Rural Tennessee's health and economic disparities have worsened since 2010 (while the state led the nation in hospital closures per capita). Guided by the Vulnerable Populations Conceptual Model, we examined the relationship between Tennessee's county-level rural mortality rates and declining access to hospital and emergency care in the decade preceding the COVID-19 pandemic (avoiding pandemic-related delayed data releases and potential statistical modeling issues). We conducted a retrospective, ecological correlational study using geographic information systems and annual cross-sectional secondary data, employing aspatial and spatial negative binomial generalized linear mixed-effects models (GLMMs). Our bivariate models revealed significant correlations between hospital and emergency care access and mortality rates, but the effect decreased when adjusted for rurality, median household income, age, and other covariates. While access to hospital and emergency care influences mortality, our findings indicate that socioeconomic and demographic factors have a greater impact, underscoring the strong health-wealth connection in rural Tennessee.

Understanding drivers of biodiversity in cities can be mutually beneficial for ecosystems and people. Crowd-sourced bird observations provide an opportunity to assess how patterns of bird diversity change across observation scales and suggest driving processes. We assessed the scale dependence of bird diversity within a 128 × 128 km extent over London’s urban–rural gradient to suggest scales at which key drivers may be operating. We quantified scale variance of bird diversity across scales from 500 m to 64,000 m for three groups of species (All, Passeriformes, and Anseriformes and Charadriiformes combined). We estimated diversity by aggregating observations into a series of grids and computed comparable diversity estimates within each cell using interpolation and rarefaction. We calculated the variance explained by each scale for common diversity metrics. The results show that bird diversity patterns around London vary by scale, and that the location of high variance shifts across the study area depending on both scale and species group. The variance of Passeriformes diversity gradually shifted from the urban core to the periphery, while variance of Anseriformes and Charadriiformes diversity occurred near water features. The results suggest that the urban–rural gradient and location of water are two properties of the study extent around London influencing the scale dependance of bird diversity that could be used to ground scale considerations of further modeling efforts.

A clear understanding of the interactions at multiple scales among ecosystem services (ESs) is essential to the reasonable spatial planning of ecosystems in large-scale ecological barrier regions. This study assessed the provision of 5 typical ESs: water yield (WY), soil conservation (SC), crop production (CP), net primary productivity (NPP), and habitat quality (HQ). We explored the tradeoff/synergy relationship, mapped the bundles and analyzed their underlying socioecological influencing factors at both grid and watershed scales in the northeast forest belt (NFB). Furthermore, we provided a spatial planning and management strategy of NFB and suggested concrete practices within the range of identified ecological bundles. Our results showed that WY, SC and CP presented an obvious spatiotemporal heterogeneity while NPP and HQ only exhibited spatial heterogeneity at both gird and watershed scales. Divergences were observed in correlations between provisioning and regulating service pairs. Specifically, CP-related correlations were all negative and WY-related correlations varied depending on the combination of ES pairs, time, and scale. Notably, the absolute value of most ES pairs increased from the grid to the watershed scale, indicating a clear scale dependence. Moreover, six ES bundles (ESBs) varying spatially during the study period were further identified at both the grid and watershed scale in NFB, respectively. Geodetector analysis revealed that ESs at the grid scale shared similar determinants with those at the watershed scale, albeit with slight variations in ranking or the number of determinants. Our findings offer detailed insights for incorporating ES interactions into spatially targeted ecosystem management and future ES payment policies.

To achieve the vacuum quality required for the operation of particle accelerators, the surface of the vacuum vessels must be clean from hydrocarbons. This is usually achieved by wet chemistry processes, e.g., degreasing chemical baths that, in case of radioactive vessels, must be disposed accordingly. An alternative way exploits the oxygen plasma produced by a downstream RF plasma source. This technique offers the possibility of operating in-situ, which is an advantageous option to avoid the handling of voluminous and/or fragile components and a more sustainable alternative to large volume disposable baths. In this work, we test a commercial plasma source in dedicated vacuum systems equipped with quartz crystal microbalances (QCMs). The evolution of the etching rates of amorphous carbon (a-C) thin films deposited on the QCMs to mimic contamination are studied as function of operating parameters. We present the results of the plasma cleaning process applied to the real case of a hydrocarbons-contaminated large vacuum vessel. The studies are complemented by transport simulations and surface contamination monitoring by X-ray photoelectron spectroscopy (XPS) analysis. The evaluation of the vessel cleanliness, which is performed via residual gas analysis (RGA) measurements, is based on CERN’s outgassing acceptance criteria and agrees with both simulations and XPS results.

