Work in Progress Simulating the Local Dynamics of Cardiovascular Health and Related Risk Factors

1. Work in ProgressSimulating the Local Dynamics of Cardiovascular Health and Related Risk Factors Jack Homer Homer Consulting jhomer@comcast.net Bobby Milstein Centers for Disease Control and Prevention bmilstein@cdc.gov University of Michigan Tobacco Modeling Meeting May 2008 This work was funded by the CDC’s Division for Heart Disease and Stroke Prevention and by the National Institutes of Health’s Office of Behavioral and Social Science Research. The work was done in collaboration with the Health and Human Services Department of Austin/Travis County, Texas, and with Indigent Care Collaboration of Central Texas. The external contractors are Sustainability Institute and RTI International.

2. Core Design Team CDC: Darwin Labarthe, Diane Orenstein, Bobby Milstein, Marilyn Metzler, Rosanne Farris Austin: Adolfo Valadez, Phil Huang, Karina Loyo, Rick Schwertfeger, Cindy Batcher, Ella Pugo, Josh Vest NIH: Patty Mabry Consultants: Kristina Wile, Jack Homer, Justin Trogdon Contributors Organizational Sponsors • Austin/Travis County Health and Human Services Department • CDC Division for Heart Disease and Stroke Prevention • CDC Division of Adult and Community Health • CDC Division of Nutrition, Physical Activity, and Obesity • CDC Division of Diabetes Translation • CDC Office on Smoking and Health • CDC NCCDPHP Office of the Director • Indigent Care Collaborative (Austin, TX) • NIH Office of Behavioral and Social Science Research • RTI International • Sustainability Institute • Texas Department of Health

3. Brief Background on System Dynamics Modeling Compartmental models resting on a general theory of how systems change (or resist change) – often in ways we don’t expect • Developed for corporate policies in the 1950s, and applied to health policies since the 1970s • Concerned with understanding dynamic complexity • Accumulation (stocks and flows) • Feedback (balancing and reinforcing loops) • Used primarily to craft far-sighted, but empirically based, strategies • Anticipate real-world delays and resistance • Identify “high leverage” interventions • Modelers engage stakeholders through interactive workshops Forrester JW. Industrial Dynamics. Cambridge, MA: MIT Press; 1961. Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin/McGraw-Hill; 2000. Homer J, Hirsch G. System dynamics modeling for public health: Background and opportunities. American Journal of Public Health 2006;96(3):452-458.

4. Purpose of the Cardiovascular Risk Model • How do local conditions affect multiple risk factors for CVD, and how do those risks, in turn, affect population health status and costs over time? • How do different local interventions affect cardiovascular health and related expenditures in the short- and long-term? • How might local health leaders better balance their policy efforts given limited resources? The CDC is partnering on this project with the Austin (Travis County), Texas, Dept. of Health and Human Services. The model is calibrated to represent the overall US, but is informed by the experience and data of the Austin team, which has been supported by the CDC’s “STEPS” program since 2004. Homer J, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the local dynamics of cardiovascular health: risk factors, context, and capacity. Preventing Chronic Disease 2008;5(2). Available at http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm

5. Direct Risk Factors

6. Based on well-established Framingham approach for calculating probability of first-time events & deaths in individuals CVD = CHD (MI, angina, cardiac arrest) + Stroke/TIA + CHF + PAD Modifies individual-level risk calculator for use with populations Uses prevalences of uncontrolled chronic disorders by sex/age group Introduces secondhand smoke and pollution as additional risk factors Combines risks multiplicatively to account for overlapping conditions Adjustment exponents reproduce synergies seen in individual-level calculator Adjustment multipliers reproduce AHA event and death frequencies for 2003 - Anderson et al, Am Heart J 1991 (based on Framingham MA population N=5573, 1968-1987) - Homer “Risk calculation in the CVD model” project document, June 19, 2007 - NHANES 1988-94 & 1999-04 - AHA Heart Disease and Stroke Statistics – 2006 Update Calculating First-Time CV Events & Deaths

7. Indirect Risk Factors

8. Tobacco and Air Quality Interventions

9. Health Care Interventions

10. Interventions Affecting Stress

11. Healthy Diet Interventions

12. Physical Activity & Weight Loss Interventions

13. Adding Up the Costs

14. Data Sources for Modeling CVD Risk • Census • Population, deaths, births, net immigration, health coverage • AHA & NIH statistical reports • Cardiovascular events, deaths, and prevalence (CHD, stroke, CHF, PAD) • National Health and Nutrition Examination Survey (NHANES) • Risk factor prevalences by age (18-29, 30-64, 65+) and sex (M, F) • Chronic disorder diagnosis and control (hypertension, high cholesterol, diabetes) • Behavioral Risk Factor Surveillance System (BRFSS) • Diet & physical activity • Primary care utilization • Lack of needed emotional/social support  Psychosocial stress • Medical Examination Panel (MEPS) / National Health Interview (NHIS) • Medical and productivity costs attributable to smoking, obesity, and chronic disorders • Research literature • CVD risk calculator, and relative risks from SHS, air pollution, obesity, and inactivity • Medical and productivity costs of cardiovascular events • Questionnaires for CDC and Austin teams (expert judgment) • Potential effects of social & services marketing on utilization behavior • Effects of behavioral services on smoking, weight loss, stress reduction • Relative risks of stress for high BP, high cholesterol, smoking, and obesity

