Inna Feldman Uppsala University [email protected]
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Transcript of Inna Feldman Uppsala University [email protected]
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Population prevalence of disease risk factors and economic consequences for
the healthcare system- possible scenarios
Inna FeldmanUppsala University
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Estimation of future costs
Future
Health (risk factors)
Health (risk factors)
Morbidity Morbidity
CostsCosts
Health (risk factors)
Morbidity
Costs
Present Past
Compare
Compare
Change
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Risk factors: BMI>30, obesity Daily smoking Lack of exercise, physical activity less than 2h/week Risk alcohol consumption (AUDIT)Source: Population survey
Age group: adults, 20-84 years old (4 age groups)
Costs: heathcare costs per patient/yearSource: Stockholm County´s VAL databases
Example for prevalence: Uppsala County (low risk factors - prevalence) Sörmland County (high risk factors - prevalence)
Base for economic consequences:: lower number of new cases (reduced incidence) due to positive development of risk factors
Starting points
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BMI>30 Smoking Lack of exercise
Risk alcohol consumption
x x x
x x x
x x x
x x x x
x
x x
x x
x x
x x x x
x x x
Diabetes
Ischaemic heart disease
Stroke
Colon cancer
Lung cancer
Breast cancer
Prostate cancer
COPD
Depression
Fractures
Diagnoses:
Disease risk factors
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Risk factors – related risks (RR)
Relative risk (RR) is the risk of an event (or of developing a disease) relative to exposure.
Relative risk is a ratio of the probability of the event occurring in the exposed group versus a non-exposed group
The model is based on related risks for these four risk factors
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Risk factors – sources
Swedish and international studies Age- and gender-specific Can be updated according to new studies and new results
Män Kvinnor Källor 20-49 50-64 65-74 75-84 20-49 50-64 65-74 75-84 Diabetes 1,1 1,2 1,15 1,15 1,1 1,2 1,2 1,2 Willi et al, 2007 Ichemisk
hjärtsjukdom 4,4 2,9 1,8 1,4 4,5 3,4 2,1 1,5 Prochaska & Hilton,
2012 Stroke 3,4 2,6 1,9 1,5 3,7 3,0 2,1 1,3 Colditz et al, 1998;
Robbins et al, 1994 Koloncancer 1,2 1,2 1,2 1,2 1,2 1,2 1,2 1,2 Giovannucci, 2001;
Parkin, 2011 Lungcancer 14,3 26,4 28,0 21,6 11,5 16,1 14,1 10,6 Parkin, 2011 KOL 3,4 10,6 12,3 11,8 2,7 9,3 10,8 7,5 Lindberg, 2007 Depression 1,02 1,02 1,02 1,02 1,02 1,02 1,02 1,02 Buden et al, 2010 Höftfraktur 1,8 1,8 1,8 1,8 1,8 1,8 1,8 1,8 Marks, 2009
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BMI>30 Smoking Lack of exercise
Risk alcohol consumption
6,4 1,2 2
1,7 2,9 1,3
1,3 2,6 2,2
1,5 1,2 1,6 1,8
26,4
- - - -
1,2 1,1
10,6 1,1
1,3 1,02 1,76 2
1,8 2 1,2
Men, 50-64 years old
Relative risks - example
Diagnoses:
Diabetes
Ischaemic heart disease
Stroke
Colon cancer
Lung cancer
Breast cancer
Prostate cancer
COPD
Depression
Fractures
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BMI>30 Smoking Lack of exercise
Risk alcohol consumption
7,3 1,2 2
1,9 3,4 1,3
1,4 3,0 2,2
1,5 1,2 1,6 1,8
16,1
1,7 - 1,2 -
- -
9,3 1,1
1,3 1,02 1,7 2
1,8 2 1,2
Women, 50-64 years old
Relative risks - example
Diagnoses:
Diabetes
Ischaemic heart disease
Stroke
Colon cancer
Lung cancer
Breast cancer
Prostate cancer
COPD
Depression
Fractures
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IF is defined as the percent reduction in desease incidence because of reduction of a risk factor prevalence to a certain level
IF=[(P2-P1)+RR(P1-P2)]/[(1-P1)+RR*P1]
Example: Smoking P1=0,13 (13%) P2=0,1 (10%)
Lung cancer RR=26 IF=0,17
A reduction in smoking rates from 13% to 10% results in a reduction in the incidence of lung cancer by 17%.Stroke
RR=2,6 IF=0,04 A reduction in smoking rates from 13% to 10% results in a reduction in the incidence of stroke by 4%.
