Central Nervous System-Active Prescriptions in Older Veterans: Trends in Prevalence, Prescribers, and High-risk Populations.

BACKGROUND: Little is known about the prevalence or chronicity of prescriptions of central nervous system-active (CNS-active) medications in older Veterans. OBJECTIVE: We sought to describe (1) the prevalence and trends in prescription of CNS-active medications in older Veterans over time; (2) variation in prescriptions across high-risk groups; and (3) where the prescription originated (VA or Medicare Part D). DESIGN: Retrospective cohort study from 2015 to 2019. PARTICIPANTS: Veterans age ≥ 65 enrolled in the Medicare and the VA residing in Veterans Integrated Service Network 4 (incorporating Pennsylvania and parts of surrounding states). MAIN MEASURES: Drug classes included antipsychotics, gabapentinoids, muscle relaxants, opioids, sedative-hypnotics, and anticholinergics. We described prescribing patterns overall and in three subgroups: Veterans with a diagnosis of dementia, Veterans with high predicted utilization, and frail Veterans. We calculated both prevalence (any fill) and percent of days covered (chronicity) for each drug class, and CNS-active polypharmacy (≥ 2 CNS-active medications) rates in each year in these groups. KEY RESULTS: The sample included 460,142 Veterans and 1,862,544 person-years. While opioid and sedative-hypnotic prevalence decreased, gabapentinoids exhibited the largest increase in both prevalence and percent of days covered. Each subgroup exhibited different patterns of prescribing, but all had double the rates of CNS-active polypharmacy compared to the overall study population. Opioid and sedative-hypnotic prevalence was higher in Medicare Part D prescriptions, but the percent of days covered of nearly all drug classes was higher in VA prescriptions. CONCLUSIONS: The concurrent increase of gabapentinoid prescribing paralleling a decrease in opioid and sedative-hypnotics is a new phenomenon that merits further evaluation of patient safety outcomes. In addition, we found substantial potential opportunities for deprescribing CNS-active medications in high-risk groups. Finally, the increased chronicity of VA prescriptions versus Medicare Part D is novel and should be further evaluated in terms of its mechanism and impact on Medicare-VA dual users.

Evaluating the relationship between facility Age-Friendly recognition and subsequent facility-free days in older Veterans.

BACKGROUND: Thousands of health systems have been recognized as "Age-Friendly" for implementing geriatric care practices aligned with the "4Ms" (What Matters, Medication, Mentation, and Mobility). However, the effect of Age-Friendly recognition on patient outcomes is largely unknown. We sought to identify this effect in the Veterans Health Administration (VHA)-one of the largest Age-Friendly integrated health systems in the United States. METHODS: There were 50 VA medical centers (VAMCs) recognized as Age-Friendly by December 2021. We used a time-event difference-in-difference analysis to identify the association of a VAMC's recognition as Age-Friendly on the change in facility-free days (days outside the hospital or nursing home) among Veterans treated at that facility. We also evaluated this association in three subgroups: Veterans at particularly high risk of nursing home entry, Veterans who lived within 10 miles of a medical center, and facilities that had reached Level 2 Age-Friendly recognition. We also evaluated individual components of the endpoint in terms of change in hospital and nursing home days separately. RESULTS: We found Age-Friendly recognition was associated with small statistically significant improvements in facility-free days (0.2% on a base of 97% facility-free days on average per year, or an additional 0.73 days per year on a base of 354 days). There were no differences in any subgroup, or any individual component of the endpoint across all groups. CONCLUSIONS: At the individual level, an increase of 0.2% in facility-free days is a weak effect. However, sites were early in implementation, and facility-free days may not be a responsive outcome measure. However, across an entire population, small changes in facility-free days may accrue large cost savings. Future evaluations should consider a broader variety of process and outcome measures.

Modeling Patient No-Show History and Predicting Future Appointment Behavior at the Veterans Administration's Outpatient Mental Health Clinics: NIRMO-2.

INTRODUCTION: No-shows are detrimental to both patients' health and health care systems. Literature documents no-show rates ranging from 10% in primary care clinics to over 60% in mental health clinics. Our model predicts the probability that a mental health clinic outpatient appointment will not be completed and identifies actionable variables associated with lowering the probability of no-show. MATERIALS AND METHODS: We were granted access to de-identified administrative data from the Veterans Administration Corporate Data Warehouse related to appointments at 13 Veterans Administration Medical Centers. Our modeling data set included 1,206,271 unique appointment records scheduled to occur between January 1, 2013 and February 28, 2017. The training set included 846,668 appointment records scheduled between January 1, 2013 and December 31, 2015. The testing set included 359,603 appointment records scheduled between January 1, 2016 and February 28, 2017. The dependent binary variable was whether the appointment was completed or not. Independent variables were categorized into seven clusters: patient's demographics, appointment characteristics, patient's attendance history, alcohol use screening score, medications and medication possession ratios, prior diagnoses, and past utilization of Veterans Health Administration services. We used a forward stepwise selection, based on the likelihood ratio, to choose the variables in the model. The predictive model was built using the SAS HPLOGISTIC procedure. RESULTS: The best indicator of whether someone will miss an appointment is their historical attendance behavior. The top three variables associated with higher probabilities of a no-show were: the no-show rate over the previous 2 years before the current appointment, the no-show probability derived from the Markov model, and the age of the appointment. The top three variables that decrease the chance of no-showing were: the appointment was a new consult, the appointment was an overbook, and the patient had multiple appointments on the same day. The average of the areas under the receiver operating characteristic curves was 0.7577 for the training dataset, and 0.7513 for the test set. CONCLUSIONS: The National Initiative to Reduce Missed Opportunities-2 confirmed findings that previous patient attendance is one of the key predictors of a future attendance and provides an additional layer of complexity for analyzing the effect of a patient's past behavior on future attendance. The National Initiative to Reduce Missed Opportunities-2 establishes that appointment attendance is related to medication adherence, particularly for medications used for treatment of mood disorders or to block the effects of opioids. However, there is no way to confirm whether a patient is actually taking medications as prescribed. Thus, a low medication possession ratio is an informative, albeit not a perfect, measure. It is our intention to further explore how diagnosis and medications can be better captured and used in predictive modeling of no-shows. Our findings on the effects of different factors on no-show rates can be used to predict individual no-show probabilities, and to identify patients who are high risk for missing appointments. The ability to predict a patient's risk of missing an appointment would allow for both advanced interventions to decrease no-shows and for more efficient scheduling.