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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.

A vast research gap in publishing between high-income (HIC) and low and middle-income countries (LMIC) exists. Given sociocultural, health systems, and geopolitical differences, results from studies carried out in high resource countries are clearly not generalizable to a truly global context. Collaborations and equal partnerships are contributing to strengthen partnerships between HIC and LMIC research, improve research capacity, and increase outputs from LMIC. This chapter aims to highlight challenges and opportunities in carrying out collaborative research in LMICs, based on the experience of UK-based researchers working with many partners across diverse settings. Cross-cultural work is not always easy and specific issues need to be addressed at different stages of the research regarding foundations for research, data collection, and impact and sustainability in order to make real impact in policy and practice. The rise of global health research is an opportunity to develop neuropsychiatric research in LMIC.

The number of people above 60 years old will double by 2050. There is a considerate variability in the health status of older people. The identification of the different trajectories that people follow as they grow older constitutes one of the aims of the ATHLOS project. In the current study, we created a metric of health in the four available waves (2001, 2003, 2012, 2015) of the Mexican Health and Aging Study (MHAS) by employing Bayesian multilevel Item Response Theory. Growth mixture modelling indicated that older Mexicans (n=14,143) age by following four distinct pathways (i.e. high-stable, moderate-stable, low-stable, decliners). Adherence to healthy lifestyle behaviours (i.e. physical activity, non-smoking, limited alcohol consumption) was associated with better health trajectories. Preliminary analyses in the ATHLOS harmonised dataset also suggest that older people age by following four distinct pathways. The impact of lifestyle behaviours within the harmonised dataset will be investigated and also presented.

Cohort Profile : The Ageing Trajectories of Health - Longitudinal Opportunities and Synergies (ATHLOS) project

One of the aims of the ATHLOS project is to develop a single metric of healthy ageing. Using as a paradigm the ELSA study – English Longitudinal Study on Aging (n= 11,906 participants, entry 1998–2001, last follow up 2011–12, 6 waves) - and applying the Item Response Theory (IRT) approach and multi-level Factor Analyses the metric of healthy aging was developed as a composite score of functioning measures. Exploratory Factor Analysis was conducted to detect the latent structure among items. After conducting the EFA and obtaining evidences for unidimensionality, a global health score was generated by means of IRT. Then, a Machine Learning (ML) approach was applied to explore patterns of healthy aging. Based on these modern analytical methods various socio-demographic and lifestyle behaviors revealed as determinants of healthy aging. Combining classical statistical approaches with machine learning methodologies, the derived pattern recognition is less biased and the conclusions more robust.

Many papers have assessed which factors are associated with healthy ageing, yet this research area has not yet been summarised properly, partly due to the heterogeneity of the studies, and the scale of the task. During this talk, we will present findings from a systematic review of the determinants of healthy ageing (socio-demographic, biological, behavioural and psychological and social). We aimed to: 1-Identify the key determinants of healthy ageing; 2-Qualify and quantify the direction of associations; 3-Summarise research trends across the last decades; 4-Identify knowledge gaps. A review protocol was registered on Prospero before the start of the project (ID=CRD42016037935). Only longitudinal cohort studies were included. No language or time limits were used. The initial title & abstract screening included 92747 papers, whereas 2341 articles were retained for full text screening. Results will be presented, together with a discussion of the research and policy implications of this evidence synthesis.

Harmonizing datasets from existing research studies has many benefits: it increases sample size, improves the generalizability and comparability of results, and provides opportunities for collaborative research. However, this process is not always straightforward. Studies are usually very heterogeneous regarding study design, variables collected and follow-up time/number of waves. One of the aims of the ATHLOS project is to create of a harmonized dataset with over 341,000 individuals from 20 existing longitudinal studies of ageing using a rigorous process. This will include information on health status and functional limitations, lifestyles, social environment, among others. The harmonization framework is supported by Maelstrom-Research, an international research program who develops tools and platforms to ensure a systematic and rigorous harmonization activity. In order to explain this comprehensive process, during this symposium, we will present the challenges and successes of this phase of the project, together with a thorough explanation of the harmonization platform.