County Mortality Research Articles

Abstract A040: Effects of food environment, income inequality, and racial segregation on obesity- related cancer mortality

Abstract BACKGROUND: Rural areas experience 7-12% higher risk of obesity-related cancer mortality compared to their urban counterparts. Complex interactions between social and structural factors in rural areas, combined with limited access to healthy foods and lower income, warrant further study to understand drivers of this disparity. Methods: County-level rates of age-adjusted obesity-related cancer mortality per 100,000 (2009-2020 were obtained from the Centers for Disease Control and Prevention’s Wide-Ranging ONline Data for Epidemiologic Research database. Based on data from the Food Environment Atlas, we calculated food swamp using the modified Retail Food Environment Index (mRFEI) as the ratio of fast-food restaurants and convenience stores to grocery and specialized food stores, supercenters, and farmers markets. Food swamp scores were then divided into quartiles. Next, we used Global G and local Gi statistics to identify the presence of spatial autocorrelation in food swamp and subsequently calculated a spatial moving average (SMA) across counties. Measures of economic segregation, racial segregation, and racialized economic segregation were calculated using the Index of Concentration at the Extremes (ICE) using population and demographic estimates from the 2005- 2009 American Community Survey. Larger values reflect concentrated extremes of disadvantage. Poisson regression was used to assess the impact of food swamp, urban-rural status, economic segregation, racial segregation, and racialized economic segregation on rates of obesity-related cancer mortality. Results: Among the 3014 counties included, median food swamp score was 3.6 (interquartile range=2.6-4.9), and counties in the 2nd, 3rd, and 4th quartiles for food swamp had 2%, 4%, and 6% higher rates (respectively) of obesity-related cancer mortality than those in the 1st quartile. Incidence rate ratios for the association of food swamp quartile with obesity-related cancer mortality were 1-3% smaller after control for food swamp SMA and for racialized economic Urban-rural differences in obesity-related cancer mortality varied with all ICE measures. Rural counties had 5% higher rates of obesity-related cancer mortality (IRR=1.05, 95%CI=1.04-1.06), even after controlling for food swamp and food swamp SMA, but there was no longer an association after controlling for economic segregation (IRR=0.99, 95%CI=0.98-1.00). Rural counties had 5% higher rates of obesity-related cancer mortality (IRR=1.05, 95%CI=1.04-1.07) when controlling for racial segregation and 2% higher rates when controlling for racialized economic segregation (IRR=1.02, 95%CI=1.01-1.03). Thus, we stratified our models by urban-rural status and found that each ICE measure had larger effects on obesity-related cancer mortality in rural counties than in urban. CONCLUSION: While food swamp is important, the socioeconomic environment of a county has a larger impact on obesity- related cancer mortality. Individuals may have access to healthy food stores, but whether they can afford the healthy food is more important. Citation Format: Elizabeth S. Davis, Sara Myers, Jeffrey A. Franks, Michael R. Poulson, Julie R. Palmer, Kelly M. Kenzik. Effects of food environment, income inequality, and racial segregation on obesity- related cancer mortality [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A040.

Evaluating the Prevalence of Maternal Health Indicators on Infant Mortality Rates in Florida.

Background The infant mortality rate is defined as the number of infant deaths for every 1000 live births. In 2020, the infant mortality rate was 5.8% in the state of Florida, compared to 7.0% in 2000. Although infant mortality rates have declined in the state of Florida, disparities influencing these rates exist across varying Florida counties, with the widest differences being compared between the healthiest versus unhealthiest counties in Florida.Many factors can contribute to high infant mortality rates in certain counties, including income inequality, access to and quality of healthcare, race/ethnicity, obesity, and disadvantaged socioeconomic status (SES). Methods This study utilized data from Florida Health Charts on infant mortality rates in the state of Florida and the Pregnancy and Young Child Profiles in 10 counties to examine how certain risk factors impact infant mortality outcomes in the state of Florida. These 10 counties consistoffive healthiest andfive unhealthiest counties, as determined by the 2022 County Health Rankings data. T-tests were used to evaluate the relationship between county health status and several county health indicators. Results The average infant mortality rate from 2011 to 2020 differed significantly among healthy and unhealthy counties (p-value=0.0000). Median household income, individuals below the poverty level, and those aged zero to 17 years old were found to differ significantly by county health status (p-values 0.0000, 0.001, and 0.009, respectively). However, mothers having no high school education, births with first-trimester care, births with adequate care, and births with late or no prenatal care were not statistically significant. Conclusion Our study suggests that counties more likely to have fewer resources than other counties, such as those considered unhealthy, are more impacted by a higher infant mortality rate. The unhealthy counties in this study were found to have lower average median household income, higher rates of no high school education among mothers, and less prenatal care in comparison to healthy counties.

