County Mortality Research Articles

Risk factors associated with mortality of COVID-19 in 3125 counties of the United States

BackgroundThe number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the United States. The risk factors associated with county-level mortality of COVID-19 with various levels of prevalence are not well understood.MethodsUsing the data obtained from the County Health Rankings and Roadmaps program, this study applied a negative binomial design to the county-level mortality counts of COVID-19 as of August 27, 2020 in the United States. In this design, the infected counties were categorized into three levels of infections using clustering analysis based on time-varying cumulative confirmed cases from March 1 to August 27, 2020. COVID-19 patients were not analyzed individually but were aggregated at the county-level, where the county-level deaths of COVID-19 confirmed by the local health agencies. Clustering analysis and Kruskal–Wallis tests were used in our statistical analysis.ResultsA total of 3125 infected counties were assigned into three classes corresponding to low, median, and high prevalence levels of infection. Several risk factors were significantly associated with the mortality counts of COVID-19, where higher level of air pollution (0.153, P < 0.001) increased the mortality in the low prevalence counties and elder individuals were more vulnerable in both the median (0.049, P < 0.001) and high (0.114, P < 0.001) prevalence counties. The segregation between non-Whites and Whites (low: 0.015, P < 0.001; median:0.025, P < 0.001; high: 0.019, P = 0.005) and higher Hispanic population (low and median: 0.020, P < 0.001; high: 0.014, P = 0.009) had higher likelihood of risk of the deaths in all infected counties.ConclusionsThe mortality of COVID-19 depended on sex, race/ethnicity, and outdoor environment. The increasing awareness of the impact of these significant factors may help decision makers, the public health officials, and the general public better control the risk of pandemic, particularly in the reduction in the mortality of COVID-19.Graphic abstract

Socioeconomic inequalities in the spread of coronavirus-19 in the United States: A examination of the emergence of social inequalities.

ObjectivesTo quantify the contribution variation in socioeconomic status in predicting the distribution of COVID-19 cases and deaths.MethodsAnalyses used incidence data on daily COVID + case counts from all counties from the initial wave of infections, merged with data from the U.S. census data to measure county-level SES and confounders. Multivariable analyses relied on survival analyses and Poisson regression to examine timing of county-level index cases and of COVID-19 incidence and mortality in infected counties to examine the spread and severity of COVID-19 while adjusting for adjusted for Black race, Hispanic ethnicity, age, gender, and urbanicity. Effect moderation by social distancing parameters was examined.ResultsResults indicate that higher SES was associated with earlier incidence of index cases, but that as social distancing took place inequalities in SES inverted so that growth in incidence was slower in higher SES counties, where case-fatality rates were lower.ConclusionsThis study is the first to date to show what happens when an opportunistic disease that could affect anyone meets the American system of inequality and is powerfully shaped by it.

Abstract PO-168: Association between geography and cause-specific mortality in gastrointestinal cancers in Georgia

