Effects of Evidence-Based Fall Reduction Programing on the Functional Wellness of Older Adults in a Senior Living Community: A Clinical Case Study.

BACKGROUND: Older adults at a high risk of falls may be referred to a physical therapist. A physical therapy episode of care is designed for the transition of an older adult from a high fall risk to a moderate to low fall risk. However, these episodes of care are limited in time and duration. There is compelling evidence for the efficacy of group-based exercise classes to address risk, and transitioning an older adult from physical therapy to a group-based program may be an effective way to manage risk through the continuum of care. OBJECTIVES: The purpose of this study was to translate research findings into a real world setting, and demonstrate the efficacy of integrating evidence-based fall prevention exercises into pre-existing exercise classes at a senior living facility as a proof of concept model for future programing. METHODS: Twenty-four participants aged 65 years and older living in a senior living community and the community were stratified into group-based exercise classes. Cutoff scores from functional outcome measures were used to stratify participants. Exercises from The Otago Exercise Program were implemented into the classes. Functional outcome measures collected included the 10-Meter Walk Test, 30-Second Sit to Stand, and Timed Up and Go (TUG). Number of falls, hospitalizations, and physical therapy episodes of care were also tracked. Data were compared to a control group in a different senior living community that offered classes with similar exercises aimed at improving strength and mobility. The classes were taught by an exercise physiologist and were of equal duration and frequency. RESULTS: Participants demonstrated significant improvements in all functional outcome measures. TUG mean improved from 13.5 to 10.4 s (p = 0.034). The 30-Second Sit to Stand mean improved from 10.5 to 13.4 (p = 0.002). The 10-Meter Walk Test improved from 0.81 to 0.98 m/s (p \u3c 0.0001). Participants did not experience any falls or hospitalizations, and two participants required physical therapy episodes of care. CONCLUSION: Implementing an evidence-based fall reduction program into a senior living program has a positive effect on strength, balance, fall risk, gait speed, fall rate, hospitalizations, and amount of physical therapy intervention

Social Implications of Teenage Parenthood

Contrary to popular impression, the absolute level of teenage childbearing in the United States has not risen during the past decade, but has actually declined. Moreover, the newly discovered epidemic of adolescent pregnancy is not recent; elevated levels of teenage childbearing can be traced to the beginning of the baby boom after the Second World War. Nevertheless, the issue does seem more pressing now than ever before. In this chapter we shall touch on some of the reasons for this issue\u27s prominence. We shall look at the evidence in the literature on the social consequences of teenage childbearing for adolescent parents, their offspring, and members of their family of origin. After assessing this evidence, we shall briefly mention some of the policy initiatives open for us for preventing premature childbearing and for ameliorating its deleterious effects when it does occur

Text Messaging in the Patient-Centered Medical Home to Improve Glucose Control and Retinopathy Screening.

Purpose: To evaluate the effectiveness of a text messaging program (TMP) to improve glucose control, retinopathy screening (RS) rates, and self-care behaviors in patients with uncontrolled type 2 diabetes. Methods: A single-group design with a quasi-systematic random sample (n=20) received educational/exhortational text messages on their cellular phones for 3 months. Subjects, 12 of whom identified as a minority ethnicity, were mostly male, aged 27-73 years. Results: Glucose control and RS rates improved significantly. Subjects (\u3e70%) reported changes in self-care behaviors. Conclusion: Leveraging ubiquitous technology, a TMP for patients with limited access to healthcare education, holds promis

Comparative effectiveness of total population versus disease-specific neural network models in predicting medical costs

The objective of this research was to compare the accuracy of two types of neural networks in identifying individuals at risk for high medical costs for three chronic conditions. Two neural network models—a population model and three disease-specific models—were compared regarding effectiveness predicting high costs. Subjects included 33,908 health plan members with diabetes, 19,264 with asthma, and 2,605 with cardiac conditions. For model development/testing, only members with 24 months of continuous enrollment were included. Models were developed to predict probability of high costs in 2000 (top 15% of distribution) based on 1999 claims factors. After validation, models were applied to 2000 claims factors to predict probability of high 2001 costs. Each member received two scores—population model score applied to cohort and disease model score. Receiver Operating Characteristic (ROC) curves compared sensitivity, specificity, and total performance of population model and three disease models. Diabetes-specific model accuracy, C = 0.786 (95%CI = 0.779–0.794), was greater than that of population model applied to diabetic cohort, C = 0.767 (0.759–0.775). Asthma-specific model accuracy, C = 0.835 (0.825–0.844), was no different from that of population model applied to asthma cohort, C = 0.844 (0.835–0.853). Cardiac-specific model accuracy, C = 0.651 (0.620–0.683), was lower than that of population model applied to cardiac cohort, C = 0.726 (0.697–0.756). The population model predictive power, compared to the disease model predictive power, varied by disease; in general, the larger the cohort, the greater the advantage in predictive power of the disease model compared to the population model. Given these findings, disease management program staff should test multiple approaches before implementing predictive models. (Disease Management 2005;8:277–287

