Josje D Schoufour1, Nicole S Erler2, Loes Jaspers3, Jessica C Kiefte-de Jong4, Trudy Voortman3, Gijsbertus Ziere3, Jan Lindemans5, Caroline C Klaver6, Henning Tiemeier3, Bruno Stricker3, Arfan M Ikram3, Joop S E Laven7, Guy G O Brusselle8, Fernando Rivadeneira9, Oscar H Franco3. 1. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands. Electronic address: j.schoufour@erasmusmc.nl. 2. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands; Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands. 3. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands. 4. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands; Leiden University College, The Hague,the Netherlands. 5. Department of Clinical Chemistry, Erasmus MC, University Medical Center, Rotterdam, the Netherlands. 6. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands; Department of Ophthalmology, Erasmus MC, Rotterdam, the Netherlands. 7. Division of Reproductive Medicine, Department of Obstetrics and Gynaecology, Erasmus MC, Rotterdam, the Netherlands. 8. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands; Department of Respiratory Medicine, Erasmus MC, Rotterdam, the Netherlands. 9. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands; Department of Internal Medicine, Erasmus MC, 3000 CA Rotterdam, the Netherlands.
Abstract
OBJECTIVES: To design a frailty index (FI) and evaluate three methods to handle missing data. Furthermore, we evaluated its construct (i.e., skewed distribution, correlation with age and sub-maximum score) and criterion validity (based on mortality risk). STUDY DESIGN: We included 11,539 participants (45± years) from a population-based cohort in the Netherlands. Frailty was measured with a FI, which we constructed based on the accumulation of 45 health-related variables, related to mood, cognition, functional status, diseases and conditions, biomarkers, and nutritional status. A total FI-score was calculated by averaging the scores of the deficits, resulting in a score between 0 and 1, with higher scores indicating increasing frailty. Mean imputation, single- and multiple imputation were applied. MAIN OUTCOME MEASURE: Mortality data were obtained by notification from the municipal administration. Median follow-up time was 9.5 years, during which 3902 (34%) participants died. RESULTS: The median FI for the full population was 0.16 (IQR=0.11-0.23). The distribution of the FI was slightly right-skewed, the absolute maximum score was 0.78 and there was a strong correlation with age (Pearson correlation=0.52;95%CI=0.51-0.54). The adjusted HR per unit increase in FI-score on mortality was 1.05 (95%CI=1.05-1.06). Multiple imputation seemed to provide more robust results than mean imputation. CONCLUSION: Based on our results we advise to the use of at least 30 deficits from different health domains to construct a FI if data are not imputed. Future research should use the continuous nature of the FI to monitor trajectories in frailty and find preventive strategies.
OBJECTIVES: To design a frailty index (FI) and evaluate three methods to handle missing data. Furthermore, we evaluated its construct (i.e., skewed distribution, correlation with age and sub-maximum score) and criterion validity (based on mortality risk). STUDY DESIGN: We included 11,539 participants (45± years) from a population-based cohort in the Netherlands. Frailty was measured with a FI, which we constructed based on the accumulation of 45 health-related variables, related to mood, cognition, functional status, diseases and conditions, biomarkers, and nutritional status. A total FI-score was calculated by averaging the scores of the deficits, resulting in a score between 0 and 1, with higher scores indicating increasing frailty. Mean imputation, single- and multiple imputation were applied. MAIN OUTCOME MEASURE: Mortality data were obtained by notification from the municipal administration. Median follow-up time was 9.5 years, during which 3902 (34%) participants died. RESULTS: The median FI for the full population was 0.16 (IQR=0.11-0.23). The distribution of the FI was slightly right-skewed, the absolute maximum score was 0.78 and there was a strong correlation with age (Pearson correlation=0.52;95%CI=0.51-0.54). The adjusted HR per unit increase in FI-score on mortality was 1.05 (95%CI=1.05-1.06). Multiple imputation seemed to provide more robust results than mean imputation. CONCLUSION: Based on our results we advise to the use of at least 30 deficits from different health domains to construct a FI if data are not imputed. Future research should use the continuous nature of the FI to monitor trajectories in frailty and find preventive strategies.
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Authors: Eline Verspoor; Trudy Voortman; Frank J A van Rooij; Fernando Rivadeneira; Oscar H Franco; Jessica C Kiefte-de Jong; Josje D Schoufour Journal: Eur J Nutr Date: 2019-11-14 Impact factor: 5.614