15. A “status quo” baseline scenario • A straightforward starting point for “what if” analysis • Assume no changes after 2000 in contextual factors or in risk factor inflow and outflow rates • Any changes in risk prevalences after 2000 are due to “bathtub” adjustment and population aging Obese % of non-CVD popn • Result: Past trends continue after 2000, but decelerate and level off • Increasing obesity, high BP, and diabetes • Decreasing smoking • High cholesterol mixed bag by age and sex, flat overall Uncontrolled hypertension % of non-CVD popn Smoking % of non-CVD popn The model is calibrated to reproduce data from NHANES 1988-94 and 1999-2004 on risk factor prevalences in the non-CVD population by age and sex.

16. Testing All 19 Interventions Combined, with Uncertainty Ranges % Change from Base Case Deaths from CVD per Capita* 4 Base Case 19.3% 20.2% All 19 Interventions with uncertainty range 2 Deaths from CVD if all risk factors = 0 0 1990 2000 2010 2020 2030 2040 There are significant gains even at the least effective end of the uncertainty range

17. Testing Selected Intervention Clusters % Change vs. Base Run Primary Care interventions (N=3): • Quality of Primary care increased • Primary care services marketed • Access to primary care increased Air Quality interventions (N=2) • Air pollution cut to half of recent value • Workplaces allowing smoking cut to zero Tobacco interventions (N=4) • Tobacco tax and sales restrictions • Social marketing against smoking • Smoke quit services marketed • Access to smoking quit services increased • The 3 (or even just the first 2) clusters together provide a large fraction of the CV deaths reduction of all 19 interventions, especially in the shorter term: • 92% (80%) by 2015, • 80% (64%) by 2040.

18. Intervention Effects on Smoking Inflows & Outflows Age 18 smokers 0.5 [0.4-0.7] Social marketing 0.65 [0.55-0.75] 0.5 [0.3-0.7] 0.6 [0.4-0.7] Adult smoking initiation/relapse Adult Smokers 0.7 [0.5-0.8] Tax & sales restrict 0.6 [0.5-0.7] 1.85 [1.5-2.5] 1.3 [1.2-1.5] Workplace ban (for those who work) 1.25 [1.2-1.4] Smoking quits 2.25 [1.5-3] Use of quit services • Sources: • Terry Pechacek CDC, personal correspondence, citing CPSTF • (re: taxes and sales restrictions and re: social marketing). • Moskowitz et al, AJPH 2000 (re: workplace bans) • Glasgow et al, Tobacco Control 1997 (re: workplace bans) • Terry Pechacek, citing multiple studies and CPSG (re: quit services) • Abby Rosenthal CDC, personal correspondence (re: quit services)

19. Use of Quit Services by Smokers • Sources: • - MEPS spending analysis, re: baseline use of quit services and products • Terry Pechacek CDC, personal correspondence, citing Group Health • Cooperative study, re: effects of marketing and quality primary care

20. Effects of Interventions on Smoking Prevalence Smoking Prevalence (non-CVD population) 0.3 Base 0.2 PC 3 PC 3 + AirQ 2 0.1 PC 3 + AirQ 2 + Tob 4 All19 0 1990 2000 2010 2020 2030 2040 In the base run, smoking prevalence among the non-CVD population declines from 17.7% in 2010 to 13.5% in 2040. The AirQ2 intervention cluster reduces the 2040 value to 12.9% (due to the effect of indoor smoking laws), and then adding the Tob4 cluster reduces it to 4.5%.

21. Effects of Interventions on Secondhand Smoke Exposure Fraction Nonsmokers SHS Exposure (non-CVD population) 0.6 Base 0.4 0.2 PC 3 PC 3 + AirQ 2 + Tob 4 PC 3 + AirQ 2 All19 0 1990 2000 2010 2020 2030 2040 In the base run, the fraction of non-smokers with significant secondhand smoke exposure declines from 19.1% in 2010 to 15.4% in 2040, tracking the decline in smoking. The AirQ2 intervention cluster reduces the 2040 value to 4.2% (due to the effect of indoor smoking laws), and then adding the Tob4 cluster reduces it to 1.5%.

