Automated Identification of Unhealthy Drinking Using Routinely Collected Data: A Machine Learning Approach

Background: Unhealthy drinking is prevalent in the United States and can lead to serious health and social consequences, yet it is under-diagnosed and under-treated. Identifying unhealthy drinkers can be time-consuming for primary care providers. An automated tool for identification would allow attention to be focused on patients most likely to need care and therefore increase efficiency and effectiveness. Objectives: To build a clinical prediction tool for unhealthy drinking based solely on routinely collected demographic and laboratory data. Methods: We obtained demographic and laboratory data on 89,325 adults seen at the University of Vermont Medical Center from 2011-2017. Logistic regression, support vector machines (SVM), k-nearest neighbor, and random forests were each used to build clinical prediction models. The model with the largest area under the receiver operator curve (AUC) was selected. Results: SVM with polynomials of degree 3 produced the largest AUC. The most influential predictors were alkaline phosphatase, gender, glucose, and serum bicarbonate. The optimum operating point had sensitivity 31.1%, specificity 91.2%, positive predictive value 50.4%, and negative predictive value 82.1%. Application of the tool increased the prevalence of unhealthy drinking from 18.3% to 32.4%, while reducing the target population by 22%. Limitations: Universal screening was not used during the time data was collected. The prevalence of unhealthy drinking among those screened was 60% suggesting the AUDIT-C was administered to confirm rather than screen for unhealthy drinking. Conclusion: An automated tool, using commonly available data, can identify a subset of patients who appear to warrant clinical attention for unhealthy drinking

Nonlinear Relationships Between The Environment And Health

Relationships between the environment and health outcomes are complex and likely nonlinear in nature. However, until recently, most studies used ordinary linear regression to model these relationships. The overall goal of this research was to investigate nonlinear relationships between the environment and health. To accomplish this goal, we used several large, national datasets across varying populations and local environments. Destination accessibility is an important measure of the built environment that is associated with active transport and body mass index (BMI). In the first study, we sought to determine the relationship between the density of nonresidential destinations (a proxy for walkability) and BMI, allowing for the possibility of a nonlinear relationship. We merged information from 17.2 million driver’s license records with the locations of 3.8 million nonresidential destinations and census tract socioeconomic data from six states. BMI peaked in the middle density, with significantly lower values in both the low and high-density extremes – a markedly nonlinear relationship. Next, we confirmed our previous nonlinear findings in an independent sample of 2,405 primary care patients with multiple chronic conditions from 13 states, and extended our analysis to include mental and physical health outcomes, in addition to BMI. Several statistical methods were used to confirm the nonlinear relationship between nonresidential destinations and BMI. We also established novel nonlinear relationships between nonresidential destinations and mental health. All three health measures were significantly worse in middle density areas with better values on either extreme. Then, we extended the previous analyses to the natural environment. We used data on 3,409 adults from 119 US counties and the natural amenities scale, a county-level measure of the natural environment, to assess the relationship between the natural environment and health at the intersection of various demographic and social factors, allowing for the possibility of a non-linear relationship. Health was generally worse in areas with poor natural environments; however, this relationship was not linear. In areas with low natural amenities, greater amenities were associated with better physical and mental health, but only for advantaged populations. Meanwhile greater amenities in high amenity areas was associated with a decrease in mental and physical health for disadvantaged populations. Finally, in the review paper, we described the current state of the literature on the nonlinear relationships between walkability and health. We argue that using linear regression techniques to model nonlinear relationships could introduce bias and be partially responsible for the conflicting findings in the literature. We conclude that there are nonlinear relationships between the environment and health. Complex relationships require complex modelling. Ignoring the possibility of a nonlinear relationship could obscure the true relationship and lead researchers and public health officials to draw incorrect conclusions. Future research should confirm these findings and investigate the mechanisms driving these relationships

Nonlinear Relationships among the Natural Environment, Health, and Sociodemographic Characteristics across US Counties

Background: The aim of this study was to explore the nonlinear relationships between natural amenities and health at the intersection of sociodemographic characteristics among primary care patients with chronic conditions. Methods: We used survey data from 3409 adults across 119 US counties. PROMIS-29 mental and physical health summary scores were the primary outcomes. The natural environment (measured using the County USDA Natural Amenities Scale (NAS)) was the primary predictor. Piecewise spline regression models were used to explore the relationships between NAS and health at the intersection of sociodemographic factors. Results: We identified a nonlinear relationship between NAS and health. Low-income individuals had a negative association with health with each increase in NAS in high-amenity areas only. However, White individuals had a stronger association with health with each increase in NAS in low-amenity areas. Conclusions: In areas with low natural amenities, more amenities are associated with better physical and mental health, but only for advantaged populations. Meanwhile, for disadvantaged populations, an increase in amenities in high-amenity areas is associated with decreases in mental and physical health. Understanding how traditionally advantaged populations utilize the natural environment could provide insight into the mechanisms driving these disparities

Intent to Change Sun-Protective Behaviors Among Hispanic People After a UV Photoaging Intervention: Cohort Study

