We sought to summarize and assess original evaluations of the economic impact of clinical pharmacy services published from 1996–2000, and to provide recommendations and methodologic considerations for future research. A systematic literature search was conducted to identify articles that were then blinded and randomly assigned to reviewers who confirmed inclusion and abstracted key information. Results were compared with those of a similar review of literature published from 1988–1995. In the 59 included articles, the studies were conducted across a variety of practice sites that consisted of hospitals (52%), community pharmacies and clinics (41%), health maintenance organizations (3%), and long‐term or intermediate care facilities (3%). They focused on a broad range of clinical pharmacy services such as general pharmacotherapeutic monitoring (47%), target drug programs (20%), disease management programs (10%), and patient education or cognitive services (10%). Compared with the studies of the previous review, a greater proportion of evaluations were conducted in community pharmacies or clinics, and the types of services evaluated tended to be more comprehensive rather than specialized. Articles were categorized by type of evaluation: 36% were considered outcome analyses, 24% full economic analyses, 17% outcome descriptions, 15% cost and outcome descriptions, and 8% cost analyses. Compared with the studies of the previous review, a greater proportion of studies in the current review used more rigorous study designs. Most studies reported positive financial benefits of the clinical pharmacy service evaluated. In 16 studies, a benefit:cost ratio was reported by the authors or was able to be calculated by the reviewers (these ranged from 1.7:1–17.0:1, median 4.68:1). The body of literature from this 5‐year period provides continued evidence of the economic benefit of clinical pharmacy services. Although the quality of study design has improved, whenever possible, future evaluations of this type should incorporate methodologies that will further enhance the strength of evidence of this literature and the conclusions that may be drawn from it.

OBJECTIVE -The objective of this study was to develop a simple tool for the U.S. population to calculate the probability that an individual has either undiagnosed diabetes or prediabetes.RESEARCH DESIGN AND METHODS -We used data from the Third National Health and Nutrition Examination Survey (NHANES) and two methods (logistic regression and classification tree analysis) to build two models. We selected the classification tree model on the basis of its equivalent accuracy but greater ease of use.RESULTS -The resulting tool, called the Diabetes Risk Calculator, includes questions on age, waist circumference, gestational diabetes, height, race/ethnicity, hypertension, family history, and exercise. Each terminal node specifies an individual's probability of pre-diabetes or of undiagnosed diabetes. Terminal nodes can also be used categorically to designate an individual as having a high risk for 1) undiagnosed diabetes or pre-diabetes, 2) pre-diabetes, or 3) neither undiagnosed diabetes or pre-diabetes. With these classifications, the sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic area for detecting undiagnosed diabetes are 88%, 75%, 14%, 99.3%, and 0.85, respectively. For pre-diabetes or undiagnosed diabetes, the results are 75%, 65%, 49%, 85%, and 0.75, respectively. We validated the tool using v-fold cross-validation and performed an independent validation against NHANES 1999 -2004 data.

The DSC-R demonstrated excellent psychometric properties when tested in a large-scale diabetes clinical trial. Responsiveness and test-retest reliability of the DSC-R warrant further evaluation.

We investigate how the scale of estimation in risk-adjustment models for health-care costs affects the covariate effect, where the scale of interest for the covariate effect may be different from the scale of estimation. As an illustrative example, we use claims data to estimate the incremental costs associated with heart failure within one year subsequent to myocardial infarction. Here, the scale of interest for the effect of heart failure on costs is additive. However, traditional methods for modeling costs use predetermined scale of estimation - for example, ordinary least squares (OLS) regression assumes an additive scale while log-transformed OLS and generalized linear models with log-link assume a multiplicative scale of estimation. We compare these models with a new flexible model that lets the data determine the appropriate scale of estimation. We use a variety of goodness-of-fit measures along with a modified Copas test to assess robustness, lack of fit, and over-fitting properties of the alternative estimators. Biases up to 19% in the scale of interest are observed due to the misrepresentation of the scale of estimation. The new flexible model is found to appropriately represent the scale of estimation and less susceptible to over-fitting despite estimating additional parameters in the link and the variance functions.