Transcript Major causes of death by age

Day 7 session 3:
Introduction to Disease Prevalence modelling
John Hamm Regional Public Health Group London
Contributors:
Dr Jenny Mindell UCL
Shelley Bradley EMPHO
James Hollinshead EMPHO
Paul Fryers EMPHO
Dave Jenner EMPHO
Tom Morgan ERPHO
Learning Objectives

Understanding why we need to model prevalence

APHO prevalence modelling work

What are the different information sources for
prevalence modelling?

Example of constructing models

Examples of use and limitations
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”
P= number of existing cases of a disease
at a given point in time
total population
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.
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
70
60
%
50
40
30
20
10
0
School A
School B
School C
School D
School E
Cornw all
average
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
Why model Prevalence?-uses

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 / JSNAs
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 Modelshttp://www.apho.org.uk/resource/item.aspx?RID=48308
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
 Age
Adjust
for
 Sex
 Time
Further
adjust for
 Body mass index

Ethnic group

Diet

Deprivation

Physical activity

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 ethnicgroup 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
CHD Prevalence Model – 1

Health Survey for England gives the prevalence of CHD as follows:
16-24 25-34 35-44 45-54 55-64 65-74 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

We can apply these to each PCT population, to get an initial
predicted prevalence

This assumes that all PCTs have characteristics in line with the
national average
CHD Prevalence Model – 2

Each PCT was assessed for deprivation and SMR from CHD

A weighting was applied to each of the PCTs based on the
relationship between deprivation and SMR from CHD

Hence an increase or decrease based on these factors is applied to
each PCTs prevalence estimate

This process was also repeated at practice level

We used these in HCfL modelling
Chronic Kidney Disease Modelling (CKD in
progress)- 1

Aim to produce estimates of CKD prevalence based on
population characteristics

A model will be developed to estimate the prevalence of
Stage 3-5 and Stage 5 CKD

The estimates will be used to inform service planning
and improve quality of care in the UK
CKD Modelling 2- 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 Modelling - 3 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- 4 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
Use of prevalence models

Assessing need and informing commissioning strategies
and plans e.g. JSNA

Validating data sources

Quality Outcomes Framework

Healthcare for London models
Assessing need: JSNA core dataset
Validating data sources: QOF
Treated Epilepsy: 00FK, Derby UA
Observed relative to expected (%)
80
60
40
20
0
-20
-40
-60
0
20
40
60
80
Expected No. of Patients
100
120
140
Hypertension prevalence in a PCT
Hypertension: England
Observed relative to expected (%)
10
0
-10
-20
-30
-40
-50
-60
-70
0
50000
100000
150000
Expected No. of Patients
200000
250000
300000
Predicting future need- POPPI
URL: http://www.poppi.org.uk/index.php
APHO Prevalence models

Four readily available with varying degree of complexity

CHD

Hypertension

COPD (includes smoking prevalence)

Diabetes (includes obesity prevalence)
APHO Prevalence models
Population
structure
(Age and sex)
Ethnicity
(Age and
sex)
Deprivation
(borough level)
Smoking
status by Age
and sex
Obesity
(National
Trends)
CHD

Hypertensio
n


COPD



PBS
Diabetes




Type 2
Type 2
Type 2
model


Estimating trends in diabetes
Source: PBS Diabetes Population Prevalence model phase 2
•Trends in BMI were predicted as shown above using national HSE
data
•For London we have assumed increase continues to 2016 – US
evidence supports linear trends to 2010 at least
Estimating trends in diabetes
Source: PBS Diabetes Population Prevalence model phase 2
•Trends in BMI were predicted as shown above using
national HSE data
•For London we have assumed increase continues to 2016
– US evidence supports linear trends to 2010 at least
Estimating trends in diabetes
Diabetes Index - Linear Extrapolation
Men and women aged 16+ years, England 1980-2004
350
y = 4.0406x + 178.78
300
R2 = 0.9882
250
y = 2.7886x + 184.64
R2 = 0.9769
200
150
Male
Female
100
Linear (Male)
Linear (Female)
50
2010
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
0
Diabetes Index* is function of %'s in BMI categories weighted using relative risks from JAMA paper
Source: PBS Diabetes model
•Using national HSE data a diabetes index, based on
proportions of normal, and obese populations and
weighted for risk was produced
Estimating trends in diabetes
Source: PBS Diabetes Population Prevalence model phase 2
•Trends in BMI were predicted as shown above using
national HSE data
•For London we have assumed increase continues to 2016
– US evidence supports linear trends to 2010 at least
Estimating trends in diabetes
Diabetes Index - Linear Extrapolation
Men and women aged 16+ years, England 1980-2004
350
y = 4.0406x + 178.78
300
R2 = 0.9882
250
y = 2.7886x + 184.64
R2 = 0.9769
200
150
Male
Female
100
Linear (Male)
Linear (Female)
50
2010
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
0
Diabetes Index* is function of %'s in BMI categories weighted using relative risks from JAMA paper
Source: PBS Diabetes model
•Using national HSE data a diabetes index, based on
proportions of normal, and obese populations and
weighted for risk was produced
NICE/NSC Vascular Risk Reduction Programme
(VRRP)
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
Learning Objectives

To increase awareness of the use of surveys in Public Health
Intelligence

To increase awareness of large national surveys and mandatory
local surveys