Spatio-temporal dynamics and influencing factors of carbon emissions (1997-2019) at county level in mainland China based on DMSP-OLS and NPP-VIIRS Nighttime Light Datasets.

Global warming caused by extensive carbon emissions is a critical global issue. However, the lack of county-level carbon emissions data in China hampers comprehensive research. To bridge this gap, we employ a deep learning method on nighttime light data sets to estimate county-level carbon emissions in mainland China from 1997 to 2019. Our key contributions include the successful derivation of more reliable data, revealing the evolution of spatial dynamics and emissions epicenters. Moreover, we identify a novel inverted N-shaped relationship between gross domestic product per capita and carbon emissions in the eastern and western regions, as well as an N-shaped relationship in the central region, challenging mainstream wisdom. Additionally, we highlight the significant impacts of population density, industrial structure, and carbon intensity on carbon emissions. Our study also unveils the nuanced effects of government spending, which exhibits both inhibitory and region-specific influences. These findings serve to enhance our understanding of the factors influencing carbon emissions and contribute to informed decision-making in addressing climate change-related challenges.

Determinants of spatiotemporal changes of land use carbon emissions for counties in Shaanxi Province, China.

In China, urban sprawl and developed land expansion challenge the country's "carbon peak" and "carbon neutrality" goals. Counties as the basic governance units are crucial for effective carbon reduction policies. This study examines land use carbon emissions (LUCE) in Shaanxi Province at the county level, essential for China's low-carbon strategy. Analyzing data from 107 counties between 2000 and 2020, we found that developed land, though increasing, is the primary carbon source with a slowing growth rate. The Conversion of Cropland to Forests and Grasslands national policy mitigated the impact on carbon absorption. Carbon emissions displayed positive autocorrelation and spatial heterogeneity, varying across the region. Using the Spatial Durbin Error Model, we linked county-level emissions to GDP per capita, population, urbanization rate, and research and development expenditure for direct and indirect influence. These factors correlate with fossil fuel use and high-quality industrial development. Promoting public transits and reducing private car use are vital for achieving local and regional low-carbon goals.

An estimation of future county-level cement production and associated air pollutant emissions in China through artificial neural networks.

Cement production and its air pollutant and carbon dioxides (CO2) emissions in China will be relocated greatly as a joint effect of diverse development of industrial economy and implementation of environmental policies for different regions. The future pathway and spatial pattern of emissions are important for policy making of air quality improvement and CO2 emission abatement, as well as coordinating regional development. In this study, we developed an artificial neural network (ANN) model to predict cement production at the county level and to calculate the associated emissions of air pollutants and CO2 at the county level till 2060. Results show that the cement production will decline from 2327 million metric tons (Mt) in 2015 to 704 Mt. in 2060 under the Shared Socioeconomic Pathways 1 (SSP1). Counties closer to provincial capital will experience greater retirement of cement industry. Likewise, the emissions of air pollutants and CO2 will experience a steady downward trend driven by the declining cement production and the improvement of pollution control technologies. There will be a more significant regional heterogeneity in the reduction of production and emissions at city level compared to the province level. With the clearance for nearly two-thirds of counties, future cement production and emissions will be more intensively distributed in a few cities. The shares of emissions in southwestern regions will grow from 2015 to 2060 while those of eastern regions will continue decreasing. The comparison between the changing spatial distributions of emissions and gross domestic product (GDP) indicates a positive effect of existing policies in reconciling regional economic development and air pollution controls. The outcome could support the analyses on the impact of industrial development on air quality and public health, and the method can be applied widely for other industrial sectors for a more comprehensive understanding of future emission relocation.

Associations of County-Level Social Determinants of Health with COVID-19 Related Hospitalization Among People with HIV: A Retrospective Analysis of the U.S. National COVID Cohort Collaborative (N3C).

