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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) &lt, 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.

The scarcity of natural resources is a challenge in Rwanda. Although Rwanda has improved water supplies, projections show a further increase in water demand. Particularly, agriculture continues to place further demands on water resources through intensification and industrialization. Similarly, although the dependence on biomass for cooking has improved over the past two decades in Rwanda, the ratio is still high and is projected to increase. Unfortunately, the heavy dependence on biomass is damaging to the environment in general, forests in particular. As the consumption of water and charcoal increases, it is important to study how people perceive their consumption. Research shows that people who perceive their consumption of natural resources are more likely to conserve them as they can see how much they are consuming. This study investigated perceptions of water and charcoal consumption among farmers in northern Rwanda. A survey was used to collect data from 323 farmers involved in a poultry development project in the district of Musanze, northern Rwanda. A Partial Proportional Odds Model (PPOM) was used to analyse the effect of different factors on the perception of natural resource consumption. Results indicate that the perception of charcoal consumption was associated with three variables: living in the urban section of the district, the amount of feed consumed by chicken, and elevation at which the coop is located. Results from this study can improve how food security projects are implemented by incorporating people’s perceptions of their consumption of natural resources.

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.

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.

Objective Electronic cigarette (e-cigarette) use has been shown to worsen asthma symptoms, but there is little information on current e-cigarette use patterns in the general US adult asthma population. We examined the unadjusted and adjusted estimates of current e-cigarette use in a nationally representative sample of US adults by asthma status and what factors influence this use. Methods We determined the prevalence of current e-cigarette use in adults with and without asthma using the 2016-2018 Behavioral Risk Factor Surveillance System datasets. A multinomial logistic regression model was used to obtain adjusted estimates of e-cigarette use by asthma status while also identifying which sociodemographic and clinical factors are associated with e-cigarette use in adults with asthma. Results Of the 23,071 adults with asthma in the study, 22.5% were currently using e-cigarettes. After adjusting for sociodemographic and clinical factors associated with asthma and current e-cigarette use, adults with asthma had similar odds of every day e-cigarette use (odds ratio (OR): 1.04, 95% confidence interval (CI): 0.93-1.15) but higher odds of e-cigarette use on some days (OR: 1.18, 95% CI: 1.10-1.27) compared to adults without asthma. Younger age, male sex, and former or current traditional cigarette use were significantly associated with both e-cigarette use on some days and every day in adults with asthma. Conclusion Current e-cigarette use estimates among US adults with asthma indicate a need for further e-cigarette education and cessation interventions that are specifically tailored to sociodemographic and clinical groups who are especially prone to e-cigarette use within this population.

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. 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. 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. 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.

Purpose Although stroke survivors who continue smoking face increased risk for subsequent strokes, little is known about U.S. poststroke smoking patterns. We examined smoking prevalence in U.S. stroke survivors and what sociodemographic factors are associated with continuation of smoking in these individuals. Methods We determined the prevalence of smoking in U.S. stroke survivors (n = 56,523) using 2016–2018 Behavioral Risk Factor Surveillance System data. A logistic regression was created to identify associations between sociodemographic factors and poststroke smoking continuation. Results 20.4% of stroke survivors continued to smoke after their stroke (14.7% smokes every day, 5.7% smokes some days). Older age, being male (OR: 1.15, 95% CI: 1.05–1.27), Asian (OR: 2.79, 95% CI: 1.52–5.11) or Hispanic (OR: 1.31, 95% CI: 1.06–1.63) ethnicity, higher income, higher educational attainment, and access to health care (OR: 1.27, 95% CI: 1.02–1.59) and a personal doctor (one doctor OR: 1.51, 95% CI: 1.25–1.83; more than one doctor OR: 1.59, 95% CI: 1.27–1.99) corresponded with increased odds of smoking continuation after a stroke. Conclusions A greater push for smoking cessation by clinicians and support programs to aid with cessation in U.S. stroke survivors is needed to decrease the high prevalence of poststroke smoking in this population.