22. Effects of Interventions on CVD deaths Note the increasing impacts over time for Tobacco4 as well as the other “gradual impact” interventions included in All19: Physical activity, Nutrition, Weight loss, Stress Deaths from CVD per 1000 (non-CVD population) 4 PC 3 Base PC 3 + AirQ 2 3 All19 PC 3 + AirQ 2 + Tob 4 2 1 Deaths from CVD if all risk factors = 0 0 1990 2000 2010 2020 2030 2040

23. Effects of Interventions on Smoking-related Cancer & COPD Deaths NonCVD deaths from smoking complications 400,000 Base 300,000 PC 3 PC 3 + AirQ 2 200,000 PC 3 + AirQ 2 + Tob 4 100,000 All19 0 1990 2000 2010 2020 2030 2040 Includes smoking-related deaths from cancers and respiratory diseases, based on 2001 data from SAMMEC (http://apps.nccd.cdc.gov/sammec/). SAMMEC = Smoking Attributable Mortality, Morbidity and Economic Costs. Male = 273,665 cancer and respir deaths due to smoking; Female = 135,296.

24. Effects of Interventions on Preventable Deaths (2010-2040 cumulative) Cumulative deaths 2010-2040 (in non-CVD population) from CV and other risk factor complications, in millions From From Other CV Complications Combined Base 19.6 10.8 30.4 PC3 17.7 10.4 28.2 PC3AirQ2 17.1 10.3 27.4 PC3AirQ2Tob4 16.6 6.8 23.5 All19 16.1 6.5 22.6 4.7 million lives saved due to air quality & tobacco interventions Over 30 years, the “Tob4” intervention cluster reduces CV deaths by 0.5m, and reduces other deaths (cancers & respiratory) by 3.4m, for a total reduction of 3.9m. Note that the CV deaths are based on the Framingham methodology, whereas the smoking-related deaths from other complications are based on the SAMMEC methodology.

25. Effects of Interventions on Costs of CV Events and Related Risk Factor Complications Complication and Risk Factor Management Costs per Capita 3,000 An average of $321 per capita could be saved—and justified for intervention spending—due to air quality and tobacco interventions Base PC 3 PC 3 + AirQ 2 2,000 PC 3 + AirQ2 + Tob 4 All19 1,000 All risk factors = 0 0 1990 2000 2010 2020 2030 2040

26. EXTRAS

27. Comparing Air Pollution vs. Tobacco-5 Interventions on Deaths and Costs CVD and NonCVD deaths from RF complications per 1000 8 6 Base Air Pollution 4 Tobacco-5 2 0 1990 2000 2010 2020 2030 2040 Complication and Risk Factor Management Costs per Capita 3,000 Base Air Pollution 2,000 Tobacco-5 1,000 All Risk Factors = 0 0 1990 2000 2010 2020 2030 2040

28. How Smoking is Modeled • Historical estimates of current smoking prevalence among non-CVD popn from NHANES 1988-94 and 1999-2004 by sex and age group. • Smoking prevalence in adults is modeled as a stock affected by flows of initiation and quitting, by the inflow of teen smokers turning age 18, and by deaths (related to CVD and otherwise). • Historical estimates of Age 18 smoking fraction by sex from YRBSS. • Baseline rates of adults quitting smoking based on Mendez & Warner AJPH 2007 and Sloan et al MIT Press 2004 (Fig. 2.1) • Baseline rates of adult initiation/relapse adjusted to reproduce NHANES adult smoking trends by sex and age. Smoking Adults Newly smoking adults Quitting or dying % Smokers 0.3 0 2040 1990

29. Effects of Interventions on Youth Smoking Smoking fraction of age 18 0.4 0.3 Base PC 3 0.2 PC 3 + AirQ 2 0.1 PC 3 + Air` 2 + Tob 4 All19 0 1990 2000 2010 2020 2030 2040

30. Effects of Interventions on Smoking Quits Smoking quit rate (combining all sex/age groups) 0.08 All19 0.06 PC 3 + Air` 2 + Tob 4 PC 3 + AirQ 2 0.04 Base PC 3 0.02 0 1990 2000 2010 2020 2030 2040

31. Effects of Interventions on Use of Quit Services & Products by Smokers Use of Quit Services & Products by Smokers 0.4 All19 0.3 PC 3 + Air` 2 + Tob 4 0.2 PC 3 0.1 PC 3 + AirQ 2 Base 0 1990 2000 2010 2020 2030 2040

32. Simulating the Local Dynamics of Cardiovascular Health and Related Risk Factors Homer J, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the local dynamics of cardiovascular health: risk factors, context, and capacity. Preventing Chronic Disease 2008;5(2). Available at http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm