Scottish Longitudinal Study
Development & Support Unit

Current Projects

Project Title:

Social inequalities in chronic disease trajectories in mid and later life: taking account of multimorbidities

Project Number:

2018_012

Researchers:

Dr Katherine Keenan (University of St Andrews)
Dr Juliana Bowles (University of St Andrews)
Prof Frank Sullivan (University of St Andrews)

Start Date:

1/08/2019

Summary:

The project objective is to investigate social and demographic factors that predict differential trajectories of chronic disease among adults in mid-and later-life living in Scotland, taking into account multimorbidities. We would create a longitudinal cohort of SLS members aged 40 and over covering 2001-2017 by linking SLS to data on hospitalisation and medications and to disease registers (cancer, diabetes). Detailed research questions / aims:

1. We will explore/ develop methods for characterising common longitudinal trajectories of multimorbidity of chronic diseases by exploring possibilities of sequence analysis, Markov state modelling and/ or survival analysis. This will involve trying to characterise the trajectory of one disease, such as diabetes based on medications and the diabetes register, and the development of others in parallel. This very complex task will start with a focus on a handful of the most commonly occurring chronic diseases: type 2 diabetes, COPD, depressive disorder, cancer, and cardiovascular disease.

2. Investigate how such trajectories are associated with cross-sectional/ non time varying measures of socio-economic and demographic factors derived from census data, including household structure and size, marital status, SIMD, ethnicity, fertility history. 3. Through sequential SLS waves investigate how changes in family and social factors (e.g widowhood, divorce, move to living alone, household members acquiring long term illnesses, change in socio-economic circumstances) may be associated with more negative trajectories. The findings will deepen our understanding of the complex interaction of co-morbid chronic diseases and social factors, identify high-risk groups, and suggest effective intervention points to reduce health inequalities.

The process of population ageing implies an increase in life expectancy and an absolute expansion of the years spent suffering with long-term complex chronic diseases [1, 2] such as cardiovascular and metabolic diseases, cancer, and mental health conditions [3]. Age-related morbidity due to one or more chronic diseases (multimorbidity) is increasing globally, resulting in a reduction in quality of life and increasing cost to health care budgets [4]. Scotland has long been dubbed ‘the sick man of Europe’ due to its consistently lower life expectancy and higher prevalence of long-term health problems compared with other European countries, which remain largely unexplained by traditional disease risk factors [5]. This study will help us develop a better understanding of the social determinants and life course risk factors for chronic disease onset and progression. The notion of health trajectory has become increasingly popular in the epidemiological and public health fields [6-9]; in this study we use this notion to mean the longitudinal pattern of common chronic diseases over the life course. The value of looking at disease trajectories vs prevalence or incidence is to understand disease progression and onset of other comorbid conditions. Thus far health trajectory research has mainly been confined to single diseases [7], those addressing multimorbidity are less common 10. Existing multimorbidity measures fail to capture longitudinal progression- many are variations on additive indices designed for cross-sectional data [4, 11]. This project will explore ways of characterising multimorbidity trajectories drawing principally on approaches from life course studies, demography and epidemiology. We will also analyse drivers of inequalities in complex chronic disease trajectories at individual and household level. Low socio-economic status is associated with poorer ageing trajectories and earlier death [12, 13]. Less advantaged groups have an earlier onset, different chronic disease trajectories, and higher risk of multimorbidity than socially advantaged groups [4]. Other well established, but less well understood risk factors for poorer health trajectories in older age include unmarried/formally married status, ethnicity, fertility history, and household structure (particularly living alone) [13, 14]. Many of these are dynamic processes which change over the life course, influencing health: events such as separation, divorce, widowhood, can alter household structure as well as induce stress, and alter social support networks. This may lead to less favourable disease trajectories through differential risk factor exposure, healthcare seeking behaviour and successful self-management. This may advance our understanding of particularly vulnerable sub-populations, and suggest targeted intervention strategies. [15]

References:

1. GBD 2016 Risk Factors Collaborators, E. et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet (London, England) 390, 1345–1422 (2017).
2. Kingston, A., Robinson, L., Booth, H., Knapp, M. & Jagger, C. Projections of multi-morbidity in the older population in England to 2035: estimates from the Population Ageing and Care Simulation (PACSim) model. Age Ageing 47, 374–380 (2018).
3. Kassebaum, N. J. et al. Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1603–1658 (2016).
4.Barnett, K. et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 380, 37–43 (2012).
5.McCartney, G. et al. Explaining the excess mortality in Scotland compared with England: pooling of 18 cohort studies. J. Epidemiol. Community Health 69, 20–7 (2015).
6. Lynch, S. M. & Taylor, M. G. Trajectory Models for Aging Research. in Handbook of Aging and the Social Sciences 23–51 (Elsevier, 2016). doi:10.1016/B978-0-12-417235-7.00002-0
7. Pinaire, J., Azé, J., Bringay, S. & Landais, P. Patient healthcare trajectory. An essential monitoring tool: a systematic review. Heal. Inf. Sci. Syst. 5, 1 (2017).
8. Oh, W. et al. Type 2 diabetes mellitus trajectories and associated risks. Big data 4, 25–30 (2016).
9. Murray, S. A., Kendall, M., Boyd, K. & Sheikh, A. Illness trajectories and palliative care. BMJ 330, 1007–11(2005).
10.Jensen, A. B. et al. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat. Commun. 5, 4022 (2014).
11. Quan, H. et al. Updating and Validating the Charlson Comorbidity Index and Score for Risk Adjustment in Hospital Discharge Abstracts Using Data From 6 Countries. Am. J. Epidemiol. 173, 676–682 (2011).
12. Stringhini, S. et al. Association of lifecourse socioeconomic status with chronic inflammation and type 2 diabetes risk: the Whitehall II prospective cohort study. PLoS Med. 10, e1001479 (2013).
13. Ploubidis, G. B., Silverwood, R. J., DeStavola, B. & Grundy, E. Life-course partnership status and biomarkers in midlife: evidence from the 1958 British Birth Cohort. Am. J. Public Health 105, 1596–1603 (2015).
15. Barclay, K., Keenan, K., Grundy, E., Kolk, M. & Myrskylä, M. Reproductive history and post-reproductive mortality: A sibling comparison analysis using Swedish register data. Soc. Sci. Med. 155, 82–92 (2016).
16. Banerjee, S. Multimorbidity—older adults need health care that can count past one. Lancet 385,587–589 (2015).

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