Studying trajectories of multimorbidity: a systematic scoping review of longitudinal approaches and evidence

Cézard, Geneviève; McHale, Calum Thomas; Sullivan, Frank; Bowles, Juliana; Keenan, Katherine

doi:10.1136/bmjopen-2020-048485

Cited by 60 publications

(53 citation statements)

References 71 publications

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“…Our work has clinical and substantive implications. Systematic reviews of trajectory analyses in health-related research [ 1 – 4 , 20 ] report heterogeneous results, suggesting that studies generally do not replicate trajectories previously identified. This may be explained partially by spurious findings [ 20 ], although differences across samples including number of data points and longer time intervals between data points may also underpin failure to replicate previous findings.…”

Section: Discussionmentioning

confidence: 99%

“…Systematic reviews of trajectory analyses in health-related research [ 1 – 4 , 20 ] report heterogeneous results, suggesting that studies generally do not replicate trajectories previously identified. This may be explained partially by spurious findings [ 20 ], although differences across samples including number of data points and longer time intervals between data points may also underpin failure to replicate previous findings. The use of GBTM in studies linking patterns of exposure to outcome measures at the end of follow-up (or subsequent clinical outcomes) may also lead to invalid results if the trajectories identified are spurious.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we describe a case-study using simulated data to illustrate that: (i) GBTM can produce spurious findings; and (ii) model adequacy criteria can fail to identify model misspecification. Despite guidelines [ 5 ] and best practices [ 16 ] recommending using numerous criteria to select the number of trajectory subgroups, recent reviews of trajectory modelling using GBTM in the health-related literature [ 17 – 20 ] suggest that BIC and APP are often the only criteria used. Herein we investigate the BIC and the APP because they are currently the only criteria for selecting the number of trajectory subgroups and model adequacy, respectively that are computed by the software used by most researchers to estimate GBTM [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Does group-based trajectory modeling estimate spurious trajectories?

Mésidor

Rousseau

O’Loughlin

et al. 2022

BMC Med Res Methodol

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Background Group-based trajectory modelling (GBTM) is increasingly used to identify subgroups of individuals with similar patterns. In this paper, we use simulated and real-life data to illustrate that GBTM is susceptible to generating spurious findings in some circumstances. Methods Six plausible scenarios, two of which mimicked published analyses, were simulated. Models with 1 to 10 trajectory subgroups were estimated and the model that minimized the Bayes criterion was selected. For each scenario, we assessed whether the method identified the correct number of trajectories, the correct shapes of the trajectories, and the mean number of participants of each trajectory subgroup. The performance of the average posterior probabilities, relative entropy and mismatch criteria to assess classification adequacy were compared. Results Among the six scenarios, the correct number of trajectories was identified in two, the correct shapes in four and the mean number of participants of each trajectory subgroup in only one. Relative entropy and mismatch outperformed the average posterior probability in detecting spurious trajectories. Conclusion Researchers should be aware that GBTM can generate spurious findings, especially when the average posterior probability is used as the sole criterion to evaluate model fit. Several model adequacy criteria should be used to assess classification adequacy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Does group-based trajectory modeling estimate spurious trajectories?

Mésidor

Rousseau

O’Loughlin

et al. 2022

BMC Med Res Methodol

View full text Add to dashboard Cite

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“…There is little established evidence on effective means of identifying risk factors or preventing and managing multimorbidity throughout the life course [8,9]. The vast majority of multimorbidity studies aim to identify disease clusters at a single point in time, providing little information about how multimorbidity develops over time within individuals [10]. This study will develop a system where innovative AI approaches are used to analyze complex longitudinal data and predict risk levels of multimorbidity.…”

Section: Rationalementioning

confidence: 99%

Life Course Digital Twins–Intelligent Monitoring for Early and Continuous Intervention and Prevention (LifeTIME): Proposal for a Retrospective Cohort Study