BackgroundWhile over half of US stroke patients were discharged to home, estimates of geographic access to outpatient stroke rehab facilities are unavailable. The objective of our study was to assess distance and travel time to the nearest outpatient stroke rehab facility in Tennessee, a high stroke prevalence state.MethodsWe systematically scraped Google Maps with the terms “stroke”, “rehabilitation”, and “outpatient” to identify Tennessee stroke rehab facilities. We then averaged/aggregated Census block-level travel distance and travel time to determine the mean travel distance/time to a facility for each of the 95 Tennessee counties and the overall state. Comparisons of mean travel time/distance were made between rural and urban counties and between low, medium, and high stroke prevalence counties.ResultsWe found that 79% of facilities were in urban areas. Significantly higher median of mean travel times and distances (p values both <0.001) were observed in rural (22.0 miles, 31.6 min) versus urban counties (10.5 miles, 18.4 min). High (21.5 miles, 32.5 min) and medium (18.7 miles, 28.3 minutes) stroke prevalence counties, which often overlap with rural counties, had significantly higher median of mean travel times and distance than low stroke prevalence counties (7.3 miles, 14.5 min).ConclusionsRural Tennessee counties were faced with high stroke prevalence, inadequate facilities, and significantly greater travel distance and time to access care. Additional efforts to address transportation barriers and accelerate telerehabilitation implementation are crucial for improving equal access to stroke aftercare in these areas.

BACKGROUND:This study sought to examine the relationship between rural residence and physical activity levels among US myocardial infarction (MI) survivors. METHODS:We conducted a cross-sectional study using nationally representative Behavioral Risk Factor Surveillance System surveys from 2017 and 2019. We determined the survey-weighted percentage of rural and urban MI survivors meeting US physical activity guidelines. Logistic regression models were used to examine the relationship between rural/urban residence and meeting physical activity guidelines, accounting for sociodemographic factors. RESULTS:Our study included 22,732 MI survivors (37.3% rural residents). The percentage of rural MI survivors meeting physical activity guidelines (37.4%, 95% CI: 35.1%-39.7%) was significantly less than their urban counterparts (45.6%, 95% CI: 44.0%-47.2%). Rural residence was associated with a 28.8% (95% CI: 20.0%-36.7%) lower odds of meeting physical activity guidelines, with this changing to a 19.3% (95% CI: 9.3%-28.3%) lower odds after adjustment for sociodemographic factors. CONCLUSIONS:A significant rural/urban disparity in physical activity levels exists among US MI survivors. Our findings support the need for further efforts to improve physical activity levels among rural MI survivors as part of successful secondary prevention in US high-MI burden rural areas.

In health disparities research, Geographic Information Systems (GIS) provide nurse researchers with powerful tools to incorporate spatial factors, such as access to care and related attributes like socioeconomic and environmental characteristics, into their studies. This article educates nurse scientists about GIS-based research benefits and considerations (focusing on access-to-care factors) and the influence of various access-to-care metrics on research outcomes. We present an overview of GIS in nursing and health disparities research, along with findings from our 2022 study examining access to care's relationship with county-level mortality rates in Tennessee, especially in areas where rural hospitals closed between 2010 and 2019. We highlight three distinct access-to-care measures (Euclidean distances and road network-based travel times based on county and census tract centroids), showcasing how different calculations impact our modeling results. Our results underscore the importance of understanding the choice of access-to-care metrics in GIS-based research to draw valid conclusions.

