Despite significant reductions in smoking prevalence nationally and changes in social norms surrounding tobacco use, tobacco use persists as the leading cause of preventable illness and death in the United States (1
). From 2000 through 2004, one-fifth (45 million) of US adults smoked, resulting in an estimated 443,000 premature deaths and $193 billion in direct health care expenditures and productivity losses each year (1
). Cigarette smoking is associated with or causally linked to myriad health conditions, including cardiovascular diseases; cancers of the lung, oral, and nasal cavities and of the esophagus, larynx, pancreas, kidney, and bladder; chronic obstructive pulmonary disease (COPD); and infertility, preterm birth, and low birth weight (3
). In the United States, smoking annually causes more than 30% of all cancer deaths and more than 80% of lung cancer deaths (1
Tobacco use control and prevention strategies (ie, education; comprehensive smoke-free policies; taxation of tobacco products; evidence-based, culturally targeted cessation approaches; and regulations on advertising, targeting, and promotion by tobacco companies) have successfully reduced the age-adjusted smoking prevalence rate among adults aged 18 or older by more than half, from 42.4% in 1965 to 19.3% in 2010 (8
). Although the reductions in smoking prevalence that occurred over the last several decades have led to a substantial reduction in deaths from coronary heart disease attributed to smoking (10
), lung cancer deaths have declined more slowly (7
Health forecasting models have become more sophisticated with advances in computer technology, the increased availability of survey data, an improved understanding of the long-term consequences of lifestyle behaviors, and more complex concepts that are translated into models, reflecting a better understanding of interactions and disease processes (12
). Smoking lends itself well to dynamic modeling because of the long delay between smoking and the manifestation of disease (eg, lung cancer), consistent data collected over many decades, and the unambiguous effect of smoking on multiple health problems.
Smoking-related health forecasts have been used to inform tobacco-use control strategies for different target populations (13
) by enhancing understanding of the potential effect of specific policies and interventions on smoking rates (14
). These models can predict short- and long-term changes in illness, death, life expectancy, quality-adjusted life years, female fertility, and health-care expenditures among smokers and the population overall (13
). Full effects of smoking cessation can require up to 50 years to measure in individuals. Because cessation efforts translate slowly into declining smoking prevalence, it may take up to 100 years to see the full population effect of cessation efforts (14
). This lag or delayed timing of benefits is rarely considered in models that estimate the magnitude of effect of smoking on outcomes.
Because morbidity and cause-specific mortality associated with smoking are affected by competing causes of death, a clearer picture of the effect of smoking on longevity would capture competing disease and injury causes of death and changes in competing risk factors for smoking-related diseases. Recent work has demonstrated that competing risks can be modeled to estimate the joint effect of smoking and obesity, the leading preventable causes of illness and death, on life expectancy and quality of life over a 15-year span (18
). Although some models have examined the effect of smoking on cause-specific mortality (15
), to our knowledge, no model has accounted for competing causes of death.
We addressed this gap by using the University of California, Los Angeles (UCLA) Health Forecasting Tool (www.health-forecasting.org
) to estimate the effect of smoking on cause-specific mortality in the United States while accounting for competing causes of mortality. We estimated the life expectancy gains in the United States under various smoking scenarios. Life expectancy was used to standardize and interpret the magnitude of interventions on health outcomes (19
The objective of this study was to use the UCLA Health Forecasting Tool to analyze the effect on US death rates of antismoking efforts and predict the nature and magnitude of future benefits.