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Liem Tran
Deparment of Geography, University of Tennessee, Knoxville, USA

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Original paper
Published: 12 July 2021 in The Journal of Primary Prevention
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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.

ACS Style

Phoebe Tran; Lam Tran; Liem Tran. A Cross-Sectional Comparison of US Adult Diabetes Screening Levels by Disability Status. The Journal of Primary Prevention 2021, 1 -13.

AMA Style

Phoebe Tran, Lam Tran, Liem Tran. A Cross-Sectional Comparison of US Adult Diabetes Screening Levels by Disability Status. The Journal of Primary Prevention. 2021; ():1-13.

Chicago/Turabian Style

Phoebe Tran; Lam Tran; Liem Tran. 2021. "A Cross-Sectional Comparison of US Adult Diabetes Screening Levels by Disability Status." The Journal of Primary Prevention , no. : 1-13.

Journal article
Published: 18 March 2021 in JMIR Public Health and Surveillance
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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.

ACS Style

Phoebe Tran; Lam Tran; Liem Tran. The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study. JMIR Public Health and Surveillance 2021, 7, e21606 .

AMA Style

Phoebe Tran, Lam Tran, Liem Tran. The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study. JMIR Public Health and Surveillance. 2021; 7 (3):e21606.

Chicago/Turabian Style

Phoebe Tran; Lam Tran; Liem Tran. 2021. "The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study." JMIR Public Health and Surveillance 7, no. 3: e21606.

Short report
Published: 17 February 2021 in The Journal of Clinical Hypertension
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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.

ACS Style

Phoebe Tran; Lam Tran; Liem Tran. A comparison of post‐stroke hypertension medication use between US Stroke Belt and Non‐Stroke Belt residents. The Journal of Clinical Hypertension 2021, 23, 1260 -1263.

AMA Style

Phoebe Tran, Lam Tran, Liem Tran. A comparison of post‐stroke hypertension medication use between US Stroke Belt and Non‐Stroke Belt residents. The Journal of Clinical Hypertension. 2021; 23 (6):1260-1263.

Chicago/Turabian Style

Phoebe Tran; Lam Tran; Liem Tran. 2021. "A comparison of post‐stroke hypertension medication use between US Stroke Belt and Non‐Stroke Belt residents." The Journal of Clinical Hypertension 23, no. 6: 1260-1263.

Journal article
Published: 20 October 2020 in Heart & Lung
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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.

ACS Style

Phoebe Tran; Lam Tran; Liem Tran. A cross-sectional analysis of binge drinking levels in US myocardial infarction survivors. Heart & Lung 2020, 1 .

AMA Style

Phoebe Tran, Lam Tran, Liem Tran. A cross-sectional analysis of binge drinking levels in US myocardial infarction survivors. Heart & Lung. 2020; ():1.

Chicago/Turabian Style

Phoebe Tran; Lam Tran; Liem Tran. 2020. "A cross-sectional analysis of binge drinking levels in US myocardial infarction survivors." Heart & Lung , no. : 1.

Journal article
Published: 15 October 2020 in Sustainability
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ACS Style

Wanwan Liang; Liem Tran; Jerome Grant; Vivek Srivastava. Estimating Invasion Dynamics with Geopolitical Unit-Level Records: The Optimal Method Depends on Irregularity and Stochasticity of Spread. Sustainability 2020, 1 .

AMA Style

Wanwan Liang, Liem Tran, Jerome Grant, Vivek Srivastava. Estimating Invasion Dynamics with Geopolitical Unit-Level Records: The Optimal Method Depends on Irregularity and Stochasticity of Spread. Sustainability. 2020; ():1.

Chicago/Turabian Style

Wanwan Liang; Liem Tran; Jerome Grant; Vivek Srivastava. 2020. "Estimating Invasion Dynamics with Geopolitical Unit-Level Records: The Optimal Method Depends on Irregularity and Stochasticity of Spread." Sustainability , no. : 1.

