1. Health impact and monetary costs of dietary salt reduction policies: the CHD Policy Model Andrew Moran MD, MPH Department of Medicine, Columbia University

2. Overview What are cardiovascular disease (CVD) computer simulation models, and what are their uses? Introduction to the Coronary Heart Disease (CHD) Policy Model and U.S. and Argentina versions Policy analyses: –United States dietary salt policy analysis –Argentina dietary salt policy analysis

3. What are CVD computer simulation models, and what are their uses?

4. What is a Markov-style computer simulation model? Well 0.2 Sick 0.1 Dead 0.05 WellSickDead Well0.750.200.05 Sick0.000.900.10 Dead0.00 1.00 State transition matrix for one model cycle:

5. What are the uses of computer simulation models? Translate epidemiologic, trial, and economic data into policy research Compare policy alternatives using a standard metric Fill gaps in current knowledge –Extend results of clinical trials –Forecast the future using demographic predictions –Scale up epidemiologic and trial results to a larger population Addressing the limitations of computer simulation models –transparency –sensitivity analysis

6. Introduction to the CHD Policy Model

7. Simplified CVD Policy Model Within a yearly cycle: Incidence or event rate 1 day case fatality 30 day case fatality 30 day CVD survivors Long term CVD survivors Probability of repeat CVD events Probability of CVD or nonCVD death Population without CVD Outcomes: Deaths Nonfatal CVD events QALYs Costs

8. CHD Policy Model Structure DEMOGRAPHIC- EPIDEMIOLOGIC MODEL Persons without CHD or stroke sorted by: Age (35-84 years) Sex Blood pressure Total or LDL chol. Smoking HDL cholesterol Diabetes BMI DEMOGRAPHIC- EPIDEMIOLOGIC MODEL Persons without CHD or stroke sorted by: Age (35-84 years) Sex Blood pressure Total or LDL chol. Smoking HDL cholesterol Diabetes BMI BRIDGE MODEL (acute stroke or CHD, first 28 days) BRIDGE MODEL (acute stroke or CHD, first 28 days) DISEASE HISTORY MODEL Persons with CHD or stroke: Age (35-84 years) Sex CHD type (angina, MI, or arrest) Stroke type (ischemic or hemorrhagic) DISEASE HISTORY MODEL Persons with CHD or stroke: Age (35-84 years) Sex CHD type (angina, MI, or arrest) Stroke type (ischemic or hemorrhagic) Multivariate risk functions CHD, stroke and non-cardiovascular deaths

9. CHD Policy Model: state transition probabilities Transition from no CVD to CVD:* Transition from chronic CVD to repeat CVD (examples): –Probability of MI in stable angina patients –Probability of stroke after MI –Probablity of revascularization if stable angina *similar for probabilty of nonCVD death

10. CHD Policy Model U.S. data inputs VariableSource Population of the U.S.US Census Bureau Incidence and case-fatality for CHD and Stroke Framingham Heart Study, National Hospital Discharge Survey CHD, stroke, non-CVD mortality U.S. Centers for Disease Control CVD risk factor means and distributions, year 2002 U.S. National Health and Nutrition Examination Survey (NHANES) Multivariate risk for CVD events, non- CVD death Framingham Heart Study CHD intervention utilization and costs (hospitalization, medications, revascularization, long-term care) California Hospital Discharge Database (OSHPD) and Medical Expenditure Panel Survey (MEPS) Stroke costs (hospitalization, medications, long-term care, background costs) California Hospital Discharge Database (OSHPD) and Medical Expenditure Panel Survey (MEPS) Disability adjustment methodsBeaver Dam Study, EuroQol Survey

11. CHD Policy Model-Argentina: data inputs VariableSource Population of ArgentinaUN Population Division Incidence and case-fatality for CHD and Stroke Hospitalized MI: Caccavo et al. Hospitalized CHD and stroke: national hospital registry SAC, ReNACer and PICSIS registries CHD, stroke, non-CVD mortality National vital statistics Multivariate risk for CVD events, non- CVD death Framingham Heart Study CVD risk factor means and joint distributions, year 2006 CARMELA Study, Buenos Aires National Risk Factor (telephone) Survey CHD intervention utilization and costs (hospitalization, medications, revascularization, long-term care) National hospital registry Stroke costs (hospitalization, medications, long-term care, background costs) National hospital registry Disability adjustment methodsGlobal Burden of Disease Study

