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Background Few studies have compared the risk of recurrent falls across various antidepressant agents—using detailed dosage and duration data—among community-dwelling older adults, including those who have a history of a fall/fracture. Objective To examine the association of antidepressant use with recurrent falls, including among those with a history of falls/fractures, in community-dwelling elders. Methods This was a longitudinal analysis of 2948 participants with data collected via interview at year 1 from the Health, Aging and Body Composition study and followed through year 7 (1997-2004). Any antidepressant medication use was self-reported at years 1, 2, 3, 5, and 6 and further categorized as (1) selective serotonin reuptake inhibitors (SSRIs), (2) tricyclic antidepressants, and (3) others. Dosage and duration were examined. The outcome was recurrent falls (≥2) in the ensuing 12-month period following each medication data collection. Results Using multivariable generalized estimating equations models, we observed a 48% greater likelihood of recurrent falls in antidepressant users compared with nonusers (adjusted odds ratio [AOR] = 1.48; 95% CI = 1.12-1.96). Increased likelihood was also found among those taking SSRIs (AOR = 1.62; 95% CI = 1.15-2.28), with short duration of use (AOR = 1.47; 95% CI = 1.04-2.00), and taking moderate dosages (AOR = 1.59; 95% CI = 1.15-2.18), all compared with no antidepressant use. Stratified analysis revealed an increased likelihood among users with a baseline history of falls/fractures compared with nonusers (AOR = 1.83; 95% CI = 1.28-2.63). Conclusion Antidepressant use overall, SSRI use, short duration of use, and moderate dosage were associated with recurrent falls. Those with a history of falls/fractures also had an increased likelihood of recurrent falls.

BACKGROUND/OBJECTIVES Clinical practice guidelines support using acetylcholinesterase inhibitors (AChEIs) and memantine to treat dementia, but conflicting evidence of effectiveness and frequent side effects limit use in practice. We examined racial/ethnic differences in initiation and time to discontinuation of antidementia medication in Medicare beneficiaries. DESIGN Retrospective cohort study. SETTING Secondary analysis of 2009/2010 enrollment, claims, and Part D prescription data for a 10% national sample of U.S. Medicare fee-for-service enrollees. PARTICIPANTS Beneficiaries aged 65+ with Alzheimer's Disease or Related Disorder (ADRD) prior to 2009 and no fills for antidementia medications in the first half of 2009 (n=84,043). MEASUREMENTS Initiation was defined as having ≥1 fill for antidementia medication in the second half of 2009, and discontinuation as a gap in coverage of ≥30 days during one year after initiation. Covariate selection was guided by the Andersen Behavioral Model. RESULTS Overall, 3,481 (4.1%) of previous non-users initiated antidementia medication in the second half of 2009. Of those initiating one drug class (AChEIs or memantine), 9% later added the other class and 2% switched classes. Among initiators, 23% discontinued within one month and 62% discontinued within one year. Hispanic beneficiaries were more likely than White beneficiaries to initiate (adjusted odds ratio [OR]=1.25, 95% CI=1.10-1.41). Black and White beneficiaries did not differ in likelihood of initiation. Hispanic and Black beneficiaries discontinued at a faster rate than White beneficiaries (adjusted hazard ratio [HR]=1.56, 95% CI=1.34-1.82 and HR=1.25, 95% CI=1.08-1.44, respectively). CONCLUSION Relative to White beneficiaries, initiation of antidementia medications was no different in Black beneficiaries and more likely in Hispanic beneficiaries. However, Black and Hispanic beneficiaries discontinued at a faster rate. More research into reasons explaining these differences is needed.

Process Monitoring involves tracking a system's behaviors, evaluating the current state of the system, and discovering interesting events that require immediate actions. In this paper, we consider monitoring temporal system state sequences to help detect the changes of dynamic systems, check the divergence of the system development, and evaluate the significance of the deviation. We begin with discussions of data reduction, symbolic data representation, and the anomaly detection in temporal discrete sequences. Time-series representation methods are also discussed and used in this paper to discretize raw data into sequences of system states. Markov Chains and stationary state distributions are continuously generated from temporal sequences to represent snapshots of the system dynamics in different time frames. We use generalized Jensen-Shannon Divergence as the measure to monitor changes of the stationary symbol probability distributions and evaluate the significance of system deviations. We prove that the proposed approach is able to detect deviations of the systems we monitor and assess the deviation significance in probabilistic manner.

The average Medicare beneficiary with SV incurs about double the annual healthcare expenditures compared to their non-SV counterparts, attributable to increased utilization of almost all categories of care.