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.

Social determinants, health status and 10-year mortality among 10,906 older adults from the English longitudinal study of aging: the ATHLOS project

“…The novelty of the present work is in defining and understanding health as not only the absence of disease, but as a vector of functioning in a sparing set of domains that matches the intuitive notion of health such that the health of people with distinct health problems or diseases could be compared, using a previously developed and validated health metric [ 3 , 4 ]. Following the World Health Organization (WHO), health is understood as: (i) an intrinsic attribute of an individual that can be aggregated to the population level; and (ii) comprising domains of human functioning that describe the actual impact of health conditions on people’s lives [ 5 ].…”

“…Nowadays, healthy aging is described by the WHO as a process of developing and maintaining the functional ability that enables well-being in older age [ 7 ]. Within the Aging Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project (EU HORIZON2020–PHC-635316, http://athlosproject.eu/ ) [ 4 ] the aim of the present work was to longitudinally explore the association between education and wealth status on 10-year all-cause mortality among the English Longitudinal Study of Aging (ELSA) participants, in relation to parameters of healthy aging, i.e., impairments in body functions and cognitive performance. Lifestyle behaviors, such as physical activity, alcohol consumption and smoking, were also evaluated as potential mediating pathways in the association between the socioeconomic determinants and healthy aging.…”

Validity of the PARADISE24 questionnaire in people with substance use disorders: A measure to assess psychosocial difficulties

“…This sample was randomly divided into two groups: 1) a developmental exploratory subsample, comprising 70% of the total outpatient's sample, and 2) a validation subsample with the remaining 30% of this sample. This two-step validation procedure has been implemented in previous studies (Caballero et al, 2017).…”

Are legume-based recipes an appropriate source of nutrients for healthy ageing? A prospective cohort study

“…This is consistent with the WHO's conceptualisation of health for purposes of measurement (51) . Measures of healthy (or unhealthy) ageing built under this approach have previously shown a good reliability and performed well when predicting mortality (52,53) . Among the potential limitations, diet was self-reported, so certain misclassification and social desirability bias may have occurred.…”