Inna Feldman Uppsala University Inna.feldman@kbh.uu.se

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 Compare Costs Costs Costs Morbidity Morbidity Morbidity Compare Health (risk factors) Health (risk factors) Health (risk factors) Change Present Future Past

3. Starting points 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

4. Disease risk factors Diagnoses:

5. Risk factors – related risks (RR) • Relative risk (RR) is the risk of an event (or ofdeveloping a disease) relative to exposure. • Relative risk is a ratioof the probabilityof the event occurring in the exposedgroupversus a non-exposedgroup • The model is based on related risks for thesefour 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. Relative risks - example Men, 50-64 years old Diagnoses:

8. Relative risks - example Women, 50-64 years old Diagnoses:

9. How the change in risk factors influences disease incidence: IF ”Impact fraction” 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%.

10. The Model 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

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. Results 1: Uppsala County • The risk factorsdevelopedpositivelywith a reduction in prevalence by 1% for every gender and age group Example: Women, 50-64 years old

14. Reduction in number of new cases Diagnoses:

15. Yearly savings If risk factors prevalence decreases by 1%: Expected yearly health care costs of the diseases in Uppsala County: 257MM

16. Results 1: Sörmland County • The risk factorsdeveloppositivelywith a reduction in prevalence by 1% for every gender and age group Example: Women, 50-64 years old

17. Reduction in number of new cases Diagnoses:

18. 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. 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. Conclusions • 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

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?