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This research compares drivers of full COVID-19 vaccinations and booster doses across US counties. Booster doses are contingent upon primary doses being received, and the risk attitudes and propensities to get vaccinated may respond to different motivations across individuals, along with distinctions in the supply chain. Our results, obtained in a quantitative framework through fractional probit, logit and quantile regressions, show that new COVID cases do not significantly impact vaccinations, while supply chain aspects via doctors' visits have a positive impact. Income, race, age, and education also play a significant role. Further, political ideologies matter, while government decentralization does not. The differences in the effects across primary versus booster doses are discussed. [ABSTRACT FROM AUTHOR]

Summary: We study the evolution of the cross‐sectional distributions of county‐level per capita income in the United States from 1970 to 2017. We confirm previous findings of convergence in pre‐transfer income during the 1970s and 1980s but present new evidence of rising inequality since the early 1990s, which is characterized by bipolarization. Cross‐county differences in education and industry composition explain much of this recent trend, which almost disappears in post‐transfer incomes. Among the various government transfer programs, medical benefits play the greatest role in making the distribution less unequal. [ABSTRACT FROM AUTHOR]

Substantial gaps in national healthcare spending and disparities in cancer mortality rates are noted across counties in the US. In this cross-sectional analysis, we investigated whether differences in local county-level social vulnerability impacts cancer-related mortality. We linked county-level age-adjusted mortality rates (AAMR) from the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research database, to county-level Social Vulnerability Index (SVI) from the CDC Agency for Toxic Substances and Disease Registry. SVI is a metric comprising 15 social factors including socioeconomic status, household composition and disability, minority status and language, and housing type and transportation. AAMRs were compared between least and most vulnerable counties using robust linear regression models. There were 4 107 273 deaths with an overall AAMR of 173 per 100 000 individuals. Highest AAMRs were noted in older adults, men, non-Hispanic Black individuals, and rural and Southern counties. Highest mortality risk increases between least and most vulnerable counties were noted in Southern and rural counties, individuals aged 45-65, and lung and colorectal cancers, suggesting that these groups may face highest risk for health inequity. These findings inform ongoing deliberations in public health policy at the state and federal level and encourage increased investment into socially disadvantaged counties. Gaps in national healthcare spending and disparities in cancer mortality rates are noted across counties in the US. This study investigated whether differences in local county-level social vulnerability impacts cancer-related mortality. [ABSTRACT FROM AUTHOR]

Research about women's representation in county governments and whether women's representation at the local level translates into different policy outcomes has been relatively unexplored (Holman 2017). Thus, we ask whether electing a woman to the county legislative body leads to different policy outputs, specifically in areas like education, health, hospitals, housing, and welfare. Our dataset builds on de Benedictis-Kessner and Warshaw's (2020) work on county legislative elections by gender coding candidates, leading to a full dataset of nearly 16,500 gender-coded races for county legislative positions, thus creating the most comprehensive, gender-coded dataset of elections in county governments. Using a regression discontinuity design, we find that electing a woman to the county's governing body does not lead to greater spending generally or on social and redistributive policy. Yet there are specific conditions in which they can more easily advocate for their preferred fiscal preferences. For instance, under commission forms of government, electing a woman leads to increased expenditures on welfare. Moreover, electing women has a positive effect on spending when the county is receiving a greater proportion of revenue from higher levels of government. [ABSTRACT FROM AUTHOR]

Policy PointsIn 2012, Colorado and Washington were the first states to legalize recreational marijuana through voter‐initiated ballots. In these states, counties could restrict or ban local marijuana facilities through a variety of regulatory methods such as ordinances and zoning.County‐level recreational marijuana policies in Washington and Colorado vary substantially, with 69.2% of Washington counties and 23.4% of Colorado counties allowing all types of recreational marijuana facilities as of April 1, 2019.After Colorado and Washington legalized recreational marijuana, many counties modified their marijuana policies over time, with shifts in county policy often preceded by advocacy and information‐seeking activities. Context: In 2012, Colorado and Washington were the first states to legalize recreational marijuana. Both allowed local governments to further regulate the availability of marijuana facilities in their jurisdictions. As early adopters, these states are important quasi‐natural experiments to examine local marijuana policy and policy change processes, including key stakeholders and arguments. Methods: We conducted a policy scan of county‐level recreational marijuana ordinances and regulations in Colorado and Washington. Data collected included policy documents from counties in both states and newspaper articles. We used a mixed‐methods approach to describe the types of county‐level recreational marijuana policies enacted by April 1, 2019; identify key policy stakeholders involved in local policy debates; and explore arguments used in support or opposition of county policies. We also selected four counties that represent three county policy environments (all marijuana facility types allowed, some marijuana facility types allowed, all marijuana facility types prohibited) and described the policy changes within these counties since recreational marijuana was legalized. Findings: By April 1, 2019, Colorado counties were less likely than Washington counties to allow marijuana facilities—48.4% of Colorado counties prohibited recreational marijuana facilities in their jurisdiction compared to 23.1% of Washington counties. Since state legalization, several counties in both states have made substantial marijuana facility policy modifications, often preceded by information‐seeking activities. Primary stakeholders involved in policy debates included elected officials, law enforcement, individual growers/farmers, marijuana business license applicants, parents, and residents. Proponents referenced local economic gain, reduced crime, and potential health benefits of marijuana as arguments in favor of permitting local facilities, whereas opponents pointed to economic loss, negative health and public health issues, public safety concerns, and existing federal law. Both sides referenced local public opinion data to support their position. Conclusions: By early 2019, a patchwork of local marijuana policies was in place in Colorado and Washington. We identify key areas of policy and public health research needed to inform future local marijuana policy decisions, including the impact of legalization on public health outcomes (particularly for youth) and public safety. [ABSTRACT FROM AUTHOR]