Abstract Background: Cancers of the gastrointestinal system represent a diverse range of diseases with different etiologies, from screening-detectable colorectal cancer (CRC) to pancreatic cancer, often diagnosed at late stage and highly lethal. While racial disparities have been observed for some of these cancers, the impact of geography is less understood. Georgia is an ideal place to examine disparities in geography because it has a diverse setting and population, with approximately 25% of residents living in a rural area and nearly one-third identifying as non-Hispanic Black (NHB), according to the 2010 decennial census. Methods: We obtained clinical (age at diagnosis, tumor stage, tumor size), sociodemographic (e.g., insurance status, marital status, SES index), and treatment information (e.g., receipt of surgery, lymph node biopsy/removal) on nearly 30,000 NHB and non-Hispanic White (NHW) men and women aged 18 years and older, diagnosed with CRC, hepatocellular carcinoma (HCC), pancreatic cancer or gastric cancer between 2009 and 2014 in Georgia from the population-based Surveillance, Epidemiology, and End Results (SEER) Program. Patients were classified as residing in a metro or non-metro county at diagnosis according to 2013 rural-urban continuum codes. Multivariable Cox proportional hazards models were used to calculate the hazard ratios (HRs) and corresponding 95% confidence intervals (95% CI) for the association between residing in a non- metro versus metro county and cause-specific mortality, stratified by patient characteristics. Results: Over 75% of the study population resided in a metro county at diagnosis, with the highest proportion among HCC cases (81%) and the lowest proportion among CRC cases (76%). Overall, NHBs were more likely to reside in metro counties (82%) than NHWs (74%). The average length of follow-up varied by cancer site and metro status, ranging from 35 months (CRC in metro areas) to 4 months (pancreatic cancer in non-metro areas). For all cancer sites, patients in metro counties were more likely to be younger, single, have localized disease (except gastric cancer), and receive surgery of the primary site. By comparison, patients in non-metro counties were more likely to be diagnosed at older ages, have Medicaid, and be widowed. Residing in a non-metro county was associated with higher cause- specific mortality among the youngest patients (age 18-49 years) for pancreatic cancer (HR=1.4, 95% CI: 1.1, 1.9) and gastric cancer (HR=1.8, 95% CI: 1.2, 2.6). We also observed a higher hazard of mortality for residing in a non-metro compared to metro county among NHB HCC patients (HR=1.3, 95% CI: 1.0, 1.7) and never married gastric cancer patients (HR=1.5, 95% CI: 1.1, 2.1). Associations among CRC patients were less robust. Conclusion: Our results suggest residing in a non-metro county is associated with higher mortality among some groups diagnosed with rarer gastrointestinal cancers. Geographic differences in access to diagnosis and treatment may contribute to this disparity. Citation Format: Rebecca Nash, Jasmine M. Miller-Kleinhenz, Maria C. Russell, Lindsay J. Collin, Katherine Ross-Driscoll, Jeffrey M. Switchenko, Lauren E. McCullough. Association between geography and cause-specific mortality in gastrointestinal cancers in Georgia [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-168.

Spatial clustering analysis and trend of liver cancer death rate in Shandong province, 1970-2013

Objective: To explore the spatial clustering and trend of liver cancer mortality in different counties of Shandong province from 1970 to 2013, and provide scientific basis for the development of liver cancer prevention and control plan. Methods: Cancer mortality data were obtained from Shandong Death Registration System and three national death cause surveys in China. Mortality rate and age adjusted mortality rate were used to describe the trend of liver cancer in different years. Difference decomposing method was applied to estimate the contribution of demographic and non-demographic factors to the change of mortality. Software ArcGIS 10.2 was used for spatial analysis, and software SaTScan 9.4 was used for spatial clustering analysis on liver cancer mortality. Results: From 2011 to 2013, the crude mortality rate of liver cancer (29.89/100 000) in Shandong increased by 208.00% and 35.37% respectively compared with that during 1970-1974 (9.72/100 000) and 1990-1992 (22.08/100 000) and was similar to that during 2004-2005 (30.44/100 000). While age standardized mortality rate (ASMR) increased first and then decreased. The ASMR during 2011-2013 (12.62/100 000) increased by 60.97% compared with that during 1970-1974 and decreased by 22.38% and 21.81% compared with that during 1990-1992 and 2004-2005, respectively. According to the difference decomposition analysis on liver cancer mortality in different years, the contribution of population factors to the liver cancer mortality rate increased from 3.38% during 1990-1992 to 29.36% during 2004-2005 and 46.16% during 2011-2013. However, the contribution of non-population factors to the increase of liver cancer mortality decreased. According to the spatial distribution of liver cancer mortality, the crude mortality rate of liver cancer in different counties were quite different, ranging from 9.33/100 000 to 65.33/100 000. Using the spatial scanning statistical software to analyze the spatial clustering of liver cancer mortality, multi areas with high mortality rate of liver cancer were found, and they were mainly distributed in Jiaodong peninsula from 2011 to 2013, covering 20 counties (cities, districts) in Qingdao, Yantai and Weihai. The risk of liver cancer mortality in this area was 1.54 times higher than that in other areas. The spatial clustering distribution of liver cancer mortality during 1970-1974 was significantly different from that during 2011-2013, the areas with high mortality rate during 1970-1974 were mainly distributed in central and western Shandong. Conclusions: There were significant temporal and spatial distribution changes in the mortality rate of liver cancer in Shandong from 1970 to 2013. According to these trends and their geographical and spatial distribution, we should further explore the risk factors of liver cancer, and formulate feasible and area specific prevention and control measures for liver cancer.

Reducing Mortality From Motor Vehicle Crashes in Rural Communities.