Novel Methodology for Linking 9-1-1 Dispatch Categories with a Death Registry: Mortality Rates of Selected Dispatch Categories

Objectives Medical Priority Dispatch System (MPDS) is a system used to assign medical 9-1-1 calls to one of 35 chief complaints that are further categorized in order of increasing priority, Alpha through Echo. In this descriptive study we demonstrate the methodology of matching MPDS codes to a county mortality registry. We also evaluated the ability of select MPDS codes (fall, respiratory, sick person, and abdominal pain) to predict mortality up to 30 d for all ages transported by Alameda County Emergency Medical Services (EMS). Methods Using Alameda County EMS data, we conducted a retrospective review of all EMS encounters that occurred from November 1, 2011, to November 1, 2016. To describe mortality in this population, we identified unique patients and linked them to the Alameda County Public Health Death Registry. We identified mortality at 48 h, 7 d, and 30 d after an EMS encounter. Results Approximately 99% of the EMS encounters were matched with unique patient identifiers, yielding a study sample of 202,431 (4% less than age 18, 53% between ages 18–65, and 43% over age 65). Patients with a respiratory chief complaint had the highest mortality percentage in each age group (0.23%, 2.7%, and 14.55% respectively). There was no correlation between the MPDS code and mortality for patients less than age 18. An increase in Alpha through Echo designation for respiratory complaints in patients 18–65 and older than 65 years corresponded with an increase in 30-day mortality. Conclusions This study demonstrates an upward trend in mortality with increasing acuity of Alpha through Echo designations for adult patients with respiratory complaints. Mortality increased with age in this cohort. Most of the deaths occurred after 7 days.

Association of race/ethnicity and severe housing problems with COVID-19 deaths in the United States: Analysis of the first three waves.

The objective of this study is to assess the associations of race/ethnicity and severe housing problems with COVID-19 death rates in the US throughout the first three waves of the COVID-19 pandemic in the US. We conducted a cross-sectional study using a negative binomial regression model to estimate factors associated with COVID-19 deaths in 3063 US counties between March 2020 and July 2021 by wave and pooled across all three waves. In Wave 1, counties with larger percentages of Black, Hispanic, American Indian and Alaska Native (AIAN), and Asian American and Pacific Islander (AAPI) residents experienced a greater risk of deaths per 100,000 residents of +22.82 (95% CI 15.09, 30.56), +7.50 (95% CI 1.74, 13.26), +13.52 (95% CI 8.07, 18.98), and +5.02 (95% CI 0.92, 9.12), respectively, relative to counties with larger White populations. By Wave 3, however, the mortality gap declined considerably in counties with large Black, AIAN and AAPI populations: +10.38 (95% CI 4.44, 16.32), +7.14 (95% CI 1.14, 13.15), and +3.72 (95% CI 0.81, 6.63), respectively. In contrast, the gap increased for counties with a large Hispanic population: +13 (95% CI 8.81, 17.20). Housing problems were an important predictor of COVID-19 deaths. However, while housing problems were associated with increased COVID-19 mortality in Wave 1, by Wave 3, they contributed to magnified mortality in counties with large racial/ethnic minority groups. Our study revealed that focusing on a wave-by-wave analysis is critical to better understand how the associations of race/ethnicity and housing conditions with deaths evolved throughout the first three COVID-19 waves in the US. COVID-19 mortality initially took hold in areas characterized by large racial/ethnic minority populations and poor housing conditions. Over time, as the virus spread to predominantly White counties, these disparities decreased substantially but remained sizable.

Disparities in Social Vulnerability and Premature Mortality among Decedents with Hepatitis B, United States, 2010-2019.