12. Model calibration

13. Incidence Cohort Studies Hospital registries Case fatality Surveillance studies Hospital registries Mortality Vital statistics Cohort studies Prevalence Population surveys Cohort studies

14. Model calibration

16. Dietary salt policy analyses

17. Degree of reduction in dietary salt and corresponding change in systolic blood pressure (SBP in mmHg) 1 gm/day salt reduction3 gm/day salt reduction Low SBP estimate High SBP estimate Low SBP estimate High SBP estimate US Population Hypertensives*1.201.873.605.61 Age ≥651.201.873.605.61 All Others0.601.171.803.51 Black US Population Hypertensives*1.803.035.409.10 Age ≥651.201.873.605.61 All Others1.201.873.605.61 Bibbins-Domingo, et.al, NEJM 2010. *based on systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg or use of antihypertensive medications

18. Percent change in incident CHD with 3 gm/day reduction in dietary salt Bibbins-Domingo, K. et. al. NEJM, 2010, 362 (7):590-99.

19. Comparing salt reduction to other preventive measures (deaths 2010-2019 )

20. Cost and effectiveness of salt reduction and hypertension treatment annually and cumulatively from 2010-2019 Cost of interventions (billions) Change in healthcare cost (billions) Change in QALYs (thousands) Cost per QALY (dollars) Healthcare cost saved per dollar spent on intervention (dollars) Population reduction in dietary salt 1 gm/day*0.3-7.0 (1.4)120 (14.6)Cost savings26.1 (5.2) 3 gm/day*0.3-20.4 (4.1)350 (42)Cost savings76.0 (15.4) Blood pressure treatment with medications among hypertensive individuals 19.5 (0.07)14.2 (2.7)360 (42)15,800 (9,900) 0.7 (0.1) Cumulative cost and effectiveness of gradually reducing dietary salt over the decade from 2010-2019 1 gm/day*2.731.6 (6.5)350 (43)Cost savings11.8 (2.4) 3 gm/day*2.795.6 (19.6)1,000 (127)Cost savings35.6 (7.3) * based on high estimate for effect of salt reduction on blood pressure and US $1 per capita cost of dietary salt reduction program Bibbins-Domingo, et.al, NEJM 2010

21. Projections of the effectiveness, of a 3 gram/day average reduction in dietary salt, by age and sex, Argentina 2011-2020* *Ferrante et al., submitted

22. Projections of the effectiveness, costs, and cost-effectiveness of a 3 gram/day average reduction in dietary salt, Argentina 2011-2020* B: Escenario de “alto efecto ” Costo intervención ($) Costos por EC ($) Costos no por EC ($) Costo marginal ($) QALY QALY marginal Costo-efectividad incremental ($ por QALY) Situación actual03108131,8660154.694.683 Reducción de sodio 142942131,063974154.956.807262124-3716 B: Escenario de “bajo efecto ” Costo intervención ($) Costos por EC ($) Costos no por EC ($) Costo marginal ($) QALY QALY marginal Costo-efectividad incremental ($ por QALY) Situación actual03108131.8660154.694.683 Reducción de sodio 143003131.407-569154.856.157161.474-3526 EC = Enfermedad coronaria (CHD); QALY = Quality-adjusted life year; $ = US dollars in millions *Ferrante et al., submitted

23. Conclusions If sufficient data are available, CVD policy models can inform dietary salt lowering policy making However, current estimates are based on limited data. There is a need for: –Policy “natural experiments” to better quantify effectiveness (example of second hand smoking) –Detailed measurement of program costs—may vary a lot across jurisdictions

24. Thank you!

25. Acknowledgements University of California, San Francisco Kirsten Bibbins-Domingo, PhD, MD, MAS Eliseo Perez-Stable, MD, MPH Pam Coxson, PhD Tekeshe Mekonnen, MS David Guzman, MSPH Jim Lightwood, PhD Mark Pletcher, MD, MPH Columbia University Lee Goldman, MD, MPH Ministry of Health, Argentina Daniel Ferrante, MD University of Buenos Aires Raul Mejia, MD