Individually, the COVID-19 and HIV pandemics have differentially impacted minoritized groups due to the role of social determinants of health (SDoH) in the U.S. Little is known how the collision of these two pandemics may have exacerbated adverse health outcomes. We evaluated county-level SDoH and associations with hospitalization after a COVID-19 diagnosis among people with (PWH) and without HIV (PWOH) by racial/ethnic groups. We used the U.S. National COVID Cohort Collaborative (January 2020-November 2023), a nationally-sampled electronic health record repository, to identify adults who were diagnosed with COVID-19 with HIV (n = 22,491) and without HIV (n = 2,220,660). We aggregated SDoH measures at the county-level and categorized racial/ethnic groups as Non-Hispanic (NH) White, NH-Black, Hispanic/Latinx, NH-Asian and Pacific Islander (AAPI), and NH-American Indian or Alaskan Native (AIAN). To estimate associations of county-level SDoH with hospitalization after a COVID-19 diagnosis, we used multilevel, multivariable logistic regressions, calculating adjusted relative risks (aRR) with 95% confidence intervals (95% CI). COVID-19 related hospitalization occurred among 11% of PWH and 7% of PWOH, with the highest proportion among NH-Black PWH (15%). In evaluating county-level SDoH among PWH, we found higher average household size was associated with lower risk of COVID-19 related hospitalization across racial/ethnic groups. Higher mean commute time (aRR: 1.76; 95% CI 1.10-2.62) and higher proportion of adults without health insurance (aRR: 1.40; 95% CI 1.04-1.84) was associated with a higher risk of COVID-19 hospitalization among NH-Black PWH, however, NH-Black PWOH did not demonstrate these associations. Differences by race and ethnicity exist in associations of adverse county-level SDoH with COVID-19 outcomes among people with and without HIV in the U.S.

Structural Racism and HIV Pre-exposure Prophylaxis Use in the Nationwide US: A County-Level Analysis.

BACKGROUND: Structural racism contributes to geographical inequalities in pre-exposure prophylaxis (PrEP) coverage in the United States (US). This study aims to investigate county-level variability in PrEP utilization across diverse dimensions of structural racism. METHODS: The 2013-2021 nationwide county-level PrEP rate and PrEP-to-need ratio (PNR) data were retrieved from AIDSVu. PrEP rate was defined as the number of PrEP users per 100,000 population, and PNR was defined as the ratio of PrEP users to new HIV diagnoses per calendar year. Linear mixed effect regression was employed to identify associations of county-level structural racism (e.g., structural racism in housing and socioeconomic status) with PrEP rate and PNR on a nationwide scale of the US. RESULTS: From 2013 to 2021, the mean PrEP rate and PNR increased from 3.62 to 71.10 and from 0.39 to 10.20, respectively. Counties with more structural racism in housing were more likely to have low PrEP rates (adjusted ß = - 5.80, 95% CI [- 8.84, - 2.75]). Higher PNR was found in counties with lower structural racism in socioeconomic status (adjusted ß = - 2.64, 95% CI [- 3.68, - 1.61]). Regionally, compared to the Midwest region, counties in the West region were more likely to have higher PrEP rate (adjusted ß = 30.99, 95% CI [22.19, 39.80]), and counties in the South had lower PNR (adjusted ß = - 1.87, 95% CI [- 2.57, - 1.17]). CONCLUSIONS: County-level structural racism plays a crucial role in understanding the challenges of scaling up PrEP coverage. The findings underscore the importance of tailored strategies across different regions and provide valuable insights for future interventions to optimize PrEP implementation.

Does vertical integration increase the costs for primary care inpatients? Evidence from a national pilot programme in China.

