Bridging the Gap in Potentially Excess Deaths Between Rural and Urban Counties in the United States

Barriers, facilitators, and priority needs related to cancer prevention, control, and research in rural, persistent poverty areas

PURPOSE

The purpose of this study was to identify the barriers, facilitators, and priority needs related to cancer prevention, control, and research in persistent poverty areas.

METHODS

We conducted three focus groups with 17 providers and staff of primary care clinics serving persistent poverty areas throughout the state of Arkansas.

RESULTS

We identified multiple barriers, facilitators, and priority needs related to cancer prevention and control at primary care clinics serving persistent poverty areas. Barriers included transportation, medical costs, limited providers and service availability, and patient fear/discomfort with cancer topics. Facilitators identified were cancer navigators and community health events/services, and priority needs included patient education, comprehensive workflows, improved communication, and integration of cancer navigators into healthcare teams. Barriers to cancer-related research were lack of provider/staff time, patient uncertainty/skepticism, patient health literacy, and provider skepticism/concerns regarding patient burden. Research facilitators included better informing providers/staff about research studies and leveraging navigators as a bridge between clinic and patients.

CONCLUSION

Our results inform opportunities to adapt and implement evidence-based interventions to improve cancer prevention, control, and research in persistent poverty areas. To improve cancer prevention and control, we recommend locally-informed strategies to mitigate patient barriers, improved patient education efforts, standardized patient navigation workflows, improved integration of cancer navigators into care teams, and leveraging community health events. Dedicated staff time for research, coordination of research and clinical activities, and educating providers/staff about research studies could improve cancer-related research activities in persistent poverty areas.

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes

Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.

A Structural Equation Modeling Analysis to Explore Diabetes Self-Care Factors in a Rural Sample

Diabetes is a public health problem that requires management to avoid health sequelae. Little is known about the determinants that influence diabetes self-care activities among rural populations. The purpose of this analysis was to explore the relationships among diabetes self-care activities, diabetes knowledge, perceived diabetes self-management, diabetes fatalism, and social support among an underserved rural group in the southern United States. A diabetes health promotion program was tested during a cluster randomized trial that tested a disease risk reduction program among adults living with prediabetes and diabetes. A structural equation model was fit to test psychosocial factors that influence diabetes self-care activities using the Information-Motivation-Behavioral Skills Model of Diabetes Self-Care (IMB-DSC) to guide the study. Perceived diabetes self-management significantly predicted self-care behaviors, and there was also a correlation between perceived diabetes self-management and diabetes fatalism. Perceived diabetes self-management influenced diabetes self-care activities in this rural sample and had an association with diabetes fatalism. The findings of this study can facilitate clinical care and community programs targeting diabetes and advance health equity among underserved rural groups.