The health risks of cardiovascular diseases, respiratory conditions, and multiple cancers associated with cigarette smoking have been well documented. Tobacco use is also a major concern to the Department of Defense DoD as it affects the general health, physical fitness, troop readiness, and active duty training costs associated with U. Military personnel. This directive prohibited smoking during basic training and increased the number of assigned non-smoking zones such as auditoriums, classrooms, hospital public areas, and official busses and vans. This ban includes all buildings and vehicles owned by the DoD.
Other tobacco-related policy initiatives in DoD aimed at reducing the prevalence of smoking include Tricare coverage for tobacco cessation, a hot line, educational campaigns such as the Tough Enough social media tobacco campaign, and targeted health programs such as the Healthy Base Initiative.
Smoking rates in DoD have decreased significantly in the past few decades. The purpose of this study is to quantify the future health and economic impact of various tobacco control initiatives and policies compared with the status quo , using an updated health promotion microsimulation model that was initially developed in — and updated in This study evaluated future health outcomes of Tricare Prime beneficiaries and the economic impact of initiatives that DoD may take to further its effort to transform the military into a tobacco-free environment.
From the 3. National Guards, Reservists, and their family members were excluded. Once an individual develops a chronic condition, we assumed that the disease remains until death or end of simulation. If the probability of an outcome exceeds a random number generated from a uniform distribution, disease onset was assumed. Details about the simulation model and the validation exercises are available upon request and have been published elsewhere. Smoking status was simulated for a status quo scenario, which assumed that DoD maintains its current tobacco prevention programs, and for an intervention scenario.
The intervention scenario assumed that the DoD would start implementing a comprehensive tobacco prevention policy in in order to achieve the Healthy People goal for smoking. The simulated smoking rates current and former were applied to the Tricare Prime population projection, based on the data provided by the Defense Health Agency, through by population characteristics such as age, gender, service, and beneficiary category.
The lifetime costs were calculated from the perspective of DoD as well as that from other payers, namely, Veterans Affairs VA , private insurance, Medicaid, other payer, and Medicare. Simulated individual-level lifetime cost savings by age and gender from quitting smoking were then applied to the number of quitters as of comparing status quo with intervention scenario to calculate the aggregated lifetime savings that the Prime population may observe if DoD were to implement a comprehensive tobacco control policy from to Procedures for handling the data and study methods were reviewed by an Institutional Review Board and considered exempt.
We simulated the changes in smoking status under different scenarios for , individuals based on their initial health profile. During the period between and , we simulated the transition for 1 non-smoker to current smoker via initiation, 2 current smoker to former smoker via cessation, and 3 former smoker to current smoker, via relapse. Under the intervention scenario, the impact of a more comprehensive tobacco control policy was imposed on smoking prevalence through initiation, cessation, and relapse from onward until The current probabilities of initiation, 16 cessation, 17 and relapse 18 were obtained from the literature.
Smoking cessation is defined as the most recent successful quit attempt. Cessation rates decreased with increasing ages, from a rate of 8. Many studies quantify the impact of various policies on the prevalence of current smoking. We relied on the price elasticity estimated by Liu and Levy on smoking initiation, cessation, and relapse, accounting for age group variation.
We were unable to find studies that quantify the impact of clean air policy by age group. For simplicity, we did not assume any age variations on the impact of implementing tighter clean air policy and intensifying media campaigns.
Table I. The price elasticity for smoking cessation is 0. We simulated the probability of each individual in the model developing any of the 25 smoking-related diseases over their lifetime and then estimated the costs associated with these diseases. The changes in SBP and cholesterol levels were modeled using ordinary least squares regression, with age, gender, and BMI as explanatory variables. The probability of developing ischemic stroke was predicted using the Framingham Heart Study.
The probability of mortality each year is dependent on demographics and disease presence. The costs per-disease case were estimated from regression analysis. Costs associated with diseases other than the 25 smoking-related diseases were estimated using the intercept from the cost regressions predicting the disease-specific costs. Medical expenditures and mortality risk were linked to demographic characteristics and disease presence rather than to smoking status.
Assuming the effect sizes as described in Table I , the prevalence of active smoking among the Prime population will decrease from This change is mainly due to the aging of the population and the fact that we assumed no smoking initiation after age 35 yr. A tighter clean air policy and a media campaign, implemented alone, would lead to a 0. Table II. Alternative Scenarios. As shown in the simulation results for cohort members aged 18—34 yr at the beginning of the simulation Table III , quitting reduces lifetime risk for many diseases.
These net reductions took into consideration that prolonged life from quitting smoking also increases the lifetime risk to some degree. Furthermore, quitting smoking would increase the life expectancy of smokers by 2. Smokers who quit at younger age 18—34 yr have a 1-yr longer extended life than smokers who quit later 55—64 yr.
Table III. A more detailed analysis not shown of simulated disease risk reduction by age group found that, for certain diseases, there is an age advantage in quitting early. These diseases include lung cancer, arterial disease, bronchitis emphysema, chronic airways obstruction, erectile dysfunction, other heart disease female only , liver cancer male only , congestive heart failure, coronary heart disease, and diabetes. The age pattern for other diseases are mixed.