Current US hepatitis B mortality rates remain three times higher than the national target. Mortality reduction will depend on addressing hepatitis B disparities influenced by social determinants of health. This study aims to describe characteristics of hepatitis B-listed decedents, which included US birthplace status and county social vulnerability attributes and quantify premature mortality. We conducted a cross-sectional analysis of 17,483 hepatitis B-listed decedents using the 2010-2019 US Multiple-Cause-of-Death data merged with the county-level Social Vulnerability Index (SVI). Outcomes included the distribution of decedents according to US birthplace status and residence in higher versus lower death burden counties by sociodemographic characteristics, years of potential life lost (YPLL), and SVI quartiles. Most hepatitis B-listed decedents were US-born, male, and born during 1945-1965. Median YPLL was 17.2; 90.0% died prematurely. US-born decedents were more frequently White, non-college graduates, unmarried, and had resided in a county with < 500,000 people; non-US-born decedents were more frequently Asian/Pacific Islander, college graduates, married, and had resided in a county with ≥ 1 million people. Higher death burden (≥ 20) counties were principally located in coastal states. US-born decedents more frequently resided in counties in the highest SVI quartile for "Household Characteristics" and "Uninsured," whereas non-US-born decedents more frequently resided in counties in the highest SVI quartile for "Racial/Ethnic Minority Status" and "Housing Type/Transportation." This analysis found substantial premature hepatitis B mortality and residence in counties ranked high in social vulnerability. Successful interventions should be tailored to disproportionately affected populations and the social vulnerability features of their geographic areas.

Recent trends in cervical cancer incidence, stage at diagnosis, and mortality according to county-level income in the United States, 2000-2019.

Early evidence suggests that declining cervical cancer incidence reversed in low-income regions in the United States in recent years; however, it is unclear whether there are distinct patterns by race/ethnicity and stage at diagnosis and if the increase has translated into rising mortality. Using Surveillance, Epidemiology, and End Results data, we evaluated trends in hysterectomy-corrected cervical cancer incidence rates (2000-2019) and mortality rates (2005-2019) by county-level income and race/ethnicity, with further stratification of incidence by stage at diagnosis. Following a period of decline, hysterectomy-corrected cervical cancer incidence increased 1.0%/year (95% CI = 0.1% to 4.5%) among Non-Hispanic White women in low-income counties. Particularly, a statistically significant 4.4%/year (95% CI = 1.7% to 7.5%) increase in distant-stage cancer occurred in this group. Additionally, recent increases in cervical cancer mortality (1.1%/year [95% CI = -1.4% to 3.7%]) were observed among this group and Non-Hispanic Black women in low-income counties (2.9%/year [95% CI = -2.3% to 18.2%]), but trends were not statistically significant. Among Hispanic women in low-income counties, distant-stage cervical cancer incidence increased 1.5%/year (95% CI = -0.6% to 4.1%), albeit not statistically significant. The increasing incidence of distant-stage cervical cancer and mortality in specific racial/ethnic groups suggests that the recent introduction of higher sensitivity screening tests may not explain increasing trends in low-income counties. Our findings suggest that the observed rise in cervical cancer incidence may reflect disruptions along the screening and treatment continuum. Future research to further comprehend these trends and continued enhancements in prevention are crucial to combat rising cervical cancer incidence and mortality in low-income counties in the United States.

COVID-19 Infection and Mortality in State Veterans Homes: A Multilevel Approach

ObjectivesSome state veterans homes (SVHs) received media attention in the wake of the COVID-19 pandemic because of allegations of poor infection control and excess mortality. However, little research has investigated how these facilities differ from community nursing homes (CNHs) and what the geographical trends of these infection and mortality differences are. We aimed to test (1) whether infection was overall lower in SVHs than CNHs, (2) whether mortality was overall lower in SVHs than CNHs, as well as the geographic distribution of nursing home infection and mortality across the United States. DesignRetrospective nationwide cohort study. Setting and ParticipantsSkilled nursing facilities in the United States from May 2020 to July 2022 during the COVID-19 pandemic. MethodsUsing multilevel negative binomial regression, we modeled COVID-19 infection and mortality rates in skilled nursing homes, testing for overall SVH differences from May 2020 to July 2022, placing random effects on counties to calculate adjusted county-level infection and mortality rates. ResultsSVHs experienced 18% fewer cases but 25% more deaths overall compared with CNHs. Counties with the highest levels of facility infection, including counties with SVHs, were situated mainly in Midwestern, Atlantic, and Southern states, with the majority of counties with low infection levels in Central and Western states. Counties with the highest levels of facility mortality emerged in Rust Belt and Midwestern states down to Southern states, with the lowest levels of county-level mortality, particularly among counties containing SVHs, occurring westward to Central and Western states. Conclusion and ImplicationsSVHs experienced lower infection levels but higher mortality levels than CNHs, and fewer extremely high infection and mortality rates in counties containing SVHs despite higher mortality risk in SVHs, calling attention to unobserved facility-level differences such as gender and age distributions and future research opportunities using more granular geographical aggregations to better understand facility-level SVH risk within the broader neighborhood context.