BackgroundShort sleep duration (SSD) (<7 hours/night) is linked with increased risk of prediabetes to diabetes progression. Despite a high diabetes burden in US rural women, existing research does not provide SSD estimates for this population. MethodsWe used national Behavioral Risk Factor Surveillance System surveys to conduct a cross-sectional study examining SSD estimates for US women with prediabetes by rural/urban residence between 2016-2020. We applied logistic regression models to the BRFSS dataset to ascertain associations between rural/urban residence status and SSD prior to and following adjustment for sociodemographic factors (age, race, education, income, health care coverage, having a personal doctor). ResultsOur study included 20,997 women with prediabetes (33.7% rural). SSD prevalence was similar between rural (35.5%, 95% CI: 33.0%-38.0%) and urban women (35.4%, 95% CI: 33.7%-37.1). Rural residence was not associated with SSD among US women with prediabetes prior to adjustment (Odds Ratio: 1.00, 95% CI: 0.87-1.14) or following adjustment for sociodemographic factors (Adjusted Odds Ratio: 1.06, 95% CI: 0.92-1.22). Among women with prediabetes, irrespective of rural/urban residence status, being Black, aged <65 years, and earning <$50,000 was linked with significantly higher odds of having SSD. ConclusionsDespite the finding that SSD estimates among women with prediabetes did not vary by rural/urban residence status, 35% of rural women with prediabetes had SSD. Efforts to reduce diabetes burden in rural areas may benefit from incorporating strategies to improve sleep duration along with other known diabetes risk factors among rural women with prediabetes from certain sociodemographic backgrounds.

Objectives Stroke symptom recognition is critical in reducing time to treatment, but it is not known whether the increased support for stroke education programs during the last several years has led to an improvement in regional stroke symptom recognition levels since they were last assessed in the mid-2010s. Methods We used the most current estimates of recognition from the 2017 National Health Interview Survey to examine regional recognition levels for individual stroke symptoms and correct identification of all five stroke symptoms. Results Recognition of individual stroke symptoms was ≥76% in all regions, but correct identification of all stroke symptoms was lower ranging from 68.8 to 70.2%. Recognition of sudden numbness or weakness of face, arm, or leg, especially on one side (Northeast: 94.9%, Midwest: 95.8%, South: 93.8%, West: 94.5%) was the highest and recognition of sudden headache with no known cause (Northeast: 77.6%, Midwest: 76.4%, South: 77.7%, West: 76.5%) was the lowest for all regions. Discussion We observed similar stroke symptom recognition levels in each US region with little improvement since the mid-2010s. Additional effort should be made to increase recognition of sudden headache with no known cause in US regions with current high prevalence of stroke risk factors.

In the United States, approximately 3.2 million adults have a visual impairment (i.e., blindness or low vision; Varma et al., 2016). These individuals face disproportionately lower levels of employment compared to those without disabilities—2017 national employment rates estimated at 44.2% and 79.4%, respectively, for these two groups (Dunn & Blank, 2018; Erickson et al., 2017; McDonnall & Sui, 2019). Although some studies have examined employment rates of adults with visual impairments at the national level in the United States, less is known about whether the disparity in employment by visual impairment status varies geographically (Bell & Silverman, 2018; McDonnall & Sui, 2019). Stateand regional-level employment rates for those with visual impairments mainly comes from American Community Survey analyses, which has small sample sizes in some states compared to other nationally representative surveys such as the Behavioral Risk Factor Surveillance System (BRFSS; Centers for Disease Control and Prevention, 2019; United States Census Bureau, 2020). Using BRFSS data, we determined whether contemporary employment rates for U.S. adults with and without visual impairments differed at the national, regional, and state levels. Methods

Ecosystem service flow dynamics which establish the linkage between human and nature is essential in an ecosystem service assessment. This study constructed an ecosystem service flow model of freshwater flow then utilized it to assess the water-related ecosystem services in northeast China. We included the provision, consumption, and spatial flow of freshwater services in an index to assess the water security condition and quantified the services trans-boundary flow from the northeast forest belt (NFB) in northeast China. Our results showed that large areas (50.54%, 55.10% and 52.90%, respectively) of northeast China received upstream freshwater service in three years. The water security condition of northeast China deteriorated from 2005 to 2015 with the change of water security index considering water flow (WSIflow), mainly influenced by precipitation and agriculture water consumption. Approximately 4.16 billion m3 of freshwater service were delivered from NFB to surrounding regions demonstrating the importance of NFB in terms of ecosystem service provision. In addition, 73 key watersheds (4.71% of total area) within NFB that significantly affect the trans-boundary flow were further identified. We suggested that local government should advocate develop water-saving agriculture and livestock water quotas. Moreover, priorities should be given to protect the key watersheds within NFB in order to maintain the supply of freshwater service. This study provided a framework for exploring suitable strategies for managing water resources and laid a foundation for promoting the ecological compensation in the future.

