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 just gaps in data — is a challenge for local leaders who want to intervene most effectively given limited resources. Our simulation model helps meet this challenge by integrating what is known about the various risk factors in a single testable framework for prospective policy analysis.
The simulations reported here point to several conclusions that local leaders and national allies may find valuable.
1) 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 as the population ages.
2) Medical and productivity costs associated with CVD risk factors have declined because of declines in first-time CVD events and consequent deaths, and because of reductions in non-CVD deaths (especially lung cancer and chronic obstructive pulmonary disease) 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.
3) Of 19 interventions that local planners may consider for lowering CVD risk, at least 15 could reduce CVD deaths without increasing total consequence costs.
4) 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 per year for 30 years (in 2005 constant dollars, without time discounting) to achieve the full implementation targets. Most local health leaders are already aware of the need for tobacco control and smoking bans, but many may not be aware of the contribution of particulate air pollution to CVD risk, even in areas like Austin/Travis County without heavy pollution.
5) Interventions aimed at improving the use and quality of primary care to diagnose and control high blood pressure, high cholesterol, and diabetes can 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 (36
6) 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 other chronic disorders. Nonetheless, their contribution grows over time and may justify intervention spending equivalent to as much as $100 per capita per year 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 model include the following:
- Modeling medical and personal costs for the post-CVD event population and targeted interventions for secondary prevention to reduce the rate of recurrent CVD events.
- Modeling the prevalence rates of borderline conditions (prehypertension, borderline cholesterol, prediabetes) 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 independent risk factors for CVD (eg, excess sodium intake, excess trans fat intake, vitamin D deficiency, periodontal disease).
The model described here was created through a close collaboration with health planners in Austin/Travis County, who are now using a locally calibrated version of the model to support local strategy design and leadership development. We plan to pursue similar engagements with colleagues elsewhere. With more widespread use, this tool may help health planners across the country transform local contexts to most effectively improve cardiovascular health.