Injuries are the leading cause of pediatric mortality, and motor vehicle crashes are the most common cause of injury.1 Efforts to ameliorate morbidity and mortality from these crashes have been focused on both prevention and optimizing care for those who are injured. Although preventive measures in the United States have decreased the number of pediatric deaths, our rate of improvement is lower than that of 19 comparable industrialized nations.2 In addition, although the advent of regional trauma centers has improved outcomes after an injury, the systems of care are not yet leveraged to their full potential and often are not readily accessible to those in rural areas. In this issue of Pediatrics , Mokdad et al3 highlight the disparities in outcomes between children injured in motor vehicle crashes in urban versus rural areas using the Fatality Analysis Reporting System database (years 2010–2017). The authors document higher mortality rates in rural counties and demonstrate lower mortality rates in counties with a regional trauma center. These findings are consistent with decades of literature revealing similar results from the United States and internationally.4–6 It is well known that children … Address correspondence to John Holcroft, MD, Division of Critical Care Medicine, Department of Pediatrics, University of California, Davis, 2556 Stockton Blvd, Sacramento, CA 95827. E-mail: jholcroft{at}ucdavis.edu

Associations between Greenness and Mortality by Ecoregion in the United States

Background: Several epidemiological studies report that residential proximity to greenness is associated with positive health benefits. However, it is unclear whether such associations vary with ecology and the type of vegetation. Therefore, we investigated whether the associations between greenness and mortality vary by ecology, as categorized by EPA ecoregions. Methods: County level data from 2010 in the contiguous United States (n=2,733) were analyzed using linear regression to determine whether greenness was associated with log-transformed age-adjusted all-cause and cardiovascular mortality rates (CDC WONDER). In specific EPA level I ecoregions, greenness was estimated using the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), which corrects for dense vegetation and distortions caused by airborne particles. Rural-urban continuum codes (RUCC) were used to categorize urbanicity. All models were adjusted for % male, % white, % smoking, median household income, population density, and fine particulate matter. Results: All-cause and cardiovascular mortality were inversely associated with EVI, and to a lesser extent, NDVI. Upon stratification by ecoregion, we observed that EVI was negatively associated with all-cause mortality (-4.5% per 0.1 EVI; 95% CI: -5.4, -3.5) and cardiovascular mortality (-4.6% per 0.1 EVI; 95% CI: -6.1, -3.0) in the Great Plains Region. In Eastern Temperate Forests, all-cause mortality was negatively associated with EVI (-1.8% per 0.1 EVI; 95% CI: -2.7, -1.0), but not NDVI (-0.1% per 0.1 NDVI; 95% CI: -1.1, 0.9). Stratifying by RUCC, we observed the strongest associations between greenness and mortality in rural counties with low populations. In cardiovascular mortality models, we observed stronger negative interactions with particulate matter and EVI than NDVI. Conclusions: The association between greenness and mortality varies by the surrounding ecology, such as the Great Plains region, an ecologically diverse region dominated by grasslands.

Geographical Variation in Traumatic Brain Injury Mortality by Proximity to the Nearest Neurosurgeon

BackgroundTrauma mortality disproportionately affects populations farther from potentially lifesaving trauma care, and traumatic brain injury (TBI) is no exception. Previous examinations have examined proximity to trauma centers as an explanation for trauma mortality, but little is known about the relationship between proximity to neurosurgeons specifically in TBI mortality. Materials and methodsIn this cross-sectional study, county-level TBI mortality rates from 2008 to 2014 were examined in relation to the distance to the nearest neurosurgeon and trauma facility. The locations of practicing neurosurgeons and trauma facilities in the United States were determined by geocoding data from the 2017 Medicare Physician and Other Supplier and Provider of Services files (respectively). The association between TBI mortality and the distance from the population-weighted centroid of the county to a closest neurosurgeon and trauma facility was examined using multivariate negative binomial regression. ResultsA total of 761 of the 3108 counties (24.5%) in the continental United States were excluded from the analysis because they had 20 or fewer TBI deaths during this time, producing unstable estimates. Excluded counties accounted for 1.67% of the US population. Multivariate analysis revealed a county's mortality increased 10% for every 25 miles from the nearest neurosurgeon (adjusted incident rate ratio: 1.10 [95% confidence interval: 1.08-1.12]; P < 0.001). The distance to the nearest trauma facility was not found to be significantly associated with mortality (adjusted incident rate ratio: 1.01 [95% confidence interval: 0.99-1.03]; P = 0.36). ConclusionsThese findings suggest that proximity to neurosurgeons may influence county-level TBI mortality. Further research into this topic with more granular data may help to allocate scarce public health resources.

