Pitfalls in understanding how multiple long-term conditions cluster: whole population and age-stratified associations in 7,490,874 people in England
Romero Moreno, G.; Restocchi, V.; Lone, N.; Fleuriot, J. D.; Palmer, J.; De Ferrari, L.; Guthrie, B.
Show abstract
Studies of how multiple long-term conditions (MLTC) cluster together in individuals vary in the populations studied, and whether they age and/or sex stratify, which limits comparison between studies and reproducibility. This study uses a large, UK primary-care dataset to examine how pairwise strength of association between 74 conditions varies by age in both men and women aged 30-99 years, and to explore implications for MLT cluster analyses. Joint prevalence of conditions was lowest in younger age-groups and progressively increased with age, whereas Association Beyond Chance (ABC) was highest in younger age-groups and progressively decreased with age. Condition clustering based on ABC identified different clusters in all men and all women aged 30-99 years, and these clusters differed from those identified in each age-group. Researchers examining how MLTC cluster should consider whether age and sex stratification is appropriate given their study aims and/or would improve comparability and reproducibility, and explicitly justify their choices.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Social isolation is linked to classical risk factors of Alzheimer's disease-related dementias 94%
- Impact of COVID-19 lockdown on psychosocial factors, health, and lifestyle in Scottish octogenarians: the Lothian Birth Cohort 1936 Study 93%
- Cohort Profile: Genetic data in the German Socio-Economic Panel Innovation Sample (Gene-SOEP) 93%
Similar papers in this journal
- Cross-classification between self-rated health and health status: longitudinal analyses of all-cause mortality and leading causes of death in the UK 94%
- Nationwide prediction of type 2 diabetes comorbidities 93%
- Comparison of COVID-19 outcomes among shielded and non-shielded populations: A general population cohort study of 1.3 million 93%
Similar papers in this journal
- Complex patterns of multimorbidity associated with severe COVID-19 and Long COVID 92%
- Sex-specific transcriptome similarity networks elucidate comorbidity relationships 92%
- The Interpretable Multimodal Machine Learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy 92%
Similar papers in this journal
- Risk factors for long COVID: analyses of 10 longitudinal studies and electronic health records in the UK 95%
- A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk 94%
- Trends and associated factors for Covid-19 hospitalisation and fatality risk in 2.3 million adults in England 93%
Similar papers in this journal
- Inequalities in healthcare disruptions during the Covid-19 pandemic: Evidence from 12 UK population-based longitudinal studies 93%
- Identifying markers of health-seeking behaviour and healthcare access in UK electronic health records 93%
- Estimating excess mortality in people with cancer and multimorbidity in the COVID-19 emergency 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.