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. Author manuscript; available in PMC: 2024 Jun 19.
Published in final edited form as: Am J Prev Med. 2023 Feb 11;64(4 Suppl 1):S53–S62. doi: 10.1016/j.amepre.2022.12.006

Mortality Relative Risks by Smoking, Race/Ethnicity, and Education

Jihyoun Jeon 1,*, Maki Inoue-Choi 2, Yoonseo Mok 1,3, Timothy S McNeel 4, Jamie Tam 5, Neal D Freedman 2, Rafael Meza 1,3
PMCID: PMC11186465  NIHMSID: NIHMS1999912  PMID: 36775754

Abstract

Introduction:

The impact of cigarette smoking on mortality is well-studied, with estimates of the relative mortality risks for the overall population widely available. However, age-specific mortality estimates for different sociodemographic groups in the US are lacking.

Methods:

Using the 1987–2018 National Health Interview Survey Linked Mortality Files through 2019, all-cause mortality relative risks (RRs) were estimated for current smokers or recent quitters and long-term quitters compared with never smokers. Stratified Cox proportional hazards regression models were used to estimate RRs by age, gender, race/ethnicity, and educational attainment. RRs were also assessed for current smokers or recent quitters by smoking intensity and for long-term quitters by years since quitting. The analysis was conducted in 2021–2022.

Results:

Compared to never smokers, all-cause mortality RRs among current smokers or recent quitters were generally highest for non-Hispanic White individuals, followed by non-Hispanic Black individuals, and lowest for Hispanic individuals. RRs varied greatly by educational attainment; generally, higher education groups had greater RRs associated with smoking compared to lower education groups. Conversely, the RRs by years since quitting among long-term quitters did not show clear differences across race/ethnicity and education groups. Age-specific RR patterns varied greatly across racial/ethnic and education groups as well as by gender.

Conclusions:

Age-specific all-cause mortality rates associated with smoking vary considerably by sociodemographic factors. Among high education groups, lower underlying mortality rates for never smokers result in correspondingly high RR estimates for current smoking. These estimates can be incorporated in modeling analyses to assess tobacco control interventions’ impact on smoking-related health disparities between different sociodemographic groups.

Introduction

Cigarette smoking has declined significantly in the United States (US), yet the extent of the decrease varies by sociodemographic factors,1,2 resulting in persistent disparities between different sociodemographic groups.1–7 Smoking behaviors and related mortality vary by several factors, including age, gender, race/ethnicity, socioeconomic status, and region.8–13 Cigarette smoking is estimated to cause more than 480,000 deaths each year in the US,1,14 but this mortality burden is unequally distributed across the nation. People with lower levels of educational attainment are known to smoke at higher rates compared to those with more education,6,13,15–17 but the smoking-related mortality rate they face has not been well-studied. Mortality rates associated with smoking likely vary by race/ethnicity as well,8,18 though this is also an understudied topic. One recent study by Inoue-Choi et al. found that the relative risk of all-cause mortality for current daily smoking compared with never smoking was higher in non-Hispanic White individuals than in non-Hispanic Black or Hispanic individuals.19 Mortality rates are also known to vary by smoking intensity, measured by the number of cigarettes smoked per day or month among current smokers and by time since quitting among former smokers, though this is also understudied for specific subpopulations.8,10,19

Empirical estimates of mortality rates associated with current and former smoking are necessary both to quantify the impact of smoking on mortality in a given population and to make projections about the future burden of smoking—typically assessed using tobacco simulation models.20–22 While previous research has produced detailed age- and gender-specific all-cause mortality relative risks (RRs) for the general US population,23 such estimates are comparatively absent for specific sociodemographic groups—an impediment to future research that aims to both quantify and monitor smoking-related disparities over time. Recent data for the general US population suggest that the risks of mortality from cigarette smoking relative to never smoking may be increasing.1,24 This further demonstrates the need for up-to-date analyses of the mortality effects of cigarette smoking across the whole US population, in addition to specific subgroups.

Assessing the long-term consequences of cigarette smoking in specific populations requires detailed estimates of their smoking-related health risks by age and smoking history. However, as discussed above, current estimates of smoking-related mortality RRs in the US do not have the required details to inform simulation models of the health consequences of smoking for relevant socio-demographic groups. To address these gaps, nationally representative data were used to estimate age-specific all-cause mortality RRs for current smokers or recent quitters who quit smoking less than two years ago and long-term quitters who quit smoking at least 2 years ago compared with never smokers, stratified by gender, race/ethnicity, and educational attainment.

