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Preventing Chronic Disease

Simulating and Evaluating Local Interventions 
to Improve Cardiovascular Health

Jack Homer, Bobby Milstein, Kristina Wile, 
Justin Trogdon, Philip Huang, Darwin Labarthe, Diane Orenstein

The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Abstract = 259 words 
Narrative = 3,496 words 
References = 30 
Tables = 4
Figures = 4
Appendices = 0

Corresponding Author

Jack Homer, PhD
Homer Consulting
4016 Hermitage Drive
Voorhees, NJ  08043
Tel: 856-810-7673
E-mail: jhomer@comcast.net

Last Revision: March 12, 2009

Simulating and Evaluating Local Interventions 
to Improve Cardiovascular Health

ABSTRACT

Introduction
Many cardiovascular disease (CVD) risks may be addressed through local interventions.  But with numerous interventions available, and limited resources, priorities must be set.  Identifying the most effective interventions is challenging, because CVD risks develop through causal pathways and gradual accumulations that defy simple calculation.  

Methods
We created a simulation model for evaluating multiple approaches to preventing and managing CVD risks.  The model incorporates data from many sources to represent all U.S. adults who have never had a CVD event.  It tracks trajectories for the leading direct and indirect risk factors from 1990-2040, and contains 19 options for simulated intervention.  The main outcomes are first-time CVD events and consequent deaths, as well as total consequence costs, which combine medical expenditures and productivity costs associated with CVD events and risk factors.  Sensitivity analyses examine the significance of uncertain impact parameters.

Results
A “status quo” scenario shows population turnover and aging strongly influencing the future trajectories of several risk factors, some favorably and others unfavorably.  At least 15 of 19 interventions are potentially cost saving, and when combined could reduce deaths from first-time CVD events by about 20% and total consequence costs by 26%.  Some interventions act quickly to reduce deaths, while others more gradually reduce risk factor-related costs.  

Discussion
The model integrates much of what is known about major CVD risks and local policy options into a single testable framework for prospective policy analysis.  Although the model is still evolving, the simulated experiments reported here can inform policy and spending decisions.

Simulating and Evaluating Local Interventions 
to Improve Cardiovascular Health

INTRODUCTION

Health planners understand that the conditions in particular neighborhoods or cities can profoundly enhance—or impede—people’s prospects for a healthy life (1).  This dependency on local context is especially evident in the case of cardiovascular health, where a constellation of documented behavioral, social, and environmental factors combine to affect the likelihood of developing disease or experiencing premature death (2).  Heart disease and stroke are largely preventable.  Yet they remain the first and third leading causes of death in the U.S., in part because we have yet to establish living conditions that minimize such modifiable risks as smoking, obesity, stress, air pollution, poor diet, and physical inactivity.  The importance of intervening in these and other areas is highlighted in A Public Health Action Plan to Prevent Heart Disease and Stroke (3), which calls for leaders throughout the country to intervene more comprehensively and effectively.

The notion that cardiovascular disease (CVD) can be prevented through local actions raises practical questions that can be addressed through systems modeling and simulation.  Working closely with colleagues in Austin/Travis County, Texas, as well as subject matter experts at the Centers for Disease Control and Prevention and the National Institutes of Health, we developed a system dynamics (SD) simulation model to address the following questions:

•	How does local context affect the major risk factors for cardiovascular disease, and, in turn, population health status and costs?
•	How might local interventions affect CVD risk, health status, and costs over time?  
•	How might local health leaders better balance their policy efforts given limited resources?

METHODS

SD models are meant to improve our ability to anticipate the likely effects of interventions in dynamically complex situations, where the pathways from interventions to outcomes may be indirect, delayed, and possibly affected by nonlinearities or feedback influences (4).  SD has been used effectively since the 1970s to model many areas of public health and social policy, including cardiovascular disease (5).  