Objectives To conduct a cross-sectional nationwide study examining how exclusion of nursing home COVID-19 cases influences the association between county level social distancing behavior and COVID-19 cases throughout the US during the early phase of the pandemic (February 2020-May 2020). Methods Using county-level COVID-19 data and social distancing metrics from tracked mobile devices, we investigated the impact social distancing had on a county’s total COVID-19 cases (cases/100,000 people) between when the first COVID-19 case was confirmed in a county and May 31 st , 2020 when most statewide social distancing measures were lifted, representing the pandemic’s exponential growth phase. We created a mixed-effects negative binomial model to assess how implementation of social distancing measures when they were most stringent (March 2020-May 2020) influenced total COVID-19 cases while controlling for social distancing and COVID-19 related covariates in two scenarios: (1) when COVID-19 nursing home cases are not excluded from total COVID-19 cases and (2) when these cases are excluded. Model findings were compared to those from February 2020, a baseline when social distancing measures were not in place. Marginal effects at the means were generated to further isolate the influence of social distancing on COVID-19 from other factors and determine total COVID-19 cases during March 2020-May 2020 for the two scenarios. Results Regardless of whether nursing home COVID-19 cases were excluded from total COVID-19 cases, a 1% increase in average % of mobile devices leaving home was significantly associated with a 5% increase in a county’s total COVID-19 cases between March 2020-May 2020 and about a 2.5% decrease in February 2020. When the influence of social distancing was separated from other factors, the estimated total COVID-19 cases/100,000 people was comparable throughout the range of social distancing values (25%-45% of mobile phone devices leaving home between March 2020-May 2020) when nursing home COVID-19 cases were not excluded (25% of mobile phones leaving home: 163.84 cases/100,000 people (95% CI: 121.81, 205.86), 45% of mobile phones leaving home: 432.79 cases/100,000 people (95% CI: 256.91, 608.66)) and when they were excluded (25% of mobile phones leaving home: 149.58 cases/100,000 people (95% CI: 111.90, 187.26), 45% of mobile phones leaving home: 405.38 cases/100,000 people (95% CI: 243.14, 567.62)). Conclusions Exclusion of nursing home COVID-19 cases from total COVID-19 case counts has little impact when estimating the relationship between county-level social distancing and preventing COVID-19 cases with additional research needed to see whether this finding is also observed for COVID-19 growth rates and mortality.

BACKGROUND Previous studies on the impact of social distancing on COVID-19 mortality in the United States have predominantly examined this relationship at the national level and have not separated COVID-19 deaths in nursing homes from total COVID-19 deaths. This approach may obscure differences in social distancing behaviors by county in addition to the actual effectiveness of social distancing in preventing COVID-19 deaths. OBJECTIVE This study aimed to determine the influence of county-level social distancing behavior on COVID-19 mortality (deaths per 100,000 people) across US counties over the period of the implementation of stay-at-home orders in most US states (March-May 2020). METHODS Using social distancing data from tracked mobile phones in all US counties, we estimated the relationship between social distancing (average proportion of mobile phone usage outside of home between March and May 2020) and COVID-19 mortality (when the state in which the county is located reported its first confirmed case of COVID-19 and up to May 31, 2020) with a mixed-effects negative binomial model while distinguishing COVID-19 deaths in nursing homes from total COVID-19 deaths and accounting for social distancing– and COVID-19–related factors (including the period between the report of the first confirmed case of COVID-19 and May 31, 2020; population density; social vulnerability; and hospital resource availability). Results from the mixed-effects negative binomial model were then used to generate marginal effects at the mean, which helped separate the influence of social distancing on COVID-19 deaths from other covariates while calculating COVID-19 deaths per 100,000 people. RESULTS We observed that a 1% increase in average mobile phone usage outside of home between March and May 2020 led to a significant increase in COVID-19 mortality by a factor of 1.18 (P<.001), while every 1% increase in the average proportion of mobile phone usage outside of home in February 2020 was found to significantly decrease COVID-19 mortality by a factor of 0.90 (P<.001). CONCLUSIONS As stay-at-home orders have been lifted in many US states, continued adherence to other social distancing measures, such as avoiding large gatherings and maintaining physical distance in public, are key to preventing additional COVID-19 deaths in counties across the country.

