Clusters Of Counties Research Articles

Filling the Treatment Gap: Geographic Expansion of Buprenorphine Providers Across the U.S.

ObjectiveWith opioid-related deaths reaching epidemic levels, gaining a better understanding of access to treatment for opioid use disorder (OUD) is critical. Buprenorphine is an effective medication for the treatment of opioid addiction. The analysis is critical post-X-waiver elimination which extended the ability to prescribe buprenorphine for the treatment of OUD to all practitioners with DEA Schedules II-V on their DEA Registration. The primary purpose of the analysis was to explore the geographic patterns of substance use treatment and establish a geographical baseline to assess the removal of the X-Waiver in the future. Data and MethodsWe assessed the expansion of buprenorphine providers across the data from all US counties up to December 31, 2022. We used all certified buprenorphine providers' data from the database of the buprenorphine waiver notification system (BWNS). We used county-level population data from the US Census of American Community Survey (ACS) of 2021 and the CDC's Drug-related mortality data. We implemented spatial scan statistics to identify the spatial clusters using SaTScan. ResultsThe results from our analysis show that Doctor of Medicine/Doctor of Osteopathic Medicine (MD/DO) have the highest numbers of certified providers at 8134 (65.08%) in 2018 and 14525 (57.87%) in 2022. Our analysis shows that the distribution of buprenorphine providers across the counties of the United States was significantly clustered. Higher clusters with RR of more than 1 (p-value <0.001) were found in the states of Washington, Oregon, Northern California, and the Western borders of Montana. Similar clusters of counties with RR more than 1 (p-value <0.001) were found in the northeastern states of Maine, Vermont, and New Hampshire. ConclusionsFrom our analysis, it is evident that buprenorphine-certified providers are clustered in areas of higher drug-related mortality filling a treatment gap. The elimination of the X-waiver will be a significant step towards increasing access to MOUD and this analysis may be used as a geographical baseline to assess in the future.

Mortality due to falls by county, age group, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities

Fall-related mortality has increased rapidly over the past two decades in the USA, but the extent to which mortality varies across racial and ethnic populations, counties, and age groups is not well understood. The aim of this study was to estimate age-standardised mortality rates due to falls by racial and ethnic population, county, and age group over a 20-year period. Redistribution methods for insufficient cause of death codes and validated small-area estimation methods were applied to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual fall-related mortality. Estimates from 2000 to 2019 were stratified by county (n=3110) and five mutually exclusive racial and ethnic populations: American Indian or Alaska Native (AIAN), Asian or Pacific Islander (Asian), Black, Latino or Hispanic (Latino), and White. Estimates were corrected for misreporting of race and ethnicity on death certificates using published misclassification ratios. We masked (ie, did not display) estimates for county and racial and ethnic population combinations with a mean annual population of less than 1000. Age-standardised mortality is presented for all ages combined and for age groups 20-64 years (younger adults) and 65 years and older (older adults). Nationally, in 2019, the overall age-standardised fall-related mortality rate for the total population was 13·4 deaths per 100 000 population (95% uncertainty interval 13·3-13·6), an increase of 65·3% (61·9-68·8) from 8·1 deaths per 100 000 (8·0-8·3) in 2000, with the largest increases observed in older adults. Fall-related mortality at the national level was highest across all years in the AIAN population (in 2019, 15·9 deaths per 100 000 population [95% uncertainty interval 14·0-18·2]) and White population (14·8 deaths per 100 000 [14·6-15·0]), and was about half as high among the Latino (8·7 deaths per 100 000 [8·3-9·0]), Black (8·1 deaths per 100 000 [7·9-8·4]), and Asian (7·5 deaths per 100 000 [7·1-7·9]) populations. The disparities between racial and ethnic populations varied widely by age group, with mortality among younger adults highest for the AIAN population and mortality among older adults highest for the White population. The national-level patterns were observed broadly at the county level, although there was considerable spatial variation across ages and racial and ethnic populations. For younger adults, among almost all counties with unmasked estimates, there was higher mortality in the AIAN population than in all other racial and ethnic populations, while there were pockets of high mortality in the Latino population, particularly in the Mountain West region. For older adults, mortality was particularly high in the White population within clusters of counties across states including Florida, Minnesota, and Wisconsin. Age-standardised mortality due to falls increased over the study period for each racial and ethnic population and almost every county. Wide variation in mortality across geography, age, and race and ethnicity highlights areas and populations that might benefit most from efficacious fall prevention interventions as well as additional prevention research. US National Institutes of Health (Intramural Research Program, National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; Intramural Research Program, National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research).