Model structure

We previously described a framework for understanding cardiovascular health in a local context (6).  That framework has been refined and quantified, using additional literature and input from veteran health planners and analysts.  The resulting simulation model—overviewed in Figure 1—focuses on primary prevention; it does not currently address those who have previously experienced a CVD event.  Causal influences move down and to the right, ending with two outcomes: (a) first-time cardiovascular events and consequent deaths, and (b) costs associated with these events and with the identified risk factors.  

(Figure 1 goes about here)

The model is initialized to represent conditions in the U.S. in 1990 and simulates continuously through 2040.  The non-CVD population and risk factor prevalences are represented as dynamic stocks, sub-divided by sex and age group (18-29, 30-64, and 65+).  Smoking and obesity are viewed as reversible conditions, whereas diabetes, high blood pressure, and high cholesterol are depicted as chronic disorders that are not reversible, but that can be controlled, with the help of good quality primary health care, to reduce their CVD risk.  

The incidence of first-time CVD events in the model is driven by the synergistic effect of several direct risk factors, in line with a widely used risk calculator from the Framingham study (7).  We modified that calculator in several ways for this study, but most fundamentally by estimating annual risks at a population level based on risk factor prevalences—rather than at the level of an individual. (A detailed description of the modified calculator is available at http://sustainer.org/cvd/documents/SELI_App1.pdf).  We also recognized the direct impacts on CVD events, and especially heart attacks, from secondhand smoke and small particulate matter (PM 2.5) air pollution (8-11).  Furthermore, because deaths from CVD have declined in part due to improvements in emergency and acute care, we incorporated a downward trend in the CVD case-fatality rate for 1990 through 2003 (12). 

Obesity is an important driver of CVD that acts largely through diabetes, high blood pressure, and high cholesterol (13).  Other indirect influences in the model include physical inactivity, poor diet, and psychosocial stress.  Table 1 summarizes what is known about these other indirect influences, as well as two indirect influences of smoking via diabetes and obesity.  

(Table 1 goes about here)

Both the direct and indirect influences in the model may be modified by 19 interventions that ring the diagram in Figure 1 (the “access to and marketing of” variables count separately).  All of those interventions could be implemented at a city, county, or state level, rather than requiring changes nationwide.  Table 2 sorts the 19 interventions into two typologies based on topical or functional characteristics.

(Table 2 goes about here)

Calculating costs
 
We use a common metric—constant 2005 dollars—to track medical and productivity costs (morbidity and mortality) that might be affected by the 19 interventions.  We measured the societal value of morbidity (sick days) and premature mortality (years of life lost) using a human capital approach, which estimates the market value of lost productivity at work and at home (14). (A detailed description of cost calculations is available at http://sustainer.org/cvd/documents/SELI_App2.pdf).  This summary of medical and productivity costs can help determine whether any intervention, or package of interventions, is justified by its likely aggregate consequences—or “total consequence costs”.  We did not estimate the costs of intervention implementation.  However the total consequence costs can inform spending decisions.  For example, suppose that for a given intervention the model calculates a cumulative consequence cost savings of $50 per capita.  Planners may then conclude that up to $50 per capita could be justifiably spent to implement that intervention and still create positive net benefits to society.  

The model tracks three types of intervention consequences:  
•	Medical and productivity costs attributable to fatal and non-fatal cardiovascular events; 
•	Medical and productivity costs attributable to non-cardiovascular complications of smoking (e.g. lung cancer), diabetes (e.g. blindness), high blood pressure (e.g. kidney failure), and obesity (e.g. colorectal cancer).  We have thus far been able to quantify these costs, but not yet costs related to non-cardiovascular complications of stress (e.g. depression), physical inactivity (e.g. back pain), and poor diet (e.g. colorectal cancer);
•	Costs of services and products to manage risk factors—these include medications and visits for managing chronic disorders, mental health services, weight loss services, and smoking cessation services and products.   

Model calibration

Although the model is meant to investigate interventions in localities such as Austin/Travis County, we began by calibrating it to represent the entire U.S.  This approach enabled more precise estimation, given that certain data were either unreliable or not available at the local level.  The results are generally reported as per capita estimates to facilitate interpretation at a local level.  Table 3 lists the major information sources upon which the model is based.  