Methods

Data

The National Health Interview Survey (NHIS) collects annual data on demographic characteristics and health information of the civilian noninstitutionalized population of the US. Adult tobacco use data has been collected since 1965. Based on the availability and consistency of smoking data/questions including use frequency and intensity, data from 1987, 1988, 1990, 1994, 1995, and 1997–2018 NHIS surveys linked with mortality data from the 2019 National Death Index were utilized.25–27 Covariates included age, gender, race/ethnicity, education, and cigarette smoking variables such as use status (never, current, former), smoking intensity measured by the average number of cigarettes smoked per day (CPD), and years since quitting (YSQ) for former smokers. People who were not eligible for mortality follow-up (27,215) and missing information for smoking status (20,434), race/ethnicity (583), or education (2,862) were excluded. A total of 611,275 participants aged 30–84 were included in the race/ethnicity analysis, and 608,996 participants in the education analysis. Individuals were followed for all-cause mortality for 10-years of follow-up from their survey interview, and a total of 27,976 and 26,662 deaths were observed for males and females in the race/ethnicity analysis and a total of 27,813 and 26,523 deaths for males and females in the education analysis, respectively.

Ever-smokers were defined as individuals who smoked 100 cigarettes or more during their lifetime. They were classified as current smokers if they reported smoking every day or some days and former smokers if they reported not smoking at all at the time of the survey interview. Former smokers were categorized into two groups: recent quitters (i.e., those who quit smoking less than two years ago) versus long-term quitters (i.e., those who quit smoking 2 or more years ago). In the main analysis, current smokers and recent quitters were combined into a single group because recent quitters are: 1) much more likely to relapse back to smoking,28–30 and 2) more likely to have quit as a response to smoking-related disease or disability.31–33 Thus, their mortality rate is inherently higher than that of an established former smoker. Age was categorized into five groups: 30–44, 45–54, 55–64, 65–74, and 75–84. Race/ethnicity was categorized into three groups: non-Hispanic White (NHW), non-Hispanic Black (NHB), and Hispanic. Non-Hispanic individuals of other racial backgrounds were excluded from the race/ethnicity-specific analyses due to insufficient sample sizes. Education was categorized into five groups: ≤8th grade, 9–11th grade, high school graduate (HSG) or General Education Development certificate (GED), some college, and at least a college degree.

Statistical Analysis

RRs of all-cause mortality, measured as Cox hazard ratios, and their 95% confidence intervals (CIs) were estimated using Cox proportional hazards models, accounting for the complex sample design of the NHIS using Taylor series linearization. Gender-specific RRs were estimated for current smokers or recent quitters vs. never smokers and long-term quitters vs. never smokers. Separate stratified analyses were conducted by age and race/ethnicity and by age and educational attainment as a socioeconomic status indicator.

First, RRs for all-cause mortality were estimated for current smokers or recent quitters and long-term quitters vs. never smokers in the US population. Further analyses were then conducted splitting the current smokers or recent quitters by smoking intensity (<10, 10–19, 20, 21+ cigarettes smoked per day [CPD] or 1–5, 6–15, 16–25, 26+ CPD) and the long-term quitters by the length of abstinence (2 to <5, 5 to <15, 15+ years since quitting [YSQ]). Analyses were conducted using the SURVEYPHREG procedure in SAS software, version 9.4.

Results

Table 1 summarizes the demographic characteristics of the study sample, stratified by smoking status: never smoker, current smoker, former smoker (YSQ < 2, 2 ≤ YSQ < 5, 5 ≤ YSQ < 15, YSQ ≥ 15). About 40% or above were in the age group 30–44 in all smoking categories, except for former users who quit smoking 15 or more years ago. This group of former users was considerably older than those in other smoking status groups. A higher percentage of females than males were never smokers. The proportion of NHB individuals was higher in current versus former smokers. The proportion of individuals with at least a college degree was lowest in current smokers and highest in never smokers, increasing with a longer length of smoking cessation among former smokers. Conversely, the percentage of individuals with 9–11th grade education or HSG or GED was highest in current smokers and lowest in never smokers, with decreasing proportion by more years since quitting in former users.

Table 1.

Demographic characteristics in different cigarette use groups in 1987–2018 NHIS data.

