Estimating benefits of past, current, and future reductions in smoking rates using a comprehensive model with competing causes of death.
ABSTRACT Despite years of declining smoking prevalence, tobacco use is still the leading preventable contributor to illness and death in the United States, and the effect of past tobacco-use control efforts has not fully translated into improvements in health outcomes. The objective of this study was to use a life course model with multiple competing causes of death to elucidate the ongoing benefits of tobacco-use control efforts on US death rates.
We used a continuous-time life course simulation model for the US population. We modeled smoking initiation and cessation and 20 leading causes of death as competing risks over the life span, with the risk of death for each cause dependent on past and current smoking status. Risk parameters were estimated using data from the National Health Interview Survey that were linked to follow-up mortality data.
Up to 14% (9% for men, 14% for women) of the total gain in life expectancy since 1960 was due to tobacco-use control efforts. Past efforts are expected to further increase life expectancy by 0.9 years for women and 1.3 years for men. Additional reduction in smoking prevalence may eventually yield an average 3.4-year increase in life expectancy in the United States. Coronary heart disease is expected to increase as a share of total deaths.
A dynamic individual-level model with multiple causes of death supports assessment of the delayed benefits of improved tobacco-use control efforts. We show that past smoking reduction efforts will translate into further increases in life expectancy in the coming years. Smoking will remain a major contributor to preventable illness and death, worthy of continued interventions.
- SourceAvailable from: Jesper Lagergren[Show abstract] [Hide abstract]
ABSTRACT: Answer questions and earn CME/CNE Esophageal adenocarcinoma (EAC) is characterized by 6 striking features: increasing incidence, male predominance, lack of preventive measures, opportunities for early detection, demanding surgical therapy and care, and poor prognosis. Reasons for its rapidly increasing incidence include the rising prevalence of gastroesophageal reflux and obesity, combined with the decreasing prevalence of Helicobacter pylori infection. The strong male predominance remains unexplained, but hormonal influence might play an important role. Future prevention might include the treatment of reflux or obesity or chemoprevention with nonsteroidal antiinflammatory drugs or statins, but no evidence-based preventive measures are currently available. Likely future developments include endoscopic screening of better defined high-risk groups for EAC. Individuals with Barrett esophagus might benefit from surveillance, at least those with dysplasia, but screening and surveillance strategies need careful evaluation to be feasible and cost-effective. The surgery for EAC is more extensive than virtually any other standard procedure, and postoperative survival, health-related quality of life, and nutrition need to be improved (eg, by improved treatment, better decision-making, and more individually tailored follow-up). Promising clinical developments include increased survival after preoperative chemoradiotherapy, the potentially reduced impact on health-related quality of life after minimally invasive surgery, and the new endoscopic therapies for dysplastic Barrett esophagus or early EAC. The overall survival rates are improving slightly, but poor prognosis remains a challenge. CA Cancer J Clin 2013;63:232-248. (©) 2013 American Cancer Society.CA A Cancer Journal for Clinicians 07/2013; 63(4):232-48. · 153.46 Impact Factor
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ABSTRACT: Chronic inflammation is a prominent feature of aging and of common age-related diseases, including atherosclerosis, cancer and periodontitis. This volume examines modifiable risk factors for periodontitis and other chronic inflammatory diseases. Oral bacterial communities and viral infections, particularly with cytomegalovirus and other herpesviruses, elicit distinct immune responses and are central in the initiation of periodontal diseases. Risk of disease is dynamic and changes in response to complex interactions of genetic, environmental and stochastic factors over the lifespan. Many modifiable risk factors, such as smoking and excess caloric intake, contribute to increases in systemic markers of inflammation and can modify gene regulation through a variety of biologic mechanisms (e.g. epigenetic modifications). Periodontitis and other common chronic inflammatory diseases share multiple modifiable risk factors, such as tobacco smoking, psychological stress and depression, alcohol consumption, obesity, diabetes, metabolic syndrome and osteoporosis. Interventions that target modifiable risk factors have the potential to improve risk profiles for periodontitis as well as for other common chronic diseases.Periodontology 2000 02/2014; 64(1):7-19. · 4.01 Impact Factor
