The Rise of Progressive Prosecutors in the United States: Politics, Prospects, and Perils

Higher COVID-19 incidence and mortality rates in rural than in urban areas are well documented (1). These disparities persisted during the B.1.617.2 (Delta) and B.1.1.529 (Omicron) variant surges during late 2021 and early 2022 (1,2). Rural populations tend to be older (aged ≥65 years) and uninsured and are more likely to have underlying medical conditions and live farther from facilities that provide tertiary medical care, placing them at higher risk for adverse COVID-19 outcomes (2). To better understand COVID-19 vaccination disparities between urban and rural populations, CDC analyzed county-level vaccine administration data among persons aged ≥5 years who received their first dose of either the BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) COVID-19 vaccine or a single dose of the Ad.26.COV2.S (Janssen [Johnson & Johnson]) COVID-19 vaccine during December 14, 2020-January 31, 2022, in 50 states and the District of Columbia (DC). COVID-19 vaccination coverage with ≥1 doses in rural areas (58.5%) was lower than that in urban counties (75.4%) overall, with similar patterns across age groups and sex. Coverage with ≥1 doses varied among states: 46 states had higher coverage in urban than in rural counties, one had higher coverage in rural than in urban counties. Three states and DC had no rural counties; thus, urban-rural differences could not be assessed. COVID-19 vaccine primary series completion was higher in urban than in rural counties. However, receipt of booster or additional doses among primary series recipients was similarly low between urban and rural counties. Compared with estimates from a previous study of vaccine coverage among adults aged ≥18 years during December 14, 2020-April 10, 2021, these urban-rural disparities among those now eligible for vaccination (aged ≥5 years) have increased more than twofold through January 2022, despite increased availability and access to COVID-19 vaccines. Addressing barriers to vaccination in rural areas is critical to achieving vaccine equity, reducing disparities, and decreasing COVID-19-related illness and death in the United States (2).

The National Risk Index (NRI) was created by the Federal Emergency Management Agency in 2021 to quantify county-level natural hazard risk. The NRI is presented as a percentile score from 0 to 100 and is calculated based on three components: (1) expected annual loss (EAL), the average economic loss resulting from natural hazards; (2) Social Vulnerability Index (SVI), the susceptibility of local population groups to the adverse impacts of natural hazards; and (3) community resilience, the local community's ability to prepare for and respond to natural hazards. We hypothe-sized that the EAL component unintentionally obscures rural versus urban differences in natural hazard vulnerability and preparedness. We tested our hypothesis using publicly available NRI data for all rural (nonmetropolitan) and urban (metropolitan) counties in the United States. We found that the population-weighted average rural county had an NRI risk score equal to 54.9 (89.1 for urban counties). Follow-up analyses suggested that differing NRI scores between rural and urban counties were driven by greater EAL in urban areas (USD 12 M and USD 347 M EAL for the population-weighted average rural county and urban county, respectively). In contrast, SVI was the strongest predictor in linear regression models of county-level premature mortality rate and years of life lost (p < .001 in both models). We conclude that the NRI primarily communicates the potential economic loss of counties due to natural disasters and under-estimates public health-related vulnerability and resilience. Community planners unaware of this finding may mistakenly overlook the needs of rural communities, leaving rural residents unnecessarily vulnerable to natural disasters. This imbalance could also lead to inequitable distribution of disaster planning resources across rural and urban counties, particularly if policymakers and individuals in charge of emergency preparedness rely on the NRI to identify areas at greatest risk.

Approximately 60 million persons in the United States live in rural counties, representing almost one fifth (19.3%) of the population.* In September 2020, COVID-19 incidence (cases per 100,000 population) in rural counties surpassed that in urban counties (1). Rural communities often have a higher proportion of residents who lack health insurance, live with comorbidities or disabilities, are aged ≥65 years, and have limited access to health care facilities with intensive care capabilities, which places these residents at increased risk for COVID-19-associated morbidity and mortality (2,3). To better understand COVID-19 vaccination disparities across the urban-rural continuum, CDC analyzed county-level vaccine administration data among adults aged ≥18 years who received their first dose of either the Pfizer-BioNTech or Moderna COVID-19 vaccine, or a single dose of the Janssen COVID-19 vaccine (Johnson & Johnson) during December 14, 2020-April 10, 2021 in 50 U.S. jurisdictions (49 states and the District of Columbia [DC]). Adult COVID-19 vaccination coverage was lower in rural counties (38.9%) than in urban counties (45.7%) overall and among adults aged 18-64 years (29.1% rural, 37.7% urban), those aged ≥65 years (67.6% rural, 76.1% urban), women (41.7% rural, 48.4% urban), and men (35.3% rural, 41.9% urban). Vaccination coverage varied among jurisdictions: 36 jurisdictions had higher coverage in urban counties, five had higher coverage in rural counties, and five had similar coverage (i.e., within 1%) in urban and rural counties; in four jurisdictions with no rural counties, the urban-rural comparison could not be assessed. A larger proportion of persons in the most rural counties (14.6%) traveled for vaccination to nonadjacent counties (i.e., farther from their county of residence) compared with persons in the most urban counties (10.3%). As availability of COVID-19 vaccines expands, public health practitioners should continue collaborating with health care providers, pharmacies, employers, faith leaders, and other community partners to identify and address barriers to COVID-19 vaccination in rural areas (2).