OBJECTIVE: To assess the impact of vertical integration (VI) within County-Level Integrated Health Organisations (CIHOs) on the costs of primary care inpatients. METHODS: This study assessed Xishui, a national pilot county for CIHOs, using inpatient claims data. The treatment group comprised 10,118 inpatients from 5 vertically integrated township health centres (THCs), while the control group consisted of 21,165 inpatients from 19 non-vertically integrated THCs. The periods from July 2020 to December 2021 and January 2022 to December 2022 were defined as pre- and post-policy intervention, respectively. The primary outcome variables were total health expenditures (THS), out-of-pocket (OOP) expenditures, and the proportion of OOP expenditures. Propensity score matching was employed to align inpatient demographics and disease characteristics between the groups, followed by a difference-in-differences analysis to evaluate the outcomes. FINDINGS: VI significantly increased THS (ß = 0.1337, p < 0.01) and OOP expenditures per case (ß = 0.1661, p < 0.001), but the increase in the proportion of OOP expenditures per case was not significant (ß = 0.0029, p > 0.05). For the basic medical insurance for urban and rural residents, THS per case (ß = 0.1343, p < 0.01) and OOP expenditures (ß = 0.1714, p < 0.001) significantly increased. For the basic medical insurance system for employees, THS per case also increased significantly (ß = 0.1238, p < 0.01), but the change in OOP expenditure proportion per case was not significant (ß = 0.1020, p > 0.05). The THS per case led by Xishui County People's Hospital, the leading county medical sub-centre (CMSC), significantly increased (ß = 0.1753, p < 0.01), whereas the increase led by Xishui County Traditional Chinese Medicine Hospital was not significant (ß = 0.0742, p > 0.05). Increases in OOP expenditures per case were significant in CMSCs led by the People's Hospital and the Traditional Chinese Medicine Hospital (ß = 0.1782, p < 0.01 and ß = 0.0757, p < 0.05, respectively). CONCLUSION: VI significantly increased THS and OOP expenditures for primary care inpatients. However, VI could exacerbate economic disparities in disease burden across different insurance categories.

Spatial-temporal pattern of ecosystem services and sustainable development in representative mountainous cities: A case study of Chengdu-Chongqing Urban Agglomeration.

The Sustainable Development Goals (SDGs) are essential measure for preserving the balance between human well-being and natural ecosystems. The benefit of preserving ecosystems health play a crucial role in promoting the SDGs by providing stable ecosystem services (ESs). However, the ecological health of mountainous cities is vulnerable, with relative low ecological resilience. To investigate the conflict between ecosystems and sustainable development, this study takes the Chengdu-Chongqing Urban Agglomeration as the study area. The major tasks and results in this study include: (1) using the entropy weighting method and the InVEST model, we combined remote sensing, geographic, and statistical data to quantify three types of SDGs (economic, social, environmental) and four ESs (water yield, soil conservation, habitat quality, carbon storage), and establish a localized sustainable development assessment framework that is applicable to the Chengdu-Chongqing Urban Agglomeration. The results show that from 2014 to 2020, the three types of SDGs exhibited an overall upward trend, with the lowest values occurring in 2016. The gap between different counties has narrowed, but significant regional differences still remain, indicating an unbalanced development status quo. Among the 142 counties, water yield and soil conservation values show a consistent downward trend but occupies significant interannual variations, while habitat quality and carbon storage values increases consistently each year. (2) using Spearman's nonparametric correlation analysis and multiscale geographically weighted regression model to explore the temporal variation and spatial heterogeneity of correlations between county ESs and SDGs. The results showed significant heterogeneity in the spatial trade-offs and synergies between ESs and SDGs, with two pairs of synergies weakening, seven pairs of trade-offs increasing, and the strongest negative correlation between Economic Sustainable Development Goals and habitat quality. (3) we applied the self-organizing mapping neural networks to analyze the spatial clustering characteristics of ESs-SDGs. Based on the spatial clustering effects, we divides the Chengdu-Chongqing Urban Agglomeration into four zones, and different zones have different levels of ESs and SDGs. The targeted strategies should be adopted according to local conditions. This work is of great practical importance in maintaining the stability and sustainable development of the Chengdu-Chongqing Urban Agglomeration ecosystem and provides a scientific reference for the optimal regulation of mountainous cities.

Motivators and Barriers to COVID-19 Vaccination Intentions Across U.S. County-Level Barriers in the COVID-19 Vaccine Coverage Index.

