Background: The Program of All-inclusive Care for the Elderly (PACE) is a program of care that enrolls nursing home eligible and offers them community-based long-term services and supports (LTSS). Many PACE enrollees are “dual eligibles” (DEs) meaning they qualify for Medicare and Medicaid services. Dental care is a unique feature of PACE among LTSS, as many LTSS do not include dental care, even though this population has difficulty in accessing these services. However, little is known about the delivery of dental care at PACE and how dental care and oral health promotion and prevention is being integrated into PACE. Thus, the purpose of this study is to describe the delivery and integration of dental care at PACE. Methods: Based on ten preliminary interviews and the PACE manual from the Centers for Medicare and Medicaid Services (CMS), a 56-question survey was developed to describe the delivery and integration of dental care at PACE across the country. In addition, the survey asked programs to rank their focus among five specialties (dentistry, mental health, optometry, audiology, podiatry), to determine if a program’s focus on dental care would influence the delivery and integration of dental services at PACE, and if this would lead the program to have a very high percentage of new and continuous enrollees with regular dental examinations. A publicly available contact list was obtained from CMS and the survey was distributed to all 124 PACE programs via email. Results: Respondents in this study represented 35 programs (28.2%) in 23 states (74.2%). Most programs had no limits for dental care, minimal waitlists, and provide most dental services without exclusions. This is evident by the 51.4% of programs that have no dental budget, 100% of programs providing preventive and basic restorative dental care, and nearly 100% offering advanced restorative services. Many programs also did not have a waitlist for non-emergent dental care. Few programs include a dentist in the routine operations of the PACE program, as evidenced by few programs having dentists conduct the dental assessment for the initial comprehensive assessment or having a dental director. A statistically significant association with a high percentage of reported utilization of dental examinations was detected with programs having a system for quality assurance for dental care (t=0.358, p=0.024), a protocol for a dental cleaning every 6-12 months (t=0.595, p<0.001), mandating a comprehensive dental examination (t=0.390, p=0.007), and providing preventive dental services onsite with built-in equipment (t=0.454, p=0.001). No factors were statistically associated with the focus ranking for dentistry among the other specialties. Conclusion: This study suggests that compared to nursing homes, PACE enrollees may have greater ability to receive dental care without limitations of the state adult Medicaid dental benefit. Dentistry also appears to be a high focus for some PACE programs. This study has begun to identify structures that support positive outcomes that can be used to develop best practices and guidelines for the delivery of dental care in PACE and other LTSS. Future studies are needed to better understand barriers and facilitators to the delivery of dental care and other specialty services.

This study examines the effectiveness of using the Normalized Difference Vegetation Index (NDVI) derived from 1326 different Landsat Thematic Mapper and Enhanced Thematic Mapper images in finding isolated housing starts within the Commonwealth of Virginia's forests. Individual NDVI images were stacked by year for the years 1995-2011 and the yearly maximum for each pixel was extracted, resulting in a 17-year image stack of all yearly maxima (a 98.7% data reduction). Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from housing starts and other forest disturbances, as well as from undisturbed forest, were used to derive vegetation index thresholds enabling separation of disturbed from undisturbed forest. Disturbances, once identified, were separated accurately (overall accuracy = 85.4 percent, F-statistic = 0.86) into housing starts and other forest disturbances using a classification tree and only two variables from the Disturbance Detection and Diagnostics (D3) algorithm: the maximum NDVI in the available recovery period and the slope between the NDVI value at the time of the disturbance and the maximum NDVI in the available recovery period. Landsat time series stacks thus show promise for identifying even the small changes associated with exurban development. / Master of Science