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.

Objective Obesity has reached epidemic proportions in the US. Physical activity is an important component of obesity reduction, but little is known about contemporary exercise levels among overweight/obese American adults. In this study, we compared current physical activity levels in overweight and obese US adults to those of normal weight individuals as well as ascertained which sociodemographic factors influence the meeting of physical activity recommendations in these three groups. Methods We used national data from 2015 and 2017 Behavioral Risk Factor Surveillance System surveys (n = 726,075). Bivariate analyses were conducted to determine unadjusted physical activity levels in normal weight, overweight, and obese adults. We fit multinomial logistic models to identify associations between sociodemographic factors and meeting physical activity recommendations in our study population. Results Around 45% of overweight and 57% of obese adults failed to meet physical activity guidelines compared to 41% of normal weight adults. Age, sex, and race were significantly associated with physical activity levels for both overweight and obese adults. In overweight and obese individuals, associations between sociodemographic factors and physical activity were more similar between the two comparisons of insufficiently active (IA) versus active (A)/highly active (HA) and A versus HA but quite different to those in the inactive (IN) versus IA/A/HA comparison. Conclusions Future physical activity interventions should be aimed at increasing the number of overweight and obese US adults who meet physical activity guidelines as well as targeted towards specific sociodemographic groups within the overweight/obese population with low exercise levels.

One proposed use of unmanned aerial systems (UAS) in crop production is to produce quantitative data to support replant decisions by assessing plant stands. Theoretically, analysis of UAS imagery could quickly determine plant populations across large areas. The objective of this research was to investigate the ability of UAS to quantify accurately varying plant populations of cotton (Gossypium hirsutum L.). Field studies were conducted in Jackson, Milan, and Grand Junction, Tennessee in three consecutive growing seasons. Treatments included five seeding rates ranging from 8,500 to 118,970 seed ha-1. After emergence, cotton plants were manually counted and images were collected in 2016 and 2017 with a MicaSense RedEdge multispectral sensor and in 2018 with a Sentera Double 4K multispectral sensor. Sensors were mounted to a quad-copter UAS flying at altitudes of 30, 60, 75, and 120 m above ground level. Spectral properties were assessed to generate normalized difference vegetation index (NDVI) thresholds that were used to limit the analysis to only plant material. Images were processed and analyzed to estimate number of plants and compared to actual plant populations within each plot. Images obtained from lower altitudes proved to be more accurate, with greatest correlations to actual ground-truthed plant populations from data collected at an altitude of 30 m. The utilization of the described novel method of estimating cotton plant population from NDVI-calculated UAS imagery might improve upon spatial and temporal efficiency in comparison to current methodology of estimation.

BackgroundAlthough the angiotensin receptor-neprilysin inhibitor (ARNI) sacubitril/valsartan started a new era in heart failure (HF) treatment, less is known about the tissue-level effects of the drug on the atrial myocardial functional reserve and arrhythmogenesis.Methods and ResultsRight atrial (RA) biopsies were retrieved from patients (n = 42) undergoing open-heart surgery, and functional experiments were conducted in muscle strips (n = 101). B-type natriuretic peptide (BNP) did not modulate systolic developed force in human myocardium during β-adrenergic stimulation, but it significantly reduced diastolic tension (p < 0.01) and the probability of arrhythmias (p < 0.01). In addition, patient's plasma NTproBNP positively correlated with isoproterenol-induced contractile reserve in atrial tissue in vitro (r = 0.65; p < 0.01). Sacubitrilat+valsartan (Sac/Val) did not show positive inotropic effects on atrial trabeculae function but reduced arrhythmogeneity. Atrial and ventricular biopsies from patients with end-stage HF (n = 10) confirmed that neprilysin (NEP) is equally expressed in human atrial and ventricular myocardium. RA NEP expression correlates positively with RA ejection fraction (EF) (r = 0.806; p < 0.05) and left ventricle (LV) NEP correlates inversely with left atrial (LA) volume (r = −0.691; p < 0.05).ConclusionBNP ameliorates diastolic tension during adrenergic stress in human atrial myocardium and may have positive long-term effects on the inotropic reserve. BNP and Sac/Val reduce atrial arrhythmogeneity during adrenergic stress in vitro. Myocardial NEP expression is downregulated with declining myocardial function, suggesting a compensatory mechanism in HF.