Objective: To estimate the association between weather and COVID-19 fatality rates during US stay-at-home orders. Methods: With a county-level longitudinal design, this study analyzed COVID-19 deaths from public health departments' daily reports and considered exposure as the 18 to 22 day-period before death. Models included state-level social distancing measures, Census Bureau demographics, daily weather information, and daily air pollution. The primary measures included minimum and maximum daily temperature, precipitation, ozone concentration, PM2.5 concentrations, and U.V. light index. Results: A 1 °F increase in the minimum temperature was associated with 1.9% (95% CI, 0.2% to 3.6%) increase in deaths 20days later. An ozone concentration increase of 1 ppb (part per billion) decreased daily deaths by 2.0% (95% CI, 0.1% to 3.6%); ozone levels below 38 ppb negatively correlated with deaths. Conclusions: Increased mobility may drive the observed association of minimum daily temperature on COVID-19 deaths. [ABSTRACT FROM AUTHOR]

To investigate how racial/ethnic density and residential segregation shape the uneven burden of COVID-19 in US counties and whether (if yes, how) residential segregation moderates the association between racial/ethnic density and infections. We first merge various risk factors from federal agencies (e.g. Census Bureau and Centers for Disease Control and Prevention) with COVID-19 cases as of June 13th in contiguous US counties (N = 3,042). We then apply negative binomial regression to the county-level dataset to test three interrelated research hypotheses and the moderating role of residential segregation is presented with a figure. Several key results are obtained. (1) Counties with high racial/ethnic density of minority groups experience more confirmed cases than those with low levels of density. (2) High levels of residential segregation between whites and non-whites increase the number of COVID-19 infections in a county, net of other risk factors. (3) The relationship between racial/ethnic density and COVID-19 infections is enhanced with the increase in residential segregation between whites and non-whites in a county. The pre-existing social structure like residential segregation may facilitate the spread of COVID-19 and aggravate racial/ethnic health disparities in infections. Minorities are disproportionately affected by the novel coronavirus and focusing on pre-existing social structures and discrimination in housing market may narrow the uneven burden across racial/ethnic groups. [ABSTRACT FROM AUTHOR]

Purpose: This study creates a COVID‐19 susceptibility scale at the county level, describes its components, and then assesses the health and socioeconomic resiliency of susceptible places across the rural‐urban continuum. Methods: Factor analysis grouped 11 indicators into 7 distinct susceptibility factors for 3,079 counties in the conterminous United States. Unconditional mean differences are assessed using a multivariate general linear model. Data from 2018 are primarily taken from the US Census Bureau and CDC. Results: About 33% of rural counties are highly susceptible to COVID‐19, driven by older and health‐compromised populations, and care facilities for the elderly. Major vulnerabilities in rural counties include fewer physicians, lack of mental health services, higher disability, and more uninsured. Poor Internet access limits telemedicine. Lack of social capital and social services may hinder local pandemic recovery. Meat processing facilities drive risk in micropolitan counties. Although metropolitan counties are less susceptible due to healthier and younger populations, about 6% are at risk due to community spread from dense populations. Metropolitan vulnerabilities include minorities at higher health and diabetes risk, language barriers, being a transportation hub that helps spread infection, and acute housing distress. Conclusions: There is an immediate need to know specific types of susceptibilities and vulnerabilities ahead of time to allow local and state health officials to plan and allocate resources accordingly. In rural areas it is essential to shelter‐in‐place vulnerable populations, whereas in large metropolitan areas general closure orders are needed to stop community spread. Pandemic response plans should address vulnerabilities. [ABSTRACT FROM AUTHOR]

