A Spatial Microsimulation Model of Co-morbidity

Lead Research Organisation: University of Liverpool


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.

Planned Impact

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.

-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
Description This research grant saw the development of a spatial microsimulation model of comorbidity, CVD, diabetes and obesity. This spatial microsimulation model is the first model to simulate comorbidity at the small area level. Previously, this data was not available. The data produced is fully geo-referenced and therefor health outcomes for each of the 7 combinations of morbidities/comorbidities are mappable. Areas with high levels of comorbidity are spatially correlated with ares of high levels of deprivation as measured by the Index of Multiple Deprivation. Furthermore, a new method to create small area data has been developed, global optimisation.
Exploitation Route The findings may be used be health practitioners and policy makers to highlight morbidity and comorbidity hotspots across England at the small area level. The simulation of additional microlevel characteristics of an area allow the impact of socio-economic circumstances to be examined.
Sectors Healthcare

Description Results from the small area simulation have been demonstrated by the East Kent Hospital Trust that they need to move from a single disease management programme to a multi disease management programme due to the rise in comorbidities.
First Year Of Impact 2014
Sector Healthcare
Impact Types Societal

Description Innovations in Small Area Estimation Methodologies
Amount £850,000 (GBP)
Funding ID ES/N011619/1 
Organisation Economic and Social Research Council 
Sector Public
Country United Kingdom
Start 01/2016 
End 12/2019
Description UKDS Innovation Fund
Amount £45,000 (GBP)
Organisation UK Data Service 
Sector Academic/University
Country United Kingdom
Start 02/2015 
End 03/2015
Description UKDS Innovation Fund
Amount £15,000 (GBP)
Organisation UK Data Service 
Sector Academic/University
Country United Kingdom
Start 03/2013 
End 07/2013
Description INFUSER provides a command line interface to published tabulations of UK Census data made available by the UK Data Service via their proprietary InFuse Application Programming Interface (API). infuser was initially conceptualised under the ESRC grant ES/K004433/1 - A Spatial Microsimulation Model of Co-morbidity. hOWEVER ADDITIONAL FUNDING WAS received from the UKDS to finalise the project. 
Type Of Material Improvements to research infrastructure 
Year Produced 2015 
Provided To Others? Yes  
Impact Downloading, saving data from the census is now much more efficient and script in R may be written so that data is easily accessed in the future. 
Title Spatial Microsimulation Model 
Description The underlining algorithm creates a spatially representative dataset by matching data from surveys and the census of population 
Type Of Material Improvements to research infrastructure 
Year Produced 2014 
Provided To Others? Yes  
Impact The model will be used by East Kent Hospital to examine current disease hotspots. A new method, global optimisation (GO) was created to update the development of spatial microsimulation models and parallel research in statistical survey reweighing. 
Title Geo-referenced Dataset of Comorbidity for England 
Description Small area estimates of Diabetes, CVD and Obesity and their comorbidity across the population have been produced 
Type Of Material Database/Collection of data 
Year Produced 2016 
Provided To Others? Yes  
Impact Our collaborative partner, East Kent Hospital Trust has received a geo-referenced dataset that they may use for policy planning Further research on prescribing and health outcomes is being conducted by colleagues in the ECEHH 
Description East Kent Hostiptal Trust Foundation collaboration 
Organisation East Kent Hospitals University NHS Foundation Trust
Country United Kingdom 
Sector Public 
PI Contribution we have provided EKHT with a spatial microsimulation model that they can use to estimate the spatial prevalence of comorbidity; CVD, diabetes and obesity
Collaborator Contribution Our research received crucial secondary data from the Hospital Trust to validation our simulation model. In return,
Impact A spatial microsimulation model has been developed
Start Year 2012
Description INFUSER provides a command line interface to published tabulations of UK Census data made available by the UK Data Service via their proprietary InFuse Application Programming Interface (API). It is designed to support the creation of scripted data extractions spanning multiple census counts, geographies and censuses. 
Type Of Technology Webtool/Application 
Year Produced 2015 
Impact INFUSER fully replicates the functionality of the existing web(based InFuse Graphical User Interface (GUI), allowing users to (i) identify and select a desired tabulation of variables; (ii) drill down to select the geographic areas for which they require census data; (iii) download 2001 and 2011 census data for the selected combination of variable tabulations and census areas. However, being a script (driven interface, INFUSER also allows users to save queries as scripts for later revision, replication or sharing. 
Description Public stakeholder Event 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Policymakers/politicians
Results and Impact Making better decisions for public health: Insights from secondary data" was a free one-day conference at The King's Fund, 11-13 Cavendish Square, London. The event discussed research findings from six health and wellbeing projects from the ESRC's Secondary Data Analysis Initiative, and was chaired by David Buck, King's Fund Senior Fellow (public health and health inequalities). Approximately 50 people attended from a range of health and wellbeing organisations.

Further correspondence relation to research outcomes with members of the audience.
Year(s) Of Engagement Activity 2014