Urban-Rural Differences in Acute Kidney Injury Mortality in the United States

Temporal Trends in Rural vs Urban Sepsis-Related Mortality in the United States, 2010-2019

Residence in urban or rural counties in relation to opioid overdose mortality among Kentucky hospitalizations before and during the COVID-19 pandemic.

To assess trends in polysubstance use among pregnant women with opioid use disorder in the United States. We conducted a time trend analysis of pooled, cross-sectional data from the National Inpatient Sample, an annual nationally representative sample of U.S. hospital discharge data. Among 38.0 million females aged 15-44 years with a hospitalization for delivery from 2007 to 2016, we identified 172,335 pregnant women with an International Classification of Diseases, Ninth Revision, Clinical Modification or International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis of opioid use disorder. Polysubstance use among pregnant women with opioid use disorder was defined as at least one co-occurring diagnosis of other substance use, including alcohol, amphetamine, cannabis, cocaine, sedative, or tobacco. We fit weighted multivariable logistic regression models to produce nationally representative estimates, including an interaction between year and rural compared with urban county of residence; controlled for age, race, and insurance type. Average predicted probabilities and 95% CIs were derived from regression results. Polysubstance use among women with opioid use disorder increased from 60.5% (95% CI 58.3-62.8%) to 64.1% (95% CI 62.8%-65.3%). Differential time trends in polysubstance use among women with opioid use disorder were found in rural compared with urban counties. Large increases in amphetamine use occurred among those in both rural and urban counties (255.4%; 95% CI 90.5-562.9% and 150.7%; 95% CI 78.2-52.7%, respectively), similarly to tobacco use (30.4%; 95% CI 16.9-45.4% and 23.2%; 95% CI 15.3-31.6%, respectively). Cocaine use diagnoses declined among women with opioid use disorder at delivery in rural (-70.5%; 95% CI -80.4% to -55.5%) and urban (-61.9%; 95% CI -67.6% to -55.1%) counties. Alcohol use diagnoses among those with opioid use disorder declined -57% (95% CI -70.8% to -37.7%) in urban counties but did not change among those in rural counties. Over the past decade, polysubstance use among pregnant women with opioid use disorder has increased more rapidly in rural compared with urban counties in the United States, with amphetamines and tobacco use increasing most rapidly.

Rurality and pediatric cancer survival in the United States: An analysis of SEER data from 2000 to 2021

Geographic Disparities in Access to Certified Hand Therapists in the United States.

Surveillance reports consistently observe that cancer mortality rates are higher in rural than urban areas, yet data on the multi-level factors that impact rural disparities have not been fully leveraged to identify the areas of greatest need for research and policy changes. To address gaps in cancer data for rural communities, we adapted the County Health Rankings model of the multiple determinants of health to cancer. Using publicly available data, we compared health factors and cancer mortality for rural versus urban counties in Wisconsin. Counties were defined as rural (N=19) or non-rural (“urban”, N=53) based on Rural Urban Continuum Codes 7-9 and 1-6, respectively. Age-adjusted county-specific cancer mortality rates for all cancer sites combined were obtained from the state cancer registry. Health factor data were obtained from multiple sources in 4 categories: health behaviors (smoking, drinking alcohol, obesity, physical activity); clinical care (HPV vaccination; breast, cervical, and colorectal cancer screening; density of primary care physicians); socioeconomic factors (Area Deprivation Index based on 17 census items); and physical environment (access to grocery stores and alcohol outlets, air quality, pesticide use). Items were ranked for the 72 counties with lower-risk values having better ranks, e.g., higher values for screening and lower values for obesity ranked closer to 1. A composite health factor ranking was defined using County Health Rankings weights, equal to 0.3*(behavioral factors) + 0.2*(clinical factors) + 0.4*(socioeconomic factors) + 0.1*(physical environment). Cancer death rates were higher in rural than in urban counties (181 vs 164 per 100,000). The composite health ranking was positively associated with cancer mortality rates (Pearson correlation coefficient 0.38, 95% CI 0.17-0.57), with worse rankings for rural (average 44, interquartile range, IQR 39-51) than for urban counties (average 34, IQR 25-42). The difference in health factor category rankings between rural and urban counties was greatest for socioeconomic factors (rural average rank 50 vs urban average rank 32) followed by clinical care (rural average rank 43 vs urban average rank 34) and behavioral factors (rural average rank 40 vs urban average rank 35). Physical environment factor rankings were slightly better for rural (average 33) than urban (average 37) counties. In conclusion, we confirmed that cancer mortality in Wisconsin is higher in rural as compared with urban areas. Future analyses will (a) refine the set of health factors used to construct the composite health factor ranking (e.g., account more fully for distance to care) and (b) optimize the weights applied to the categories to calculate the composite ranking. These initial findings suggest that, to increase the impact of future research and policy efforts, clinical and behavioral interventions targeting cancer health disparities in rural counties should include strategies to address socioeconomic factors. Citation Format: Amy Trentham-Dietz, Noelle K LoConte, Betsy Rolland, Lisa Cadmus-Bertram, Tracy M Downs, John M Eason, Cody M Fredrick, John M Hampton, Xiao Zhang, Ronald E Gangnon. Associations between multilevel health factors and cancer mortality according to rural residence [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr D002.

