County-level Sociodemographic Characteristics Research Articles

County-level industrial composition of the labor force and drug overdose mortality rates in the United States in 2018-2021.

Drug mortality risks vary among industries, creating distinctive geographic patterns across US counties. However, less is known about how local labor market structure relates to drug overdose mortality amid the synthetic opioid era in the United States. This study investigates the relationship between industry-specific job composition and drug overdose mortality at the county level while exploring how fentanyl's presence in illicit drug supplies may moderate the relationship. Data were derived from the National Center for Health Statistics' Multiple Cause of Death files for the rates of drug overdose mortality of any intent, linked with four other sources on industry-specific job shares, drug supply, and county-level sociodemographic characteristics and opioid prescribing rates from the US Census Bureau, the CDC, and the Drug Enforcement Administration. Negative binomial regression models were employed to examine associations between county industry-specific job composition and drug overdose mortality, with tests for moderating effects of state-level fentanyl seizure rates. Our models indicate negative associations between job shares of manufacturing, retail trade, and educational services industries and drug overdose mortality. Positive associations were found for arts/entertainment/recreation and public administration. State-level fentanyl seizure rates had moderating effects on administrative/support/waste management/remediation (A/S/WM/R) and educational services. Counties with a higher concentration of arts/entertainment/recreation and public administration jobs need targeted efforts to mitigate drug-related overdose risks. Additionally, areas with higher concentrations of A/S/WM/R service jobs, particularly where fentanyl seizure rates are higher, may require proactive harm reduction strategies for reducing overdose risks.

Associations of Medical Debt With Health Status, Premature Death, and Mortality in the US

Medical debt is increasingly common in the US. Little is known regarding its association with population health. To examine the associations of medical debt with health status, premature death, and mortality at the county level in the US. This cross-sectional study was conducted at the US county level using 2018 medical debt data from the Urban Institute Debt in America project linked with 2018 data on self-reported health status and premature death from the County Health Rankings & Roadmaps and with 2015 to 2019 mortality data from the National Center for Health Statistics. Data analysis was performed from August 2022 to May 2023. Share of population with any medical debt in collections and median amount of medical debt. Health status was measured as (1) the mean number of physically and mentally unhealthy days in the past 30 days per 1000 people, (2) the mean number of premature deaths measured as years of life lost before age 75 years per 1000 people, and (3) age-adjusted all-cause and 18 cause-specific mortality rates (eg, malignant cancers, heart disease, and suicide) per 100 000 person-years. Multivariable linear models were fitted to estimate the associations between medical debt and health outcomes. A total of 2943 counties were included in this analysis. The median percentage of the county population aged 65 years or older was 18.3% (IQR, 15.8%-20.9%). Across counties, a median 3.0% (IQR, 1.2%-11.9%) of the population were Black residents, 4.3% (IQR, 2.3%-9.7%) were Hispanic residents, and 84.5% (IQR, 65.7%-93.3%) were White residents. On average, 19.8% (range, 0%-53.6%) of the population had medical debt. After adjusting for county-level sociodemographic characteristics, a 1-percentage point increase in the population with medical debt was associated with 18.3 (95% CI, 16.3-20.2) more physically unhealthy days and 17.9 (95% CI, 16.1-19.8) more mentally unhealthy days per 1000 people during the past month, 1.12 (95% CI, 1.03-1.21) years of life lost per 1000 people, and an increase of 7.51 (95% CI, 6.99-8.04) per 100 000 person-years in age-adjusted all-cause mortality rate. Associations of medical debt and elevated mortality rates were consistent for all leading causes of death, including cancer (1.12 [95% CI, 1.02-1.22]), heart disease (1.39 [95% CI, 1.21-1.57]), and suicide (0.09 [95% CI, 0.06-0.11]) per 100 000 person-years. Similar patterns were observed for associations between the median amount of medical debt and the aforementioned health outcomes. These findings suggest that medical debt is associated with worse health status, more premature deaths, and higher mortality rates at the county level in the US. Therefore, policies increasing access to affordable health care, such as expanding health insurance coverage, may improve population health.

County-Level Sociodemographic Characteristics and Availability of COVID-19 Therapeutic Drugs

This cross-sectional study examines access to COVID-19 treatments in US counties by race and ethnicity, poverty rate, uninsured rate, and other population and community characteristics.

County-level jail incarceration and cancer mortality in the US.