Association Between Cardiologist Density and Mortality in Urban and Rural Counties in the United States

Association Between Cardiologist Density and Mortality in Urban and Rural Counties in the United States

Clinico-epidemiological profile of cerebrovascular accident in eastern Nepal: A descriptive cross-sectional study.

cerebrovascular accident (CVA) has contributed to a significant increase in the morbidity and mortality rates in lower middle-income counties like Nepal. Despite being a common noncommunicable disease in Nepal, little attention has been paid to it, in terms of formulating national health plans and policies by the concerned authorities. This was a descriptive cross-sectional study in patients diagnosed with cerebrovascular accidents at a tertiary care hospital in Eastern Nepal. We analyzed 128 diagnosed cases of cerebrovascular accidents from February 26, 2023 to June 26, 2023 after taking ethical clearance from the Institutional Review Committee (Reference no. IRC-PA-283/2078-79). Data were analyzed by Statistical Package for the Social Sciences version 23. The objective of this study was to explore the age and sex distribution of CVA, its association with medical co-morbidities, and known risk factors like Type-2-Diabetes Mellitus, hypertension, thyroid disorders, smoking and alcohol. Together with calculating the distribution of ischemic CVA and hemorrhagic CVA we had also staged the disease based on the National Institute of Health Stroke Scale.

Postpartum haemorrhage: a review from evidence

Postpartum hemorrhage although highly preventable, it is the leading cause of maternal mortality in both resource abundant counties and resource limited countries, despite the presence of known risk factors every woman has a chance of developing postpartum hemorrhage during delivery, but identification of high-risk women and active management of third stage of labor has been shown to have positive effects in reducing mortality from postpartum hemorrhage.

Racial, ethnic and gender trends in lung cancer mortality rates in the United States-Mexico border and non-border areas

BackgroundGeographic patterns of lung cancer mortality rate differ in the region bordering Mexico in contrast to the US. This study compares lung cancer mortality between border and non-border counties by race/ethnicity and gender. MethodsThis study utilized population-level death certificate data from US Centers for Disease Control and Prevention Public Internet Wide-Ranging Online Data for Epidemiologic Research dataset between 1999 and 2020. Established algorithms were implemented to examine lung cancer deaths among US residents. We analyzed the age-adjusted data by year, race/ethnicity, gender, and geographic region. Joinpoint regression was used to determine mortality trends across time. ResultsLung cancer mortality rates were lower in border counties compared to non-border counties across time (p < 0.05). Hispanic lung cancer mortality rates were not different in border counties compared to non-border counties during the same period (p > 0.05). Lung cancer mortality among non-Hispanic White living in border counties was lower than non-Hispanic White residing in non-border counties (p < 0.01), and non-Hispanic Black living in border counties had lower lung cancer mortality than non-Hispanic Black in non-border counties in all but three years (p < 0.05). Both female and male mortality rates were lower in border counties compared to non-border counties (p < 0.05). ConclusionDifferences in lung cancer mortality between border counties and non-border counties reflect lower mortality in Hispanics overall and a decline for non-Hispanic White and non-Hispanic Black living in border counties experiencing lower lung cancer mortality rates than non-border counties. Further studies are needed to identify specific causes for lower mortality rates in border counties.

