Use of a Financial Incentive Code for In-Home COVID-19 Vaccination of Homebound Older Adults.

This study evaluated the uptake of Healthcare Common Procedure Coding System code M0201 after initial implementation to inform future policy related to in-home preventive care.

Which older adults are at highest risk of prescribing cascades? A national study of the gabapentinoid-loop diuretic cascade.

BACKGROUND: Prescribing cascades are important contributors to polypharmacy. Little is known about which older adults are at highest risk of experiencing prescribing cascades. We explored which older veterans are at highest risk of the gabapentinoid (including gabapentin and pregabalin)-loop diuretic (LD) cascade, given the dramatic increase in gabapentinoid prescribing in recent years. METHODS: Using Veterans Affairs and Medicare claims data (2010-2019), we performed a prescription sequence symmetry analysis (PSSA) to assess loop diuretic initiation before and after gabapentinoid initiation among older veterans (≥66 years). To identify the cascade, we calculated the adjusted sequence ratio (aSR), which assesses the temporality of LD relative to gabapentinoid initiation. To explore high-risk groups, we used multivariable logistic regression with prescribing order modeled as a binary dependent variable. We calculated adjusted odds ratios (aORs), measuring the extent to which factors are associated with one prescribing order versus another. RESULTS: Of 151,442 veterans who initiated a gabapentinoid, there were 1,981 patients who initiated a LD within 6 months after initiating a gabapentinoid compared to 1,599 patients who initiated a LD within 6 months before initiating a gabapentinoid. In the gabapentinoid-LD group, the mean age was 73 years, 98% were male, 13% were Black, 5% were Hispanic, and 80% were White. Patients in each group were similar across patient and health utilization factors (standardized mean difference <0.10 for all comparisons). The aSR was 1.23 (95% CI: 1.13, 1.34), strongly suggesting the cascade's presence. People age ≥85 years were less likely to have the cascade (compared to 66-74 years; aOR 0.74, 95% CI: 0.56-0.96), and people taking ≥10 medications were more likely to have the cascade (compared to 0-4 drugs; aOR 1.39, 95% CI: 1.07-1.82). CONCLUSIONS: Among older adults, those who are younger and taking many medications may be at higher risk of the gabapentinoid-LD cascade, contributing to worsening polypharmacy and potential drug-related harms. We did not identify strong predictors of this cascade, suggesting that prescribing cascade prevention efforts should be widespread rather than focused on specific subgroups.

Development and External Validation of Models to Predict Need for Nursing Home Level of Care in Community-Dwelling Older Adults With Dementia.

Importance: Most older adults living with dementia ultimately need nursing home level of care (NHLOC). Objective: To develop models to predict need for NHLOC among older adults with probable dementia using self-report and proxy reports to aid patients and family with planning and care management. Design, Setting, and Participants: This prognostic study included data from 1998 to 2016 from the Health and Retirement Study (development cohort) and from 2011 to 2019 from the National Health and Aging Trends Study (validation cohort). Participants were community-dwelling adults 65 years and older with probable dementia. Data analysis was conducted between January 2022 and October 2023. Exposures: Candidate predictors included demographics, behavioral/health factors, functional measures, and chronic conditions. Main Outcomes and Measures: The primary outcome was need for NHLOC defined as (1) 3 or more activities of daily living (ADL) dependencies, (2) 2 or more ADL dependencies and presence of wandering/need for supervision, or (3) needing help with eating. A Weibull survival model incorporating interval censoring and competing risk of death was used. Imputation-stable variable selection was used to develop 2 models: one using proxy responses and another using self-responses. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (calibration plots). Results: Of 3327 participants with probable dementia in the Health and Retirement Study, the mean (SD) age was 82.4 (7.4) years and 2301 (survey-weighted 70%) were female. At the end of follow-up, 2107 participants (63.3%) were classified as needing NHLOC. Predictors for both final models included age, baseline ADL and instrumental ADL dependencies, and driving status. The proxy model added body mass index and falls history. The self-respondent model added female sex, incontinence, and date recall. Optimism-corrected iAUC after bootstrap internal validation was 0.72 (95% CI, 0.70-0.75) in the proxy model and 0.64 (95% CI, 0.62-0.66) in the self-respondent model. On external validation in the National Health and Aging Trends Study (n = 1712), iAUC in the proxy and self-respondent models was 0.66 (95% CI, 0.61-0.70) and 0.64 (95% CI, 0.62-0.67), respectively. There was excellent calibration across the range of predicted risk. Conclusions and Relevance: This prognostic study showed that relatively simple models using self-report or proxy responses can predict need for NHLOC in community-dwelling older adults with probable dementia with moderate discrimination and excellent calibration. These estimates may help guide discussions with patients and families in future care planning.