 (Table 3 goes about here)  

The model specifies initial (1990) inflow and outflow rates for smoking, obesity, diabetes, high blood pressure, and high cholesterol.  These parameters have been set so that the model accurately simulates the observed changes in prevalence in the NHANES survey from 1988-1994 to 1999-2004.   

The model also contains 56 causal linkages requiring the estimation of relative risks, effect sizes, or initial values.  Many of these “impact” parameters were estimated through the use of published studies, meta-analyses, and in some instances, ad hoc surveys of veteran practitioners (15).  Because most of these parameter estimates have some level of uncertainty we also identified lower and upper bounds to be used for sensitivity analysis.     

Model testing

Having calibrated the model to accurately reproduce observed trends in risk factor prevalences as well as CVD events and deaths, we then explored plausible futures.  A base case scenario assumed no changes after 2004 in many of the local determinants of risk in the adult population, including healthiness of diet, extent of physical activity, stress, utilization of quality primary care, air pollution, and the prevalences of smoking and obesity among incoming 18-year olds.  This “status quo” base case should not be taken as a statement about what is most likely to happen absent intervention, but rather serves as a straightforward and easily understood benchmark against which to compare intervention scenarios.  

We tested interventions singly and in topical clusters.  For all interventions, we assumed a one year ramp-up during 2009, followed by full implementation from 2010 through 2040.  The significance of “full implementation” depends upon the intervention in question, but in all cases is based on effect sizes that the research literature or veteran practitioners indicate should be possible (see Table 4).  

(Table 4 goes about here)

We also tested the sensitivity of intervention results to uncertainties in the model’s impact parameters.  For each intervention scenario, we conducted separate simulations using the mid-point, lower-end, and upper-end values for all impact parameters.  This yielded an envelope of plausible outcomes for each intervention scenario.

RESULTS

Base case results

Figure 2 shows results from the base case simulation in small graphs overlaid on the overview diagram.  Knowing that the base case assumed no changes to the model’s inputs after 2004, it may seem surprising to see further declines in prevalence of smoking (and, thus, secondhand smoke exposure) and high cholesterol, as well as further growth in high blood pressure and diabetes.  The projected continuation of past trends reflects in part the eventual death of older cohorts being replaced by younger cohorts with different habits and characteristics.  For instance, the continued decline of smoking prevalence reflects the lower rate of smoking among teens and young adults today than in previous decades.  Such demographic turnover also helps explain the continued growth of high blood pressure and diabetes, which occurs in the model as a legacy of the large increase in obesity—a leading risk factor for both disorders—from 1980 to 2004.  The projected continuation of trends also reflects the future aging of the population—with the over-65 population growing dramatically from 2010 through 2030.  This aging effect contributes to the projected decline in smoking, because smoking is much less common among the elderly than it is among the non-elderly.  It also contributes to the projected growth of high blood pressure and diabetes, because these disorders are more common in the elderly than in the non-elderly. 

(Figure 2 goes about here)

Deaths from first-time CVD events, which declined on a per capita basis by 35% from 1990 to 2004, are projected in the base case to rebound by 33% from 2004 to 2040.  Much of the past decline is attributable to a 28% reduction in the event fatality rate, due to improvements in emergency and acute care.  But it also reflects an 11% decline in the per-capita rate of CVD events, which occurred, despite increases in high blood pressure and diabetes, because of decreases in smoking, secondhand smoke, PM 2.5 air pollution, and uncontrolled high cholesterol.  

Looking to the future, the potential rebound in deaths reflects a 17% increase in overall event fatality, and a 15% increase in the per-capita rate of CVD events.  Both of these increases reflect aging of the population.  Although the base case projects no future increase in per-capita CVD events or deaths within each age group, the aging of the population will lead to a significant increase in the overall population rate of CVD events and deaths.

