Structural characteristics and drivers of greenhouse gas emissions at county-level and long-time scales: A case study of the Anji County, China

To achieve carbon neutrality, the Chinese government needs to gain a comprehensive understanding of the sources and drivers of greenhouse gas (GHG) emissions, particularly at the county level. Anji County in eastern China is a typical example of an industrial transformation from quarrying to a low-carbon economy. This study analyzed the decoupling types and structural characteristics of GHG emissions and the driving factors of carbon dioxide (CO2) emissions in the Anji from 2006 to 2019, and explored the differences between county-level and provincial-level or city-level results. It was observed that energy-related activities are the main source of GHG emissions in Anji and that economic development is the driving factor behind the increasing CO2 emissions. However, industrial transformation and upgradation coupled with the alternative use of clean energy limit the growth of GHG emissions. This study details the GHG emissions of county during the industrial transformation stage and provides corresponding policy recommendations for county governments.

Disparity in hospital beds' allocation at the county level in China: an analysis based on a Health Resource Density Index (HRDI) model

BACKGROUND

As approximately 3/4 of the population lives in county-level divisions in China, the allocation of health resources at the county level will affect the realization of health equity. This study aims to evaluate the disparity in hospital beds at the county level in China, analyze its causes, and discuss measures to optimize the allocation.

METHODS

Data were drawn from the Chinese County/City Statistical Yearbook (2001-2020). The health resource density index (HRDI) was applied to mediate between the influence of demographic and geographical factors on the allocation of hospital beds. The trends of HRDI allocation were evaluated through the growth incidence curve and the probability density function. The regional disparity in the HRDI was examined through the Lorenz curve, and Dagum Gini coefficient. The contribution of the Gini coefficient and its change were assessed by using the Dagum Gini decomposition method.

RESULTS

From 2000 to 2019, the number of hospital beds per thousand people at the county level in China increased dramatically by 1.49 times. From the aspect of the HRDI, there were large regional disparities at the national level, with a Gini coefficient of 0.367 in 2019 and in the three subregions. In 2019, the Gini coefficient of the HRDI exhibited regional variations, with the highest value observed in the western region, followed by the central region and the eastern region. Decomposition reveals that the contribution of interregional disparity changed from the dominant factor to the least important factor, accounting for 29.79% of the overall disparity and the contribution of trans-variation intensity increased from 29.19% to 39.75%, whereas the intraregional disparity remained stable at approximately 31% and became the second most important factor.

CONCLUSION

The regional disparity in hospital beds allocation at the county level in China was large and has not improved substantially. Trans-variation intensity was the main reason for the overall disparity and changes, and the intraregional disparity was more important than the interregional disparity for the overall disparity.

Spatialization and driving factors of carbon budget at county level in the Yangtze River Delta of China

The county is the basic administrative unit of China, and the spatialization of carbon budget at the county scale plays an irreplaceable role in deepening the understanding of the carbon emission mechanism and spatial pattern. Yueqing County, an economically developed county in the Yangtze River Delta of China, was selected as the study area, the spatial pattern of the carbon budget and the optimal resolution of the spatialization at the county level were dissected on the basis of accurate accounting, and driving factors of carbon emissions were further identified using the geographically weighted regression model. The results indicated that (1) the carbon emissions were mainly generated from fossil fuel combustion related to energy, accounting for 98.8% of the total carbon budget in the study area; (2) the optimal resolution of spatialization was 200 m and carbon emissions were concentrated in the southeast of the study area; (3) energy intensity, energy structure, per capita GDP, and urbanization rate were positively correlated with carbon emissions, while population played a bidirectional role in carbon emissions. This study not only strengthens the understanding of the patterns and drivers of the carbon budget but also establishes a theoretical framework and operational tools for policymakers to formulate solutions to mitigate the carbon crisis.

Spatial-temporal characteristics and influencing factors of county-level carbon emissions in Zhejiang Province, China

