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Aalto UL, Knuutila M, Lehti T, Jansson A, Kautiainen H, Öhman H, Strandberg T, Pitkälä KH. Being actively engaged in life in old age: determinants, temporal trends, and prognostic value. Aging Clin Exp Res 2023:10.1007/s40520-023-02440-9. [PMID: 37225934 DOI: 10.1007/s40520-023-02440-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/08/2023] [Indexed: 05/26/2023]
Abstract
PURPOSE Recently, the concept of successful ageing has shifted from healthy ageing to active ageing, the latter emphasising even more the subjective perspective. Active agency is a marker for better functioning. However, the concept of active ageing lacks a clear definition so far. The specific aims of the study were to identify the determinants of being actively engaged in life (BAEL), to explore the changes in BAEL over 3 decades, and to explore the prognostic value of BAEL. METHODS This is a repeated cross-sectional cohort study of older (≥ 75 years) community-dwelling people in Helsinki in 1989 (N = 552), 1999 (N = 2396), 2009 (N = 1492), and 2019 (N = 1614). The data were gathered by a postal questionnaire at each time point. Being actively engaged in life was defined by two questions "Do you feel needed?" and "Do you have plans for the future?", which was further converted into BAEL score. RESULTS An increasing temporal trend in BAEL score was observed through the study years. Male sex, good physical functioning and subjective health, and meaningful social contacts were determinants for higher BAEL score. Active agency measured by BAEL score predicted lower 15-year mortality. CONCLUSIONS Older home-dwelling, urban Finnish people have become more actively engaged in recent years. The underlying causes are diverse but improved socioeconomic status observed over the study years was one of them. Social contacts and not feeling lonely were found to be determinants for being actively engaged. Two simple questions describing active engagement in life may help to predict mortality among older people.
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Affiliation(s)
- Ulla L Aalto
- Department of Geriatrics, Helsinki University Hospital, Helsinki, Finland.
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland.
| | - Mia Knuutila
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- Primary Health Care Unit, Helsinki University Hospital, Helsinki, Finland
- Social Services and Health Care, City of Helsinki, Helsinki, Finland
| | - Tuuli Lehti
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- Primary Health Care Unit, Helsinki University Hospital, Helsinki, Finland
- Social Services and Health Care, City of Helsinki, Helsinki, Finland
- Oulunkylä Rehabilitation Center, Helsinki, Finland
| | - Anu Jansson
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- The Finnish Association for the Welfare of Older Adults, Helsinki, Finland
| | - Hannu Kautiainen
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
| | - Hanna Öhman
- Department of Geriatrics, Helsinki University Hospital, Helsinki, Finland
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
| | - Timo Strandberg
- Department of Geriatrics, Helsinki University Hospital, Helsinki, Finland
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Kaisu H Pitkälä
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
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Barmpas P, Tasoulis S, Vrahatis AG, Georgakopoulos SV, Anagnostou P, Prina M, Ayuso-Mateos JL, Bickenbach J, Bayes I, Bobak M, Caballero FF, Chatterji S, Egea-Cortés L, García-Esquinas E, Leonardi M, Koskinen S, Koupil I, Paja̧k A, Prince M, Sanderson W, Scherbov S, Tamosiunas A, Galas A, Haro JM, Sanchez-Niubo A, Plagianakos VP, Panagiotakos D. A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project. Health Inf Sci Syst 2022; 10:6. [PMID: 35529251 PMCID: PMC9013733 DOI: 10.1007/s13755-022-00171-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/30/2022] [Indexed: 01/13/2023] Open
Abstract
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). Supplementary Information The online version contains supplementary material available at 10.1007/s13755-022-00171-1.