Persistent Poverty and Cancer Mortality Rates: An Analysis of County-Level Poverty Designations.

Cancer mortality is higher in counties with high levels of (current) poverty, but less is known about associations with persistent poverty. Persistent poverty counties (with ≥20% of residents in poverty since 1980) face social, structural, and behavioral challenges that may make their residents more vulnerable to cancer. We calculated 2007 to 2011 county-level, age-adjusted, and overall and type-specific cancer mortality rates (deaths/100,000 people/year) by persistent poverty classifications, which we contrasted with mortality in counties experiencing current poverty (≥20% of residents in poverty according to 2007-2011 American Community Survey). We used two-sample t tests and multivariate linear regression to assess mortality by persistent poverty, and compared mortality rates across current and persistent poverty levels. Overall cancer mortality was 179.3 [standard error (SE) = 0.55] deaths/100,000 people/year in nonpersistent poverty counties and 201.3 (SE = 1.80) in persistent poverty counties (12.3% higher, P < 0.0001). In multivariate analysis, cancer mortality was higher in persistent poverty versus nonpersistent poverty counties for overall cancer mortality as well as for several type-specific mortality rates: lung and bronchus, colorectal, stomach, and liver and intrahepatic bile duct (all P < 0.05). Among counties experiencing current poverty, those counties that were also experiencing persistent poverty had elevated mortality rates for all cancer types as well as lung and bronchus, colorectal, breast, stomach, and liver and intrahepatic bile duct (all P < 0.05). Cancer mortality was higher in persistent poverty counties than other counties, including those experiencing current poverty. Etiologic research and interventions, including policies, are needed to address multilevel determinants of cancer disparities in persistent poverty counties.

Trends in Heart Failure-related Cardiovascular Mortality in Rural Versus Urban Counties in the United States, 2011-2018

Introduction Mortality rates due to heart failure (HF) have increased since 2011, but estimates of heterogeneity at the county-level in HF-related mortality are not known. Differences between rural and urban counties and the impact of county-level factors on HF-related mortality are needed to inform public health strategies. Hypotheses Substantial variation in HF-related mortality trends between 2011-2018 exists by rural-urban designation. In addition, county-level factors account for a large proportion rural-urban differences in HF-related mortality rates. Methods We queried CDC WONDER to identify HF deaths between 2011-2018 defined as any mention of HF (I50) on the death certificate and cardiovascular disease (I00-78) as the underlying cause of death. First, we calculated age-adjusted mortality rates (AAMR) and examined trends stratified by rural-urban status (defined using 2013 NCHS Urban-Rural Classification Scheme), age (35-64 and 65-84 years), and race-sex subgroups per year. Second, we combined all deaths from 2011-2018 and estimated incidence rate ratios (IRR) in HF-related mortality for rural versus urban counties using multivariable negative binomial regression models with adjustment for sociodemographic factors, risk factor prevalence, and physician density. Results Between 2011-2018, AAMRs were consistently higher for rural compared with urban counties (e.g., 73.2 [95% CI: 72.2-74.2] vs. 57.2 [56.8-57.6] in 2018, respectively). The highest AAMR was observed in rural black men (131.1 [123.3-138.9] in 2018), and the greatest increases in HF-related mortality occurred in rural black men, age 35-64 years (+6.1%/year). Unadjusted models demonstrated significantly higher HF-related mortality in rural versus urban counties across the entire study period (IRR = 1.67 [95% CI: 1.57-1.78] and 1.16 [95% CI: 1.13-1.18] for younger and older adults, respectively). The rural-urban IRR was significantly attenuated after adjustment for county-level factors among both younger (1.10 [1.04-1.16]) and older adults (1.04 [1.02-1.07]). Conclusions Differences in county-level factors account for a significant amount of the observed variation in HF-related mortality between rural and urban counties.

Improving Health Equity for Women Involved in the Criminal Legal System.

Improving Health Equity for Women Involved in the Criminal Legal System.

Association between coal and firewood combustion and hospital admissions and mortality in Chile 2015 - An ecological approach.

