Development and use of methods to estimate chronic disease prevalence in small populations

Introduction National data on the prevalence of chronic diseases on general practice registers is now available. The aim of this PhD was to develop and validate epidemiological models for the expected prevalence of chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), stroke, hypertension, overall cardiovascular disease (CVD) and high CVD risk at general practice and small area level, and to explore the extent of undiagnosed disease, factors associated with it, and its impact on population health. Methods Multinomial logistic regression models were fitted to pooled Health Survey for England data to derive odds ratios for disease risk factors. These were applied to general practice and small area level population data, split by age, sex, ethnicity, deprivation, rurality and smoking status, to estimate expected disease prevalence at these levels. Validation was carried out using external data, including population-based epidemiological research and case-finding initiatives. Practice-level undiagnosed disease prevalence i.e. expected minus registered disease prevalence, and hospital admission rates for these conditions, were evaluated as outcome indicators of the quality and supply of primary health care services, using ordinary least squares (OLS) regression, geographically-weighted regression (GWR), and other spatial analytic methods. Results Risk factors, odds of disease and expected prevalence were consistent with external data sources. Spatial analysis showed strong evidence of spatial non-stationarity of undiagnosed disease prevalence, with high levels of undiagnosed disease in London and other conurbations, and associations with low supply of primary health care services. Higher hospital admission rates were associated with population deprivation, poorer quality and supply of primary health care services and poorer access to them, and for COPD, with higher levels of undiagnosed disease. Conclusion The epidemiologic prevalence models have been implemented in national data sources such as NHS Comparators, the Association of Public Health Observatories website, and a number of national reports. Early experience suggests that they are useful for guiding case-finding at practice level and improving and regulating the quality of primary health care. Comparisons with external data, in particular prevalence of disease detected by general practices, suggest that model predictions are valid. Practice-level spatial analyses of undiagnosed disease prevalence and hospital admission rates failed to demonstrate superiority of GWR over OLS methods. Disease modellers should be encouraged to collaborate more effectively, and to validate and compare modelling methods using an agreed framework. National leadership is needed to further develop and implement disease models. It is likely that prevalence models will prove to be most useful for identifying undiagnosed diseases with a slow and insidious onset, such as COPD, diabetes and hypertension.EThOS - Electronic Theses Online ServiceESRC, Dept of Health, Eastern Region Public Health Observatory, CQCGBUnited Kingdo