Zhejiang Province is a "demonstration area for high-quality development and construction of common prosperity" in China. Moreover, the county is the basic unit and power source for the economic development of Zhejiang Province. Therefore, the research on the spatial-temporal characteristics and influencing factors of county-level carbon emissions is of great significance for Zhejiang Province to achieve the strategic goal of carbon peak and carbon neutrality. Based on the carbon emissions and socio-economic data of 62 counties in Zhejiang Province from 2014 to 2020, the spatial dependence and agglomeration of county-level carbon emissions are analyzed through the spatial autocorrelation test and local spatial autocorrelation test respectively. According to the spatial-temporal characteristics of county-level carbon emissions revealed by the index of Moran's I and local Moran's I, the spatial error STIRPAT model is used to study the influencing factors of county-level carbon emissions in Zhejiang Province, China. The main results are as follows: (1) The total amount of county-level carbon emissions of 62 counties fluctuates from 259.69 to 326.28 million tons and shows a growth trend. (2) Moran's I index is between 0.369 and 0.399. The county-level carbon emissions have a significant spatial correlation, and the spatial agglomeration trend is relatively stable, which is consistent with the hypothesis of the geographical polarization effect. (3) High-high agglomeration counties are concentrated in the northeast of Zhejiang Province, while low-low agglomeration counties are mainly in the southwest. (4) The relationship between county per capita GDP and carbon emissions has not been "decoupled," because when other variables remain unchanged, the county's total carbon emissions will increase by 2.866% for every 1% increase in the county's per capita GDP; the increase of the proportion of secondary industry contributes to the decline of carbon emissions, and the low-carbon effect brought by large-scale industrial development as well as scientific and technological innovation has not yet appeared. (5) The estimate of the spatial coefficient λ was 0.324, which illustrates that the carbon emission of a single county is positively affected by the carbon emission of the neighboring counties, and other socio-economic factors affecting carbon emission among counties also have a spatial correlation. Therefore, the policy of realizing regional coordinated development as well as the carbon peaking and carbon neutrality goals should not only focus on industrial layout, but also take a dynamic and comprehensive consideration from a spatial perspective.

Study on the extension of the dynamic benchmark system of per capita carbon emissions in China's county

County is the center of China's socio-economic development and the key node for urban-rural integration. Also, the county is an important carrier for promoting urban and rural green development. Improving green and low-carbon development capabilities and formulating county-level low-carbon standards will play a significant role in promoting China's new people-oriented urbanization and rural revitalization. Although there have been extensive studies on low-carbon benchmarks, over half of the benchmarks tend to ignore the development stage of the evaluated region and its needs. When the region's economy reaches a certain level, constraints from low-carbon targets may limit the local development process. This study firstly allocated county carbon intensity reduction targets (CIRT) by considering the differences in county carbon reduction capacity and responsibility. Secondly, a dynamic benchmark system of per capita carbon emissions (PCCE) in counties in China is constructed. Finally, we took Changxing County in Zhejiang Province as a research case to verify the dynamic benchmark of PCCE. According to the carbon intensity target reduction rate (CITRR), China's counties can be divided into three categories: low carbon emissions reduction capability-responsible counties (L-CERCRC), medium carbon emissions reduction capability-responsible counties (M-CERCRC), and high carbon emissions reduction capability-responsible counties (H-CERCRC). The results show that (1) due to the national CO₂ emission reduction target in 2030, the carbon intensity will be 60% lower than in 2005, the CITRR for China's 1510 counties range from 8.36 to 137.83%; the average CITRR is 48.40%. (2) Changxing County's CITRR is 57.71%, which belongs to the H-CERCRC. The PCCE of Changxing County will be much higher than the benchmark when the carbon peak is reached in the future. (3) For reaching the aiming benchmark, Changxing County is suggested to adjust its relevant influencing factors of PCCE for converting local's PCCE reaching to the benchmark within a certain time period. The dynamic benchmark system for PCCE in China's counties established in this study is economically sensitive, which not only takes the differences of counties into account, but also meets the needs of counties' diverse development form stages. This system provides counties a few coordinated directions which can improve the local's economic development and reduce greenhouse gas (GHGs) emissions through the development progress.

Spatiotemporal variations and structural characteristics of carbon emissions at the county scale: a case study of Wu'an City

In China, the county is not only an important component of industrial areas and a large contributor of carbon emissions, but also a key administrative unit for the implementation of carbon peak and carbon neutrality goals and policies. The spatiotemporal variations and structural characteristics of carbon emissions at the county scale are of great significance to China's dual goals of regional carbon policy implementation and low carbon spatial planning. Thus, it is important and insightful to conduct an in-depth and detailed examination of these characteristics while focusing on a typical iron and steel industry county-level city in North China. This study systematically calculated the carbon emissions of the county-level city of Wu'an from 2008 to 2017, and explored their structural characteristics and spatiotemporal variations. The results showed that (1) under the influence of macroeconomic and national policies, the carbon emissions of county-level cities dominated by the iron and steel industry show obvious phased characteristics; (2) there is a significant negative correlation between industry carbon emission concentrations and industrial carbon emissions; (3) within the steel industry system, sintering, iron smelting, steelmaking, and metal product processing are the main sources of carbon emissions, and the coal-based production process of the iron and steel industry needs a fundamental reformation; and (4) the carbon emission of Wu'an City shows obvious spatial differentiation characteristics. The geographic distribution of carbon emissions in Wu'an City is very unbalanced and tended to cluster together in urban areas, industrial and mining areas, and major towns. Taking 2014 as the turning point, the spatial pattern of carbon emissions in Wu'an City presents different variation characteristics.

