A Spatial Microsimulation Model of Co-morbidity

Informed health care policy requires spatially detailed data on health outcomes and patterns of health service utilisation. Health differentials across space arise due to the clustering of individuals who share similar demographic profiles and lifestyle choices into areas where similar contextual characteristics come into play, for example, area-based effects related to low levels of health service provision. To establish the key determinants of health status at both the individual and small area level a large variety of spatially referenced demographic, socio-economic and health data is required at the individual level. Although there are a number of national datasets that contain detailed health and health usage data, these data tend to be aspatial or at too high a spatial resolution to permit health data analysis at the required local level. One solution to this problem that has emerged in the last two decades is the growing use of spatial microsimulation models to represent and simulate health processes at the individual and small area level. Drawing on the experience of PI, Morrissey and Co-I's Williamson and Singleton this project seeks to develop a spatial microsimulation for England to meet the information needs of health policymakers and practitioners. Pre-project collaboration with the Director of Information in East Kent Hospital Trust (EKHT), Marc Farr and his colleagues has identified the increased rates of patients presenting with two or more diseases (co-morbidity) as a pressing issue, of direct policy relevance, that requires detailed spatial analysis. Co-morbidity is associated with a significant decline in both life expectancy and quality of life and increased costs in the provisioning of health services for individuals. The World Health Organisation has identified increased rates of co-morbidity as a major challenge for health policy. Utilising data holdings made available to academics via the ESRC-supplied data services, this project will develop a spatial microsimulation model of co-morbidity for England. This model will be validated, in collaboration with EKHT, to develop a case study of co-morbidity for the East Kent region, based on the disease combinations of most concern for policy planning.

(1) Identify the underlying determinants of co-morbidity for CVD, diabetes & obesity at both the national and health authority region in England using the Health Survey for England (HSE)
(2) Develop baseline small-area population estimates of co-morbidity outcomes and associated health service utilisation for the whole of England, using spatial microsimulation techniques to combine information from the Census of Population and the HSE 2008-2010. Access to the required Census and HSE data for this project is reliant upon, and exploits, the ESRC-funded data services CASWEB and ESDS.
(3) Validate the baseline estimates of health service utilisation associated with those co-morbidities placing most burden on the NHS - CVD, diabetes & obesity - using administrative data supplied by East Kent Health Trust
(4) Inform health policy, using East Kent as a case study area, by
a. Identifying areas of apparent under/over-utilisation, so that local causes such as GP 'gatekeeper' and 'intervention' behaviours can be investigated further, with a view to identifying and sharing best practise
b. Undertake a cost-benefit analysis of alternative potential early-intervention strategies and the rolling-out 'best practise' treatment regimes for co-morbid patients.

On completion of the health model, public health policymakers and practitioners will have a powerful geographical based tool that may be used to develop and target evidence-based health policies to areas with the highest risk profiles. Furthermore, once validated, the model estimates will have the potential for application to a wide range of public policy issues (income; smoking; etc) across each region for England.

One of the key strategic goals of the ESRC is the promotion of social science research that will underpin policies that promote improved societal health and well-being using innovative research techniques. This research combines national expertise in spatial microsimulation and it's application to health research from the University of Liverpool with health policymakers and practitioners concerned with the increased rates of co-morbidity at both the regional (East Kent) and national level. The successful completion of the project will create an impetus for a continued exchange circle and consultative forum for data-led knowledge that supports the development of effective health policy. Documenting each step of the framework and making the synthetic baseline population available to interested third parties creates a social research capacity within the public, private and third sector for those involved in the public policy domain.

Users:
-Medical practitioners interested in developing risk profiles for their patients
-Education professionals required to (a) deliver health education may use the results to target their services to health practitioners in areas with the highest risk profile and (b) provide information to patients
-Health policymakers e.g. Department of Health, NHS, regional hospital trusts interested in targetting health service provision in a more spatially targetted manner
-Primary Care Trusts (PCTs) interested in developing comorbidity risk profiles for their patients and their catchment areas
-Office of National Statistics (ONS): With the proposed discontinuation of the British census, the development and testing of different techniques to simulate small area data is an important policy issue. Of particular interest to statisticians and policymakers will be the use of administrative data to (a) validate newly created data and (b) the ability of administrative to augment survey data with regard to policy decisions.
-Public policymakers: Once the initial case study is completed the framework may be used to examine a variety of public policy issues (income; smoking; obesity etc) across each region for England.
-Academics: Members of the Royal Geographical Society quantitative health modelling group and the international microsimulation group