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After applying the average cost per-disease case in the initial and subsequent years to the simulated years of life after the disease onset, we calculated the lifetime cost savings by comparing the lifetime cost of smokers when they remain a smoker for life vs. Table IV. Overall, females benefited more from quitting, which was primarily the result of a more significant risk reduction for respiratory conditions and heart diseases than their male counterparts. The savings seemed to increase with age, which resulted from several factors that will be discussed later.
Across payers, Tricare and Medicare would benefit the most from individuals quitting smoking. The reduced risk of chronic airway obstruction, lung cancer, and congestive heart failure from cessation accounts for the largest reductions in lifetime medical expenditures. Applying the reduction of nearly 2 percentage points in smoking prevalence in comparing the combined comprehensive policy scenario with the status quo to the projected Tricare Prime population, DoD could observe a total of 81, more quitters by Although the number of female quitters is only one-third of the number of male quitters, the total savings by gender was close, due to a much larger per-person savings for females.
We simulated the impact of DoD-wide comprehensive tobacco control initiatives on the prevalence of current smoking from to , as well as the future health and economic consequences of such initiatives. The success of tobacco control policy initiatives heavily depend on how these initiatives are implemented. For instance, price increase of tobacco products should consider factors such as the effect of state sales tax and inflation as well as the average income in a particular locality. Similarly, military personnel with roommates who smoke and who perceive their leaders or classmates to be tobacco users are more likely to initiate smoking.
Program components or contents that address the weight gain problems after smoking cessation is another important area for DoD to focus on. The surgeon general has concluded that smoking cessation programs are some of the most cost-effective health promotion interventions. This estimate is similar to estimates in the literature.
Our estimates show an increasing trend in per-person savings by age group.
However, this should not be interpreted as people would benefit more from quitting late. Several factors contributed to the cost variations across age, including the variation in disease risk reduction by disease type and age, the timing of disease onset, younger people having a 1-yr longer extended life as a result of quitting. Additionally, the estimated cost per-disease case was higher for people who enter the simulation at older age and hence have fewer years for discounting. When looking at the disease risk reduction only, quitting at a younger age generated more health benefits on most of the respiratory and heart conditions, and comparable benefits on the majority of cancers compared with quitting at an older age.
However, given the current trend in both the general and the Tricare population, such an aggressive reduction is unlikely to occur. The estimated lifetime savings from our analyses are conservative as we only measured the impact of comprehensive tobacco control policy on initiation, cessation, and relapse rate, but not on the possible reduction in quantity of cigarettes smoked. The effects of tobacco control policies are only assumed to have short-term effects lasting from the year when a comprehensive initiative is assumed to begin to The harms of secondhand smoke were also not considered in this analysis due to a lack of sufficient evidence and data needed to model the long-term relationship between smoking, secondhand smoke, and health outcomes.
Productivity gain over time from quitting smoking was also excluded from our analysis. Data limitations and measurement issues presented several challenges in this study. First, the estimates of the relationship between patient characteristics and risk for low-mortality diseases used 2 yr of MHS medical claims and lab test records; a longer longitudinal file could provide more precise estimates of key relationships.
Additionally, model estimates of the relationship between age and clinical measures were obtained from cross-sectional data rather than longitudinal data that track the same cohort of people over time. However, we compared the clinical movement with possible evidence from the literature and calibrated our estimates when necessary. In addition, we were unable to include important social determinants of smoking behaviors such as race and ethnicity due to a lack of sufficient information in the MHS administrative data.
MDH works in the community, collaborates with other departments, and partners with local organizations to reduce secondhand smoke, promote smoking cessation and prevention, and increase health equity in the state. Both organizations lead several initiatives where they work with the community, public health leaders, and other stakeholders to reduce tobacco use.
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These include efforts to:. Minnesota has a rich history of statewide coalitions working together to advance tobacco prevention policies and reduce tobacco use. By working together, says Moilanen, MDH and ClearWay Minnesota have been able to effect large changes that have a measurable impact on smoking rates and health care costs.tr.ywowuremawax.gq
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Oliven agrees. Across the board, their focus has been on close communication to achieve a shared goal. She explains that most states have limited funding for tobacco prevention, and few have sufficient resources to support media campaigns. Both Oliven and Moilanen are also proud to say that their work is informing similar efforts elsewhere. For example, research funded by ClearWay Minnesota is being used by addiction and tobacco programs around the country and the world.
When asked about specific success stories, both organizations highlight their recent efforts around the emerging youth e-cigarette epidemic.
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Together, they identified specific actions that local governments could take to address rising e-cigarette use among youth. Then MDH mobilized their grantees to increase awareness while ClearWay Minnesota took the lead in advocating for and passing policy. Over the past 20 years, ClearWay Minnesota, MDH, and their partners have worked to save thousands of lives and billions of dollars.
Minnesota is among the 27 states that have comprehensive clean indoor air policies , and its cigarette tax is the eighth highest in the Nation.
Related Smoking, Healthy People 2020, and Tobacco Policy in the U.S.
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