Introduction

According to the 2014 Surgeon General’s report, more than 20 million Americans died as a result of smoking or exposure to secondhand tobacco smoke in the 50 years since the first Surgeon General’s report was released in 1964 (U.S. Department of Health and Human Services [USDHHS] 2014). The 2014 Surgeon General’s report showed that smoking impacts nearly every organ of the body (USDHHS 2014). Smoking is causally associated with 12 cancers and is the leading cause of lung cancer, which is the largest cause of cancer deaths in the United States (USDHHS 2014; Cronin et al. 2022). In addition, smoking is a major contributor to incidence and mortality from cardiovascular disease (including coronary heart disease, stroke, congestive heart failure, coronary artery disease, and peripheral arterial disease) and chronic obstructive pulmonary disease (COPD; including chronic bronchitis and emphysema) (USDHHS 2014; Tsao et al. 2023; Centers for Disease Control and Prevention [CDC] n.d.b). Exposure to secondhand tobacco smoke causes lung cancer, coronary heart disease and stroke (USDHHS 2014).

The current report fills critical gaps by describing disparities in incidence and mortality due to smoking-caused diseases including cancer, COPD, and cardiovascular disease, and in smoking- and secondhand tobacco smoke-attributable mortality using various analytic and modeling techniques. This chapter begins by providing a brief overview of differences in select smoking-related health outcomes by sociodemographic characteristics, using the latest available published reports. Modeling is also used to conduct a comprehensive analysis of recent trends and disparities in smoking-attributable mortality by sex and age, race and ethnicity, educational attainment, geographic region, and urbanicity (urban versus rural residency). Data from multiple sources are used to estimate the number of deaths caused by cigarette smoking and exposure to secondhand tobacco smoke in the United States overall and by race and ethnicity. Data sources include the American Community Survey, the National Health Interview Survey (NHIS), the National Health and Nutrition Examination Survey (NHANES), the linked NHIS-National Death Index (NHIS-NDI), the National Vital Statistics System Multiple Cause of Death file, and National Center for Health Statistics’ National Vital Statistics Reports.

The chapter also reviews the findings from various simulation models which are important tools to project the potential effects of large-scale interventions on smoking-attributable morbidity and mortality and on disparities in tobacco use. The chapter concludes with a discussion about gaps in data and research that can be used to further assess the health impacts of tobacco-related health disparities.

Conclusions from Previous Surgeon General’s Reports

The 1998 Surgeon General’s report was the first to focus exclusively on tobacco use and health outcomes among members of four racial and ethnic population groups. That report concluded that cigarette smoking is a major cause of disease in African American, American Indian and Alaska Native, Asian American and Pacific Islander, and Hispanic people; African American people experienced the greatest health burden from cigarette smoking; and lung cancer is the leading cause of cancer deaths for each of the aforementioned racial and ethnic groups (USDHHS 1998).

The 2014 Surgeon General’s report provided a comprehensive review of the health consequences of smoking and reflected on 50 years of progress in tobacco prevention and control. The 2014 Surgeon General’s report acknowledged that tobacco use causes or worsens cardiovascular diseases, diabetes, eye diseases, pneumonia, COPD, tuberculosis, periodontitis, adverse reproductive health outcomes, congenital defects, hip fractures, rheumatoid arthritis, male sexual dysfunction, and immune dysfunction and diminishes overall health (USDHHS 2014). Additionally, exposure to secondhand tobacco smoke causes lung cancer, stroke, cardiovascular disease, adverse reproductive health outcomes, middle ear disease, impaired lung function, lower respiratory illness, and sudden infant death syndrome (USDHHS 2014). Lower relative risks (RRs) for smoking in women than men in earlier studies reflected historical differences in population-level smoking patterns. However, by the 1960s, smoking patterns had largely converged for men and women and by the 21st century, the disease risks from smoking for women are now similar to those of men for lung cancer, COPD, and cardiovascular disease. The 2014 report also concluded that very large disparities in tobacco use remain across groups defined by race, ethnicity, level of educational attainment, socioeconomic status (SES), and geographic region.

A major conclusion of the 2020 Surgeon General’s report on tobacco cessation was that smoking cessation is beneficial at any age (USDHHS 2020). The 2020 report showed that quitting smoking reduces the risk of premature death and the risk of 12 types of cancer compared with continued smoking. It also concluded that disparities in the prevalence of smoking persist, aligning with the conclusions of the 2014 Surgeon General’s report. The 2020 Surgeon General’s report also identified disparities in key indicators of smoking cessation, including quit attempts, having received advice to quit smoking from a health professional, and using counseling and medications to facilitate quitting (USDHHS 2020). As described in Chapter 2 of the present report, the prevalence of smoking is higher among some population groups than it is among others in the United States. Furthermore, cessation behaviors, such as quit attempts, are lower among some groups than among other groups based on level of educational attainment, poverty status, age, health insurance status, race and ethnicity, and geographic region.

Differences in Smoking-Caused Diseases Across Population Groups

The 2010 and 2014 Surgeon General’s reports outlined the biologic mechanisms linking active smoking and exposure to secondhand tobacco smoke with cancer, COPD, cardiovascular disease, and other conditions (USDHHS 2010, 2014). Substantial declines in the prevalence of smoking contributed to a 46.4% decline in the age-standardized rate of years of life lost (YLL) due to premature death associated with smoking from 1990 to 2019, although smoking remained the leading risk factor for YLL during this period (Tsao et al. 2023). Smoking was also the leading risk factor for years of life lived with disability or injury (YLD) in 1990. However, the age-standardized YLD rate attributable to smoking declined by 25.8% from 1990 to 2019, such that smoking ranked as the third most common risk factor for YLD in 2019 (Tsao et al. 2023).

This section provides a brief overview of differences in select smoking-related outcomes by sociodemographic characteristics, using data from the latest available published reports. Consistent with Chapter 2 of this report, differences in select outcomes by sociodemographic characteristics are reported where 95% confidence intervals (CIs) do not overlap, when applicable.

Smoking-Attributable Mortality Across U.S. Populations

CDC has produced estimates of the health and economic burdens attributable to cigarette smoking for almost 30 years through the Smoking-Attributable Mortality, Morbidity, and Economic Costs (SAMMEC) system. Epidemiologists, policymakers, public health practitioners, and other professionals have used these estimates to inform and support programs and policy initiatives that are intended to reduce smoking in the United States. The common approach for calculating PAF is as follows:

Chapter 12 of the 2014 Surgeon General’s report offers additional details about the SAMMEC methodology, including its assumptions and limitations, and how SAFs are influenced by underlying RRs (USDHHS 2014). The health burden component of SAMMEC is based on the approach of Levin (1953), which involves the computation of PAFs (known as SAFs in SAMMEC) for diseases determined by the U.S. Surgeon General to have a causal relationship with cigarette smoking (Levin 1953; USDHHS 2004, 2014). In this chapter, estimates of the number of deaths attributable to smoking are calculated by multiplying the corresponding SAF for each population group by the total number of deaths in that population group.

