Background Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations. Objective To provide recommendations for systematic evaluation of evidence from the scientific literature, drug product labeling, and regulatory documents with respect to DDIs for clinical decision support. Methods A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 15 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations. Results We developed expert-consensus answers to three key questions: 1) What is the best approach to evaluate DDI evidence?; 2) What evidence is required for a DDI to be applicable to an entire class of drugs?; and 3) How should a structured evaluation process be vetted and validated? Conclusion Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug information systems that implement these recommendations should be able to provide higher quality information about DDIs in drug compendia and clinical decision support tools.

In a representative sample of Medicare beneficiaries, despite low income and health status, veterans with dual Medicare/VHA use were as likely as veterans without dual use to have any ACSH, perhaps due to expanded healthcare access and emphasis on primary care in the VHA system.

We assessed the short-term association between antidepressant drug use and the risk of new-onset diabetes in 2-years of observation. This study used longitudinal data from the Medical Expenditure Panel Survey for years 2004-2007. Chi-square tests and logistic regressions were used to examine the link between use of antidepressants with and without depression, and new-onset diabetes, after controlling for independent variables in blocks. In unadjusted models, the risk of new-onset diabetes was significantly increased for persons using antidepressants with depression compared with those without antidepressant use or depression [odds ratio (OR)=2.12, 95% confidence interval (CI): 1.45-3.09]. When lifestyle risk factors were entered in the model, statistical significance disappeared [adjusted OR (AOR)=1.42, 95% CI: 0.98-2.08]. Independently, lifestyle risk factors significantly increased the risk of new-onset diabetes: hypertension (AOR=2.55, 95% CI: 1.86-3.50, P<0.001), lipid disorders (AOR=1.60, 95% CI: 1.14-2.24), overweight (AOR=2.01, 95% CI: 1.35-2.98), obesity (AOR=3.57, 95% CI: 2.50-5.10), and no physical exercise (AOR=1.98, 95% CI: 1.53-2.57, P<0.001). Future studies on the risk of new-onset diabetes by duration and intensity of antidepressant use and depression are needed.

This study examined the association between index hospitalization characteristics and the risk of all-cause 30-day readmission among high-risk Medicaid beneficiaries using multi-level analyses. A retrospective cohort with a baseline and a follow-up period was used. The study population consisted of Medicaid beneficiaries (21-64 years) with selected chronic conditions, continuous fee-for-service enrollment through the observation period, and at least one inpatient encounter during the follow-up period (N=15,806). The outcome of 30-day readmission was measured using inpatient admissions within 30-days from the discharge date of the first observed hospitalization. Key independent variables included length of stay, reason for admission, and month of index hospitalization (seasonality). Multi-level logistic regression that accounted for beneficiaries nested within counties was used to examine this association, after controlling for patient-level and county-level characteristics. In this study population, 16.7% had all-cause 30-day readmissions. Adults with greater lengths of stay during the index hospitalization were more likely to have 30-day readmissions [AOR=1.03, 95% CI 1.02,1.04]. Adults who were hospitalized for cardiovascular conditions [AOR=1.20, 95% CI 1.08,1.33], diabetes [AOR=1.23, 95% CI 1.10,1.39], cancer [AOR=1.55, 95% CI 1.26,1.90], and mental health conditions [AOR=2.17, 95% CI 1.98,2.38] were more likely to have 30-day readmissions compared to those without these conditions.

Background. Individuals with multimorbidity are vulnerable to poor quality of care due to issues related to care coordination. Ambulatory care sensitive hospitalizations (ACSHs) are widely accepted quality indicators because they can be avoided by timely, appropriate, and high-quality outpatient care. Objective. To examine the association between multimorbidity, mental illness, and ACSH. Study Design. We used a longitudinal panel design with data from multiple years (2000–2005) of Medicare Current Beneficiary Survey. Individuals were categorized into three groups: (1) multimorbidity with mental illness (MM/MI); (2) MM/no MI; (3) no MM. Multivariable logistic regressions were used to analyze the association between multimorbidity and ACSH. Results. Any ACSH rates varied from 10.8% in MM/MI group to 8.8% in MM/No MI group. Likelihood of any ACSH was higher among beneficiaries with MM/MI (AOR = 1.62; 95% CI = 1.14, 2.30) and MM (AOR = 1.54; 95% CI = 1.12, 2.11) compared to beneficiaries without multimorbidity. There was no statistically significant difference in likelihood of ACSH between MM/MI and MM/No MI groups. Conclusion. Multimorbidity (with or without MI) had an independent and significant association with any ACSH. However, presence of mental illness alone was not associated with poor quality of care as measured by ACSH.

To examine the association between changes in BMI categories and health-care expenditures among elderly Medicare beneficiaries using longitudinal data of the Medicare Current Beneficiary Survey (MCBS) 2000–2005. Changes in BMI were (i) Stayed Normal: individuals with a normal BMI at baseline and follow-up; (ii) Stayed Overweight individuals with overweight BMI at baseline and follow-up; (iii) Stayed Obese individuals with obese BMI at baseline and follow-up; (iv) Normal-Overweight: individuals with normal BMI at baseline and overweight BMI at follow-up; (v) Overweight-Obese: individuals with overweight BMI at baseline and obese BMI at follow-up; (vi) Overweight-Normal: individuals with overweight BMI at baseline and normal BMI at follow-up; (vii) Obese-Overweight: individuals with obese BMI at baseline and overweight BMI at follow-up. Ordinary Least Squares (OLS) models on logged Year 3 expenditures were used to analyze changes in expenditures between BMI categories. Overall, 35% Stayed Normal, 34% Stayed Overweight, 18% Stayed Obese, 4% gained weight from Normal-Overweight BMI, 3% gained weight from Overweight-Obese BMI, 5% lost weight from Overweight-Normal BMI, and 3% lost weight from Obese-Overweight BMI. Adjusted models revealed those who Stayed Obese had increased total and multiple expenditure types that were significantly higher than Stayed Normal including total (11%), outpatient (25%), prescription (9%), and medical provider (4%). Compared to Stayed Normal, total expenditures were both 26% higher for Obese-Overweight and Overweight-Obese. The current findings highlight the importance of maintaining a normal BMI in the elderly.

Because less than two thirds of pediatricians and one third of other PCPs would use mL alone in dosing instructions, additional education to encourage prescribing and communicating with patients/caregivers using mL alone may be needed.

Despite the fact that the use of medication histories through e-prescribing networks in the ambulatory care setting has not been encouraged through federal incentive programs, there has been substantial growth in the use of medication histories offered through e-prescribing networks.