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Index or illusion: The case of frailty indices in the Health and Retirement Study.

ABSTRACT: INTRODUCTION:Frailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (FIs). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of FIs. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power of FIs regarding mortality. METHODS:Three FIs, the Functional Domains Model proposed by Strawbridge et al. (1998), the Burden Model by Rockwood et al. (2007) and the Biologic Syndrome Model by Fried et al. (2004), were directly compared using the data from the Health and Retirement Study (HRS), a longitudinal study since 1996 mainly following up Americans aged 50 years and over. The FIs were reproduced according to Cigolle et al. (2009) and interpreted with their input variables through forward-stepwise regression. Biases were the residuals of the FIs that could not be explained by own input variables. Any four of the input variables were used to create alternative indices. Discrete-time survival analysis was conducted to compare the predictive power of FIs, input variables and alternative indices on mortality. RESULTS:We found frailty a syndrome not unique to the elderly. The FIs were produced with different degrees of bias. The FIs could not be fully interpreted with the theory-based input variables. The bias induced by the Biological Syndrome Model better predicted mortality than frailty status. A complicated FI, the Burden Model, could be simplified. The input variables better predicted mortality than the FIs. The continuous FIs predicted mortality better than the frailty statuses. At least 6865 alternative indices better predicted mortality than the FIs. CONCLUSION:FIs have been used as outcome in clinical trials and need to be reviewed for adequacy based on our findings. The three FIs are not closely linked to the theories because of bias introduced by data manipulation and excessive numbers of input variables. We are developing new algorithms to develop and validate innovative indices.

SUBMITTER: Chao YS

PROVIDER: S-EPMC6051600 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Index or illusion: The case of frailty indices in the Health and Retirement Study.

Chao Yi-Sheng YS Wu Hsing-Chien HC Wu Chao-Jung CJ Chen Wei-Chih WC

PloS one 20180718 7

<h4>Introduction</h4>Frailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (FIs). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of FIs. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power ...[more]

PMID: 30020923

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Project description:BackgroundMortality prediction models are useful to guide clinical decision-making based on prognosis. The frailty index, which allows prognostication and personalized care planning, has not been directly compared with validated prognostic models.ObjectiveTo compare the discrimination of mortality, disability, falls, and hospitalization between a frailty index and validated prognostic indices.DesignSecondary Analysis of the National Health and Aging Trends Study.ParticipantsSeven thousand thirty-three Medicare beneficiaries 65 years or older.MeasurementsWe measured a deficit-accumulation frailty index, Schonberg index, and Lee index at the 2011 baseline assessment. Primary outcome was mortality at 5 years. Secondary outcomes were decline in activities of daily living (ADL), decline in instrumental activities of daily living (IADL), fall, and hospitalization at 1 year. We used C-statistics to compare discrimination between indices, adjusting for age and sex.ResultsThe study population included 4146 (44.8%) with age ≥ 75 years, with a median frailty index of 0.15 (interquartile range 0.09-0.25). A total of 1385 participants died (14.7%) and 2386 (35.2%) were lost to follow-up. Frailty, Schonberg, and Lee indices predicted mortality similarly: C-statistics (95% confidence interval) were 0.78 (0.77-0.80) for frailty index; 0.79 (0.78-0.81) for Schonberg index; and 0.78 (0.77-0.80) for Lee index. The frailty index had higher C-statistics for decline in ADL function (frailty index, 0.80 [0.78-0.83]; Schonberg, 0.74 [0.72-0.76]; Lee, 0.74 [0.71-0.77]) and falls (frailty index, 0.66 [0.65-0.68]; Schonberg, 0.61 [0.58-0.63]; Lee, 0.61 [0.59-0.63]). C-statistics were similar for decline in IADL function (frailty index, 0.61 [0.59-0.63]; Schonberg, 0.60 [0.59-0.62]; Lee, 0.60 [0.58-0.62]) and hospitalizations (frailty index, 0.68 [0.66-0.70]; Schonberg, 0.68 [0.66-0.69]; Lee, 0.65 [0.63-0.67]).ConclusionsA deficit-accumulation frailty index performs as well as prognostic indices for mortality prediction, and better predicts ADL disability and falls in community-dwelling older adults. Frailty assessment offers a unifying approach to risk stratification for key health outcomes relevant to older adults.