Bioenergy crops can provide a reliable and adequate supply of biomass feedstocks to support the bioenergy industry. However, promoting bioenergy crops would require major change in land use and management practices that can have long term socio-economic and environmental impacts. Therefore, it is important to evaluate potential opportunities and challenges presented by bioenergy crop production. We developed a multi-objective optimization model to analyze trade-offs among various components of bioenergy crop production to help make better production decisions. Our model integrated sustainability dimensions including social, economic and environmental factors for bioenergy crop production. As bioenergy crop production may incorporate various objectives, we ran the model by optimizing one objective at a time to measure the magnitude of change in one objective with respect to change in other objectives given a set of constraints. In addition, our model had the ability to assess how trade-offs would be affected by chancing preferences for different factors in the production decisions. The model was applied for a four-county study area in northern Kentucky and it provided a regional examination for the potential of bioenergy crops for energy production. The model can serve as an effective tool for making bioenergy production planning and management decisions.

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 31st, 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.

This research assesses the social vulnerability of five villages (Jora, Kiteghe, Makwasinyi, Bungule, and Rukanga) in Mt. Kasigau, Kenya. Our goal was to develop a social vulnerability model by adopting a vulnerability conceptual framework that conceptualizes vulnerability into three major components: exposure, sensitivity, and adaptive capacity and using the analytical hierarchy process (AHP). Employing the AHP, the three components of vulnerability were decomposed into its constituent components and structured into a hierarchical format where each component was represented by different societal and environmental criterions and stressors. Next, we performed a pairwise comparison at each level of the hierarchy to obtain local priorities. Finally, we aggregated the local priorities from the bottom up to obtain global priorities of the social vulnerability of each village. The results from this study revealed that Makwasinyi was the most vulnerable village followed by Bungule, Kiteghe, Jora, and Rukanga respectively. Further, the results suggested that adaptive capacity and exposure played a critical role in determining the social vulnerability compared to sensitivity. Considering this, reducing social vulnerability in the area should focus much on improving the adaptive capacity of the people and reducing their exposure specifically in Makwasinyi village.

Transferability of species distribution models (SDMs) is key to predicting invasion patterns and can be challenged if niche shift occurs in the invaded range. When using native occurrences to estimate potential invasions with presence-only modeling methods, it is important to constrain the pseudo-absence (PA) sampling to the species’ native range. However, some studies including highly cited ones, do not follow this approach to selecting PA samples. In this research, we addressed two questions using an invasive species in the United States (U.S.), kudzu bug (Megacopta cribraria): 1) is model transferability challenged by a non-adaptive niche shift? and 2) is model performance affected by use of PA samples from outside the native range of the species? Kudzu bug is native to Asia, with recently observed non-adaptive niche shift in the U.S. To answer the first question, we quantified the environmental space anisotropy and non-adaptive niche change, and then evaluated the performances of seven SDMs. To answer the second question, we further compared the interpolation and transferability of seven SDMs trained with PAs from the native range and from both native and invaded ranges. We confirmed that the environmental space anisotropy (P = 0.01) and non-adaptive niche change (P = 0.01) are both statistically significant. Of the seven SDMs used, four models had transferability indices higher than 0.9. Boosted regression tree and random forests both had good interpolation and transferability (AUC>0.80 and kappa>0.60), whereas three other models showed good interpolation and fair transferability (AUC>0.70 and kappa>0.40). Inclusion of pseudo-absences from the invaded range significantly increased the interpolation (P