Journal article
Published: 29 September 2020 in Forests
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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’ participation and it is crucial to understand whether they intend to promote bioenergy crop production. This study evaluated landowners’ 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’ 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’ 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.

ACS Style

Sandhya Nepal; Liem T. Tran; Donald G. Hodges. Determinants of Landowners’ Willingness to Participate in Bioenergy Crop Production: A Case Study from Northern Kentucky. Forests 2020, 11, 1052 .

AMA Style

Sandhya Nepal, Liem T. Tran, Donald G. Hodges. Determinants of Landowners’ Willingness to Participate in Bioenergy Crop Production: A Case Study from Northern Kentucky. Forests. 2020; 11 (10):1052.

Chicago/Turabian Style

Sandhya Nepal; Liem T. Tran; Donald G. Hodges. 2020. "Determinants of Landowners’ Willingness to Participate in Bioenergy Crop Production: A Case Study from Northern Kentucky." Forests 11, no. 10: 1052.

Journal article
Published: 01 August 2020 in Diabetes & Metabolism
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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.

ACS Style

Phoebe Tran; Lam Tran; Liem Tran. Influence of sexual orientation on diabetes management in US adults with diabetes. Diabetes & Metabolism 2020, 47, 101177 .

AMA Style

Phoebe Tran, Lam Tran, Liem Tran. Influence of sexual orientation on diabetes management in US adults with diabetes. Diabetes & Metabolism. 2020; 47 (1):101177.

Chicago/Turabian Style

Phoebe Tran; Lam Tran; Liem Tran. 2020. "Influence of sexual orientation on diabetes management in US adults with diabetes." Diabetes & Metabolism 47, no. 1: 101177.

Preprint content
Published: 22 June 2020
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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.

ACS Style

Phoebe Tran; Lam Tran; Liem Tran. The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study (Preprint). 2020, 1 .

AMA Style

Phoebe Tran, Lam Tran, Liem Tran. The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study (Preprint). . 2020; ():1.

Chicago/Turabian Style

Phoebe Tran; Lam Tran; Liem Tran. 2020. "The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study (Preprint)." , no. : 1.

Research article
Published: 20 April 2020 in Journal of Environmental Planning and Management
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Local communities can play a very important role in evaluating their environmental conditions and in developing innovative, practical, and effective solutions to improve community environmental health. Thus, community involvement in decision-making is one of the keys to improving environmental and public health. However, such a process is unquestionably complicated and demands well-organized collaboration between local communities and authoritative partners, as well as suitable decision-aiding methods/tools that facilitate a multiple-step decision process, starting from the identification and prioritization of hazards, risks and concerns, to the development and ranking of potential solutions. We introduce a new multi-criteria decision-making method named BESTMAP (Bounding, Eliciting, and Sliding Technique for Multi-Criterion Analysis of Priorities). BESTMAP inherits the strengths of several popular MCDM models and retains their respective merits by tackling myriad concerns with a practical, yet rigorous, approach to derive preference. BESTMAP has been developed in a familiar (offline) web-browser interface to facilitate stakeholder use. Integrating practicality and methodological rigor, BESTMAP serves as an effective model for MCDM applications, especially those with a large number of criteria and alternatives in general, and for prioritizing concerns for community environmental health in particular, where the list of concerns is often numerous, unclear, and diverse between different stakeholders.

ACS Style

Liem Tran; Timothy Barzyk; Mark Ridgley; Robert O’Neill. Prioritizing community environmental concerns with a hybrid approach to multi-criteria decision-making – a case study of Newport News, Virginia, USA. Journal of Environmental Planning and Management 2020, 63, 2501 -2517.

AMA Style

Liem Tran, Timothy Barzyk, Mark Ridgley, Robert O’Neill. Prioritizing community environmental concerns with a hybrid approach to multi-criteria decision-making – a case study of Newport News, Virginia, USA. Journal of Environmental Planning and Management. 2020; 63 (14):2501-2517.