Colon cancer epidemiology, race and socioeconomic status: Comparing trends in counties served by an urban hospital in Newark, NJ with overall NJ-state and nation-wide patterns

PurposeDisparities in colorectal cancer (CRC) trends are linked with socioeconomic status (SES) and race. To better understand the colon cancer trends at our medical center, this study characterizes the racial and socioeconomic profile of the population served by our center to identify modifiable risk factors amenable to interventions. MethodsColon cancer data from our center as well as New Jersey (NJ) and United States (US) were obtained from National Cancer Database. Demographic data on race and SES for NJ counties were obtained from public databases that sourced data from the American Community Survey and the US census. We compared the odds of being diagnosed with early-onset and late-stage colon cancer (III or IV), respectively in NJ and US, across different racial groups. We also quantified the association between Social Vulnerability Index (SVI) and age-adjusted CRC mortality in NJ counties, with and without accounting for the racial composition of each county. ResultsIn 2015, our center recorded higher proportions of late-stage and early-onset colon cancer diagnoses compared to all hospitals in NJ and US. Trends for stage and patient age at diagnosis of colon cancer for NJ and the US (2010–2019) showed that Black, Hispanic, and Asian/Pacific Islander individuals had greater odds of being diagnosed with early-onset (age<50) and late-stage colon cancer (Stage III/IV) when compared to White population. NJ counties served by our center showed an overrepresentation of either Black or Hispanic-Latino populations and reported significant disadvantage in SES. For NJ counties, each 25 percentile increase in social vulnerability was associated with 1.04 times the rate of age-adjusted colorectal cancer death (95 % CI: 1.00–1.07). ConclusionPublic data on race and SES of the target population can help identify areas of social disparities at the county-level to guide targeted interventions such as improving healthcare access and screening rates.

Monthly excess mortality across counties in the United States during the COVID-19 pandemic, March 2020 to February 2022.

Excess mortality is the difference between expected and observed mortality in a given period and has emerged as a leading measure of the COVID-19 pandemic's mortality impact. Spatially and temporally granular estimates of excess mortality are needed to understand which areas have been most impacted by the pandemic, evaluate exacerbating factors, and inform response efforts. We estimated all-cause excess mortality for the United States from March 2020 through February 2022 by county and month using a Bayesian hierarchical model trained on data from 2015 to 2019. An estimated 1,179,024 excess deaths occurred during the first 2 years of the pandemic (first: 634,830; second: 544,194). Overall, excess mortality decreased in large metropolitan counties but increased in nonmetropolitan counties. Despite the initial concentration of mortality in large metropolitan Northeastern counties, nonmetropolitan Southern counties had the highest cumulative relative excess mortality by July 2021. These results highlight the need for investments in rural health as the pandemic's rural impact grows.

Demographic and Regional Trends of Cardiovascular Diseases and Diabetes Mellitus-Related Mortality in the United States From 1999 to 2019

ObjectiveThe purpose of this research was to study the contemporary trends in cardiovascular disease (CVD) and diabetes mellitus (DM)-related mortality. MethodsWe used the Centers for Disease Control and Prevention Wide-Ranging OnLine Data for Epidemiologic Research (CDC WONDER) database to identify adults ≥25 years old where both CVD and DM were listed as an underlying or contributing cause of death between 1999 and 2019. Crude and age-adjusted mortality rates per 100,000 population were determined. ResultsThe overall age-adjusted mortality rate was 99.18 in 1999 and 91.43 in 2019, with a recent increase from 2014-2019 (annual percent change 1.0; 95% confidence interval [CI], 0.3-1.6). Age-adjusted mortality rate was higher for males compared with females, with increasing mortality in males between 2014 and 2019 (annual percent change 1.5; 95% CI, 0.9-2.0). Age-adjusted mortality rate was highest for non-Hispanic Black adults and was ∼2-fold higher compared with non-Hispanic White adults. Young and middle-aged adults (25-69 years) had increasing age-adjusted mortality rates in recent years. There were significant urban-rural disparities, and age-adjusted mortality rates in rural counties increased from 2014 to 2019 (annual percent change 2.2; 95% CI, 1.5-2.9); states in the 90th percentile of mortality had age-adjusted mortality rates that were ∼2-fold higher than those in the bottom 10th percentile of mortality. ConclusionAfter an initial decrease in DM + CVD-related mortality for a decade, this trend has reversed, with increasing mortality from 2014 to 2019. Significant geographic and demographic disparities persist, requiring targeted health policy interventions to prevent the loss of years of progress.