6535 Background: Incarceration has been linked to poor community health outcomes, such as worse preterm births and mortality in the US, although little research has examined cancer outcomes. This study examined associations of county-level jail incarceration and cancer mortality. Methods: Annual county-level local jail incarceration rates (1990-2018) were obtained from the Vera Institute of Justice. We calculated annual county-level mortality rates (2000 -2019) with invasive cancer as the underlying cause of death (ICD-10 codes: C00-C97) using National Vital Statistics System. Associations of county-level jail incarceration and cancer mortality overall, and by sex, race, and common cancer sites were examined with generalized estimating equations with Poisson distribution and standard errors clustered at county level. To assess the short-, medium-, and long-term associations, we used lagged county-level incarceration rates by 1, 5, and 10 years prior to mortality rates in separate analyses. Results: Over the 20-year study period, each 1 per 1000 increase in county jail incarceration rate was associated with a 1.2% increase in cancer mortality rate in the short-term (model with 1-year lags, rate ratio (RR): 1.012, 95% CI: 1.009–1.015) and 1.1% increase in medium-term (5-year lags (RR: 1.011, 95% CI: 1.008–1.014) and 0.8% increase long-term (10-year lags (RR: 1.008, 95% CI: 1.004–1.012). After adjusting for county-level sociodemographic characteristics, the RRs were attenuated but remained statistically significant. Jail incarceration was associated with higher cancer mortality for all common cancers included in the study (Table) and for White people, but not for Black people. The magnitude of associations was similar for medium- and long-term effects (5- and 10-year lags, respectively). Conclusions: Higher county-level jail incarceration rates were associated with higher county-level cancer mortality rates, underscoring the collateral health consequences of mass incarceration. Efforts to identify interventions to decrease the cancer mortality burden in these communities are warranted. [Table: see text]

Association of County-degree Social Vulnerability with Chronic Respiratory Disease Mortality in the United States.

Rationale: Chronic respiratory diseases, the third leading cause of death worldwide, have been associated with significant morbidity, mortality, and increased economic burden that make a profound impact on individuals and communities. However, limited research has delineated complex relationships between specific sociodemographic disparities and chronic respiratory disease outcomes among U.S. counties. Objectives: To assess the association of county-level sociodemographic vulnerabilities with chronic respiratory disease mortality in the United States. Methods: Chronic respiratory disease mortality data among U.S. counties for 2014-2018 was obtained from the CDC WONDER (Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research) database. The social vulnerability index (SVI), including subindices of socioeconomic status, household composition and disability, minority status and language, and housing type and transportation, is a composite, percentile-based measure developed by the CDC to evaluate county-level sociodemographic vulnerabilities to disasters. We examined county-level sociodemographic characteristics from the SVI and classified the percentile rank into quartiles, with a higher quartile indicating greater vulnerability. The associations between chronic respiratory disease mortality and overall SVI, its four subindices, and each county characteristic were analyzed by negative binomial regression. Results: From 2014 to 2018, the age-adjusted mortality per 1,000,000 population attributed to chronic lower respiratory disease was 406.4 (95% confidence interval [CI], 405.5-407.3); chronic obstructive pulmonary disease (COPD), 393.7 (392.8-394.6); asthma, 10.0 (9.9-10.2); interstitial lung disease (ILD), 50.5 (50.1-50.8); idiopathic pulmonary fibrosis (IPF), 37.0(36.7-37.3); and sarcoidosis, 5.3 (5.2-5.4). Counties in the higher quartile of overall SVI were significantly associated with greater disease mortality (chronic lower respiratory disease, incidence rate ratios: fourth vs. first quartile, 1.43 [95% CI, 1.39-1.48]; COPD, 1.44 [1.39-1.49]; asthma, 2.06 [1.71-2.48]; ILD, 1.07 [1.02-1.13]; IPF, 1.14 [1.06-1.22]; sarcoidosis, 2.01 [1.44-2.81]). In addition, higher mortality was also found in counties in the higher quartile of each subindex and most sociodemographic characteristics. Conclusions: Chronic respiratory disease mortalities were significantly associated with county-level sociodemographic determinants as measured by the SVI in the United States. These findings suggested sociodemographic determinants may add a considerable barrier to establishing health equity. Multidegree public health strategies and clinical interventions addressing inequitable outcomes of chronic respiratory disease should be developed and targeted in areas with greater social vulnerability and disadvantage.

