prevalence modelling – an apho perspective hannah walford eastern region pho with contributions...
DESCRIPTION
APHO Prevalence Modelling Commissioned by Dept of Health Prevalence estimates for PCTs and LAs –CHD –Hypertension –Stroke –COPD –CKD (EMPHO) PBS Diabetes model (YHPHO)TRANSCRIPT
![Page 1: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/1.jpg)
Prevalence Modelling – an APHO perspective
Hannah Walford Eastern Region PHO
With contributions fromJulian Flowers, ERPHO
Michael Soljak, Informing Healthier Choices Implementation Team
![Page 2: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/2.jpg)
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
![Page 3: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/3.jpg)
APHO Prevalence Modelling
• Commissioned by Dept of Health• Prevalence estimates for PCTs and
LAs– CHD– Hypertension– Stroke– COPD– CKD (EMPHO)
• PBS Diabetes model (YHPHO)
![Page 4: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/4.jpg)
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
![Page 5: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/5.jpg)
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
![Page 6: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/6.jpg)
Model application
Population by age-sex-
ethnicity(ONS)
Smoking status
(modelled estimates)
Deprivation(IMD2004)
Relative Risks
Prevalence estimates
Rurality(COPD model)
![Page 7: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/7.jpg)
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
![Page 8: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/8.jpg)
CHD Results
Lowest prevalence:WandsworthLambethOxfordWokinghamCambridge
Highest prevalence:West SomersetEasingtonTendringHartlepoolSandwell
![Page 9: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/9.jpg)
CHD Results
![Page 10: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/10.jpg)
CHD Results
![Page 11: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/11.jpg)
Stroke results
![Page 12: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/12.jpg)
Hypertension results
![Page 13: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/13.jpg)
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?
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Chronic Obstructive Pulmonary Disease
Model QOF
![Page 15: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/15.jpg)
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
![Page 16: Prevalence Modelling – an APHO perspective Hannah Walford Eastern Region PHO With contributions from Julian Flowers, ERPHO Michael Soljak, Informing Healthier](https://reader035.vdocuments.us/reader035/viewer/2022062600/5a4d1b517f8b9ab0599a7de9/html5/thumbnails/16.jpg)
Next Steps
• Practice level modelling• Collaboration with NHS Comparators• Mental Health modelling
– Psychosis– Neurosis and Personality Disorder– Drug or alcohol dependence
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Links
• All results and technical documentation are available through the APHO website http://www.apho.org.uk/resource/view.aspx?RID=48308