Former Smoker
Characteristic Never Smoker (N=330,500) Current Smoker (N=133,448) YSQ < 2 (N=4,337) 2 ≤ YSQ < 5 (N=16,895) 5 ≤ YSQ < 15 (N=44,674) YSQ ≥ 15 (N=82,004)
% (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI) % (95% CI)
Age group (years)
 30–44 41.3 (41.0–41.6) 44.6 (44.3–45.0) 46.9 (45.0–48.7) 43.0 (42.0–43.9) 38.0 (37.5–38.6) 10.0 (9.7–10.3)
 45–54 23.1 (22.9–23.3) 27.2 (26.9–27.5) 21.3 (19.6–23.0) 22.3 (21.5–23.1) 22.4 (21.9–22.9) 20.1 (19.8–20.5)
 55–64 16.9 (16.7–17.1) 17.6 (17.4–17.9) 17.8 (16.4–19.3) 18.6 (17.9–19.3) 19.6 (19.2–20.1) 26.3 (25.9–26.7)
 65–74 11.3 (11.1–11.4) 8.0 (7.9–8.2) 10.9 (9.9–12.1) 11.9 (11.3–12.5) 14.2 (13.8–14.6) 25.9 (25.6–26.3)
 75–84 7.4 (7.3–7.5) 2.5 (2.4–2.6) 3.1 (2.5–3.8) 4.3 (3.9–4.6) 5.7 (5.5–6.0) 17.6 (17.2–17.9)
Sex
 Male 41.5 (41.3–41.8) 53.0 (52.6–53.3) 55.2 (53.4–57.0) 54.5 (53.5–55.5) 55.1 (54.5–55.7) 58.4 (57.9–58.8)
 Female 58.5 (58.2–58.7) 47.0 (46.7–47.4) 44.8 (43.0–46.6) 45.5 (44.5–46.5) 44.9 (44.3–45.5) 41.6 (41.2–42.1)
Race/ethnicity
 Non-Hispanic White 67.2 (66.7–67.6) 76.1 (75.6–76.5) 79.8 (78.3–81.1) 77.4 (76.6–78.3) 79.1 (78.6–79.7) 84.2 (83.8–84.6)
 Non-Hispanic Black 12.1 (11.8–12.5) 12.3 (11.9–12.7) 9.1 (8.2–10.2) 9.0 (8.5–9.5) 8.0 (7.7–8.4) 6.5 (6.2–6.7)
 Hispanic 6.5 (6.3–6.7) 3.5 (3.3–3.7) 3.2 (2.5–4.1) 3.9 (3.5–4.3) 3.7 (3.5–4.0) 2.7 (2.5–2.9)
 Non-Hispanic Other 14.2 (13.9–14.6) 8.1 (7.9–8.4) 7.9 (7.0–8.9) 9.7 (9.1–10.3) 9.1 (8.8–9.5) 6.7 (6.4–6.9)
Education
 ≤ 8th grade 6.8 (6.6–7.0) 6.3 (6.1–6.5) 7.4 (6.6–8.3) 5.9 (5.5–6.4) 5.9 (5.7–6.2) 6.3 (6.1–6.5)
 9–11th grade 5.6 (5.5–5.7) 12.8 (12.6–13.0) 9.8 (8.8–10.9) 8.1 (7.6–8.6) 7.6 (7.3–7.9) 6.9 (6.7–7.1)
 HSG or GED 27.2 (27.0–27.5) 40.2 (39.9–40.6) 36.4 (34.7–38.2) 33.9 (33.0–34.8) 32.1 (31.5–32.6) 29.5 (29.1–29.9)
 Some college 25.1 (24.9–25.3) 27.7 (27.4–28.1) 26.6 (24.9–28.3) 29.2 (28.4–30.1) 29.0 (28.5–29.6) 27.6 (27.2–28.0)
 At least a college degree 35.3 (34.9–35.7) 12.9 (12.6–13.2) 19.8 (18.3–21.4) 22.9 (22.0–23.7) 25.4 (24.8–25.9) 29.7 (29.2–30.2)
Cigarettes per daya
 < 6 22.0 (21.7–22.3) 15.9 (14.5–17.4) 14.7 (13.3–16.2) 15.3 (14.4–16.1) 19.8 (19.1–20.6)
 6–15 32.4 (32.1–32.8) 27.3 (25.6–29.0) 26.7 (24.9–28.5) 25.5 (24.5–26.5) 26.4 (25.6–27.2)
 16–25 31.7 (31.3–32.0) 33.6 (31.8–35.4) 32.7 (30.9–34.6) 33.4 (32.3–34.4) 31.7 (30.9–32.6)
 26+ 13.9 (13.6–14.2) 23.3 (21.8–24.8) 25.9 (24.4–27.5) 25.8 (24.9–26.8) 22.1 (21.4–22.8)
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Note: YSQ=years since quitting; CI=confidence interval; HSG=high school graduate; GED=General Education Development certificate

a

Average number of cigarettes smoked per day (current smokers) and average number of cigarettes usually smoked per day when they smoked regularly (former smokers). Information for average cigarettes per day for former smokers was only available in five years of survey: 1987, 1988, 1990, 2005, 2010.