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Affiliation(s)
- Petros Barmpas
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Sotiris Tasoulis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Aristidis G. Vrahatis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | | | - Panagiotis Anagnostou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Matthew Prina
- Social Epidemiology Research Group. Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Global Health Institute, King’s College London, London, UK
| | - José Luis Ayuso-Mateos
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain
- Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IIS Princesa), Madrid, Spain
| | - Jerome Bickenbach
- Swiss Paraplegic Research, Guido A. Zäch Institute (GZI), Nottwil, Switzerland
- Department of Health Sciences & Health Policy, University of Lucerne, Lucerne, Switzerland
| | - Ivet Bayes
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Research, Innovation and Teaching Unit. Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Martin Bobak
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Francisco Félix Caballero
- Department Preventive Medicine and Public Health, Universidad Autónoma de Madrid, Idipaz, Madrid, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, CIBERESP, Madrid, Spain
| | - Somnath Chatterji
- Information, Evidence and Research, World Health Organization, Geneva, Switzerland
| | - Laia Egea-Cortés
- Research, Innovation and Teaching Unit. Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Esther García-Esquinas
- Department Preventive Medicine and Public Health, Universidad Autónoma de Madrid, Idipaz, Madrid, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, CIBERESP, Madrid, Spain
| | | | - Seppo Koskinen
- National Institute for Health and Welfare (THL), Helsinki, Finland
| | - Ilona Koupil
- Centre for Health Equity Studies, Department of Public Health Sciences, Stockholm University, Stockholm, Sweden
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Andrzej Paja̧k
- Department of Epidemiology and Population Studies, Jagienllonian University, Krakow, Poland
| | - Martin Prince
- Global Health Institute, King’s College London, London, UK
- Centre for Global Mental Health. Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Warren Sanderson
- International Institute for Applied Systems Analysis, World Population Program, Wittgenstein Centre for Demography and Global Human Capital, Laxenburg, Austria
- Department of Economics, Stony Brook University, Stony Brook, NY USA
| | - Sergei Scherbov
- International Institute for Applied Systems Analysis, World Population Program, Wittgenstein Centre for Demography and Global Human Capital, Laxenburg, Austria
- Austrian Academy of Science, Vienna Institute of Demography, Vienna, Austria
- Russian Presidential Academy of National Economy and Public Administration (RANEPA), Moscow, Russian Federation
| | | | - Aleksander Galas
- Department of Epidemiology and Preventive Medicine, Jagiellonian University, Krakow, Poland
| | - Josep Maria Haro
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Research, Innovation and Teaching Unit. Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Albert Sanchez-Niubo
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Research, Innovation and Teaching Unit. Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Vassilis P. Plagianakos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Demosthenes Panagiotakos
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
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Porhcisaliyan VD, Wang Y, Tan NC, Jafar TH. Socioeconomic status and ethnic variation associated with type 2 diabetes mellitus in patients with uncontrolled hypertension in Singapore. BMJ Open Diabetes Res Care 2021; 9:e002064. [PMID: 34301679 PMCID: PMC8728350 DOI: 10.1136/bmjdrc-2020-002064] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/30/2021] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION The burden of type 2 diabetes mellitus (T2DM) and related vascular complications is particularly high in Asians and ethnic minorities living in the West. However, the association of T2DM with socioeconomic status (SES) and ethnicity has not been widely studied in populations living in Asia. Therefore, we investigated these associations among the multiethnic population with uncontrolled hypertension in Singapore. RESEARCH DESIGN AND METHODS In a cross-sectional study using baseline data of a 2-year randomized trial in Singapore, we obtained demographic, SES, lifestyle and clinical factors from 915 patients aged ≥40 years with uncontrolled hypertension. T2DM was defined as having either: (i) self-reported 'physician-diagnosed diabetes confirmed through medical records' or taking antidiabetes medications, (ii) fasting blood glucose levels ≥7.0 mmol/dL or (iii) hemoglobin A1c ≥6.5%. The SES proxies included education, employment status, housing ownership and housing type, and the ethnicities were Chinese, Malays and Indians. Logistic regression analyses were used to evaluate the association of T2DM with SES and ethnicity. RESULTS Higher proportion of T2DM was observed in Malays (40.0%) and Indians (56.0%) than Chinese (26.8%) (p<0.001), and in patients with lower SES (ranging from 25.7% to 66.2% using different proxies) than those with higher SES (19.4% to 32.0%). In a multivariate model comprising age, gender, ethnicity and SES, Malay ethnicity (OR 1.59; 95% CI 1.04 to 2.44, p=0.031) or Indian ethnicity (OR 3.65; 95% CI 2.25 to 5.91, p<0.001) versus Chinese and housing type (residing in one to three rooms (OR 2.00; 95% CI 1.16 to 3.43, p=0.012) or four to five rooms public housing (OR 1.86; 95% CI 1.13 to 3.04, p=0.013) vs private housing) were associated with higher T2DM odds. The associations of Indians and one to three rooms public housing with T2DM met the significance after accounting for multiple testing (p≤0.0125). CONCLUSION Our study suggests that housing type and ethnic variation are independently associated with higher T2DM risk in patients with uncontrolled hypertension in Singapore. Further studies are needed to validate our results. TRIAL REGISTRATION NUMBER NCT02972619.