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

Adolescent handgun carrying is associated with increased risk of firearm-related violence. Most evidence on adolescent handgun carrying is from urban areas, but these findings may not generalize to rural areas. To examine differences in associations of adolescent interpersonal violence with handgun carrying across the rural-urban continuum. This cross-sectional study used nationally representative data from the US National Survey on Drug Use and Health among adolescents aged 12 to 17 years from 2002 to 2019 to estimate time-varying prevalence ratios (PRs) and prevalence differences (PDs) between interpersonal violence and handgun carrying across the rural-urban continuum. Analyses were conducted in April to July 2022. Any past-year serious fighting, group fighting, and attacking with intent to harm. Any past-year handgun carrying. Associations were estimated within county rural-urban strata using the US Department of Agriculture's Rural-Urban Continuum Codes. In each year, the sample included a weighted count of almost 25 million adolescents, with 50.9% (95% CI, 50.2%-51.6%) males and 24.7% (95% CI, 23.8%-25.6%) Hispanic adolescents, 13.5% (95% CI, 12.8%-14.2%) non-Hispanic Black adolescents, and 51.8% (95% CI, 50.8%-52.8%) non-Hispanic White adolescents in 2019. More rural counties had less racial and ethnic diversity. For example, 81.1% (95% CI, 75.9%-85.4%) of adolescents were non-Hispanic White in the most rural counties vs 43.1% (95% CI, 41.7%-44.6%) of adolescents were non-Hispanic White in the most urban counties in 2019. Adolescent handgun carrying increased over time, with the largest increases in the most rural counties, where the prevalence of adolescent handgun carrying increased from 5.2% (95% CI, 3.8%-7.0%) in 2003 to 12.4% (95% CI, 8.9%-16.9%) in 2019. PRs for the association of violence and handgun carrying were greater in more urban counties. For example, in the most urban counties in 2019, adolescents involved in a group fight had 3.7 (95% CI, 2.9-4.8) times the prevalence of handgun carrying vs those not involved in a group fight; this PR was 3.1 (95% CI, 1.6-5.6) in the most rural counties. PDs were similar and, in some cases, larger in rural areas. For example, in the most urban counties in 2019, handgun carrying prevalence was 7.5% (95% CI, 5.7%-9.5%) higher among adolescents who were involved in a group fight compared with those who were not; this PD was 21.8% (95% CI, 8.2%-37.8%) in the most rural counties, where handgun carrying was more common. This cross-sectional study found that associations of interpersonal violence with handgun carrying were stronger in relative terms in urban areas than in rural areas; however, a higher percentage of rural than urban adolescents carried handguns, resulting in a greater absolute prevalence of handgun carrying associated with violence in rural areas than in urban areas. These findings suggest opportunities for preventing handgun carrying-related harms may differ between rural and urban communities.