For both genders, all-cause mortality RRs for current smokers or recent quitters were highest in NHW individuals, followed by NHB individuals and then Hispanic individuals in most age groups (Table 2 & Figure 1). Mortality RRs increased until ages 55–64, then declined in NHW males. Conversely, RRs were relatively constant between ages 30–54, then increased and peaked at age 65–74, then decreased in ages 75–84 in NHB males. RRs were highest at ages 65–74, the second highest at ages 30–44, and relatively constant at the other age groups in Hispanic males. RRs in NHW females showed similar age patterns as NHS males. The RRs in NHB females are similar across all age groups. Among current smokers or recent quitters, the difference in RRs between females and males varied by age and race/ethnicity, although the differences were not statistically significant in some categories, in which 95% CIs for males and females were overlapping. In NHW individuals, the RRs were lower in males than females for the age groups 30–44 and 75–84. The RRs were higher in NHB males than females at ages 65–74. RRs in Hispanic individuals do not show a clear pattern by gender. The RRs for long-term quitters vs. never smokers increased until ages 65–74 and then declined in both NHW males and females, but there were no clear age patterns in NHB and Hispanic populations (Table 2 & Appendix Figure 1).

Table 2.

All-cause mortality Relative risks for current smoker or recent quitters and long-term quitters by race/ethnicity.

Males Females
Current Smoker or Recent Quittera Long-term Quitterb Current Smoker or Recent Quittera Long-term Quitterb Current Smoker or Recent Quittera Long-term Quitterb
RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI) Males/Females RR Ratio Males/Females RR Ratio
All races
 30–44 2.14 (1.86–2.46) 1.16 (0.92–1.44) 2.28 (1.93–2.70) 1.09 (0.81–1.45) 0.94 1.06
 45–54 2.66 (2.37–2.97) 1.19 (1.02–1.37) 2.60 (2.31–2.93) 1.16 (0.98–1.37) 1.02 1.03
 55–64 3.38 (3.09–3.69) 1.41 (1.28–1.56) 3.20 (2.91–3.51) 1.44 (1.29–1.61) 1.06 0.98
 65–74 3.35 (3.12–3.60) 1.51 (1.41–1.62) 2.96 (2.77–3.17) 1.55 (1.45–1.67) 1.13 0.97
 75–84 2.36 (2.20–2.53) 1.40 (1.32–1.47) 2.72 (2.57–2.88) 1.47 (1.40–1.55) 0.87 0.95
Non-Hispanic White
 30–44 2.29 (1.92–2.74) 1.18 (0.87–1.58) 3.10 (2.46–3.91) 1.34 (0.93–1.93) 0.74 0.88
 45–54 3.15 (2.74–3.63) 1.17 (0.96–1.41) 2.81 (2.40–3.29) 1.21 (0.98–1.49) 1.12 0.97
 55–64 3.75 (3.38–4.16) 1.51 (1.33–1.70) 3.60 (3.21–4.04) 1.51 (1.32–1.72) 1.04 1.00
 65–74 3.45 (3.16–3.76) 1.51 (1.39–1.64) 3.22 (2.98–3.48) 1.62 (1.50–1.76) 1.07 0.93
 75–84 2.43 (2.24–2.64) 1.41 (1.32–1.50) 2.82 (2.65–3.00) 1.51 (1.43–1.59) 0.86 0.93
Non-Hispanic Black
 30–44 1.99 (1.43–2.77) 1.44 (0.86–2.42) 2.01 (1.40–2.87) 1.48 (0.66–3.34) 0.99 0.97
 45–54 1.84 (1.47–2.30) 1.51 (1.08–2.09) 2.31 (1.83–2.91) 1.21 (0.87–1.68) 0.80 1.25
 55–64 2.76 (2.25–3.40) 1.48 (1.18–1.86) 2.26 (1.89–2.71) 1.51 (1.17–1.95) 1.22 0.98
 65–74 3.08 (2.61–3.63) 1.60 (1.33–1.93) 2.30 (1.96–2.69) 1.39 (1.17–1.66) 1.34 1.15
 75–84 2.01 (1.68–2.42) 1.21 (1.04–1.42) 1.99 (1.67–2.37) 1.10 (0.96–1.28) 1.01 1.10
Hispanic
 30–44 2.26 (1.58–3.24) 1.51 (0.92–2.48) 1.17 (0.72–1.88) 1.10 (0.54–2.24) 1.93 1.37
 45–54 1.73 (1.25–2.37) 1.34 (0.95–1.89) 1.81 (1.20–2.75) 1.35 (0.79–2.30) 0.96 0.99
 55–64 1.84 (1.36–2.49) 0.98 (0.72–1.35) 2.19 (1.52–3.15) 1.42 (0.97–2.07) 0.84 0.69
 65–74 2.58 (2.03–3.27) 1.52 (1.20–1.92) 1.56 (1.15–2.11) 1.42 (1.08–1.87) 1.65 1.07
 75–84 1.65 (1.27–2.15) 1.17 (0.94–1.46) 2.14 (1.59–2.89) 1.41 (1.12–1.78) 0.77 0.83
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Note: Data for all-cause mortality within 10-year follow-up period ranges from 1987–2019. RR=Relative risk (i.e., hazard ratio) estimated from the Cox proportional hazard regression analysis; CI=confidence interval.

a

Recent quitters are individuals who quit smoking less than 2 years ago.

b

Long-term quitters are individuals who have been quitting smoking longer than or equal to 2 years.