Chicago/Turabian Style

Liem Tran; Timothy Barzyk; Mark Ridgley; Robert O’Neill. 2020. "Prioritizing community environmental concerns with a hybrid approach to multi-criteria decision-making – a case study of Newport News, Virginia, USA." Journal of Environmental Planning and Management 63, no. 14: 2501-2517.

Journal article
Published: 12 February 2020 in Remote Sensing
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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.

ACS Style

Wanwan Liang; Mongi Abidi; Luis Carrasco; Jack McNelis; Liem Tran; Yingkui Li; Jerome Grant. Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu. Remote Sensing 2020, 12, 609 .

AMA Style

Wanwan Liang, Mongi Abidi, Luis Carrasco, Jack McNelis, Liem Tran, Yingkui Li, Jerome Grant. Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu. Remote Sensing. 2020; 12 (4):609.

Chicago/Turabian Style

Wanwan Liang; Mongi Abidi; Luis Carrasco; Jack McNelis; Liem Tran; Yingkui Li; Jerome Grant. 2020. "Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu." Remote Sensing 12, no. 4: 609.

Preprint content
Published: 02 January 2020
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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.

ACS Style

Wanwan Liang; Liem Tran; Gregory Wiggins; Jerome Grant. Estimating invasion dynamics with geopolitical-unit level records: performance and similarity of common methods using both simulated data and a real case. 2020, 1 .

AMA Style

Wanwan Liang, Liem Tran, Gregory Wiggins, Jerome Grant. Estimating invasion dynamics with geopolitical-unit level records: performance and similarity of common methods using both simulated data and a real case. . 2020; ():1.

Chicago/Turabian Style

Wanwan Liang; Liem Tran; Gregory Wiggins; Jerome Grant. 2020. "Estimating invasion dynamics with geopolitical-unit level records: performance and similarity of common methods using both simulated data and a real case." , no. : 1.

Journal article
Published: 12 November 2019 in Ecological Modelling
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Challenges associated with developing species distribution models (SDMs) with high-resolution data (including from lidar) prompted our investigation into a complementary approach to enhance the performance of SDMs using spatial data with different resolutions. In our experiment we developed a model with Maxent (a presence-background SDM) with variables that had a 30-m resolution, and then used the output of the model to restrict the background sampling area for models developed with variables that had a 10-m resolution. According to common measures of model quality, this approach produced better models than both a model developed with the default Maxent background sampling area and a model developed using the conventional approach of resampling environmental data to a common spatial resolution. We then reviewed the ecological meaning of this approach and observed how model mechanics were impacted as restricting the background sampling areas led to background points that had a greater contrast with the presence points, and therefore different environmental characteristics than background points sampled from the default background sampling area.

ACS Style

Adam G. Alsamadisi; Liem T. Tran; Monica Papeş. Employing inferences across scales: Integrating spatial data with different resolutions to enhance Maxent models. Ecological Modelling 2019, 415, 108857 .

AMA Style

Adam G. Alsamadisi, Liem T. Tran, Monica Papeş. Employing inferences across scales: Integrating spatial data with different resolutions to enhance Maxent models. Ecological Modelling. 2019; 415 ():108857.

Chicago/Turabian Style

Adam G. Alsamadisi; Liem T. Tran; Monica Papeş. 2019. "Employing inferences across scales: Integrating spatial data with different resolutions to enhance Maxent models." Ecological Modelling 415, no. : 108857.