Association of county level poverty with mortality from primary liver cancers.

The highly variable occurrence of primary liver cancers across the United States emphasize the relevance of location-based factors. Social determinants such as income, educational attainment, housing, and other factors may contribute to regional variations in outcomes. To evaluate their impact, this study identified and analyzed clusters of high mortality from primary liver cancers and the association of location-based determinants with mortality across the contiguous United States. A geospatial analysis of age-adjusted incidence and standardized mortality rates from primary liver cancers from 2000 to 2020 was performed. Local indicators of spatial association identified hot-spots, clusters of counties with significantly higher mortality. Temporal analysis of locations with persistent poverty, defined as high (>20%) poverty for at least 30 years, was performed. Social determinants were analyzed individually or globally using composite measures such as the social vulnerability index or social deprivation index. Disparities in county level social determinants between hot-spots and non-hot-spots were analyzed by univariate and multivariate logistic regression. There are distinct clusters of liver cancer incidence and mortality, with hotspots in east Texas and Louisiana. The percentage of people living below the poverty line or Hispanics had a significantly higher odds ratio for being in the top quintile for mortality rates in comparison to other quintiles and were highly connected with mortality rates. Current and persistent poverty were both associated with an evolution from non-hotspots to new hotspots of mortality. Hotspots were predominantly associated with locations with significant levels of socioeconomic vulnerability or deprivation. Poverty at a county level is associated with mortality from primary liver cancer and clusters of higher mortality. These findings emphasize the importance of addressing poverty and related socio-economic determinants as modifiable factors in public health policies and interventions aimed at reducing mortality from primary liver cancers.

De Novo Exposomic Geospatial Assembly of Chronic Disease Regions with Machine Learning & Network Analysis.

Determining spatial relationships between disease and the exposome is limited by available methodologies. aPEER (algorithm for Projection of Exposome and Epidemiological Relationships) uses machine learning (ML) and network analysis to find spatial relationships between diseases and the exposome in the United States. Using aPEER we examined the relationship between 12 chronic diseases and 186 pollutants. PCA, K-means clustering, and map projection produced clusters of counties derived from pollutants, and the Jaccard correlation of these clusters with counties with high rates of disease was calculated. Pollution correlation matrices were used together with network analysis to identify the strongest disease-pollution relationships. Results were compared to LISA, Moran's I, univariate, elastic net, and random forest regression. aPEER produced 68,820 maps with human interpretable, distinct pollution-derived regions. Diseases with the strongest pollution associations were hypertension (J=0.5316, p=3.89x10-208), COPD (J=0.4545, p=8.27x10-131), stroke (J=0.4517, p=1.15x10-127), stroke mortality (J=0.4445, p=4.28x10-125), and diabetes mellitus (J=0.4425, p=2.34x10-127). Methanol, acetaldehyde, and formaldehyde were identified as strongly associated with stroke, COPD, stroke mortality, hypertension, and diabetes mellitus in the southeast United States (which correlated with both the Stroke and Diabetes Belt). Pollutants were strongly predictive of chronic disease geography and outperformed conventional prediction models based on preventive services and social determinants of health (using elastic net and random forest regression). aPEER used machine learning to identify disease and air pollutant relationships with similar or superior AUCs compared to social determinants of health (SDOH) and healthcare preventive service models. These findings highlight the utility of aPEER in epidemiological and geospatial analysis as well as the emerging role of exposomics in understanding chronic disease pathology. Boston Public Health Commission, NHLBI (R03 HL157890) and the CDC.