How the change in risk factors influences disease incidence: IF ”Impact fraction”
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Relative risks: Swedish and international scientific studies, gender- and age-specific
Incidence: Swedish registers and scientific studies
Prevalence of gender- and age-specific risk factors used to estimate number of new cases
Development of ealier models from Uppsala County
The model can be adapted to different populations by taking into account the existing age structure and the prevalence of risk factors
The Model
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The costs
Annual health care costs for a person with a respective diagnosis
Based on Stockholm County’s database
Mainly costs for the first year of disease
Did not include medication costs
Can be updated
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Time perspective
How long does it take to reduce the risk?Differs for different diseases and risk factorsLack of studies
Assumption:
Changing in RR
0
2
4
6
8
10
12
14
16
18
Time
RR
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The risk factors developed positively with a reduction in prevalence by 1% for every gender and age group
Example: Women, 50-64 years old
BMI>30 Smoking Lack of exercise
Risk alcohol consumption
2011 16% 14% 22% 6%
2016 15% 13% 21% 5%
Results 1: Uppsala County
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BMI>30 Smoking Lack of exercise
Risk alcohol consumption
-39 -2 -12
-8 -19 -4
-1 -1
-1 -1 -1
-7
-1
-1 -1
-34 -1
-1 -1 -2
-3 -3 -3 -1
-49 -67 -24 -4
Diabetes
Ischaemic heart disease
Stroke
Colon cancer
Lung cancer
Breast cancer
Prostate cancer
COPD
Depression
Fractures
Total:
Diagnoses:
Reduction in number of new cases
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If risk factors prevalence decreases by 1%:
BMI>30 2MM -1%
Smoking 4MM -2%
Lack of exercise 1,2MM -0,5%
Risk alcohol consumption
0,2MM -0,1%
Expected yearly health care costs of the diseases in Uppsala County: 257MM
Yearly savings
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BMI>30 Smoking Lack of exercise
Risk consumption
of alcohol
2011 18% 19% 21% 8%
2016 17% 18% 20% 7%
Results 1: Sörmland County
The risk factors develop positively with a reduction in prevalence by 1% for every gender and age group
Example: Women, 50-64 years old
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BMI>30 Smoking Lack of exercise
Risk alcohol consumption
-30 -2 -11
-7 -15 -4
-1 -1
-1 -1 -1
-6
-1
0 -29 -1
0 -1 -1
0 -3 -3
-39 -57 -22 -3
Diabetes
Ischaemic heart disease
Stroke
Colon cancer
Lung cancer
Breast cancer
Prostate cancer
COPD
Depression
Fractures
Total:
Diagnoses:
Reduction in number of new cases
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BMI>30 1,4MM -0,6%
Smoking 3,5MM -1,5%
Lack of exercise 1,0MM -0,4%
Risk alcohol consumption
0,1MM -0,06%
Yearly savings
If risk factors prevalence decreases by 1%:
Expected yearly healthcare costs of the diseases in Sörmland County: 237MM
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Uppsala - Sörmland: comparison of BMI and smoking
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Risk factors Uppsala Sörmland
BMI>30 -1% -0,6%
Smoking -2% -1,5%
Lack of exercise -0,5% -0,5%
Risk alcohol consumption -0,1% -0,06%
Uppsala - Sörmland: relative savings
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Strengths
Can include as many diagnoses as we have data for:IncidenceRisk factors and RRCosts
Can be used to calculate other HE-parameters, as QALY
Easy to understand and to use
Can be applied to local data
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Weaknesses
Based on the population at baseline, should include population prognosis
Time aspect, more careful estimation
Some risk factors significantly correlate, overestimation
The model estimates only one-year reduction in morbidity, but changes in life style are likely to are affect morbidity for several more years - underestimation
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Policy relevance
Policy options
Risk Factors
Disease prevalence
Economic consequences
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The decrease in the prevalence risk factors can result in significant
cost savings for the healthcare system
Relative savings depend on the baseline level of the risk factor
which influences the amount of cost savings
The model takes into account only healthcare costs (it can include
other societal costs and health effects)
This model may be used in other relevant studies
Conclusions
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Development
Just now: Data program with user - friendly interface to make different
estimations
Coming soon: Inclusion of other societal costs Calculation of QALY Possible to make estimations for different time perspectives
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Discussion?