Figure 1.

Figure 1.

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Relative risks of all-cause mortality for current smokers or recent quitters vs. never smokers by age group and race/ethnicity (top panels), and by age group and education attainment level (bottom panels). These relative risks are hazard ratios estimated from the Cox proportional hazard regression analysis stratified by gender (Males-left panels, Females-right panels). Recent quitters are individuals who quit smoking less than 2 years ago. All=all race/ethnicity combined; HSG=high school graduate; GED=General Education Development certification.

Table 3 & Figure 1 show RR estimates by educational attainment. The results show in general a positive gradient in all-cause mortality RRs for current smokers or recent quitters by educational attainment level among males aged 30–44, 45–54, and 55–64. RRs were generally higher among lower education groups than higher education groups in males aged 65–74, but they were lowest in the ≤8th grade and some college groups, and highest in the at least a college degree group in males aged 75–84. The RRs patterns by educational attainment level in females differed from those in males. The RRs in females with at least a college degree were lowest in ages 30–44 but became highest in ages 65–74 and 75–84. In addition, there was a clear positive gradient in all-cause mortality RRs by educational attainment level among females aged 65–74 and 75–84. The age pattern in RRs varied depending on educational attainment, but in general, it increased by age and peaked either at ages 55–64 or 65–74 and then declined in males. In contrast, RRs increased by age among females with at least a college degree, but the age pattern is less clear in the other education groups in females. Gender differences in RRs for current smokers or recent quitters varied by age and educational attainment, although differences were not statistically significant in some categories (95% CIs were overlapping). The RRs for those with at least a college degree were higher in males than females in ages below 65, but lower after age 65; they were lower in males than females with some college education at ages 30–44 and 75–84. No clear RR patterns were observed by educational attainment level for long-term quitters vs. never smokers (Table 3 & Appendix Figure 1).

Table 3.

All-cause mortality Relative risks for current smoker or recent quitters and long-term quitters by education.

