽

Irma T. Elo ◽

Katherine Hempstead ◽

Jacob Bor ◽

...

Keyword(s):

United States ◽

Formal Education ◽

Excess Mortality ◽

Time Lag ◽

The United States ◽

County Level ◽

Level Variation ◽

Cross Sectional ◽

All Cause Mortality ◽

Excess Deaths

Background Coronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics. Methods and findings In this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics. Conclusions In this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.

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County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model

JMIR Public Health and Surveillance ◽

10.2196/23902 ◽

2020 ◽

Vol 6 (4) ◽

pp. e23902

Author(s):

Kevin L McKee ◽

Ian C Crandell ◽

Alexandra L Hanlon

Keyword(s):

United States ◽

Differential Equation ◽

Population Density ◽

Household Income ◽

The United States ◽

Policy Impact ◽

County Level ◽

Median Household Income ◽

Social Distancing

Background Social distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data. Objective We propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices. Methods A differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered. Results Mobility dynamics show moderate correlations with two census covariates: population density (Spearman r ranging from 0.11 to 0.31) and median household income (Spearman r ranging from –0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (r=–0.37 and r=–0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (r=0.51, r=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from –0.12 to –0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect. Conclusions Chronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.

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Home Infusions for Inflammatory Bowel Disease are Safe: United States Experience and Patient Perspectives

Crohn's & Colitis 360 ◽

10.1093/crocol/otab063 ◽

2021 ◽

Author(s):

Bharati Kochar ◽

Yue Jiang ◽

Wenli Chen ◽

Yuting Bu ◽

Edward L Barnes ◽

...

Keyword(s):

United States ◽

Inflammatory Bowel Disease ◽

Patient Experience ◽

Bowel Disease ◽

The United States ◽

Cross Sectional Survey ◽

Cross Sectional ◽

Increased Risk ◽

Inflammatory Bowel ◽

States Experience

Abstract Background Home-infusions (HI) for biologic medications are an option for inflammatory bowel disease (IBD) patients in the United States (US). We aimed to describe the population receiving HI and report patient experience with HI. Methods We conducted a retrospective cohort study in the Quintiles-IMSLegacy PharMetrics Adjudicated Claims Database from 2010-2016 to describe the population receiving infliximab and vedolizumab HI and determine predictors for an urgent/emergent visit post-HI. We then administered a cross-sectional survey to IBD-Partners Internet-based cohort participants to assess knowledge and experience with infusions. Results We identified claims for 11,892 conventional infliximab patients, 1,573 home infliximab patients, 438 conventional vedolizumab patients and 138 home vedolizumab patients. There were no differences in demographics or median charges with infliximab home and conventional infusions. Home vedolizumab infusions had a greater median charge than conventional vedolizumab infusion. Less than 4% of patients had an urgent/emergent visit post-HI. Charlson comorbidity index >0 (OR:1.95, 95% CI:1.01-3.77) and Medicaid (OR:3.01, 95%CI:1.53-5.94) conferred significantly higher odds of urgent/emergent visit post-HI. In IBD-Partners, 644 IBD patients responded; 56 received HI. The majority chose HI to save time and preferred HI to conventional infusions. Only 2 patients reported an urgent/emergent visit for HI-related problems. Conclusions HI appears to be safe in IBD patients receiving infliximab and vedolizumab. However, patients with fewer resources and more co-morbidities are at increased risk for an urgent/emergent visit post-HI. The overall patient experience with HI is positive. Expansion of HI may result in decreased therapy-related logistic burden for carefully selected patients.

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Air Pollution Can Worsen the Death Rate from COVID-19

Eos ◽

10.1029/2020eo142660 ◽

2020 ◽

Vol 101 ◽

Author(s):

Tim Hornyak

Keyword(s):

United States ◽

Air Pollution ◽

Death Rate ◽

The United States

Scientists find that highly polluted counties in the United States will have a COVID-19 death rate 4.5 times higher than those with low pollution if they’re otherwise similar.

