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.

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.

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.

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.

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.

Examining the Correlation Between PrEP Use and Black:White Disparities in HIV Incidence in the Ending the HIV Epidemic Priority Jurisdictions.

INTRODUCTION: HIV incidence remains high in the U.S. as do disparities in new HIV diagnosis between White and Black populations and access to preventive therapies like pre-exposure prophylaxis (PrEP). The federal Ending the HIV Epidemic (EHE) initiative was developed to prioritize resources to 50 jurisdictions with high HIV incidence. METHODS: We conducted secondary analyses of data (2013-2019) from the CDC, Census Bureau, and AIDSVu to evaluate the correlation between PrEP use, HIV incidence, and HIV incidence disparities. We compared the PrEP-to-need ratio (PnR) with the ratio of Black and White HIV incidence rates in 46 EHE counties. Subsequent analyses were performed for the seven states that contained multiple EHE counties. RESULTS: These 46 counties represented 25.9% of the U.S. population in 2019. HIV incidence ranged from 10.5 in Sacramento County, CA, to 59.6 in Fulton County, GA (per 100,000). HIV incidence disparity ranged from 1.5 in Orleans Parish, LA, to 12.1 in Montgomery County, MD. PnR ranged from 26.8 in New York County, NY, to 1.46 in Shelby County, TN. Change in HIV incidence disparities and percent change in PnR were not significantly correlated (ρ = 0.06, p = 0.69). Change in overall HIV incidence was significantly correlated with increase in PnR (ρ = -0.42, p = 0.004). CONCLUSIONS: PrEP has the potential to significantly decrease HIV incidence; however, this benefit has not been conferred equally. Within EHE priority counties, we found significant HIV incidence disparities between White and Black populations. PrEP has decreased overall HIV incidence, but does not appear to have decreased HIV incidence disparity.

Extreme temperatures, mortality, and adaptation: Evidence from the county level in China.

Extreme temperatures are known to cause adverse health outcomes. Yet knowledge on the magnitude of this effect in developing countries is limited due to data availability and reliability issues. Collecting data for 2872 counties in China, we estimate the effects of daily temperatures on the monthly mortality rate. The results indicate that an additional day for which the maximum temperature is 38°C or above on average increases the monthly mortality rate by about 1.7% relative to if that day's maximum temperature had been in the range 16-21°C. This is after deducting deaths harvested from the subsequent month. Higher gross domestic product per capita at the county level is associated with lower mortality effects of hot and cold days. Improved dwelling conditions are found to be associated with a lower mortality effect of hot days and improved local healthcare infrastructure to be associated with a lower mortality effect of cold days. In the absence of strong adaptation efforts, the estimates suggest net upward pressure on annual mortality rates over coming decades in many populous counties, especially under more extreme climate change scenarios.

Understanding social risk factors of county-level disparities in COVID-19 tests per confirmed case in South Carolina using statewide electronic health records data.

BACKGROUND: COVID-19 testing is essential for pandemic control, and insufficient testing in areas with high disease burdens could magnify the risk of poor health outcomes. However, few area-based studies on COVID-19 testing disparities have considered the disease burden (e.g., confirmed cases). The current study aims to investigate socioeconomic drivers of geospatial disparities in COVID-19 testing relative to disease burden across 46 counties in South Carolina (SC) in the early (from April 1, 2020, to June 30, 2020) and later (from July 1, 2020, to September 30, 2021) phases of the pandemic. METHODS: Using SC statewide COVID-19 testing data, the COVID-19 testing coverage was measured by monthly COVID-19 tests per confirmed case (hereafter CTPC) in each county. We used modified Lorenz curves to describe the unequal geographic distribution of CTPC and generalized linear mixed-effects regression models to assess the association of county-level social risk factors with CTPC in two phases of the pandemic in SC. RESULTS: As of September 30, 2021, a total of 641,201 out of 2,941,227 tests were positive in SC. The Lorenz curve showed that county-level disparities in CTPC were less apparent in the later phase of the pandemic. Counties with a larger percentage of Black had lower CTPC during the early phase (ß = -0.94, 95%CI: -1.80, -0.08), while such associations reversed in the later phase (ß = 0.28, 95%CI: 0.01, 0.55). The association of some other social risk factors diminished as the pandemic evolved, such as food insecurity (ß: -1.19 and -0.42; p-value is < 0.05 for both). CONCLUSIONS: County-level disparities in CTPC and their predictors are dynamic across the pandemic. These results highlight the systematic inequalities in COVID-19 testing resources and accessibility, especially in the early stage of the pandemic. Counties with greater social vulnerability and those with fewer health care resources should be paid extra attention in the early and later phases, respectively. The current study provided empirical evidence for public health agencies to conduct more targeted community-based testing campaigns to enhance access to testing in future public health crises.