BackgroundMost health effects studies of ozone and temperature have been performed in urban areas, due to the available monitoring data. We used observed and interpolated data to examine temperature, ozone, and mortality in 91 urban and non-urban counties.MethodsOzone measurements were extracted from the Environmental Protection Agency’s Air Quality System. Meteorological data were supplied by the National Center for Atmospheric Research. Observed data were spatially interpolated to county centroids. Daily internal-cause mortality counts were obtained from the National Center for Health Statistics (1988–1999). A two-stage Bayesian hierarchical model was used to estimate each county’s increase in mortality risk from temperature and ozone. We examined county-level associations according to population density and compared urban (≥1,000 persons/mile2) to non-urban (<1,000 persons/mile2) counties. Finally, we examined county-level characteristics that could explain variation in associations by county.ResultsA 10 ppb increase in ozone was associated with a 0.45% increase in mortality (95% PI: 0.08, 0.83) in urban counties, while this same increase in ozone was associated with a 0.73% increase (95% PI: 0.19, 1.26) in non-urban counties. An increase in temperature from 70°F to 90°F (21.2°C 32.2°C) was associated with a 8.88% increase in mortality (95% PI: 7.38, 10.41) in urban counties and a 8.08% increase (95% PI: 6.16, 10.05) in non-urban counties. County characteristics, such as population density, percentage of families living in poverty, and percentage of elderly residents, partially explained the variation in county-level associations.ConclusionsWhile most prior studies of ozone and temperature have been performed in urban areas, the impacts in non-urban areas are significant, and, for ozone, potentially greater. The health risks of increasing temperature and air pollution brought on by climate change are not limited to urban areas.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-069X-14-3) contains supplementary material, which is available to authorized users.

Background:Temperature-related mortality risks have mostly been studied in urban areas, with limited evidence for urban–rural differences in the temperature impacts on health outcomes.Objectives:We investigated whether temperature–mortality relationships vary between urban and rural counties in China.Methods:We collected daily data on 1 km gridded temperature and mortality in 89 counties of Zhejiang Province, China, for 2009 and 2015. We first performed a two-stage analysis to estimate the temperature effects on mortality in urban and rural counties. Second, we performed meta-regression to investigate the modifying effect of the urbanization level. Stratified analyses were performed by all-cause, nonaccidental (stratified by age and sex), cardiopulmonary, cardiovascular, and respiratory mortality. We also calculated the fraction of mortality and number of deaths attributable to nonoptimum temperatures associated with both cold and heat components. The potential sources of the urban–rural differences were explored using meta-regression with county-level characteristics.Results:Increased mortality risks were associated with low and high temperatures in both rural and urban areas, but rural counties had higher relative risks (RRs), attributable fractions of mortality, and attributable death counts than urban counties. The urban–rural disparity was apparent for cold (first percentile relative to minimum mortality temperature), with an RR of 1.47 [95% confidence interval (CI): 1.32, 1.62] associated with all-cause mortality for urban counties, and 1.98 (95% CI: 1.87, 2.10) for rural counties. Among the potential sources of the urban–rural disparity are age structure, education, GDP, health care services, air conditioners, and occupation types.Conclusions:Rural residents are more sensitive to both cold and hot temperatures than urban residents in Zhejiang Province, China, particularly the elderly. The findings suggest past studies using exposure–response functions derived from urban areas may underestimate the mortality burden for the population as a whole. The public health agencies aimed at controlling temperature-related mortality should develop area-specific strategies, such as to reduce the urban–rural gaps in access to health care and awareness of risk prevention. Future projections on climate health impacts should consider the urban–rural disparity in mortality risks. https://doi.org/10.1289/EHP3556

A recent study shows that among the three age groups of youth, adult and older adult, youth‐older adult has the highest age segregation while youth‐adult has the lowest. Similar to many previous age segregation studies, racial‐ethnic differences, an important population axis in segregation studies, were not considered. Prior studies are also limited to using two‐group measures, failing to compare multiple groups together. We explore the complexity in measuring intersectional segregation focusing on the two axes of age and race‐ethnicity and propose a conditional approach to measure age segregation by racial‐ethnic groups, and racial‐ethnic segregation by age groups. Using this approach, we empirically study the 2010 age‐race‐ethnic segregation at the county and state levels in the United States, using census tracts as the basic units. Both the two and multigroup dissimilarity indices were used. Results show that the racial‐ethnic axis had been a stronger force in segregation than the age axis. Results also show disparities of racial‐ethnic segregation across age groups with the highest levels present among older adults and in urban counties. For all three age groups, segregation levels involving Natives and Asians tend to be higher than those without them. In contrast, age segregation was the highest between youths and older adults, and the levels varied across racial‐ethnic groups with Natives at the highest levels. Although age segregation was significantly different between urban and rural counties, higher segregation in urban areas were mostly involving Whites as opposed to higher segregation in rural counties involving minority racial groups. Studying age segregation should not be colour blinded, as nonwhite older adults in rural counties were more likely to experience higher levels of age segregation than other groups.