Sub regional estimates of morbidities in the English elderly population

Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.

A multinomial model for comorbidity in England of long-standing cardiovascular disease, diabetes and obesity

Comorbidity has been found to be significantly related to increased levels of mortality, decreased functional status and quality of life, increasing dependence on health services and an increased risk of mental and social problems. Previous research into comorbidity has mainly focused on identifying the most common groupings of illnesses found among elderly healthcare users. In contrast, this paper pools data from the Health Survey for England from 2008 to 2012 to form a representative sample of individuals in private households in England to explore the risk of comorbidity among the general population; and to take account of not only the demographic but also the socioeconomic and area-level determinants of comorbidity. Using a multinomial logistic model, this research confirms that age and gender are significant predictors of cardiovascular disease, diabetes and obesity, whether examined singly or in any comorbidity combination. Across the seven possible disease combinations, the odds ratios are lowest for those individuals with a high income (6 of 7), home-owning (5 of 7), degree educated (7 of 7) and living in the least deprived area (6 of 7), when controlling for demographic and smoking characteristics. The important influence of socioeconomic factors associated with comorbidity risk indicates that healthcare policy needs to move from a focus on age profiles to take better account of individual and local area socioeconomic circumstances.