The Association of a Geographically Wide Social Media Network on Depression: County-Level Ecological Analysis.

BACKGROUND: Social connectedness decreases human mortality, improves cancer survival, cardiovascular health, and body mass, results in better-controlled glucose levels, and strengthens mental health. However, few public health studies have leveraged large social media data sets to classify user network structure and geographic reach rather than the sole use of social media platforms. OBJECTIVE: The objective of this study was to determine the association between population-level digital social connectedness and reach and depression in the population across geographies of the United States. METHODS: Our study used an ecological assessment of aggregated, cross-sectional population measures of social connectedness, and self-reported depression across all counties in the United States. This study included all 3142 counties in the contiguous United States. We used measures obtained between 2018 and 2020 for adult residents in the study area. The study's main exposure of interest is the Social Connectedness Index (SCI), a pair-wise composite index describing the "strength of connectedness between 2 geographic areas as represented by Facebook friendship ties." This measure describes the density and geographical reach of average county residents' social network using Facebook friendships and can differentiate between local and long-distance Facebook connections. The study's outcome of interest is self-reported depressive disorder as published by the Centers for Disease Control and Prevention. RESULTS: On average, 21% (21/100) of all adult residents in the United States reported a depressive disorder. Depression frequency was the lowest for counties in the Northeast (18.6%) and was highest for southern counties (22.4%). Social networks in northeastern counties involved moderately local connections (SCI 5-10 the 20th percentile for n=70, 36% of counties), whereas social networks in Midwest, southern, and western counties contained mostly local connections (SCI 1-2 the 20th percentile for n=598, 56.7%, n=401, 28.2%, and n=159, 38.4%, respectively). As the quantity and distance that social connections span (ie, SCI) increased, the prevalence of depressive disorders decreased by 0.3% (SE 0.1%) per rank. CONCLUSIONS: Social connectedness and depression showed, after adjusting for confounding factors such as income, education, cohabitation, natural resources, employment categories, accessibility, and urbanicity, that a greater social connectedness score is associated with a decreased prevalence of depression.

Assessing County-Level Behavioral Health and Justice Systems with the Sequential Intercept Model Practices, Leadership, and Expertise Scorecard.

The Sequential Intercept Model has helped conceptualize interventions for people with serious mental illness in the criminal/legal system. This paper operationalizes the Sequential Intercept Model into a 35-item scorecard of behavioral health and legal practices. Using interviews, survey, and observational methods, the scorecard assesses an exploratory sample of 19 counties over 27 independent data collections. A series of ordinary least squares regression models assessed the predictor scores on four jail outcomes: prevalence of serious mental illness, length of stay, connections to treatment, and recidivism. Increases in pre-booking scores showed significant decreases in jail prevalence of serious mental illness at the p < 0.05 level, and post-booking scores and overall scores showed significant positive associations with connections to treatment at the p < 0.05 level, though these were non-significant after correcting for multiple comparisons. Preliminary findings suggest a combination of practices across the Sequential Intercept Model could have synergistic impacts on key jail diversion outcomes.

The Social Factors Influencing Cover Crop Adoption in the Midwest: A Controlled Comparison.