MP75-09 RURAL AND URBAN GENITOURINARY CANCER INCIDENCE AND MORTALITY IN THE PENNSYLVANIA CANCER REGISTRY FROM 1990-2019

You have accessJournal of UrologyCME1 Apr 2023MP75-09 RURAL AND URBAN GENITOURINARY CANCER INCIDENCE AND MORTALITY IN THE PENNSYLVANIA CANCER REGISTRY FROM 1990-2019 Jonathan Pham, Ahmad Alzubaidi, Tullika Garg, and Jay Raman Jonathan PhamJonathan Pham More articles by this author , Ahmad AlzubaidiAhmad Alzubaidi More articles by this author , Tullika GargTullika Garg More articles by this author , and Jay RamanJay Raman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003349.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Cancer mortality is steadily rising in rural communities in the United States compared to urban counterparts. The underlying causes of these disparities are complex and multifactorial. Our objective was to describe trends in rural and urban incidence and mortality of genitourinary (GU) cancers in the Pennsylvania Cancer Registry. METHODS: We conducted a retrospective review of age-adjusted incidence and mortality rates of GU (prostate (PC), bladder (BC), and kidney (KC)) cancers by county from 1990-2019. Counties were designated as urban or rural using the Center for Rural Pennsylvania’s population-density based definition defined as 284 people per square mile using 2019 county census data. In Pennsylvania, 48 rural counties and 19 urban counties were identified. Joinpoint regression was used to model average annual percent changes (AAPC) in age-adjusted incidence and mortality rates in urban and rural counties. RESULTS: From 1990-2019, overall age-adjusted GU cancer incidence significantly decreased in rural (AAPC -7.5%, p=0.04) and in urban counties (-6.6%, p=0.02). For PC, age-adjusted incidence decreased in rural counties by -10.5% (p=0.02) and in urban counties by -9.1% (p=0.01). Age-adjusted incidence of BC was unchanged for both urban and rural counties. KC incidence increased in both rural counties (AAPC=+11.2, p=0.002) and in urban counties (AAPC=+9.3%, p=0.01).Overall GU cancer age-adjusted mortality decreased in rural counties (AAPC=-11.6, p=0.047) and in urban counties (AAPC -12.2, p=0.01). PC mortality for rural and urban counties decreased at similar rates (AAPC -15.5, p=0.03 and -15.4, p=0.02, respectively). Age-adjusted mortality for BC was unchanged for rural and urban counties over the study period. KC mortality in urban counties decreased (AAPC -6.9% p=0.03), while mortality in rural counties remained stable. CONCLUSIONS: From 1990-2019, overall GU cancer incidence and mortality have decreased in both rural and urban Pennsylvania counties. Across all counties, PC incidence and mortality have decreased, and BC incidence and mortality have remained stable. KC incidence increased across all counties; however, mortality remained stable in rural counties. These data may inform targeted interventions to improve outcomes for specific GU cancers in rural areas. Source of Funding: N/A © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1082 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jonathan Pham More articles by this author Ahmad Alzubaidi More articles by this author Tullika Garg More articles by this author Jay Raman More articles by this author Expand All Advertisement PDF downloadLoading ...

The association between grandparents as caregivers and overdose mortality in Appalachia and non-Appalachia counties.

To assess the association of drug overdose mortality with grandparents serving as caregivers of children in Appalachia and non-Appalachia in the U.S. This study used a cross-sectional design, with percent of grandparents as caregivers and overdose mortality rates being of primary interest. County-level data were combined, and descriptive, bivariate, and multivariable statistics were applied. Multiple sociodemographic and geographic variables were included: median age of the population, percent of the population that is uninsured, percent of the population that is non-Hispanic white, teen birth rate, percent of high school dropouts, and rurality. The percent of grandparents as caregivers increased as the overdose mortality rate increased (p < 0.01). For every 1% increase in the overdose mortality rate, the percent of grandparents as caregivers increased by 56% in Appalachian counties compared to 24% in non-Appalachian counties. After adjusting for sociodemographic characteristics, the interaction between overdose mortality and Appalachian vs. non-Appalachian counties was no longer significant (p = 0.3). Counties with higher overdose mortality rates had greater rates of grandparents as caregivers, with Appalachian counties experiencing greater rates of grandparents as caregivers than non-Appalachian counties. Sociodemographic characteristics that are often more prevalent in Appalachia may be driving the observed differences. Policies and programs are needed to support grandparents providing caregiving for children impacted by substance use disorders including reform to federal child welfare financing to support children, parents, and grandparent caregivers such as kinship navigation, substance use treatment and prevention services, mental health services and in-home supports.

