Clustering & trajectories of multimorbidity across the lifecourse: a 70 year birth cohort study. (13th November 2019)

• Record Type: Journal Article
• Authors: Khanolkar, A R; Patalay, P
• Is Part Of: European journal of public health. Volume 29(2019)Supplement 4
• Publication Date: 2019-11-13
• Subjects: Epidemiology -- Europe -- Periodicals; Public health -- Europe -- Periodicals; 362.109405
• DOI: 10.1093/eurpub/ckz186.228
• Languages: English
• ISSNs: 1101-1262

Abstract

Abstract: Background: Multimorbidity (≥2 chronic diseases) is increasingly prevalent in ageing populations and presents a public health challenge in successful disease management. Most evidence for multimorbidity at different ages comes from cross-sectional data, hindering understanding the extent and types of multimorbidity across the lifecourse, how they develop and associated risk factors. The aim of this study is to investigate the clustering and patterning of multimorbidity over the lifecourse using one of the longest running national birth cohort studies. Methods: This ongoing study is based on the National Survey of Health & Development (NSHD), a birth cohort study following 5, 362 individuals born in 1946 with detailed sociodemographic and clinical data collected from 22 waves across the lifecourse till date. This study will investigate the trajectories and clustering of 24 diseases (physical and mental health) and potential differences by sex and socioeconomic status using mixed-effects linear spline modelling. Results: Preliminary analyses indicate that as expected, the rates of many conditions increase with age (e.g. 13% obese at age 43 to 31% at age 69), increasing the likelihood of individuals suffering from multiple conditions with age. At age 63, 73% with diabetes had hypertension, increasing to 85% with hypertension at age 69. We will estimate longitudinal trajectories of multimorbidity for individuals and whether the age of onset and rate of accumulation vary by sex, life-period and SES. Given the longitudinal nature of the data, we will investigate the extent to which multimorbidity earlier in the lifecourse predicts the rate of further multimorbidity later in the lifecourse. Conclusions: Understanding patterning and trajectories of multimorbidity over the lifecourse and associated inequalities will better inform health care provision planning including appropriate window periods for intervention, specifically for the disadvantaged at higher risk of high multimorbidity. Key messages: This is the first study to investigate trajectories of multimorbidity with data from birth to old age. Understanding how early life factors predict later life multimorbidity will better inform healthcare planning.