As of 2014, cigarette smoking was causally associated with 27 diseases or adverse outcomes (Table 6.1). Five of these chronic diseases—lung cancer, coronary heart disease, other heart disease, cerebrovascular disease, and COPD—account for an estimated 80% of smoking-attributable mortality in the United States (USDHHS 2014). According to the 2014 Surgeon General’s report, cigarette smoking is causally linked to 12 cancers (USDHHS 2014). A range of estimates exists for the PAF of smoking for all cancer deaths, which represent calculations of specific population groups, prevalence estimates, and RR estimates for a given time period (USDHHS 2014). Additionally, analyses suggest that the PAF of cancer deaths attributable to smoking has declined over time due to the decline in the prevalence of smoking. For example, an analysis of the PAF for cancer deaths attributable to cigarette smoking in 2011 estimated PAFs of 51.5% for men and 44.5% for women (Siegel et al. 2015). More recently, Islami and colleagues (2022) estimated that, of the 418,563 total cancer deaths in 2019 among adults 25–79 years of age, 122,951 were attributable to smoking, for an overall PAF of 29.4% (men: 74,508 deaths, PAF = 33.1%; women: 48,536 deaths, PAF = 25.0%). By state, the estimated proportion of cancer deaths attributable to smoking in 2019 was lowest in Utah (PAF = 16.5%) and highest in Kentucky (PAF = 37.8%), which corresponds to states with lower and higher prevalence of smoking (see Chapter 2).

Table 6.1

Diseases that are causally associated with or worsened by cigarette smoking.

Historically, national and state-based estimates of smoking-attributable mortality have been generated and reported by sex. However, reports on smoking-attributable mortality that are stratified by other factors relevant to health and smoking-related disparities, such as race and ethnicity, level of educational attainment, urbanicity, and geographic region (e.g., U.S. Census Region) are lacking, representing an important knowledge gap.

One reason for the absence of such estimates has been the general lack of group-specific RR estimates for smoking-attributable diseases, which are needed to estimate smoking-attributable mortality. Such data are often lacking because many population groups are underrepresented in large cohort studies of people who smoke and do not smoke. For example, some ethnic populations with lower SES are underrepresented in the Cancer Prevention Study II (Calle et al. 2002; USDHHS 2004), which is a well-known cohort study of 1.2 million American men and women volunteers that serves as a common source of data for RR estimates in analyses of SAFs (Calle et al. 2002; USDHHS 2004). Although analyses using estimates of smoking-attributable mortality based on Cancer Prevention Study II and other cohorts are considered scientifically sound for the overall U.S. population (Malarcher et al. 2000; USDHHS 2004, 2014), contemporary cohorts that are more representative of the current U.S. population are needed to produce appropriate population group estimates of smoking-attributable mortality.

The National Health Interview Survey and Linked Mortality Files

The National Center for Health Statistics (NCHS) links records from participants in large national surveys, such as the NHIS and the NHANES, which are designed to be representative of the civilian, noninstitutionalized U.S. population, to mortality information in the NDI. Information from these data sources enables researchers to conduct longitudinal analyses that describe health outcomes in relation to the baseline characteristics measured in these surveys.

RRs for mortality were computed using the NHIS Linked Mortality File (LMF) by current and former smoking status. To protect the privacy of decedents, work was conducted in the NCHS Research Data Center (CDC 2020). Specifically, this analysis used NHIS respondents who were surveyed during 1999 to 2014 with linked mortality data beginning in 2000 through December 31, 2015. All participants with sufficient identifying data were eligible for mortality linkage. Each survey record was screened to determine if it contained at least one of the following combinations of identifying data elements:

1. Social Security number (SSN) (nine digits [SSN9] or last four digits [SSN4]), last name, first name
2. SSN (SSN9 or SSN4), sex, month of birth, day of birth, year of birth
3. Last name, first name, month of birth, year of birth

Any survey participant records that did not meet these minimum data requirements were ineligible for record linkage. On average, an estimated 94.8% of NHIS participants for the years 1999 to 2014 were eligible for mortality follow-up (NCHS 2019). Mortality for eligible survey respondents was primarily determined by matching identifying data between survey records to the NDI and supplemented with information from NHIS linkages with other sources, such as the Social Security Administration and Centers for Medicare & Medicaid Services. If a match was found for the NHIS respondent in the NDI, the respondent was assumed to be dead; if no match was found, the participant was assumed to be alive as of December 31, 2015.

The respondent’s smoking status was collected only at the time of their NHIS interview and not at mortality follow-up. To minimize bias in RR estimates attributable to potential changes in smoking status over time (e.g., initiation, relapse, or quitting), follow-up was restricted to 10 years, and participant data were censored thereafter. The demographic characteristics of the NHIS-NDI cohort by race (for non-Hispanic people or people of Hispanic origin) are shown in Table 6.2.

Table 6.2

Demographic characteristics of participants by race and ethnicity, NHIS Linked Mortality File, 1999–2014.

Cause of Death

The NHIS-NDI LMF reports respondents’ specific underlying cause of death, which was coded using ICD-10 (Table 6.1). Initially, lung cancer, coronary heart disease, other heart disease, cerebrovascular disease, and COPD were examined as causes of death, as were grouped conditions that included all smoking-attributable diseases, all cancers, and all deaths. However, preliminary analyses of individual causes of death for sociodemographic groups resulted in small sample sizes, introducing sizeable error into the resulting estimates. Therefore, the following analyses focus on mortality from all causes combined (hereafter referred to as “all-cause mortality”).

Estimates of Relative Risk of Mortality for Selected Sociodemographic Groups

Weighted NHIS-NDI LMF data were used to estimate the RR of mortality by group. The original NHIS adult sample weights were adjusted to account for respondents who were ineligible for linkage to the mortality follow-up (through December 31, 2015). Following standard procedures, the adjusted adult sample weights were divided by 17 (equal to the number of NHIS survey years) to compute the final analytic weights per person; more details about the NHIS-NDI LMF methodology can be found elsewhere (NCHS 2019).

The RRs for smoking-attributable diseases were computed for people who currently smoked and people who smoked in the past; the referent group was people who never smoked. For all sociodemographic groups considered, computed RRs were stratified by gender³ and age group. Deaths were assumed to be Poisson-distributed and conditional on number of years of follow-up. A multivariable Poisson regression model was built to compute RRs by smoking status for each age group and racial ethnic group. RRs were computed with a 95% CI; CIs were based on a normal approximation for the natural log RRs. RR estimates for American Indian and Alaska Native and Asian American, Native Hawaiian, and Other Pacific Islander people are omitted from this section because of insufficient numbers of deaths in each age group during the period of analysis.

Relative Risks of All-Cause Mortality

Race and Ethnicity

Table 6.3 presents estimated RRs of all-cause mortality by gender, race and ethnicity, and age group. For Hispanic, non-Hispanic White, and non-Hispanic Black men, and non-Hispanic White women who currently smoked (at the time of their interview), compared with people who never smoked, the RR of all-cause mortality increased from the 35- to 54-year-old age group to the 55- to 64-year-old age group before declining for the oldest age groups (65–74 years of age and 75 years of age and older). For non-Hispanic Black and Hispanic women who currently smoked compared with women who never smoked, the RR of all-cause mortality increased from the 35- to 54-year-old age group to the 65- to 74-year-old age group before declining among women 75 years of age and older. In general, these estimates were largest for non-Hispanic White people, followed by non-Hispanic Black adults, and lowest for Hispanic adults. This was true across all age groups and among men and women.

Table 6.3

Estimated relative risks for all-cause mortality among people, 35 years of age and older, who currently smoked and who formerly smoked, by gender, race and ethnicity, and age; NHIS Linked Mortality File, 1999–2014.

Non-Hispanic White men and women who currently smoked and were 35–64 years of age had the highest RR estimates for all-cause mortality, with more than three times the mortality risk of people who never smoked. For Hispanic men and women 75 years of age and older, current smoking was not associated with a statistically significantly elevated RR of mortality compared with never smoking, although these results may have been the product of small numbers of observed deaths in those categories. White women who were younger than 65 years of age and currently smoked had an estimated risk of death that was three times that of their never-smoking counterparts. Black women who were younger than 75 years of age and who currently smoked had more than twice the risk of death compared with Black women in that age group who never smoked. Estimates of RR were generally highest in the 55- to 64-year-old age group across the three racial and ethnic groups for men and for non-Hispanic White women. For Black and Hispanic women, estimates of RR were highest in the 65- to 74-year-old age group.