Background Mortality rates from melanoma are higher among Hispanic populations than non-Hispanic White (NHW) populations. Interventions to improve sun safety are needed. The Reveal Imager is a camera that uses standard cross-polarized flash photography to record surface and subsurface skin conditions. Objective This study aims to determine the intervention’s effectiveness in increasing awareness of sun damage and exposure reduction between Hispanic and NHW populations. Methods A cohort of 322 participants, aged ≥18 years, were recruited from community events in 2018. Baseline information was collected on demographics, sun exposure, and perception of risk factors. A facial image was then captured using the Reveal Imager. The results were explained and counseling on sun safety was given, followed by filling out an immediate postimage survey. Chi-square tests, analysis of variance, Wilcoxon signed-rank test, McNemar tests, and multivariable logistic regression were used. Results At follow-up, 125 of 141 (89%) Hispanic participants reported that viewing the UV photoaged image influenced intent-to-change sun protection behaviors, compared to 88 of 121 (73%) NHW participants (odds ratio 2.9, 95% CI 1.5-5.6). Of 141 Hispanic participants, 96 (68%) reported that they intended to increase sunscreen use, compared to only 41 of 121 (34%) NHW participants (P<.001). Conclusions We demonstrated an application of Reveal Imager for education and risk assessment. The Reveal Imager was especially helpful in motivating intention to change sun exposure among Hispanic populations

Urban–Rural Differences in Mental and Physical Health among Primary Care Patients with Multiple Chronic Conditions: A Secondary Analysis from a Randomized Clinical Trial

Purpose: Rural health disparities are largely attributable to access to healthcare, socioeconomic status, and health behaviors. Little is known about the persistence of these disparities when differences in access to care are eliminated. We sought to investigate urban–rural differences in physical and mental health in primary care patients with demonstrated access to primary care. Methods: We obtained cross-sectional survey responses from a multicenter randomized controlled trial on 2726 adult primary care patients with multiple chronic medical or behavioral conditions from 42 primary care practices in 13 states. Study outcomes include measures of mental health including: The Patient-Reported Outcomes Measurement Information System (PROMIS-29®), General Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9), as well as physical health including: the PROMIS-29® and the Duke Activity Status Index (DASI). Urban–rural residence was indicated by census-tract Rural Urban Commuting Areas of the participant’s home address. Differences in mental and physical health outcomes attributable to rurality were assessed using multilevel models with a random intercept for census-tract. Results: After adjustment for demographic and neighborhood characteristics, urban residents had significantly worse generalized anxiety disorder (GAD-7) (ß = 0.7; 95% CI = 0.1, 1.3; p = 0.027), depression (PHQ-9) (ß = 0.7; 95% CI = 0.1, 1.4; p = 0.024), and functional capacity (DASI) (ß = −0.4; 95% CI = −0.5, −0.2; p ® compared to their rural counterparts. There were no urban–rural differences in the other PROMIS-29® subdomains. Conclusions: Among adults with demonstrated access to care and multiple diagnosed chronic conditions, rural residents had better mental health and functional capacity than their urban counterparts. This finding is not consistent with prior research documenting rural health disparities and should be confirmed

Community-Dwelling Older Adults and Physical Activity Recommendations: Patterns of Aerobic, Strengthening, and Balance Activities.

Though it is known that most older adults do not meet the recommended physical activity (PA) guidelines, little is known regarding their participation in balance activities or the full guidelines. Therefore, we sought to describe PA patterns among 1,352 community-dwelling older adult participants of the Adult Changes in Thought study, a longitudinal cohort study exploring dementia-related risk factors. We used a modified version of the Community Healthy Activities Model Program for Seniors questionnaire to explore PA performed and classify participants as meeting or not meeting the full guidelines or any component of the guidelines. Logistic regression was used to identify factors associated with meeting PA guidelines. Despite performing 10&nbsp;hr of weekly PA, only 11% of participants met the full guidelines. Older age, greater body mass index, needing assistance with instrumental daily activities, and heart disease were associated with decreased odds of meeting PA guidelines. These results can guide interventions that address PA among older adults

Integrating Behavioral Health and Primary Care (IBH-PC) to improve patient-centered outcomes in adults with multiple chronic medical and behavioral health conditions: study protocol for a pragmatic cluster-randomized control trial.

BackgroundChronic diseases that drive morbidity, mortality, and health care costs are largely influenced by human behavior. Behavioral health conditions such as anxiety, depression, and substance use disorders can often be effectively managed. The majority of patients in need of behavioral health care are seen in primary care, which often has difficulty responding. Some primary care practices are providing integrated behavioral health care (IBH), where primary care and behavioral health providers work together, in one location, using a team-based approach. Research suggests there may be an association between IBH and improved patient outcomes. However, it is often difficult for practices to achieve high levels of integration. The Integrating Behavioral Health and Primary Care study responds to this need by testing the effectiveness of a comprehensive practice-level intervention designed to improve outcomes in patients with multiple chronic medical and behavioral health conditions by increasing the practice's degree of behavioral health integration.MethodsForty-five primary care practices, with existing onsite behavioral health care, will be recruited for this study. Forty-three practices will be randomized to the intervention or usual care arm, while 2 practices will be considered "Vanguard" (pilot) practices for developing the intervention. The intervention is a 24-month supported practice change process including an online curriculum, a practice redesign and implementation workbook, remote quality improvement coaching services, and an online learning community. Each practice's degree of behavioral health integration will be measured using the Practice Integration Profile. Approximately 75 patients with both chronic medical and behavioral health conditions from each practice will be asked to complete a series of surveys to measure patient-centered outcomes. Change in practice degree of behavioral health integration and patient-centered outcomes will be compared between the two groups. Practice-level case studies will be conducted to better understand the contextual factors influencing integration.DiscussionAs primary care practices are encouraged to provide IBH services, evidence-based interventions to increase practice integration will be needed. This study will demonstrate the effectiveness of one such intervention in a pragmatic, real-world setting.Trial registrationClinicalTrials.gov NCT02868983 . Registered on August 16, 2016