Modelling the potential economic impact of investment in Public

Health

Professor Malcolm Whitfield Director of The Centre for Health & Social Care Research

Sheffield Hallam University, UK

The Key Problem

“In the most industrialized countries of North America, Europe and the Asian Pacific, at least one-third of all disease burden is caused by tobacco, alcohol, blood pressure, cholesterol and obesity”.

“More than three-quarters of cardiovascular disease (the world’s leading cause of death) results from tobacco use, high blood pressure or cholesterol, or their combination”.

“Overall, cholesterol causes more than 4 million premature deaths a year, tobacco causes almost 5 million, and blood pressure causes around 7 million”

WHO 2002

The health problem

Issue•If we can get people to change their lifestyle i.e. diet, exercise, smoking and alcohol consumption we could reduce the burden of disease in society by up to 33% and reduce the cost of healthcare by over 70%

Questions•How much would we have to change the risk factors to reduce the burden of disease? •What order of savings could we achieve on healthcare costs in the first five years?•How much could we realistically invest in getting lifestyle change?

The model idea

The model The model

The risk factorsThe risk factors

Demographic profile Framingham

Population smoking rates Framingham

Mean total and HDL Cholesterol (mmol/l)

Framingham

Mean systolic blood pressure Framingham

Mean Body Mass Index (BMI) Diabetes / Heart Failure

Mean HBA1c levels UKPDS

Measures of CKD prevalence eGFR etc

Nanes II

Does it work?Does it work?

To validate the model, we estimated how many people in five Primary Care Trusts (n=620,000 population) would have a heart attack, stroke, heart failure, kidney failure and heart surgery.

We then compared the predicted number with the actual number

NB The models has since been tested in 15 PCTs

The validation – S/Yorks

Admission data 2005/06 for 5 PCTs

0500

1000150020002500300035004000

P op. 1 P op. 2 P op. 3 P op. 4 P op. 5 All P ops.

All acute MI events - actual v predicted adjusted (Brindle 2003)

Actual Predicted Adjusted

The validation - Liverpool

0

1000

2000

3000

4000

5000

6000

7000

16-24 25-24 35-44 45-54 55-64 55-74 75+ All events

All acute CHD events - actual v predicted Liverpool PCT (Weighted population)

Actual Predicted

The validation - Birmingham

02000400060008000

1000012000140001600018000

16-24 25-24 35-44 45-54 55-64 55-74 75+ All events

All acute CHD events - actual v predicted Birmingham PCTs

Actual Predicted

Admissions avoided (364,912

pop)

Estimated reduction in annual acute events/admissions over a five year period assuming 20% move toward risk factor reduction target per annum

0

1,000

2,000

3,000

4,000

5,000

6,000

Baseline Year 1 Year 2 Year 3 Year 4 Year 5

Scenario 1 Scenario 2

Scenario Annual acute admission events avoided after 5 years

5 year cumulative acute admission events avoided

Current risk 0

0

Scenario 1 1,707

5,120

Scenario 2 1,075

3,225

Deaths avoided (364,912 pop)

Estimated reduction annual premature deaths over a five year period assuming 20% move toward risk factor reduction target per annum

0

500

1,000

1,500

2,000

2,500

Baseline Year 1 Year 2 Year 3 Year 4 Year 5

Scenario 1 Scenario 2

Scenario Annual premature deaths avoided after 5 years

5 year cumulative premature deaths avoided

Current risk 0

0

Scenario 1 490

1,470

Scenario 2 326

979

Revenue savings (364,912 pop)

Estimated reduction in acute hospital admission costs over a five year period assuming 20% move toward risk factor reduction target per annum

£0

£5,000,000

£10,000,000

£15,000,000

£20,000,000

£25,000,000

Baseline Year 1 Year 2 Year 3 Year 4 Year 5

Scenario 1 Scenario 2

Scenario Annual acute admission costs avoided after 5 years

5 year cumulative admission costs avoided

Current risk 0

0

Scenario 1 6.6 million

19.8 million

Scenario 2 4.1 million

12.5 million

Estimated impact of health determinants on Estimated impact of health determinants on population healthpopulation health

Key

10% Physical Environment15% Genetic endowment25% Health System50% Socio-economic environment

Linkage to Decipher model

Original model

North Karelia

Main risk factors in North Karelia between 1972 and 2007 among men and women aged 30-59 years

DeathsRate in 1969-1971

Rate in 2006 Change from 1969-1971 to 2006

All causes 1509 572 -62%

All cardiovascular 855 182 -79%

Coronary heart disease

672 103 -85%

All cancers 271 96 -65%

Lung cancers 147 30 -80%

Age-adjusted mortality rates of coronary heart disease in North Karelia and the whole of Finland among males aged 35–64 years from 1969 to 2006.

Nuffield Bio-Ethical

http://www.nuffieldbioethics.org/news/council-cited-white-paper-public-health

Public health intelligence (case finding)

Pathway to equality Pathway to equality

EPHP'sEngagem

ent

Social marketing Health- literacy Informed choiceEnvironmental initiatives

Lifestyle change: Health Trainers Smoking Cessation Five a day Healthy schools meals Housing Green spaces Etc…

Primary care: StatinsHypotensivesObesity treatments

PH Intelligence Public Health Interventions Primary care

TE TETETE TE

AE

(Example) - CVD

Cardiac risk checks Disease registers

TE

http://www.sportseng.org/sheftool//

Decipher model