OBJECTIVE: To evaluate the effectiveness and safety of pharmacological interventions for the treatment of psychological distress in people with asthma. DATA SOURCES: Electronic searches were performed in Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, PubMed/Medline, Embase, PsycInfo, Health Technology Assessment Database and Web of Science (inception to April 2019). STUDY SELECTIONS: Included studies were randomised controlled trials (RCT) or controlled clinical trials investigating the effect of pharmacological interventions for psychological distress in people with asthma. Records were screened and data extracted by two independent authors into standardised pilot-tested extraction templates. Data was analysed according to standard Cochrane methodology and entered into Review Manager Software version 5.3. RESULTS: From 5,689 studies, six RCTs (n = 215) met inclusion criteria and were included in the systematic review, of which four studies were included in the meta-analysis. A meta-analysis of four studies (n = 158) indicated no evidence of an effect for selective serotonin reuptake inhibitors (Citalopram or Escitalopram) on reduction of psychological distress in adult patients with asthma. Similarly, antiepileptic medication (Levetiracetam) was no better than placebo in the treatment of psychological distress in people with asthma. Adverse events were poorly reported across all studies but were slightly increased among intervention participants compared to control participants. CONCLUSIONS: There was great heterogeneity between studies and overall poor methodological quality providing insufficient evidence to make recommendations for or against the use of pharmacotherapy in asthma patients with psychological distress. Further confirmatory trials are warranted to make recommendations for clinical practice. Refereed/Peer-reviewed

Bioenergy crops are considered as potential biomass feedstocks to support the bioenergy industry in the southern US. Even though there are suitable areas to grow bioenergy crops, commercial scale production of bioenergy crops has not been established to meet the increasing energy demand. Establishing bioenergy crops in the region requires landowners&rsquo, participation and it is crucial to understand whether they intend to promote bioenergy crop production. This study evaluated landowners&rsquo, perception of bioenergy and their willingness to supply lands for bioenergy crops in northern Kentucky. A questionnaire survey of randomly selected landowners was administered in four selected counties. Results indicated that landowners&rsquo, land use decisions for bioenergy crop production were based on their current land management practices, socio-economic and environmental factors. Overall, there was a low willingness of landowners to participate in bioenergy crop production. Those who were interested indicated that a higher biomass price would be required to promote bioenergy crops on their land. This information could be useful to plan for policies that provide economic incentives to landowners for large-scale production of bioenergy crops in the study area and beyond. Further, results showed how landowners&rsquo, opinion on bioenergy affected their preferences for land use decisions. Younger landowners with positive attitude towards bioenergy were more willing to promote bioenergy crops. This information could be useful to develop outreach programs for landowners to encourage them to promote bioenergy crops in the study area.

Background Although binge drinking is associated with higher myocardial infarction (MI) incidence, little is known about binge drinking patterns in US MI survivors, at elevated risk for recurrent MIs. Objectives To determine the prevalence of and what factors are associated with binge drinking in US MI survivors. Methods We compared the prevalence of binge drinking between MI survivors and those without a MI history in 2016-2018 Behavioral Risk Factor Surveillance System data. Logistic regression was used to examine which sociodemographic factors are associated with binge drinking in these groups. Results 8.7% of MI survivors (1.1 million people nationwide) were binge drinkers. Among MI survivors; being young, male, Hispanic, having higher income, and having lower educational attainment were associated with increased binge drinking. Conclusions The sizable number of US MI survivors who binge drink suggests interventions to reduce this behavior are warranted, especially among specific sociodemographic groups of this population.

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 (PP 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 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 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.

