Introduction to Disease Prevalence modelling
Day 6 23rd September 2009
James Hollinshead
Paul FryersBen Kearns
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
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.
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
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.
The proportion of 5 yr olds who have some experience of tooth decay (decayed (untreated), missing, or filled teeth).
1997/98
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School A School B School C School D School E Cornw allaverage
%
• PH action: service development in the area of school A
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
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
• 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
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
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
APHO Models-
Current
• Hypertension• COPD • CHD• Diabetes • Stroke• Chronic Kidney Disease • Cancer
Under development• Mental health
http://www.apho.org.uk/resource/view.aspx?RID=48308
APHO prevalence modelling webpage
What different sources of information are used in prevalence modelling?
• Prevalence estimates
• Population denominators/demographic information
• What sources can you think of ?
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
Adjustment
Adjust for
• Age• Sex• Ethnic group• Deprivation
Further adjust for
• Time• Body mass index• Diet• Physical activity• Smoking• Family history
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
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
How models are constructed- some examples
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
16-24 25-3435-4445-5455-6465-74 75+
Men 0.0 0.0 1.0 3.4 11.1 21.6 26.5 Women 0.3 0.0 0.5 1.9 5.8 9.7 18.1
Prevalence of CHD in under 16s is assumed to be
zero
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)
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
y = 260.39x + 25.969
R2 = 0.466
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15% 20% 25% 30% 35% 40% 45%
UV67 Score
CH
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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 PCTSMR for CHD
• Finally adjust for individual practice deprivation scores
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Age/Sex Based Prevalence Doncaster SMR Adjustment Practice DeprivationAdjustment
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vale
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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
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)
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 18-24 25-34 35-44 45-54 55-64 65-74 75-84 85+
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%
The CKD prevalence model
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
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
Examples of the use of prevalence models
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)
Assessing need: JSNA core dataset
NHS Comparators
Source NHS Comparators The NHS Information Centre http://www.ic.nhs.uk/services/nhs-comparators
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
Validating data sources: QOF
Treated Epilepsy: 00FK, Derby UA
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Observed relative to expected (%)
Hypertension prevalence in a PCT
Hypertension: England
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Predicting future need- POPPI and PANSI URL: http://www.poppi.org.uk/index.php
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