Risk of mortality in older adults with loss of appetite: An analysis of Medicare fee-for-service data.

OBJECTIVES: Prior research suggested that loss of appetite (LOA) among adults with Medicare fee-for-service (FFS) insurance in the United States increased the risk of mortality within 1 year; those findings were not adjusted for risk factors and confounders. The objective of this study was to compare the risk of mortality among Medicare FFS beneficiaries with LOA to a control group without LOA while controlling or adjusting for age, comorbidities, body mass index (BMI), and weight loss. DESIGN: Retrospective and observational analysis of Medicare FFS health insurance claims data from October 1, 2015 to December 31, 2021. SETTING: Claims from all settings (e.g., hospital inpatient/outpatient, office, assisted living facility, skilled nursing facility, hospice, rehabilitation facility, home) were included in these analyses. PARTICIPANTS: The LOA group included all individuals aged 65-115 years with continuous Medicare FFS medical coverage (Parts A and/or B) for at least 12 months before a claim with ICD-10 diagnosis code "R63.0 Anorexia". The control group was drawn from individuals aged 65-115 years with continuous Medicare FFS coverage who did not have a diagnosis of R63.0. Individuals with LOA were matched 1:3 to those in the control group based on age, sex, and race/ethnicity. MEASUREMENTS: Mortality in the LOA group was compared to mortality in the control group using Kaplan-Meier and Cox regression analyses and stratified or adjusted in terms of Charlson Comorbidity Index (CCI), claims-based frailty index (CFI), BMI, and weight loss. RESULTS: The study population of 1,707,031 individuals with LOA and 5,121,093 controls without LOA was 61.7% female and 82.2% White. More individuals with LOA compared with the control group had a CCI score 5+ (52.4% vs. 19.4%), CFI score 5+ (31.6% vs. 6.4%), and BMI < 20 kg/m2 (11.2% vs. 2.1%). Median follow-up was 12 months (individuals with LOA) and 49 months (control group). In a matched population, the risk of mortality was significantly higher (unadjusted hazard ratio 4.40, 95% confidence interval 4.39-4.42) for individuals with LOA than the control group. Median survival time was 4 months (individuals with LOA) and 26 months (control group); differences in survival time remained when stratifying by CCI, BMI, and weight loss. CONCLUSION: Individuals with LOA had a substantially increased risk of death even after matching for age, sex, race/ethnicity, and adjusting for comorbidities. These findings highlight the burden of illness in older adults with LOA and the need for therapies.

Adherence and persistence to tafamidis treatment among Medicare beneficiaries in the presence of a patient assistance program.

Tafamidis is the only disease-modifying therapy approved to treat patients in the United States with transthyretin amyloid cardiomyopathy (ATTR-CM), which most commonly affects patients aged ≥ 65 years. The manufacturer operates a patient assistance program (PAP) to support access to tafamidis. This study conducted Privacy Preserving Record Linking (PPRL) using Datavant tokens to match patients across Medicare prescription drug plan (PDP) and PAP databases to evaluate the impact of PAPs on treatment exposure classification, adherence, and persistence determined using Medicare PDP data alone. We found 35% of Medicare PDP patients received tafamidis through the PAP only; 14% through both Medicare PDP and the PAP, and 51% through Medicare PDP only. Adherence and persistence were comparable between these cohorts but underestimated among patients who received ≥ 2 prescriptions through Medicare PDP and ≥ 1 through the PAP when solely using Medicare data versus pooled Medicare and PAP data (modified Medication Possession Ratio: 84% [69% ≥ 80% adherent] vs. 96% [93%]; Proportion of Days Covered: 77% [66% ≥ 80% adherent] vs. 88% [88%]; mean days to discontinuation: 186 vs. 252; total discontinuation: 13% vs. 11%). Cross-database PPRL is a valuable method to build more complete treatment journeys and reduce the risk of exposure misclassification in real-world analyses.