Comprehensive Clustering Analysis and Profiling of COVID-19 Vaccine Hesitancy and Related Factors across U.S. Counties: Insights for Future Pandemic Responses.

This study employs comprehensive clustering analysis to examine COVID-19 vaccine hesitancy and related socio-demographic factors across U.S. counties, using the collected and curated data from Johns Hopkins University. Utilizing K-Means and hierarchical clustering, we identify five distinct clusters characterized by varying levels of vaccine hesitancy, MMR vaccination coverage, population demographics, and political affiliations. Principal Component Analysis (PCA) was conducted to reduce dimensionality, and key variables were selected based on their contribution to cumulative explained variance. Our analysis reveals significant geographic and demographic patterns in vaccine hesitancy, providing valuable insights for public health strategies and future pandemic responses. Geospatial analysis highlights the distribution of clusters across the United States, indicating areas with high and low vaccine hesitancy. In addition, multiple regression analyses within each cluster identify key predictors of vaccine hesitancy in corresponding U.S. county clusters, emphasizing the importance of socio-economic and demographic factors. The findings underscore the need for targeted public health interventions and tailored communication strategies to address vaccine hesitancy across the United States and, potentially, across the globe.

The intervention of local public authorities and the impact of the COVID-19 pandemic in Romania: a subnational analysis.

The COVID-19 pandemic had a strong territorial dimension, with a highly asymmetric impact among Romanian counties, depending on pre-existing vulnerabilities, regions' economic structure, exposure to global value chains, specialization, and overall ability to shift a large share of employees to remote working. The aim of this paper is to assess the role of Romanian local authorities during this unprecedented global medical emergency by capturing the changes of public spending at the local level between 2010 and 2021 and amid the COVID-19 pandemic, and to identify clusters of Romanian counties that shared similar characteristics in this period, using a panel data quantitative model and hierarchical cluster analysis. Our empirical analysis shows that between 2010-2021, the impact of social assistance expenditures was higher than public investment (capital spending and EU funds) on the GDP per capita at county level. Additionally, based on various macroeconomic and structural indicators (health, labour market performance, economic development, entrepreneurship, and both local public revenues and several types of expenditures), we determined seven clusters of counties. The research contributes to the discussion regarding the increase of economic resilience but also to the evidence-based public policies implementation at local level.

County-level factors associated with a mismatch between opioid overdose mortality and availability of opioid treatment facilities.

Opioid overdose deaths in the United States remain a major public health crisis. Little is known about counties with high rates of opioid overdose mortality but low availability of opioid use disorder (OUD) treatment facilities. We sought to identify characteristics of United States (US) counties with high rates of opioid overdose mortality and low rates of opioid treatment facilities. Rates of overdose mortality from 3,130 US counties were compared with availability of opioid treatment facilities that prescribed or allowed medications for OUD (MOUD), from 2018-2019. The outcome variable, "risk-availability mismatch" county, was a binary indicator of a high rate (above national average) of opioid overdose mortality with a low (below national average) rate of opioid treatment facilities. Covariates of interest included county-level sociodemographics and rates of insurance, unemployment, educational attainment, poverty, urbanicity, opioid prescribing, depression, heart disease, Gini index, and Theil index. Multilevel logistic regression, accounting for the clustering of counties within states, was used to determine associations with being a "risk-availability mismatch" county. Of 3,130 counties, 1,203 (38.4%) had high rates of opioid overdose mortality. A total of 1,098 counties (35.1%) lacked a publicly-available opioid treatment facility in 2019. In the adjusted model, counties with an additional 1% of: white residents (odds ratio, OR, 1.02; 95% CI, 1.01-1.03), unemployment (OR, 1.11; 95% CI, 1.05-1.19), and residents without insurance (OR, 1.04; 95% CI, 1.01-1.08) had increased odds of being a mismatch county. Counties that were metropolitan (versus non-metropolitan) had an increased odds of being a mismatch county (OR, 1.85; 95% CI, 1.45-2.38). Assessing mismatch between treatment availability and need provides useful information to characterize counties that require greater public health investment. Interventions to reduce overdose mortality are unlikely to be effective if they do not take into account diverse upstream factors, including sociodemographics, disease burden, and geographic context of communities.

