1. Population prevalence of disease risk factors and economic consequences for the healthcare system - possible scenarios Inna Feldman Uppsala University Inna.feldman@kbh.uu.se

2. Estimation of future costs Future Health (risk factors) Morbidity Costs Health (risk factors) Morbidity Costs Present Past Compare Change

3. 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/year Source: 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

4. BMI>30SmokingLack of exercise Risk alcohol consumption xxx xxx xxx xxxx x xx xx xx xxxx xxx Diabetes Ischaemic heart disease Stroke Colon cancer Lung cancer Breast cancer Prostate cancer COPD Depression Fractures Diagnoses: Disease risk factors

5. 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 groupratioprobability The model is based on related risks for these four risk factors

6. Risk factors – sources Swedish and international studies Age- and gender-specific Can be updated according to new studies and new results

7. BMI>30SmokingLack of exercise Risk alcohol consumption 6,41,22 1,72,91,3 2,62,2 1,51,21,61,8 26,4 ---- 1,21,1 10,61,1 1,31,021,762 1,821,2 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

8. BMI>30SmokingLack of exercise Risk alcohol consumption 7,31,22 1,93,41,3 1,43,02,2 1,51,21,61,8 16,1 1,7-1,2- -- 9,31,1 1,31,021,72 1,821,2 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

9. 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”

10. 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

11. 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

12. Time perspective How long does it take to reduce the risk? Differs for different diseases and risk factors Lack of studies Assumption:

13.  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>30SmokingLack of exercise Risk alcohol consumption 201116%14%22%6% 201615%13%21%5% Results 1: Uppsala County

14. BMI>30SmokingLack of exercise Risk alcohol consumption -39-2-12 -8-19-4 -7 -34 -2 -3 -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

15. 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

16. BMI>30SmokingLack of exercise Risk consumption of alcohol 201118%19%21%8% 201617%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

17. BMI>30SmokingLack of exercise Risk alcohol consumption -30-2-11 -7-15-4 -6 0-29 0 0-3 -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

18. 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

19. Uppsala - Sörmland: comparison of BMI and smoking

20. Risk factorsUppsalaSörmland BMI>30-1%-0,6% Smoking-2%-1,5% Lack of exercise-0,5% Risk alcohol consumption -0,1% -0,06% Uppsala - Sörmland: relative savings

21. Strengths Can include as many diagnoses as we have data for: Incidence Risk factors and RR Costs Can be used to calculate other HE-parameters, as QALY Easy to understand and to use Can be applied to local data

22. 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

23. Policy relevance Policy options Risk Factors Disease prevalence Economic consequences

24.  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

25. 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

26. Discussion?