BACKGROUND: County-level barriers (sociodemographic barriers, limited healthcare system resources, healthcare accessibility barriers, irregular healthcare seeking behaviors, low vaccination history) may impact individuals' reasons for receiving the COVID-19 vaccine. METHODS: This study linked data from REACH-US (Race-Related Experiences Associated with COVID-19 and Health in the United States), a nationally representative, online survey of 5475 adults living in the U.S (January-March 2021) to county-level barriers in the COVID-19 Vaccine Coverage Index. County-level vaccination barriers were measured using the COVID-19 Vaccine Coverage Index. Participants reported why they would or would not receive the COVID-19 vaccine in an open-ended item and their responses were coded using thematic analysis. Descriptive statistics and chi-square tests assessed whether reasons for COVID-19 vaccination intentions varied by county-level barriers and whether these distributions varied across racial/ethnic groups. RESULTS: Thematic analysis revealed twelve themes in participants' reasons why they would or would not receive the COVID-19 vaccine. Themes of societal responsibility (9.8% versus 7.7%), desire to return to normal (8.1% versus 4.7%), and trust in science/healthcare/government (7.7% versus 5.1%) were more frequently reported in counties with low/medium barriers (versus high/very high) (p-values < 0.05). Concerns of COVID-19 vaccine side effects/safety/development (25.3% versus 27.9%) and concerns of access/costs/availability/convenience (1.9% versus 3.6%) were less frequently reported in counties with low/medium barriers (versus high/very high) (p-values < 0.05). Trends in the prevalence of these themes varied across racial/ethnic groups (p-values < 0.05). CONCLUSIONS: Future pandemic responses should consider potential ways county-level barriers shape reasons for COVID-19 vaccination.

Associations Between Drug Overdose Mortality and Recovery Ecosystems in the United States: A County-Level Analysis Using a Novel Index.

BACKGROUND: Communities with robust recovery ecosystems could reduce negative outcomes associated with substance use disorders (SUDs) and facilitate the recovery process. This cross-sectional study examined the relationship between drug overdose mortality rates in the United States and the strength of county-level recovery ecosystems, as measured by the Recovery Ecosystem Index (REI). METHODS: The REI assesses the strength of county-level recovery ecosystems in the United States. Comprised of 14 indicators across 3 component classes, overall and component scores ranging from "one" (strongest) to "five" (weakest) were calculated for each county using standardized values of the indicators. County-level analyses included: (1) correlational analyses between drug overdose mortality rates (n = 2076) and REI scores (overall score and by component); and (2) quadrant analysis (n = 2076), dividing counties based on their drug overdose mortality rates and overall REI scores. RESULTS: Drug overdose mortality rates were inversely related to REI overall, SUD treatment component, and continuum of SUD support component scores, indicating that lower (stronger) scores corresponded to higher rates. Conversely, REI infrastructure and social component scores were positively related to rates. Counties were relatively evenly distributed across quadrants, with 26% (n = 537) with a strong REI score and high overdose mortality rate, 24% (n = 489) with a strong REI score and low overdose mortality rate, 20% (n = 409) with a weak REI and high overdose mortality rate, and 31% (n = 641) with a weak REI and low overdose mortality rate. CONCLUSIONS: REI scores were generally inversely associated with drug overdose mortality rates in US counties, suggesting that communities have stronger recovery systems and services as the burden of SUD increases. Given relative variation in the scale of drug overdose mortality and strength of recovery ecosystems among counties, results could guide the identification of communities where the need for expanded recovery systems and services may be particularly critical.

Evaluation and obstacle analysis of sustainable development in small towns based on multi-source big data: A case study of 782 top small towns in China.

