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      The Burden of Frailty Among U.S. Veterans and Its Association With Mortality, 2002–2012

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          Abstract

          Background

          Frailty is a key determinant of clinical outcomes. We sought to describe frailty among U.S. Veterans and its association with mortality.

          Methods

          Nationwide retrospective cohort study of regular Veterans Affairs (VA) users, aged at least 65 years in 2002–2012, followed through 2014, using national VA administrative and Medicare and Medicaid data. A frailty index (FI) for VA (VA-FI) was calculated using the cumulative deficit method. Thirty-one age-related deficits in health from diagnostic and procedure codes were included and were updated biennially. Survival analysis assessed associations between VA-FI and mortality.

          Results

          A VA-FI was calculated for 2,837,152 Veterans over 10 years. In 2002, 35.5% were non-frail (FI = 0–0.10), 32.6% were pre-frail (FI = 0.11–0.20), 18.9% were mildly frail (FI = 0.21–0.30), 8.7% were moderately frail (FI = 0.31–0.40), and 4.3% were severely frail (FI > 0.40). From 2002 to 2012, the prevalence of moderate frailty increased to 12.7%and severe frailty to 14.1%. Frailty was strongly associated with survival and was independent of age, sex, race, and smoking; the VA-FI better predicted mortality than age alone. Although prevalence of frailty rose over time, compared to non-frail Veterans, 2 years’ hazard ratios (95% confidence intervals) for mortality declined from a peak in 2004 of 2.01 (1.97–2.04), 3.49 (3.44–3.55), 5.88 (5.79–5.97), and 10.39 (10.23–10.56) for pre-frail, mildly, moderately, and severely frail, respectively, to 1.51 (1.49–1.53), 2.36 (2.33–2.39), 3.68 (3.63–3.73), 6.62 (6.53–6.71) in 2012. At every frailty level, risk of mortality was lower for women versus men and higher for blacks versus whites.

          Conclusions

          Frailty affects at least 3 of every 10 U.S. Veterans aged 65 years and older, and is strongly associated with mortality. The VA-FI could be used to more accurately estimate life expectancy and individualize care for Veterans.

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          Most cited references23

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          Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index

          Background Frailty is a key determinant of health status and outcomes of health care interventions in older adults that is not readily measured in Medicare data. This study aimed to develop and validate a claims-based frailty index (CFI). Methods We used data from Medicare Current Beneficiary Survey 2006 (development sample: n = 5,593) and 2011 (validation sample: n = 4,424). A CFI was developed using the 2006 claims data to approximate a survey-based frailty index (SFI) calculated from the 2006 survey data as a reference standard. We compared CFI to combined comorbidity index (CCI) in the ability to predict death, disability, recurrent falls, and health care utilization in 2007. As validation, we calculated a CFI using the 2011 claims data to predict these outcomes in 2012. Results The CFI was correlated with SFI (correlation coefficient: 0.60). In the development sample, CFI was similar to CCI in predicting mortality ( C statistic: 0.77 vs. 0.78), but better than CCI for disability, mobility impairment, and recurrent falls (C statistic: 0.62–0.66 vs. 0.56–0.60). Although both indices similarly explained the variation in hospital days, CFI outperformed CCI in explaining the variation in skilled nursing facility days. Adding CFI to age, sex, and CCI improved prediction. In the validation sample, CFI and CCI performed similarly for mortality (C statistic: 0.71 vs. 0.72). Other results were comparable to those from the development sample. Conclusion A novel frailty index can measure the risk for adverse health outcomes that is not otherwise quantified using demographic characteristics and traditional comorbidity measures in Medicare data.
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            Inequalities in Life Expectancy Among US Counties, 1980 to 2014: Temporal Trends and Key Drivers.

            Examining life expectancy by county allows for tracking geographic disparities over time and assessing factors related to these disparities. This information is potentially useful for policy makers, clinicians, and researchers seeking to reduce disparities and increase longevity.
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              Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration.

              Statistical models that identify patients at elevated risk of death or hospitalization have focused on population subsets, such as those with a specific clinical condition or hospitalized patients. Most models have limitations for clinical use. Our objective was to develop models that identified high-risk primary care patients.
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                Author and article information

                Journal
                The Journals of Gerontology: Series A
                Oxford University Press (OUP)
                1079-5006
                1758-535X
                August 2019
                July 12 2019
                October 11 2018
                August 2019
                July 12 2019
                October 11 2018
                : 74
                : 8
                : 1257-1264
                Affiliations
                [1 ]New England Geriatric Research, Education, and Clinical Center (GRECC), VA Boston Healthcare System, Massachusetts
                [2 ]Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Boston, Massachusetts
                [3 ]Division of Aging, Department of Medicine, Brigham & Women’s Hospital, Boston, Massachusetts
                [4 ]Department of Biostatistics, Boston University School of Public Health, Massachusetts
                [5 ]Department of Gerontology, University of Massachusetts Boston, Massachusetts
                [6 ]Department of Psychiatry, Boston University School of Medicine, Massachusetts
                [7 ]Renal Section, Department of Medicine, VA Boston Healthcare System, Massachusetts
                [8 ]Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
                [9 ]Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
                [10 ]Atlanta VA Medical Center, Decatur, Georgia
                [11 ]Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
                [12 ]Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta
                Article
                10.1093/gerona/gly232
                6625596
                30307533
                b781980b-1a10-4947-a2e9-7a038d2b4384
                © 2018
                History

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