@article {12565, title = {Development of a common scale for measuring healthy ageing across the world: results from the ATHLOS consortium.}, journal = {International Journal of Epidemiology}, volume = {50}, year = {2021}, pages = {880-892}, abstract = {

BACKGROUND: Research efforts to measure the concept of healthy ageing have been diverse and limited to specific populations. This diversity limits the potential to compare healthy ageing across countries and/or populations. In this study, we developed a novel measurement scale of healthy ageing using worldwide cohorts.

METHODS: In the Ageing Trajectories of Health-Longitudinal Opportunities and Synergies (ATHLOS) project, data from 16 international cohorts were harmonized. Using ATHLOS data, an item response theory (IRT) model was used to develop a scale with 41 items related to health and functioning. Measurement heterogeneity due to intra-dataset specificities was detected, applying differential item functioning via a logistic regression framework. The model accounted for specificities in model parameters by introducing cohort-specific parameters that rescaled scores to the main scale, using an equating procedure. Final scores were estimated for all individuals and converted to T-scores with a mean of 50 and a standard deviation of 10.

RESULTS: A common scale was created for 343~915 individuals above 18 years of age from 16 studies. The scale showed solid evidence of concurrent validity regarding various sociodemographic, life and health factors, and convergent validity with healthy life expectancy (r = 0.81) and gross domestic product (r = 0.58). Survival curves showed that the scale could also be predictive of mortality.

CONCLUSIONS: The ATHLOS scale, due to its reliability and global representativeness, has the potential to contribute to worldwide research on healthy ageing.

}, keywords = {Aging, Cohort Studies, Health Status, healthy aging, Humans, Reproducibility of Results}, issn = {1464-3685}, doi = {10.1093/ije/dyaa236}, author = {Sanchez-Niubo, Albert and Forero, Carlos G and Wu, Yu-Tzu and Gin{\'e}-V{\'a}zquez, Iago and Prina, Matthew and de la Fuente, Javier and Daskalopoulou, Christina and Critselis, Elena and De La Torre-Luque, Alejandro and Panagiotakos, Demosthenes and Arndt, Holger and Ayuso-Mateos, Jos{\'e} Luis and Bayes-Marin, Ivet and Bickenbach, Jerome and Bobak, Martin and Caballero, Francisco F{\'e}lix and Chatterji, Somnath and Egea-Cort{\'e}s, Laia and Garc{\'\i}a-Esquinas, Esther and Leonardi, Matilde and Koskinen, Seppo and Koupil, Ilona and Mellor-Mars{\'a}, Blanca and Olaya, Beatriz and Paj{\k a}k, Andrzej and Prince, Martin and Raggi, Alberto and Rodr{\'\i}guez-Artalejo, Fernando and Sanderson, Warren and Scherbov, Sergei and Tamosiunas, Abdonas and Tobias-Adamczyk, Beata and Tyrovolas, Stefanos and Haro, Josep Maria} } @article {9475, title = {Determinants of health trajectories in England and the US: an approach to identify different patterns of healthy aging.}, journal = {Journals of Gerontology Series A: Biological Sciences and Medical Sciences}, volume = {73}, year = {2018}, pages = {1512-1518}, abstract = {

Background: Aging is a multidimensional process with a remarkable inter-individual variability. This study is focused on identifying groups of population with similar aging patterns, and to define the health trajectories of these groups. Socio-demographic and health determinants of these trajectories are also identified.

Methods: Data from the English Longitudinal Study of Aging (ELSA) and the Health and Retirement Study (HRS) were used. A set of self-reported health items and measured tests were used to generate a latent health metric by means of a Bayesian multilevel IRT model, assessing the ability of the metric to predict mortality. Then, a Growth Mixture Model (GMM) was conducted in each study to identify latent classes and assess health trajectories. Kaplan-Meier survival curves were obtained for each class and a multinomial logistic regression was used to identify determinants of these trajectories.

Results: The health score generated showed an adequate ability to predict mortality over ten years in ELSA [AUC=0.74; 95\% CI=(0.72,0.75)] and HRS [AUC=0.74; 95\% CI=(0.73,0.75)]. By means of GMM, four latent classes were identified in ELSA and five in HRS. Chronic conditions, no qualification and low level of household wealth were associated to the classes which showed a higher mortality in both studies.

Conclusion: The method based on the creation of a common metric of health and the use of GMM to identify similar patterns of aging, allows for the comparison of trajectories of health across longitudinal surveys. Multimorbidity, educational level and household wealth could be considered as determinants associated to these trajectories.

}, keywords = {Cross-National, Health Trajectories, Successful aging}, issn = {1758-535X}, doi = {10.1093/gerona/gly006}, author = {de la Fuente, Javier and Francisco F{\'e}lix Caballero and Albert S{\'a}nchez-Niub{\'o} and Demosthenes B Panagiotakos and Matthew Prina and Arndt, Holger and Haro, Josep Maria and Chatterji, Somnath and Ayuso-Mateos, Jos{\'e} Luis} }