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ABSTRACT: The aim of this study is to analyse longitudinally, the annual effects of age group and birth cohort on smoking in the Swedish population during a 24-year period and to analyse the smoking trends for different levels of education. A random sample of adult, non-institutionalized persons aged 16-71 years was interviewed every 8 years by professional interviewers. In addition to three time-related variables-year of interview, age at the time of the interview, and year of birth-we included the following explanatory variables in the analyses: sex, educational level, and urbanization. We found significant decreases in smoking prevalence in all studied subgroups. The adjusted odds ratios for age were 0.89 (95 % CI 0.88-0.90) and 0.92 (95 % CI 0.91-0.93) for men and women, respectively. The decreases in smoking over time were significant in all levels of education, except for in women with low educational level. In Sweden, the prevalence of smoking has decreased in most age groups and cohorts, and in persons in most levels of education, albeit less so in women with low educational level.International Journal of Public Health 12/2013; · 1.99 Impact Factor
Estimating Benefits of Past, Current, and Future
Reductions in Smoking Rates Using a Comprehensive
Model With Competing Causes of Death
Jeroen van Meijgaard, PhD; Jonathan E. Fielding, MD, MPH, MBA
Suggested citation for this article: van Meijgaard J, Fielding JE. Estimating Benefits of Past, Current, and Future
Reductions in Smoking Rates Using a Comprehensive Model With Competing Causes of Death. Prev Chronic Dis
2012;9:110295. DOI: http://dx.doi.org/10.5888/pcd9.110295
Despite years of declining smoking prevalence, tobacco use is still the leading preventable contributor to illness and
death in the United States, and the effect of past tobacco-use control efforts has not fully translated into improvements
in health outcomes. The objective of this study was to use a life course model with multiple competing causes of death
to elucidate the ongoing benefits of tobacco-use control efforts on US death rates.
We used a continuous-time life course simulation model for the US population. We modeled smoking initiation and
cessation and 20 leading causes of death as competing risks over the life span, with the risk of death for each cause
dependent on past and current smoking status. Risk parameters were estimated using data from the National Health
Interview Survey that were linked to follow-up mortality data.
Up to 14% (9% for men, 14% for women) of the total gain in life expectancy since 1960 was due to tobacco-use control
efforts. Past efforts are expected to further increase life expectancy by 0.9 years for women and 1.3 years for men.
Additional reduction in smoking prevalence may eventually yield an average 3.4-year increase in life expectancy in the
United States. Coronary heart disease is expected to increase as a share of total deaths.
A dynamic individual-level model with multiple causes of death supports assessment of the delayed benefits of
improved tobacco-use control efforts. We show that past smoking reduction efforts will translate into further increases
in life expectancy in the coming years. Smoking will remain a major contributor to preventable illness and death,
worthy of continued interventions.
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 (1,2). 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 (3-6). In the United States, smoking annually causes more
than 30% of all cancer deaths and more than 80% of lung cancer deaths (1,7).
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
Page 1 of 10CDC - Preventing Chronic Disease: Volume 9, 2012: 11_0295
aged 18 or older by more than half, from 42.4% in 1965 to 19.3% in 2010 (8,9). 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 (7,11).
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 (13,15-17). 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
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.
The simulation model is based on a dynamic and continuous-time framework previously developed for the UCLA
Health Forecasting Model (12,21,22). Continuous-time modeling reduces the complexity of simulating multiple
processes with many events that otherwise would explode the number of possible state transitions in a discrete-time
model. The simulation framework provides an algorithm to generate individual lifetime histories starting at birth and
using probabilities to determine which events happen during the life course. Smoking behavior is simulated by using
initiation and cessation rates conditional on smoking status and age. Time since cessation is implicitly updated as the
lifetime history is simulated. Mortality hazards are updated when age and smoking status change, including changes in
the time since cessation.