Increased funding and resources have become available in recent years for agricultural producers to plant cover crops to improve soil health and prevent nutrient loss and erosion; however, cover crop adoption remains relatively low and has been uneven across different Midwestern counties. This study employed a controlled comparison method to investigate the social factors affecting cover crop adoption in Iowa, Illinois, and Indiana. In each state, the authors compared pairs of neighboring counties, where one county was a relatively higher adopter and the other was a lower adopter of cover crops, while controlling for variations in climate conditions. Results show that there were multiple factors explaining the difference in cover crop adoption among county pairs. Social factors included attitudes toward cover crops; conservation agency influence; presence of cover crop experts, advocates, and/or entrepreneurs; and collaboration between agencies and the private sector. Other important factors included topography, cattle raising, organic production, and local incentive-based programs. Among these, collaborations between agencies and the private sector played the most important role in explaining why some counties had higher rates of cover crop adoption compared to their neighbors.

Closing Greenhouse Gas Emission Gaps of Staple Crops in China.

China is facing the dual challenge of achieving food security and agricultural carbon neutrality. Developing spatially explicit crop emission profiles can help inform policy to mitigate agricultural greenhouse gases (GHGs), but previous life-cycle studies were conducted mostly at national and provincial levels. Here, we estimate county-level carbon footprint of China's wheat and maize production based on a nationwide survey and determine the contribution of different strategies to closing regional emission gaps. Results show that crop carbon footprint varies widely between regions, from 0.07 to 3.00 kg CO2e kg-1 for wheat and from 0.09 to 2.30 kg CO2e kg-1 for maize, with inter-county variation generally much higher than interprovince variation. Hotspots are mainly concentrated in Xinjiang and Gansu provinces, owing to intensive irrigation and high plastic mulch and fertilizer inputs. Closing the regional emission gaps would benefit mostly from increasing crop yields and nitrogen use efficiency, but increasing manure use (e.g., in Northeast, East, and Central China) and energy use efficiency (e.g., in North and Northwest China) can also make important contributions. Our county-level carbon footprint estimates improve upon previous broad-scale results and will be valuable for detailed spatial analysis and the design of localized GHG mitigation strategies in China.

Long-term exposure to fine particulate matter air pollution: An ecological study of its effect on COVID-19 cases and fatality in Germany.

BACKGROUND: COVID-19 is a lung disease, and there is medical evidence that air pollution is one of the external causes of lung diseases. Fine particulate matter is one of the air pollutants that damages pulmonary tissue. The combination of the coronavirus and fine particulate matter air pollution may exacerbate the coronavirus' effect on human health. RESEARCH QUESTION: This paper considers whether the long-term concentration of fine particulate matter of different sizes changes the number of detected coronavirus infections and the number of COVID-19 fatalities in Germany. STUDY DESIGN: Data from 400 German counties for fine particulate air pollution from 2002 to 2020 are used to measure the long-term impact of air pollution. Kriging interpolation is applied to complement data gaps. With an ecological study, the correlation between average particulate matter air pollution and COVID-19 cases, as well as fatalities, are estimated with OLS regressions. Thereby, socioeconomic and demographic covariates are included. MAIN FINDINGS: An increase in the average long-term air pollution of 1 µg/m3 particulate matter PM2.5 is correlated with 199.46 (SD = 29.66) more COVID-19 cases per 100,000 inhabitants in Germany. For PM10 the respective increase is 52.38 (SD = 12.99) more cases per 100,000 inhabitants. The number of COVID-19 deaths were also positively correlated with PM2.5 and PM10 (6.18, SD = 1.44, respectively 2.11, SD = 0.71, additional COVID-19 deaths per 100,000 inhabitants). CONCLUSION: Long-term fine particulate air pollution is suspected as causing higher numbers of COVID-19 cases. Higher long-term air pollution may even increase COVID-19 death rates. We find that the results of the correlation analysis without controls are retained in a regression analysis with controls for relevant confounding factors. Nevertheless, additional epidemiological investigations are required to test the causality of particulate matter air pollution for COVID-19 cases and the severity.

Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study.

BACKGROUND: Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. METHODS: We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. RESULTS: Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. CONCLUSIONS: County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

The relationship between Social Determinants of Health (SDoH) and death from cardiovascular disease or opioid use in counties across the United States (2009-2018).

