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Objectives To examine the association between CNS medication burden and serious falls in those with a recent fall history. Design Nested-case control study; cases matched to four controls by age, gender, and date. Setting US nursing homes. Participants 5,556 residents age ≥65 with a recent fall history admitted to a nursing home between 1/1-9/30/2010 and followed until discharge, death, or December 31, 2010. Measurements Outcome was serious falls as per Medicare Part A and B ICD/ CPT codes. CNS burden, from Medicare Part D data, was calculated by dividing the daily dose of each CNS agent (i.e., specific antidepressants, antiepileptic, antipsychotic, benzodiazepine and opioid receptor agonists) received during the 6 days prior to the index (outcome) date by the minimum effective geriatric daily dose and summing the results across medications. Results There were 367 cases and 1,468 matched controls. Those taking 3 + CNS standardized daily doses were more likely to have a serious fall than those not taking any CNS medications (Adjusted Odds Ratio 1.83; 95% confidence interval 1.35-2.48). There was no significant difference in fall risk for residents taking >0 to <3 CNS standardized daily doses compared to residents taking no CNS medications (Adjusted Odds Ratio 0.85; 95% CI 0.63-1.15). Conclusion CNS medication burden, approximately 3 + standardized daily doses, was associated with an increased risk of serious falls in nursing home residents with recent fall. Clinicians should be vigilant for opportunities to discontinue or decrease the doses of individual CNS medications and/or consider non-pharmacological alternatives. Such interventions that reduce use of CNS medications in nursing homes could reduce fall rates but further research is needed to confirm this. [ABSTRACT FROM AUTHOR]

Background: Numerous interventions are available to boost medication adherence, but the targeting of these interventions often relies on crude measures of poor adherence. Group-based trajectory models identify individuals with similar longitudinal prescription filling patterns. Identifying distinct adherence trajectories may be more useful for targeting interventions, although the association between adherence trajectories and clinical outcomes is unknown.Objective: To examine the association between adherence trajectories for oral hypoglycemics and subsequent hospitalizations among diabetes patients.Design: Retrospective cohort study.Patients: A total of 16,256 Pennsylvania Medicaid enrollees, non-dually eligible for Medicare, initiating oral hypoglycemics between 2007 and 2009.Main Measures: We used group-based trajectory models to identify trajectories of oral hypoglycemics in the 12 months post-treatment initiation, using monthly proportion of days covered (PDC) as the adherence measure. Multivariable Cox proportional hazard models were used to examine the association between trajectories and time to first diabetes-related hospitalization/emergency department (ED) visits in the following year. We used the C-index to compare prediction performance between adherence trajectories and dichotomous cutpoints (annual PDC <80 vs. ≥80 %).Results: The mean annual PDC was 0.58 (SD 0.32). Seven trajectories were identified: perfect adherers (9 % of the cohort), nearly perfect adherers (31.4 %), moderate adherers (21.0 %), low adherers (11.0 %), late discontinuers (6.8 %), early discontinuers (9.7 %), and non-adherers with only one fill (11.1 %). Compared to perfect adherers, trajectories of moderate adherers (HR = 1.48, 95 % CI 1.25, 1.75), low adherers (HR = 1.51, 95 % CI 1.25, 1.83), and non-adherers with only one fill (HR = 1.35, 95 % CI 1.09, 1.67) had greater risk of diabetes-related hospitalizations/ED visits. Predictive accuracy was improved using trajectories compared to dichotomized cutpoints (C-index = 0.714 vs. 0.652).Conclusions: Oral hypoglycemic treatment trajectories were highly variable in this large Medicaid cohort. Low and moderate adherers and those filling only one prescription had a modestly higher risk of hospitalizations/ED visits compared to perfect adherers. Trajectory models may be valuable in identifying specific non-adherence patterns for targeting interventions. [ABSTRACT FROM AUTHOR]