Predicting high utilization of emergency department services among patients with a diagnosis of psychosis in a Medicaid managed care organization

Studies have demonstrated increased utilization of medical services for patients with behavioral health diagnoses. Medicaid managed care organizations (MMCOs) that operate under behavioral health carve-outs face the challenge of effectively targeting disease management initiatives in the absence of information on behavioral diagnoses. This study sought to develop a predictive model of emergency department (ED) utilization for patients where a diagnosis of psychosis could be identified from a claim associated with a medical service provider visit. A retrospective cohort analysis was performed using medical and pharmacy claims from an MMCO in Philadelphia, Pennsylvania, to identify patients known to have a diagnosis of psychosis and to develop the predictive model. Demographics, comorbidities, medical utilization, and medications were assessed as predictor variables. Within the MMCO, 764 members were identified with at least one medical claim having a psychosis diagnosis. Ordinary least squares multiple regression analysis was performed to measure the correlation between independent variables and ED visits. Variables with significant F ratios in the regression analysis were retained as factors in a risk model to evaluate their additive and cumulative effects. Four variables were significant predictors of high ED utilization: prior number of ED visits, prior number of hospitalizations, history of alcohol abuse, and history of depression. ED utilization increased as the number of risk factors increased: With no risk factors, mean ED use was 0.58 visits (per 6 months), while the cumulative effects of all four factors equated to 8.5 ED visits. The model may be useful to other MMCOs, or similar organizations, seeking to risk-stratify their ED-related disease management activities for patients identified with psychosis

Transport, Growth Mechanisms, and Material Quality in GaN Epitaxial Lateral Overgrowth

Growth kinetics, mechanisms, and material quality in GaN epitaxial lateral over-growth (ELO) were examined using a single mask of systematically varied patterns. A 2-D gas phase reaction/diffusion model describes how transport of the Ga precursor to the growth surface enhances the lateral rate in the early stages of growth. In agreement with SEM studies of truncated growth runs, the model also predicts the dramatic decrease in the lateral rate that occurs as GaN over-growth reduces the exposed area of the mask. At the point of convergence, a step-flow coalescence mechanism is observed to fill in the area between lateral growth-fronts. This alternative growth mode in which a secondary growth of GaN is nucleated along a single convergence line, may be responsible for producing smooth films observed to have uniform cathodoluminescence (CL) when using 1{micro}m nucleation zones. Although emission is comprised of both UV ({approximately}365nm) and yellow ({approximately}550nm) components, the spectra suggest these films have reduced concentrations of threading dislocations normally associated with non-radiative recombination centers and defects known to accompany growth-front convergence lines

Avalanches in the lung: A statistical mechanical model

We study a statistical mechanical model for the dynamics of lung inflation which incorporates recent experimental observations on the opening of individual airways by a cascade or avalanche mechanism. Using an exact mapping of the avalanche problem onto percolation on a Cayley tree, we analytically derive the exponents describing the size distribution of the first avalanches and test the analytical solution by numerical simulations. We find that the tree-like structure of the airways together with the simplest assumptions concerning opening threshold pressures of each airway, is sufficient to explain the existence of power-law distributions observed experimentally.Comment: 4 pages, Figures avaliable by mail from [email protected], REVTE

Prevalence of obesity, type II diabetes mellitus, hyperlipidemia, and hypertension in the United States: findings from the GE Centricity Electronic Medical Record database.

This study analyzed GE Centricity Electronic Medical Record (EMR) data to examine the effects of body mass index (BMI) and obesity, key risk factor components of metabolic syndrome, on the prevalence of 3 chronic diseases: type II diabetes mellitus, hyperlipidemia, and hypertension. These chronic diseases occur with high prevalence and impose high disease burdens. The rationale for using Centricity EMR data is 2-fold. First, EMRs may be a good source of BMI/obesity data, which are often underreported in surveys and administrative databases. Second, EMRs provide an ideal means to track variables over time and, thus, allow longitudinal analyses of relationships between risk factors and disease prevalence and progression. Analysis of Centricity EMR data showed associations of age, sex, race/ethnicity, and BMI with diagnosed prevalence of the 3 conditions. Results include uniform direct correlations between age and BMI and prevalence of each disease; uniformly greater disease prevalence for males than females; varying differences by race/ethnicity (ie, African Americans have the highest prevalence of diagnosed type II diabetes and hypertension, while whites have the highest prevalence of diagnosed hypertension); and adverse effects of comorbidities. The direct associations between BMI and disease prevalence are consistent for males and females and across all racial/ethnic groups. The results reported herein contribute to the growing literature about the adverse effects of obesity on chronic disease prevalence and about the potential value of EMR data to elucidate trends in disease prevalence and facilitate longitudinal analyses