Why Covid-19 restrictions lose effectiveness over time

Teleworking, social capital, and subjective well-being: Evidence from US county-level data

Study on regional rapeseed yield estimation based on data assimilation technology considering canopy photosynthesis and component succession characteristics

Projecting lyme disease risk in the United States: A machine learning approach integrating environmental, socioeconomic and vector factors.

• Using panel data for United States counties for years 2001-2011, this study applies mediation analysis to examine the extent to which wildfires influence mortality through air pollution. • The first empirical finding demonstrates that local wildfire events are strongly associated with elevated fine particulate matter (PM 2.5 ) concentrations. • The second empirical finding is that there is a strong positive relationship between wildfire occurrences and all-cause mortality, as well as mortality due to respiratory and circulatory system diseases. • Mediation analysis indicates that increased PM 2.5 from wildfires accounts for about 58 % of wildfire-induced all-cause fatalities, 47 % for respiratory mortality, and 22 % for circulatory mortality. • The analysis also identifies significant spillover effects on counties downwind from wildfires. Wildfires have become an increasingly severe public health threat in the United States, driven by rising frequency and intensity. While the health impacts of wildfire-related air pollution are well documented, less is known about the full extent to which wildfires affect mortality through both pollution-mediated and other pathways. This study aims to quantify the direct and indirect effects of wildfires on mortality, with a focus on fine particulate matter (PM 2.5 ) as the primary environmental mediator. Using county-level panel data from 2001 to 2011, we apply a causal mediation framework that incorporates an instrumental variable strategy to address endogeneity. We find that wildfires significantly increase all-cause, respiratory, and circulatory mortality. Mediation analysis reveals that PM 2.5 accounts for 58% of the effect on all-cause mortality, 47% on respiratory mortality, and 21% on circulatory mortality. These results underscore the substantial role of air pollution in transmitting wildfire impacts to health, while also highlighting the potential importance of non-pollution pathways such as psychological stress and healthcare disruptions.

Abstract Living in a rural area (10+ miles from a population center) is associated with increased morbidity and mortality. Rural populations are typically older, engage in fewer healthy behaviors, and have poorer access to basic resources like healthcare. Understanding the relationships between rurality, lifestyle, and health outcomes is critical to reducing disparities, particularly among older adults. Moreover, operationalizing population needs/interests supports meaningful development of actionable interventions. However, in Oregon, insufficient county-level data remains a persistent barrier to such efforts. A pilot study was therefore launched in two rural Oregon counties (Grant/Gilliam). State and local health authorities collaborated with community members to iteratively develop a comprehensive, mixed-method survey instrument (online/paper) with tailored questions on health/wellbeing, behavior, service access, and community needs. Older adults were prioritized, given Oregon’s elevated proportion of residents aged 65+, especially in rural areas (US = 17.7%, Oregon=19.6%, Grant/Gilliam=30-32%). Although data collection is ongoing (2/28/25-4/20/25, 35% complete, N = 281/800), preliminary results indicate respondents were predominantly older (55% aged 65+, range=18-85+) and female (68%). Approximately 40% reported joint/muscle/mobility issues; nearly 20% reported caregiving responsibilities. Emerging themes suggest significant barriers to healthcare, recreation, and quality produce, pointing to marked regional deficiencies. There is notable demand for local health professionals/services (primary care, dentistry, telehealth, etc.), emphasizing critical gaps in access which may compound rural health disparities. Overall, results suggest aging issues are prominent in rural Oregon, potentially exacerbating individual challenges and cyclically diminishing quality of life. This pilot can inform future development and implementation of population-led assessments to improve data availability in small/rural communities.