We estimated smoking initiation and cessation rates using sequential cohorts from the National Health Interview
Survey (NHIS). Initiation is modeled through young adulthood with a constant initiation rate through age 24, after
which initiation is considered negligible (23). We estimated cumulative initiation through age 24 using the “Have you
ever smoked” response on the NHIS survey and cessation using the change in prevalence of current smokers over a 5-
year period to obtain the cessation rate of successful quitters. We estimated cessation rates for different age groups;
the age cutoffs were selected after visual inspection of the smoothed cessation rates over the life span. We assumed
negligible relapse after 5 years of smoking abstention. We calibrated initiation and cessation rates by using the
simulation model to account for the decline in smoking prevalence from excess mortality among smokers. This
approach yielded cumulative rates of initiation of 35% among women and 39% among men for the 1980 birth cohort
(24-year-olds in 2004), with annual cessation rates of 4.2%, 3.1%, 2.5%, and 4.5% for women aged 15 to 27, 28 to 32,
33 to 47, and 48 or older, respectively, and 4.0%, 2.8%, 2.1%, and 6.0% for men aged 15 to 27, 28 to 32, 33 to 47, and
48 or older, respectively. These rates are consistent with observed rates reported elsewhere (24-27). The increase in
cessation rates as age increases may be driven by health events, such as the onset of heart disease, of the individual or
Page 2 of 10 CDC - Preventing Chronic Disease: Volume 9, 2012: 11_0295
friends and relatives later in life (24,28,29). The smoking prevalence and time since last smoked, as generated by the
model, were subsequently validated against the observed rates using NHIS data.
Population and causes of death
We chose to simulate a representation of the 2004 US population, which gave us access to a robust data set that
allowed estimating excess mortality related to tobacco use linked to follow-up data on cause-specific mortality. We
created a synthetic population based on 2004 population and mortality data from the National Center for Health
Statistics (NCHS) (30) and obtained cause-specific mortality rates for 2004 from the National Vital Statistics System.
NCHS provides recodes for 39, 113, 130, and 358 selected causes of death, with varying degrees of specificity (31). We
used the 39-cause list to identify the top 20 causes of death after excluding 4 nonspecific causes: “Other malignant
neoplasms,” “Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified,” “All other
diseases (Residual),” and “All other external causes.”
We estimated the parameters of our analysis by pooling health behavior data from NHIS for 1997 through 2004 and
linked these with follow-up data on cause-specific mortality through the end of 2006 (32). Data for relative risks of
smoking on cause-specific mortality are available for select causes and populations (33) but not for each of the 20
leading causes of death separately for men and women. Therefore, we estimated a Cox proportional hazards model for
each cause of death to obtain relative risk parameters of smoking (never, current, former [0–4 y, 5–9 y, 10–19 y, or
≥20 y since quit]) on cause-specific mortality. The estimates were stratified by sex, and the baseline hazards were
stratified by age (5-year age categories). Relative risk estimates were adjusted for race/ethnicity (6 categories),
education (less than high school, high school diploma, more than high school), income (<100%, 100%–400%, >400%
of the federal poverty level), body mass index (BMI, continuous), physical activity (metabolic equivalent time,
continuous), and alcohol consumption (no alcohol, 0–2 drinks/d, >2 drinks/d).
We simulated 2 sets of scenarios. The first set of scenarios estimated smoking attributable deaths for the 2004
population and validated the relative risk of smoking on all-cause mortality by comparing our estimates with other
studies. To estimate smoking-attributable deaths, we ran the simulation model for a 2004 reference scenario, applying
2004 smoking rates to 40 million simulated individuals reflecting the 2004 population. Next, we ran the
counterfactual scenario with all relative risks of smoking on mortality set to 1, assuming that smoking has no effect on
mortality. We compared our estimates of smoking-attributable deaths with estimates from the Centers for Disease
Control and Prevention (CDC) (1) to validate our model.
The second set of scenarios estimated the effect of past, current, and future changes in initiation and cessation rates
using a cohort analysis. We used 2004 mortality rates throughout the life course, similar to life table calculations, and
compared mortality and life expectancy in a birth cohort followed from birth to death. We held initiation and cessation
rates fixed at levels specified in each scenario. We simulated cohorts of 4 million individuals in each scenario, by using
different assumptions about smoking initiation and cessation rates to estimate the timing of changes in smoking
initiation and cessation on mortality (Box). Comparing scenarios 3 and 6, for example, yields the difference in
mortality, life expectancy, and distribution of causes of death between never smokers and always smokers (if viewed
from the individual perspective, the probabilistic outcome of death with continuous lifelong smoking vs never
smoking). We compared age-adjusted mortality rates and life expectancy with the reference scenario to estimate past
and potential future gains from tobacco-use control efforts.
Timing of benefits
Smoking-related deaths occur among people of all ages.
Gains in life expectancy occur across a significant
portion of the life span and not just later in life. We
estimated expected gains at the individual level for men
and women by repeated simulation of individuals
quitting smoking at various ages and comparing the
total remaining life years to those of lifetime smokers.