Evaluating the sustainable development level and obstacle factors of small towns is an important guarantee for implementing China's new-type urbanization and rural revitalization strategies, and is also a key path to promoting the United Nations Sustainable Development Goal 11 (SDG11). Traditional evaluation methods (such as Analytic Hierarchy Process, AHP, and Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) mainly calculate the comprehensive score of each indicator through weighting. These methods have limitations in handling multidimensional data and system nonlinearity, and they cannot fully reveal the complex relationships and interactions within the sustainability systems of small towns. In contrast, the evaluation model combining Principal Component Analysis (PCA) and Catastrophe Progression Method (CPM) used in this study can better handle multidimensional data and system nonlinear relationships, reducing subjectivity in evaluation and improving the accuracy and reliability of the assessment results. The specific research process is as follows: First, based on the United Nations SDG11 framework, using multi-source big data, a theoretical framework and evaluation index system for the sustainable development of small towns suitable for the Chinese context were established. The impact of county-level factors on the sustainable development of small towns was also considered, and an entropy weight-grey correlation model was used to measure these impacts, resulting in a town-level dataset incorporating county-level influences. Secondly, the sustainability levels of 782 top small towns in China were evaluated using the comprehensive evaluation model based on PCA-CPM Model. Finally, an improved diagnostic model was used to identify obstacles influencing the sustainable development of small towns. The main findings include: 52.69% of the small towns have a sustainable development score exceeding 0.7255, indicating that the overall performance of small towns is at a medium to high development level. The development of small towns exhibits significant differences across regions and types, which are closely linked to county-level effects. Economic and social factors are the main obstacles to the sustainable development of small towns, and the impact of these obstacles intensifies from the eastern to the central, western, and northeastern regions. This study provides valuable insights for policymakers and scholars, promoting a deeper understanding of the sustainable development of small towns.

Field survey data for conservation: Evaluating suitable habitat of Chinese pangolin at the county-level in eastern China (2000-2040).

The scarcity of up-to-date data on the distribution and dynamics of the Chinese pangolin (Manis pentadactyla) presented a significant challenge in developing effective conservation strategies and implementing protective measures within China. Currently, most of China's national-level nature reserves and administrative departments operate at the county level, thereby limiting the applicability of larger-scale analyses and studies for these administrative entities. This study employed 11 widely used modeling techniques created within the Biomod2 framework to predict suitable habitats for the pangolin at the county scale, while examining the correlation between environmental variables and pangolin distribution. The results revealed that highly suitable habitats in Mingxi County of China encompassed only 49 km2. Within the county-managed nature reserve, the proportion of highly suitable habitats reached as high as 52%. However, nearly half of these areas, both moderately and highly suitable habitats, remained inadequately addressed and conserved. We found nine administrative villages that necessitated prioritized conservation efforts. The study anticipated an overall expansion in suitable habitats over the ensuing two decades, with significant growth projected in the eastern regions of Xiayang and Hufang Town. This research offered a clear and applicable research paradigm for the specific administrative level at which China operates, particularly pertinent to county-level jurisdictions with established nature reserves.

Land scale division and multifunctional evaluation for Fuping County, China, based on DEM-based watershed analysis.

Land is the spatial background and basic carrier of human survival and development. The study of land function evaluation at different scales can promote the harmonious coexistence of humans and nature. Taking Fuping County, Hebei Province, China, as an example, this study establishes the theoretical framework of county-level land scale division using a digital elevation model (DEM)-based watershed analysis method and establishes the theory and methodological system of land function evaluation from the perspective of the characteristic scale. The multifunctionality of the land was evaluated using the Carnegie-Ames-Stanford approach (CASA), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and comprehensive index evaluation. By using the methods of DEM-based watershed analysis, dominant factor differentiation and layer superposition, a three-level scale system of 'subwatershed scale-land chain scale-land segment scale' and a multifunctional multiscale evaluation index system containing 18 evaluation indices were established. The single-function and multifunction evaluation results of land at different scales were obtained by the comprehensive index method and Getis-Ord Gi* index method. The accuracy of land function evaluation results mainly depends on the selection of the measurement scale. The land measurement scale determined by DEM-based watershed analysis is close to the intrinsic scale of land function evaluation. The scale effect of land function in different temporal and spatial ranges is also evident and shows obvious spatial heterogeneity and difference. At larger scales, individual functions show synergistic effects.

Dynamics of urban expansion and form changes impacting carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area counties.

