Disproportionate multimorbidity among veterans in middle age.

BACKGROUND: Little is known about the prevalence of multimorbidity among middle-aged veterans. Multimorbidity holds implications for planning for a population with high health care utilization, poor quality of life and marked need for interdisciplinary care. METHODS: The current study used the US 2017 Behavior Risk Factor Surveillance System to measure multimorbidity in three ways: (1) reporting two or more health conditions, (2) reporting two or more conditions controlling for demographic characteristics (e.g. income) and health risk behaviors (e.g. smoking) and (3) a weighted index using health-related quality of life. RESULTS: After age 25, veterans' risk for multimorbidity increased across all age groups. The increased odds of reporting multimorbidity was highest when comparing veterans aged 35-44 to non-veterans of the same ages. Veterans aged 35-44 are 50% (adjusted odds ratios (AOR) 1.50, 95% confidence interval (CI) 1.16, 1.94) to 80% (AOR 1.80, 95% CI 1.46, 2.23) more likely to report multimorbidity when compared with same aged non-veterans. CONCLUSIONS: Younger veterans may benefit from comprehensive interdisciplinary services to aid in the treatment of multiple medical conditions. Failure to account for the impact of chronic conditions on quality of life may lead to an underestimate of the health care needs of veterans across the lifespan.

Quality management and federal workers' compensation: the Veterans Health Administration workers' compensation program model.

OBJECTIVE: The federal workers' compensation program includes under a single employer five commonly encountered roles and responsibilities-injured patient, clinical provider, third-party administrator, adjudicator, and insurer. Data within the Veterans Health Administration (VHA) provide a unique opportunity to apply a simple model of health care quality improvement, exploring interactions between structures, processes, and outcomes. METHODS: A facility survey identified reporting structures, levels of education and training, policies and processes, tool availability and use, and perceptions of role adherence. Administrative data included process and outcome metrics, including short-term disability, long-term disability, and lost time cases. RESULTS: Improved collaboration between clinical and administrative staff within VHA and with the Department of Labor was associated with improved performance. CONCLUSIONS: Applying a clinical quality improvement model clarifies roles, expectations, and likely relationships for improved program management.