Per-capita total consequence costs, which the model calculates to have declined by 25% from 1990 to 2004, are projected in the base case to decline by another 5% from 2004 to 2040.  Recall that total consequence costs reflect not only cardiovascular events (which account for 44% of the total costs in 2004), but also non-cardiovascular complications of risk factors (also 44%), as well as management of risk factors (12%).  Although per-capita cardiovascular event costs are projected to rise by 12% from 2004 to 2040 (reflecting the rise in the frequency of the events themselves), and per-capita risk management costs are projected to rise by 8% (reflecting the growing demand for blood pressure and diabetes treatment), these increases are more than offset by a 25% decrease in non-cardiovascular complications.  This decrease is due to the projected decline of smoking, which in 2004 was responsible for more than 300,000 non-cardiovascular deaths, primarily from lung cancer and COPD.  These premature non-CVD deaths from smoking account for a large fraction (about 28% in 2004) of the total consequence costs calculated in the model.      

Intervention scenario results 

Individual tests of the 19 interventions suggest that each can reduce deaths from first-time CVD events, and most can reduce total consequence costs.  Four of the interventions, however, raise total consequence costs, meaning that they increase risk factor management costs more than they decrease the costs of medical events and complications.  These four interventions include the two that encourage utilization of mental health services and the two that encourage utilization of weight loss services.  However, due to limitations in the current model, planners should not be too quick to dismiss these interventions in the real world.  In the case of mental health services, we have not yet estimated the non-cardiovascular costs of depression.  In the case of weight loss services for the obese, our estimates of cost and benefit are based on conventional dieting and exercise programs, rather than on bariatric surgery, which, while more costly also appears to be much more effective (16).  

For clarity we present simulation results for only those 15 interventions that do not in the current model increase total consequence costs.  Figure 3 shows projected changes in the death rate from first-time CVD events and in total consequence costs per capita when all 15 of these interventions are combined.  Such a multi-pronged approach may or may not be feasible in practice, given resource limitations, but it is useful to look at what it might achieve.  

(Figure 3 goes about here)

The model suggests that if all risk factors in the model were eliminated (gray line), the death rate could be reduced by about 60% below the base case.  This is somewhat more than the 50% potential reduction that some authors have asserted, but less than the 75% that others claim (17).  It is important to note that our model dichotomizes blood pressure, cholesterol, and diabetes as “high” or “not high”, and does not further subdivide the “not high” between normal and borderline.  Reducing borderline conditions (pre-hypertension, borderline cholesterol, pre-diabetes) to normal could yield a greater reduction in CVD, but we cannot explore this possibility with the current model.  (A static analysis of the potential benefits of reducing both high and borderline conditions is available at http://sustainer.org/cvd/documents/SELI_App3.pdf). 

The model projects that a 15-component intervention could reduce the first-time CVD event death rate relative to the base case by 20% (range based on sensitivity analysis: 15% to 26%) in 2015, and by 19% (range: 14% to 25%) in 2040.  Thus, the interventions that could significantly reduce CVD deaths seem to act relatively rapidly.  

The story differs somewhat with regard to consequence costs.  If all risk factors in the model were eliminated, consequence costs could be reduced by about 80% below the base case.  Relative to the base run, the 15-component intervention reduces consequence costs by 16% (range: 11.5% to 23%) in 2015, eventually reaching 26% (range: 19% to 33%) in 2040.  The reduction in consequence costs is $348 per capita (range: $254 to $514) in 2015, and $565 per capita (range: $416 to $722) in 2040.  Thus, as Figure 3 shows, the effect of the interventions is more gradual with regard to total consequence costs than it is with regard to CVD deaths; nearly 40% of the eventual impact on costs occurs after 2015.  

The 15-component intervention may be better understood by examining the incremental contributions of its components grouped by topical cluster.  Again looking at CVD deaths and total consequence costs, Figure 4 shows the same base case graph as in Figure 3, and then incrementally adds 
•	the 3 interventions that improve the utilization and quality of primary care (‘Care’); 
•	the 6 interventions related to air quality and smoking (‘Air’); and 
•	the 5 interventions related to improved nutrition and physical activity and the 1 intervention that would reduce sources of stress (‘Lifestyle’).  

