Evidence map›Paper›PMID 29346518›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2018

Determinants of Health Trajectories in England and the United States: An Approach to Identify Different Patterns of Healthy Aging.

Javier de la Fuente, Francisco Félix Caballero, Albert Sánchez-Niubó, Demosthenes B Panagiotakos, A Matthew Prina, Holger Arndt, Josep Maria Haro, Somnath Chatterji, José Luis Ayuso-Mateos

Abstract read
In one paragraph

Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

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  8. Exploring Social Determinants of Health in Healthy Aging Among Older Adults: A Qualitative Study.Progress in community health partnerships : research, education, and action · 2023
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  19. The impact of physical activity on healthy ageing trajectories: evidence from eight cohort studies.The international journal of behavioral nutrition and physical activity · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Javier de la FuenteDepartment of Psychiatry, Universidad Autónoma de Madrid, Spain.
Francisco Félix CaballeroDepartment of Psychiatry, Universidad Autónoma de Madrid, Spain.
Albert Sánchez-NiubóParc Sanitari Sant Joan de Déu, Barcelona, Spain.
Demosthenes B PanagiotakosDepartment of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece.
A Matthew PrinaDepartment of Health Service and Population Research, King's College London, Institute of Psychiatry, Psychology and Neuroscience, UK.
Holger ArndtSPRING TECHNO GMBH & Co. KG, Bremen, Germany.
Josep Maria HaroCIBER of Mental Health, Madrid, Spain.
Somnath ChatterjiInformation, Evidence and Research, World Health Organization, Geneva, Switzerland.
José Luis Ayuso-MateosDepartment of Psychiatry, Universidad Autónoma de Madrid, Spain.

Funding

HRS Yrs29-34: Y33 SSA CoFundingU01AG009740 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jessica Faul, KENNETH M LANGA · 1990 to 2026
$555.8M
NIA NIH HHS U01 AG009740World Health Organization 001
6 · The paper itself

Abstract

Background: Aging is a multidimensional process with a remarkable interindividual variability. This study is focused on identifying groups of population with similar aging patterns, and to define the health trajectories of these groups. Sociodemographic and health determinants of these trajectories are also identified. Methods: Data from the English Longitudinal Study of Aging (ELSA) and the Health and Retirement Study (HRS) were used. A set of self-reported health items and measured tests were used to generate a latent health metric by means of a Bayesian multilevel IRT model, assessing the ability of the metric to predict mortality. Then, a Growth Mixture Model (GMM) was conducted in each study to identify latent classes and assess health trajectories. Kaplan-Meier survival curves were obtained for each class and a multinomial logistic regression was used to identify determinants of these trajectories. Results: The health score generated showed an adequate ability to predict mortality over 10 years in ELSA (AUC = 0.74; 95% CI: 0.72, 0.75) and HRS (AUC = 0.74; 95% CI: 0.73, 0.75). By means of GMM, four latent classes were identified in ELSA and five in HRS. Chronic conditions, no qualification and low level of household wealth were associated to the classes which showed a higher mortality in both studies. Conclusion: The method based on the creation of a common metric of health and the use of GMM to identify similar patterns of aging, allows for the comparison of trajectories of health across longitudinal surveys. Multimorbidity, educational level, and household wealth could be considered as determinants associated to these trajectories.

Indexed as

Healthy AgingAgedAged, 80 and overChronic DiseaseEducational StatusEnglandFemaleHealth SurveysHumansIncomeKaplan-Meier EstimateLongitudinal StudiesMaleMiddle AgedModels, StatisticalMortality

Identifiers

PMID29346518
PMCPMC6175023

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.