County-Level Socio-Environmental Factors Associated With Stroke Mortality in the United States: A Cross-Sectional Study.

We used machine learning methods to explore sociodemographic and environmental determinants of health (SEDH) associated with county-level stroke mortality in the USA. We conducted a cross-sectional analysis of individuals aged ≥15 years who died from all stroke subtypes between 2016 and 2020. We analyzed 54 county-level SEDH possibly associated with age-adjusted stroke mortality rates/100,000 people. Classification and Regression Tree (CART) was used to identify specific county-level clusters associated with stroke mortality. Variable importance was assessed using Random Forest analysis. A total of 501,391 decedents from 2397 counties were included. CART identified 10 clusters, with 77.5% relative increase in stroke mortality rates across the spectrum (28.5 vs 50.7 per 100,000 persons). CART identified 8 SEDH to guide the classification of the county clusters. Including, annual Median Household Income ($), live births with Low Birthweight (%), current adult Smokers (%), adults reporting Severe Housing Problems (%), adequate Access to Exercise (%), adults reporting Physical Inactivity (%), adults with diagnosed Diabetes (%), and adults reporting Excessive Drinking (%). In conclusion, SEDH exposures have a complex relationship with stroke. Machine learning approaches can help deconstruct this relationship and demonstrate associations that allow improved understanding of the socio-environmental drivers of stroke and development of targeted interventions.

The variation in preventable hospitalization in patients with type 2 diabetes in Kentucky before and after the Medicaid expansion.

Hospitalizations are more resource intensive and expensive than outpatient care. Therefore, type 2 diabetes-related preventable hospitalization are a major topic of research efficiency in the healthcare system. Analyze county level variation in type 2 diabetes-related preventable hospitalization rates in Kentucky before the Medicaid expansion (2010-2013) and after the Medicaid expansion (2014-2017). Geographic mapping and cluster analysis. Data for a state of the United States of America. We used the KID data to generate geographic mapping for type 2 diabetes-related preventable hospitalizations to visualize rates. We included all Kentucky discharges of age 18 years and older with the ICD9/10 principal diagnosis code for type 2 diabetes. Then, we conducted cluster analysis techniques to compare county-level variation in type 2 diabetes-related preventable hospitalization rates across Kentucky counties pre- and post-Medicaid expansion. County type 2 diabetes-related preventable hospitalization pre- and post-Medicaid expansion. From 2010-2017, type 2 diabetes-related preventable hospitalization discharge rates reduced significantly in the period of the post-Medicaid expansion (P=.001). The spatial statistics analysis revealed a significant spatial clustering of counties with similar rates of type 2 diabetes-related preventable hospitalization in the south, east, and southeastern Kentucky pre- and post-Medicaid expansion (positive z-score and positive Moran's Index value (P>.05). Also, there was a significant clustering of counties with low type 2 diabetes-related preventable hospitalization rates in the north, west, and central regions of the state pre-Medicaid expansion and post-Medicaid expansion (positive z-score and positive Moran's Index value (P>.05). Kentucky counties in the southeast have experienced a significant clustering of highly avoidable hospitalization rates during both periods. Focusing on the vulnerable counties and the economic inequality in Kentucky could lead to efforts to lowering future type 2 diabetes-related preventable hospitalization rates. We used de-identified data which does not provide insights into the frequency of hospitalizations per patient. An individual patient may be hospitalized several times and counted as several individuals.

The Impact of Primary Care Physician Capacity on Preventable Hospitalizations: Identifying Bright Spots in the Appalachian & Mississippi Delta Regions.