For most population groups, people who smoked in the past (at the time of interview) had a moderately increased risk of mortality by gender, age group, and race or Hispanic origin compared with people who never smoked (RR <2.0). Specific groups of people who smoked in the past had RR estimates that were not statistically significantly different from that of people who never smoked, as indicated by the CIs crossing 1.0: non-Hispanic Black men (75 years of age and older: RR = 1.08), Hispanic women (35–64 years of age: RR = 0.81; 55–64 years of age: RR = 1.24; 75 years of age and older: RR = 1.14), and non-Hispanic Black women (35–54 years of age: RR = 1.23; 75 years of age and older: RR = 1.12). These non-significant findings may reflect declining RRs with increasing time since quit smoking (USDHHS 2020) or may be due to small sample sizes resulting in wider CIs.

Level of Educational Attainment

Table 6.4 presents RR estimates of all-cause mortality for men and women who, at the time of interview, currently smoked and people who smoked in the past by level of educational attainment and age group compared with people of the same level of educational attainment and age who never smoked. Across all age groups, genders, and levels of educational attainment, people who currently smoked had a significantly higher RR of all-cause mortality compared with their never-smoking counterparts. Currently smoking, college-educated men 65–74 years of age and currently smoking college-educated women 55–64 years of age at the time of interview had the highest RR (3.39 and 3.43, respectively) for mortality compared with their never-smoking counterparts.

Table 6.4

Estimated relative risks for all-cause mortality among people, 35 years of age and older, who currently smoked and who formerly smoked, by gender, level of educational attainment, and age group, NHIS Linked Mortality File, 1999–2014.

With some exceptions, the magnitude of the RR was generally lower among people with an eighth-grade education or less who currently smoked at the time of interview (compared to their never-smoking counterparts) than were RRs among people with other levels of educational attainment. Furthermore, with some exceptions, RR estimates were generally higher among people with more than an eighth-grade education but less than a college degree who smoked (compared to their never-smoking counterparts) than they were among people with other educational attainment levels. Among men and women 75 years of age and older in all levels of educational attainment, RR estimates were similar and overlapping, ranging from 1.29 to 1.67.

Mortality RRs among people who smoked in the past (at the time of interview) were much lower than those among people who currently smoked (RRs among people who smoked in the past were ≤1.7); CIs overlapped for most estimates, leading to few differences by level of educational attainment, age group, and sex. Adults 35–54 years of age and 75 years of age and older who smoked in the past had lower mortality RR estimates (compared to people who never smoked) than the RR estimates among those 55–74 years of age. Compared to their never-smoking counterparts, mortality RR estimates for people with specific levels of educational attainment who smoked in the past did not differ significantly for men and women 35–54 years of age with less than an eighth-grade education; women 35–54 years of age with 9th- to 12th-grade education but no diploma; women 35–54 years of age with a high school degree or GED; and men 75 years of age and older with some college education but no degree. As stated previously, these non-significant findings may be due to small sample sizes.

Geographic Region

Across all geographic regions, people 75 years of age and older who currently smoked at the time of their NHIS interview had the lowest RR for mortality compared with their counterparts never smoked (Table 6.5), which may be due to competing causes of mortality and temporal trends in smoking by birth cohort. For example, because older birth cohorts started smoking at earlier ages and had higher smoking prevalence than younger cohorts, more members of older birth cohorts may have died from smoking-attributable causes before reaching age 75 (USDHHS 2014). People 55–64 years of age who currently smoked had the highest RR for mortality, on average, led by men in the Northeast (RR = 3.30) and women in the Midwest (RR = 3.38), which were more than three times the RR for mortality compared with people who never smoked in their respective regions and age groups. RR estimates for people who smoked in the past were highest in the Midwest for men and women 35–74 years of age in the Midwest; men and women 65–74 years of age who smoked in the past had more than 1.7 times the risk for mortality compared with their never-smoking counterparts.

Table 6.5

Estimated relative risks for all-cause mortality among people, 35 years of age and older, who currently smoked and who formerly smoked, by gender, geographic region, and age group; NHIS Linked Mortality File, 1999–2014.

Urbanicity

Table 6.6 presents RR estimates for people who currently smoked and people who smoked in the past by urbanicity (at the time of their NHIS interview) compared to their never-smoking counterparts. In rural areas, women who currently smoked at the time of interview and were 35–54 years of age had higher RRs for mortality compared with women in the same age group who currently smoked in urban areas. There were no major differences in RR for mortality between people living in urban and rural places for other age and gender groups. Estimates followed similar patterns of highest RRs for men and women 55–65 years of age and lowest RRs for men and women 75 years of age and older. With respect to former smoking, the RR for mortality was highest (1.64) among women 55–64 years of age who lived in rural areas, although the magnitude of the RR for mortality was similar among other population groups studied based on overlapping CIs. The RR for mortality among women 35–54 years of age who smoked in the past and lived in either rural or urban areas was not statistically significant.

Table 6.6

Estimated relative risks for all-cause mortality among people, 35 years of age and older, who currently smoked and who formerly smoked, by gender, urbanicity, and age group; NHIS Linked Mortality File, 1999–2014.

Smoking-Attributable Fractions in the United States, 2010–2018

To facilitate calculations of the SAF, the population-level prevalence estimates of current, former, and never smoking—pooled across the public use 2010–2018 NHIS data files—were stratified by age group, gender, race and ethnicity, educational attainment, and geographic region (Appendix Tables 6A.1–6A.3). Weights were pooled across years by dividing annual weights by 9 (equal to the number of NHIS survey years) following standard procedures. Pooled prevalence estimates by urbanicity were not obtained because of the absence of this information in 2010–2018 NHIS public-use files.

Race and Ethnicity

Among men, an estimated 9–22% of all deaths of Hispanic, 12–39% of non-Hispanic White, and 7–39% of non-Hispanic Black men were attributed to smoking; the range varied by age group (Table 6.7). Among women, smoking was attributed to 1–13% of all deaths of Hispanic, 10–38% of non-Hispanic White, and 7–22% of non-Hispanic Black women. Among men, SAFs were highest among non-Hispanic White men who were 35–64 years of age (38–39%) and among non-Hispanic Black men who were 55–64 years of age (39%). Among women, SAFs were highest among non-Hispanic White women who were 35–64 years of age (35–38%) and lowest among Hispanic women who were 35–54 years of age and 75 years of age and older (<5%). For non-Hispanic Black and White people, SAFs peaked at 55–64 years of age before declining at older ages. For Hispanic people, SAFs peaked at 65–74 years of age.

Table 6.7

Smoking-attributable fractions for all-cause mortality by gender, age group, and race and ethnicity; NHIS Linked Mortality File, 1999–2014.

Level of Educational Attainment

Table 6.8 shows SAFs by level of educational attainment. Among men, 12–16% of all deaths for those with an 8th-grade education or less were attributed to smoking, 15–44% for those with a 9th- to 12th-grade education but no diploma, 13–35% for those with a high school diploma or GED, 8–37% for those with some college education but no degree, and 9–17% for those with a college degree or above. Among women, 6–19% of all deaths for those with an 8th-grade education or less were attributed to smoking, 14–40% for those with a 9th- to 12th-grade education but no diploma, 12–33% for those with a high school diploma or GED, 12–30% for those with some college education but no degree, and 6–21% for those with a college degree or above. SAFs peaked at 55–64 years of age among men and women with more than an 8th-grade education but less than a college degree and among women with college degrees. In contrast, SAFs peaked at 65–74 years of age among men and women with an 8th-grade education or less and among men with a college degree or higher.