Cities are the main carriers of social and economic development, and they are also important sources of carbon emissions. Therefore, it is essential to explore the impact of urban expansion and form changes on carbon emissions. Here, we attempted to analyzes the relationship between urban expansion and carbon emissions at the county level in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1997 to 2017. It further decomposes the driving effects of carbon emissions from multiple factors, and considers the spatial heterogeneity between different urban form changes and driving effects. The results show that: The relationship between urban expansion and carbon emissions in the GBA has gone through three stages from 1997 to 2017, with 2012 as a turning point. Optimization of economic development models and strict protection of the ecological environment can effectively control carbon emissions. After 2012, the economic development effect (GE) and population scale effect (PE) are the driving factors of carbon emissions, while the carbon emission intensity effect (CE) and urban land intensity effect (UE) are the inhibitory factors of carbon emissions. The contribution rate of UE to carbon emission reduction can reach 86 %. The impact of urban form changes on carbon emissions has spatial heterogeneity. The changes in urban form have a significant impact on the carbon emissions of counties in Dongguan and Shenzhen. The increase in fragmentation indirectly promotes carbon emissions. In 2007-2012, the increase in centrality significantly weakened the economic development effect, which is conducive to emission reduction. After 2007, the increase in compactness in counties in the eastern part of the GBA, including Zhongshan and Zhuhai, is not conducive to emission reduction.

Investigating the role of county-level colorectal cancer screening rates on stage at diagnosis of colorectal cancer in rural Georgia.

BACKGROUND: To examine the impact of county-level colorectal cancer (CRC) screening rates on stage at diagnosis of CRC and identify factors associated with stage at diagnosis across different levels of screening rates in rural Georgia. METHODS: We performed a retrospective analysis utilizing data from 2004 to 2010 Surveillance, Epidemiology, and End Results Program. The 2013 United States Department of Agriculture rural-urban continuum codes were used to identify rural Georgia counties. The 2004-2010 National Cancer Institute small area estimates for screening behaviors were applied to link county-level CRC screening rates. Descriptive statistics and multinominal logistic regressions were performed. RESULTS: Among 4,839 CRC patients, most patients diagnosed with localized CRC lived in low screening areas; however, many diagnosed with regionalized and distant CRC lived in high screening areas (p-value = 0.009). In multivariable analysis, rural patients living in high screening areas were 1.2-fold more likely to be diagnosed at a regionalized and distant stage of CRC (both p-value < 0.05). When examining the factors associated with stage at presentation, Black patients who lived in low screening areas were 36% more likely to be diagnosed with distant diseases compared to White patients (95% CI, 1.08-1.71). Among those living in high screening areas, patients with right-sided CRC were 38% more likely to have regionalized disease (95% CI, 1.09-1.74). CONCLUSION: Patients living in high screening areas were more likely to have a later stage of CRC in rural Georgia. IMPACT: Allocating CRC screening/treatment resources and improving CRC risk awareness should be prioritized for rural patients in Georgia.

The impacts of national centralized drug procurement policy on drug utilization of medical institutions: an empirical study in a county-level hospital in China.

PURPOSE: Under the background of the regular implementation of the National Centralized Drug Procurement (NCDP) policy, this study aimed to assess the impacts of the NCDP policy on drug utilization of county-level medical institutions, and probe into the influencing factors of the changes in drug utilization. METHOD: A pre-post study was applied using inpatient data from a county-level medical institution in Nanjing. Drug utilization behavior of medical institutions of 88 most commonly used policy-related drugs (by generic name, including bid-winning and bid-non-winning brands) was analyzed, and the substitution of bid-winning brands for brand-name drugs after policy intervention was evaluated. RESULTS: After policy intervention, 43.18% of policy-related drugs realized the substitution of bid-winning brands for bid-non-winning brands (6.82% of complete substitution, 36.36% of partial substitution). Meanwhile, 40.90% of policy-related drugs failed to realize brand substitution. Multiple factors affected brand substitution, including: (1) Policy effect: brand substitution was more obvious after the intervention of the first and third round of NCDP. (2) Drug market competition: the greater the price reduction of bid-non-winning brands, the more the drugs for the same indication, the more likely that medical institutions keep using the same brands as they did before policy intervention. (3) Previous drug utilization of medical institutions: brand substitution was more obvious in drugs with large number of prescriptions and weak preference for brand-name drugs. CONCLUSION: The NCDP policy promoted the substitution of bid-winning brands for bid-non-winning brands. However, the NCDP policy remained to be further implemented in county-level medical institutions. Policy implememtation efforts, drug market competition and drug utilization of medical institutions would affect the implementation of the NCDP policy.

