UTILIZATION OF DILATED EYE EXAMS AMONG ADULTS WITH DIABETES

Purpose: The purpose of this study is to determine the utilization rate of dilated eye exams among adults with diabetes, and to examine the differences in receiving dilated eye exams by predisposing, need, and enabling factors. Methods: National survey data from the 2010 Behavioral Risk Factor Surveillance System. Univariate, weighted bivariate, and logistic regression analyses were performed. Independent variables include demographic information. The dependent variable is the receipt of a dilated eye exam. Results: Some characteristics of individuals who were more likely to receive a dilated eye exam include adults 65 or older, non-Hispanic Blacks, individuals with a health plan, individuals who had a physical exam within the past year, individuals with some type of formal diabetes education, and individuals earning at least $50,000 annually. Conclusion: The Andersen Behavior Model that predisposing, need, and enabling factors are positively associated to the receipt of a dilated eye examination was supported. All enabling factors used in our study are strong predictors of receiving a dilated eye examination. Developing effective recommendations and guidelines for dilated eye exam utilization targeting at-risk adults with diabetes may be beneficial for increasing the number of adults with diabetes who receive annual dilated eye exams

Approaches to Engaging Low-Income Communities in Improving Their Diabetes Health: A Review of the Literature Published in the 21 st Century

Diabetes is a serious health condition and can lead to a variety of health complications and increase

Racial/ethnic and geographic differences in access to a usual source of care that follows the patient-centered medical home model: Analyses from the Medical Expenditure Panel Survey data

This study examined racial and geographic differences in access to a usual source of care (USC) and it further explored these differences among individuals who had a USC that followed the patient-centered medical home (PCMH) model. Using cross-sectional data from the Household Component of the Medical Expenditure Panel Survey (2008-2013), our sample consisted of non-institutionalized US civilians ages 18-85 (n= 146,233; weighted n = 229,487,016). Our analysis included weighted descriptive statistics and weighted logistic regressions. Although 76% of the respondents had a USC, only 11% of them had a USC that followed the PCMH model. Among respondents who had a USC that followed the PCMH model, 80% were White, 13% Black, 5% Asian, and 12% were of Hispanic ethnicity. Across U.S. regions, 88% percent of those who had a USC that followed the PCMH model resided in metropolitan statistical areas (MSAs), 22% resided in the West, 26% in the Northeast, 25% in the Midwest, and 27% in the South. Results from logistic regression analyses indicated that race and ethnicity were not significant predictors of having a USC that followed the PCMH model. Northeastern U.S. residents (OR: 1.30; 95% CI:1.06-1.61) were more likely to have a USC that followed the PCMH model compared with southern residents. In conclusion, only a small percentage of respondents in our sample had a USC with the PCMH model. Further, race and ethnicity were not predictors of having a USC with the PCMH model

Does the Provision of High-Technology Health Services Change After the Privatization of Public Hospitals?

Background: Public hospitals hold a key role in providing health care services especially to individuals without health insurance, those who are partially covered by health insurance, and low income population. However, some of these hospitals have converted to private status. The objective of this study was to assess the effect of the ownership conversion of public hospitals into private status on the provision of high-technology health services. Methods: This study used a non-experimental longitudinal design based on merged secondary data from the American Hospital Association annual survey, the Area Health Resources File, and the Local Area Unemployment Statistics [1997–2013]. The dependent variable “high-technology health services” was measured using Saidin index. There were 492 non-federal acute care public hospitals (n=8,335 hospital-year observations) in our sample, of which 104 (21%) converted to private status (75 converted to private not-for-profit and 29 converted to for-profit hospitals). The independent variable “privatization” referred to ownership conversion from public to either private not-for-profit or private for-profit status. We ran two fixed-effects linear regressions to measure the impact of privatization on high-technology services offering. Results: Our key findings suggested that privatization was associated with a decrease in Saidin index (β=−0.74; P=0.016; 95% CI: −1.34 to −1.38). For-profit privatization was associated with a greater decrease in Saidin index (β=−1.29; P=0.024; 95% CI: −2.41 to −0.17), compared with an insignificant decrease for not-for-profit privatization (β=−0.56; P=0.106; 95% CI: −1.25 to 0.12). Conclusions: Given the excessive cost of high-technology health services and the change in the hospitals’ mission after privatization, privatized hospitals tend to reduce the number of high-technology health services they provide

