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Aim: To quantify variations in health‐related behaviors (HRB) clustering of older adults in Western and Eastern countries. Methods: Using six aging cohorts from the USA, England, Europe, Japan, Korea and China, latent class analysis was applied to access the clustering of smoking, alcohol consumption, physical activity and social activity. Results: A total of 104 552 participants (55% women) aged ≥50 years in 2010 were included. Despite a different number of clusters identified, three consistent cluster profiles emerged: "Multiple‐HRB" (ex‐/never smoking, moderate drinking, frequent physical and social activity); "Inactives" (socially and physically inactive without other risk behaviors); and "(ex‐)Smokers with Risk Behaviors". Sex and cohort variations were shown. For men in Western cohorts, "Multiple‐HRB" was the predominant cluster, whereas their Asian counterparts were more likely to be members of the "Smokers with risk behavior" and "Inactives" clusters. Most women, particularly those in Asian cohorts, were never smokers and non‐drinkers, and most of them belonged to the socially "Inactives" cluster. Conclusions: We provide a person‐centered understanding of HRB clustering of older adults over selected countries by sex, informing tailored health promotion for the target population. Geriatr Gerontol Int 2019; 19: 930–937. [ABSTRACT FROM AUTHOR]

Current mortality prediction indexes are mainly based on functional morbidity and comorbidity, with limited information for risk prevention. This study aimed to develop and validate a modifiable lifestyle-based mortality predication index for older adults. Data from 51,688 participants (56% women) aged ≥50 years in 2002 Health and Retirement Study, 2002 English Longitudinal Study of Ageing and 2004 Survey of Health Ageing and Retirement in Europe were used to estimate coefficients of the index with cohort-stratified Cox regression. Models were validated across studies and compared to the Lee index (having comorbid and morbidity predictors). Over an average of 11-year follow-up, 10,240 participants died. The lifestyle index includes smoking, drinking, exercising, sleep quality, BMI, sex and age; showing adequate model performance in internal validation (C-statistic 0.79; D-statistic 1.94; calibration slope 1.13) and in all combinations of internal-external cross-validation. It outperformed Lee index (e.g. differences in C-statistic = 0.01, D-statistic = 0.17, P < 0.001) consistently across health status. The lifestyle index stratified participants into varying mortality risk groups, with those in the top quintile having 13.5% excess absolute mortality risk over 10 years than those in the bottom 50th centile. Our lifestyle index with easy-assessable behavioural factors and improved generalizability may maximize its usability for personalized risk management.