Doctor of Philosophy

dissertationSeveral studies have demonstrated an association between prediabetes (preDM) and the incidence of Type II Diabetes Mellitus (T2DM). Many preventable factors can contribute to this association, namely behavioral and environmental conditions that lead to physiological changes and symptomology. Earlier identification of disease through combining common laboratory studies that demonstrate an elevated fasting glucose may be one mechanism to identify the vast majority of patients who are unaware of their preDM condition. Also, it has been widely demonstrated that T2DM can be effectively prevented or delayed with interventions geared towards weight management, physical activity, goal setting, and stress management. However, it is not entirely known whether education provided within a healthcare delivery system is effective in supporting patients to reach a 5% weight loss while reducing their overall incidence of T2DM disease. Furthermore, study is needed to evaluate such health interventions beyond effectiveness, to better identify effect and transferability through measuring the reach, adoption, and implementation. The objective of this dissertation was to determine: (a) the risk of T2DM among patients with confirmed and unconfirmed preDM relative to an at-risk group; (b) the association of a 5% weight loss with participation in the Intermountain Healthcare (IH) Diabetes Prevention Program (DPP); and, subsequently, (c) the reach, effectiveness, adoption, and implementation of the IH DPP intervention. The IH Enterprise Data Warehouse was utilized to evaluate these objectives. Patients with unconfirmed preDM iv (HR 1.74; CI 1.59, 1.91; p<0.0001) and confirmed preDM (HR 2.77; CI 2.38, 3.23; p<0.0001) were more likely to develop T2DM when compared to at-risk patients. DPP participants were more likely to achieve a 5% weight loss within 6 months (OR 1.72; 95% CI 1.29, 2.34; p<0.001) and less likely to have incident T2DM (OR 0.45; 95% CI 0.24, 0.84; p=0.012) when compared to the no-DPP group. Lastly, DPP-based lifestyle interventions deployed within IH's delivery system demonstrated moderate effectiveness in the short term, yet the proportion of patients (8%) who enrolled was low. Broad adoption across regions by providers and leadership revealed organizational buy-in (194 providers at 53 clinics referred patients), while demonstrating that much of the clinical effect was seen when patients participated in interventions that were far less resource intensive (only 2.3 DPP counseling encounters on average). In conclusion, confirmed and unconfirmed preDM was associated with T2DM, however when patients participated in a DPP-based intervention, there was significant weight loss and reduction in T2DM incidence. Finally, the IH DPP demonstrated encouraging potential when evaluating organizational adoption and short-term effectiveness, yet may benefit from leveraging technology to scale these established interventions for those at risk for disease

Implementation of Diabetes Prevention in Health Care Organizations: Best Practice Recommendations

Approximately 1 in 3 American adults has prediabetes, a condition characterized by blood glucose levels that are above normal, not in the type 2 diabetes ranges, and that increases the risk of developing type 2 diabetes. Evidence-based treatments can be used to prevent or delay type 2 diabetes in adults with prediabetes. The American Medical Association (AMA) has collaborated with health care organizations across the country to build sustainable diabetes prevention strategies. In 2017, the AMA formed the Diabetes Prevention Best Practices Workgroup (DPBP) with representatives from 6 health care organizations actively implementing diabetes prevention. Each organization had a unique strategy, but all included the National Diabetes Prevention Program lifestyle change program as a core evidence-based intervention. DPBP established the goal of disseminating best practices to guide other health care organizations in implementing diabetes prevention and identifying and managing patients with prediabetes. Workgroup members recognized similarities in some of their basic steps and considerations and synthesized their practices to develop best practice recommendations for 3 strategy maturity phases. Recommendations for each maturity phase are classified into 6 categories: (1) organizational support; (2) workforce and funding; (3) promotion and dissemination; (4) clinical integration and support; (5) evaluation and outcomes; (6) and program. As the burden of chronic disease grows, prevention must be prioritized and integrated into health care. These maturity phases and best practice recommendations can be used by any health care organization committed to diabetes prevention. Further research is suggested to assess the impact and adoption of diabetes prevention best practices

Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes

Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.https://doi.org/10.3390/ijerph1710365

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents&rsquo; risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.https://doi.org/10.3390/ijerph1717635

Incidental Risk of Type 2 Diabetes Mellitus among Patients with Confirmed and Unconfirmed Prediabetes

<div><p>Objective</p><p>To determine the risk of type 2 diabetes (T2DM) diagnosis among patients with confirmed and unconfirmed prediabetes (preDM) relative to an at-risk group receiving care from primary care physicians over a 5-year period.</p><p>Study Design</p><p>Utilizing data from the Intermountain Healthcare (IH) Enterprise Data Warehouse (EDW) from 2006–2013, we performed a prospective analysis using discrete survival analysis to estimate the time to diagnosis of T2DM among groups.</p><p>Population Studied</p><p>Adult patients who had at least one outpatient visit with a primary care physician during 2006–2008 at an IH clinic and subsequent visits through 2013. Patients were included for the study if they were (a) at-risk for diabetes (BMI ≥ 25 kg/m2 and one additional risk factor: high risk ethnicity, first degree relative with diabetes, elevated triglycerides or blood pressure, low HDL, diagnosis of gestational diabetes or polycystic ovarian syndrome, or birth of a baby weighing >9 lbs); or (b) confirmed preDM (HbA1c ≥ 5.7–6.49% or fasting blood glucose 100–125 mg/dL); or (c) unconfirmed preDM (documented fasting lipid panel and glucose 100–125 mg/dL on the same day).</p><p>Principal Findings</p><p>Of the 33,838 patients who were eligible for study, 57.0% were considered at-risk, 38.4% had unconfirmed preDM, and 4.6% had confirmed preDM. Those with unconfirmed and confirmed preDM tended to be Caucasian and a greater proportion were obese compared to those at-risk for disease. Patients with unconfirmed and confirmed preDM tended to have more prevalent high blood pressure and depression as compared to the at-risk group. Based on the discrete survival analyses, patients with unconfirmed preDM and confirmed preDM were more likely to develop T2DM when compared to at-risk patients.</p><p>Conclusions</p><p>Unconfirmed and confirmed preDM are strongly associated with the development of T2DM as compared to patients with only risk factors for disease.</p></div