Understanding the prevalence of multimorbidity in people of later working age and the impact of multimorbidity definition on characteristics of the cohort
Principal Investigator: Dr Sarah Damery
Approved Research ID: 33503
Approval date: April 24th 2018
We aim to understand the prevalence of multimorbidity in people of later working age (40 to 64) in a large sample of older UK adults. Specific objectives are to: ? Assess different approaches to defining and identifying multimorbidity in adults of later working age ? Investigate multimorbidity prevalence in the cohort identified using each definitional approach and assess any overlaps between groups ? Determine the socio-demographic and lifestyle characteristics of patients identified in each approach to defining multimorbidity ? To compare prevalence and characteristics of the later working age cohort(s) with existing evidence on multimorbidity in the population aged 65+ The UK Biobank supports research that intends to improve prevention, diagnosis and treatment of illness and promotion of health. This project aligns closely with these objectives: multimorbidity presents a growing challenge for organising and delivering NHS and social care services. However, prevalence varies according to definitions used (e.g. 2+ conditions vs. 3+ conditions), and this may have implications for service design and patient management. The nature of multimorbidity is known to vary with age, and understanding multimorbidity in people of working age is a pre-requisite to developing effectively targeted strategies to identify patients and for designing appropriate services. We will quantify multimorbidity prevalence using several definitions (2+ or 3+ chronic conditions, ?complex? multimorbidity with multiple conditions affecting multiple body systems, influence of ?sentinel? conditions like cardiovascular disease). Descriptive statistics will assess the extent to which the same patients feature in different groups. Sociodemographic (e.g. age, gender) and lifestyle characteristics will be described, and binary logistic regression will generate odds ratios and p values for associations between specific characteristics and multimorbidity presence/absence. Future research using linked data will investigate the relationship between multimorbidity and health-related outcomes (e.g. primary and secondary care use when data permit). We request access to the full cohort as information on medical conditions and self-reported chronic diseases was collected from all participants. This will allow the identification of patient groups with multimorbidity according to different definitions, including a comparison group with 1 or no chronic conditions. It will also allow us to fully scope which linked data would be most appropriate to assess health-related outcomes and healthcare utilisation in a future UK Biobank application.