A Health Profile of Senior-Aged Women Veterans: A Latent Class Analysis of Condition Clusters. Issue 2 (20th November 2017) - Gonsoulin, Margaret E; Durazo-Arvizu, Ramon A; Goldstein, Karen M; Cao, Guichan; Zhang, Qiuying; Ramanathan, Dharani; Hynes, Denise M
Abstract: Background and Objectives: This study characterizes the multiple morbidities experienced by senior-aged women Veterans so that the Veterans Health Administration (VHA) and other health care systems may be better prepared to meet the health care needs of this growing cohort. Research Design and Methods: Using the VHA's Corporate Data Warehouse, we conducted a retrospective observational study of the 38, 597 female veteran patients who were at least 65 years old and received care in the VHA during 2013 and 2014. We use a latent class analysis model to cluster diagnoses associated with inpatient and outpatient events over the years. Results: The senior-aged women Veterans are characterized by six major classes of disease clusters. We defined these classes as: Healthy (16.24% of the cohort); Ophthalmological Disorders (13.84%); Musculoskeletal Disorders (14.22%); At Risk for Cardiovascular Disease (37.53%); Diabetic with Comorbidities (9.05%); and Multimorbid (9.12%). The patterns and prevalence of these condition classes vary by race, age, and marital status. Discussion and Implications: Each of the six clusters can be used to develop clinical practice guidelines that are appropriate for senior-aged women Veterans. Consistent with past literature, the most common conditions in this cohort are hypertension and hyperlipidemia; together they form the most common class, "At Risk of Cardiovascular Disease (CVD)". Results also show evidence of race-related disparities, with Blacks being more likely to be in the highest risk classes. Also, members of the cohort who are currently married having improved chances of being in the healthy class. And finally, we see a "healthy survivor" effect with the oldest women in our cohort having low overall rates of disease.