(Figure 4 goes about here)

The relative impacts of the clusters are different for CVD deaths than they are for total consequence costs.  Of the three topical clusters, the largest contributor to projected reductions in CVD deaths, both in 2015 and 2040, is ‘Care’, followed by ‘Air’, followed by a smaller (though growing) contribution from  ‘Lifestyle’.  In contrast, the largest contributor to projected cost reductions, both in 2015 and 2040, is ‘Air’, followed by ‘Lifestyle’, followed by a smaller (and ultimately negligible) contribution from ‘Care’.  The contributions to per-capita cost reduction in 2015 are $235 from ‘Air’, $71 from ‘Lifestyle’, and $42 from ‘Care’; the contributions in 2040 are $393 from ‘Air’, $165 from ‘Lifestyle’, and $7 from ‘Care’.
   
DISCUSSION

The major factors that affect cardiovascular health at a population level interact through causal pathways and develop through gradual accumulations that defy simple calculation.  This dynamic complexity—and not only gaps in data—creates a challenge for local leaders who want to intervene most effectively given limited resources.  Our SD simulation model helps address this challenge by integrating what is known about the various risk factors in a single testable framework for prospective policy analysis.

The simulated experiments reported here point to several conclusions that local leaders and national allies may find valuable:

First, the CVD death rate has declined in recent years, not only because of  improvements in emergency and acute care, but also because of reductions in the CVD event rate itself, due to reductions in smoking, secondhand smoke, particulate air pollution, and uncontrolled high cholesterol.  If this progress does not continue at a similar pace in the future, however, the CVD death rate will likely rebound strongly due to aging of the population.

Second, medical and productivity costs associated with CVD risk factors have declined along with first-time CVD events and consequent deaths, but also due to reductions in non-CVD deaths (especially lung cancer and COPD) associated with smoking.  Population aging will likely keep smoking prevalence on a path of decline into the future, so that even if CVD deaths rebound, the total consequence costs need not rebound.

Third, of 19 intervention options that local planners may consider for addressing CVD risk, at least 15 have the potential to reduce CVD deaths without increasing total consequence costs.  

Fourth, interventions aimed at reducing smoking and improving indoor and outdoor air quality can save lives relatively quickly and can justify intervention spending equivalent to as much as $300 per capita for 30 years (in 2005 constant dollars, without time discounting) to achieve the full implementation targets in Table 4.  Most local health leaders are already aware of the importance of tobacco control and smoking bans, but many may not be aware of the significant contribution of particulate air pollution to CVD risk, even in relatively less polluted areas like Austin/Travis County.  

Fifth, interventions aimed at improving the utilization and quality of primary care to diagnose and control high blood pressure, high cholesterol, and diabetes, can also save lives quickly, but should not be expected to save much on total costs, primarily because of the high cost of medications.  Consequently, the intervention spending to achieve and maintain such improvement should not exceed the equivalent of $25 per capita per year for 30 years.  Other researchers have similarly found that good preventive care for chronic conditions may be cost-effective but is not necessarily cost saving (28, 29).

Sixth, interventions to improve nutrition and physical activity and to reduce sources of stress take more time to affect CVD deaths, as they gradually reduce obesity and the chronic conditions.  Nonetheless, their contribution grows over time and may justify intervention spending equivalent to as much as $100 per capita for 30 years.

The ability of particular localities to achieve full implementation within these cost limits may vary depending on context and implementation factors.