Along party Lines: Examining the gubernatorial party difference in COVID-19 mortality rates in U.S. Counties

Drawing upon the literatures on risk factors for COVID-19 and the roles of political party and political partisanship in COVID-19 policies and outcomes, this study quantifies the extent to which differences in Republican- and Democrat-governed counties' observable characteristics explain the Republican - Democrat gap in COVID-19 mortality rate in the United States. We analyze the county COVID-19 mortality rate between February 1 and December 31, 2020 and employ the Blinder-Oaxaca decomposition method. We estimate the extent to which differences in county characteristics - demographic, socioeconomic, employment, health status, healthcare access, area geography, and Republican vote share, explain the difference in COVID-19 mortality rates in counties governed by Republican vs Democrat governors. Among 3,114 counties, Republican-governed counties had significantly higher COVID-19 mortality than did Democrat-governed counties (127 ± 86 vs 97 ± 80 per 100,000 population, p < 0.001). Results are sensitive to which weights are used: of the total gap of 30.3 deaths per 100,000 population, 12.8 to 20.5 deaths, or 42.2–67.7 %, are explained by differences in observable characteristics of Republican- and Democratic-governed counties. Difference in support for President Trump between Republican- and Democrat-governed counties explains 25 % of the additional deaths in Republican counties. Policies aimed at improving population health and lowering racial disparity in COVID-19 outcomes may also be correlated with reducing the partisan gap in COVID-19 mortality.

Differences in county-level cardiovascular disease mortality rates due to damage caused by hurricane Matthew and the moderating effect of social capital: a natural experiment

BackgroundAs the climate continues to warm, hurricanes will continue to increase in both severity and frequency. Hurricane damage is associated with cardiovascular events, but social capital may moderate this relationship. Social capital is a multidimensional concept with a rich theoretical tradition. Simply put, social capital refers to the social relationships and structures that provide individuals with material, financial, and emotional resources throughout their lives. Previous research has found an association between high levels of social capital and lower rates of cardiovascular (CVD) mortality. In post-disaster settings, social capital may protect against CVD mortality by improving access to life-saving resources. We examined the association between county-level hurricane damage and CVD mortality rates after Hurricane Matthew, and the moderating effect of several aspects of social capital and hurricane damage on this relationship. We hypothesized that (1) higher (vs. lower) levels of hurricane damage would be associated with increased CVD mortality rates and (2) in highly damaged counties, higher (vs. lower) levels of social capital would be associated with lower CVD mortality.MethodsAnalysis used yearly (2013-2018) county-level sociodemographic and epidemiological data (n = 183). Sociodemographic data were compiled from federal surveys before and after Hurricane Matthew to construct, per prior literature, a social capital index based on four dimensions of social capital (sub-indices): family unity, informal civil society, institutional confidence, and collective efficacy. Epidemiological data comprised monthly CVD mortality rates constructed from monthly county-level CVD death counts from the CDC WONDER database and the US Census population estimates. Changes in CVD mortality based on level of hurricane damage were assessed using regression adjustment. We used cluster robust Poisson population average models to determine the moderating effect of social capital on CVD mortality rates in both high and low-damage counties.ResultsWe found that mean levels of CVD mortality increased (before and after adjustment for sociodemographic controls) in both low-damage counties (unadjusted. Mean = 2.50, 95% CI [2.41, 2.59], adjusted mean = 2.50, 95% CI [2.40, 2.72]) and high-damage counties (mean = 2.44, CI [2.29, 2.46], adj. Mean = 2.51, 95% CI [2.49, 2.84]). Among the different social capital dimensions, institutional confidence was associated with reduced initial CVD mortality in low-damage counties (unadj. IRR 1.00, 95% CI [0.90, 1.11], adj. IRR 0.91 CI [0.87, 0.94]), but its association with CVD mortality trends was null. The overall effects of social capital and its sub-indices were largely nonsignificant.Conclusion Hurricane damage is associated with increased CVD mortality for 18 months after Hurricane Matthew. The role of social capital remains unclear. Future research should focus on improving measurement of social capital and quality of hurricane damage and CVD mortality data.