Males Females
Current Smoker or Recent Quittera Long-term Quitterb Current Smoker or Recent Quittera Long-term Quitterb Current Smoker or Recent Quittera Long-term Quitterb
RR (95% CI) RR (95% CI) RR (95% CI) RR (95% CI) Males/Females RR Ratio Males/Females RR Ratio
≤ 8th grade
 30–44 1.56 (0.98–2.49) 1.15 (0.53–2.47) 1.85 (1.00–3.45) - 0.84 -
 45–54 1.97 (1.31–2.97) 0.88 (0.53–1.48) 2.11 (1.39–3.22) 1.61 (0.78–3.32) 0.93 0.55
 55–64 2.52 (1.92–3.30) 1.47 (1.08–2.01) 3.03 (2.34–3.93) 1.71 (1.17–2.50) 0.83 0.86
 65–74 3.22 (2.69–3.84) 1.70 (1.42–2.03) 2.46 (2.04–2.97) 1.80 (1.47–2.22) 1.31 0.94
 75–84 2.00 (1.74–2.29) 1.28 (1.15–1.44) 2.40 (2.07–2.77) 1.43 (1.26–1.63) 0.83 0.90
9–11th grade
 30–44 1.69 (1.10–2.59) 0.47 (0.20–1.10) 2.30 (1.46–3.63) 2.25 (0.90–5.63) 0.73 0.21
 45–54 1.39 (1.02–1.89) 0.66 (0.41–1.08) 2.69 (1.96–3.70) 1.70 (0.97–2.98) 0.52 0.39
 55–64 2.44 (1.85–3.21) 1.34 (0.97–1.84) 2.47 (1.94–3.14) 1.69 (1.24–2.31) 0.99 0.79
 65–74 2.95 (2.39–3.64) 1.58 (1.28–1.95) 2.71 (2.30–3.19) 1.74 (1.45–2.09) 1.09 0.91
 75–84 2.41 (1.95–2.97) 1.69 (1.42–2.02) 2.51 (2.16–2.92) 1.66 (1.46–1.88) 0.96 1.02
HSG or GED
 30–44 1.78 (1.40–2.25) 0.91 (0.63–1.33) 1.76 (1.33–2.33) 1.04 (0.62–1.75) 1.01 0.88
 45–54 2.02 (1.68–2.43) 1.00 (0.79–1.28) 1.89 (1.54–2.32) 1.06 (0.80–1.42) 1.07 0.94
 55–64 2.63 (2.25–3.07) 1.24 (1.04–1.48) 2.85 (2.45–3.30) 1.52 (1.25–1.83) 0.92 0.82
 65–74 2.97 (2.60–3.40) 1.49 (1.30–1.70) 2.76 (2.46–3.09) 1.60 (1.43–1.79) 1.08 0.93
 75–84 2.34 (2.05–2.67) 1.41 (1.28–1.54) 2.60 (2.38–2.85) 1.62 (1.49–1.75) 0.90 0.87
Some college
 30–44 1.65 (1.27–2.13) 1.21 (0.83–1.78) 2.44 (1.83–3.27) 1.18 (0.74–1.86) 0.68 1.03
 45–54 2.62 (2.12–3.24) 1.29 (0.99–1.70) 2.57 (2.07–3.18) 1.01 (0.74–1.37) 1.02 1.28
 55–64 3.04 (2.55–3.64) 1.25 (1.02–1.53) 3.15 (2.59–3.83) 1.33 (1.07–1.65) 0.97 0.94
 65–74 2.67 (2.28–3.12) 1.26 (1.08–1.46) 2.73 (2.35–3.18) 1.41 (1.20–1.65) 0.98 0.89
 75–84 2.00 (1.66–2.41) 1.28 (1.13–1.45) 2.96 (2.57–3.40) 1.42 (1.27–1.60) 0.68 0.90
At least a college degree
 30–44 2.09 (1.44–3.03) 1.52 (0.93–2.48) 1.52 (0.91–2.54) 1.02 (0.57–1.80) 1.38 1.49
 45–54 3.06 (2.30–4.07) 1.51 (1.12–2.04) 2.32 (1.58–3.40) 1.32 (0.92–1.89) 1.32 1.14
 55–64 3.54 (2.79–4.50) 1.42 (1.16–1.74) 2.73 (2.07–3.60) 1.38 (1.08–1.77) 1.30 1.03
 65–74 3.06 (2.57–3.65) 1.33 (1.14–1.54) 3.78 (3.01–4.75) 1.58 (1.25–2.00) 0.81 0.84
 75–84 2.56 (2.08–3.15) 1.27 (1.12–1.44) 3.76 (3.11–4.55) 1.40 (1.20–1.62) 0.68 0.91
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Note: Data for all-cause mortality within 10-year follow-up period ranges from 1987–2019. RR= Relative risk (i.e., hazard ratio) estimated from the Cox proportional hazard regression analysis; CI=confidence interval; HSG=high school graduate; GED=General Education Development certificate.

a

Recent quitters are individuals who quit smoking less than 2 years ago.

b

Long-term quitters are individuals who have been quitting smoking longer than or equal to 2 years.

Age-specific patterns of all-cause mortality RRs by smoking intensity groups (1–5, 6–15, 16–25, and 26+ CPD) for each race/ethnic subgroup are shown in Appendix Tables 1 & 5, Appendix Figures 2 & 6. RRs increased with higher CPD levels for both males and females. However, some estimates, especially among individuals with 26+ CPD, had large 95% CIs due to small sample sizes. Among long-term quitters, RRs tended to decrease by increasing YSQ across racial/ethnic groups and gender. RRs declined to the level of never smokers for individuals with 15 or more YSQ, especially among those who were younger than 65 years (Appendix Tables 2 & 6, Appendix Figures 3 & 7).

Age-specific all-cause mortality RRs by smoking intensity for each education group are shown in Appendix Tables 3 & 7, Appendix Figures 4 & 8. Mortality RRs increased by increasing smoking intensity across education groups in general. RRs for long-term quitters decreased by increasing YSQ across all education groups (Appendix Tables 4 & 8, Appendix Figures 5 & 9).

Overall all-cause mortality RR estimates adjusted by age for each gender, race/ethnic group, and education group are also shown in the supplement (Appendix Tables 9–13).

Discussion

This study estimated all-cause mortality RRs associated with smoking in the US stratified by age, gender, race/ethnicity, and educational attainment. Among current smokers or recent quitters who quit smoking less than two years ago, all-cause mortality RRs compared to never smokers were generally higher in NHW individuals than in NHB or Hispanic individuals. RRs also increased by the level of educational attainment, with higher RRs seen for higher education groups. In the analyses further stratified by smoking intensity among current smokers or recent quitters, RRs generally increased in both genders with higher levels of smoking intensity across racial/ethnic and education groups. Gender differences in RRs for current smokers or recent quitters varied by age, race/ethnicity, and educational attainment. Interestingly, females tended to have higher RRs than males among individuals above age 65 who had some college education or at least a college degree. The all-cause mortality RRs for long-term quitters were negatively associated with the length of abstinence from smoking in all racial/ethnic and education groups for both genders, with similar mortality rates compared with never smokers for individuals with 15 years or more of quitting smoking, particularly among those who were younger than 65 years.

