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The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP).

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.

Preventive healthcare is a crucial pillar of health as it contributes to staying healthy and having immediate treatment when needed. Mining knowledge from longitudinal studies has the potential to significantly contribute to the improvement of preventive healthcare. Unfortunately, data originated from such studies are characterized by high complexity, huge volume, and a plethora of missing values. Machine Learning, Data Mining and Data Imputation models are utilized a part of solving these challenges, respectively. Toward this direction, we focus on the development of a complete methodology for the ATHLOS Project – funded by the European Union’s Horizon 2020 Research and Innovation Program, which aims to achieve a better interpretation of the impact of aging on health. The inherent complexity of the provided dataset lies in the fact that the project includes 15 independent European and international longitudinal studies of aging. In this work, we mainly focus on the HealthStatus (HS) score, an index that estimates the human status of health, aiming to examine the effect of various data imputation models to the prediction power of classification and regression models. Our results are promising, indicating the critical importance of data imputation in enhancing preventive medicine’s crucial role.

Although we are currently overwhelmed by the astonishing speed of infection of the Covid-19 pandemic, and the daily onslaught of new, and ever-worsening predictions, it is vital that we begin to prepare for the aftershocks of the pandemic. Prominent among this will be the cohort of post-intensive case survivors who have been mechanically ventilated and will like experience short- and medium-term consequences of the experience. The notion that patients surviving intensive care and mechanical ventilation for several weeks can be discharged home without further medical attention is a dangerous illusion. Post Intensive Care Syndrome and other severe conditions will require not only adequate screening but early rehabilitation and other interventions. Action must be taken now to prepare for this inevitable shock to the healthcare system.

On 2 December 2020 the World Health Organization (WHO) announced key findings from the newly released “Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019” published in The Lancet (1), promoting the upcoming WHO Rehabilitation Need Estimator aligned with these global estimate data. Using data from the 2019 Global Burden of Disease, Injuries, and Risk Factors Study (2), the estimates were based on the prevalence and years of life lived with disability (YLDs) of 25 diseases and impairments selected as amenable to rehabilitation. The Lancet paper presents the headline number that, globally, 2.41 billion individuals live with conditions who could benefit from rehabilitation. Musculoskeletal disorders contributed the most to this total, with a prevalence of 1.71 billion people. The Lancet paper discusses implications for the field of rehabilitation and for country-level rehabilitation priority-setting and decisions on programmes to address rehabilitation needs. The objective of this paper is to put this milestone study of global estimates of rehabilitation need into an historical perspective, and, in particular, to reflect on its implications for the WHO’s broader agenda, “Rehabilitation 2030: A Call for Action”, and for Physical and Rehabilitation Medicine more generally. This paper also briefly elaborates the opportunities for the European Academy of Rehabilitation Medicine (EARM) to contribute to the WHO’s Call for Action., EARM members EARM members collaborating in this paper (in alphabetical order): Jari Arokoski (University of Helsinki, Helsinki, Finland), Mercè Avellanet (Hospital Nostra Senyora de Meritxell, Escaldes-Engordany, Andorra), Kristian Borg (Karolinska Institutet, Danderyd University Hospital, Stockholm, Sweden), Helena Burger (University Rehabilitation Institute, Ljubljana, Slovenia), Maria Gabriella Ceravolo (“Politecnica delle Marche” University, Ancona, Italy), Joaquim Chaler (EUSES, Universitat de Girona-Universitat de Barcelona, Barcelona, Spain), Anne Chamberlain (University of Leeds Faculty of Medicine, Leeds, UK), Richard Crevenna (Medical University of Vienna, Vienna, Austria), Thierry Deltombe (CHU UCL Namur site Godinne, Yvoir, Belgium), Jean-Pierre Didier (Université de Bourgogne, Dijon, France), Gabor Fazekas (National Institute for Medical Rehabilitation, Budapest, Hungary), Joan García Alsina (Hospital Universitario de Bellvitge. Barcelona, Spain), Christoph Gutenbrunner (Hannover Medical School, Hannover, Germany), Elena Ilieva (Medical University of Plovdiv, Bulgaria), Alvydas Juocevicius (Klaipeda University, Klaipeda, Lithuania), Jaro Karppinen (Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland), Jolanta Ewa Kujawa (Medical University of Lodz, Lodz, Poland), Jorge Lains (Rehabilitation Medicine Center for the Central Region, Rovisco Pais, Tocha, Portugal), Antti Malmivaara (National Institute for Health and Welfare, Helsinki, Finland), Angela McNamara (National Rehabilitation Hospital, Dublin, Ireland), Xanthi Michail (University of West Attica, Athens, Greece), Rory J. O’Connor (University of Leeds, Leeds, UK), Jean Paysant (Regional Rehabilitation Institute, Nancy, France), Brigitte Perrouin-Verbe (University of Nantes, France), E. Diane Playford (University of Warwick, Coventry, UK), Konstantina Petropoulou (National Rehabilitation Center, Athens, Greece), Hans Rietman (University of Twente, Enschede, The

We investigated the relation between alcohol drinking and healthy ageing by means of a validated health status metric, using individual data from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. For the purposes of this study, the ATHLOS harmonised dataset, which includes information from individuals aged 65+ in 38 countries, was analysed (n = 135,440). Alcohol drinking was reflected by means of three harmonised variables: alcohol drinking frequency, current and past alcohol drinker. A set of 41 self-reported health items and measured tests were used to generate a specific health metric. In the harmonised dataset, the prevalence of current drinking was 47.5% while of past drinking was 26.5%. In the pooled sample, current alcohol drinking was positively associated with better health status among older adults ((b-coef (95% CI): 1.32(0.45 to 2.19)) and past alcohol drinking was inversely related (b-coef (95% CI): −0.83 (−1.51 to −0.16)) with health status. Often alcohol consumption appeared to be beneficial only for females in all super-regions except Africa, both age group categories (65–80 years old and 80+), both age group categories, as well as among all the financial status categories (all p < 0.05). Regional analysis pictured diverse patterns in the association for current and past alcohol drinkers. Our results report the need for specific alcohol intake recommendations among older adults that will help them maintain a better health status throughout the ageing process.