Background: To examine diabetes screening by sugar sweetened beverage (SSB) consumption levels among US adults who fall under the American Diabetes Association’s (ADA) recommended screening guidelines. Methods: Using 2017 Behavioral Risk Factor Surveillance System survey data, we determined screening estimates by SSB consumption levels for US adults who belong to the ADA’s two recommended screening groups: (1) <45 years with body mass index ≥25 kg/m2 and (2) ≥45 years. Unadjusted and adjusted screening estimates by SSB consumption levels for each recommended screening group were obtained from logistic regressions. Results: Differences in screening by SSB consumption were primarily observed in the younger screening group (0 drinks/day: 64.5%, between 0 and 1 drink/day: 57.1%, ≥1 drink/day: 57.8%). Unadjusted (between 0 and 1 drink/day OR: 0.73 (95% CI: 0.56–0.96), ≥1 drink/day OR: 0.75 (95% CI: 0.56–1.01)) and adjusted (between 0 and 1 drink/day OR: 0.76 (95% CI: 0.57–1.00), ≥1 drink/day OR: 0.87 (95% CI: 0.64–1.18)) estimates show an association between SSB consumption and lower screening in younger individuals. Conclusions: SSB consumption was associated with lower diabetes screening receipt in the younger screening group. Additional research examining factors contributing to low screening among SSB drinkers in the younger screening group are needed to develop screening interventions for these individuals.

Objective Effects of stroke (i.e., memory loss, paralysis) may make effective diabetes care difficult which can in turn contribute to additional diabetes related complications and hospitalization. However, little is known about US post-stroke diabetes care levels. This study sought to examine diabetes care levels among US adults with diabetes by stroke status. Methods Using 2015-2018 Behavioral Risk Factor Surveillance System surveys, the prevalence of nonadherence with the American Diabetes Association's diabetes care measures (<1 eye exam annually, <1 foot exam annually, <1 blood glucose check daily, <2 A1C tests annually, no receipt of annual flu vaccination) was ascertained in people with diabetes by stroke status. A separate logistic regression model was run for each diabetes care measure to determine if nonadherence patterns differed by stroke status after adjustment for stroke and diabetes associated factors. Results Our study included 72,630 individuals, with 9.8% having had a stroke. Nonadherence levels varied for each diabetes care measure ranging from 20.4-42.2% for stroke survivors and 22.8-44.0% for those who had never had stroke. By stroke status, nonadherence with diabetes management measures was comparable except for stroke survivors having both a lower prevalence (30.2% versus 40.1%) and odds of nonadherence (OR: 0.73, 95% CI: 0.65, 0.82) with daily blood glucose check than those who had never had stroke. Conclusion While nonadherence with diabetes management does not vary by stroke status, considerable nonadherence still exists among stroke survivors with diabetes. Additional interventions to improve diabetes care may help to reduce risk of further diabetes complications in this population.

Diabetes is a potentially life-threatening metabolic condition that disproportionately affects US adults with a disability. Diabetes screening is key to early disease detection and prompt treatment, but it is not known whether US adults with a disability receive similar levels of diabetes screening as individuals without a disability. We compared diabetes screening levels in US adults with a disability to those without one. Using national 2017 Behavioral Risk Factor Surveillance System surveys, we determined the prevalence of diabetes screening by disability status in US adults who fall under the American Diabetes Association's recommended screening guidelines: those younger than 45 years old with a body mass index (BMI) ≥ 25 kg/m2 and those aged 45 years and older. We used logistic regression modelling to examine the impact of disability status on diabetes screening while adjusting for diabetes associated sociodemographic and clinical factors. In people with a disability, around 50% of those younger than 45 years old with a BMI ≥ 25 kg/m2 and 33% of those 45 years or older did not receive screening. In the under 45 years with a BMI ≥ 25 kg/m2 screening group, individuals with a disability had a slightly higher but non-significant prevalence, but a lower adjusted odds of diabetes screening compared to those without a disability. People with a disability under age 45 had a slightly lower but again non-significant prevalence but a higher adjusted odds of diabetes screening than did those without a disability who were age 45 or older. Additional interventions are needed to improve diabetes screening levels among US adults with a disability at high risk of developing diabetes as screening is a critical initial step in the diabetes management process.