Burning coal and firewood generates toxic emissions that are associated with respiratory illness, cardiovascular disease, and even death. The aim of the study is to evaluate the association between county-level prevalence of household coal and firewood use and health outcomes, including total, respiratory, and cardiovascular mortality, as well as total and respiratory hospitalization rates. The ecological study included data on the use of household coal and firewood in 139 counties obtained from the 2015 Chilean National Socio-economic Characterization Survey. Total, respiratory, and cardiovascular mortality, as well as total and respiratory hospitalization rates, were obtained from the Department of Health Statistics. Poisson models with robust error variance, Pearson linear correlation coefficients, and scatterplots were used to explore associations between household coal and firewood use and morbidity-mortality, stratifying by geographic zone. Total, respiratory, and cardiovascular mortality and total and respiratory hospitalization rates were 5.7 per 1,000, 552 per 100,000, 157 per 100,000, 92.5 per 1000, and 8.8 per 1000 inhabitants, respectively. The median prevalence of coal use for residential cooking, heating, or water heating was 3.64%, while the median prevalence of firewood combustion was 12%. In southern counties, age- and gender-adjusted respiratory mortality increased 2.02 (95% CI: 1.17-3.50), 1.5 (95% CI: 1.11-1.89), and 1.76-fold (95% CI: 1.19-2.60) for each percentage increase in household coal and firewood use for heating, cooking and heating water, respectively. The prevalence of household coal and firewood used for heating and cooking was positively correlated with respiratory mortality and hospitalization in southern zone counties.

Abstract IA10: Colorectal cancer mortality along the U.S.-Mexico border

Abstract Colorectal cancer (CRC) incidence and mortality have changed strikingly in the U.S. over the past three decades. Incidence and mortality rates have decreased among older adults since the early 1990s, largely due to improvements in average-risk screening. By contrast, incidence rates have rapidly increased in younger (age &amp;lt;50 years) adults, but little is known about the mechanisms contributing to young-onset CRC. These changes in incidence and mortality rates have not occurred equally by race/ethnicity or geography. To better understand the burden of CRC among Hispanics, we examined CRC mortality along the U.S.-Mexico border. We used data from the National Center for Health Statistics to estimate age-adjusted (to the 2000 U.S. standard population) mortality rates per 100,000 persons. We compared mortality rates between counties on the U.S.-Mexico border (41 counties in California, Arizona, New Mexico, and Texas) to non-border counties in the same states, overall and by ethnicity (Hispanic vs. non-Hispanic white) and age (+/- 50 years). From 1990 to 2016, there were 25,057 CRC deaths in counties along the U.S.-Mexico border and 1,453,410 deaths in non-border counties. Age-adjusted mortality rate was 21.1 per 100,000 and 25.9 per 100,000 in border and non-border counties, respectively (rate ratio [RR] 1.23; 95% CI 1.21, 1.24). Rates decreased over time in both border and non-border counties: from 26.9 per 100,000 to 17.8 per 100,000 in border counties and from 33.9 per 100,000 to 19.9 per 100,000 in non-border counties. There were differences in mortality by county and ethnicity in older adults (age ≥50 years). Specifically, the lowest overall mortality rate was among Hispanics living in border counties (43.1 per 100,000), and the highest was among whites in non-border counties (58.6 per 100,000). These differences disappeared over time, and in 2012-16, mortality rates across the four subgroups were similar (border: 42.3 per 100,000 non-Hispanic whites, 41.6 per 100,000 Hispanics; non-border: 44.8 per 100,000 non-Hispanic whites, 41.8 per 100,000 Hispanics). Mortality rates in younger adults increased from 1990 to 2016, and rates were slightly higher in non-border counties. For example, in 2012-16, age-adjusted mortality of young-onset CRC was 2.6 per 100,000 in border counties compared to 2.9 per 100,000 in non-border counties (RR 1.11; 95% CI 1.00, 1.23). We observed lower CRC mortality rates in counties along the U.S.-Mexico border, particularly for older Hispanics. The mortality advantage in this subgroup declined over time, driven by improvements in non-border counties and among non-Hispanic whites. Rates remained stable among Hispanics in border and non-border counties. Younger adults experienced CRC-related mortality, regardless of county or ethnicity. These findings may reflect generational or birth cohort differences in cancer risk. Citation Format: Caitlin C. Murphy. Colorectal cancer mortality along the U.S.-Mexico border [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 IA10.

Did the ACA Medicaid expansion save lives?

