Google Street View Images as Predictors of Patient Health Outcomes, 2017-2019.

Collecting neighborhood data can both be time- and resource-intensive, especially across broad geographies. In this study, we leveraged 1.4 million publicly available Google Street View (GSV) images from Utah to construct indicators of the neighborhood built environment and evaluate their associations with 2017-2019 health outcomes of approximately one-third of the population living in Utah. The use of electronic medical records allows for the assessment of associations between neighborhood characteristics and individual-level health outcomes while controlling for predisposing factors, which distinguishes this study from previous GSV studies that were ecological in nature. Among 938,085 adult patients, we found that individuals living in communities in the highest tertiles of green streets and non-single-family homes have 10-27% lower diabetes, uncontrolled diabetes, hypertension, and obesity, but higher substance use disorders-controlling for age, White race, Hispanic ethnicity, religion, marital status, health insurance, and area deprivation index. Conversely, the presence of visible utility wires overhead was associated with 5-10% more diabetes, uncontrolled diabetes, hypertension, obesity, and substance use disorders. Our study found that non-single-family and green streets were related to a lower prevalence of chronic conditions, while visible utility wires and single-lane roads were connected with a higher burden of chronic conditions. These contextual characteristics can better help healthcare organizations understand the drivers of their patients' health by further considering patients' residential environments, which present both risks and resources.

Characterization of diffuse fibrosis in the failing human heart via diffusion tensor imaging and quantitative histological validation.

Non-invasive imaging techniques are highly desirable as an alternative to conventional biopsy for the characterization of the remodeling of tissues associated with disease progression, including end-stage heart failure. Cardiac diffusion tensor imaging (DTI) has become an established method for the characterization of myocardial microstructure. However, the relationships between diffuse myocardial fibrosis, which is a key biomarker for staging and treatment planning of the failing heart, and measured DTI parameters have yet to be investigated systematically. In this study, DTI was performed on left ventricular specimens collected from patients with chronic end-stage heart failure as a result of idiopathic dilated cardiomyopathy (n = 14) and from normal donors (n = 5). Scalar DTI parameters, including fractional anisotropy (FA) and mean (MD), primary (D1 ), secondary (D2 ) and tertiary (D3 ) diffusivities, were correlated with collagen content measured by digital microscopy. Compared with hearts from normal subjects, the FA in failing hearts decreased by 22%, whereas the MD, D2 and D3 increased by 12%, 14% and 24%, respectively (P < 0.01). No significant change was detected for D1 between the two groups. Furthermore, significant correlation was observed between the DTI scalar indices and quantitative histological measurements of collagen (i.e. fibrosis). Pearson's correlation coefficients (r) between collagen content and FA, MD, D2 and D3 were -0.51, 0.59, 0.56 and 0.62 (P < 0.05), respectively. The correlation between D1 and collagen content was not significant (r = 0.46, P = 0.05). Computational modeling analysis indicated that the behaviors of the DTI parameters as a function of the degree of fibrosis were well explained by compartmental exchange between myocardial and collagenous tissues. Combined, these findings suggest that scalar DTI parameters can be used as metrics for the non-invasive assessment of diffuse fibrosis in failing hearts.

Physician assistants and their intent to retire.

OBJECTIVES: To determine predictors of physician assistants (PAs) to retire or to permanently leave clinical practice. The intent was to create a measure of retention and attrition for purposes of forecasting PA supply. STUDY DESIGN: All PAs 55 years or older who were nationally certified in 2011 were surveyed. METHODS: Statistical analysis included descriptive measures utilizing means, standard deviations, range, and proportions for all survey questions. Univariable analysis using χ² test for the categorical variables determined gender differences in participants' intent to retire. A studentized t test analysis for continuous variables was used to compare differences across genders. The estimated time interval until retirement was calculated using reported values from participants and then subtracting their projected retirement age from current age. The same calculation was used for estimating PA career length from date of graduation to retirement. For all analyses, a P value < .05 was considered statistically significant. RESULTS: A total of 12,005 were eligible and surveyed online; 4767 responded (38%). The mean age was 60 years and the years in clinical practice was 25. When asked to predict a retirement date or age, the mean duration of working beyond age 55 years was 12 years (range 5 to 21). Most respondents reported being confident they were on track to retire with an adequate income. The significant differences that emerged were that men were more confident than women in preparing to retire, having enough money for medical expenses, and being able to live comfortably in retirement. Men more than women stated that, if forced to retire, they were more confident in the preparation to do so. CONCLUSIONS: PAs 55 years and older report they are likely to delay retirement from practice until age 67 years, on average. Women were less confident than men in retirement preparation. This age prediction expands career projections and refines forecasting models for the profession. Correlations based on expectation-action chain of events should be developed by periodically measuring how often intent and reality coalesce.