Factors Associated With Geographic Disparities in Gastrointestinal Cancer Mortality in the United States

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 R².

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.

County-level vaccination coverage and rates of COVID-19 cases and deaths in the United States: An ecological analysis

Background

On Dec 14, 2020, the United States initiated a nationwide COVID-19 vaccination campaign. Demonstrating clear population-level impact following vaccine introduction helps to further elucidate and quantify the public-health benefits of vaccination.

Methods

Using a negative binomial regression model we evaluated the ecological association between county-level COVID-19 vaccine uptake and rates of COVID-19 cases and deaths in the United States from April 1, 2021 through October 31, 2021 controlling for a broad set of county-level environmental, sociodemographic, economic, and health-status-related characteristics. County-level data were obtained from several publicly available databases that were merged for analysis.

Findings

After adjustment for county-level characteristics, US counties with ≥ 80% of their residents ≥ 12 years of age fully vaccinated against COVID-19 had 30% (95% CI: 25−35; P < .001) and 46% (38−52; P < .001) lower rates of COVID-19 cases and deaths, respectively, versus those with <50% coverage (reference group). A dose response was observed: counties with 70−79% uptake had 20% (95% CI: 16−24; P < .001) and 35% (29−40; P < .001) lower rates of cases and deaths, respectively; counties with 60−69% uptake had 8% (5−11; P < .001) and 20% (15−24; P < .001) lower rates; and counties with 50−59% uptake had 2% (0−4; P =.09) and 8% (4−12; P < .001) lower rates. Restricting the analysis to the period when the Delta variant was predominant (June 1, 2021 ‒ October 31, 2021) showed similar findings.

Interpretation

Our results showed that US counties with higher proportions of persons ≥ 12 years of age fully vaccinated against COVID-19 had substantially lower rates of COVID-19 cases and deaths—a finding that showed dose response and persisted even in the period when Delta was predominant.

Funding

Pfizer.

How to obtain industrial waste data at the county scale: Two downscaling models and their application in Dongguan, China

The availability of environmental emission data is critical in evaluation of countries' ecological security and the implementation of environmental management. However, access to environmental emission data at the county level is not provided by statistical publications and bulletins. Therefore, in this paper, we develop two novel data downscaling models, an environmental Kuznets curve downscaling model (EKCDM) and a scale model (SM), to obtain county-level environmental emission data. The EKCDM relies on the EKC hypothesis and the assumption that the same model applies across scales, whereas the SM depends on the assumption that the share of a region's environmental pollution is equivalent to its share of economic output. Subsequently, environmental emission data above the county scale can be obtained through model transformation and simple calculations. By verifying and analyzing the official data with the one obtained through downscaling at municipal level and above, we verify the feasibility of the models, after which we apply the models to extrapolate information on the industrial waste of the counties of Dongguan city in Guangdong Province, China. We find that the EKCDM should be given priority in most cases, especially for the quadratic parameter model, and that the SM can be adopted when per capita gross domestic product differs greatly between adjacent levels of administrative units. In general, scholars can synthesize the characteristics of these two models, and obtain more accurate data by supplementing and verifying one with the other. Compared with other downscaling methods, our methods require far less data and the concepts are easily understood, which makes them more feasible and increases applicability. This study provides scholars with powerful tools to explore the relationship between industrial pollution and economic development in depth by obtaining industrial waste data at the county scale, thereby supporting scientific research and policy design.

Mortality versus Municipal and State Government Spending in American Cities

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.

Describing the changing relationship between opioid prescribing rates and overdose mortality: A novel county-level metric

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.

County-Level Variation in Utilization of Surgical Resection for Early-Stage Hepatopancreatic Cancer Among Medicare Beneficiaries in the USA

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.

Assessment of county-level proxy variables for household firearm ownership

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.

