Reliability, Validity, and Interpretation of the Dependence Scale in Mild to Moderately Severe Alzheimer's Disease

INTRODUCTION: The Dependence Scale (DS) was designed to measure dependence on others among patients with Alzheimer's disease (AD). The objectives of this research were primarily to strengthen the psychometric evidence for the use of the DS in AD studies. METHODS: Patients with mild to moderately severe AD were examined in 3 study databases. Within each data set, internal consistency, validity, and responsiveness were examined, and structural equation models were fit. RESULTS: The DS has strong psychometric properties. The DS scores differed significantly across known groups and demonstrated moderate to strong correlations with measures hypothesized to be related to dependence (|r| >/= .31). Structural equation modeling supported the validity of the DS concept. An anchor-based DS responder definition to interpret a treatment benefit over time was identified. DISCUSSION: The DS is a reliable, valid, and interpretable measure of dependence associated with AD and is shown to be related to--but provides information distinct from--cognition, functioning, and behavior

Accounting for the relationship between per diem cost and LOS when estimating hospitalization costs

Abstract Background Hospitalization costs in clinical trials are typically derived by multiplying the length of stay (LOS) by an average per-diem (PD) cost from external sources. This assumes that PD costs are independent of LOS. Resource utilization in early days of the stay is usually more intense, however, and thus, the PD cost for a short hospitalization may be higher than for longer stays. The shape of this relationship is unlikely to be linear, as PD costs would be expected to gradually plateau. This paper describes how to model the relationship between PD cost and LOS using flexible statistical modelling techniques. Methods An example based on a clinical study of clevidipine for the treatment of peri-operative hypertension during hospitalizations for cardiac surgery is used to illustrate how inferences about cost-savings associated with good blood pressure (BP) control during the stay can be affected by the approach used to derive hospitalization costs. Data on the cost and LOS of hospitalizations for coronary artery bypass grafting (CABG) from the Massachusetts Acute Hospital Case Mix Database (the MA Case Mix Database) were analyzed to link LOS to PD cost, factoring in complications that may have occurred during the hospitalization or post-discharge. The shape of the relationship between LOS and PD costs in the MA Case Mix was explored graphically in a regression framework. A series of statistical models including those based on simple logarithmic transformation of LOS to more flexible models using LOcally wEighted Scatterplot Smoothing (LOESS) techniques were considered. A final model was selected, using simplicity and parsimony as guiding principles in addition traditional fit statistics (like Akaike’s Information Criterion, or AIC). This mapping was applied in ECLIPSE to predict an LOS-specific PD cost, and then a total cost of hospitalization. These were then compared for patients who had good vs. poor peri-operative blood-pressure control. Results The MA Case Mix dataset included data from over 10,000 patients. Visual inspection of PD vs. LOS revealed a non-linear relationship. A logarithmic model and a series of LOESS and piecewise-linear models with varying connection points were tested. The logarithmic model was ultimately favoured for its fit and simplicity. Using this mapping in the ECLIPSE trials, we found that good peri-operative BP control was associated with a cost savings of $5,366 when costs were derived using the mapping, compared with savings of$7,666 obtained using the traditional approach of calculating the cost. Conclusions PD costs vary systematically with LOS, with short stays being associated with high PD costs that drop gradually and level off. The shape of the relationship may differ in other settings. It is important to assess this and model the observed pattern, as this may have an impact on conclusions based on derived hospitalization costs.</p

Impact of Cost Display on Ordering Patterns for Hospital Laboratory and Imaging Services

BackgroundPhysicians "purchase" many health care services on behalf of patients yet remain largely unaware of the costs of these services. Electronic health record (EHR) cost displays may facilitate cost-conscious ordering of health services.ObjectiveTo determine whether displaying hospital lab and imaging order costs is associated with changes in the number and costs of orders placed.DesignQuasi-experimental study.ParticipantsAll patients with inpatient or observation encounters across a multi-site health system from April 2013 to October 2015.InterventionDisplay of order costs, based on Medicare fee schedules, in the EHR for 1032 lab tests and 1329 imaging tests.Main measuresOutcomes for both lab and imaging orders were (1) whether an order was placed during a hospital encounter, (2) whether an order was placed on a given patient-day, (3) number of orders placed per patient-day, and (4) cost of orders placed per patient-day.Key resultsDuring the lab and imaging study periods, there were 248,214 and 258,267 encounters, respectively. Cost display implementation was associated with a decreased odds of any lab or imaging being ordered during the encounter (lab adjusted odds ratio [AOR] = 0.97, p&nbsp;= .01; imaging AOR = 0.97, p&nbsp;&lt; .001), a decreased odds of any lab or imaging being ordered on a given patient-day (lab AOR = 0.95, p&nbsp;&lt; .001; imaging AOR = 0.97, p&nbsp;&lt; .001), a decreased number of lab or imaging orders on patient-days with orders (lab adjusted count ratio = 0.93, p&nbsp;&lt; .001; imaging adjusted count ratio = 0.98, p&nbsp;&lt; .001), and a decreased cost of lab orders and increased cost of imaging orders on patient-days with orders (lab adjusted cost ratio = 0.93, p&nbsp;&lt; .001; imaging adjusted cost ratio = 1.02, p&nbsp;= .003). Overall, the intervention was associated with an 8.5 and 1.7% reduction in lab and imaging costs per patient-day, respectively.ConclusionsDisplaying costs within EHR ordering screens was associated with decreases in the number and costs of lab and imaging orders