Journal article
Published: 01 November 2019 in Journal of Diabetes and its Complications
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To determine US diabetes screening estimates in Whites, Blacks, Hispanics, Asians, Native Hawaiians/Pacific Islanders, American Indians/Alaska Natives, and Others at the national, regional, and state level. In this study of 2011, 2013, 2015, and 2017 Behavioral Risk Factor Surveillance System data, we used logistic regression results to generate national, regional, and state screening marginal probabilities (average adjusted predictions (AAPs)) for each race in the two American Diabetes Association recommended screening groups1: asymptomatic overweight/obese people <45y with ≥1 diabetes risk factor and2 people ≥45y. Even after adjusting for sociodemographic and clinical factors, significant racial disparities in screening (p-value<.05) persist at all three geographic levels. Asians had the worst national, regional, and state level AAPs of all the races. Across all races, the Northeast had the highest regional screening levels (regional AAP: 48.4-78.58%) while the West had the lowest (regional AAP: 41.98-75.18%). Study findings indicate that sociodemographic and clinical factors do not fully explain racial disparities in diabetes screening. Further research on clinician and patient attitudes towards diabetes screening are warranted in order to design and implement initiatives in US areas where certain racial groups have particularly low diabetes screening levels.

ACS Style

Lam Tran; Phoebe Tran; Liem Tran. A cross-sectional analysis of racial disparities in US diabetes screening at the national, regional, and state level. Journal of Diabetes and its Complications 2019, 34, 107478 .

AMA Style

Lam Tran, Phoebe Tran, Liem Tran. A cross-sectional analysis of racial disparities in US diabetes screening at the national, regional, and state level. Journal of Diabetes and its Complications. 2019; 34 (1):107478.

Chicago/Turabian Style

Lam Tran; Phoebe Tran; Liem Tran. 2019. "A cross-sectional analysis of racial disparities in US diabetes screening at the national, regional, and state level." Journal of Diabetes and its Complications 34, no. 1: 107478.

Comparative study
Published: 11 October 2019 in Journal of Stroke and Cerebrovascular Diseases
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Background: The Stroke Belt is a region of the United States with elevated stroke incidence and prevalence of stroke risk factors. Physical inactivity is an important stroke risk factor, but little is known about whether current physical activity levels differ between Stroke Belt and non-Stroke Belt states. In this nationally representative study, we determined whether unadjusted and adjusted physical activity levels differ between the Stroke Belt region and the rest of the United States. Methods: Using 2017 Behavioral Risk Factor Surveillance System data, we conducted bivariate analyses to obtain unadjusted physical activity levels in Stroke Belt and non-Stroke Belt states. Logistic regressions that controlled for sociodemographic and stroke risk factors were created to estimate adjusted associations between Stroke Belt residence and physical activity. Results: A higher percentage of Stroke Belt residents were inactive (Stroke Belt: 35.3%, non-Stroke Belt: 29.4%) and failed to meet physical activity guidelines (Stroke Belt: 53.7%, non-Stroke Belt: 47.8%) compared to non-Stroke Belt residents. Stroke Belt residence was significantly associated with lower odds of meeting physical activity guidelines in a model that adjusted for sociodemographic factors only (odds ratio [OR]: 0.85, 95% confidence interval [CI]: 0.78-0.91) and one that adjusted for both sociodemographic and stroke risk factors (OR: 0.87, 95% CI: 0.81-0.93). Conclusions: The considerably lower physical activity levels and likelihood of meeting physical activity guidelines in Stroke Belt residents compared to their non-Stroke Belt counterparts demonstrates a need for clinician attention and public health interventions to increase regular physical activity as part of a stroke reduction strategy in this region.

ACS Style

Phoebe Tran; Lam Tran; Liem Tran. A Cross-Sectional Analysis of Differences in Physical Activity Levels between Stroke Belt and Non-Stroke Belt US Adults. Journal of Stroke and Cerebrovascular Diseases 2019, 28, 104432 .

AMA Style

Phoebe Tran, Lam Tran, Liem Tran. A Cross-Sectional Analysis of Differences in Physical Activity Levels between Stroke Belt and Non-Stroke Belt US Adults. Journal of Stroke and Cerebrovascular Diseases. 2019; 28 (12):104432.

Chicago/Turabian Style

Phoebe Tran; Lam Tran; Liem Tran. 2019. "A Cross-Sectional Analysis of Differences in Physical Activity Levels between Stroke Belt and Non-Stroke Belt US Adults." Journal of Stroke and Cerebrovascular Diseases 28, no. 12: 104432.

Journal article
Published: 02 August 2019 in International Journal of Environmental Research and Public Health
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Human health is inextricably tied to ecosystem services (ES), including those associated with greenspace in urban communities. EnviroAtlas provides close to 100 maps of ES metrics based on high-resolution land cover data in featured communities across the contiguous United States. Using selected EnviroAtlas ES metrics, a Community EcoHealth Index (CEHI) was created based on an ecohealth framework including health promotion and hazard buffering domains. Aggregation of eight selected ES metrics in these domains entailed a weighted distance measure, where objective, data-driven weights were generated. CEHI was calculated by Census Block Group (CBG) at both the local level and the national level for 22 EnviroAtlas communities. Results were mapped to show one- to five-star CBGs or neighborhoods within and across all 22 featured communities. At the national level, CEHI favors communities in forested ecoregions. The local version of CEHI is more appropriate to inform social, economic, and environmental decision-making for improving community ES associated with human health.

ACS Style

Ferdouz Cochran; Laura Jackson; Anne Neale; John Lovette; Liem Tran. A Community EcoHealth Index from EnviroAtlas Ecosystem Services Metrics. International Journal of Environmental Research and Public Health 2019, 16, 2760 .

AMA Style

Ferdouz Cochran, Laura Jackson, Anne Neale, John Lovette, Liem Tran. A Community EcoHealth Index from EnviroAtlas Ecosystem Services Metrics. International Journal of Environmental Research and Public Health. 2019; 16 (15):2760.

Chicago/Turabian Style

Ferdouz Cochran; Laura Jackson; Anne Neale; John Lovette; Liem Tran. 2019. "A Community EcoHealth Index from EnviroAtlas Ecosystem Services Metrics." International Journal of Environmental Research and Public Health 16, no. 15: 2760.

Journal article
Published: 26 September 2018 in Ecological Modelling
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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 < 0.001) but decreased the transferability (P < 0.01) of almost all models. Our findings suggest that SDMs can show good transferability with non-adaptive niche shift, thus native occurrence information should be used in similar situation. We confirmed that it is crucial to constrain the PAs to the same spatial range as presences to accurately model potential invasions.

ACS Style

Wanwan Liang; Monica Papeş; Liem Tran; Jerome Grant; Robert Washington-Allen; Scott Stewart; Gregory Wiggins. The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift. Ecological Modelling 2018, 388, 1 -9.

AMA Style

Wanwan Liang, Monica Papeş, Liem Tran, Jerome Grant, Robert Washington-Allen, Scott Stewart, Gregory Wiggins. The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift. Ecological Modelling. 2018; 388 ():1-9.

Chicago/Turabian Style

Wanwan Liang; Monica Papeş; Liem Tran; Jerome Grant; Robert Washington-Allen; Scott Stewart; Gregory Wiggins. 2018. "The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift." Ecological Modelling 388, no. : 1-9.

Research article
Published: 16 April 2018 in Journal of Environmental Planning and Management
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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.

ACS Style

Liem T. Tran; Ryan McManamay; Hyun Kim. A non-parametric distance-based method using all available indicators for integrated environmental assessment – a case study of the Mid-Atlantic Region, USA. Journal of Environmental Planning and Management 2018, 62, 766 -778.

AMA Style

Liem T. Tran, Ryan McManamay, Hyun Kim. A non-parametric distance-based method using all available indicators for integrated environmental assessment – a case study of the Mid-Atlantic Region, USA. Journal of Environmental Planning and Management. 2018; 62 (5):766-778.

Chicago/Turabian Style

Liem T. Tran; Ryan McManamay; Hyun Kim. 2018. "A non-parametric distance-based method using all available indicators for integrated environmental assessment – a case study of the Mid-Atlantic Region, USA." Journal of Environmental Planning and Management 62, no. 5: 766-778.

Journal article
Published: 01 February 2016 in Ecological Indicators
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ACS Style

Liem Tran. An interactive method to select a set of sustainable urban development indicators. Ecological Indicators 2016, 61, 418 -427.