Table 6.8

Smoking-attributable fractions for all-cause mortality by gender, age group, and level of educational attainment; NHIS Linked Mortality File, 1999–2014.

Geographic Region

Table 6.9 shows SAFs by geographic region. Among men, 12–34% of all deaths in the Northeast, 14–39% in the Midwest, 10–36% in the South, and 14–31% in the West were attributable to smoking. Among women, 12–27% of all deaths in the Northeast, 7–37% in the Midwest, 9–30% in the South, and 9–22% in the West were attributable to smoking.

Table 6.9

Smoking-attributable fractions for all-cause mortality by gender, age, and geographic region; NHIS Linked Mortality File, 1999–2014.

Urbanicity

SAFs could not be calculated by urbanicity because the prevalence of smoking stratified by the intersection of urbanicity, gender, and age group was not available in the 2010–2018 NHIS public use data file.

Smoking-Attributable Mortality in the United States by Race and Ethnicity, 2010–2018

Tables 6.10 and 6.11 present the estimated number of deaths from all causes attributable to smoking among non-Hispanic Black, non-Hispanic White, and Hispanic adults who smoked and formerly smoked from 2010 to 2018 by gender, age group, and race and ethnicity. Estimates are presented after being rounded to the nearest 100 people.

Table 6.10

Total deaths, smoking-attributable mortality, and average annual smoking-attributable mortality per 100,000 population for all causes of death among people who currently smoked and who formerly smoked, by gender and age group; 2010–2018; United (more...)

Table 6.11

Total deaths, smoking-attributable mortality, and average annual smoking-attributable mortality per 100,000 population for all causes of death among people who currently smoked and who formerly smoked, by gender and race and ethnicity; 2010–2018; (more...)

To calculate the estimated total number of smoking-attributable deaths, SAFs were applied to the total number of deaths during 2010–2018 for selected populations from the National Vital Statistics System mortality data extracted from CDC Wonder among adults 35 years of age and older (CDC n.d.a). The average annual smoking-attributable mortality was calculated as the total number of smoking-attributable deaths divided by the number of years (n = 9) included in the analysis. The average annual smoking-attributable mortality rate (per 100,000 population) was calculated as the total number of smoking-attributable deaths divided by the total population size for selected population groups 35 years of age and older from the U.S. Census Bureau single-race population estimates extracted from CDC Wonder (CDC n.d.c).

Data for deaths are from the Multiple Cause of Death Files for 1999–2019, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program (CDC n.d.a). National mortality and population data by race and Hispanic origin, age group, and gender were obtained from CDC Wonder. Death counts were restricted to select populations with non-missing data. For this analysis, deaths among American Indian and Alaska Native people, Asian American and Pacific Islander people, and people with unknown ethnicity were excluded because of small sample sizes that limit estimates of RR. Due to data issues that would complicate presentation of results, analyses of smoking-attributable deaths by urbanicity, geographic region, and level of educational attainment were not conducted.

From 2010 to 2018, at least 4,259,400 smoking-attributable deaths were estimated to have occurred. On average each year, at least 473,300 smoking-attributable deaths were estimated to have occurred (277,100 deaths among men and 196,200 among women) among non-Hispanic White, non-Hispanic Black, and Hispanic adults (Table 6.10). The average annual smoking-attributable death rate among non-Hispanic White, non-Hispanic Black, and Hispanic adults from 2010 to 2018 was an estimated 289.9 deaths per 100,000 population (365.9 deaths per 100,000 men and 237.5 deaths per 100,000 women). The estimated number of annual smoking-attributable deaths increased by age group for women, but for men, the estimated number peaked at 65–74 years of age before declining among those 75 years of age and older. However, the smoking-attributable death rate was highest among men and women 75 years of age and older: 914.5 deaths per 100,000 men and 680.5 deaths per 100,000 women.

Table 6.11 shows the estimated number of smoking-attributable deaths among Hispanic, non-Hispanic Black, and non-Hispanic White men and women. On average, an estimated 15,100 smoking-attributable deaths occurred each year among Hispanic adults (11,400 deaths among men and 3,800 deaths among women), 50,600 among non-Hispanic Black adults (31,700 deaths among men and 19,000 deaths among women), and 407,500 among non-Hispanic White adults (234,100 deaths among men and 173,500 deaths among women). Of all deaths that occurred among Hispanic men and women, 10% were attributed to smoking. In addition, 18% of all deaths for non-Hispanic Black men and women and 20% of all deaths for non-Hispanic White men and women were attributed to smoking.

The average annual smoking-attributable death rate from 2010 to 2018 was approximately 69.2 deaths per 100,000 Hispanic people (106.0 deaths per 100,000 Hispanic men and 33.7 deaths per 100,000 Hispanic women), 266.9 deaths per 100,000 Black people (369.1 deaths per 100,000 Black men and 182.5 deaths per 100,000 Black women), and 346.7 deaths per 100,000 White people (414.7 deaths per 100,000 White men and 283.9 deaths per 100,000 White women). From 2010 to 2018, men accounted for 75% of all smoking-attributable deaths in the Hispanic population, 63% of such deaths in the non-Hispanic Black population, and 57% of such deaths in the non-Hispanic White population.

The NHIS-NDI-LMF did not include sufficient data on American Indian and Alaska Native people and Asian American and Pacific Islander people to allow for longitudinal analysis of these population groups. Therefore, conclusions about mortality rates attributable to smoking for these populations cannot be made using these data. However, evidence from other sources, such as the Strong Heart Study and the Indian Health Service, shows that smoking is a large contributor to heart disease, stroke, and cancer mortality in American Indian and Alaska Native populations (Mowery et al. 2015; Zhang et al. 2015). Lung cancer is the leading causes of cancer deaths in Alaska Native people (Nash et al. 2022). Another study showed significant heterogeneity in tobacco-caused cancers among Asian American and Native Hawaiian and Other Pacific Islander people (Medina et al. 2021). Each of these groups are unique, and additional data sources are needed to assess mortality within each group.

The aggregate group of the Asian American and Native Hawaiian and Other Pacific Islander population has historically had the lowest prevalence of smoking (see Chapter 2). However, rates of smoking differ within specific groups, and aggregation of data masks disparities in cancer incidence and mortality rates for such groups as Vietnamese, Chinese, Filipino, Japanese, Native Hawaiian, and Samoan people. An evaluation of the prevalence of smoking by ethnic population group that used data from the NHIS and the National Survey on Drug Use and Health revealed disparities in smoking within the Asian American and Native Hawaiian and Other Pacific Islander population (Martell et al. 2016). This study showed that the prevalence of smoking is historically higher among Korean and Vietnamese American people than among Chinese and Indian American people. Furthermore, differences in the prevalence of smoking by ethnic population and gender have been observed. For example, Gorman and colleagues (2014) reported that Vietnamese men were significantly more likely to smoke than Chinese men in models adjusted for various acculturation measures, but this association was not observed among women.

Other analyses have shown high SAFs of cancer deaths among specific Asian American and Pacific Islander groups, such as Korean men who live in California and the aggregate category of Asian and Pacific Islander or Hawaiian men and women who live in Hawaii (Leistikow et al. 2006). Similarly, lower than average prevalence of smoking among Hispanic people may mask differences in smoking and in the SAF of deaths by ethnic origin. For example, Puerto Rican and Cuban American people are more likely to smoke than Mexican American people (Martell et al. 2016).

Limitations

Smoking status in the NHIS-NDI LMF is assessed at only baseline, and thus a particular respondent’s smoking status recorded at the time of interview might not reflect that person’s status at time of death. For example, it is possible that observed deaths occurred among people who had quit smoking but were recorded as currently smoking at the time of their NHIS interview. Such cases would lead to an underestimation of the true RRs of mortality associated with current smoking among specific population groups. Conversely, the RR of mortality associated with former smoking may partially reflect the risk of those who recently quit smoking because of smoking-related illness, potentially inflating the risk associated with being a person who used to smoke.