BACKGROUND: Death from cardiovascular disease (CVD) has been a longstanding public health challenge in the US, whereas death from opioid use is a recent, growing public health crisis. While population-level approaches to reducing CVD risk are known to be effective in preventing CVD deaths, more targeted approaches in high-risk communities are known to work better for reducing risk of opioid overdose. For communities to plan effectively in addressing both public health challenges, they need information on significant community-level (vs individual-level) predictors of death from CVD or opioid use. This study addresses this need by examining the relationship between 1) county-level social determinants of health (SDoH) and CVD deaths and 2) county-level SDoH and opioid-use deaths in the US, over a ten-year period (2009-2018). METHODS: A single national county-level ten-year 'SDoH Database' is analyzed, to address study objectives. Fixed-effects panel-data regression analysis, including county, year, and state-by-year fixed effects, is used to examine the relationship between 1) SDoH and CVD death-rate and 2) SDoH and opioid-use death-rate. Eighteen independent (SDoH) variables are included, spanning three contexts: socio-economic (e.g., race/ethnicity, income); healthcare (e.g., system-characteristics); and physical-infrastructure (e.g., housing). RESULTS: After adjusting for county, year, and state-by-year fixed effects, the significant county-level positive SDoH predictors for CVD death rate were, median age and percentage of civilian population in armed forces. The only significant negative predictor was percentage of population reporting White race. On the other hand, the four significant negative predictors of opioid use death rate were median age, median household income, percent of population reporting Hispanic ethnicity and percentage of civilian population consisting of veterans. Notably, a dollar increase in median household income, was estimated to decrease sample mean opioid death rate by 0.0015% based on coefficient value, and by 20.05% based on effect size. CONCLUSIONS: The study provides several practice and policy implications for addressing SDoH barriers at the county level, including population-based approaches to reduce CVD mortality risk among people in military service, and policy-based interventions to increase household income (e.g., by raising county minimum wage), to reduce mortality risk from opioid overdoses.

A Novel Causal Risk-Based Decision-Making Methodology: The Case of Coronavirus.

Either in the form of nature's wrath or a pandemic, catastrophes cause major destructions in societies, thus requiring policy and decisionmakers to take urgent action by evaluating a host of interdependent parameters, and possible scenarios. The primary purpose of this article is to propose a novel risk-based, decision-making methodology capable of unveiling causal relationships between pairs of variables. Motivated by the ongoing global emergency of the coronavirus pandemic, the article elaborates on this powerful quantitative framework drawing on data from the United States at the county level aiming at assisting policy and decision makers in taking timely action amid this emergency. This methodology offers a basis for identifying potential scenarios and consequences of the ongoing 2020 pandemic by drawing on weather variables to examine the causal impact of changing weather on the trend of daily coronavirus cases.

Efficiency and productivity of county-level traditional Chinese medicine hospitals in Hubei Province, China: A retrospective study based on 17 years of panel data.

BACKGROUND: The county-level traditional Chinese medicine hospitals have significantly expanded in recent decades. This study aims to assess the changes in the efficiency and productivity of the county-level traditional Chinese medicine hospitals and explore the possible causes of such changes. METHODS: Sixty one hospitals spanning from 2001 to 2017 were selected as samples in this study. And a slacks-based measure of super-efficiency in Data Envelopment Analysis and Malmquist index were used to respectively measure the changes in the efficiency and productivity. RESULTS: The scale of sample hospitals in Hubei continuously expanded from 2001 to 2017. The mean values of technical efficiency, pure technical efficiency and scale efficiency in 2017 were 0.686, 0.74 and 0.933, respectively. The technical efficiency changes in 2017 was 1.97 times that of 2001, and the technological changes in 2017 was 1.45 times that of 2001. CONCLUSIONS: The medical environment and resources have been greatly improved due to the expansion of the sample hospitals, but the technical efficiency value indicates that the operation efficiency of sample hospitals still needs to be significantly improved. Decision-makers are advised to attach importance to the efficiency of operation management and consider the impact of multiple factors on the change in productivity.

The socio-economic determinants of COVID-19: A spatial analysis of German county level data.