Project description:Description: The University of Michigan Health and Retirement Study (HRS) is a longitudinal survey of a representative sample of Americans over the age of 50. Supported by the National Institute on Aging (NIA U01AG009740) and the Social Security Administration, the HRS is designed to provide reliable data on the decisions, choices, and behaviors of people as they age and respond to changes in public policy, the economy, and health. The study obtains information every two years about income and wealth, health and use of health services, work and retirement, and family connections. Through its unique and in-depth interviews, the HRS provides an invaluable and growing body of multidisciplinary data that researchers can use to address important questions about the challenges and opportunities of aging. Because of its innovation and importance, the HRS has become the model and hub for a growing network of harmonized longitudinal aging studies around the world. Origins of the HRS. As the population ages it is increasingly important to obtain reliable data about aging and topics that are relevant to a range of policy issues in aging. To address this need, the National Institutes on Aging (NIA) established a cooperative agreement with the University of Michigan Institute for Social Research to collect such data. The HRS launched data collection in 1992 and has re-interviewed the original sample of respondents every two years since then. By adding new cohorts and refreshing the sample, the HRS has grown to become the largest, most representative longitudinal panel study of Americans 50 years and older. HRS Study Design. The target population for the original HRS cohort includes all adults in the contiguous United States born during the years 1931-1941 who reside in households, with a 2:1 oversample of African-American and Hispanic populations. The original sample is refreshed with new birth cohorts (51-56 years of age) every six years. The sample has been expanded over the years to include a broader range of birth cohorts as well. The target population for the AHEAD survey consists of United States household residents who were born in 1923 or earlier. Children of the Depression (CODA) recruits households born 1924-1930, Warbabies 1942-47, Early Boomers 1948-53, and Mid-Boomers 1954-59. The current sample is over 26,000 persons in 17,000 households. Data collection includes a mixed mode design combining in-person, telephone, mail, and Internet. For consenting respondents, HRS data is linked at the individual level to administrative records from Social Security and Medicare claims. Genetic Research in the HRS. The HRS is now genotyping 2.5 million single nucleotide polymorphisms (SNPs) on respondents using Illumina's Human Omni2.5-Quad (Omni2.5) BeadChip methodology. The genotyping is being performed by the NIH Center for Inherited Disease Research (CIDR). Saliva was collected on half of the HRS sample each wave starting in 2006. In 2006, saliva was collected using a mouthwash collection method. In 2008, the data collection method switched to the Oragene kit. Saliva completion rates were 83% in 2006 and 84% in 2008. Based on prior rates of consent and completion, we expect an additional 6,000 Oragene samples to be added by 2012, including a substantial expansion of the minority sample. HRS Phenotypic data. Phenotypic data are available on a variety of dimensions. Health measures include physical/psychological self-report, various health conditions, disabilities; cognitive performance; health behaviors (smoking, drinking, exercise), physical performance and anthropomorphic measures, and biomarkers (HbA1c, Total Cholesterol, HDL, CRP, Cystatin-C). Data are also available on health services including utilization, insurance and out-of-pocket spending with linkage to Medicare records. Economic measures include employment status/history, earnings, disability, retirement, type of work, income by source, wealth by asset type, capital gains/debt, consumption, linkage to pensions, Social Security earnings/benefit histories. There is also extensive information on family structure, proximity, transfers to/from of money, time, social and psychological characteristics, as well as a wide range of demographics. Performance on a cognitive test combining immediate and delayed word recall was selected as an example trait for the dbGaP data release. In the immediate word recall task the interviewer reads a list of 10 nouns to the respondent and asks the respondent to recall as many words as possible from the list in any order. After approximately five minutes of asking other survey questions, the respondent is asked to recall the nouns previously presented as part of the immediate recall task. The total recall score is the sum of the correct answers to these two tasks, with a range of 0 to 20. Researchers who wish to link to other HRS measures not in dbGaP will be able to apply for access from HRS. A separate Data Use Agreement (DUA) will be required for linkage to the HRS data. See the HRS website (<a href="http://hrsonline.isr.umich.edu/gwas" target="_blank">http://hrsonline.isr.umich.edu/gwas</a>) for details.

Project description:IntroductionNight sleep duration and total sleep duration are associated with frailty. However, the association between daytime nap duration and the risks of frailty has not been explored thoroughly.MethodsThis study used data from the China Health and Retirement Longitudinal Study (CHARLS). Participants aged 60 years and older at baseline were included in this study. Individuals with daytime nap duration were categorized into four groups: no napping, short napping (< 30 min), moderate napping (30-89 min), and extended napping (≥90 min). Frailty was assessed using a modified Physical Frailty Phenotype (PFP) scale. Non-frail participants at baseline were followed up for 4 years. The association between nap duration and risks of frailty at baseline and incident frailty was evaluated by logistic regression and discrete-time Cox regression analyses, respectively.ResultsIn total, 5,126 participants were included in this study. For individuals with night sleep duration of ≥9 h, short nappers showed higher odds [odds ratio (OR) = 4.08, 95% confidence interval (CI): 1.30-12.78] for frailty compared with non-habitual nappers at baseline, while moderate nappers were less likely to be frail (OR = 0.18, 95% CI: 0.04-0.73). In the follow-up study, short nappers showed higher risks for frailty compared with participants of the no napping group with night sleep duration of < 6 h [hazard ratio (HR) = 1.91, 95% CI: 1.07-3.43] or 6-9 h (HR = 1.97, 95% CI: 1.18-3.30). Compared with short nappers, older adults with extended napping (HR = 0.41, 95% CI: 0.22-0.77) showed lower risks for frailty in those with night sleep duration of 6-9 h. For individuals with night sleep duration of ≥9 h, moderate napping (HR = 0.20, 95% CI: 0.05-0.77) decreased the risks for frailty compared with short napping.ConclusionAmong older adults with night sleep duration of < 9 h, short nappers posed higher risks for frailty compared with non-habitual nappers. Extended naps for those with a night sleep duration of 6-9 h or moderate naps for those with night sleep duration of ≥9 h could lower the risk of frailty compared with short naps. Future studies on the timing, purpose, frequency, and quality of daytime napping and objectively measured nap duration are needed to explore the association between daytime napping and risks of frailty.

Dataset Information

Index or illusion: The case of frailty indices in the Health and Retirement Study.

Publications

Index or illusion: The case of frailty indices in the Health and Retirement Study.

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