The aim of the present study was to estimate the prevalence of mental distress at the county-level for the service member or veteran (SMV) population in Illinois. Multilevel small-area estimation methodology (SAE) was used to estimate and map the prevalence of SMV mental distress in Illinois counties using data from the 2016 Behavioral Risk Factor Surveillance System. Furthermore, local indicators of spatial association analysis (LISA) was conducted in order to identify hotspots of SMV mental distress in Illinois. For 2016, the average model-based mental distress rate for SMVs in Illinois counties was 8.6%. LISA analysis revealed a significant cluster (p < 0.01) of "high–high" SMV mental distress prevalence in St. Clair County and Clinton County. These findings highlight the importance of examining SMV health from a population perspective and reveal the need for further examination of geographic-based SMV health disparities. [ABSTRACT FROM AUTHOR]

Background Recent news reports state that racial minority groups, such as African–Americans, are experiencing a greater COVID-19 burden, as measured by confirmed cases and deaths. Limited racial data is available on a national level. Methods We conducted the first nationwide analysis to examine COVID-19 and race on a county level. We obtained datasets on COVID-19 cases and deaths, and racial population totals, by US county. We examined if correlations exist between the racial percentages and percentages of confirmed COVID-19 cases and deaths by county. Results A positive correlation existed between percentages of African–Americans living in a county and who have COVID-19 (r  = 0.254, P  < 0.0001), who have died from COVID-19 (r  = 0.268, P  < 0.0001), and case mortality (r  = 0.055, P  = 0.003). Positive correlations also existed between percentages of Asian–Americans living in counties and these factors. Negative correlations existed between percentages of Whites living in counties and these factors. Conclusions A weak, albeit very significant, positive relationship exists between the percentage of African–Americans living in a county and the percentage of COVID-19 confirmed cases, confirmed deaths and case mortality in the county. This is in support of many city and statewide analyses, and we urge for targeted resources towards work that further examine these racial associations. [ABSTRACT FROM AUTHOR]

Objectives We tested three alternative hypotheses regarding the relationship between income inequality and individual risk of obesity at two geographical scales: U.S. Census tract and county. Methods Income inequality was measured by Gini coefficients, created from the 2000 U.S. Census. Obesity was clinically measured in the 2003–2008 National Health and Nutrition Examination Survey (NHANES). The individual measures and area measures were geo-linked to estimate three sets of multi-level models: tract only, county only, and tract and county simultaneously. Gender was tested as a moderator. Results At both the tract and county levels, higher income inequality was associated with lower individual risk of obesity. The size of the coefficient was larger for county-level Gini than for tract-level Gini; and controlling income inequality at one level did not reduce the impact of income inequality at the other level. Gender was not a significant moderator for the obesity-income inequality association. Conclusions Higher tract and county income inequality was associated with lower individual risk of obesity, indicating that at least at the tract and county levels and in the context of cross-sectional data, the public health goal of reducing the rate of obesity is in line with anti-poverty policies of addressing poverty through mixed-income development where neighborhood income inequality is likely higher than homogeneous neighborhoods. [ABSTRACT FROM AUTHOR]

Objective: Rank county health using a Bayesian factor analysis model.Data Sources: Secondary county data from the National Center for Health Statistics (through 2007) and Behavioral Risk Factor Surveillance System (through 2009).Study Design: Our model builds on the existing county health rankings (CHRs) by using data-derived weights to compute ranks from mortality and morbidity variables, and by quantifying uncertainty based on population, spatial correlation, and missing data. We apply our model to Wisconsin, which has comprehensive data, and Texas, which has substantial missing information.Data Collection Methods: The data were downloaded from www.countyhealthrankings.org.Principal Findings: Our estimated rankings are more similar to the CHRs for Wisconsin than Texas, as the data-derived factor weights are closer to the assigned weights for Wisconsin. The correlations between the CHRs and our ranks are 0.89 for Wisconsin and 0.65 for Texas. Uncertainty is especially severe for Texas given the state's substantial missing data.Conclusions: The reliability of comprehensive CHRs varies from state to state. We advise focusing on the counties that remain among the least healthy after incorporating alternate weighting methods and accounting for uncertainty. Our results also highlight the need for broader geographic coverage in health data. [ABSTRACT FROM AUTHOR]

In both 2008 and 2012, about one third of the American electorate cast their votes early. While early voting programs are established by states, counties have considerable discretion with regard to implementing them. While some counties offer only a single early voting site, others offer dozens. Previous research suggests that site density may affect the degree to which programs increase turnout. I use county-level data from the 2008 and 2012 elections to measure whether a county’s racial and ethnic composition predicts high (or low) levels of site density. Applying county-level data from the Election Assistance Commission and American Community Survey, I find that the percentage of a county identifying as Black has a significantly negative association with early voting site density. This relationship persists when numerous demographic covariates are included in ordinary least squares models. These site disparities suggest that early voting may not be achieving its full potential in heavily African American communities. [ABSTRACT FROM PUBLISHER]