Disparities in COVID-19 Mortality Rates: Implications for Rural Health Policy and Preparedness.

Context:It is well established that rural communities face geographic and socioeconomic challenges linked to higher rates of health disparities across the United States, though the coronavirus disease 2019 (COVID-19) impact on rural communities is less certain.Objective:To understand the COVID-19 pandemic's impact on rural communities in Tennessee, investigate differences in rural-urban mortality rates after controlling for confounding variables, and inform state pandemic response policy.Design:A cross-sectional analysis of cumulative COVID-19 morality rates.Setting/Participants:Tennessee county-level COVID-19 mortality data from March 1, 2020, to January 31, 2021, were matched with county-level sociodemographic and health data from public datasets: Agency for Healthcare Research and Quality Social Determinants of Health, PLACES: Local Data for Better Health County Data, and the US Census Bureau. County status was defined using the 2013 National Center for Health Statistics Urban-Rural Classification.Main Outcome Measures:A negative binomial regression model estimated adjusted incidence rate ratio and 95% confidence intervals (CI) for rural compared with urban mortality. Unadjusted rate ratios and rate differences for COVID-19 mortality in rural versus urban counties were compared with those for influenza and pneumonia and all-cause mortality over the past 5 years.Results:During the study period, 9650 COVID-19 deaths occurred across 42 urban and 53 rural counties. Controlling for county-level sociodemographic characteristics, health care access, and comorbidities, incidence rate ratio was 1.13 (95% CI, 1.00-1.28, P < .05) for rural as compared with urban deaths. Unadjusted COVID-19 mortality risk difference between rural and urban counties was greater (61.85, 95% CI, 54.31-69.31) than 5-year influenza and pneumonia rural-urban risk difference (12.57, 95% CI, 11.16-13.00) during 2015-2019.Conclusions:COVID-19 mortality rates were greater for populations living in Tennessee's rural as compared with urban counties during the study period. This differential impact must be considered in public health decision making to mitigate COVID-19.

Association between county-level sociodemographic characteristics and county-level differences in opioid dispensing

BackgroundWhile overall opioid prescribing has been decreasing in the United States, the rates of prescribing at the county level have been variable. Previous studies show that social determinants of health (the social and economic conditions in which we live) may play a role in opioid prescribing; however, researchers have not examined this relationship across US counties. This cross-sectional study seeks to determine whether county-level sociodemographic characteristics (e.g., economic, housing, social environment, healthcare environment, and population characteristics) are associated with county level differences in opioid dispensing. MethodsData from 2,881 counties in the United States from 2017 to 2018 were used for this study. Opioid dispensing was measured using morphine milligram equivalents (MME) per capita. Spatial error models were used to measure the association between county-level sociodemographic characteristics and MME per capita while adjusting for spatial correlation between neighboring counties. ResultsIn the adjusted model, counties with a higher percentage of people below the poverty line, with less than a 4-year college degree, and without health insurance were associated with higher MME dispensed per capita, as were counties with higher percentages of families headed by a single parent, persons separated or divorced, and those with disabilities. Conversely, minority race/ethnicity and rural population were associated with lower opioid dispensing. ConclusionsCounty-level sociodemographics can differ in their association with opioid dispensing, hence examining which county-level factors help in improving opioid prescribing, and implementing overdose prevention strategies that tackle these factors is important.

High School Cardiac Emergency Response Plans and Sudden Cardiac Death in the Young.

Surveys were sent to representatives from all public high schools in 30 randomly selected Michigan counties. Counties with greater than 50% response rate were included (n=19). Association of county-level sociodemographic characteristics with incidence of SCD in the young and existence of CERPs were evaluated using Spearman correlation coefficient. Factors related to the presence of AEDs were similar across counties. Schools in counties of lower socioeconomic status (SES; lower-median income, lower per capita income, and higher population below poverty level) were less likely to have a CERP than those with higher SES (all P<.01). Lack of a CERP was associated with a higher incidence of SCD in youth (r=-0.71; P=.001). Overall incidence of SCD in youth was higher in lower SES counties (r=-0.62 in median income and r=0.51 in population below poverty level; both P<.05). County SES is associated with the presence of CERPs in schools, suggesting a link between school cardiac emergency preparedness and county financial resources. Additionally, counties of lower SES demonstrated higher incidence of SCD in the young. Statewide and national studies are required to further explore the factors relating to geographic and socioeconomic differences in cardiac emergency preparedness and the incidence of SCD in the young. White MJ , Loccoh EC , Goble MM , Yu S , Odetola FO , Russell MW . High school cardiac emergency response plans and sudden cardiac death in the young. Prehosp Disaster Med. 2017;32(3):269-272.