Comprehensive risk assessment of typhoon disasters in China's coastal areas based on multi-source geographic big data.

Typhoons can bring substantial casualties and economic ramifications, and effective prevention strategies necessitate a comprehensive risk assessment. Nevertheless, existing studies on its comprehensive risk assessment are characterized by coarse spatial scales, limited incorporation of geographic big data, and rarely considering disaster mitigation capacity. To address these problems, this study combined multi-source geographic big data to develop the Comprehensive Risk Assessment Model (CRAM). The model integrated 17 indicators from 4 categories of factors, including exposure, vulnerability, hazard, and mitigation capacity. A subjective-objective combination weighting method was introduced to generate the indicator weights, and comprehensive risk index of typhoon disasters was calculated for 987 counties along China's coastal regions. Results revealed a pronounced spatial heterogeneity of the comprehensive typhoon risk, which exhibited an overall decreasing trend from the southeast coastal areas toward the northwest inland territories. 61.7 % of the counties exhibited a medium-to-high level of comprehensive risk, and counties with very-high risks are predominantly concentrated in the Shandong Peninsula, Yangtze River Delta, Hokkien Golden Triangle, Greater Bay Area, Leizhou Peninsula, and Hainan Province, mainly due to high exposure and hazard factors. The correlation coefficient between the risk assessment results and typhoon-induced direct economic losses reached 0.702, indicating the effectiveness and reliability of the CRAM. Meanwhile, indicators from intrinsic attributes of typhoons and geographic big data had pronounced importance, and regional mitigation capacity should be improved. Our proposed method can help to scientifically understand spatial patterns of comprehensive risk and mitigate the effects of typhoon disasters in China's coastal regions.

Coupling coordination analysis of population, economy and grain in major grain-producing counties.

This study discusses the coupling and coordination relationship among population, economy, and grain production in the central primary grain-producing counties. It aims to find a dynamic balance between the responsibility of the grain-producing areas in ensuring food security and the development of the economy and population. This study focuses on the main grain-producing provinces of Jilin and Jiangsu in China. Based on county-level data on population, economy, and grain production, it constructs an index system for the population, economy, and grain systems. The study employs the entropy weighting method and coupling coordination model to analyze the coupling coordination degree and coordinated development of the three systems in Jilin and Jiangsu provinces from 2000 to 2020, covering a span of 21 years. The coupling coordination degree and coordinated development of the three systems in the main grain-producing areas have gradually moved towards high-quality coordination. In the economically underdeveloped province of Jilin, factors such as geographical environment, population size, and industrial structure impose constraints on system coordination. In the economically developed region of Jiangsu, there is a high labour force and better development of the secondary and tertiary industries, but relatively less investment in agriculture, which affects overall coordination. It is necessary to promote regions' development with high-quality coordination by leveraging their advantages in economic foundations, and further advance the construction of the main grain-producing areas. Additionally, efforts should be made to strengthen policy support for underdeveloped regions, clearly define the industrial types and positioning of counties, and focus on industrial transformation and upgrading.

The impact of clean energy demonstration province policies on carbon intensity in Chinese counties based on the multi-phase PSM-DID method.

