title: Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the ATHLOS project.
abstract: A most challenging task for scientists that are involved in the study of ageing is the development of a measure to quantify health status across populations and over time. In the present study, a Bayesian multilevel Item Response Theory approach is used to create a health score that can be compared across different waves in a longitudinal study, using anchor items and items that vary across waves. The same approach can be applied to compare health scores across different longitudinal studies, using items that vary across studies. Data from the English Longitudinal Study of Ageing (ELSA) are employed. Mixed-effects multilevel regression and Machine Learning methods were used to identify relationships between socio-demographics and the health score created. The metric of health was created for 17,886 subjects (54.6% of women) participating in at least one of the first six ELSA waves and correlated well with already known conditions that affect health. Future efforts will implement this approach in a harmonised data set comprising several longitudinal studies of ageing. This will enable valid comparisons between clinical and community dwelling populations and help to generate norms that could be useful in day-to-day clinical practice.
authors.affiliation: Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain.|CIBER of Mental Health, Spain.|Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IP), Madrid, Spain.
authors.forename: Francisco Félix
authors.initials: FF
authors.lastname: Caballero
authors.affiliation: Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece.
authors.forename: George
authors.initials: G
authors.lastname: Soulis
authors.affiliation: The Centre for Applied Genomics, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.
authors.forename: Worrawat
authors.initials: W
authors.lastname: Engchuan
authors.affiliation: Parc Sanitari Sant Joan de Déu, Barcelona, Spain.|CIBER of Epidemiology and Public Health, Spain.
authors.forename: Albert
authors.initials: A
authors.lastname: Sánchez-Niubó
authors.affiliation: SPRING TECHNO GMBH &Co. KG, Bremen, Germany.
authors.forename: Holger
authors.initials: H
authors.lastname: Arndt
authors.affiliation: Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain.|CIBER of Mental Health, Spain.|Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IP), Madrid, Spain.
authors.forename: José Luis
authors.initials: JL
authors.lastname: Ayuso-Mateos
authors.affiliation: CIBER of Mental Health, Spain.|Parc Sanitari Sant Joan de Déu, Barcelona, Spain.
authors.forename: Josep Maria
authors.initials: JM
authors.lastname: Haro
authors.affiliation: Information, Evidence and Research, World Health Organization, Geneva, Switzerland.
authors.forename: Somnath
authors.initials: S
authors.lastname: Chatterji
authors.affiliation: Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece.
authors.forename: Demosthenes B
authors.initials: DB
authors.lastname: Panagiotakos
mesh_terms: D005163:Factor Analysis, Statistical; D006304:Health Status; D000073656:Healthy Aging; D006801:Humans; D008137:Longitudinal Studies; D000069550:Machine Learning