These individual-level gains were aggregated over a
simulated cohort for each scenario, yielding gains in life
years across the lifespan for the entire cohort. We
calculated gains in life years relative to scenario 4,
which used initiation and cessation rates from the
1950s, to calculate past and future gains from
reductions in smoking. To calculate gains in life years
for the 2004 population, we applied age-specific 2004
mortality rates to a standard cohort, similar to life
Box. Smoking Initiation and Cessation
calibrated to 2004
Used as a reference to
where smoking has
no effect on
Used to calculate smoking
attributable deaths by
comparing to the reference
Page 3 of 10CDC - Preventing Chronic Disease: Volume 9, 2012: 11_0295
We estimated that 18.9% of adult women and 23.1% of
adult men were current smokers in 2004, as compared
with observed rates of 18.5% and 23.4%, respectively
(34). We estimated smoking-attributable deaths at
420,000 in 2004 (Table 1), which is comparable to
CDC estimates of 443,000 deaths annually from 2000
through 2004 (1). We found that 15% of all deaths
among women and 20% of all deaths among men were
attributable to smoking. Most of the deaths avoided
derive from a reduction in cancer of the trachea,
bronchus, or lung (27%) and chronic lower respiratory
diseases (21%). The data presented in Table 2,
simulated smoking rates, life expectancy, and mortality
by cause of death for 2004, are a reference to compare
the 4 scenarios. In 2004, nearly half of adult men and
approximately 37% of adult women had smoked at
some point in their adult life; life expectancy was 75.1
years for men and 80.2 years for women.
Life expectancy benefits
Table 3 summarizes estimates of the effect of smoking initiation and cessation on smoking prevalence and mortality
outcomes for scenarios 3 through 6. Smoking prevalence is age-adjusted to the 2004 US population by sex to facilitate
comparison of the cohort scenarios with the reference scenario (scenario 1). Initiation and cessation rates in the 1940s
and 1950s, scenario 4, result in a lifetime smoking prevalence of 33% for women and 48% for men; life expectancy is
reduced to 79.6 years for women and 73.9 years for men.
Comparing scenario 4 with the reference scenario shows that changes in initiation and cessation rates since 1960 have
resulted in a life expectancy increase of 0.6 years for women and 1.2 years for men. Moreover, at 2004 initiation and
cessation rates (scenario 5), an additional 0.9 years for women (life expectancy, 81.1 y) and 1.3 years for men (life
expectancy, 76.4 y) may be realized in the future, because 2004 initiation and cessation rates will continue to yield
reductions in current smokers and ever smokers and their related mortality (scenario 5 vs reference). Fully eliminating
tobacco use in the population (scenario 6) would yield an increase in life expectancy of 1.2 years for women and 1.6
years for men, or 82.3 and 78.0 years, respectively.
Causes of mortality
Although overall age-adjusted mortality declines with reductions in smoking prevalence, trends vary by disease.
Reductions in smoking prevalence lead to a substantial reduction in deaths from lung cancer and COPD. However,
although smoking increases the risk of coronary heart disease, heart disease as a percentage of total deaths is higher
for never smokers than for always smokers (scenario 3 compared with scenario 6). In fact, the share of total deaths
held by ischemic heart disease (IHD) has risen and is expected to continue to increase if smoking prevalence declines
further (Table 3).
Timing of benefits
Longevity gains of quitters relative to lifetime smokers were recorded across the lifespan (Figure 1). Using a cohort of
10,000 people at birth, we plotted years of life gained across the life span (Figure 2). The life years already gained is the
difference between the years of life for the 2004 population (reference scenario) and scenario 4. The life years yet to be
gained if 2004 initiation and cessation rates persist are the difference between scenario 5 and the reference scenario,
and the life years that may be gained if no one ever smoked is the difference between scenario 6 and scenario 5. The
area under the curve is equal to the gain in life expectancy (∆LE). Substantial gains have already been realized during
the life course (life expectancy gain of 0.6 y for women and 1.2 y for men), and additional gains will occur mostly at
older ages if initiation and cessation rates stay at 2004 levels (additional life expectancy gain of 0.9 y for women and
1.3 y for men) (Figure 2). The largest gains in years of life across the lifespan may be realized if all individuals in the
cohort remain never smokers, providing an additional gain in life expectancy of 1.2 years for women and 1.6 years for
men (scenario 6).