Among current smokers or recent quitters, the age pattern of all-cause mortality RRs from smoking varied by gender, race/ethnicity, and educational attainment. In general, RRs increased at younger ages, peaking at age 55–64 or 65–74 and then declining, although differences were not statistically significant for some age categories (overlapping 95% CIs). However, there were some noticeable departures from this pattern. For instance, the RRs in females with at least a college degree kept increasing by age and became highest at ages 65–74 and 75–84. Moreover, the RRs for long-term quitters were lower in younger ages, 30–44 & 45–54, compared to the older age groups in both NHW males and females, but there was no clear age pattern among other racial/ethnic or education groups.

The results are generally consistent with previous literature. A pooled cohort analysis based on follow-up time from 2000–2010 in five U.S. cohort studies showed that all-cause mortality is much higher in males than females, but the all-cause mortality RRs between current and never smokers is similar in both genders within the same age stratum; 2.92 (95% CI, 2.69–3.18), 3.00 (95% CI, 2.89–3.13), and 2.36 (95% CI, 2.24–2.48) among males and 2.64 (95% CI, 2.43–2.86), 2.87 (95% CI, 2.76–2.99), and 2.47 (95% CI, 2.37–2.58) among females for ages 55–64, 65–74, and 75+, respectively.1 A recent study also based on the 1997–2018 NHIS adults aged 25–84 who were followed up for mortality through 2019 showed that all-cause mortality RRs for current vs. never smokers were 2.91 (95% CI, 2.79–3.04), 2.21 (95% CI, 2.02–2.41), and 1.99 (95% CI, 1.77–2.24) among NHW, NHB, and Hispanic males and 3.15 (95% CI, 3.03–3.28), 2.20 (95% CI, 2.02–2.41), and 2.05 (95% CI, 1.80–2.33) among NHW, NHB, and Hispanic females.34 The RRs by age and gender for all races/ethnicity combined in the present study are consistent with these studies, although this present analysis combined current smokers or recent quitters in a single group. Another study using pooled 1997–2003 NHIS adult samples aged 50–84 linked to the National Death Index through 2006 found an all-cause mortality hazard ratio for NHB males relative to NHW males of 1.49 after adjusting for age at baseline, body mass index (BMI), and survey year. This estimate decreased to 1.39 after additional adjustment for smoking status,8 and decreased further to 1.15 when marital status, education, family income, and region were also adjusted. These results suggest that smoking and socioeconomic status are important determinants of racial disparities in overall mortality. A study by Inoue-Choi et al.19 showed that the RR of all-cause mortality increases by the number of CPD up to the hazard ratio of 2.94 (95% CI, 2.75–3.14) in current daily smokers who smoked more than 30 CPD. This is slightly lower than this study’s age-adjusted hazard ratio for current smokers or recent quitters who smoked more than 26 CPD (Hazard ratio: 3.57 (95% CI, 3.36–3.78) and 3.99 (95% CI, 3.68–4.32) for males and females, respectively).

Interestingly the study findings suggest lower mortality RRs from smoking for those with lower education, which have higher smoking prevalence than those with higher education.6,15,17,35–40 This apparently counterintuitive finding is likely due to greater competing mortality risks from non-smoking sources among those with lower education, which affect both never, current and former smokers.41–43 In contrast, those with higher educational levels have fewer comorbidities or competing causes of risk, which likely then results in higher RRs when comparing people who smoke to never smoked. This phenomenon, in addition to differences in other relevant factors such as smoking intensity and duration,34 might also help explain higher mortality RRs among NHW vs. NHB individuals, given the higher overall mortality rates in the Black vs. White populations in the US.44–47

Smoking-related all-cause mortality RRs vary greatly by age, gender, race/ethnicity, and educational attainment. It is thus crucial to characterize the differences in RRs between key population subgroups and to consider these when assessing the impact of tobacco control interventions on smoking-related health outcomes and disparities.48–53 Not doing so can result in over/underestimation of the burden and impact of interventions in specific subgroups and mischaracterize how different interventions could affect smoking-related health disparities.

This study used nationally representative data of the US non-institutionalized population covering mortality data with a large sample from 1987–2019. This data provided detailed smoking information on use status, intensity, and years since quitting as well as the other demographic variables, which allowed extensive analyses of all-cause mortality for people with different smoking experiences in various combinations of age, gender, and race/ethnicity or educational attainment. The estimates from this study can be readily incorporated into smoking and mortality simulation models to assess the impact of smoking tobacco control interventions on these subgroups.1,48–50 The large differences in mortality patterns between subgroups found in this study suggest that group-specific estimates are essential for models to properly capture smoking risks and the impact of interventions in different sociodemographic groups.