Background Urban sprawl has the potential to influence cancer mortality via direct and indirect effects on obesity, access to health services, physical activity, transportation choices and other correlates of sprawl and urbanization. Methods This paper presents a cross-sectional analysis of associations between urban sprawl and cancer mortality in urban and suburban counties of the United States. This ecological analysis was designed to examine whether urban sprawl is associated with total and obesity-related cancer mortality and to what extent these associations differed in different regions of the US. A major focus of our analyses was to adequately account for spatial heterogeneity in mortality. Therefore, we fit a series of regression models, stratified by gender, successively testing for the presence of spatial heterogeneity. Our resulting models included county level variables related to race, smoking, obesity, access to health services, insurance status, socioeconomic position, and broad geographic region as well as a measure of urban sprawl and several interactions. Our most complex models also included random effects to account for any county-level spatial autocorrelation that remained unexplained by these variables. Results Total cancer mortality rates were higher in less sprawling areas and contrary to our initial hypothesis; this was also true of obesity related cancers in six of seven U.S. regions (census divisions) where there were statistically significant associations between the sprawl index and mortality. We also found significant interactions (p < 0.05) between region and urban sprawl for total and obesity related cancer mortality in both sexes. Thus, the association between urban sprawl and cancer mortality differs in different regions of the US. Conclusions Despite higher levels of obesity in more sprawling counties in the US, mortality from obesity related cancer was not greater in such counties. Identification of disparities in cancer mortality within and between geographic regions is an ongoing public health challenge and an opportunity for further analytical work identifying potential causes of these disparities. Future analyses of urban sprawl and health outcomes should consider exploring regional and international variation in associations between sprawl and health. [ABSTRACT FROM AUTHOR]

This study explores three features of the contemporary foreclosure crisis that have been highlighted in the literature but relatively neglected in existing empirical research. First, the study evaluates the capacity for levels of mortgage fraud to serve as a potentially importantleading indicatorof significant foreclosure activity. Second, the study examines the possibility that high foreclosure rates may exhibit spatial dependence, affecting the foreclosure realities of surrounding areas in ways similar to how they have been shown to influence housing prices. Finally, the research considers whether key factors emphasized in scholarly and policy discussions interacted to produce particularly high rates of foreclosure in some areas of the United States. The findings indicate that foreclosure rates in 2008 were significantly higher in United States. counties where “profit-motivated” mortgage fraud was more prevalent several years earlier (i.e., 2004–2006). This study also reveals that, net of a wide variety of other factors, high rates of foreclosure can adversely affect nearby counties by elevating their foreclosure rates. Overall, spatial variation in foreclosure rates appears to be due to additive effects of selected factors rather than interactions of those factors, although the study does show that affordable housing can lessen the tendency for high levels of subprime lending to translate into high foreclosure rates. [ABSTRACT FROM AUTHOR]

In 1995, a study entitled 'Does Marriage Matter?' was published by Linda Waite in the journal of Demography, which was concerned with the direction of such causal relationships. While Waite's examination of the causal relationships associated with marriage, and most other analyses of this type, is primarily concerned with the individual level effects of marriage on a variety of outcomes, little is understood concerning the ecological effect of community marriage rates on levels of aggregate well-being. This study aims to contribute to this gap through the implementation of a recent conceptualization of social well-being as a multi-dimensional measure incorporating both biological, operationalized as average life expectancy, and social phenomena, operationalized as, community level crime rates (Raphael, Making the links: what do health promotion, crime prevention, and social development have in common? in ). It is important to understand such aggregate level effects in the face of the existing literature, which relies heavily on relational associations which could be subject to ecological fallacy. Analytic techniques incorporate Exploratory Spatial Data Analysis and spatial regression techniques, due to the high existence of spatial autocorrelation often evident in census data, as a way of understanding the effect of the aggregate level marriage rate on the constructed social well being indicator. [ABSTRACT FROM AUTHOR]