California Autism Prevalence by County and Race/Ethnicity: Declining Trends Among Wealthy Whites

County-level ASD prevalence was estimated using an age-resolved snapshot from the California Department of Developmental Services (DDS) for birth years 1993–2013. ASD prevalence increased among all children across birth years 1993–2000 but plateaued or declined thereafter among whites from wealthy counties. In contrast, ASD rates increased continuously across 1993–2013 among whites from lower income counties and Hispanics from all counties. Both white ASD prevalence and rate of change in prevalence were inversely correlated to county income from birth year 2000–2013 but not 1993–2000. These disparate trends within the dataset suggest that wealthy white parents, starting around 2000, may have begun opting out of DDS in favor of private care and/or making changes that effectively lowered their children’s risk of ASD.

Small Area Estimation of Mental Distress Among United States Military Veterans in Illinois

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.

Predicting geographical variation in health-related quality of life

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.

County-level socioeconomic and crime risk factors for substantiated child abuse and neglect

Rates of substantiated child abuse and neglect vary significantly across counties. Despite strong cross-sectional support for links between social-contextual characteristics and abuse and neglect, few longitudinal studies have tested relations between these risk factors and substantiated rates of abuse/neglect. The goal of this study was to identify county-level socioeconomic and crime factors associated with substantiated abuse/neglect rates over 13 years (2004-2016). Annual county-level data for Tennessee, obtained from the KIDS COUNT Data Center, included rates of substantiated child abuse and neglect, children's race and ethnicity, births to unmarried women, teen birth rate, children in families receiving Supplemental Nutrition Assistance Program (SNAP) benefits, and children in families receiving Temporary Assistance for Needy Families. Annual county-level crime report data, obtained from the Tennessee Incident Based Reporting System, included sexual offenses, non-sexual assaults, stalking incidents, thefts, property damage, and drug-related offenses. Bayesian spatio-temporal models indicated that substantiated child abuse and neglect rates were independently and positively associated with teen birth rates, percentages of births to unmarried mothers, drug-related offenses, and percentages of children receiving SNAP benefits. In contrast, substantiated child abuse and neglect rates were negatively associated with percentages of African-American youth. The findings highlighted distinct demographic, socioeconomic, and crime factors associated with substantiated child abuse and neglect rates and have the potential to enhance identification of high-risk counties that could benefit from targeted abuse and neglect prevention efforts.

Before the here and now: What we can learn from variation in spatiotemporal patterns of changing heart disease mortality by age group, time period, and birth cohort

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.

How does availability of county-level healthcare services shape terminal decline in well-being?

Both lifespan psychology and life course sociology highlight that contextual factors influence individual functioning and development. In the current study, we operationalize context as county-level care services in inpatient and outpatient facilities (e.g., number of care facilities, privacy in facilities) and investigate how the care context shapes well-being in the last years of life. To do so, we combine 29 waves of individual-level longitudinal data on life satisfaction from now deceased participants in the nationwide German Socio-Economic Panel Study (N = 4557; age at death: M = 73.35, SD = 14.20; 47% women) with county-level data from the Federal Statistical Office. Results from three-level growth models revealed that having more inpatient care facilities, more employees per resident, and more staff in administration are each uniquely associated with higher late-life well-being, independent of key individual (age at death, gender, education, disability) and county (affluence, demographic composition) characteristics. Number of employees in physical care, residential comfort, and flexibility and care indicators in outpatient institutions were not found to be associated with levels or change in well-being. We take our results to provide empirical evidence that some contextual factors shape well-being in the last years of life and discuss possible routes how local care services might alleviate terminal decline.

Individual- and county-level predictors of cervical cancer screening: a multi-level analysis

OBJECTIVES

Despite the gains in cervical cancer screening, there remain persistent socio-economic, geographical, racial, and ethnic disparities. This study examines the combined effect of individual- and county-level characteristics on the use of cervical cancer screening tests such as Papanicolaou (Pap) tests in Texas.

STUDY DESIGN

Cross-sectional study.

METHODS

Individual-level information was obtained from 2014-2015 Texas Behavioral Risk Factor Surveillance System (BRFSS). Using the county of residence of the study population, the BRFSS data were linked to the American Community Survey (2010-2014) and the Area Health Resources File (2015). Women aged between 21 and 65 years, with no history of hysterectomy, and residing in 47 counties in Texas were included in the study (n = 4276). Multi-level logistic regression was used to assess the independent influences of individual- and county-level covariates on receipt of a Pap test in the past 3 years.