3. 100% initiation
and 0% cessation for
all adult men and
Used to generate distribution
of causes of death for
4. 55% initiation for
initiation for men and
cessation half of
(described in text)
Used to generate distribution
of causes of death for cohort
assuming smoking rates
observed in the 1940–1950s
before public health action to
reduce smoking prevalence
(to estimate the effect of
antismoking public health
5. 2004 initiation and
(described in text)
Used to generate distribution
of causes of death for cohort
assuming 2004 initiation and
6. 0% initiationUsed to generate distribution
of causes of death for “never
Page 4 of 10CDC - Preventing Chronic Disease: Volume 9, 2012: 11_0295
Figure 1. Expected gain in years of life across the lifespan by age of quitting, relative to a lifetime smoker, by sex.
After quitting smoking, individuals are more likely to be alive at every age after the quit age. The largest gain is around
age 80, but gains are smaller for those who quit later in life. [A tabular version of this figure is also available.]
Figure 2. Gains in years of life relative to mid-1900s initiation and cessation rates, by age and sex. Lower initiation
and cessation rates have yielded additional life years in the population at all ages (area under the curve is the gain in
life expectancy (∆LE), and additional gains are expected if initiation and cessation rates stay at 2004 levels. [A tabular
version of this figure is also available.]
Our simulation model is a dynamic tool to estimate health effects of various scenarios, taking into account the timing
of smoking initiation, cessation, and the effect on health outcomes. We can evaluate what may have happened if
smoking behavior had not changed and estimate what could be attained with further tobacco-use control efforts. We
found that, as of 2004, reductions in smoking prevalence resulted in life expectancy gains equal to nearly 9% of the
total gain in female life expectancy and 14% of the total gain in male life expectancy from 1960 to 2004. However, at
current initiation and cessation rates, additional life expectancy gains approximately equal to the total observed gains
from 1995 through 2004 are expected (35). The magnitude of these gains and the potential for additional gains if
smoking rates can be reduced further underscore the importance of continuing tobacco-use control efforts.
The simulation also helps assess the distribution of gains across the lifespan and how the fractions of mortality
attributable to various diseases may change as smoking prevalence is reduced. For example, as a share of total deaths,
IHD is expected to increase, despite a decline in smoking rates and a decline in age-adjusted IHD mortality, reflecting
a shift in mortality away from lung cancer and COPD to IHD and other causes of death. Causes of death minimally
Page 5 of 10 CDC - Preventing Chronic Disease: Volume 9, 2012: 11_0295
affected by smoking, such as injuries, or occurring primarily at older ages, such as Alzheimer’s disease, will also
increase their share of total deaths.
We have inherent limitations in our modeling approach. First, our model treats people separately from their
environment. Therefore, passive smoking and effects of air pollution are ignored. Similarly ignored are
intergenerational effects, including how smoking by pregnant mothers affects the health and mortality risk of infants
and how parents who smoke are more likely to have children who eventually initiate smoking. Furthermore, smoking
intensity was not modeled in the simulation. This exclusion may bias effect estimates if ongoing cessation efforts have
also changed smoking intensity. However, our estimates of the benefits of reduced smoking initiation and increased
cessation are likely conservative with the exclusion of passive smoking, intergenerational effects, and decline in
smoking intensity (8). Moreover, by focusing on smoking-attributable mortality, we omit the quality-of-life benefits
from reductions in smoking-related morbidity.
Although this study incorporates competing causes of death, it did not include comorbidities and competing behavioral
risks for illness and death, despite their potential relevance. For example, mental illness is associated with higher
smoking prevalence and other unhealthy behaviors as well as increased mortality (36,37). Also, obesity differentially
affects smoking-related diseases (18), and current increases in obesity prevalence are likely to further increase
cardiovascular mortality relative to lung cancer and COPD mortality.
Our model can help inform future public health campaigns and assist in prioritizing scarce resources. Future work
should focus on adding additional health risk factors, such as obesity or other morbidities, to better understand how
reductions in smoking prevalence will reduce and shift the burden of disease. Moreover, expanding the framework to
include passive smoking and intergenerational effects would better capture the full benefits of reductions in smoking
prevalence, and stratification by race/ethnicity would provide insight into causes of health disparities.