Abstract: The U.S. has experienced a resurgence of income inequality in the past decades. The evidence regarding the mortality implications of this phenomenon has been mixed. This study employs a rarely used method in mortality research, quantile regression (QR), to provide insight into the ongoing debate of whether income inequality is a determinant of mortality and to investigate the varying relationship between inequality and mortality throughout the mortality distribution. Analyzing a U.S. dataset where the five-year (1998–2002) average mortality rates were combined with other county-level covariates, we found that the association between inequality and mortality was not constant throughout the mortality distribution and the impact of inequality on mortality steadily increased until the 80th percentile. When accounting for all potential confounders, inequality was significantly and positively related to mortality; however, this inequality–mortality relationship did not hold across the mortality distribution. A series of Wald tests confirmed this varying inequality–mortality relationship, especially between the lower and upper tails. The large variation in the estimated coefficients of the Gini index suggested that inequality had the greatest influence on those counties with a mortality rate of roughly 9.95 deaths per 1000 population (80th percentile) compared to any other counties. Furthermore, our results suggest that the traditional analytic methods that focus on mean or median value of the dependent variable can be, at most, applied to a narrow 20 percent of observations. This study demonstrates the value of QR. Our findings provide some insight as to why the existing evidence for the inequality–mortality relationship is mixed and suggest that analytical issues may play a role in clarifying whether inequality is a robust determinant of population health. [Copyright &y& Elsevier]

Objectives We estimate the prevalence of children with special health care needs (CSHCN) in 70 metropolitan and four micropolitan statistical areas across the United States. Methods The data are from the 2001 National Survey of CSHCN, which was sponsored by the Maternal and Child Health Bureau and conducted by the National Center for Health Statistics. Prevalence estimates were generated for 74 metropolitan and micropolitan statistical areas (M/MSAs) and 45 individual counties that were represented by at least 1,000 children in the sample. To generate the estimates, the child-level sample weights (representative at the national and state level) were recalibrated within each M/MSA and county to match Census 2000 counts of the child population by age, sex, and Hispanic ethnicity. Results M/MSA-level and county-level prevalence of CSHCN are compared with national- and state-level prevalence, and within M/MSAs and counties, prevalence is reported by age, sex and race/ethnicity. Most, but not all, M/MSA- or county-level prevalence estimates did not differ significantly from state-level estimates. Some M/MSAs and counties that did not differ from their states in overall prevalence of CSHCN did show some differences in prevalence for certain demographic subgroups. Conclusions Metropolitan health departments and Maternal and Child Health agencies that serve urban areas may find these new small area estimates useful for program planning purposes. This study demonstrates the importance of assessing whether state estimates may approximate local area estimates of the prevalence of CSHCN. [ABSTRACT FROM AUTHOR]

Identifying the spatial pattern of wildfire occurrences and its relationships with environmental and social conditions at a state and county scale may help to reinforce strategic and diverse solutions in wildfire and land management throughout different environmental conditions and management practices. In this study, a dataset of wildfire occurrences from 1999 to 2004 in Alabama, USA, was used to analyze the spatial distribution of wildfire occurrences and the relationships with environmental factors (climate, forest types and physiographic zones) and the human dimension (total population, road length and land ownership). A spatial pattern existed for wildfire occurrence and density at county level. It appeared that there were more wildfire occurrences in central Alabama within the latitude 32°-35°N and longitude 86°-88°W, but there was no significant difference in wildfire density (wildfire occurrences at unit area) among five general physiographic zones. The relationship between each climate factor and a wildfire occurrence was not significant. As for different forest types, significantly higher wildfire density only occurred in Oak-Pine forest in comparison with Longleaf-Slash Pine forest. Most wildfires occurred in the counties with population size between 10,000 and 63,000 or total road length from 250 to 320 km. There was significant higher wildfire density in the national forest area than at the area without national forest. These results may have implications for strategic policy-making for integrated wildfire management in this region. [ABSTRACT FROM AUTHOR]

Background: Opioid use disorder (OUD) among older adults (age ≥ 65) is a growing yet underexplored public health concern and previous research has mainly assumed that the spatial process underlying geographic patterns of population health outcomes is constant across space. This study is among the first to apply a local modeling perspective to examine the geographic disparity in county-level OUD rates among older Medicare beneficiaries and the spatial non-stationarity in the relationships between determinants and OUD rates., Methods: Data are from a variety of national sources including the Centers for Medicare & Medicaid Services beneficiary-level data from 2020 aggregated to the county-level and county-equivalents, and the 2016-2020 American Community Survey (ACS) 5-year estimates for 3,108 contiguous US counties. We use multiscale geographically weighted regression to investigate three dimensions of spatial process, namely "level of influence" (the percentage of older Medicare beneficiaries affected by a certain determinant), "scalability" (the spatial process of a determinant as global, regional, or local), and "specificity" (the determinant that has the strongest association with the OUD rate)., Results: The results indicate great spatial heterogeneity in the distribution of OUD rates. Beneficiaries' characteristics, including the average age, racial/ethnic composition, and the average hierarchical condition categories (HCC) score, play important roles in shaping OUD rates as they are identified as primary influencers (impacting more than 50% of the population) and the most dominant determinants in US counties. Moreover, the percentage of non-Hispanic white beneficiaries, average number of mental health conditions, and the average HCC score demonstrate spatial non-stationarity in their associations with the OUD rates, suggesting that these variables are more important in some counties than others., Conclusions: Our findings highlight the importance of a local perspective in addressing the geographic disparity in OUD rates among older adults. Interventions that aim to reduce OUD rates in US counties may adopt a place-based approach, which could consider the local needs and differential scales of spatial process., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yang, Shoff, Choi and Sun.)

