PRISM is a nationally representative computer model that simulates the effects of diverse clinical and population-level interventions aimed at reducing risks for CVD events (ie, acute events coded as coronary heart disease, stroke, heart failure, or peripheral artery disease) and for other chronic conditions and diseases (eg, hypertension, diabetes, renal disease, obstructive pulmonary disease, certain cancers) (15,18,19). It is a compartmental system dynamics model and, like other such models, depicts processes of multiple and nonlinear influence, accumulation, delay, and feedback that result in movements among healthy and ill population subgroups (20–22). The conceptual and numerical assumptions of PRISM are drawn from scientific literature, national survey data, and subject matter expertise. PRISM provides a user-friendly interface and in seconds produces 50 years of simulated output, from 1990 through 2040, answering “what if” questions posed in interactive workshops and study sessions. The model has been calibrated to represent the United States and some local areas (18,23,24).

In PRISM, the population is segmented by 6 childhood and adult age categories, by sex, and by CVD event status (not-yet or non-CVD, and already-had or post-CVD). The model simulates changes in the population through birth, death, migration, aging, and movement from non-CVD to post-CVD status. It also depicts flows into and out of 3 blood pressure categories (normal, borderline, high), 3 blood cholesterol categories (normal, borderline, high), 3 blood glucose categories (normal, prediabetic, diabetic), 4 smoking categories (never, current, recent ex-smoker, long-term ex-smoker), and 2 body mass index categories (non-obese, obese). PRISM also includes risk factors of poor nutrition (ie, inadequate consumption of fruits and vegetables and excess consumption of junk food, sodium, or trans fats), inactivity, psychological distress, secondhand smoke, periodontal disease, sleep apnea, small particulate air pollution, and inadequate use of aspirin for primary prevention. Co-occurrence of these risk factors in individuals is not modeled explicitly (PRISM is a compartmental model of subpopulations rather than separate individuals), but interactions among the risk factors are represented algebraically in equations for cardiovascular events and deaths and also in equations describing how the prevalence of one risk factor (eg, smoking) may affect another (eg, diabetes). The model simulates changes by quarter-year increments in more than 4,000 variables, including changes in population mix, risk factor prevalence, and risk factor–related morbidity, mortality, and costs (19).

The current version of PRISM, version 3p, includes 50 intervention levers that may prevent or mitigate the cardiovascular risks described above. All interventions in the model are based on peer-reviewed literature and discussions with experts working in the field who helped to specify and quantify their potential effects (19).

We grouped PRISM’s 50 intervention levers into established and emerging interventions that we crossed with policy categories of care (clinical, behavioral, and mental health services), air (smoking, secondhand smoke, and air pollution), and lifestyle (nutrition and physical activity), resulting in 6 clusters (Table 1). For each intervention, the table gives several pieces of information: estimated size of the target population in 2010, the existing recipient population in 2010 (for care interventions), the unit cost per recipient per year (for care interventions), the initial (pre-2012) lever setting for the intervention, and the best plausible (full extent) lever setting (19).

Within the care policy domain, the established interventions include the use of quality preventive care for diagnosis and basic control (that is, according to current standards) of high blood pressure, high cholesterol, and diabetes; quality acute and rehabilitative care after cardiovascular events; quality care for post-CVD patients to prevent recurrent events; behavioral services for smoking cessation and weight loss for the obese; mental health services to address psychological distress and depression; diagnosis of sleep apnea and its effective treatment through the use of continuous positive airway pressure (CPAP) devices; prevention of periodontal disease through regular dental care; and the use of aspirin for primary prevention among middle-aged and older people (men aged 45 to 79, women aged 55 to 79) who have not had a cardiovascular event. Emerging care interventions include tighter control (that is, based on lower thresholds than current standards) of high blood pressure, high cholesterol, and diabetes and the control of borderline hypertension, borderline cholesterol, and prediabetes.

Within the air domain, the established interventions include several policies for tobacco: taxation, counter-marketing, and prohibitions on workplace smoking. Emerging interventions in the air domain are tobacco marketing restrictions and the reduction of small particulate (PM 2.5, also known as soot) air pollution. Although the sources of PM 2.5 are well understood and reductions of 50% or more were achieved from 1990 to 2010, no consensus exists on how to accomplish further reduction as a matter of national policy (25).

Within the lifestyle domain, the established interventions consist of policies for increasing access to and promoting physical activity for adults and older children. Emerging interventions in this domain include strategies to reduce sodium and trans fat consumption (through legislative or market-based actions), the taxation and counter-marketing of energy-dense (junk) food, improving physical activity in child care, and policies for increasing access to and promoting the consumption of fruits and vegetables.

In this analysis, we report the results of testing these 6 clusters one at a time and in combination. When an intervention lever was included in a cluster being tested, it was ramped up linearly to a level of best plausible implementation from 2012 to 2017 and remained in full effect thereafter to the end of the simulation in 2040. For some of the interventions (whether established or emerging), the best plausible lever setting was directly measurable (eg, milligrams of sodium consumed daily, fraction of workplaces allowing smoking), whereas for others it was defined as 100% of an ideal or best practice as described in the literature or by subject matter experts.