This research received no specific grant from any funding agency in the public, commercial, or nonprofit sectors.
Corresponding Author: Jeroen van Meijgaard, PhD, Department of Health Services, University of California, Los
Angeles (UCLA) School of Public Health, Box 951722, Room 61-253 CHS, Los Angeles, CA 90095-1772. Telephone: 310
-206-6236. E-mail: firstname.lastname@example.org.
Author Affiliation: Jonathan E. Fielding, UCLA and Los Angeles County Department of Health, Los Angeles,
Centers for Disease Control and Prevention. Smoking-attributable mortality, years of potential life lost, and
productivity losses — United States, 2000-2004. MMWR Morb Mortal Wkly Rep 2008;57(45):1226-8. PubMed
Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA
2004;291(10):1238-45. CrossRef PubMed
Agrawal A, Scherrer JF, Grant JD, Sartor CE, Pergadia ML, Duncan AE, et al. The effects of maternal smoking
during pregnancy on offspring outcomes. Prev Med 2010;50(1-2):13-8. CrossRef
Cornfield J, Haenszel W, Hammond EC, Lilienfeld AM, Shimkin MB, Wynder EL. Smoking and lung cancer:
recent evidence and a discussion of some questions. Int J Epidemiol 2009;38(5):1175-91. CrossRef
The health consequences of smoking: a report of the Surgeon General. Atlanta (GA): US Department of Health
and Human Services; 2004.
White WB. Smoking-related morbidity and mortality in the cardiovascular setting. Prev Cardiol 2007;10(2Suppl
1)1-4. CrossRef PubMed
Shopland DR, Eyre HJ, Pechacek TF. Smoking-attributable cancer mortality in 1991: is lung cancer now the
leading cause of death among smokers in the United States? J Natl Cancer Inst 1991;83(16):1142-8. CrossRef
Centers for Disease Control and Prevention. Vital signs: current cigarette smoking among adults aged ≥18 years —
United States, 2005–2010. MMWR Morb Mortal Wkly Rep 2011;60(35):1207-12. PubMed
Giovino GA, Schooley MW, Zhu BP, Chrismon JH, Tomar SL, Peddicord JP, et al. Surveillance for selected
tobacco-use behaviors — United States, 1900-1994. MMWR CDC Surveill Summ 1994;43(3):1-43. PubMed
Page 6 of 10CDC - Preventing Chronic Disease: Volume 9, 2012: 11_0295
Ford ES, Ajani UA, Croft JB, Critchley JA, Labarthe DR, Kottke TE, et al. Explaining the decrease in US deaths
from coronary disease, 1980-2000. N Engl J Med 2007;356(23):2388-98. CrossRef
Thun MJ, Jemal A. How much of the decrease in cancer death rates in the United States is attributable to
reductions in tobacco smoking? Tob Control 2006;15(5):345-7. CrossRef
van Meijgaard J, Fielding JE, Kominski GF. Assessing and forecasting population health: integrating knowledge
and beliefs in a comprehensive framework. Public Health Rep 2009;124(6):778-89. PubMed
Tengs TO, Osgood ND, Lin TH. Public health impact of changes in smoking behavior: results from the Tobacco
Policy Model. Med Care 2001;39(10):1131-41. CrossRef
Jha P. Avoidable global cancer deaths and total deaths from smoking. Nat Rev Cancer 2009;9(9):655-64.
Akushevich I, Kravchenko JS, Manton KG. Health-based population forecasting: effects of smoking on mortality
and fertility. Risk Anal 2007;27(2):467-82. CrossRef PubMed
Hurley SF, Matthews JP. The Quit Benefits Model: a Markov model for assessing the health benefits and health
care cost savings of quitting smoking. Cost Eff Resour Alloc 2007;5:2. CrossRef
Wang H, Preston SH. Forecasting United States mortality using cohort smoking histories. Proc Natl Acad Sci U S
A 2009;106(2):393-8. CrossRef PubMed
Stewart ST, Cutler DM, Rosen AB. Forecasting the effects of obesity and smoking on US life expectancy. N Engl J
Med 2009;361(23):2252-60. CrossRef PubMed
Wright JC, Weinstein MC. Gains in life expectancy from medical interventions — standardizing data on outcomes.