Background & Aims: Significant geographic variability in gastrointestinal (GI) cancer-related death has been reported in the United States. We aimed to evaluate both modifiable and nonmodifiable factors associated with intercounty differences in mortality due to GI cancer., Methods: Data from the Centers for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research platform were used to calculate county-level mortality from esophageal, gastric, pancreatic, and colorectal cancers. Multivariable linear regression models were fit to adjust for county-level covariables, considering both patient (eg, sex, race, obesity, diabetes, alcohol, and smoking) and structural factors (eg, specialist density, poverty, insurance prevalence, and colon cancer screening prevalence). Intercounty variability in GI cancer-related mortality explained by these covariables was expressed as the multivariable model R 2 ., Results: There were significant geographic disparities in GI cancer-related county-level mortality across the US from 2010-2019 with the ratio of mortality between 90th and 10th percentile counties ranging from 1.5 (pancreatic) to 2.1 (gastric cancer). Counties with the highest 5% mortality rates for gastric, pancreatic, and colorectal cancer were primarily in the Southeastern United States. Multivariable models explained 43%, 61%, 14%, and 39% of the intercounty variability in mortality rates for esophageal, gastric, pancreatic, and colorectal cancer, respectively. Cigarette smoking and rural residence (independent of specialist density) were most strongly associated with GI cancer-related mortality., Conclusions: Both patient and structural factors contribute to significant geographic differences in mortality from GI cancers. Our findings support continued public health efforts to reduce smoking use and improve care for rural patients, which may contribute to a reduction in disparities in GI cancer-related death., (Copyright © 2022 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Previous literature on the uneven development of the opioid crisis across U.S. counties fails to account for the temporal and spatial dependency simultaneously. Assembling a spatiotemporal dataset from 2006 to 2018 based on the U.S. Opioid Dispensing Rate Maps, the American Community Survey, and other national data sources, this study examines how rurality impacts the county-level opioid prescribing rates. The results show significant spatial clustering patterns of opioid prescribing rates over the years. Taking the spatial structures into account, it is found that counties with a higher degree of rurality have higher opioid prescribing rates and this association could be explained by higher percentages of whites, higher unemployment rates, less nurse practitioners and physician assistants, and more specialized opioid prescribers such as surgeons and oncologists. Higher level of social capital is related to higher opioid prescribing rates, but it cannot explain the association between rurality and opioid prescribing. The findings highlight the role of healthcare services play in shaping the spatial inequality of opioid prescribing. • It is among the first to examine the crisis from a spatiotemporal perspective. • The time effects are no longer significant when considering spatial structures. • The spatial clustering patterns of opioid prescribing rates persist over the years. • The geography of opioid prescribing is largely shaped by healthcare services. [ABSTRACT FROM AUTHOR]

The current debate about relationships between American origins and European precedents has engaged geographers at a number of levels. A major problem has been the lack of discussion of the institutional environments of early Americans and the regional settings in which they were established. A study of the early Chesapeake reveals the importance of its profit-oriented, socially stratified beginnings based upon the success of tobacco cultivation in the southern Virginia tidewater. This placed paramount importance on the drive for land, the search for a suitable labor supply, and individual desire for upward social mobility. The dependence on British agricultural servants created early class distinctions and an initial demographic profile marked by young, unmarried males. Family life was thus less structured than it was in England or New England, and cultural patterns were subject to greater change. The principal institutions of the family, plantation, and county were recognizable by the 1660s, to which hereditary slavery was added during the 1680s. Regional differences nevertheless emerged between early Virginia and Maryland, and between the Chesapeake's eastern and western shores. Political and religious patterns in particular remained more varied and unstable in Maryland. These findings emphasize the importance of examining institutions as well as structures and patterns in the early life of American settlers. They highlight the differential significance of English social origins, the need to identify the Chesapeake yeoman, and the limitations of the culture hearth concept. [ABSTRACT FROM AUTHOR]