This analysis does not separate estimates of mortality risk for people who quit smoking recently from those who quit smoking longer ago. The analysis by Jeon and colleagues (2023) found that the RR of all-cause mortality due to smoking for people who had quit smoking for longer periods was negatively associated with the time since quitting in groups defined by race and ethnicity and educational attainment; mortality rates for people who quit smoking 15 or more years ago were similar to those who had never smoked, particularly among those who were younger than 65 years of age. To partially address this limitation, follow-up for each respondent in the NHIS-NDI LMF is restricted to 10 years (an approach called right censoring). Although this approach does not completely address the potential for misclassification of smoking status, other longitudinal data sources that could address this issue may not be as representative of the U.S. population as the NHIS. Prevalence of smoking is only one factor that influences RR of death. For example, socioeconomic indicators; alcohol use; dietary behaviors; biological factors, such as metabolic pathways, inflammation, and pathophysiological processes; and other risk behaviors may influence the risk of mortality (Thun et al. 2000; National Institutes of Health State-of-the-Science Panel 2006; Orsi et al. 2010; Du et al. 2011; Jin et al. 2013; Deng et al. 2016; Singh and Jemal 2017; Milajerdi et al. 2018).

Additionally, the current analysis does not account for smoking intensity among people who currently smoked or who formerly smoked at baseline. However, in a recent analysis by Jeon and colleagues (2023), which stratified people by smoking intensity (as measured by the number of cigarettes smoked per day), RRs generally increased with higher levels of smoking intensity across population groups defined by race and ethnicity and by educational attainment. Notwithstanding, disparities in cancer incidence by race and ethnicity and smoking intensity have been observed. Specifically, data from the Multiethnic Cohort Study showed that Native Hawaiian people and Black people have higher risks for lung cancer than do White, Japanese American, and Hispanic or Latino people who smoke similar numbers of cigarettes; disparities were more pronounced among those who smoked 10 cigarettes per day than among those who smoked 35 cigarettes per day (Stram et al. 2019).

Several aspects of the methodology may have resulted in underestimates of RR and, thus, smoking-attributable mortality. First, the stratified nature of this analysis reduces the number of observable deaths in each relevant age, gender, and demographic category. When evaluating some racial and ethnic populations, this can translate into small numbers of observations. Nonsignificant RR estimates reported in Tables 6.3–6.6 may reflect insufficient power to detect statistical significance and may not be an accurate assessment of risk of mortality associated with current or former smoking.

Second, the long latency period between onset of smoking and subsequent smoking-related morbidity and mortality further limits the number of observable incidents in the NHIS-NDI LMF. With longer follow-up periods, RRs associated with death tend to increase. As such, restricting the follow-up period to 10 years (right censoring) may have resulted in an underestimate of RRs. The tradeoffs associated with methods to limit different forms of bias, while unavoidable, are important to acknowledge.

Another limitation is that RR estimates for some population groups exhibit wide CIs, which translates into wide ranges in the calculation of SAFs (Tables 6.7–6.9). Because SAFs for each population group of interest reflect the underlying prevalence and RRs specific to that group, direct comparisons between different smoking-attributable mortality estimates are not always appropriate.

The estimates of smoking-attributable mortality presented in this chapter include deaths for non-Hispanic Black, non-Hispanic White, and Hispanic adults only. Thus, overall estimates presented likely represent underestimates of the total smoking-attributable mortality for the United States. As such, the findings reported in this chapter should be considered only a first estimate of the contribution of smoking to the increased morbidity and mortality burdens faced by specific population groups in the United States; comprehensive data are critical for more refined estimates.

Finally, the estimates reported here focused on only cigarette-smoking-attributable mortality. Use of other tobacco products, including cigars, pipes, and smokeless tobacco, either alone or in combination with cigarette smoking and other tobacco products also contributes to mortality risk (Nonnemaker et al. 2014). Patterns of use of other combustible tobacco and smokeless tobacco products vary by race and ethnicity, level of educational attainment, geographic region, and other sociodemographic factors and may influence tobacco-related mortality across the U.S. population (Hirschtick et al. 2021).

Deaths Due to Exposure to Secondhand Tobacco Smoke Among Infants and Nonsmoking Adults, by Race and Ethnicity

The 2014 Surgeon General’s report included estimates from Max and colleagues (2012) of the number of deaths attributable to exposure to secondhand tobacco smoke among nonsmoking adults. The authors noted that 41,280 deaths among nonsmoking adults were attributable to exposure to secondhand tobacco smoke in 2006, including 33,950 deaths from coronary heart disease and 7,330 deaths from lung cancer. However, previous Surgeon General’s reports did not included estimates of deaths due to exposure to secondhand tobacco smoke in the United States by race and ethnicity. Leveraging the methodology developed by Max and colleagues (2012), the present report estimates deaths among infants for 2020 and among nonsmoking adults 18 years of age and older for 2019, overall and by race and ethnicity.

Detailed documentation of the methodology, including epidemiological formulas, is presented in Max and colleagues (2012). For newborns, estimates of exposure to secondhand tobacco smoke by race and ethnicity were obtained from National Vital Statistics Reports for 2020, the most recent year available at the time of analysis (Osterman et al. 2022). In brief, exposure to second-hand tobacco smoke among newborns was determined by whether the mother smoked anytime during pregnancy, as reported on the U.S. Standard Certificate of Live Birth. RRs of death due to exposure to secondhand tobacco smoke for sudden infant death syndrome and prenatal conditions were based on data from Dietz and colleagues (2010).

For adults 18 years of age and older, estimates of exposure to secondhand tobacco smoke were obtained from the NHANES during 2017 to March 2020 and based on serum cotinine (0.05–10.0 ng/mL). NHANES participants with missing serum cotinine measurements were excluded from the analysis. Following the methodology used in the study by Tsai and colleagues (2021), people who currently smoked and those who used any nicotinecontaining product, including nicotine replacement therapy, were excluded from the analysis. RRs of death from exposure to secondhand tobacco smoke for men and women were based on estimates from the 2006 Surgeon General’s report (USDHHS 2006) for coronary heart disease and a 2005 California Environmental Protection Agency report for lung cancer (Max et al. 2012, citing California Environmental Protection Agency 2005). The estimates of the prevalence of former, never, and current cigarette smoking were obtained from the 2019–2020 NHIS. The total number of deaths among infants less than 1 year of age (in 2020) and adults 18 years of age and older (in 2019) were from NCHS’s National Vital Statistics System, Multiple Causes of Death file, obtained from the CDC WONDER database.

Max and colleagues (2012) described the steps to estimate deaths among nonsmoking adults (people who never smoked and people who smoked in the past, combined), which were applied to produce the estimates for 2020. In short, deaths among nonsmoking adults were determined by (a) estimating the number of excess deaths attributable to current smoking; (b) subtracting these excess deaths attributable to active smoking from the total number of deaths among all people; and (c) apportioning the remaining deaths to people who did and did not smoke by applying the proportion of the population who did and did not smoke. The SAFs of deaths from exposure to secondhand tobacco smoke were estimated, then applied to total deaths among nonsmoking people who did not use nicotine-containing products in the previous 5 days for each condition category, by race and ethnicity.