We estimate the effect of the Affordable Care Act Medicaid expansion on county-level mortality in the first four years following expansion using restricted-access microdata covering all deaths in the United States. To adjust for pre-expansion differences in mortality rates between treatment and control, we use a propensity-score weighting model together with techniques from machine learning to match counties in expansion and non-expansion states. We find a reduction in all-cause mortality in ages 20 to 64 equaling 11.36 deaths per 100,000 individuals, a 3.6 percent decrease. This estimate is largely driven by reductions in mortality in counties with higher pre-expansion uninsured rates and for causes of death likely to be influenced by access to healthcare. A cost-benefit analysis shows that the improvement in welfare due to mortality responses may offset the entire net-of-transfers expenditure associated with the expansion.

Abstract P467: Premature Coronary Artery Disease Mortality in the Appalachian Region: Is the Problem Driven by Rural-urban or Socioeconomic Disparities?

Background: Premature mortality under the age of 75 years exacerbates the burden of coronary artery disease (CAD) as the leading cause of death in the nation, with particular concerns being raised about its disproportionate effect on the vulnerable population of the Appalachian region compared to other regions of the United States. It is unknown whether the problem of premature CAD mortality in the Appalachian residents is further aggravated by disparities across various population groups within the Appalachian region itself. We hypothesized that within-region socioeconomic and geographic (rural-urban) disparities, independently of each other, aggravate the premature CAD mortality problem in the Appalachian population. Methods: A population-based study of age-adjusted premature CAD mortality among 25-74 year old residents of the Appalachian region was conducted (n=63,630,221). Counties’ rurality status was defined using the U.S. Department of Agriculture Economic Research Service’s (USDA ERS) Rural-Urban Continuum Codes: of 420 Appalachian counties, 217 were classifies as rural and 203 - as urban. To adjust for multiple covariates, general linear modeling (GLM) analysis was conducted (SAS, ver. 9.4). Results: Age-adjusted premature CAD mortality rates were higher in rural than urban Appalachian counties: 99.0 + 40.2 vs. 78.8 + 26.4 per 100,000 (p&lt;0.01). Compared to urban counties, their rural counterparts had a higher prevalence of poverty (30.8 + 7.3% vs. 24.4 + 5.7%, p&lt;0.01), unemployment (8.1 + 2.0% vs. 6.7 + 1.2%, p&lt;0.01), lack of insurance (18.4 + 3.5% vs. 16.0 + 3.6%, p&lt;0.01), smoking (22.2 + 3.8% vs. 20.2 + 2.9%, p&lt;0.01), obesity (33.1 + 3.8% vs. 32.0 + 3.5%, p&lt;0.01), sedentary lifestyle (32.2 + 4.5% vs. 29.6 + 4.7%, p&lt;0.01). In the multivariate-adjusted analysis, smoking ( beta 3.322, p&lt;0.01), sedentary lifestyle ( beta 0.947, p&lt;0.01), healthcare services accessibility and utilization ( beta -0.532, p&lt;0.05), unemployment ( beta 2.761, p&lt;0.05) and poverty ( beta 0.652, p&lt;0.05) were statistically significantly associated with age-adjusted premature CAD mortality. Conclusions: Premature CAD mortality in rural Appalachian counties is statistically significantly higher than in urban Appalachian counties. Socioeconomic disparities and modifiable CAD risk factors further aggravate the problem.

County Jail Incarceration Rates and County Mortality Rates in the United States, 1987-2016.

Objectives. To evaluate the relationship between changes in county jail incarceration rates and subsequent county mortality rates across the United States.Methods. We analyzed county jail incarceration rates from the Bureau of Justice Statistics from 1987 to 2016 for 1884 counties and mortality rates from the National Vital Statistics System. We fit 1-year-lagged quasi-Poisson 2-way fixed-effects models, controlling for unmeasured stable county characteristics, and measured time-varying confounders, including county poverty and crime rates.Results. A within-county increase in jail incarceration rates from the first to second quartile was associated with a 2.5% increase in mortality rates, adjusting for confounders (risk ratio [RR] = 1.03; 95% confidence interval [CI] = 1.02, 1.03). This association followed a dose-response relationship and was stronger for mortality among those aged 15 to 34 years (RR = 1.07; 95% CI = 1.06, 1.09).Conclusions. Within-county increases in jail incarceration rates are associated with increases in subsequent mortality rates after adjusting for important confounders.Public Health Implications. Our findings add to the growing body of empirical evidence of the harms of mass incarceration. The criminal justice reform and decarceration movements can use these findings as they develop strategies to end mass incarceration.