Impact of Social Vulnerability on Comorbid Cancer and Cardiovascular Disease Mortality in the United States

BackgroundRacial and social disparities exist in outcomes related to cancer and cardiovascular disease (CVD). ObjectivesThe aim of this cross-sectional study was to study the impact of social vulnerability on mortality attributed to comorbid cancer and CVD. MethodsThe Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database (2015-2019) was used to obtain county-level mortality data attributed to cancer, CVD, and comorbid cancer and CVD. County-level social vulnerability index (SVI) data (2014-2018) were obtained from the CDC’s Agency for Toxic Substances and Disease Registry. SVI percentiles were generated for each county and aggregated to form SVI quartiles. Age-adjusted mortality rates (AAMRs) were estimated and compared across SVI quartiles to assess the impact of social vulnerability on mortality related to cancer, CVD, and comorbid cancer and CVD. ResultsThe AAMR for comorbid cancer and CVD was 47.75 (95% CI: 47.66-47.85) per 100,000 person-years, with higher mortality in counties with greater social vulnerability. AAMRs for cancer and CVD were also significantly greater in counties with the highest SVIs. However, the proportional increase in mortality between the highest and lowest SVI counties was greater for comorbid cancer and CVD than for either cancer or CVD alone. Adults <45 years of age, women, Asian and Pacific Islanders, and Hispanics had the highest relative increase in comorbid cancer and CVD mortality between the fourth and first SVI quartiles, without significant urban-rural differences. ConclusionsComorbid cancer and CVD mortality increased in counties with higher social vulnerability. Improved education, resource allocation, and targeted public health interventions are needed to address inequities in cardio-oncology.

COVID-19 mortality and excess mortality among working-age residents in California, USA, by occupational sector: a longitudinal cohort analysis of mortality surveillance data

BackgroundDuring the first year of the COVID-19 pandemic, workers in essential sectors had higher rates of SARS-CoV-2 infection and COVID-19 mortality than those in non-essential sectors. It is unknown whether disparities in pandemic-related mortality across occupational sectors have continued to occur during the periods of SARS-CoV-2 variants and vaccine availability.MethodsIn this longitudinal cohort study, we obtained data from the California Department of Public Health on all deaths occurring in the state of California, USA, from Jan 1, 2016, to Dec 31, 2021. We restricted our analysis to residents of California who were aged 18–65 years at time of death and died of natural causes. We classified the occupational sector into nine essential sectors; non-essential; or unemployed or without an occupation provided on the death certificate. We calculated the number of COVID-19 deaths in total and per capita that occurred in each occupational sector. Separately, using autoregressive integrated moving average models, we estimated total, per-capita, and relative excess natural-cause mortality by week between March 1, 2020, and Nov 30, 2021, stratifying by occupational sector. We additionally stratified analyses of occupational risk into counties with high versus low vaccine uptake, categorising high-uptake regions as counties where at least 50% of the population were fully vaccinated according to US guidelines by Aug 1, 2021.FindingsFrom March 1, 2020, to Nov 30, 2021, 24 799 COVID-19 deaths were reported in residents of California aged 18–65 years and an estimated 28 751 (95% prediction interval 27 853–29 653) excess deaths. People working in essential sectors were associated with higher COVID-19 deaths and excess deaths than were those working in non-essential sectors, with the highest per-capita COVID-19 mortality in the agriculture (131·8 per 100 000 people), transportation or logistics (107·1 per 100 000), manufacturing (103·3 per 100 000), facilities (101·1 per 100 000), and emergency (87·8 per 100 000) sectors. Disparities were wider during periods of increased infections, including during the Nov 29, 2020, to Feb 27, 2021, surge in infections, which was driven by the delta variant (B.1.617.2) and occurred during vaccine uptake. During the June 27 to Nov 27, 2021 surge, emergency workers had higher COVID-19 mortality (113·7 per 100 000) than workers from any other sector. Workers in essential sectors had the highest COVID-19 mortality in counties with low vaccination uptake, a difference that was more pronounced during the period of the delta infection surge during Nov 29, 2020, to Feb 27, 2021.InterpretationWorkers in essential sectors have continued to bear the brunt of high COVID-19 and excess mortality throughout the pandemic, particularly in the agriculture, emergency, manufacturing, facilities, and transportation or logistics sectors. This high death toll has continued during periods of vaccine availability and the delta surge. In an ongoing pandemic without widespread vaccine coverage and with anticipated threats of new variants, the USA must actively adopt policies to more adequately protect workers in essential sectors.FundingUS National Institute on Aging, Swiss National Science Foundation, and US National Institute on Drug Abuse.