1. Introduction to Disease Prevalence modelling Day 6 23 rd September 2009 James Hollinshead Paul Fryers Ben Kearns

2. Contents What is prevalence? Why should we model prevalence? APHO prevalence modelling work What are the different information sources for prevalence modelling? Example of constructing models Examples of use

3. What is prevalence Prevalence is the total number of cases of disease in a population at one point in time, taken as a proportion of the total number of persons in that population. Also referred to as “point prevalence” Period prevalence is a variation which represents the number of persons who were a case at any time during a specified (short) period as a proportion of the total number of persons in that population.

4. Prevalence Prevalence is expressed as a proportion, which lies between 0-100%, or as a rate (e.g. x cases per 100,000 population) It does not take into account WHEN people became infected / diseased

5. Measuring prevalence 29 of the 49 five year olds examined in school ‘A’ had experienced tooth decay (29/49)*100 = 59% Cross sectional surveys can only measure prevalence, not incidence. PH action: service development in the area of school A

6. Why look at disease prevalence? Identify the burden of disease (or health- related condition) –in the population –on the health service Important for allocation of resources and funds –now –future

7. Why model prevalence?- uses Local prevalence data not always available and collecting information e.g. surveys is expensive Assess the level of case-finding in primary care and the completeness of disease registers Compare the level of service demand with population need Inform the planning and the commissioning of health and social care services

8. Estimate the number of diagnosed cases and estimate the number of undiagnosed cases Forecast future levels of demand by predicting the future burden Inform health equity audits Why model prevalence?-uses

9. Prevalence modelling- limitations Monitoring performance e.g. impact of an intervention to reduce obesity Assessing progress towards targets e.g. monitoring the number of people with CHD Ranking areas (league tables) e.g. comparisons of prevalence in different PCT areas

10. APHO prevalence modelling work For the 2007/8 Local Delivery Plan APHO was commissioned by the DH to produce PCT level prevalence estimates for hypertension and CHD APHO are now steering a number of prevalence modelling projects –consistent approach –improve and update –new models

11. APHO Models- Current Hypertension COPD CHD Diabetes Stroke Chronic Kidney Disease Cancer Under development Mental health

12. http://www.apho.org.uk/resource/view.aspx?RID=48308 APHO prevalence modelling webpage

13. What different sources of information are used in prevalence modelling? Prevalence estimates Population denominators/demographic information What sources can you think of ?

14. Data required for prevalence modelling Prevalence estimates from –Surveys e.g. Health Survey for England –Research –Primary Care Data Denominator data –Population –Deprivation/ethnicity etc

15. Adjustment Adjust for Age Sex Ethnic group Deprivation Further adjust for Time Body mass index Diet Physical activity Smoking Family history

16. Information sources used in hypertension model Prevalence estimates –Hypertension prevalence is known to be correlated with age, sex and ethnic-group –Health Survey for England data 2004 –Hypertension prevalence modified by ethnic-group age- standardised risk ratios Population denominators –Primary Care Trust registered populations –In the absence of age by sex by ethnic-group PCT populations, age by sex registered populations of current PCTs were attributed the ethnic-group distributions of their constituent former PCT/s at 2001 census

17. Information sources used COPD model Prevalence estimates –Based on the estimates from the 2001 Health Survey for England –Logistic regression identified Sex, Ethnicity, Age, Rurality, Deprivation, smoking status as risk factors Population denominators –Local Authority registered populations –ONS measures of rurality –IMD scores –LA Smoking estimates

18. How models are constructed- some examples

19. Doncaster CHD Prevalence Model – 1 Health Survey for England gives the prevalence of CHD as follows: These prevalence estimates can be applied to each practice population extracted from Exeter, to get an initial predicted prevalence This assumes that practices all have characteristics in line with the national average 16-2425-3435-4445-5455-6465-7475+ Men0.00.01.03.411.121.626.5 Women0.30.00.51.9 5.8 9.718.1 Prevalence of CHD in under 16s is assumed to be zero

20. CHD Prevalence Model – 2 NCHOD publishes SMRs for CHD for each local authority in the country Doncaster’s 2002-04 SMR for CHD was 116.0 Assume that if Doncaster has 16% more deaths from CHD than the national average, then there are also 16% more people with CHD Hence apply a 16% increase to each practice’s prevalence estimate This still assumes that all practices in Doncaster have similar characteristics to each other (apart from age/sex distributions)

21. CHD Prevalence Model – 3 In order to take account of differences between Doncaster’s practices, need to adjust for deprivation From the Census, deprivation scores were calculated for each local authority These were plotted against the SMRs from NCHOD The gradient of the regression fit was used to adjust practices prevalence in line with practice deprivation scores Hence an increase or decrease based on these factors is applied to each practice prevalence estimate

22. CHD Prevalence Model – 4 The graph summarises the process for practices in the former Doncaster Central PCT: First estimates reflect differences in basic demographics Second adjust all practices for PCT SMR for CHD Finally adjust for individual practice deprivation scores

23. Chronic Kidney Disease Modelling (CKD in progress) National Service Framework for Renal disease Aim to produce estimates of CKD prevalence based on population characteristics A model to estimate the prevalence of Stage 3-5 CKD

24. CKD Modelling- Literature review Higher in females Increases with age Ethnicity differences Wide range of estimates (5%-11% adults) UK GP practice estimates (8-9% adults) Compared with 2006/07 QOF estimate of 3% (adults)

25. CKD Current model - phase I Based on research NEOERICA estimates Applied to ONS mid year estimates Compared to the QOF prevalence Self input section for population denominators Age group 18-2425-3435-4445-54 55-64 65-7475-8485+ Males:0.0%0.2%0.7%3.1%6.9%17.7%33.2%44.8% Females:0.2%0.8%2.7%2.8%13.1%27.9%41.7%48.6%

26. The CKD prevalence model

27. CKD Modelling the future- Design Work with St George’s primary care data base A cross sectional study of CKD prevalence, using estimated glomerular filtration rate (eGFR) on GP records Study sample 750,000 (registered with London, Surrey, Kent, Leicester and Manchester GPs ) Logistic regression will be used to adjust for the demographic variables age, sex, deprivation and ethnicity

28. CKD Modelling- Outcomes Statistical model based on the study sample will be developed to estimate the population prevalence of CKD Two further outputs based on this model will be produced; –CKD prevalence estimates for Local Authorities (LA) and Primary Care Trusts (PCT) in the UK –a resource to enable prevalence estimation at a General Practice and Practice Based Commissioning Cluster level

29. Examples of the use of prevalence models

30. Use of prevalence models -examples Assessing need and informing commissioning strategies and plans e.g. JSNA Improving case finding Validating data sources –Quality Outcomes Framework Predictions of future need POPPI (Health and social care predictions)

31. Assessing need: JSNA core dataset

32. NHS Comparators Source NHS Comparators The NHS Information Centre http://www.ic.nhs.uk/services/nhs-comparators

33. Case finding- Variation in CKD recorded prevalence at Practice level Ratio of observed vs expected Source NHS Comparators The NHS Information Centre http://www.ic.nhs.uk/services/nhs-comparators

34. Validating data sources: QOF

35. Hypertension prevalence in a PCT

36. Predicting future need- POPPI and PANSI URL: http://www.poppi.org.uk/index.php

37. What you have covered What is prevalence? Why should we model prevalence? APHO prevalence modelling work What are the different information sources for prevalence modelling? Example of constructing models Examples of use