Table 6.12 shows the percentage of newborn infants who were exposed to secondhand tobacco smoke in utero in 2020 and the percentage of nonsmoking men and women 18 years of age and older who were exposed to secondhand tobacco smoke from 2017 to March 2020, by race and ethnicity and for all races and ethnicities combined. Among all racial and ethnic groups, 5.5% of infants, 21.1% of men, and 19.3% of women were exposed to secondhand tobacco smoke. Among infants born in 2020, 1.4% of Hispanic, 4.5% of Black, and 8.1% of White infants were exposure to secondhand tobacco smoke in utero. Among women, exposure to secondhand tobacco smoke ranged from 14.5% among Hispanic women to 38.4% among Black women; 16.7% of White women and 24.4% of women of non-Hispanic Other races were exposed to secondhand tobacco smoke. Among men, exposure to secondhand tobacco smoke ranged from 18.2% for White men to 42.3% for Black men; 20.6% of Hispanic men and 24.7% of men of non-Hispanic Other race and ethnicity were exposed to secondhand tobacco smoke.

Table 6.12

Prevalence of exposure to maternal smoking among newborn infants in utero and of cotinine-measured exposure to secondhand tobacco smoke among nonsmoking adults, by race and ethnicity.

Overall, these estimates suggest a decline in exposure to secondhand tobacco smoke since 2006, which is consistent with findings presented in Chapter 2 of this report. Specifically, Max and colleagues (2012) noted that in 2006, 13.2% of infants and 39.1% of adults 20 years of age (the minimum adult age included for that study) and older were exposed to secondhand tobacco smoke.

As shown in Table 6.13, an estimated 19,600 deaths attributable to exposure to secondhand tobacco smoke occurred among infants and nonsmoking adults in 2019 and 2020. Of these deaths, 70.7% (13,800) occurred among non-Hispanic White people, 19.1% (3,700) occurred among non-Hispanic Black people, 6.0% (1,200) occurred among Hispanic people, and 4.1% (800) occurred among non-Hispanic people of other races. In contrast, Max and colleagues (2012) found that in 2006, of the estimated 42,147 total deaths due to exposure to secondhand tobacco smoke, nonsmoking non-Hispanic White people accounted for 80% (33,746) of deaths, while non-Hispanic Black people accounted for 12.8% (5,410) of deaths, Hispanic people accounted for 4.1% (1,745) of deaths, and non-Hispanic, other racial and ethnic groups accounted for 3.0% (1,247) of deaths (Figure 6.1). Deaths among non-Hispanic Black, Hispanic, and other non-Hispanic racial groups accounted for a larger proportion of estimated deaths attributable to exposure to secondhand tobacco smoke in 2019 (adults) and 2020 (infants) (combined, 29.2%) compared with the proportion in 2006 (combined, 19.9%). Furthermore, among non-Hispanic White people, the estimated number of deaths attributable to exposure to secondhand tobacco smoke declined by nearly 60% from 2006 (n = 33,746) to 2019–2020 (n = 13,800). Smaller declines were observed among non-Hispanic Black people (by 31%; from 5,410 to about 3,700 deaths), Hispanic people (by 33%; from 1,745 to about 1,200 deaths) and non-Hispanic people from other races (by 35%; from 1,247 to about 800 deaths) during this period (Figure 6.1).

Table 6.13

Estimated total number of deaths attributable to exposure to secondhand tobacco smoke among infants and nonsmoking adults, by cause, gender, and race and ethnicity, 2019 and 2020.

Figure 6.1

Estimated number of deaths due to exposure to secondhand tobacco smoke among infants and nonsmoking adults, 2006 and 2019 and 2020. ^aData on deaths in 2006 among infants younger than 1 year of age and among adults, 20 years of age and older are from Max (more...)

The rate of death (per 100,000 population) due to exposure to secondhand tobacco smoke in 2019 was calculated from the estimated number of deaths due to exposure to secondhand tobacco smoke (Table 6.14). The denominator included adults 18 years of age and older who did not smoke. The total population of adults, averaged across 2019 and 2020 from the U.S. Census Bureau single-race population estimate, was obtained from CDC WONDER (CDC n.d.d). For each racial and ethnic group presented in Table 6.14, the prevalence of cigarette smoking was estimated from the 2019–2020 NHIS. The number of nonsmoking adults was estimated by multiplying the total adult population by the prevalence of never and non-current cigarette smoking. The rate of death was calculated as the estimated number of deaths due to exposure to secondhand tobacco smoke divided by the estimated population of nonsmoking adults.

Table 6.14

Estimated number of deaths attributable to exposure to secondhand tobacco smoke and rates of death per 100,000 population among nonsmoking adults, by race and ethnicity, 2019.

Among all nonsmoking adults 18 years of age and older in 2019, an estimated 19,300 deaths were due to exposure to secondhand tobacco smoke, which equates to an estimated 8.7 deaths per 100,000 population (10.6 deaths per 100,000 men and 6.9 deaths per 100,000 women) (Table 6.14). Differences were noted by race and ethnicity. Although the greatest absolute number of deaths due to exposure to secondhand tobacco smoke occurred among White men and women, the highest population-specific death rates occurred among non-Hispanic Black men (16.4 deaths per 100,000 population) and non-Hispanic Black women (11.4 deaths per 100,000 population). The lowest rates of death due to exposure to secondhand tobacco smoke occurred among Hispanic adults: 3.9 deaths per 100,000 men and 2.1 deaths per 100,000 women.

Simulation Modeling of Smoking Disparities

Simulation models—also known as computational models—have been used to show the relationship between tobacco control policies and patterns of tobacco use and their downstream health outcomes (Mendez et al. 2013; Holford et al. 2014b; Feirman et al. 2016, ²⁰¹⁷; Levy et al. 2016, 2017b). These models can complement the analyses of smoking-attributable morbidity and mortality by (1) combining information from different sources and (2) including cross-sectional and longitudinal surveys and policy and health evaluation studies to examine how the effects of tobacco use and tobacco control policies could unfold over time. Numerous tobacco simulation models were reviewed in the 2014 Surgeon General’s report (USDHHS 2014) and by Feirman and colleagues (2016; ²⁰¹⁷⁾, but a limited number of tobacco models have considered explicitly groups that are disproportionately affected by tobacco-related health harms.

Chapter 12 of the NCI Tobacco Control Monograph 22 (NCI 2017a) discusses the SimSmoke disparity model—a modified version of the SimSmoke tobacco control simulation model—which was used to examine the potential effect of tobacco control policies on smoking and smoking-attributable deaths by SES in the United States. As illustrated in Chapter 2 of the current report, the prevalence of smoking is generally highest among people of lower SES—measured by poverty level and level of educational attainment—resulting in large disparities in the burden of tobacco-related disease and death by SES. Because levels of educational attainment are generally increasing in the U.S. population, the SimSmoke disparity model relies on income quintiles, which are a relative measure of SES and thus a more stable metric over time. Results of the SimSmoke disparity model are described next.

The SimSmoke model begins in 2006, when the prevalence of smoking among adults 18 years of age and older in the lowest income quintile was 30.2% for men and 22.7% for women. At that time, the prevalence of smoking among adults in the second lowest income quintile was 25.3% for men and 18.4% for women. The prevalence of smoking was higher in the lowest and second lowest income quintiles than it was in higher income quintiles. The model estimated that 119,526 people in the lowest income quintile and 95,986 people in the second lowest income quintile died prematurely from smoking in 2014 (NCI 2017a).