Around the EQUATOR with Clin-STAR: Prediction modeling opportunities and challenges in aging research.

The 2015 Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement was published to improve reporting transparency for prediction modeling studies. The objective of this review is to highlight methodologic challenges that aging-focused researchers will encounter when designing and reporting studies involving prediction models for older adults and provide guidance for addressing these challenges. In following the 22-item TRIPOD checklist, researchers must consider the representativeness of cohorts used (e.g., whether older adults with frailty, cognitive impairment, and social isolation were included), strategies for incorporating common geriatric predictors (e.g., age, comorbidities, functional status, and frailty), methods for handling missing data and competing risk of death, and assessment of model performance heterogeneity across important subgroups (e.g., age, sex, race, and ethnicity). We provide guidance to help aging-focused researchers develop, validate, and report models that can inform and improve patient care, which we label "TRIPOD-65."

Frequency of Screening for Colorectal Cancer by Predicted Life Expectancy Among Adults 76-85 Years.

This study assesses whether colorectal cancer screening varied by predicted life expectancy in a national sample of Veterans Affairs patients aged 76 to 85 years.

Medication misuse and overuse in community-dwelling persons with dementia.

BACKGROUND: Persons with dementia (PWD) have high rates of polypharmacy. While previous studies have examined specific types of problematic medication use in PWD, we sought to characterize a broad spectrum of medication misuse and overuse among community-dwelling PWD. METHODS: We included community-dwelling adults aged ≥66 in the Health and Retirement Study from 2008 to 2018 linked to Medicare and classified as having dementia using a validated algorithm. Medication usage was ascertained over the 1-year prior to an HRS interview date. Potentially problematic medications were identified by: (1) medication overuse including over-aggressive treatment of diabetes/hypertension (e.g., insulin/sulfonylurea with hemoglobin A1c < 7.5%) and medications inappropriate near end of life based on STOPPFrail and (2) medication misuse including medications that negatively affect cognition and medications from 2019 Beers and STOPP Version 2 criteria. To contextualize, we compared medication use to people without dementia through a propensity-matched cohort by age, sex, comorbidities, and interview year. We applied survey weights to make our results nationally representative. RESULTS: Among 1441 PWD, median age was 84 (interquartile range = 78-89), 67% female, and 14% Black. Overall, 73% of PWD were prescribed ≥1 potentially problematic medication with a mean of 2.09 per individual in the prior year. This was notable across several domains, including 41% prescribed ≥1 medication that negatively affects cognition. Frequently problematic medications included proton pump inhibitors (PPIs), non-steroidal anti-inflammatory drugs (NSAIDs), opioids, antihypertensives, and antidiabetic agents. Problematic medication use was higher among PWD compared to those without dementia with 73% versus 67% prescribed ≥1 problematic medication (p = 0.002) and mean of 2.09 versus 1.62 (p < 0.001), respectively. CONCLUSION: Community-dwelling PWD frequently receive problematic medications across multiple domains and at higher frequencies compared to those without dementia. Deprescribing efforts for PWD should focus not only on potentially harmful central nervous system-active medications but also on other classes such as PPIs and NSAIDs.

Development and External Validation of a Mortality Prediction Model for Community-Dwelling Older Adults With Dementia.