Little is known about routine diabetes screening levels (test for high blood glucose or diabetes every three years) in US individuals who identify as lesbian, gay or bisexual (LGB) despite a high risk of diabetes in these groups. We compared routine diabetes screening levels between US adult LGBs and heterosexuals who belonged to either of the two American Diabetes Association's (ADA) recommended diabetes screening groups (age <45 years and body mass index (BMI) >= 25, age >= 45 years). Sexual orientation, diabetes screening and sociodemographic/diabetes risk factor information was collected from the 2015 and 2017 Behavioral Risk Factor Surveillance System surveys. We calculated the unadjusted prevalence of diabetes screening in LGBs and heterosexuals for both ADA screening groups. Logistic regression modelling was used to obtain adjusted diabetes screening estimates by sexual orientation. LGBs had slightly higher unadjusted diabetes screening prevalences (age <45 years and BMI >= 25: 53.2%; age >= 45 years: 72.3%) compared to heterosexuals (age <45 years and BMI >= 25: 51.7%; age >= 45 years: 69.9%) for both ADA recommended screening groups. Irrespective of sexual orientation, diabetes screening prevalence was considerably lower in the younger ADA screening group with around 50% of individuals who should be screened not receiving screening. After adjustment for sociodemographic/diabetes risk factors, LGBs and heterosexuals had similar odds of diabetes screening in the age <45 years and BMI >= 25 (odds ratio [OR]: 0.98, 95% confidence interval [CI]: 0.80-1.21) and age >= 45 years (OR: 1.11, 95% CI: 0.91-1.35) groups. As a result of not receiving routine diabetes screening, more than 100,000 LGB individuals at high risk of developing diabetes face potential delays in diabetes diagnosis and treatment as well as increased future diabetes complications. Copyright (c) 2021 John Wiley & Sons.

Although hypertension is a contributing factor to higher stroke occurrence in the Stroke Belt, little is known about post-stroke hypertension medication use in Stroke Belt residents. Through the use of national Behavioral Risk Factor Surveillance System surveys from 2015, 2017, and 2019; we compared unadjusted and adjusted estimates of post-stroke hypertension medication use by Stroke Belt residence status. Similar levels of post-stroke hypertension medication use were observed between Stroke Belt residents (OR: 1.09, 95% CI: 0.89, 1.33) and non-Stroke Belt residents. After adjustment, Stroke Belt residents had 1.14 times the odds of post-stroke hypertension medication use (95% CI: 0.92, 1.41) compared to non-Stroke Belt residents. Findings from this study suggest that there is little difference between post-stroke hypertension medication use between Stroke Belt and non-Stroke Belt residents. However, further work is needed to assess whether use of other non-medicinal methods of post-stroke hypertension control differs by Stroke Belt residence status.

Aim Asian Americans have high levels of undiagnosed diabetes, but little is known about what influences diabetes screening in this group. We determined which sociodemographic, socioeconomic, and clinical factors were associated with diabetes screening in the American Diabetes Association’s (ADA) recommended screening groups for Asian Americans. Subjects and methods We included Asian Americans from the 2015 and 2017 Behavioral Risk Factor Surveillance System who fit the ADA’s diabetes screening guidelines and responded to a diabetes screening question. Logistic regression models were created to examine associations between sociodemographic, socioeconomic, and clinical factors and diabetes screening in Asian Americans. Results Being a college graduate and having high blood pressure were associated with higher levels of diabetes screening for the two screening groups. A trend of decreased diabetes screening with less educational attainment was observed in both screening groups. Conclusion Diabetes screening in Asian Americans is influenced by socioeconomic and clinical factors. Additional work is needed to identify other Asian American-specific cultural factors that may have an impact on the decision to seek diabetes screening.