Mortality of the population of the Siberian Federal District as a result of accidental alcohol poisoning in 2009-2018

Purpose of research. To study the dynamics of the death rate of the population of the Siberian Federal District (SFD) as a result of accidental acute alcohol poisoning for the period from 2009 to 2018, as well as a comparative analysis of the death rates of the population of the SFD regions with general district indicators. Materials and methods. The analysis of mortality of the population of the Russian Federation, Siberian Federal District and regions of the Siberian Federal district (Altai Republic, Tuva, Khakassia, Altai, Krasnoyarsk, Irkutsk, Kemerovo, Novosibirsk, Omsk, Tomsk region) as a result of accidental alcohol poisoning, the comparison of regional indicators with county mortality rates have been carried out. Traditional methods of calculating extensive and intensive indicators, the average error of the indicator, and evaluating the reliability of differences have been used in statistical processing of the material. Results and discussion . The death rate of the SFD population as a result of accidental alcohol poisoning has decreased by 2.0 times for the period from 2009 to 2018. The decrease in population mortality due to accidental alcohol poisoning has been revealed in all regions of the Siberian Federal District, with the exception of the Tomsk region, where the three-fold increase in death rate has been registered. The most rapid decrease in the death rate due to accidental alcohol poisoning occurred in the Krasnoyarsk territory (4.4 times) and the Republic of Altai (3.5 times) in the period from 2009 to 2018. The study of the dynamics of population mortality in the regions of the Siberian Federal District as a result of accidental alcohol poisoning allows us to identify the features of the toxicological situation at the regional level and determine strategic directions for regional chemical safety in the future.

Factors influencing high respiratory mortality in coal-mining counties: a repeated cross-sectional study

BackgroundPrevious studies have associated elevated mortality risk in central Appalachia with coal-mining activities, but few have explored how different non-coal factors influence the association within each county. Consequently, there is a knowledge gap in identifying effective ways to address health disparities in coal-mining counties. To specifically address this knowledge gap, this study estimated the effect of living in a coal-mining county on non-malignant respiratory diseases (NMRD) mortality, and defined this as “coal-county effect.” We also investigated what factors may accentuate or attenuate the coal-county effect.MethodsAn ecological epidemiology protocol was designed to observe the characteristics of three populations and to identify the effects of coal-mining on community health. Records for seven coal-mining counties (n = 19,692) were obtained with approvals from the Virginia Department of Health Office of Vital Statistics for the years 2005 to 2012. Also requested were records from three adjacent coal counties (n = 10,425) to provide a geographic comparison. For a baseline comparison, records were requested for eleven tobacco-producing counties (n = 27,800). We analyzed the association of 57,917 individual mortality records in Virginia with coal-mining county residency, county-level socioeconomic status, health access, behavioral risk factors, and coal production. The development of a two-level hierarchical model allowed the coal-county effect to vary by county-level characteristics. Wald tests detected sets of significant factors explaining the variation of impacts across counties. Furthermore, to illustrate how the model estimations help explain health disparities, two coal-mining county case studies were presented.ResultsThe main result revealed that coal-mining county residency increased the probability of dying from NMRD. The coal-county effect was accentuated by surface coal mining, high smoking rates, decreasing health insurance coverage, and a shortage of doctors. In Virginia coal-mining regions, the average coal-county effect increased by 147% (p-value< 0.01) when one doctor per 1000 left, and the effect increased by 68% (p-value< 0.01) with a 1% reduction of health insurance rates, holding other factors fixed.ConclusionsThis study showed a high mortality risk of NMRD associated with residents living in Virginia coal-mining counties. Our results also revealed the critical role of health access in reducing health disparities related to coal exposure.

Spatial clusters of life expectancy and association with cardiovascular disease mortality and cancer mortality in the contiguous United States: 1980-2014.

