Evidence map›Paper›PMID 33822803›Full record

ArticlePloS one2021

Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts.

Hai Nguyen, Dario Moreno-Agostino, Kia-Chong Chua, Silia Vitoratou, A Matthew Prina

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In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

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3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Hai NguyenHealth Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.ORCID 0000-0003-2171-1955
Dario Moreno-AgostinoHealth Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
Kia-Chong ChuaCentre for Implementation Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
Silia VitoratouBiostatistics and Health Informatics Department, Psychometrics and Measurement Lab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
A Matthew PrinaHealth Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Funding

Medical Research Council MC_PC_MR/T037423/1Medical Research Council MR/S028188/1Medical Research Council MR/T037423/1Medical Research Council MR/T038500/1
6 · The paper itself

Abstract

objectivesIn this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time. SETTING AND

participantsOur study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2-7 of the English Longitudinal Study of Ageing (ELSA).

methodsUsing a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors.

resultsThree similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a 'high stable' group (76% in ATHLOS, 61% in ELSA), 2) a 'low stable' group (22% in ATHLOS, 36% in ELSA) and 3) a 'rapid decline' group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4-2.1) more likely to be in the 'rapid decline' group and 11.7 times (OR = 11.7 95% CI: 10.9-12.6) more likely to be in the 'low stable' group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the 'rapid decline' and the 'low stable' groups (OR = 2.1, 95% CI: 1.2-3.8 and OR = 9.8, 95% CI: 7.5-12.7 respectively).

conclusionHealthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity.

Indexed as

Healthy AgingMultimorbidityAgedAged, 80 and overFemaleHumansLife StyleLongitudinal StudiesMaleMiddle AgedRisk Factors

Identifiers

PMID33822803
PMCPMC8023455

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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.