Health Disparities Among Children with Disabilities

Dr. DeLawnia Comer-Hagans, Ph.D., Assistant Professor, Department of Health Administration & Dr. Zo Ramamonjiarivelo, Ph.D., Assistant Professor, Department of Health Administration. This presentation addresses some of the health disparities that exist among children with disabilities. For example, children with disabilities experience higher rates of obesity and diabetes than children without health disparities. The presenters will also discuss the impact of health policies within the health care system on children with disabilities

Assessing Health Services Utilizations of Individuals Diagnosed with Coronary Heart Disease and Diabetes

Coronary heart disease (CHD) is the leading co-morbid condition among individuals with diabetes. Individuals with diabetes are four times more likely to develop CHD than individuals without diabetes. Our study was designed to assess health services utilization among patients with diabetes who have CHD. Through an analysis of cross sectional data from the Medical Expenditure Panel Survey (MEPS) from 2008 to 2013 using a population of adults 18 years or older we examined the data for health care utilization and found that individuals with both diabetes and CHD tended to have more office-based physician visits and emergency room visits compared with individuals with diabetes and individuals with CHD. Individuals with CHD tended to have more hospitalizations compared with individuals with both diabetes and CHD as well as individuals with diabetes. Individuals with diabetes tended to have more hospital-based outpatient visits compared with individuals with both diabetes and CHD as well as individuals with CHD

The Characteristics of Individuals Who Access Health Care at a Full vs. Partial Patient-Centered Medical Home: A Patient Perspective

The patient-centered medical home (PCMH) model has been promoted and supported by the Affordable Care Act. It is expected to improve health care quality, enhance patient experience, and reduce costs. The purpose of this study was to examine the characteristics of individuals who access health care at a full versus partial PCMH from patients’ perspective. This study used pooled cross sectional data from the Medical Expenditure Panel Survey (MEPS) from 2008 to 2013. Our sample consisted of all American adults\u3e18 years old who have a usual source of care (n = 103,676; weighted n=174,630,895). We constructed a dichotomous dependent variable coded “1” if the individual has access to a full PCMH and coded as “0” if the individual has access to a partial PCMH. Our independent variables consisted of individual’s characteristics that may affect access to PCMH. We used weighted logistic regression, based on the weight provided by MEPS. We found that compared with individuals aged between 18 and 24, individuals aged between 25 and 44 (OR=0.85,

Assessing the Effectiveness of Intergenerational Virtual Service-Learning Intervention on Loneliness and Ageism: A Pre-Post Study

Background: Service-learning is an effective intervention to solve social issues. The purpose of this study is to assess the effectiveness of intergenerational virtual service-learning on loneliness and ageism. Method: This study used a pre-post design. A group of undergraduate students were randomly assigned to a “service-learning” project (n = 18). They were paired with seniors (n = 22) to have at least a 30-min weekly virtual interaction for six weeks. The following scales were used: the Aging Semantic Differential (ASD) Scale, the UCLA Loneliness Scale, a one-item researcher generated Likert-rating of loneliness, and two-item researcher generated Likert-rating of student competence. Results: Among college students, the service-learning group showed lower ASD and ageism scores at the post-test compared to the non-service-learning group, t (1, 40) = −2.027, p = 0.049; t (1, 40) = −2.102, p = 0.042, respectively. Among seniors, loneliness scores on the UCLA Scale and the one-item scale of loneliness dropped significantly from pre- to post-interactions with students, t (1, 19) = 2.301, p = 0.033, and t (1, 22) = 2.412, p = 0.009, respectively. Conclusion: Virtual service-learning is an effective way to solve social issues such as loneliness and ageism

The Predictive Factors of Hospital Bankruptcy—An Exploratory Study

The United States healthcare industry has witnessed a number of hospitals declare bankruptcy. This has a meaningful impact on local communities with vast implications on access, cost, and quality of care available. In our research, we seek to determine what contemporary structural and operational factors influence a bankruptcy outcome, and craft predictive models to guide healthcare leaders on how to best avoid bankruptcy in the future. In this exploratory study we performed, a single-year cross-sectional analysis of short-term acute care hospitals in the United States and subsequently developed three predictive models: logistic regression, a linear support vector machine (SVM) model with hinge function, and a perceptron neural network. Data sources include Definitive Healthcare and Becker’s Hospital Review 2019 report with 3121 observations of 32 variables with 27 observed bankruptcies. The three models consistently indicate that 18 variables have a significant impact on predicting hospital bankruptcy. Currently, there is limited literature concerning financial forecasting models and knowledge detailing the factors associated with hospital bankruptcy. By having tailored knowledge of predictive factors to establish a sound financial structure, healthcare institutions at large can be empowered to take proactive steps to avoid financial distress at the organizational level and ensure long-term financial viability