Based on China's empirical data from 2000 to 2020 of 1875 county-level administrative units, combined with the multi-phase by the propensity score matching and difference-in-difference (PSM-DID) model, this paper studies the impact of clean energy demonstration province policies on the carbon intensity of pilot counties, and its further impact on carbon emissions and economic development level. The results showed that 1. from a county-level perspective, although the economic development level of the pilot areas of clean energy demonstration provinces has improved as the carbon emissions have also increased, what is more, the carbon intensity has also significantly improved in this process; 2. there is no time lag in the impact of policies on the carbon intensity of counties, and the impact effects gradually increase over time along with strong regional heterogeneity; 3. the clean energy demonstration policy has weakened the technological level of the county and reduced the proportion of industrial-added value to GDP, thereby increasing the carbon intensity of the county through these intermediaries.

County-level colorectal cancer screening rates on colorectal cancer survival in the state of Georgia: Does county-level rurality matter?

PURPOSE: Investigating CRC screening rates and rurality at the county-level may explain disparities in CRC survival in Georgia. Although a few studies examined the relationship of CRC screening rates, rurality, and/or CRC outcomes, they either used an ecological study design or focused on the larger population. METHODS: We conducted a retrospective analysis utilizing data from the 2004-2010 Surveillance, Epidemiology, and End Results Program. The 2013 United States Department of Agriculture rural-urban continuum codes and 2004-2010 National Cancer Institute small-area estimates for screening behaviors were used to identify county-level rurality and CRC screening rates. Kaplan-Meier method and Cox proportional hazard regression were performed. RESULTS: Among 22,160 CRC patients, 5-year CRC survival rates were lower among CRC patients living in low screening areas in comparison with intermediate/high areas (69.1% vs. 71.6% /71.3%; p-value = 0.030). Patients living in rural high-screening areas also had lower survival rates compared to non-rural areas (68.2% vs. 71.8%; p-value = 0.009). Our multivariable analysis demonstrated that patients living in intermediate (HR, 0.91; 95% CI, 0.85-0.98) and high-screening (HR, 0.92; 95% CI, 0.85-0.99) areas were at 8%-9% reduced risk of CRC death. Further, non-rural CRC patients living in intermediate and high CRC screening areas were 9% (HR, 0.91; 95% CI, 0.83-0.99) and 10% (HR, 0.90; 95% CI, 0.82-0.99) less likely to die from CRC. CONCLUSIONS: Lower 5-year survival rates were observed in low screening and rural high-screening areas. Living in intermediate/high CRC screening areas was negatively associated with the risk of CRC death. Particularly, non-rural patients living in intermediate/high-screening areas were 8%-9% less likely to die from CRC. Targeted CRC screening resources should be prioritized for low screening and rural communities.

Population Aging and Heat Exposure in the 21st Century: Which U.S. Regions Are at Greatest Risk and Why?

BACKGROUND AND OBJECTIVES: The co-occurring trends of population aging and climate change mean that rising numbers of U.S. older adults are at risk of intensifying heat exposure. We estimate county-level variations in older populations' heat exposure in the early (1995-2014) and mid (2050) 21st century. We identify the extent to which rising exposures are attributable to climate change versus population aging. RESEARCH DESIGN AND METHODS: We estimate older adults' heat exposure in 3,109 counties in the 48 contiguous U.S. states. Analyses use NASA NEX Global Daily Downscaled Product (NEX-GDDP-CMIP6) climate data and county-level projections for the size and distribution of the U.S. age 69+ population. RESULTS: Population aging and rising temperatures are documented throughout the United States, with particular "hotspots" in the Deep South, Florida, and parts of the rural Midwest. Increases in heat exposure by 2050 will be especially steep in historically colder regions with large older populations in New England, the upper Midwest, and rural Mountain regions. Rising temperatures are driving exposure in historically colder regions, whereas population aging is driving exposure in historically warm southern regions. DISCUSSION AND IMPLICATIONS: Interventions to address the impacts of temperature extremes on older adult well-being should consider the geographic distribution and drivers of this exposure. In historically cooler areas where climate change is driving exposures, investments in warning systems may be productive, whereas investments in health care and social services infrastructures are essential in historically hot regions where exposures are driven by population aging.