The SimSmoke disparity model showed that stronger tobacco control policies have the potential to reduce considerably the prevalence of smoking in lower income groups. The model simulated the specific effects of cigarette tax increases, smokefree policies, mass media antitobacco campaigns, marketing restrictions, health warnings, cessation treatment policies, and youth access policies for people in the two lowest income quintiles, taking into account moderating factors such as level of policy enforcement and intensity of publicity (NCI 2017a). The model simulated a status quo scenario by maintaining 2014 policy levels through 2064 and modeled the incremental effects of stronger policies implemented and maintained from 2015 through 2064 relative to the status quo. As shown in Table 6.15 (Parts A and B), raising the average cigarette tax by $3.00 per pack was projected to reduce the prevalence of smoking in the lowest quintile by 19.6% among men and by 19.5% among women, averting a total of 275,760 deaths from 2015 to 2064 (Table 6.16). For the second lowest quintile, the prevalence of smoking was similarly reduced, and 238,759 deaths were estimated to be averted over 50 years (NCI 2017a). All seven modeled policies were projected to reduce the prevalence of smoking and prevent smoking-attributable deaths in the lowest income quintile from 2015 to 2064, with the policy to raise the average cigarette tax to $3.00 per pack projected to have the largest effect (Table 6.15).

Table 6.16

Smoking-attributable deaths among men and women in the lowest income quintile, according to the U.S. SimSmoke model.

The SimSmoke disparity model also evaluated the effects of implementing a combination of the individual policies, including a cigarette tax increase ($1.00, $2.00, or $3.00 per pack); comprehensive, well-enforced smokefree air laws (smoking banned in worksites, bars, restaurants, and other public places); high-intensity mass media antitobacco campaigns; comprehensive, well-enforced marketing restrictions; strong health warnings (on cigarette packages); cessation treatment policies; and strong youth access enforcement (to prohibit minors from accessing tobacco products). In the combined policy scenario that specifically included a $3.00 tax increase per pack of cigarettes, the modified SimSmoke model projected that, from 2015 to 2064, (a) the prevalence of smoking for people in the lowest income quintile would fall by 42.8% among men and by 43.7% among women and (b) a total of 845,401 smoking-attributable deaths would be averted (Tables 6.15 and 6.16). For the second lowest income quintile, the model projected that this same policy combination would avert 676,821 smoking-attributable deaths among men and women from 2015 to 2064 (NCI 2017a).

Furthermore, two U.S. simulation models examined populations with comorbidities and higher-than-average prevalences of smoking, specifically adults with depression (Tam et al. 2020) and adults living with HIV (Reddy et al. 2017). According to data from the 2017 National Survey on Drug Use and Health, the prevalence of smoking among people with depression was greater than 23%, relative to about 15% among people without depression (Weinberger et al. 2020). Among people living with HIV, the prevalence of smoking was greater than 40% (Mdodo et al. 2015; Asfar et al. 2021).

Tam and colleagues (2020) evaluated smoking disparities by mental health status, focusing on adults with a common mental health condition: major depression. They showed that in the absence of intervention, people with depression would remain disproportionately affected by tobacco-related mortality from 2018 to 2060. This model estimated that during this period, 484,000 smoking-attributable deaths and 11.3 million life-years would be lost among adults with major depression (Tam et al. 2020). Reddy and colleagues (2017) simulated lung cancer mortality among people living with HIV and showed that patients who adhere to antiretroviral therapy were 6 to 13 times more likely to die from lung cancer than from AIDS-related causes.

Table 6.15

Status quo policies and SimSmoke-recommended policies: prevalence of smoking and percentage change among men and women, 18–85 years of age, in the lowest income quintile (percentages).

Investigators with the Cancer Intervention and Surveillance Modeling Network (CISNET) consortium generated smoking initiation, cessation, and intensity parameters that can be used to simulate long-term smoking and related health outcomes. For example, Holford and colleagues (2016) used age-period-cohort statistical models to estimate smoking history patterns over the life course among African American and White people in the United States. They found that the probabilities of smoking initiation and cessation and the intensity of cigarette smoking are historically lower among African American people than among White people. This translates into longer smoking durations but lower levels of cumulative exposure in pack-years for African American people. These age-period-cohort analyses and the resulting parameters for modeling have been extended for additional racial population groups by Hispanic origin (Meza et al. 2019), by levels of educational attainment (Cao et al. 2018), and by family income in relation to the federal poverty line (Jeon et al. 2019).

Numerous simulation models have used estimates of smoking from CISNET to evaluate long-term health outcomes at the population level (Jeon et al. 2012; Moolgavkar et al. 2012; Vugrin et al. 2015; Apelberg et al. 2018). The smoking histories modeled by CISNET can be modified in an interactive web based Tobacco Control Policy Tool (TCPT) (CISNET 2021) to estimate the impact of various tobacco control policies (e.g., enacting smokefree air laws, increasing cigarette taxes, raising the minimum age of legal access to tobacco products, increasing the level of expenditures for tobacco control programs, and adding graphic health warnings to cigarette packaging) on projections of adult smoking prevalence, number of life-years gained, and number of deaths avoided in the United States (Tam et al. 2018).

Simulation Modeling of the Prevalence and Attributable Mortality of Menthol Cigarette Use

Research shows that menthol cigarette use contributes to increased rates of smoking initiation and is associated with reduced smoking cessation among the general population and notably among Black people (Giovino et al. 2004; Ahijevych and Ford 2010; Delnevo et al. 2011; Levy et al. 2011a, ^b; Rath et al. 2015; Villanti et al. 2017, ^{2019, 2021}; D’Silva et al. 2018; Nonnemaker et al. 2019; Azagba et al. 2020; Cwalina et al. 2020; Mantey et al. 2021; Mills et al. 2021; Center for Tobacco Products 2022). Further, research shows that the tobacco industry has heavily marketed menthol cigarettes to Black people, who are more likely to smoke menthol cigarettes than are people from other racial and ethnic groups who smoke (Tobacco Products Scientific Advisory Committee 2011; Alexander et al. 2016; Mendez and Le 2021; Levy et al. 2023; U.S. Food and Drug Administration n.d.). Therefore, the availability of menthol cigarettes in the marketplace differentially affects Black people compared with people from other races. This is true even though the majority of people who smoke menthol cigarettes are White, because White people make up the largest racial group in the United States (U.S. Census Bureau 2022). Thus, although a smaller proportion of White people who smoke report smoking menthol cigarettes, the total number of people who smoke menthol cigarettes is largest for White people.

As of February 2024, nearly 200 cities and counties and two states (Massachusetts and California) have implemented restrictions on the sale of menthol cigarettes and other flavored tobacco products (Campaign for Tobacco Free Kids 2024). Chapter 7 discusses studies that evaluate the impact of those subnational policies.

In the absence of experimental studies or an actual prohibition on menthol cigarettes at the federal level, simulation modeling can project the potential effects of a federal prohibition under reasonable scenarios on tobacco use and tobacco-related health outcomes. Models developed by Levy and colleagues (2011b; ²⁰²³⁾, Mendez (2011), Le and Mendez (2021), and Mendez and Le (2021) have assessed the impact of menthol cigarettes on the prevalence of smoking and smoking-attributable deaths among Black or African American people and the total U.S. population. The model by Levy and colleagues (2011b) estimated the effects of a menthol ban, and the model by Mendez (2011) assessed the effects of menthol cigarettes on the population by contrasting a world with and without menthol cigarettes. In projecting future prevalence estimates for menthol and nonmenthol cigarette smoking, both models considered differential initiation and cessation rates among those who start with or become established menthol cigarette users. The results of various simulation models and their updates are summarized in Table 6.17 and discussed in this section.

Table 6.17

Summary of simulation models estimating cumulative smoking-attributable deaths averted, if menthol cigarettes were banned, and premature (excess) deaths caused by menthol cigarettes: total population and Black or African American population, United States, (more...)