The study explores the association of socioeconomic, demographic, and health-related variables at the regional level with COVID-19 related cases and deaths in Germany during the so-called first wave through mid-June 2020. Multivariate spatial models include the 401 counties in Germany to account for regional interrelations and possible spillover effects. The case and death numbers are, for example, significantly positively associated with early cases from the beginning of the epidemic, the average age, the population density and the share of people employed in elderly care. By contrast, they are significantly negatively associated with the share of schoolchildren and children in day care as well as physician density. In addition, significant spillover effects on the case numbers of neighbouring regions were identified for certain variables, with a different sign than the overall effects, giving rise to further future analyses of the regional mechanisms of action of COVID-19 infection. The results complement the knowledge about COVID-19 infection beyond the clinical risk factors discussed so far by a socio-economic perspective at the ecological level.

Measuring the completeness of death registration in 2844 Chinese counties in 2018.

BACKGROUND: Death registration completeness has never been assessed at the county level in China. Such analyses would provide critical intelligence to monitor the performance of the vital registration system and yield adjustment factors to correct death registration data, thereby increasing their policy utility. METHODS: We estimated the completeness of death registration for 31 provinces and 2844 counties of China in 2018 based on death data from the China Cause of Death Reporting System (CDRS) by using the empirical completeness method. We computed the root mean square difference (RMSD) of county-level completeness compared with provincial-level completeness to study intra-provincial variations. A two-level (province and county) logistic regression model was fitted to explore the association between county-level registration completeness and a set of covariates reflecting socioeconomic status, healthcare quality, and specific strategies and regulations designed to improve registration. RESULTS: In 2018, the overall death registration completeness for the CDRS in China was 74.2% (95% uncertainty interval [UI] 66.2-80.4), with very little difference for males and females. Geographical differences in completeness were higher across counties than across provinces. The county-level completeness ranged from 2.4% (95% UI 1.0-5.0%) in Burang County, Tibet, to 100.0% (95% UI 99.9-100.0%) in Guandu District, Yunnan. The coastal provinces of Jiangsu, Guangdong, and Fujian, with higher overall completeness, contained counties with low completeness; conversely, the underdeveloped provinces of Guangxi and Guizhou, with lower overall completeness, included some counties with high completeness. GDP, education, population density, minority population, healthcare access, and registration strategies were important drivers of the geographical differences in registration completeness. CONCLUSIONS: There are marked inequalities in registration completeness at the county level and within provinces in China. The socioeconomic condition, the implementation of specific registration-enhancing initiatives, and the availability and quality of medical care were the primary drivers of the observed geographical variation. A more strategic approach, with more research, is required to identify the main reasons for death under-reporting, especially in the poorer performing counties, to guide remedial action.

Development and Validation of a County-Level Social Determinants of Health Risk Assessment Tool for Cardiovascular Disease.

PURPOSE: Social determinants of health (SDoH) have been linked to a variety of health conditions, but there are no multivariate measures of these determinants to estimate the risk of morbidity or mortality in a community. We developed a score derived from multivariate measures of SDoH that predicts county-level cardiovascular disease (CVD) mortality. METHODS: Using county-level data from 3,026 US counties, we developed a score considering variables of neighborhood socioeconomic status, food/lifestyle environment, and health care resource availability and accessibility to predict the 3-year average (2015-2017) age-adjusted county-level mortality rate for all CVD. We used one 50% random sample to develop the score and the other to validate the score. A Poisson regression model was developed to estimate parameters of variables while accounting for intrastate correlation. RESULTS: The index score was based on 7 SDoH factors: percentage of the population of minority (nonwhite) race, poverty rate, percentage of the population without a high school diploma, grocery store ratio, fast-food restaurant ratio, after-tax soda price, and primary care physician supply. The area under the curve for the development and validation groups was similar, 0.851 (95% CI, 0.829-0.872) and 0.840 (95% CI, 0.817-0.863), respectively, indicating excellent discriminative ability. The index had better predictive performance for CVD burden than other area-level indexes: poverty only (area under the curve= 0.808, P <.001); the Centers for Disease Control and Prevention's Social Vulnerability Index (CDC-SVI) (area under the curve =0.786, P <.001); and the Agency for Healthcare Research and Quality's Socioeconomic Status (AHRQ-SES) index (area under the curve =0.835, P = .03). CONCLUSIONS: Our validated multivariate SDoH index score accurately classifies counties with high CVD burden and therefore has the potential to improve CVD risk prediction for vulnerable populations and interventions for CVD at the county level.