RESULTS

The odds of timely Pap testing were lower among women aged greater than 50 years, single women, and those with low education and income (<$25,000). Black women who reside in counties with higher percentages of Hispanics (quartile 4) were less likely to be screened compared with black women living in counties with a low Hispanic population (adjusted odds ratio [OR] = 0.08 [95% confidence interval [CI]: 0.02-0.37]). County-level socio-economic status, although associated with timely screening in bivariate analysis, was not a significant predictor of screening after controlling for individual characteristics.

CONCLUSIONS

There are significant disparities in the uptake of cervical cancer screening across Texas counties. Individual-level socio-economic disparities as well as the number of obstetric-gynecologic physicians in a county are predictors of these disparities.

The convergence of Chinese county government health expenditures: capitation and contribution

BACKGROUND

The disparity between government health expenditures across regions is more severe in developing countries than it is in developed countries. The capitation subsidy method has been proven effective in developed countries in reducing this disparity, but it has not been tested in China, the world's largest developing country.

METHODS

The convergence method of neoclassical economics was adopted to test the convergence of China's regional government health expenditure. Data were obtained from Provinces, Prefectures and Counties Fiscal Statistical Yearbook (2003-2007) edited by the Chinese Ministry of Finance, and published by the Chinese Finance & Economics Publishing House.

RESULTS

The existence of σ-convergence and long-term and short-term β-convergence indicated the effectiveness of the capitation subsidy method in the New Rural Cooperative Medical Scheme on narrowing county government health expenditure disparities. The supply-side variables contributed the most to the county government health expenditure convergence, and factors contributing to convergence of county government health expenditures per capita were different in three regions.

CONCLUSION

The narrowing disparity between county government health expenditures across regions supports the effectiveness of the capitation subsidy method adopted by China's New Rural Cooperative Scheme. However, subsidy policy still requires further improvement.

Employment and income among first-time cases diagnosed with non-affective psychosis in Stockholm, Sweden: a follow-up study 2004/2005-2010

PURPOSE

Non-affective psychoses (F20-F29) are serious conditions causing a high degree of disability. Loss of income and increasing costs for personal care and treatment are severe consequences following the disorders, but less is known about employment and income in different social strata. The aim was to study these conditions among persons with non-affective psychosis compared to the general population, and possible social differentials.

METHODS

A population-based follow-up study with 530,350 persons (aged 18-44), including 756 first-time cases diagnosed with non-affective psychosis registered in in- or outpatient psychiatric care in 2005 or 2006. Age-standardised rates of non-employment, disability pension, social assistance and poverty were calculated at baseline and at follow-up in 2010. Odds ratios of poverty were estimated using logistic regression, adjusting for employment status, age, education and country of birth.

RESULTS

Before diagnosis, rates of non-employment, disability pension and social assistance were higher among persons with non-affective psychosis compared to the general population. At the follow-up, rates of disability pension had doubled, most pronounced among women with only compulsory education. Rates of social assistance were twice as high for foreign-born women. Among persons with non-affective psychosis, non-employment, lower education (among men) and being foreign born (among women) were associated with an increased risk of poverty at follow-up.

CONCLUSIONS

Poor employment and income conditions were found among persons with non-affective psychosis, but the social insurance system seemed to alleviate the poor income conditions. Early and preventative support to encourage employment and income security is needed, which could support recovery.

Tract- and county-level income inequality and individual risk of obesity in the United States

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.

Modeling Area-Level Health Rankings

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.

Multilevel built environment features and individual odds of overweight and obesity in Utah

Based on the data from the Behavioral Risk Factor Surveillance System (BRFSS) in 2007, 2009 and 2011 in Utah, this research uses multilevel modeling (MLM) to examine the associations between neighborhood built environments and individual odds of overweight and obesity after controlling for individual risk factors. The BRFSS data include information on 21,961 individuals geocoded to zip code areas. Individual variables include BMI (body mass index) and socio-demographic attributes such as age, gender, race, marital status, education attainment, employment status, and whether an individual smokes. Neighborhood built environment factors measured at both zip code and county levels include street connectivity, walk score, distance to parks, and food environment. Two additional neighborhood variables, namely the poverty rate and urbanicity, are also included as control variables. MLM results show that at the zip code level, poverty rate and distance to parks are significant and negative covariates of the odds of overweight and obesity; and at the county level, food environment is the sole significant factor with stronger fast food presence linked to higher odds of overweight and obesity. These findings suggest that obesity risk factors lie in multiple neighborhood levels and built environment features need to be defined at a neighborhood size relevant to residents' activity space.