The average life expectancy varies greatly from county to county in USA and there are also spatial variations in the county mortality rates for cardiovascular disease (CVD) and cancer, the top two causes of death. An association between these two groups of diseases has not been identified by cluster analysis previously. The main objective in this study was to investigate and quantify the associations between mortality due to CVD, cancer mortality and life expectancy based on US county data between 1980 and 2014. Regression analysis was used to adjust life expectancy for the mortality due to CVD and that due to cancer. In addition to the spatial life expectancy trends, we also studied existing trends over time with the software JOINPOINT to see how life expectancy is influenced by changes in mortality due to CVD and cancer mortality. The study setting was the 48 contiguous US states, while participants were 3,100 counties and their populations of all ages during the period 1980-2014. The main outcomes are spatial clusters of unusually low or high levels of life expectancy in addition to identifying which county level life expectancy locations were significantly associated with mortality due to CVD and/or cancer. Life expectancy has been improving steadily from 1980 to 2014, but the rate of increase per year (indicated by variation of the trend slope) changed significantly at five joinpoints, the latest of which occurred in 2010 when the slope changed from 0.29 (1980-1982) to 0.03 (2010-2014). Our results reveal that there are significant, purely spatial clusters in some geographical areas where life expectancy rates are significantly higher (or lower) than in the rest of the contiguous US. It is also shown that there is a significant association between the life expectancy level and the corresponding CVD mortality, and there is also a significant association between life expectancy level and the corresponding overall cancer mortality. The general trends (regression slopes) over time for the USA in life expectancy mortality, CVD mortality and cancer mortality have changed significantly after 2009-2010.

Long-term impact of the Garrett Lee Smith Youth Suicide Prevention Program on youth suicide mortality, 2006-2015.

Comprehensive suicide prevention programs funded through the Garrett Lee Smith Memorial Youth Suicide Prevention Program (GLS) have previously been shown to be associated with lower youth suicide mortality rates 1year following program implementation. However, longer term effects of GLS have yet to be examined. The impact of GLS implementation on youth suicide mortality through 2015 was estimated for U.S. counties initially exposed to state and tribal GLS activities between 2006 and 2009. The analytic approach combined propensity score-based techniques to address potential confounding arising from differences between counties exposed and not exposed to the program along an extensive set of characteristics, including historical suicide rates. Counties exposed to GLS during a single year had youth suicide mortality rates lower than expected and for longer than previously reported following implementation of GLS activities. Youth suicide mortality rates in counties implementing GLS were estimated to be 0.9 per 100,000 youths lower than control counties (p=.029) 1year after the implementation, and 1.1 per 100,000 youths lower than control counties (p=.010) 2years after the implementation. Further, persistent implementation during multiple years was associated with larger effects during longer periods. Additionally, among rural counties, the youth suicide rates 2years after exposure were estimated to be 2.4 per 100,000 youths lower than in the absence of the program (p=.003). There was no significant evidence of a decrease in youth suicide rates three or more years after the GLS activities were discontinued. The effects of GLS comprehensive suicide prevention program were found to be stronger and longer lasting than previously reported, particularly in rural counties. In the face of well-documented increases in national suicide prevention rates, these results support the widespread and persistent implementation of comprehensive, community-based youth suicide prevention programs.

Identifying Priority and "Bright-Spot" Counties for Diabetes Preventive Care in Appalachia: An Exploratory Analysis.

IntroductionType 2 diabetes mellitus (T2DM) prevalence and mortality in Appalachian counties is substantially higher when compared to non-Appalachian counties, although there is significant variation within Appalachia.PurposeThe objectives of this research were to identify low-performing (priority) and high-performing (bright spot) counties with respect to improving T2DM preventive care.MethodsUsing data from the Centers for Medicare and Medicaid (CMS), the Dartmouth Atlas of Health Care, and the Appalachia Regional Commission, conditional maps were created using county-level estimates for T2DM prevalence, mortality, and annual hemoglobin A1c (HbA1c) testing rates. Priority counties were identified using the following criteria: top 33rd percentile for T2DM mortality; top 33rd percentile for T2DM prevalence; bottom 50th percentile for A1c testing rates. Bright spot counties were identified as counties in the bottom 33rd percentile for T2DM mortality, the top 33rd percentile for T2DM prevalence; and the top 50th percentile for HbA1c testing rates.ResultsForty-one priority counties were identified (those with high T2DM mortality, high T2DM prevalence, and low HbA1c testing rates), which were located primarily in Central and North Central Appalachia; and 17 bright spot counties were identified (high T2DM prevalence, low T2DM mortality, and high HbA1c testing rates), which were scattered throughout Appalachia. Eight of the 17 bright spot counties were adjacent to priority counties.ImplicationsBy employing conditional mapping to T2DM, multiple variables can be summarized into a single, easily interpretable map. This could be valuable for T2DM-prevention programs seeking to prioritize diagnostic and intervention resources for the management of T2DM in Appalachia.