N Engl J Med 1998;339(6):380-6. CrossRef PubMed
Parrish RG. Measuring population health outcomes. Prev Chronic Dis 2010;7(4):A71.. PubMed
Will BP, Berthelot JM, Nobrega KM, Flanagan W, Evans WK. Canada’s Population Health Model (POHEM): a
tool for performing economic evaluations of cancer control interventions. Eur J Cancer 2001;37(14):1797-804.
Shi L, van Meijgaard J, Fielding J. Forecasting diabetes prevalence in California: a microsimulation. Prev Chronic
Dis 2011;8(4):A80.http://www.cdc.gov/pcd/issues/2011/jul/10_0177.htm. PubMed
National household survey of drug abuse: advance report no. 18. Washington (DC): US Department of Health and
Human Services, SAMHSA, Office of Applied Studies; 1991.
Hyland A, Li Q, Bauer JE, Giovino GA, Steger C, Cummings KM. Predictors of cessation in a cohort of current and
former smokers followed over 13 years. Nicotine Tob Res 2004;6(Suppl 3)S363-9. CrossRef
Centers for Disease Control and Prevention. Smoking cessation during previous year among adults — United
States, 1990 and 1991. MMWR Morb Mortal Wkly Rep 1993;42(26):504-7. PubMed
DeCicca P, Kenkel DS, Mathios AD; National Bureau of Economic Research. Cigarette taxes and the transition
from youth to adult smoking smoking initiation, cessation, and participation. In: NBER working paper series no.
14042. Cambridge (MA): National Bureau of Economic Research; 2008.
Hatziandreu EJ, Pierce JP, Lefkopoulou M, Fiore MC, Mills SL, Novotny TE, et al. Quitting smoking in the United
States in 1986. J Natl Cancer Inst 1990;82(17):1402-6. CrossRef
Reichert VC, Folan P, Bartscherer D, Jacobsen D, Fardellone C, Metz C, et al. A comparison study of older
smokers vs younger smokers being treated for tobacco dependence. Chest 2007;132(4):489s-489s.
Sachs-Ericsson N, Schmidt NB, Zvolensky MJ, Mitchell M, Collins N, Blazer DG. Smoking cessation behavior in
older adults by race and gender: the role of health problems and psychological distress. Nicotine Tob Res 2009;11
(4):433-43. CrossRef PubMed
Estimates of the July 1, 2000-July 1, 2004, United States resident population from the Vintage 2004 postcensal
series by year, county, age, sex, race, and Hispanic origin, prepared under a collaborative arrangement with the
US Census Bureau. Hyattsville (MD): National Center for Health Statistics; 2005.
Heron M. Deaths: leading causes for 2004. Natl Vital Stat Rep 2007;56(5):1-95. PubMed
Data file documentation, National Health Interview Survey 1997-2004 and linked mortality files (machine
readable data file and documentation). Hyattsville (MD): National Center for Health Statistics; 2010.
Kenfield SA, Wei EK, Rosner BA, Glynn RJ, Stampfer MJ, Colditz GA. Burden of smoking on cause-specific
mortality: application to the Nurses’ Health Study. Tob Control 2010;19(3):248-54. CrossRef
Centers for Disease Control and Prevention. Cigarette smoking among adults — United States, 2004. MMWR
Morb Mortal Wkly Rep 2005;54(44):1121-4. PubMed
Page 7 of 10CDC - Preventing Chronic Disease: Volume 9, 2012: 11_0295
Health, United States, 2010: with special feature on death and dying. Hyattsville (MD): National Center for
Health Statistics; 2011.