We report annual outcome variables that include preventable deaths per thousand adults (death rate) that include CVD deaths and non-CVD deaths attributable to CVD risk factors, such as chronic obstructive pulmonary disease, end-stage renal disease, and various cancers (lung, colorectal, breast, and others); years of potential life lost per thousand adults; 3 categories of per-adult cost (direct risk factor management costs, direct acute and extended care costs, and indirect productivity costs); and the sum of these per-adult costs (19). Costs were discounted by 3% per year back to 2012. Cumulative effects are reported as of 2020 and as of 2040, in terms of percentage changes relative to the status quo base case of no change in intervention levers after 2012.

To test the robustness of results, each of the 6 cluster intervention scenarios was tested by using probabilistic sensitivity analysis. In 200 Monte Carlo runs of PRISM, 89 different parameters quantifying intervention effect sizes were set to randomly selected values along assumed uniform probability distributions with estimated mean, minimum, and maximum. These distributional parameters were taken from means and confidence intervals reported in the research literature where available and otherwise from the estimates of subject matter experts (19).

We calculated cumulative results from testing the 6 intervention clusters (care, air, and lifestyle in both established and emerging interventions) from 2012 to 2020 (Table 2). Of the 3 policy domains, combining the care interventions led to the greatest reduction in the death rate (−30.4%; 95% uncertainty range, −33.5% to −29.3%) and per capita acute and extended care costs (−17.9%; 95% uncertainty range, −21.6% to −15.6%). However, the care interventions also led to a substantial increase in risk factor management costs (105.8%; 95% uncertainty range, 94.9% to 117.4%), which includes the direct costs of office visits, medications, equipment, and ambulatory medical procedures. As a result, the care interventions together did not lead to much, if any, reduction in combined costs (−0.8%; 95% uncertainty range, −4.4% to 1.7%). The health effects were greater for the established care interventions than for the emerging care interventions, but both made significant contributions.

The air and lifestyle interventions had modest effects through 2020. The combined air interventions led to modest reductions in the death rate (−2.3%; 95% uncertainty range, −2.6% to −1.9%) and combined costs (−1.9%; 95% uncertainty range, −2.2% to −1.6%). The health and cost effects were evenly divided between the established and emerging air interventions. The combined lifestyle interventions provided greater reductions in the death rate (−5.7%; 95% uncertainty range, −7.3% to −4.4%) and combined costs (−4.3%; 95% uncertainty range, −5.5% to −3.3%). The emerging lifestyle interventions contributed more than the established interventions did.

The combined care, air, and lifestyle interventions resulted in large reductions in the death rate (−32.6%; 95% uncertainty range, −34.7% to −30.6%) and a moderate reduction in combined costs (−4.5%; 95% uncertainty range, −7.4% to −1.7%). Both established and emerging interventions contributed to these reductions, but the established interventions contributed more than the emerging ones did with respect to health effects, while the emerging interventions contributed more than the established ones did with respect to total costs.

We calculated cumulative results from testing the 6 intervention clusters from 2012 to 2040 (Table 3). The combined care interventions resulted in large reductions in the death rate (−47.7%; 95% uncertainty range, −51.9% to −45.7%) and a moderate reduction in combined costs (−9.4%; 95% uncertainty range, −14.6% to −6.1%). Both established and emerging care interventions contributed to these improvements, but the established interventions contributed more than the emerging ones did.

The combined air interventions resulted in moderate reductions in the death rate (−5.6%; 95% uncertainty range, −6.3% to −4.8%) and combined costs (−4.9%; 95% uncertainty range, −5.6% to −4.1%). Both established and emerging air interventions contributed to these improvements, but the established air interventions contributed more than the emerging ones did.

The combined lifestyle interventions resulted in moderate reductions in the death rate 
(−10.8%; 95% uncertainty range, −12.8% to −8.5%) and combined costs (−8.6%; 95% uncertainty range, −10.2% to −6.7%). Both established and emerging lifestyle interventions contributed to these improvements, but the emerging interventions contributed more than the established interventions did.

The combined care, air, and lifestyle interventions resulted in large reductions in the death rate (−51.3%; 95% uncertainty range, −54.1% to −48.7%) and a moderate reduction in combined costs (−16.5%; 95% uncertainty range, −20.7% to −12.8%). Both established and emerging interventions contributed to these improvements, but the established interventions contributed more than the emerging interventions did with respect to health impacts, whereas the emerging interventions contributed more than the established ones with respect to total costs.

Results from the probabilistic sensitivity analysis are represented in the uncertainty ranges (Table 2, Table 3). All of the results described above regarding the relative importance of established and emerging interventions within each of the 3 policy domain areas and in total are not qualitatively affected by uncertainties.