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Affiliation(s)
| | - Yeli Wang
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Ngiap Chuan Tan
- SingHealth Polyclinics, Singapore
- SingHealth-Duke NUS Family Academic Clinical Program, Singapore
| | - Tazeen H Jafar
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore
- Department of Renal Medicine, Singapore General Hospital, Singapore
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
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The WHO active ageing pillars and its association with survival: Findings from a population-based study in Spain. Arch Gerontol Geriatr 2020; 90:104114. [PMID: 32526561 DOI: 10.1016/j.archger.2020.104114] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 05/11/2020] [Accepted: 05/13/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND The World Health Organization's active ageing model is based on the optimisation of four key "pillars": health, lifelong learning, participation and security. It provides older people with a policy framework to develop their potential for well-being, which in turn, may facilitate longevity. We sought to assess the effect of active ageing on longer life expectancy by: i) operationalising the WHO active ageing framework, ii) testing the validity of the factors obtained by analysing the relationships between the pillars, and iii) exploring the impact of active ageing on survival through the health pillar. METHODS Based on data from a sample of 801 community-dwelling older adults, we operationalised the active ageing model by taking each pillar as an individual construct using principal component analysis. The interrelationship between components and their association with survival was analysed using multiple regression models. RESULTS A three-factor structure was obtained for each pillar, except for lifelong learning with a single component. After adjustment for age, gender and marital status, survival was only significantly associated with the physical component of health (HR = 0.66; 95% CI = 0.47-0.93; p = 0.018). In turn, this component was loaded with representative variables of comorbidity and functionality, cognitive status and lifestyles, and correlated with components of lifelong learning, social activities and institutional support. CONCLUSION According to how the variables clustered into the components and how the components intertwined, results suggest that the variables loading on the biomedical component of the health pillar (e.g. cognitive function, health conditions or pain), may play a part on survival chances.
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Liotta G, Ussai S, Illario M, O'Caoimh R, Cano A, Holland C, Roller-Wirnsberger R, Capanna A, Grecuccio C, Ferraro M, Paradiso F, Ambrosone C, Morucci L, Scarcella P, De Luca V, Palombi L. Frailty as the Future Core Business of Public Health: Report of the Activities of the A3 Action Group of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15122843. [PMID: 30551599 PMCID: PMC6313423 DOI: 10.3390/ijerph15122843] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 12/06/2018] [Accepted: 12/09/2018] [Indexed: 12/19/2022]
Abstract
Background: The prevalence of frailty at population-level is expected to increase in Europe, changing the focus of Public Health. Here, we report on the activities of the A3 Action Group, focusing on managing frailty and supporting healthy ageing at community level. Methods: A three-phased search strategy was used to select papers published between January 2016 and May 2018. In the third phase, the first manuscript draft was sent to all A3-Action Group members who were invited to suggest additional contributions to be included in the narrative review process. Results: A total of 56 papers were included in this report. The A3 Action Group developed three multidimensional tools predicting short–medium term adverse outcomes. Multiple factors were highlighted by the group as useful for healthcare planning: malnutrition, polypharmacy, impairment of physical function and social isolation were targeted to mitigate frailty and its consequences. Studies focused on the management of frailty highlighted that tailored interventions can improve physical performance and reduce adverse outcomes. Conclusions: This review shows the importance of taking a multifaceted approach when addressing frailty at community level. From a Public Health perspective, it is vital to identify factors that contribute to successful health and social care interventions and to the health systems sustainability.
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Affiliation(s)
- Giuseppe Liotta
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy.
| | - Silvia Ussai
- International Healthcare Programs, Lombardy Region/LISPA, 20124 Milan, Italy.
| | - Maddalena Illario
- Unità Operativa Dipartimentale 14 Promozione e Potenziamento dei Programmi di Healths Innovation, Direzione Generale per la Tutela della Salute ed il Coordinamento del Sistema Sanitario Regionale, Regione Campania, 80143 Naples, Italy.
- Dipartimento di Scienze Mediche Traslazionali, Università degli Studi di Napoli Federico II, 80138 Naples, Italy.
| | - Rónán O'Caoimh
- Clinical Sciences Institute, National University of Ireland, Galway, Galway City, H91 TK33 Ireland.
| | - Antonio Cano
- Department of Pediatrics, Obstetrics and Gynecology, University of Valencia-INCLIVA, 46010 Valencia, Spain.
| | - Carol Holland
- Centre for Ageing Research, University of Lancaster, Lancaster, LA1 4YG, UK.
| | | | - Alessandra Capanna
- School of Specialization in Hygiene and Medicine Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy.
| | - Chiara Grecuccio
- School of Specialization in Hygiene and Medicine Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy.
| | - Mariacarmela Ferraro
- School of Specialization in Hygiene and Medicine Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy.
| | - Francesca Paradiso
- School of Specialization in Hygiene and Medicine Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy.
| | - Cristina Ambrosone
- School of Specialization in Hygiene and Medicine Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy.
| | - Luca Morucci
- School of Specialization in Hygiene and Medicine Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy.
| | - Paola Scarcella
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy.
| | - Vincenzo De Luca
- Unità Operativa Semplice Ricerca e Sviluppo, Azienda Ospedaliera Universitaria Federico II, 80138 Naples, Italy.
| | - Leonardo Palombi
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", 00133 Rome, Italy.
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