The association between wealth and sleep medication use in a nationally-representative sample of older Medicare beneficiaries.

BACKGROUND: Sleep disorders are common among older adults, leading to high prevalence of over-the-counter and prescription sleep medication use. Socioeconomically disadvantaged individuals have higher prevalence of sleep disorders. Frequent use of sleep medications can increase the risk of falls. Little is known about the association between wealth and sleep medication use in older adults. METHODS: We conducted a cross-sectional analysis using a nationwide sample of 7603 Medicare beneficiaries (65+ years) from Round 1 (2011) of the National Health and Aging Trends Study. We measured self-reported wealth as the sum of assets (retirement savings, stocks/bonds, checking/savings accounts, business assets, and home value) minus liabilities (mortgage, credit card, and medical debt). Self-reported sleep medication use in the past month was categorized as frequent (5-7 nights/week), sometimes (1-4 nights/week), or never (0 night/week). We estimated differences in the prevalence of sleep medication use by quintiles of wealth using crude and adjusted binomial regression models. Individuals missing sleep medication information were excluded. RESULTS: Median wealth was $152 582 (IQR: $24 023-412 992). Sixteen percent reported frequent sleep medication use, 15% reported some use, and 70% reported no use. Frequent sleep medication use was more common in lower wealth quintiles (lowest: 20%, highest: 12%). Alternatively, some use was more common in higher wealth quintiles (lowest: 11%, highest: 18%). Results were similar after adjustment for demographic factors, anxiety, depression, and sleep disorders. CONCLUSIONS: In this study, less wealthy older adults had higher prevalence of frequent sleep medication use. This may lead to dependency or increased fall risk in this vulnerable population.

Translation of a Claims-Based Frailty Index From the International Classification of Diseases, Ninth Revision, Clinical Modification to the Tenth Revision.

The Faurot frailty index (FFI) is a validated algorithm that uses enrollment and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-based billing information from Medicare claims data as a proxy for frailty. In October 2015, the US health-care system transitioned from the ICD-9-CM to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Applying the Centers for Medicare and Medicaid Services General Equivalence Mappings, we translated diagnosis-based frailty indicator codes from the ICD-9-CM to the ICD-10-CM, followed by manual review. We used interrupted time-series analysis of Medicare data to assess the comparability of the pre- and posttransition FFI scores. In cohorts of beneficiaries enrolled in January 2015-2017 with 8-month frailty look-back periods, we estimated associations between the FFI and 1-year risk of aging-related outcomes (mortality, hospitalization, and admission to a skilled nursing facility). Updated indicators had similar prevalences as pretransition definitions. The median FFI scores and interquartile ranges (IQRs) for the predicted probability of frailty were similar before and after the International Classification of Diseases transition (pretransition: median, 0.034 (IQR, 0.02-0.07); posttransition: median, 0.038 (IQR, 0.02-0.09)). The updated FFI was associated with increased risks of mortality, hospitalization, and skilled nursing facility admission, similar to findings from the ICD-9-CM era. Studies of medical interventions in older adults using administrative claims should use validated indices, like the FFI, to mitigate confounding or assess effect-measure modification by frailty.

Missing data approaches in longitudinal studies of aging: A case example using the National Health and Aging Trends Study.

PURPOSE: Missing data is a key methodological consideration in longitudinal studies of aging. We described missing data challenges and potential methodological solutions using a case example describing five-year frailty state transitions in a cohort of older adults. METHODS: We used longitudinal data from the National Health and Aging Trends Study, a nationally-representative cohort of Medicare beneficiaries. We assessed the five components of the Fried frailty phenotype and classified frailty based on their number of components (robust: 0, prefrail: 1-2, frail: 3-5). One-, two-, and five-year frailty state transitions were defined as movements between frailty states or death. Missing frailty components were imputed using hot deck imputation. Inverse probability weights were used to account for potentially informative loss-to-follow-up. We conducted scenario analyses to test a range of assumptions related to missing data. RESULTS: Missing data were common for frailty components measured using physical assessments (walking speed, grip strength). At five years, 36% of individuals were lost-to-follow-up, differentially with respect to baseline frailty status. Assumptions for missing data mechanisms impacted inference regarding individuals improving or worsening in frailty. CONCLUSIONS: Missing data and loss-to-follow-up are common in longitudinal studies of aging. Robust epidemiologic methods can improve the rigor and interpretability of aging-related research.