Racial bias is associated with ingroup death rate for Blacks and Whites: Insights from Project Implicit

RationaleResearch suggests that, among Whites, racial bias predicts negative ingroup health outcomes. However, little is known about whether racial bias predicts ingroup health outcomes among minority populations. ObjectiveThe aim of the current research was to understand whether racial bias predicts negative ingroup health outcomes for Blacks. MethodWe compiled racial bias responses from 250,665 Blacks and 1,391,632 Whites to generate county-level estimates of Blacks' and Whites’ implicit and explicit biases towards each other. We then examined the degree to which these biases predicted ingroup death rate from circulatory-related diseases. ResultsIn counties where Blacks harbored more implicit bias towards Whites, Blacks died at a higher rate. Additionally, consistent with previous research, in counties where Whites harbored more explicit bias towards Blacks, Whites died at a higher rate. These links between racial bias and ingroup death rate were independent of county-level socio-demographic characteristics, and racial biases from the outgroup in the same county. ConclusionFindings indicate that racial bias is related to negative ingroup health outcomes for both Blacks and Whites, though this relationship is driven by implicit bias for Blacks, and explicit bias for Whites.

Trends and Correlates of Disparities in Alcohol-Related Mortality Between Hispanics and Non-Hispanic Whites inthe United States, 1999 to 2014.

Among Hispanics, chronic liver disease and cirrhosis are among the leading causes of death despite generally lower alcohol consumption rates. Moreover, recent national studies have suggested temporal changes in Hispanic consumption and alcohol mortality, which raises the question of whether Hispanic white disparities in alcohol-related mortality are also changing over time. This study aimed to describe temporal trends of alcohol-related mortality between Hispanics and non-Hispanic (NH) whites in the United States from 1999 to 2014 and to assess county-level sociodemographic characteristics that are associated with racial/ethnic disparities in age-adjusted alcohol-related mortality. We conducted a population-based, cross-sectional, ecologic study using multiple cause-of-death mortality data linked, at the county level, to census data from the American Community Survey. Overall, 77% of alcohol-related deaths were among men, and Hispanic men had the highest age-adjusted alcohol-related mortality rate (41.6 per 100,000), followed by NH white men (34.8), NH white women (10.8), and Hispanic women (6.7). Whereas the relative gap in alcohol-related mortality between NH white and Hispanic women increased from 1999 to 2014, the disparity between NH white and Hispanic men that was pronounced in earlier years was eliminated by 2012. From 2007 to 2014, when the race/ethnic disparity among men was decreasing, county-specific Hispanic:NH white age-adjusted mortality ratios (AAMRs) ranged from 0.29 to 2.64. Lower Hispanic rates were associated with large metropolitan counties, and those counties that tended to have Hispanic populations were less acculturated, as evidenced by their higher rates of being foreign-born, non-U.S. citizens or citizens through naturalization, and a higher proportion that do not speak English "very well." Since 1999, whereas the increasing mortality rate among whites is leading to a widening gap among women, mortality differences between Hispanic and white men have been eliminated. The understanding of contextual factors that are associated with disparities in alcohol-related mortality may assist in tailoring prevention efforts that meet the needs of minority populations.

Displacement, county social cohesion, and depression after a large-scale traumatic event

Depression is a common and potentially debilitating consequence of traumatic events. Mass traumatic events cause wide-ranging disruptions to community characteristics, influencing the population risk of depression. In the aftermath of such events, population displacement is common. Stressors associated with displacement may increase risk of depression directly. Indirectly, persons who are displaced may experience erosion in social cohesion, further exacerbating their risk for depression. Using data from a population-based cross-sectional survey of adults living in the 23 southernmost counties of Mississippi (N = 708), we modeled the independent and joint relations of displacement and county-level social cohesion with depression 18-24 months after Hurricane Katrina. After adjustment for individual- and county-level socio-demographic characteristics and county-level hurricane exposure, joint exposure to both displacement and low social cohesion was associated with substantially higher log-odds of depression (b = 1.34 [0.86-1.83]). Associations were much weaker for exposure only to low social cohesion (b = 0.28 [-0.35-0.90]) or only to displacement (b = 0.04 [-0.80-0.88]). The associations were robust to additional adjustment for individually perceived social cohesion and social support. Addressing the multiple, simultaneous disruptions that are a hallmark of mass traumatic events is important to identify vulnerable populations and understand the psychological ramifications of these events.