Importance: Estimating mortality risk in older adults with dementia is important for guiding decisions such as cancer screening, treatment of new and chronic medical conditions, and advance care planning. Objective: To develop and externally validate a mortality prediction model in community-dwelling older adults with dementia. Design, Setting, and Participants: This cohort study included community-dwelling participants (aged ≥65 years) in the Health and Retirement Study (HRS) from 1998 to 2016 (derivation cohort) and National Health and Aging Trends Study (NHATS) from 2011 to 2019 (validation cohort). Exposures: Candidate predictors included demographics, behavioral/health factors, functional measures (eg, activities of daily living [ADL] and instrumental activities of daily living [IADL]), and chronic conditions. Main Outcomes and Measures: The primary outcome was time to all-cause death. We used Cox proportional hazards regression with backward selection and multiple imputation for model development. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (plots of predicted and observed mortality). Results: Of 4267 participants with probable dementia in HRS, the mean (SD) age was 82.2 (7.6) years, 2930 (survey-weighted 69.4%) were female, and 785 (survey-weighted 12.1%) identified as Black. Median (IQR) follow-up time was 3.9 (2.0-6.8) years, and 3466 (81.2%) participants died by end of follow-up. The final model included age, sex, body mass index, smoking status, ADL dependency count, IADL difficulty count, difficulty walking several blocks, participation in vigorous physical activity, and chronic conditions (cancer, heart disease, diabetes, lung disease). The optimism-corrected iAUC after bootstrap internal validation was 0.76 (95% CI, 0.75-0.76) with time-specific AUC of 0.73 (95% CI, 0.70-0.75) at 1 year, 0.75 (95% CI, 0.73-0.77) at 5 years, and 0.84 (95% CI, 0.82-0.85) at 10 years. On external validation in NHATS (n = 2404), AUC was 0.73 (95% CI, 0.70-0.76) at 1 year and 0.74 (95% CI, 0.71-0.76) at 5 years. Calibration plots suggested good calibration across the range of predicted risk from 1 to 10 years. Conclusions and Relevance: We developed and externally validated a mortality prediction model in community-dwelling older adults with dementia that showed good discrimination and calibration. The mortality risk estimates may help guide discussions regarding treatment decisions and advance care planning.

Comparing Machine Learning to Regression Methods for Mortality Prediction Using Veterans Affairs Electronic Health Record Clinical Data.

BACKGROUND: It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods. OBJECTIVE: The objective of this study was to compare machine learning and traditional regression models for 10-year mortality prediction using EHR data. DESIGN: This was a cohort study. SETTING: Veterans Affairs (VA) EHR data. PARTICIPANTS: Veterans age above 50 with a primary care visit in 2005, divided into separate training and testing cohorts (n= 124,360 each). MEASUREMENTS AND ANALYTIC METHODS: The primary outcome was 10-year all-cause mortality. We considered 924 potential predictors across a wide range of EHR data elements including demographics (3), vital signs (9), medication classes (399), disease diagnoses (293), laboratory results (71), and health care utilization (149). We compared discrimination (c-statistics), calibration metrics, and diagnostic test characteristics (sensitivity, specificity, and positive and negative predictive values) of machine learning and regression models. RESULTS: Our cohort mean age (SD) was 68.2 (10.5), 93.9% were male; 39.4% died within 10 years. Models yielded testing cohort c-statistics between 0.827 and 0.837. Utilizing all 924 predictors, the Gradient Boosting model yielded the highest c-statistic [0.837, 95% confidence interval (CI): 0.835-0.839]. The full (unselected) logistic regression model had the highest c-statistic of regression models (0.833, 95% CI: 0.830-0.835) but showed evidence of overfitting. The discrimination of the stepwise selection logistic model (101 predictors) was similar (0.832, 95% CI: 0.830-0.834) with minimal overfitting. All models were well-calibrated and had similar diagnostic test characteristics. LIMITATION: Our results should be confirmed in non-VA EHRs. CONCLUSION: The differences in c-statistic between the best machine learning model (924-predictor Gradient Boosting) and 101-predictor stepwise logistic models for 10-year mortality prediction were modest, suggesting stepwise regression methods continue to be a reasonable method for VA EHR mortality prediction model development.