Patients with heart conditions in rural areas may have different responses to health promotion-disease Self-management interventions compared to their urban counterparts.
To estimate the impact of a multi-component health promotion nurse intervention on physical function and total health care expenditures among elderly adults with heart conditions and to examine the impact of rural residence on the intervention effect.
We analyzed data on 281 community-living Medicare beneficiaries with heart conditions from the Medicare Primary and Consumer-Directed Care Demonstration (a randomized controlled trial). We estimated ordinary least squares (OLS) models to determine the effect of the intervention on the change in functional status and log-linear models to determine the impact of the intervention on total health care expenditures over a 2-year period.
The OLS models showed that the nurse intervention resulted in fewer impairments in Activities of Daily Living (ADL) (-0.307 on 0-6 scale, P = .055) at the end of 2 years. The effect of the intervention on ADL appeared to be stronger for rural than for urban participants (-0.490 vs -0.162, respectively). However, the difference was not statistically significant (P = .150). The effect of the intervention on Instrumental Activities of Daily Living (IADL) was not significant (P = .321). Average total health care expenditures were 6.5% ($1,981, 95% CI: -$8,048, $4,087) lower in the nurse group.
The nurse intervention led to better physical functioning and has potential to reduce total health care expenditures among high-risk Medicare beneficiaries with heart conditions.
"Work-based weight loss programs for nurses, such as Weight Watchers®, are common in practice, but results are not found in the literature. Much more has been written about nurses' delivering wellness programs (Casey, 2007; Kaewthummanukul & Brown, 2006; Kelley & Abraham, 2007; McKey & Huntington, 2004; Meng, Wamsley, Eggert, & Van Nostrand, 2007; Nauta, Byrne, & Wesley, 2009; Wood et al., 2008) than about their participating in these programs. "
[Show abstract][Hide abstract] ABSTRACT: This study examined perceptions of general and emotional health among a statewide sample of nurses, and their assessment of employers' workplace health and safety initiatives. These variables and demographic data were then used to model predictors of intention to leave their work positions. A survey was mailed to all registered nurses in one state. Fifty-three percent responded (n = 3,955). Findings suggested marked differences in perception of emotional health by age, with younger nurses reporting less positive perceptions of their emotional health. Perceptions of employers' safety and health initiatives varied by age, setting, and work role. Predictors of intention to leave included lower perceived emotional health among younger nurses and employer safety initiatives for both age groups. This exploratory study suggests a relationship among employer health and safety practices, nurses' emotional health, and intention to leave. Implications for occupational health nurses are detailed.
[Show abstract][Hide abstract] ABSTRACT: Background: Cost data in healthcare are often skewed across patients. Thus, researchers have used either a log transformation of the dependent variable or generalized linear models with log links. However, frequently these nonlinear approaches produce non-linear incremental effects: the incremental effects differ at different levels of the covariates, and this can cause dramatic effects on predicted cost.
Objectives: The aim of this study was to demonstrate that when modelling skewed data, log link functions or log transformations are not necessary and have unintended effects.
Methods: We simulated cost data using a linear model with a ‘treatment’, a covariate and a specified number of observations with excessive cost (skewed data). We also used actual data from a pain-relief intervention among hip-replacement patients. We then estimated cost models using various functional approaches suggested to handle skew and calculated the incremental cost of treatment at various levels of the covariate(s).
Results: All of these methods provide unbiased estimates of the incremental effect of treatment on costs at the mean level of the covariate. However, in some log-based models the implied incremental treatment cost doubled between extreme low and high values of the covariate in a manner inconsistent with the underlying linear model.
Conclusions: Although specification checks are always needed, the potential for misleading incremental estimates resulting from log-based specifications is often ignored. In this era of cost containment and comparisons of treatment effectiveness it is vital that researchers and policymakers understand the limitation of the inferences that can be made using log-based models for patients whose characteristics differ from the sample mean.
Applied Health Economics and Health Policy 09/2012; 10(5). DOI:10.1007/BF03261866
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