Suchergebnisse

ObjectivesMany studies use a reductionist approach to isolate the influence of one factor in childhood on multimorbidity rather than consider the combined effect of wider determinants. We aimed to explore how potential multiple early-life determinants of multimorbidity can be audited and characterised across three UK cohort studies.
ApproachWe used the 1958 National Child Development Study (NCDS), the 1970 British Cohort Study (BCS70), and the Aberdeen Children of the 1950s Study (ACONF) to identify and categorise early-life variables that fit into 12 previously conceptualised domains of early-life determinants of multimorbidity. A data audit manually assigned variables into the domain they best represented. Principal component analysis (PCA) reduced the dimensionality of the data and structured variables into subgroups. Important PCA components defined as the component that contributed the greatest proportion of the overall variance were identified.
ResultsSeven domains were characterised by 74 variables in ACONF, ten domains by 143 variables in the NCDS, and twelve domains by 289 variables in the BCS70. PCA analysis reduced the dimensionality of ACONF variables from 74 to 41, from 143 to 73 in the NCDS, and from 289 to 149 in the BCS70. Important PCA components included maternal fertility histories, long-term illnesses, educational ability, ethnicity, housing status and parental-child interactions.
Conclusions and ImplicationsConceptualising the risk of future multimorbidity as lifecourse domains composed of multiple factors can help challenge the existing understanding of disease aetiology and inform strategies for the prevention of multimorbidity.

ObjectivesThrough the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) project, we established the SAIL MELD-B e-cohort (SMC) and the SAIL MELD-B Young adults e-cohort (SMYC), with the aim to enhance the understanding of 'burdensomeness' in individuals living with multimorbidity, including identifying new clusters of burdensome indicators, exploring early life risk factors and modelling potential preventative scenarios.
ApproachWe use routinely-collected anonymised linked demographic, health and administrative data sources available within the SAIL Databank to define SMC and SMYC. These cohorts were developed using a reproducible, maintainable, methodological pipeline that allows for dynamic updates as data coverage expands. The pipeline efficiently processes new burdensomeness concepts, facilitating the extraction of relevant records associated with the concepts identified for use in the SMC and SMYC.
ResultsSMC and SMYC comprises of 5,180,602 and 896,155 individuals registered with a Welsh General Practice at any time between 1st January 2000 and 31st December 2022 respectively. Analysis of primary and secondary care health data reveals that the most common conditions in SMC were depression (21.6%), anxiety (21.1%), asthma (17.5%), hypertension (16.2%), and atopic eczema (14.1%). In SMYC, the most common conditions were atopic eczema (21.2%), asthma (11.6%), anxiety (6.0%), deafness (4.6%), and depression (4.3%).
Conclusions and ImplicationsSMC and SMYC provide two generalisable population samples, which can be used to address various research questions across MELD-B. The adaptability of the methodological pipeline allows cohort curation to be repurposed for other projects accessing population-scale data sources and trusted research environments.