Background and Aims: In the United States, the rate of drug overdose death has more than tripled over the past two decades, a trend that is often attributed to changes in opioid prescribing practices. We developed a novel, longitudinal metric to summarize the relationship between prescription opioid prescribing practices and drug overdose mortality and to assess if longitudinal changes in that relationship differ by characteristics of place.Methods: We constructed a single county-level measure of overdose deaths per 100,000 opioid prescriptions annually from 2006 to 2018. We used latent profile analysis to classify all U.S. counties into classes based on demographic and socioeconomic characteristics and fit a mixed Poisson log-linear model to quantify temporal changes in our measure by county-type classes.Results: Latent profile analysis resulted in 7 classes with high separation between classes (overall entropy = 0.916). Across all groups, the average number of overdose deaths per opioid prescription remained steady from 2006 to 2011 and increased from 2012-2018. The largest increases were in the high GDP (average annual change: 18.1 %, 95 %CI: 17.5, 18.6) and high education classes (16.6 %, 95 %CI: 16.0, 17.1).Conclusions: This novel summary metric enhances our understanding of the shift in overdose mortality and the role of geography and place characteristics. [ABSTRACT FROM AUTHOR]

Our objective in this study was to evaluate how well proxy variables for firearm ownership used in county-level studies measure firearm ownership. We applied Bayesian spatial smoothing methods to calculate county-level estimates of household firearm ownership using Behavioral Risk Factor Surveillance System (BRFSS) data (2013-2018). We compared these estimates to four proxies for county-level firearm ownership: the proportion of suicides that were firearm suicides, the average of the proportion of suicides that were firearm suicides and the proportion of homicides that were firearm homicides, gun shops per capita, and federal firearm licenses per capita. U.S. counties for which BRFSS data on household firearm ownership were collected and available for release (n = 304) were included. The median (interquartile range) prevalence of household firearm ownership was 46.6% (37.2%, 56.4%). The per capita rate of federal firearm licenses was most strongly correlated with household firearm ownership (r = 0.70; 95% CI: 0.63, 0.75) followed by the proportion of suicides that were firearm suicides (r = 0.45; 95% CI: 0.36, 0.54). These correlations were stronger among counties with populations of ≥250,000 people. The per capita rate of federal firearm licenses was the best proxy variable for firearm ownership at the county level, however, a better proxy should be identified. [ABSTRACT FROM AUTHOR]

The USA leads the world in healthcare spending but trails dozens of countries in life expectancy. Government spending may reduce overall mortality by redistributing resources from the rich to the poor. We linked mortality data from 2006 to 2015 to municipal and state government spending in 149 of the largest American cities. We modeled the association of mortality with city and state government spending per capita in 2005 using weighted linear regression. A 10% increase in state government expenditures was associated (P = 0.008) with a 1.4% (95%CI: 0.4-2.4%) reduction in mortality in American cities. Total city government expenditures were not associated with mortality (P > 0.10). However, among Whites, increases in city government spending were associated with a reduction in mortality of 4.8% (2.1-7.5%), but among Blacks and Asians, increased city government spending was associated with respective mortality increases of 1.7% (0.6-2.9%) and 5.1% (2.1-6.2%). State government spending is associated with reduced mortality in American cities. City government spending appears to benefit White longevity and hurt non-White longevity., (© 2021. The New York Academy of Medicine.)

Introduction: Geographic variations in access to care exist in the USA. We sought to characterize county-level disparities relative to access to surgery among patients with early-stage hepatopancreatic (HP) cancer., Methods: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER)-Medicare Linked database from 2004 to 2015 to identify patients undergoing surgery for early-stage HP cancer . County-level information was acquired from the Area Health Resources Files (AHRF). Multivariable logistic regression analysis was performed to assess factors associated with utilization of HP surgery on the county level., Results: Among 13,639 patients who met inclusion criteria, 66.9% (n = 9125) were diagnosed with pancreatic cancer and 33.1% (n = 4514) of patients had liver cancer. Among patients diagnosed with early-stage liver and pancreas malignancy, two-thirds (n = 8878, 65%) underwent surgery. Marked county-level variation in the utilization of surgery was noted among patients with early-stage HP cancer ranging from 57.1% to more than 83.3% depending on which county a patient resided. After controlling for patient and tumor-related characteristics, counties with the highest quartile of patients living below the poverty level had 35% lower odds of receiving surgery for early stage HP cancer compared patients who lived in a county with the lowest proportion of patients below the poverty line (OR 0.65, 95% CI 0.55-0.77). In addition, patients residing in counties with the highest surgeon-to-population ratio (OR 2.01, 95% CI 1.52-2.65), as well as the highest hospital bed-to-population ratio (OR 1.29, 95% CI 1.07-1.54), were more likely to undergo surgical treatment for an early-stage HP malignancy., Conclusion: Area-level variations among patients undergoing surgery for early-stage HP cancer were mainly due to differences in structural measures and county-level factors. Policies targeting high-poverty counties and improvement in structural measures may reduce variations in utilization of surgery among patients diagnosed with early-stage HP cancer., (© 2020. The Society for Surgery of the Alimentary Tract.)