Potential extensions and improvements to the current model include:  
•	Modeling the medical and personal costs of the post-CVD population, and targeted interventions for secondary prevention to reduce the rate of recurrent CVD events; 
•	Modeling the prevalences of borderline conditions (pre-hypertension, borderline cholesterol, pre-diabetes) and incorporating them in the CVD risk calculations;
•	Modeling the prevalence of former smokers and incorporating their differential risks in the CVD event and cost calculations; 
•	Incorporating the non-CVD consequences of stress, physical inactivity, and poor diet; 
•	Estimating intervention implementation costs to better inform intervention priorities;
•	Incorporating additional risk factors (e.g., sodium, vitamin D deficiency, periodontal disease, C-reactive protein) as their independent relative risks become established. 

The model described here was created through a close collaboration with stakeholders in Austin/Travis County, who are now using a locally calibrated version of the model to support local strategy design and leadership development (30).  We plan to pursue similar engagements with colleagues in other locations. With more widespread use, this tool may help stakeholders across the country transform local contexts to most effectively improve cardiovascular health.  

DISCLAIMER

The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

ACKNOWLEDGMENTS

The authors gratefully acknowledge the contributions of Terry Pechacek, Dave Buchner, Roseanne Farris, Parakash Pratibhu, Deb Galuska, Adolfo Valadez, Karina Loyo, Rick Schwertfeger, Cindy Batcher, Ella Pugo, Jessie Patton-Levine, Josh Vest, Patty Mabry, John Robitscher, Alyssa Easton, Nancy Williams, and Larry Fine.

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Table 1. Effects of Indirect Risk Factors

   RISK FACTOR	EFFECTS 	
Lack of physical activity	– Increases the risk of obesity, high blood pressure, high cholesterol,            
and diabetes 	
	– Can exacerbate stress 	
Lack of a healthy diet                                            defined by the USDA’s Healthy Eating Index	– Increases the risk of high obesity, high blood pressure, and high  
cholesterol	
Stress                                                                  which may result from poverty, crime, discrimination, social isolation, or other persistent adversities	– Increases the risk of high blood pressure	
	– Can lead to greater caloric intake	
	– Increases the likelihood of smoking	
Smoking 	– Increases the risk of diabetes	
	– Reduces weight gain and obesity onset	

Sources for these effects are listed at http://sustainer.org/cvd/documents/Addl_sources.pdf

Table 2. Intervention Typology

The interventions fall into six broad topical categories, which include efforts to: 
•	Improve indoor or outdoor air quality (2 types);
•	Reduce smoking (4 types);
•	Improve the utilization or quality of primary health care (3 types);
•	Reduce stress preventively or through mental health services (3 types);
•	Improve nutrition or physical activity for the general population (5 types); and 
•	Facilitate weight loss specifically among the obese (2 types).

One may also categorize the 19 intervention types by their mode of action, as follows:
•	Social marketing of physical or mental health-related services (in 7 areas);
•	Regulations and/or taxes to deter unhealthy behaviors (in 4 areas);
•	Promoting primary care provider adherence to quality guidelines;
•	Providing broader access to physical or mental health-related services and products (in 6 areas); and
•	Reducing sources of stress through neighborhood improvements and social supports. 

The last two of these modes of action may involve targeting investments toward poorer neighborhoods and individuals within the larger community.

Table 3. Information Sources

   TOPICS	   SOURCES	
Population size, growth, and aging, and on health care coverage	US Census	
Rates of cardiovascular events and deaths	Reports from American Heart Association (18) and National Institutes of Health (12)	
Prevalences of smoking, obesity, and chronic disorders, and rates of diagnosis and control of the chronic disorders	National Health and Nutrition Examination Survey (NHANES), 1988-1994 and 1999-2004	
Fractions of teens age 18 who smoke, are obese, or have chronic disorders	NHANES and Youth Risk Behavior Surveillance System (YRBSS)	
Prevalence of psychosocial stress	Behavioral Risk Factor Surveillance System (BRFSS)	
Access to and utilization of good nutrition, physical activity, and primary care	Behavioral Risk Factor Surveillance System (BRFSS)	
Rates of smoking cessation	Published studies (19, 20)	
Rates of people moving from obese to non-obese	Published studies (21)	
Trend in fraction of workplaces allowing smoking	Published studies (11)	
Trend in PM 2.5 air pollution	Published studies (9)	
Average quality of primary care 	Published studies (22)	
Medical costs, sick days, and years of life lost due to cardiovascular events and deaths	Social Security actuarial life tables and published studies (14, 23, 24)	
Non-cardiovascular medical costs and sick days due to smoking, obesity, diabetes, and high blood pressure	Linked files of Medical Examination Panel Survey (MEPS) and National Health Interview Survey (NHIS)	
Non-cardiovascular mortality and years of life lost due to smoking, obesity, diabetes, and high blood pressure	CDC Smoking-Attributable Mortality, Morbidity, and Economic Costs (SAMMEC), WHO Statistical Information System, and published studies (25, 26, 27)	