Several studies have documented that higher rates of primary care physicians are associated with lower rates of preventable hospitalizations. Counties with higher rates of preventable hospitalizations are found in the Appalachian and Mississippi (MS) Delta Regions. (1) To determine if the association of primary care capacity with preventable hospitalizations is different in the Appalachian and MS Delta regions compared to the rest of the U.S., and (2) to explore primary care capacity in counties with lower-than-expected preventable hospitalization rates. This study modeled preventable hospitalizations with primary care physicians (PCP) per 100,000 (PCP capacity) while controlling for several factors. A spatial regime variable was also included, which modeled Appalachian and MS Delta regions separately. Next, PCP capacity was removed from the model and a geospatial residual analysis was performed to identify geographic clusters of counties with lower-than-expected rates of preventable hospitalizations (bright spots). PCP capacity in bright spots was then compared to that in counties with higher-than-expected rates (cold spots). Higher PCP capacity was significantly associated with lower rates of preventable hospitalizations in the rest of U.S. model, though was not significant for the Appalachian or MS Delta models. The residual analysis showed that compared to counties with higher-than-expected rates (cold spots), counties with lower-than-expected rates (bright spots) had significantly higher PCP capacity, though not in the MS Delta region. Consistent with previous literature, it was found that the factors associated with preventable hospitalizations vary by region, though the results are mixed when looking at the Appalachian and MS Delta regions separately. Future research should explore characteristics of bright spots within the Appalachian and MS Delta regions.

Multimethod evaluation of health services integration for neglected tropical diseases requiring case management in Liberia

IntroductionThe WHO neglected tropical disease (NTD) roadmap stresses the importance of integrating NTDs requiring case management (CM) within the health system. The NTDs programme of Liberia is among the first...

EXPLORING PROPERTY TAX BURDENS AND AGING IN PLACE: A GEOSPATIAL INVESTIGATION IN PENNSYLVANIA

Abstract Utilizing Geographic Information System (GIS) techniques and a composite index, this project delves into the intricate geographic patterns of property tax burdens in Pennsylvania, with a specific focus on older residents contemplating aging in place amidst limited financial resources. The study scrutinizes various factors, including property tax rates, home values, income, demographics, and other housing-related variables, to comprehensively grasp their effects on the financial feasibility of aging in place. Pennsylvania displays notable disparities in property taxes, averaging approximately 1.5% of home value, across counties due to distinct assessment systems and varying assessed-to-market ratios (CLRs). Tax rates are also influenced and specified by smaller administrative and geographic entities, including municipalities and school districts. Despite alternatives (e.g., assisted living), aging in place remains the preferred choice for most older Americans. This accentuates the significance of assessing property tax affordability that facilitates potential aging in place, particularly for financially constrained but “healthy” seniors who may not qualify for tax rebates or local freezes. Moreover, escalating housing prices and assessed values further emphasize the urgency. Cartographic and tabular findings from the study unveil challenging situations for seniors in specific county clusters of Pennsylvania, encompassing not only “expensive” urban but also “typically affordable” rural settings. Additionally, areas with the highest tax rates are not always the most financially burdensome (and vice versa) with all other factors taken into account. This study offers practical insights that have the potential to inform adjustments in housing and tax policies aimed at benefiting older adults in Pennsylvania.

Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China.

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO2 emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO2 emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO2 emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO2 emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO2 emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO2 emission reduction is realized from both the source of industry and low-carbon modes of transport.

Geographic Variation, Economic Activity, and Labor Market Characteristics in Trajectories of Suicide in the United States, 2008-2020.

Suicide rates in the United States have increased over the past 15 years, with substantial geographic variation in these increases; yet there have been few attempts to cluster counties by the magnitude of suicide rate changes according to intercept and slope or to identify the economic precursors of increases. We used vital statistics data and growth mixture models to identify clusters of counties by their magnitude of suicide growth from 2008 to 2020 and examined associations with county economic and labor indices. Our models identified 5 clusters, each differentiated by intercept and slope magnitude, with the highest-rate cluster (4% of counties) being observed mainly in sparsely populated areas in the West and Alaska, starting the time series at 25.4 suicides per 100,000 population, and exhibiting the steepest increase in slope (0.69/100,000/year). There was no cluster for which the suicide rate was stable or declining. Counties in the highest-rate cluster were more likely to have agricultural and service economies and less likely to have urban professional economies. Given the increased burden of suicide, with no clusters of counties improving over time, additional policy and prevention efforts are needed, particularly targeted at rural areas in the West.

Travel distance and social vulnerability index: Impact on liver-related mortality among patients with end-stage liver disease.

The reasons for the geographic disparities in liver-related mortality across the US remain ill-defined. We sought to investigate the impact of travel distance to liver transplantation (LT) programs and social vulnerability on county differences in liver-related mortality. Data on LT registrants were obtained from the Scientific Registry of Transplant Recipients Standard Analytic Files (SRTR SAFs) between 2004 and 2019. Liver-related mortality data were obtained from the Center for Disease Control and Prevention's Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) platform. Spatial epidemiological clustering of county-level LT registration and liver-related mortality rates was determined using local Moran's I. Comparison analyses assessed social vulnerability index (SVI) and travel distance within various county clusters. Among 151864 LT waitlist registrants who were diagnosed with liver disease due to hepatitis C virus (HCV) or hepatitis B virus (HBV) (n=68479, 45.1%), alcohol (n=38328, 25.2%), non-alcoholic steatohepatitis (NASH) (n=17485, 11.5%), liver tumors (n=16644, 11.0%), and other diseases (n = 10928, 7.2%), median SVI was 59.3 (IQR, 40.1-83.4). SVI (76.2 vs. 24.3, p<.001) was greater in the highest versus lowest liver-related mortality quartiles. The travel distances to LT centers (143.1 miles vs. 107.2 miles, p<.001) was longer in the lowest versus highest LT registration quartiles. Counties with low LT registration rates and high liver-related mortality rates were associated with long travel distances and high SVI. In contrast, while counties with high LT registration rates and high liver-related mortality rates had comparable SVI, travel distance was relatively shorter. Counties with greater SVIs were associated with higher liver-related mortality, with the highest SVI counties having the highest overall liver-related mortality. Longer travel distances were associated with higher liver-related mortality. These findings highlight the impact of social determinants of health (SDOH) on liver disease outcomes.

Empirical Analysis of China’s Agricultural Total Factor Productivity and the Reform of “County Administrated by Province”: Insights from Agricultural Enterprise Data

The study focuses on examining how the total factor productivity (TFP) of China’s agriculture-related enterprises is affected by the reform of “County Administrated by Province” (CAP). Using panel data from 1998 to 2013, with a sample size of 292, 423 agriculture-related enterprises, the study investigates the influence of CAP reform on the TFP. The findings reveal a significant dampening effect of the CAP reform on the TFP of agriculture-related enterprises. These results are further supported with a series of robustness tests including placebo test, multidimensional fixed effects test, and clustering of cities or counties. Heterogeneity analysis reveals that the CAP reform has significantly restrained the TFP of agriculture-related enterprises in high level cities, while showing no significant impact on cities with lower administrative levels. In the eastern region, the impact of the CAP reform on the TFP of agriculture-related enterprises is significantly negative, whereas it is not statistically significant in the central and western regions. the study on mechanisms elucidates that the inhibitory impact of CAP policy on the TFP of agriculture-related enterprises is enhanced by urban property prices, labor resource allocation, and banking competition. The research conclusion is of significance in guiding the practices of agriculture-related enterprises and deepening the reform of “County Administrated by Province”.

