Conference Proceeding from International Population Data Linkage Conference 202

Agrawal, U

Akbari, A

Bailey, R

Dezateux, C

Fry, R

Harper, G

Lyons, J

Lyons, R

McCowan, C

Rafferty, J

Robson, J

Watkins, A

International Journal for Population Data Science

English

Queen Mary Research Online

International Journal of Population Data Science (2020) 5:5:011International Journal ofPopulation Data ScienceJournal Website: www.ijpds.orgIdentifying Patterns of Comorbidities with Cancers of the Colon and Rectum,as Related to age at DiagnosisLyons, J1, Agrawal, U2, Harper, G1, Akbari, A1, Bailey, R1, Fry, R1, Rafferty, J1, Watkins, A1, Robson, J3, Dezateux, J3, McCowan,C2, and Lyons, R11Health Data Research UK, Swansea University2Health Data Research UK, St Andrews University3Health Data Research UK, Queen Mary University of LondonIntroductionMulti-morbidity is a widely recognised but poorly understoodglobal issue that appears to be increasing in prevalence, ac-cording to the UK’s Academy of Medical Sciences (AMS) re-port in 2018. Disease clustering, their determinants and con-sequences are poorly researched. Better understanding wouldhelp drive prevention and improved clinical care, services andpatient outcomes.Objectives and ApproachDevelopment of two comprehensive population-wide e-cohorts,derived utilising data linkage techniques and including multi-sourced anonymised routine health and demographic data heldwithin the SAIL Databank. The objective is to characterisemulti-morbidity and its clustering, determinants and outcomesand compare methods using a) prospective cohort design us-ing multiple data sources in Wales and b) retrospective cohortdesign to examine household level and environment clusteringusing GP data in demographically diverse populations (Walesand North East London).The prospective e-cohort focuses on adults living in Waleson 1st January 2000 and followed up to 2020, including datafrom the NHS population register, deaths, inpatients, outpa-tients, Emergency Department, GP, disease registries, labora-tory data, and population surveys with QoL measures. Thise-cohort will be harmonised with other sites across the UK.The retrospective e-cohort is designed to harmonise with aNorth East London e-cohort, including all individuals living inWales on 24 th April 2018 and registered with a GP.Results2.8 and 2.2 million individuals have been included in theprospective and retrospective cohorts respectively, with 43.6million person years of follow up. Established comorbidity in-dices and published phenotypes from libraries are being appliedto the data to create initial prevalence and incidence estimatesfor further analysis. Important clusters will be determined byassociations with mortality and excess healthcare utilisation.Conclusion/ImplicationsBuilding the e-cohorts has involved multiple disciplines acrossorganisations. Multi- morbidity prevalence estimates andstudy designs will be compared prior to statistical analysesand machine learning methods to evaluate clustering and de-terminants.∗Corresponding Author:Email Address: J.Lyons@Swansea.ac.uk (J Lyons)https://doi.org/10.23889/ijpds.v5i5.1430November 26, 2020 © The Authors. Open Access under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.en)

Identifying Patterns of Comorbidities with Cancers of the Colon and Rectum, as Related to age at Diagnosis

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Identifying Patterns of Comorbidities with Cancers of the Colon and Rectum, as Related to age at Diagnosis

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