Goods and services provided by businesses can either promote health or represent an additional risk factor. We assessed the association between business pattern indicators and the prevalence of adult obesity, diabetes, physical inactivity, fair or poor health and frequent physical and mental distress. Data on business types were obtained from the 2013 U.S. Census Bureau County Business Patterns. County health data were obtained from the Centers for Disease Control and Prevention Diabetes Interactive Atlas, Behavior Risk Factor Surveillance System and Fatality Analysis Reporting System. We explored the relationship at county level using the global (Ordinary Least Square regression) and local (Geographically Weighted Regression (GWR)) models in 3108 U.S. counties. Density of full service restaurants and fitness centers was associated with a significant decrease in adult obesity, diabetes, fair or poor health, physical inactivity, physical and mental distress. Conversely, density of payday loan centers was associated with an increase in these adverse health outcomes. However, our GWR models revealed substantial geographical variations in these relationships across the U.S. counties. Better understanding of the association between area-level structures and important health outcomes at the local level is important for developing targeted context-specific policy interventions. Full service restaurants and fitness centers may provide places for people to access higher quality food, socialize and exercise. Conversely, payday loans provide an expensive form of short-term credit and this debt may degrade an individual or family's ability to achieve or maintain health. Our study emphasizes the influence of local built environment characteristics on important health outcomes. [ABSTRACT FROM AUTHOR]

One hypothesized explanation for the recent slowing of declines in heart disease death rates is the generational shift in the timing and accumulation of risk factors. However, directly testing this hypothesis requires historical age-group-specific risk factor data that do not exist. Using national death records, we compared spatiotemporal patterns of heart disease death rates by age group, time period, and birth cohort to provide insight into possible drivers of trends. To do this, we calculated county-level percent change for five time periods (1973-1980, 1980-1990, 1990-2000, 2000-2010, 2010-2015) for four age groups (35-44, 45-54, 55-64, 65-74), resulting in eight birth cohorts for each decade from the 1900s through the 1970s. From 1973 through 1990, few counties experienced increased heart disease death rates. In 1990-2000, 49.0% of counties for ages 35-44 were increasing, while all other age groups continued to decrease. In 2000-2010, heart disease death rates for ages 45-54 increased in 30.4% of counties. In 2010-2015, all four age groups showed widespread increasing county-level heart disease death rates. Likewise, birth cohorts from the 1900s through the 1930s experienced consistently decreasing heart disease death rates in almost all counties. Similarly, with the exception of 2010-2015, most counties experienced decreases for the 1940s birth cohort. For birth cohorts in the 1950s, 1960s, and 1970s, increases were common and geographically widespread for all age groups and calendar years. This analysis revealed variation in trends across age groups and across counties. However, trends in heart disease death rates tended to be generally decreasing and increasing for early and late birth cohorts, respectively. These findings are consistent with the hypothesis that recent increases in heart disease mortality stem from the beginnings of the obesity and diabetes epidemics. However, the common geographic patterns within the earliest and latest time periods support the importance of place-based macro-level factors., (Published by Elsevier Ltd.)

Rationale: Previous studies have observed a positive association between automation risk and employment loss. Based on the job insecurity-health risk hypothesis, greater exposure to automation risk could also be negatively associated with health outcomes., Objective: The main objective of this paper is to investigate the county-level association between prevalence of workers in jobs exposed to automation risk and general, physical, and mental health outcomes., Methods: As a preliminary assessment of the job insecurity-health risk hypothesis (automation risk → job insecurity → poorer health), a structural equation model was used based on individual-level data in the two cross-sectional waves (2012 and 2014) of General Social Survey (GSS). Next, using county-level data from County Health Rankings 2017, American Community Survey (ACS) 2015, and Statistics of US Businesses 2014, Two Stage Least Squares (2SLS) regression models were fitted to predict county-level health outcomes., Results: Using the 2012 and 2014 waves of the GSS, employees in occupational classes at higher risk of automation reported more job insecurity, that, in turn, was associated with poorer health. The 2SLS estimates show that a 10% increase in automation risk at county-level is associated with 2.38, 0.8, and 0.6 percentage point lower general, physical, and mental health, respectively., Conclusion: Evidence suggests that exposure to automation risk may be negatively associated with health outcomes, plausibly through perceptions of poorer job security. More research is needed on interventions aimed at mitigating negative influence of automation risk on health., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Local health officials have been forced to make do with public health resources stretched thin., (Project HOPE—The People-to-People Health Foundation, Inc.)