Abstract
Analysis of healthy life expectancy is typically based on a binary distinction between health and ill-health. By contrast, this paper considers spatial modelling of disease free life expectancy taking account of the number of chronic conditions. Thus the analysis is based on population sub-groups with no disease, those with one disease only, and those with two or more diseases (multiple morbidity). Data on health status is accordingly modelled using a multinomial likelihood. The analysis uses data for 258 small areas in north London, and shows wide differences in the disease burden related to multiple morbidity. Strong associations between area socioeconomic deprivation and multiple morbidity are demonstrated, as well as strong spatial clustering.
Highlights
A number of recent studies stress the health care implications of the increasing prevalence of long term chronic conditions, in particular people having two or more conditions (e.g. Bahler et al 2015; Reeve et al 2006; Mercer et al 2009; Wolff et al 2002; Diederichs et al 2011)
There is currently little evidence regarding socioeconomic differences in the burden of multiple long term conditions, such as average years lived with multiple chronic conditions as against years lived with a single condition
The analysis here focuses on single condition and multiple condition morbidity based on patient register data for diagnosed conditions treated in primary care by the National Health Service (NHS)
Summary
A number of recent studies stress the health care implications of the increasing prevalence of long term chronic conditions, in particular people having two or more conditions (e.g. Bahler et al 2015; Reeve et al 2006; Mercer et al 2009; Wolff et al 2002; Diederichs et al 2011). Regarding impacts of geographic risk factors, there is evidence from the community health literature that the burden of multiple conditions is unequally distributed according to area socioeconomic status, and that the burden in deprived areas extends significantly into age groups under 65 (Barnett et al 2012) Such differentials have not so far been expressed in life year terms. The analysis here addresses these questions and is oriented to small area comparisons in the context of growing multiple morbidity It focuses on estimation of spatial life tables considering illness from a multinomial perspective, based on levels of treated prevalence of a range of chronic conditions. A Bayesian analysis provides a natural framework for modelling spatially clustered area random effects, reflecting unmeasured area risk factors
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