Identification and Optimization of County-Level Ecological Spaces under the Dual-Carbon Target: A Case Study of Shaanxi Province, China

County-level ecological space, as a crucial level in optimizing the land spatial system, plays a pivotal role in “undertaking superior planning and guiding subordinate implementation”. From a spatial optimization perspective, effectively implementing the dual-carbon goal by increasing carbon sinks in specific ecological space units is essential. This study focused on 107 districts and counties in Shaanxi Province, China, aiming to construct a comprehensive multivariate identification system for ecological space under the dual-carbon target based on an analysis of the spatiotemporal distribution characteristics and driving factors of county-level carbon sinks. Furthermore, by analyzing the ecological spatial distribution pattern, carbon sink land structure, and county clustering characteristics, the study explored differential optimization strategies for ecological spaces of different county types to enhance carbon sinks in the ecosystem. The results demonstrated that: (1) From 2000 to 2020, the total carbon sink in Shaanxi Province exhibited an initial increase followed by a decrease, with a decline from 864.39 × 104 t to 863.21 × 104 t. The county-level distribution of total carbon sink displayed significant spatial heterogeneity, with an overall pattern of south &gt; north &gt; central. (2) The interaction among factors enhanced the explanatory power for spatial differentiation of county-level carbon sinks compared to individual factors, exerting an important impact on the spatial distribution pattern of carbon sinks. (3) The distribution of ecological space in Shaanxi Province was highly uneven, with the core ecological space primarily concentrated in the southern and north-central regions. The proportions of low carbon sink (Type I), medium carbon sink (Type II), and high carbon sink (Type III) counties were 35.51%, 18.69%, and 45.80%, respectively. For different types of county-level ecological spaces, this study proposed a differentiated optimization strategy aimed at reducing carbon emissions and enhancing carbon sink. The results will provide theoretical and technical support for regional ecological construction and land spatial optimization, holding significant practical implications for achieving the dual-carbon goal and addressing climate change.

Alzheimer`s disease mortality in the United States: Cross-sectional analysis of county-level socio-environmental factors

BackgroundGeographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods. MethodsWe performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a “hold-out” set of counties. ResultsOverall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016–2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%). ConclusionML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.

Harvested winter rye energy cover crop: multiple benefits for North Central US

Cover crops (CCs) can reduce nitrogen (N) loss to subsurface drainage and can be reimagined as bioenergy crops for renewable natural gas production and carbon (C) benefits (fossil fuel substitution and C storage). Little information is available on the large-scale adoption of winter rye for these purposes. To investigate the impacts in the North Central US, we used the Root Zone Water Quality Model to simulate corn-soybean rotations with and without winter rye across 40 sites. The simulations were interpolated across a five-state area (IA, IL, IN, MN, and OH) with counties in the Mississippi River basin, which consists of ∼8 million ha with potential for rye CCs on artificially drained corn-soybean fields (more than 63 million ha total). Harvesting fertilized rye CCs before soybean planting in this area can reduce N loads to the Gulf of Mexico by 27% relative to no CCs, and provide 18 million Mg yr−1 of biomass-equivalent to 0.21 EJ yr−1 of biogas energy content or 3.5 times the 2022 US cellulosic biofuel production. Capturing the CO2 in biogas from digesting rye in the region and sequestering it in underground geologic reservoirs could mitigate 7.5 million Mg CO2 yr−1. Nine clusters of counties (hotspots) were identified as an example of implementing rye as an energy CC on an industrial scale where 400 Gg yr−1 of rye could be sourced within a 121 km radius. Hotspots consisted of roughly 20% of the region’s area and could provide ∼50% of both the N loss reduction and rye biomass. These results suggest that large-scale energy CC adoption would substantially contribute to the goals of reducing N loads to the Gulf of Mexico, increasing bioenergy production, and providing C benefits.

Multivariate and geospatial analysis of technology utilization in US counties

We examine geographic patterns and socio-economic and social capital correlates of the adoption and utilization of information and communication technologies (ICTs) in 3,108 counties of the United States. Access and use of ICTs are found to vary significantly among counties in metropolitan, micropolitan, and rural parts of the country. Clusters of high, moderate, and low ICT utilization counties are characterized by geodemographic and socio-economic attributes. Regression findings indicate that young dependency ratio, college education, working age population, and race and ethnic variables are major correlates of ICT use. We analyze and explain novel findings on associations of income, ethnicity, and social capital variables with ICT usage in light of the technology-use landscape in the US. Spatial bias and large sample size fallacy issues are addressed and policy recommendations to remediate the digital divide in US counties are provided.