Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier Choices Implementation Team

Why Model Disease Prevalence? Disease and risk factor prevalence models can be used for: assessing completeness of disease registers in primary care assessing completeness of case finding comparing outcomes e.g. admission rates after adjustment for variation in expected prevalence comparing service provision with population need planning and commissioning services, including projecting future levels of demand undertaking health equity audits

APHO Prevalence Modelling Commissioned by Dept of Health Prevalence estimates for PCTs and LAs –CHD –Hypertension –Stroke –COPD –CKD (EMPHO) PBS Diabetes model (YHPHO)

COPD, CHD, stroke, hypertension Multinomial logistic regression models using pooled Health Survey for England data Developed by Dept of Primary Care and Social Medicine, Imperial College Applied to real populations by ERPHO

Health Survey for England Direct measures: BP measures, FEV1, cotinine, BMI etc. Patient reported measures: doctor diagnosed disease, smoking, etc. Age-sex specific prevalence estimates Geography down to old SHA Used to build logistic models for predictors of disease

Model application Population by age-sex- ethnicity (ONS) Smoking status (modelled estimates) Deprivation (IMD2004) Relative Risks Prevalence estimates Rurality (COPD model)

Smoking status Require proportion of smokers, ex-smokers and non- smokers Model-based estimates of lifestyle behaviours only gives prevalence of smokers Combine with national smoking and ex-smoking prevalence by age and sex (HSE) Assume same ex-smoking prevalence everywhere Assume same distribution of smoking status across ethnic groups

CHD Results Lowest prevalence: Wandsworth Lambeth Oxford Wokingham Cambridge Highest prevalence: West Somerset Easington Tendring Hartlepool Sandwell

CHD Results

Stroke results

Hypertension results

Predicting the future Modelled risk combined with population projections to generate projected prevalence Assumes constant risk for ageing population Cannot use model for scenario modelling e.g. How does prevalence change if smoking prevalence decreases?

Chronic Obstructive Pulmonary Disease ModelQOF

How to use modelled estimates As an indication of likely disease prevalence The estimates are only as good as the input data Smaller areas have greater uncertainty May be inaccurate for areas with special characteristics not captured by input data (e.g. ethnic population with very low/high smoking prevalence) Be careful with denominators, especially when comparing to QOF

Next Steps Practice level modelling Collaboration with NHS Comparators Mental Health modelling –Psychosis –Neurosis and Personality Disorder –Drug or alcohol dependence

Links All results and technical documentation are available through the APHO website