Abstract Racially segregated communities often experience concentrated poverty, yet the impact of racialized economic segregation on mental health across the life course remains understudied. This study examined associations between life stage-specific, county-level racialized economic segregation and the number of mentally unhealthy days over the past 30 days among U.S. adults aged 50 and older. Racialized economic segregation during young and middle adulthood was measured using the Index of Concentration at the Extreme (ICE), which quantifies spatial social polarization between deprived (Black individuals in poverty) and privileged (White individuals not in poverty) groups. ICE scores (range: -1 to1) were derived from U.S. Census (1970–2000) and the American Community Survey (2010–2020) data and categorized into quintiles. Life-course addresses were geocoded and linked to county-level data corresponding to their ages. Negative binomial regression models were fitted separately for each life stage and racialized group. This analysis included 365 Black and 885 White participants (56.1% male; 24.3% aged 80+). After adjusting for age, gender, and population density, living in a county in the 4th or 5th ICE quintile during young adulthood —but not middle adulthood—was associated with a 61% or 56% lower likelihood of currently experiencing poor mental health days among White participants (IRR = 0.39, 95% CI: 0.17, 0.91; IRR = 0.44, 95% CI: 0.19, 1.00), compared to those in the 1st quintile. This pattern was not observed among Black participants. This finding contributes to research delineating sensitive periods of exposure to racialized economic segregation and mental health in older adults.

Urban environments may alter the landscape of disease transmission with implications for control. Yet, it is unclear whether urban-rural differences exist in the dynamics of childhood respiratory diseases, given specific mixing patterns in younger age groups. Here, we leverage county-level data on respiratory syncytial virus (RSV) from the United States to reveal an urban-rural gradient in both the intensity and age structure of the RSV epidemic, where urban locations experience more prolonged epidemics with higher burden in infants (under 1 year of age). We develop a mechanistic epidemiological model to show that these differences can be explained by daycare utilization rates in children under 5. Using our model to consider control measures, we find that expanding seasonal immunization access in urban and rural areas may limit the risk of off season RSV epidemics.

The national park system pilot program is a significant initiative in China's ecological civilization institutional reform, and its impact on regional industrial development warrants further exploration. This study utilizes county-level data on tea industry enterprise registration from 2000 to 2022 and innovatively employs Double Machine Learning (DML) model to assess the impact of the national park system pilot program on tea industry aggregation within Wuyishan National Park, examining both horizontal and vertical aggregation dimensions. The research findings indicate the following: (1) The national park system pilot program has significantly promoted horizontal clustering and vertical integration of the tea industry in Wuyishan National Park. After removing outliers, resetting the sample division ratio, and changing the machine learning algorithm, the model results remain robust and reliable. (2) The national park system pilot program can promote tea industry clustering by strengthening fiscal support, technological innovation, and ecological protection. (3) The pilot program has a stronger impact on tea industry clustering in counties with higher economic levels and higher informatization levels. (4) The spatial distribution of horizontal and vertical agglomeration in the tea industry exhibits significant spatial autocorrelation. This study contributes to research on the economic effects of the national park system pilot program and provides theoretical support and practical insights for the green clustering development of agricultural industries.

Background Rural communities face challenges of insufficient healthcare professionals. Studies show that rural populations experience disparities in skin cancer care and delayed diagnoses. Methods Data describing Texas dermatologist locations from 2015 to 2022 were obtained from the Texas Department of Health Services. County-level data, including estimated population, rural-urban continuum codes, public health regions, and poverty percent, were collected from the Texas Department of Health Services, the Economic Research Service at the US Department of Agriculture, and the US Census Bureau. Results Only 2 public health regions met the recommended ratio of 4 dermatologists per 100,000 people in 2022. While dermatologist density has increased with a compound annual growth rate of 2.47%, rural areas with <5000 people showed a negative compound annual growth rate of −9.12%. Dermatologist density in public health regions surrounding the largest cities in Texas—San Antonio, Houston, Austin, and Dallas—was 4.01 per 100,000 dermatologists in 2022, which was significantly higher than the rest of Texas at 1.86 (P < 0.05). Conclusions These findings highlight insufficient access to Texas dermatologists and describe disparities linked to population, poverty, and urban proximity. Efforts are needed to address these inequalities and their consequences on dermatologic outcomes in rural communities.