Lasser K, Boyd JW, Woolhandler S, Himmelstein DU, McCormick D, Bor DH. Smoking and mental illness: a
population-based prevalence study. JAMA 2000;284(20):2606-10. CrossRef
Lawrence D, Coghlan R. Health inequalities and the health needs of people with mental illness. N S W Public
Health Bull 2002;13(7):155-8. CrossRef PubMed
Table 1. Estimated Deaths Avoided in the Absence of Smoking, Causes of
Death, Simulation Model, 2004 US Population
Cause of Death
Deaths Avoided (in Thousands), n
Age-Adjusted Mortality (per 100,000), n
(% Total Mortality)
FemaleMale Female Male
Ischemic heart diseases25 (15)41 (17)103 (18) 172 (21)
Cancer of trachea,
bronchus, and lung
49 (70)66 (71)12 (2)22 (3)
Chronic lower respiratory
47 (72)40 (66)10 (2)18 (2)
All other causes 60 (7) 92 (11) 277 (48)354 (44)
Table 2. Life Expectancy and Mortality, by Cause of Death, United States,
181 (15) 239 (20) 579 (100)808 (100)
Current smokers (adults
Former smokers (adults
Life expectancy, y80.275.1
Cause of Death
Total Deaths (in
Total Deaths (in
All1,222 6881,193 1,002
Ischemic heart diseases 219 118236205
Other heart diseases (no
Cancer of trachea,
bronchus, and lung
70 42 92 73
Cerebrovascular diseases 92505953
Chronic lower respiratory
65 3761 53
Diabetes mellitus38 2236 29
Unspecified accidents and
Alzheimerâ€™s disease 472420 20
Influenza and pneumonia33182725
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Life Expectancy FemaleMale
Cancer of colon, rectum,
27 16 2622
Motor vehicle accidents15 103122
syndrome, and nephrosis
22 1221 19
Cancer of breast 412500
Intentional self-harm75 26 18
Cancer of pancreas1691612
Cancer of prostateNANA 2927
Chronic liver disease and
Cancer of urinary tract95 1714
Cancer of cervix uteri,
corpus uteri, and ovary
All other causes348198 348286
Abbreviation: NA, not applicable.
Numbers in the table are from model output for 2004 after calibration with vital statistics data from the National Center
for Health Statistics.
Table 3. Life Expectancy, Age-Adjusted Mortality, and Causes of Deaths in a
Cohort Using Different Smoking Rates, United States
Cause of Death/Life
Scenario 3: All
Scenario 4: 1940s-
Scenario 6: No
FemaleMaleFemaleMale FemaleMale Female Male
Current adult smokers, % 10010032.9 47.914.4 16.400
Former adult smokers, %0020.230.319.221.200
Life expectancy, y74.970.179.673.981.176.482.378.0
Age-adjusted mortality1,1761,644 7251,080 646896579805
Distribution of Deaths by Cause, %
Ischemic heart diseases17.921.818.921.019.521.920.522.8
Other heart diseases (no CHD)184.108.40.206.6 220.127.116.11.0
Cancer of trachea, bronchus, and
Cerebrovascular diseases 18.104.22.168.22.214.171.124 7.0
Chronic lower respiratory
Diabetes mellitus126.96.36.199 188.8.131.52.43.5
Unspecified accidents and
Alzheimer’s disease 2.01.24.11.9 184.108.40.206 2.8
Influenza and pneumonia220.127.116.11 18.104.22.168.4 3.0
Page 9 of 10CDC - Preventing Chronic Disease: Volume 9, 2012: 11_0295
For Questions About This Article Contact email@example.com
Page last reviewed: July 12, 2012
Page last updated: July 12, 2012
Content source: National Center for Chronic Disease Prevention and Health Promotion
Centers for Disease Control and Prevention 1600 Clifton Rd. Atlanta, GA 30333, USA
800-CDC-INFO (800-232-4636) TTY: (888) 232-6348 - firstname.lastname@example.org
Cause of Death/Life
Scenario 3: All
Scenario 4: 1940s-
Scenario 6: No
Cancer of colon, rectum, and
Motor vehicle accidents0.81.30.81.22.214.171.124.8
Nephritis, nephrotic syndrome,
1.11.1 1.81.82.02.2 2.12.4
Cancer of breast 1.40 2.80 3.003.10
Intentional self-harm 0.81.8 0.4 126.96.36.199.2 1.1
Hypertensive heart disease 0.80.71.41.11.6 188.8.131.52
Cancer of pancreas 184.108.40.206.2 220.127.116.11 1.5
Cancer of prostateNA 1.6NA2.6 NA3.3 NA 3.7
Chronic liver disease and
Cancer of urinary tract0.41.50.71.5 0.71.20.80.9
Cancer of cervix uteri, corpus
uteri, and ovary
1.3NA 1.8NA1.9 NA2.0NA
All other causes23.023.327.527.428.528.428.928.6
Abbreviation: CHD, coronary heart disease; NA, not applicable.
Cohorts simulated from birth to death to calculate eventual cause of death for alternative scenarios.
Aged ≥18 y, age-adjusted.
Per 100,000 2004 population.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S.
Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention,
or the authors’ affiliated institutions.
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