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Epidemiologic Prevalence Modelling Project

Epidemiologic Prevalence Modelling Project

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Epidemiologic Prevalence Modelling Project

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  1. Epidemiologic Prevalence Modelling Project Michael Soljak Informing Healthy Choices ImplementationTeam Julian Flowers Eastern Region PHO

  2. Why Model Prevalences? Epidemiologic disease and risk factor prevalence models can be used for: • assessing the completeness of disease registers in primary care or assessing the completeness of case finding • comparing outcomes such as complication rates or admission rates after adjustment for variation in expected prevalence • comparing service provision with population need • Planning and commissioning health & social care services, including projecting future levels of demand • undertaking health equity audits

  3. Existing examples: PBS diabetes modelhttp://www.yhpho.org.uk/PBS_diabetes.aspx#1 • a spreadsheet model that generates expected total numbers of persons with Type 1 and Type 2 diabetes mellitus (diagnosed plus undiagnosed combined) in 2001 for England, GO Regions, SHAs, LAs, PCTs, electoral wards and user-defined populations including GP practices • applies age/sex/ethnic group-specific estimates of diabetes prevalence rates, derived from epidemiological population studies, to 2001 Census resident populations • forecasts of 2010 diabetes prevalence are also presented for sub-national areas based on projected population change and trends in obesity: • Scenario 1 population change only, holding 2001 BMI pattern constant • Scenario 2 population change and predicted BMI in 2010 if trends in obesity prevalence continue • Scenario 3 Population change and a return to 1995 BMI patterns

  4. Existing examples: PBS diabetes modelhttp://www.yhpho.org.uk/PBS_diabetes.aspx#1 See also: forecasting the burden of diabetes on secondary care http://www.nwph.net/nwpho/Publications/AintreeDiabetes.pdf

  5. Existing examples: PBS diabetes modelhttp://www.yhpho.org.uk/PBS_diabetes.aspx#1

  6. HPA HIV Prevalence Modelhttp://www.hpa.org.uk/infections/topics_az/hiv_and_sti/publications/publications.htm • Direct method= categorising the population into a set of mutually exclusive risk groups of known size, and applying estimates of risk group-specific HIV prevalence to each group • Model was based on Bayesian multi-parameter evidence synthesis (MPES) of surveillance data • Uses WinBUGS package for Bayesian Markov chain Monte Carlo to incoporate multiple evidence sources • estimates the risk group size, group-specific HIV prevalence and proportion diagnosed in 13 risk groups, in each of 3 regions (Inner London, Outer London, Rest of England and Wales)

  7. UK estimates of prevalent HIV infections – adults aged 15-59, 2005 Multi-parameter Evidence Synthesis method - Goubar A et al. 2005, SOPHID, Health Protection Scotland, Natsal 2000, Unlinked Anonymous Programme, National Study of HIV in Pregnancy & Childhood, ICH.

  8. COPD Prevalence Model: PCT examplehttp://tools.erpho.org.uk/copdsel.aspx • Based on data from 2001 Health Survey for England • Logistic regression analysis used to choose risk factors for inclusion based on the strength of association between selected risk factors and COPD • Risk factors are age, sex, smoking and ethnicity, degrees of urbanisation and deprivation • Validated against a direct model obtained from epidemiologic studies • 7-fold variation in the prevalence across subgroups of the population, with lowest values in Asian women from wealthy rural areas (1.7 %), and highest in black men from deprived urban areas (12.5 %)

  9. COPD Prevalence Model: PCT examplehttp://tools.erpho.org.uk/copdsel.aspx

  10. APHO CHD & hypertension modelshttp://www.apho.org.uk/apho/models.aspx • Hypertension: April 2006 PCT registered populations multiplied by hypertension prevalence rates identified in the 2003 and 2004 HSfE, modified by ethnic-group age-standardised risk ratios from the 2004 HSfE • CHD: stage 1- prevalence of doctor diagnosed CHD in each age/sex stratum based on national data from the HSfE; stage 2- assumes that areas with higher CHD mortality rates have comparably higher prevalence with a linear relationship CHD SMR = (2.604 × UV67) + 25.97. Using UV67 scores calculated for each PCT, get a multiplying factor for each PCT • Both models need validation and further development e.g. effect of diabetes prevalence on CHD prevalence • NB NatCen obesity prevalence modelling to 2010, by GOR

  11. O:E CHD Prevalence, English PCTs Benchmarking tool developed by Paul Fryers, Doncaster PCT

  12. Healthcare for London: impact of prevalence on utilisation

  13. Projecting Older People Population Information System (POPPI) http://www.poppi.org.uk/ • will support Joint Strategic Needs Assessment • includes LA population projections to 2025 • includes data on prevalence of depression, dementia, heart attack, stroke, bronchitis\emphysema, falls, continence, visual impairment, mobility, obesity • Forecasts are provided for numbers: • Helped to live at home • Intensive home care • Community based services • Supported residents in care homes • Admissions to permanent residential and nursing care • Carers receiving services

  14. 2007-8 Work Programme • Update/improve/validate existing models and methodology • Consider development of case-finding strategies • Develop new models for a cancer site (with ACR), chronic kidney disease, and (serious?) mental illness (with NIMHE/CSIP) • Further develop intervention modelling- ASSET stroke model, health inequalities intervention model, overall mortality? • With stakeholders e.g. academia, encourage a consensus view about methodology and future requirements