Table 4. Definition of “Full Implementation” for Intervention Options

•	For the 7 marketing interventions and for taxes on tobacco or junk food, full implementation means doing the maximum that has been demonstrated or seriously proposed somewhere in the U.S.  
•	For the 6 access interventions, it means raising access to 100%.  
•	For smoking restriction, it means reducing secondhand exposures in workplaces and public places to zero.  
•	For air pollution, it means reducing PM 2.5 by 50% from its 2001-2003 value.  
•	For sources of chronic stress, it means a reduction by 50%.  
•	For the quality of primary care (i.e., adherence to guidelines), it means improvement from a national average of 54% (ref. 40) to 75%.

Figure 1.  Overview of model structure

Note:  This diagram depicts major concepts in the model and their causal linkages.  Boxes identify risk factor prevalences modeled as dynamic stocks.  The population flows associated with these stocks—including people entering the adult population, entering the next age category, immigration, risk factor incidence, recovery, cardiovascular event survival, and death—are not shown for the sake of clarity.

Key:
Blue solid arrows: causal linkages affecting risk factors and cardiovascular events and deaths;
Brown dashed arrows: influences on costs;
Purple italics: factors amenable to direct intervention;
Black italics (population aging, CV event fatality): other specified trends;
Black non-italics: all other variables, affected by italicized variables and by each other. 

Figure 2.  Base case trajectories

Note:  The graphed variables sum across all sex and age groups in the adult non-CVD population.

Figure 3.  Estimated impacts of a 15-component intervention, with ranges based on sensitivity testing

Note:  The 15 components include interventions focused on: air pollution control regulations, smoking bans at work and public places; access to and marketing of smoking quit products and services; tobacco taxes and marketing/sales regulations; anti-smoking social marketing; access to and marketing of primary care; quality of primary care provision; sources of stress; access to and marketing of healthy food options; junk food taxes and sales/marketing regulations; and access to and marketing of physical activity options.

Key:
Blue line = base case results;  
Black line = expected reduction in death rate or costs from the 15-component intervention when the impact parameters are all set to their baseline values;
Orange shaded area around the black line = envelope of plausible outcomes in the 15-component intervention outcomes based on sensitivity testing.  Upper edge (least impact) results when all uncertain impact parameters set to their lowest values, while lower edge (most impact) results when all uncertain impact parameters set to their greatest values;
Gray line = the model’s calculation of what the death rate or costs would be if all of the risk factors in the model—smoking, PM 2.5 air pollution, high blood pressure, high cholesterol, diabetes, obesity, poor nutrition, inactivity, and stress—were reduced to zero. 

Figure 4.  Contributions of intervention clusters to the 15-component intervention results

Key:
Blue line = base case results (as in Figure 3);
Gray line = outcomes if all risk factors were reduced to zero (as in Figure 3); 
Red line = implement the 3 interventions that improve the utilization and quality of primary care (‘Care’);
Green line = add the 6 interventions related to air quality and smoking (‘Air’);
Black line = add the 5 interventions related to improved nutrition and physical activity and the 1 intervention that would reduce sources of stress (‘Lifestyle’).  This scenario includes all 15 interventions and is identical to the black line in Figure 3.