As global warming intensifies, extreme weather phenomena such as heatwaves, flash droughts, torrential floods, cold waves, and blizzards are becoming increasingly frequent. Against this backdrop, traditional static food security assessment methods fail to capture the dynamic transmission patterns of agricultural productivity risks and their regional heterogeneity. Therefore, it is imperative to reconstruct a resilience analysis paradigm for food production systems, dynamically investigate the mechanisms through which climate change affects China’s agricultural productivity and discern the interactive effects between technological evolution and climate constraints. This will provide theoretical foundations for building a climate-resilient food security system. Accordingly, this study establishes a multidimensional resilience measurement index system for China’s grain productivity by integrating agricultural factor elasticity analysis with disaster impact response modeling. Through production function decomposition and hybrid forecasting models, we reveal the evolutionary patterns of China’s grain productivity under climate risk shocks and trace the transmission pathways of risk fluctuations. Key findings indicate the following: (1) Extreme climate events exhibit significant negative correlations with grain production, with drought and flood impacts demonstrating pronounced regional heterogeneity. (2) A dynamic game relationship exists between agricultural technological progress and climate risk constraints, where the marginal contribution of resource efficiency improvements to productivity growth shows diminishing returns. (3) Climate-sensitive factors vary substantially across agricultural zones: Northeast China faces dominant cold damage, North China experiences drought stress, while South China contends with humid-heat disasters as primary regional risks. Consequently, strengthening foundational agricultural infrastructure and optimizing regionally differentiated risk mitigation strategies constitute critical pathways for enhancing food security resilience. (4) Future research should leverage higher-resolution, county-level data and incorporate a wider range of socio-economic variables to enhance granular understanding and predictive accuracy.

People’s social networks are fragmenting and partisan animosity is rising. Three studies highlight the connection between social network density and partisan animosity. Americans with denser networks (where more social contacts know each other) appear to often harbor less animosity toward political opponents. One explanation for this association is feelings of unity: dense social networks feel more unified, and people project these feelings of connection and common identity onto distant fellow Americans. Study 1 supports this idea with a representative sample of Americans: social network density predicts feelings of local unity, which predicts feelings of national unity, which predicts warmth toward and willingness to interact with political opponents. Study 2 attempts to manipulate perceived social network density and finds a “causal cascade” of indirect effects culminating in lower partisan animosity, suggesting that more involved interventions may reduce partisan animosity. Study 3 uses nationwide county-level data from the Social Capital Atlas and Project Implicit to reveal that communities with higher social network density exhibit lower partisan animosity. The results suggest that considering the overarching structures of social networks is crucial to understanding partisan animosity, and that fostering social cohesion could help mend political divides.

Abstract Pituitary adenomas often require urgent surgical intervention to prevent irreversible visual or hormonal dysfunction. Yet, access to experienced pituitary surgeons remains highly centralized. We analyzed national patterns in provider distribution and travel burden, identifying geographic and sociodemographic factors that limit timely access to specialized surgical care. We performed a cross-sectional geospatial and sociodemographic analysis across 40 U.S. County catchment areas. Pituitary surgeons were identified from CMS billing data as providers who submitted ≥10 annual claims for CPT codes 62165, 61546, or 61548 over five years. Provider locations were geocoded to define access points. County-level demographic and health data were obtained from the UCSF Health Atlas. Two primary outcomes were calculated: mean Euclidean distance to the nearest pituitary surgeon and surgeon density per 100,000 population. Multivariate linear regression models identified predictors of both outcomes. Among 83 identified pituitary surgeons, access varied markedly across regions. Travel distances ranged from 5.9 to over 150 miles. A multivariate model (adjusted R² = 0.706, p &amp;lt; 0.001) revealed that higher proportions of Native American residents predicted significantly longer travel distances (β = +18.2 miles per 1%, p &amp;lt; 0.00001), alongside cognitive disability prevalence, depression, lack of vehicle access, and poor internet infrastructure (p &amp;lt; 0.05). Surgeon density was positively associated with population density, income, education, and vehicle access. Latino-majority regions had significantly fewer providers per capita (β = -0.059, p &amp;lt; 0.01). Substantial disparities exist in access to pituitary surgery, driven by overlapping social, economic, and geographic barriers. Native American and socioeconomically disadvantaged populations face the longest travel distances, while wealthier urban areas benefit from greater provider density. Interventions such as telehealth, transportation support, and workforce redistribution are needed to address these inequities.

ABSTRACT Urban-rural income inequality poses a significant challenge for developing countries, particularly in border regions with ethnic diversity. This paper employs the ‘Program to Prosper Border Ethnic Areas and Enrich Local Residents’ (PBE) as a quasi-natural experiment, using county-level data from China’s border ethnic provinces from 2004 to 2022 to examine its impact on urban-rural income inequality. The results indicate that PBE significantly reduces the urban-rural income gap. Mechanism analysis reveals that PBE mitigated income inequality by increasing local fiscal investment, enhancing infrastructure development, and promoting agricultural growth. Heterogeneity analysis shows that the common prosperity effect of PBE is more pronounced in counties with higher proportions of ethnic minority populations and greater initial income disparities. We provide valuable insights for countries worldwide in reducing urban-rural income inequality and promoting prosperity in ethnic border areas.

Public health professionals frequently engage with residents of rural Georgia to conduct needs-based initiatives, which aim to identify deficiencies and shortcomings in community health. However, this process can exacerbate existing stereotypes and lead community members to feel a sense of despair in their own communities. The Community Health Resource Project (CHRP) offers a counterbalance through a strengths-based approach by highlighting animal, plant, human, and environmental resources, or “One Health” assets, that currently exist in the community. CHRP begins by analyzing publicly available county-level data to gain an initial understanding of the health landscape before proceeding to the field. Next, the team engages in Participatory Asset Mapping (PAM) to gather community-driven qualitative insights on existing One Health assets in participating rural or underserved counties. Data gathered from community engagement strategies inform the development of comprehensive county-specific asset maps and reports. This paper describes the methods of applying a strengths-based approach to highlight community One Health-related assets. These strategies can be a valuable tool for developing targeted workforce development efforts in resource-limited counties for the benefit of all species.

Background: Emerging evidence links the local food environment to chronic disease outcomes, yet its relationship with cardiac mortality remains under explored at the population level. This study examines the association between the Retail Food Environment Index (RFEI), a marker of community food healthiness, and cardiac death rates across U.S. counties. Hypothesis: We propose that higher RFEI scores are significantly linked to greater cardiac mortality, independent of socioeconomic status, lifestyle behaviours, and demographic characteristics. Methods: A cross-sectional ecological study was conducted using county-level data from 2,793 U.S. counties, integrating cardiac mortality information from the CDC WONDER database for the years 2018–2020 and the food accessibility data from the USDA Food Environment Atlas. The primary outcome was age-adjusted cardiac mortality per 100,000 population. The main exposure variable was the Retail Food Environment Index (RFEI), defined as the ratio of fast-food outlets and convenience stores to supermarkets and farmers' markets. To test the robustness of the RFEI, two alternate indices (RFEI1 and RFEI2) were developed by varying the inclusion criteria for superstore classification. Descriptive statistics, along with univariable and multivariable regression analyses, were performed, adjusting for socioeconomic indicators, racial/ethnic composition, health behaviours, metabolic risk factors, and food accessibility. Results: The mean cardiac mortality rate was 246.6 per 100,000 (SD = 58.3). RFEI showed a positive association with cardiac mortality in both univariate (β = 1.75; 95% CI, 1.25–2.24; P &lt; 0.001) and multivariable analyses (β = 0.96; 95% CI, 0.60–1.34; P &lt; 0.001). RFEI1 and RFEI2 yielded consistent results (β = 2.17 and 2.37, respectively; both P &lt; 0.001). Among covariates, smoking (β = 5.47; P &lt; 0.001), diabetes (β = 2.26; P = 0.008), and poverty rate (β = 0.76; P = 0.008) were significant predictors. The final model explained 50% of the variation in mortality (adjusted R square = 0.50). Conclusion: A higher density of unhealthy food outlets is independently associated with increased cardiac mortality across U.S. counties. These findings underscore the importance of local food environments as modifiable population-level determinants of cardiovascular health and support public health strategies aimed at improving equitable access to nutritious food.

Background: Community-level social disadvantage is a recognized determinant of adverse cardiovascular outcomes, yet its relationship with hypertension-related hospitalizations (HTNH) among older adults remains unclear. Composite indices such as the Social Vulnerability Index (SVI) and Social Deprivation Index (SDI) have emerged as tools to quantify area-level social disadvantage. This study assessed the association of county-level SVI and SDI scores with HTNH rates among Medicare beneficiaries aged ≥65 years and compared the predictive utility of each index. Methods: We conducted a retrospective cross-sectional analysis using publicly available county-level data. The SDI, developed by the Robert Graham Center, is a measure of deprivation based on seven demographic characteristics. The SVI, developed by the CDC, is based on 16 social determinants of health. We linked 2019 SDI and 2018 SVI data to HTNH data from the CDC’s Interactive Atlas of Heart Disease and Stroke for adults aged 65 and older in the United States from 2019 to 2021 for each county. The linked dataset was divided into quartiles (Q1 = least disadvantaged; Q4 = most disadvantaged) based on SDI scores (0–100) and SVI scores (0–1). Mean HTNH rates per 1,000 person-years were calculated with 95% confidence intervals (CIs) and stratified by quartile, gender, and race/ethnicity. A higher rate in Q4 vs Q1, with nonoverlapping CIs, indicated a negative impact of SDI or SVI. Rate differences were calculated as excess or fewer hospitalizations per 1,000 person-years by subtracting Q1 from Q4. Results: The overall HTNH rate was 13.6 (95% CI: 13.4–13.8). For SDI, rates rose from 10.9 (10.6–11.2) in Q1 to 16.8 (16.4–17.2) in Q4, representing a 5.9 excess hospitalizations. Among men, rates increased from 12.6 to 18.2; among women, from 9.9 to 15.7. For SVI, rates rose from 10.6 (10.3–11.0) in Q1 to 16.5 (16.1–16.9) in Q4, again reflecting 5.9 excess hospitalizations. Disparities were also evident by race/ethnicity, with Q4 counties showing significantly higher HTNH rates than Q1 across Black, White, and Hispanic groups. Conclusion: Older adults with hypertension living in counties with high social disadvantage experience markedly higher HTNH rates. Both SDI and SVI effectively identify counties with elevated hospitalization risk and may support targeted public health interventions to reduce HTNH and healthcare resource utilization among Medicare beneficiaries.

Health-related social needs (HRSNs) have been associated with increased emergency department (ED) use among older adults. The Accountable Health Communities (AHC) Model, through its Assistance and Alignment Tracks, was implemented to reduce healthcare use by connecting Medicare beneficiaries to community resources that addressed their HRSNs. This study employed a two-way fixed effects difference-in-differences (DiD) analysis using county-level claims data from the Colorado All-Payer Claims Database (2014-2019) to assess the impact of the AHC Model's Alignment Track on ED visits among Medicare beneficiaries. Results show a reduction of nearly 26 all-cause and nine preventable ED visits per 1,000 people in counties served by one of the two implementing organizations. However, pre-existing declining trends raise concerns about the effects being directly attributed to the program. Further research is needed to determine whether, and through what mechanisms, the AHC Alignment Track influences ED use among older adults in Colorado.

Background: Peripheral artery disease (PAD) continues to pose a significant public health burden in the United States (U.S.), contributing to increased morbidity, functional decline, and mortality. While social determinants of health have been shown to influence overall cardiovascular outcomes, their specific effect on PAD-related mortality remains underexplored. We therefore analyzed the association between county-level Social Vulnerability Index (SVI), Social Deprivation Index (SDI), and PAD-related mortality across the U.S. Methods: County-level PAD-related mortality data for adults aged &gt;25 years were obtained from the CDC WONDER database (1999–2020) and linked to the corresponding 2022 SVI and 2019 SDI data. Counties with PAD related deaths fewer than 10 were excluded from the CDC database to protect confidentiality and ensure statistical reliability. SDI data which measures area-level socioeconomic disadvantage was derived from the American Community Survey and SVI data were obtained from the CDC. PAD-related mortalities were stratified by SVI and SDI quartiles, with the 1st quartile (Q1) representing the least vulnerable and the 4th quartile (Q4) the most vulnerable. PAD age adjusted mortality rates(AAMR) per 100,000 deaths with 95% confidence intervals (CI) were calculated for each quartile and stratified by sex and race. A higher PAD related mortality in Q4 than Q1 with non-overlapping CIs was considered evidence of a negative impact. Results: A total of 396 counties from 43 states were included. For SDI, there was no linear association with PAD-related mortality. The AAMR in Q1 was 2.12 (95% CI: 1.17–3.08) compared to 1.95 (1.47–2.42) in Q4, with overlapping CIs indicating no significant difference. This trend was consistent across sexes and racial groups (Table 1). A similar pattern was observed for SVI, with a rate of 2.12 (1.45–2.79) in Q1 and 1.58 (1.17–1.99) in Q4, again with overlapping CIs. Subgroup analyses by SDI also showed overlapping CIs between Q1 and Q4 (Table 2). Conclusions: Higher SDI and SVI scores were not associated with increased PAD-related mortality. Further research exploring alternative social indices may improve risk prediction and help identify truly vulnerable populations.

Introduction: Cardiovascular disease (CVD) is a major cause of morbidity and mortality among older adults in the U.S. The Social Vulnerability Index (SVI), developed by the Centers for Disease Control and Prevention (CDC), is a comprehensive measure aggregating various social determinants of health, including socioeconomic status, household composition, minority status, and housing/transportation factors. Social determinants, measured by the CDC’s Social Vulnerability Index (SVI), affect healthcare access and outcomes. This study examines the link between social vulnerability and CVD-related outcomes among Medicare beneficiaries. Objectives: To assess the impact of county-level social vulnerability, stratified by SVI quartiles, on total CVD-related hospitalization and mortality rates between 2019 and 2021, with subgroup analyses by gender and race/ethnicity. Methods: We conducted a retrospective analysis of total CVD-related hospitalizations and mortality rates using ICD-10 codes (I00-I78) among Medicare beneficiaries aged ≥65 years from 2019 to 2021. County-level SVI data from the CDC was categorized into quartiles (Q1: 0–0.25, lowest vulnerability; Q4: 0.75–1.0, highest vulnerability). Age-standardized hospitalization and mortality rates per 100,000 beneficiaries were analyzed across SVI quartiles, gender, and race/ethnicity. Pearson correlation and linear regression models assessed the associations between SVI and outcomes, with rate differences calculated between SVI Q4 and Q1. Results: The total age-standardized CVD hospitalization rate was 53.61 per 1,000(95% CI: 53.14–54.09), and mortality was 1,526.51 per 100,000 (95% CI: 1,516.57–1,536.45) beneficiaries. Both rose with SVI: hospitalizations from 46.94 (Q1) to 60.08 (Q4), and mortality from 1,376.46 to 1,681.66. Men had higher rates than women (61.52 vs. 47.19 per 1,000; 1,796.07 vs. 1,311.08 per 100,000). Black beneficiaries had the highest rates: hospitalizations at 70.18 per 1,000 and mortality at 1,811.22 per 100,000. SVI correlated with hospitalization (r = 0.363, p = &lt; 0.001 R 2 = 0.132) and mortality (r = 0.405, p = &lt; 0.001 R 2 = 0.164). A 0.1 unit increase in SVI was linked to 1.7 more hospitalizations per 1,000 and 39.8 more deaths per 100,000. Conclusion: Higher social vulnerability is associated with increased rates of CVD-related hospitalizations and mortality among Medicare beneficiaries, with greater disparities by gender and race, underscoring the need for targeted social interventions.