The SimSmoke model provided by Levy and colleagues (2011b) simulated the effect of a national-level menthol prohibition implemented in 2011 in three potential scenarios: (1) 10% of people who smoked menthol cigarettes permanently quit smoking, and 10% of those who would have initiated smoking with menthol cigarettes did not initiate smoking (10% change); (2) 20% of people who smoked menthol cigarettes quit, and 20.0% of those who would have initiated smoking with menthol cigarettes did not initiate (20% change); and (3) 30.0% of people who smoked menthol cigarettes quit, and 30.0% of those who would have initiated smoking with menthol cigarettes did not initiate (30% change). This model predicted that, in the absence of a federal menthol ban (i.e., the status quo), the prevalence of smoking would decline slowly and the proportion of remaining people who smoke menthol cigarettes would increase. However, in the presence of a prohibition on menthol cigarettes, the model projected greater reductions in the prevalence of smoking and fewer smoking-attributable deaths.

The SimSmoke model also showed that Black or African American people were projected to experience larger health gains from the menthol prohibition compared with the general population. Over a 40-year period (from 2010 through 2050), the 10% change scenario projected nearly a 5% relative reduction in the prevalence of smoking for the total population and a 9% relative reduction among Black or African American people (Levy et al. 2011b). Similarly, under the 20% and 30% change scenarios, the prevalence of smoking would decline by over 7% and nearly 10%, respectively, for all adults. However, these declines would be 17% and nearly 25%, respectively, among Black or African American people (Levy et al. 2011b).

FDA’s Tobacco Products Scientific Advisory Committee (TPSAC) completed a review of the scientific evidence related to menthol cigarettes in 2011. Based on smoking models reviewed, TPSAC specified model parameters to compare smoking outcomes under two scenarios, with and without the availability of menthol cigarettes (Mendez 2011; Tobacco Products Scientific Advisory Committee 2011). The model projected that more than 327,500 premature (or excess) deaths would be attributable to the availability of menthol cigarettes over a 40-year period (2010 to 2050), of which, more than 66,500 would be among Black or African American people (Mendez 2011). Mendez (2011) also conducted sensitivity analyses of the model parameters specified by the TPSAC on results for the total number of premature (or excess) deaths overall and with varying menthol-specific parameters in the model; results are included in summary Table 6.17.

The SimSmoke (Levy et al. 2011b) and Mendez (2011) models yield relatively consistent estimates for the general population. Both models find that menthol cigarettes disproportionately harm Black people, although, the Mendez (2011) model yields estimates of cumulative menthol-attributable deaths among Black people that are lower than estimates in the SimSmoke model under various scenarios.

The two models have limitations when viewed as forecasts of the effects of potential regulatory actions on menthol. Mendez (2011) modeled a hypothetical world without menthol cigarettes to estimate the present and future harms attributable to menthol cigarettes, but that study did not evaluate a specific model of menthol regulation and its consequences. Levy and colleagues (2011b) explicitly modeled the potential future effects of a national-level prohibition on menthol cigarettes under various hypothetical scenarios but did not estimate which, if any, of the scenarios was most likely. In addition, Levy and colleagues (2011b) did not consider the potential impact of mass media campaigns or increased access to cessation services when implementing a menthol prohibition. Chapter 7 describes implementation evaluations and considerations in more detail.

The Mendez model (2011) and the SimSmoke model (Levy et al. 2011b) were originally developed using data from before 2010, when smoking patterns were relatively stable and cigarettes were the overwhelmingly dominant form of tobacco product use. Since then, evidence on the relationship between the use of menthol cigarettes and smoking initiation and cessation has expanded (Villanti et al. 2017); Chapter 2 of this report provides data on prevalence and trends in menthol cigarette smoking by race and ethnicity and age.

To provide more contemporary estimates to address these developments, Le and Mendez (2021) updated the model Mendez developed for TPSAC (Mendez 2011) to estimate the burden of menthol cigarettes in the United States from 1980 to 2018. Their analysis suggests that the prevalence of smoking in the United States was 2.6 percentage points higher in 2018 than it would have been if menthol cigarettes had not been available from 1980 onward (13.7% versus 11.1%). Furthermore, the availability of menthol cigarettes was estimated to result in smoking initiation among 10.1 million people from 1980 to 2018, and 3 million years of life lost (Le and Mendez 2021). This model also estimated that 378,000 premature deaths occurred from 1980 to 2018 as a result of menthol cigarettes (Table 6.17), or approximately 9,900 premature deaths per year (Le and Mendez 2021).

Mendez and Le (2021) also used the updated model to examine the impact of menthol cigarettes on African American people from 1980 to 2018. Results from this study estimated that from 1980 to 2018, menthol cigarettes were responsible for initiation of smoking among 1.5 million African American people, 1.5 million years of life lost, and nearly 157,000 premature deaths among African American people. This study noted that, compared with estimates of the total menthol-related harm among the general population (Le and Mendez 2021), African American people experienced a disproportionate share of menthol-related harm with respect to these measures (15%, 50%, and 41%, respectively), as African American people constituted about 12% of the U.S. population during the study period.

Conclusions

1. Smoking is the primary cause of lung and bronchus cancers—the leading cause of cancer death in the United States. Recent declines in the lung and bronchus cancer death rate have occurred among both men and women. Among men, the death rate for lung and bronchus cancer is highest among Black men, followed by White men, American Indian and Alaska Native men, Asian and Pacific Islander men, and Hispanic men. Among women, the death rate for lung and bronchus cancer is highest among White women, followed by American Indian and Alaska Native women, Black women, Asian and Pacific Islander women, and Hispanic women.
2. Cigarette smoking is a primary cause of COPD and the primary risk factor for the worsening of COPD. The overall prevalence of COPD is highest among American Indian and Alaska Native adults and lowest among Asian adults. There is a clear socioeconomic gradient for COPD prevalence and mortality, with higher prevalence and mortality occurring among people with lower income and lower educational attainment.
3. Cigarette smoking and exposure to secondhand tobacco smoke have adverse effects on overall cardiovascular health and cause cardiovascular disease. Among men, the prevalence of cardiovascular disease in 2017–2020 was highest among non-Hispanic Black (11.3%) and non-Hispanic White (11.3%) men, followed by Hispanic men (8.7%) and non-Hispanic Asian men (6.9%). Among women, the prevalence of cardiovascular disease was highest among non-Hispanic Black women (11.1%), followed by non-Hispanic White (9.2%), Hispanic (8.4%), and non-Hispanic Asian (4.9%) women.
4. From 2010 to 2018, an estimated 4.26 million smoking-attributable deaths occurred among non-Hispanic Black, Hispanic, and non-Hispanic White adults in the United States. Among those groups, at least 473,000 cigarette smoking-attributable deaths are estimated to have occurred each year.
5. Smoking causes about 1 in 5 deaths among non-Hispanic White and non-Hispanic Black people and about 1 in 10 deaths among Hispanic people.
6. An estimated 19,600 deaths attributable to exposure to secondhand tobacco smoke occurred among nonsmoking people in the United States based on data from 2019 and 2020. Deaths attributable to exposure to secondhand tobacco smoke have declined considerably since 2006, but this is largely due to the declines in death observed among non-Hispanic White people. Declines occurred at lower rates during this period among non-Hispanic Black, Hispanic, and other non-Hispanic racial groups.
7. Simulation models can be useful tools to project the potential effects of large-scale interventions on smoking-attributable morbidity and mortality and on disparities in tobacco use across various populations. Future modeling efforts would benefit from (a) more detailed data on patterns of smoking and the use of noncigarette tobacco products; and (b) more robust data for racial and ethnic groups; minoritized sexual orientation and gender identity groups; urban and rural communities; and other focused populations.
8. Aggregation of data on tobacco product use, disease incidence, and mortality may mask disparities within population groups, such as within Asian American and Native Hawaiian and Other Pacific Islander groups. Disaggregation of data reporting and oversampling among disparate populations will foster greater understanding of tobacco-related health disparities.

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Chapter 6. Appendix