Although the risk of developing diabetes is high among US sexual minorities (SM) (lesbian, gay, bisexual), little is known about diabetes management in this population. We examined the impact of sexual orientation on current US diabetes management levels in a geographically diverse sample of people with diabetes (PWD).Adult PWDs were identified from the 2015-2018 cross-sectional Behavioural Risk Factor Surveillance System surveys. We determined the unadjusted percentage and the adjusted odds ratios (OR) of noncompliance with American Diabetes Association (ADA) diabetes management measures (1 eye exam annually,1 foot exam annually,1 blood glucose check daily,2 A1C tests annually, no receipt of annual flu vaccination, never receiving pneumococcal vaccination, never taking a diabetes management course) in PWDs by SM status.Unadjusted analyses revealed a high level of noncompliance with diabetes management among SMs and especially for annual flu vaccination (40.1-52.3%) and diabetes management education (38.4-48.4%). Compared to heterosexuals, lesbian women were more noncompliant for most and bisexual men and bisexual women for all diabetes management measures. We observed that SMs had slightly higher adjusted levels of noncompliance than heterosexuals only for annual foot exams (OR: 1.09, 95% confidence interval (CI): 0.81-1.46) and diabetes management education (OR: 1.06, 95% CI: 0.81-1.41).High levels of noncompliance with ADA diabetes management guidelines in SM PWDs indicates a need for additional efforts to elucidate the factors that contribute to noncompliance in SMs, information that can be used to develop appropriate interventions to improve diabetes management for this population.

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

Estimating invasion dynamic is important to the management of invasive species, and geopolitical-unit level data are usually the most abundant and available records of invasive species. Here, for the first time we evaluated performances and similarities of eight common methods to estimate spread pattern and spread dynamic of invasive species with geopolitical-unit level data, and assessed impacts of variations in geopolitical-units on each method using simulated spread data. We also formulated a concave hull boundary displacement method (i.e., CEB) and an area-based regression method (i.e., AER) for estimating spread with geopolitical-unit data. Three regions with different sized counties in the United States (U.S.) were selected to conduct simulations and three spread scenarios were simulated. R2 and root mean square error were used to evaluate the abilities of all methods to estimate spread. Correlation coefficients were used to assess the similarity pattern of all methods. Finally, kudzu bug Megacopta cribraria, an invasive insect in the U.S., was used as a case study to test the generality of some results concluded from the simulated research. We found the CEB and two regression methods consistently estimated the right expansion patterns. Two boundary displacement and two area-based regression methods estimated highly correlated spread and were the best four methods, among which CEB had the best estimation. Distance-based regression methods are sensitive to irregularity and stochasticity in spread, and the minimum spread distance method had low ability to estimate spread. The case study showed consistent results with the simulated research. Both regression and boundary displacement methods can estimate spread patterns, overall rate, and spread dynamics of invasive species. Boundary displacement methods best estimate spread rates and dynamics; however, for spread without clear infestation outlines, area-based regression methods can be good alternatives.

Biological invasions are an ongoing threat for sustainability of ecosystems, and estimating the spread of invasive species is critical for making management decisions. Geopolitical unit-level data (GULD) are often used to estimate invasions due to their wide availability, and researchers had evaluated the abilities of multiple methods to estimate invasion with GULD. However, earlier studies were case based and only addressed limited information on the spread, thus making it inadequate to determine which method to choose to estimate invasions with GULD under various spread scenarios. Here, we conducted a simulation study to (1) evaluate performances of eight methods on estimating expansion patterns, spread rates, and spread dynamics of invasive species with GULD, (2) assess the impact of size and homogeneity of size of geopolitical unit on the estimations by studied methods, (3) evaluate the similarities of all studied methods. Additionally, we presented a concave hull boundary displacement method (Ctd_BD) and an area-based regression method (SqrtNA_R) to estimate spread with GULD. Three regions with varying sizes of counties in the United States (U.S.) were selected to conduct the simulations, and three spread scenarios and three expansion patterns were simulated. AIC, and R2 and root mean square error (RMSE) were used to evaluate the accuracy of methods on estimating expansion pattern, and overall spread rate and spread dynamics, respectively. Correlation coefficient and RMSE were used to assess the similarity of eight methods. We found Ctd_BD and area-based regression methods consistently estimated the right expansion patterns. Boundary displacement and area-based regression methods estimated highly correlated spread rates and dynamics. Distance-based regression methods provided a high accuracy on estimating overall spread rate without long-distance jump dispersal but performed poorly on estimating the spread dynamics. We recommend boundary displacement method, especially Ctd_BD, for estimating spread with GULD, whereas for spread without clear infestation boundaries, distance-based regression can be used to estimate overall spread rate and area-based regression can be used to estimate spread dynamics.

Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.

Biological invasions have long placed challenges on ecosystems, agricultural production, and human health. Modeling potential invasion of an introduced organism becomes a critical tool for early management of damaging species, such as kudzu bug, Megacopta cribraria (F.) (Hemiptera:Heteroptera:Plataspidae). Since it was first found in the United States in 2009, kudzu bug has spread rapidly, economically impacted agricultural production, and became a household pest. To better predict the potential invasion of kudzu bug in North and South America, we used the species distribution models Genetic Algorithm for Rule-set Production (GARP) and Maximum Entropy (Maxent). We used the D metric to test for niche equivalency and similarity between native and invaded populations of kudzu bug. We found that kudzu bugs currently occupied unequal environmental space between the two ranges. Therefore, distribution models using GARP and Maxent were constructed using occurrences in both native and invaded ranges. Area under the curve (AUC), true skill statistics (TSS), and omission rate (OR) were used to evaluate and compare the models. Results indicated both models had good performance, but Maxent (AUC = 0.971, TSS = 0.946, OR = 0.019) performed better than GARP (AUC = 0.922, TSS = 0.860, OR = 0.037). This research confirmed the effectiveness of using occurrence data in both ranges to predict potential invasions. Kudzu bugs prefer warm (annual mean temperature around 15 °C) and humid (annual mean precipitation around 1300 mm) regions. Distribution models generated by both methods indicated similar regions with high invasion risk. Management programs that include quarantine and prevention measures are suggested for these regions to avoid outbreaks of kudzu bug.

The paper presents a multivariate measure useful for integrated environmental assessments. It is a weighted distance measure applied to metric data but based on nonparametric statistical procedures. The proposed measure allows all environmental indicators to be used directly without any reduction in dimension (e.g. as in principal component analysis) nor losing variance while being able to tolerate possible non-normality of the indicators, as well as non-linear relationships among them. Results of the hypothetical example and the Mid-Atlantic case study show that the proposed measure is suitable and valuable for integrating multiple indicators into a single index, an important task in integrated environmental assessment.

In-channel large wood (LW) plays an important role in the eco-morphological functionality of many river systems. This importance has been widely recognized, yet there continues to be a poor understanding of relationships between morphodynamics and locations of wood deposition within the channel, particularly in low-gradient, semi-confined rivers. This research investigates the following hypotheses: 1) LW deposition locations (DLs) occur periodically in relation to the periodic arrangement of morphological features in the Big River, Missouri, USA, a low-gradient, variably-confined, alluvial river system; 2) geomorphic controls on DLs in the Big River exert varying levels of influence at different spatial scales. A large-scale field inventory of LW DLs was performed along the Big River. A spectral analysis was then used to identify periodic patterns of DLs along the Big River and various statistical tests of association were used to investigate the relationships between DLs and morphological variables, and between periodicity (where identified) and morphological variables. The results suggest that under certain circumstances, DLs are spatially periodic, with periodicities ranging from 270 m to 1371 m, and in some cases exhibit periodicity at different spatial scales. Regression analysis was unable to statistically associate periodicity with morphological features; however, correlation and stepwise Poisson regression models suggest that channel-scale (100 m to 500 m) sinuosity, and valley width exert more influence on DLs than other variables. The lack of strong statistical associations suggests that either 1) LW dynamics in the Big River contain a high level of stochasticity or 2) controlling variables were not included in this analysis. These results support the need for better theoretical and numerical models of stochastic LW processes in order to better manage LW in complex geomorphic systems.