A Spatial Analysis of County-level Variation in Syphilis and Gonorrhea in Guangdong Province, China

BackgroundSexually transmitted infections (STI) have made a resurgence in many rapidly developing regions of southern China, but there is little understanding of the social changes that contribute to this spatial distribution of STI. This study examines county-level socio-demographic characteristics associated with syphilis and gonorrhea in Guangdong Province.Methods/Principal FindingsThis study uses linear regression and spatial lag regression to determine county-level (n = 97) socio-demographic characteristics associated with a greater burden of syphilis, gonorrhea, and a combined syphilis/gonorrhea index. Data were obtained from the 2005 China Population Census and published public health data. A range of socio-demographic variables including gross domestic product, the Gender Empowerment Measure, standard of living, education level, migrant population and employment are examined. Reported syphilis and gonorrhea cases are disproportionately clustered in the Pearl River Delta, the central region of Guangdong Province. A higher fraction of employed men among the adult population, higher fraction of divorced men among the adult population, and higher standard of living (based on water availability and people per room) are significantly associated with higher STI cases across all three models. Gross domestic product and gender inequality measures are not significant predictors of reported STI in these models.Conclusions/SignificanceAlthough many ecological studies of STIs have found poverty to be associated with higher reported STI, this analysis found a greater number of reported syphilis cases in counties with a higher standard of living. Spatially targeted syphilis screening measures in regions with a higher standard of living may facilitate successful control efforts. This analysis also reinforces the importance of changing male sexual behaviors as part of a comprehensive response to syphilis control in China.

ENVIRONMENTAL JUSTICE, SWINE PRODUCTION AND FARM LOSS IN NORTH CAROLINA

Since the early 1990s, North Carolina has been the fastest growing swine-producing state in the country and the leading innovator in vertically integrated, industrially structured hog farming. Although the growth and concentration of swine production has been associated with a host of negative social and environmental impacts on the states air, land, and waterways, environmental justice and farm loss concerns have played a particularly key role in the evolution of the controversy in North Carolina. Using multivariate analysis of statewide census and agricultural data, we identified the county-level sociodemographic characteristics associated with farm loss between 1982 and 1997. We found that recent patterns of farm loss were more pronounced in Black communities, regardless of income, and low-income communities, regardless of race. Furthermore, counties that had greater hog industry growth in the early 1980s and had large hog populations by 1992 have suffered greater farm loss since the early 1980s than counties where the hog industry growth did not intensify until more recently. The implications of these findings with reference to an expanded environmental justice framework regarding the discriminatory impacts of swine facilities on minority and low-income rural communities are discussed.

Access to Public Mental Health Services

This study examined factors that affect access to Veterans Administration mental health services. Data from national Veterans Affairs databases and the 1990 Decennial Census were used to estimate rates of Veterans Affairs mental health service use in each US county (n = 3,156) among all US veterans and in three subpopulations defined by eligibility and clinical status. Independent variables examined in standard multivariate analyses and using hierarchical linear modeling techniques included county-level sociodemographic characteristics (age, race, and income); "unmanaged" service system characteristics (those not directly controlled by Veterans Affairs program managers, eg, distance from residence to Veterans Affairs and to non-Veterans Affairs services, local supply of non-Veterans Affairs services); and "managed" service system factors (those directly controlled by Veterans Affairs program managers, eg, per capita Veterans Affairs funding level and the efficiency of Veterans Affairs service delivery). Altogether, 2.0% of US veterans used Veterans Affairs mental health services. More than one third (36%) of the variance in utilization was explained by sociodemographic factors; 8% was explained by unmanaged service system factors and 7% was explained by managed service system factors, with variations among subgroups. Substitution effects were demonstrated between Veterans Affairs and non-Veterans Affairs systems and appeared to be diagnosis-specific. Both per capita funding levels and efficient service delivery were significantly associated with increased access to mental health services. Implications for health system performance assessment and management are discussed.