Milne‐Ives¹,

Fraser²,

Khan³

et al. 2022

JMIR Res Protoc

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Background Multimorbidity, which is associated with significant negative outcomes for individuals and health care systems, is increasing in the United Kingdom. However, there is a lack of knowledge about the risk factors (including health, behavior, and environment) for multimorbidity over time. An interdisciplinary approach is essential, as data science, artificial intelligence, and engineering concepts (digital twins) can identify key risk factors throughout the life course, potentially enabling personalized simulation of life-course risk for the development of multimorbidity. Predicting the risk of developing clusters of health conditions before they occur would add clinical value by enabling targeted early preventive interventions, advancing personalized care to improve outcomes, and reducing the burden on health care systems. Objective This study aims to identify key risk factors that predict multimorbidity throughout the life course by developing an intelligent agent using digital twins so that early interventions can be delivered to improve health outcomes. The objectives of this study are to identify key predictors of lifetime risk of multimorbidity, create a series of simulated computational digital twins that predict risk levels for specific clusters of factors, and test the feasibility of the system. Methods This study will use machine learning to develop digital twins by identifying key risk factors throughout the life course that predict the risk of later multimorbidity. The first stage of the development will be the training of a base predictive model. Data from the National Child Development Study, the North West London Integrated Care Record, the Clinical Practice Research Datalink, and Cerner’s Real World Data will be split into subsets for training and validation, which will be done following the k-fold cross-validation procedure and assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). In addition, 2 data sets—the Early-Life Data Cross-linkage in Research study and the Children and Young People’s Health Partnership randomized controlled trial—will be used to develop a series of digital twin personas that simulate clusters of factors to predict different risk levels of developing multimorbidity. Results The expected results are a validated model, a series of digital twin personas, and a proof-of-concept assessment. Conclusions Digital twins could provide an individualized early warning system that predicts the risk of future health conditions and recommends the most effective intervention to minimize that risk. These insights could significantly improve an individual’s quality of life and healthy life expectancy and reduce population-level health burdens. International Registered Report Identifier (IRRID) PRR1-10.2196/35738

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“…The incidence of multimorbidity is latent, and the progression is slow [8]. If early detection and diagnosis are not efficient and timely, this not only will delay treatment and prognosis and affect the development of the disease but also may lead to premature death [9]. Therefore, understanding the patterns and associations among multimorbid conditions should be given priority, which may assist the early diagnosis of multimorbidity and thus improve quality of life of older adults [10].…”

Section: Introductionmentioning

confidence: 99%

Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records

Zhang¹,

Cc²,

Huang³

et al. 2022

JMIR Public Health Surveill

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Background Multimorbidity has become a new challenge for medical systems and public health policy. Understanding the patterns of and associations among multimorbid conditions should be given priority. It may assist with the early detection of multimorbidity and thus improve quality of life in older adults. Objective This study aims to comprehensively analyze and compare associations among multimorbid conditions by age and sex in a large number of middle-aged and older Chinese adults. Methods Data from the home pages of inpatient medical records in the Shenzhen National Health Information Platform were evaluated. From January 1, 2017, to December 31, 2018, inpatients aged 50 years and older who had been diagnosed with at least one of 40 conditions were included in this study. Their demographic characteristics (age and sex) and inpatient diagnoses were extracted. Association rule mining, Chi-square tests, and decision tree analyses were combined to identify associations between multiple chronic conditions. Results In total, 306,264 hospitalized cases with available information on related chronic conditions were included in this study. The prevalence of multimorbidity in the overall population was 76.46%. The combined results of the 3 analyses showed that, in patients aged 50 years to 64 years, lipoprotein metabolism disorder tended to be comorbid with multiple chronic conditions. Gout and lipoprotein metabolism disorder had the strongest association. Among patients aged 65 years or older, there were strong associations between cerebrovascular disease, heart disease, lipoprotein metabolism disorder, and peripheral vascular disease. The strongest associations were observed between senile cataract and glaucoma in men and women. In particular, the association between osteoporosis and malignant tumor was only observed in middle-aged and older men, while the association between anemia and chronic kidney disease was only observed in older women. Conclusions Multimorbidity was prevalent among middle-aged and older Chinese individuals. The results of this comprehensive analysis of 4 age-sex subgroups suggested that associations between particular conditions within the sex and age groups occurred more frequently than expected by random chance. This provides evidence for further research on disease clusters and for health care providers to develop different strategies based on age and sex to improve the early identification and treatment of multimorbidity.

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Studying trajectories of multimorbidity: a systematic scoping review of longitudinal approaches and evidence

Cited by 60 publications

References 71 publications

Does group-based trajectory modeling estimate spurious trajectories?

Does group-based trajectory modeling estimate spurious trajectories?

Life Course Digital Twins–Intelligent Monitoring for Early and Continuous Intervention and Prevention (LifeTIME): Proposal for a Retrospective Cohort Study

Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records

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