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Secondary organic aerosol association with cardiorespiratory disease mortality in the United States

Nature Communications ◽

10.1038/s41467-021-27484-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Havala O. T. Pye ◽

Cavin K. Ward-Caviness ◽

Ben N. Murphy ◽

K. Wyat Appel ◽

Karl M. Seltzer

Keyword(s):

United States ◽

Secondary Organic Aerosol ◽

Carbon Sources ◽

Organic Aerosol ◽

The United States ◽

County Level ◽

Risk Of Death ◽

Predictive Algorithms ◽

Increased Risk ◽

The U.S

AbstractFine particle pollution, PM2.5, is associated with increased risk of death from cardiorespiratory diseases. A multidecadal shift in the United States (U.S.) PM2.5 composition towards organic aerosol as well as advances in predictive algorithms for secondary organic aerosol (SOA) allows for novel examinations of the role of PM2.5 components on mortality. Here we show SOA is strongly associated with county-level cardiorespiratory death rates in the U.S. independent of the total PM2.5 mass association with the largest associations located in the southeastern U.S. Compared to PM2.5, county-level variability in SOA across the U.S. is associated with 3.5× greater per capita county-level cardiorespiratory mortality. On a per mass basis, SOA is associated with a 6.5× higher rate of mortality than PM2.5, and biogenic and anthropogenic carbon sources both play a role in the overall SOA association with mortality. Our results suggest reducing the health impacts of PM2.5 requires consideration of SOA.

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Abstract 16562: Association of County-Level Social Vulnerability Index and Cardiovascular Disease in the United States, 1999-2018

Circulation ◽

10.1161/circ.142.suppl_3.16562 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Quentin R Youmans ◽

Megan E McCabe ◽

Clyde W Yancy ◽

Lucia Petito ◽

Kiarri N Kershaw ◽

...

Keyword(s):

United States ◽

Cardiovascular Disease ◽

Social Vulnerability ◽

Cardiovascular Health ◽

Vulnerability Index ◽

The United States ◽

County Level ◽

Social Vulnerability Index ◽

Cross Sectional ◽

Cvd Mortality

Introduction: Social determinants of health are multi-dimensional and span various interrelated domains. In order to inform community-engaged clinical and policy efforts, we sought to examine the association between a national social vulnerability index (SVI) and age-adjusted mortality rate (AAMR) of CVD. Hypothesis: Higher county-level SVI or greater vulnerability will be associated with higher AAMR of CVD between 1999-2018 in the United States. Methods: In this serial, cross-sectional analysis, we queried CDC WONDER for age-adjusted mortality rates (AAMRs) per 100,000 population for cardiovascular disease (I00-78) at the county-level between 1999-2018. We quantified the association of county-level SVI and CVD AAMR using Spearman correlation coefficients and examined trends in CVD AAMR stratified by median SVI at the county-level. Finally, we performed geospatial county-level analysis stratified by combined median SVI and CVD AAMR (high/high, high/low, low/high, and low/low). Results: We included data from 2766 counties (representing 95% of counties in the US) with median SVI 0.53 (IQR 0.28, 0.76). Overall SVI and the household and socioeconomic subcomponents were strongly correlated with 2018 CVD AAMR (0.47, 0.50, and 0.56, respectively with p<0.001 for all). CVD mortality declined between 1999-2011 and was stagnant between 2011-2018 with similar patterns in high and low SVI counties (FIGURE). Counties with high SVI and CVD AAMR were clustered in the South and Midwest (n=977, 35%). Conclusion: County-level social vulnerability is associated with higher CVD mortality. High SVI and CVD AAMR coexist in more than 1 in 3 US counties and have persisted over the past 2 decades. Identifying counties that are disproportionately vulnerable may inform targeted and community-based strategies to equitably improve cardiovascular health across the country.

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