AMA Style

Liem Tran. An interactive method to select a set of sustainable urban development indicators. Ecological Indicators. 2016; 61 ():418-427.

Chicago/Turabian Style

Liem Tran. 2016. "An interactive method to select a set of sustainable urban development indicators." Ecological Indicators 61, no. : 418-427.

Research article
Published: 07 January 2015 in Progress in Physical Geography: Earth and Environment
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Estimates of annual streamflow in connection with key natural and anthropogenic factors are necessary and important for different purposes, such as water resource planning and management, sediment and nutrient loading in streams and rivers, hydropower, and navigation. This study is an attempt to use the spatial statistical regression approach to develop regression models for mean annual streamflow at regional scale while adequately dealing with the common spatial dependency issue in input and output variables used in regression models. The proposed modeling approach is illustrated with a case study of the Upper Mississippi River Basin. The R-squared and the Nash–Sutcliffe model efficiency coefficient of the regional model were 0.993 and 0.985, respectively, while those of the sub-regional model were 0.995 and 0.990, respectively. Methodologically, the proposed model provided an effective way to utilize an extensive spatial dataset of various climatic, geomorphologic, and land cover variables for a large region like the Upper Mississippi River Basin to assess and compare the impact of various factors on mean annual streamflow at regional scale. Furthermore, the model was able to handle spatial dependency in data.

ACS Style

Liem T. Tran; Robert V. O’Neill; Randall J. F. Bruins; Elizabeth R. Smith; Carol Harden. Linking land use/land cover with climatic and geomorphologic factors in regional mean annual streamflow models with geospatial regression approach. Progress in Physical Geography: Earth and Environment 2015, 39, 258 -274.

AMA Style

Liem T. Tran, Robert V. O’Neill, Randall J. F. Bruins, Elizabeth R. Smith, Carol Harden. Linking land use/land cover with climatic and geomorphologic factors in regional mean annual streamflow models with geospatial regression approach. Progress in Physical Geography: Earth and Environment. 2015; 39 (2):258-274.

Chicago/Turabian Style

Liem T. Tran; Robert V. O’Neill; Randall J. F. Bruins; Elizabeth R. Smith; Carol Harden. 2015. "Linking land use/land cover with climatic and geomorphologic factors in regional mean annual streamflow models with geospatial regression approach." Progress in Physical Geography: Earth and Environment 39, no. 2: 258-274.

Journal article
Published: 27 August 2014 in Geosciences
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This research examines risk factors for sporadic cryptosporidiosis and Escherichia coli (E. coli) O157 infection in East Tennessee, using a case-control approach and spatial logistic regression models. The risk factors examined are animal density, land use, geology, surface water impairment, poverty rate and availability of private water supply. Proximity to karst geology, beef cow population density and a high percentage of both developed land and pasture land are positively associated with both diseases. The availability of private water supply is negatively associated with both diseases. Risk maps generated using the model coefficients show areas of elevated risk to identify the communities where background risk is highest, so that limited public health resources can be targeted to the risk factors and communities most at risk. These results can be used as the framework upon which to develop a comprehensive epidemiological study that focuses on risk factors important at the individual level.

ACS Style

Ingrid Luffman; Liem Tran. Risk Factors for E. coli O157 and Cryptosporidiosis Infection in Individuals in the Karst Valleys of East Tennessee, USA. Geosciences 2014, 4, 202 -218.

AMA Style

Ingrid Luffman, Liem Tran. Risk Factors for E. coli O157 and Cryptosporidiosis Infection in Individuals in the Karst Valleys of East Tennessee, USA. Geosciences. 2014; 4 (3):202-218.

Chicago/Turabian Style

Ingrid Luffman; Liem Tran. 2014. "Risk Factors for E. coli O157 and Cryptosporidiosis Infection in Individuals in the Karst Valleys of East Tennessee, USA." Geosciences 4, no. 3: 202-218.