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

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• 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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UV67 Score

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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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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