Previously, all-cause mortality RRs for current (by smoking intensity) and former smokers vs. never smokers by age and gender have been used as input parameters for the development of the Cancer Intervention and Surveillance Modeling Network-Lung Working Group (CISNET-LWG) Smoking History Generator (SHG) for US population. The SHG is a micro-simulation model that generates detailed smoking histories by gender and birth cohort for the US population, which are inputs for multiple simulation models.21,22,54–58 The new estimates in this study will facilitate the extension of the SHG and other models to simulate racial/ethnic- and educational attainment level-specific individual smoking histories. Combined with the smoking parameters provided by Meza et al.5 and Cao et al,6 these all-cause mortality RRs by race/ethnicity and education will offer relevant data to inform modeling, evaluation, and surveillance tools, which can evaluate the impact of specific tobacco control interventions on smoking-related health disparities.50,59,60

Limitations

This study has some limitations. Although data were combined from multiple NHIS surveys, sample sizes in some subgroups were relatively small, resulting in wide CIs. This was particularly true when estimating RRs by smoking intensity among current smokers or recent quitters and by the length of abstinence among long-term quitters. The mortality analysis did not account for underlying tobacco-related and non-tobacco-related comorbidities and other health risks, which may partially explain the lower mortality RRs from smoking among lower education or NHB individuals compared to higher education or NHW individuals. Plans to extend this analysis using the Tobacco Longitudinal Mortality Study (TLMS) are underway; this links the 1993–2019 Tobacco Use Supplement to the Current Population Survey and the National Death Index and has considerably larger sample sizes. Using the TLMS data, additional covariates can be accounted for when investigating differences in smoking-related mortality by race/ethnicity or education. Cox proportional hazard regression analyses did not adjust for any other relevant smoking determinants, such as income, US region, BMI, health insurance coverage, and education (for the race/ethnicity analysis) and race/ethnicity (for the education analysis).8–11,19,61 This is because simulation models usually do not include all of these covariates, so unadjusted estimates for specific subgroups are required to inform such models.1,22,49,50,52–54,62 Another limitation is that all Hispanic individuals are combined into a single group. Some studies have suggested that smoking patterns are different between Hispanic subgroups63,64 as well as between other US subpopulations (e.g, Asian subpopulations and American Indian and Alaskan Native subgroups). Future analyses using larger samples (TLMS) should attempt to estimate RRs for these subpopulations.

Conclusions

All-cause mortality RRs associated with smoking vary by age, gender, race/ethnicity, and educational attainment. RRs are lower in Hispanic and NHB individuals compared to NHW individuals and increase by level of educational attainment. The resulting estimates can be incorporated into simulation models for smoking and related health outcomes to evaluate the potential differential impact of tobacco control policies on different sociodemographic groups and on smoking-related health disparities.

Supplementary Material

Supplement

Conflict of interest statement:

This project was funded through National Cancer Institute (NCI) grants U01CA199284 & U01CA253858 and the Intramural Research Program of the National Cancer Institute. The authors also acknowledge support from NCI grant U54CA229974. The study sponsor had no role in the study design; collection, analysis, and interpretation of data; writing the report; or the decision to submit the report for publication.

Footnotes

Financial disclosure: No financial disclosures were reported by the authors of this paper.

References

  • 1.U.S. Department of Health and Human Services. The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2014. Accessed December 25, 2021. https://www.ncbi.nlm.nih.gov/books/NBK179276/pdf/Bookshelf_NBK179276.pdf [Google Scholar]
  • 2.U.S. National Cancer Institute. A Socioecological Approach to Addressing Tobacco-Related Health Disparities. National Cancer Institute Tobacco Control Monograph 22. NIH Publication No. 17-CA-8035A. Bethesda, MD: U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute;. Published online 2017. [Google Scholar]
  • 3.Current Cigarette Smoking Among Adults — United States, 2005–2014. Accessed December 25, 2021. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6444a2.htm
  • 4.Kanjilal S, Gregg EW, Cheng YJ, et al. Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adults, 1971–2002. Arch Intern Med. 2006;166(21):2348–2355. doi: 10.1001/archinte.166.21.2348 [DOI] [PubMed] [Google Scholar]
  • 5.Meza R, Cao P, Jeon J, et al. Patterns of Birth Cohort-Specific Smoking Histories by Race and Ethnicity in the US. Am J Prev Med. In Press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Cao P, Jeon J, Tam J, et al. Smoking Disparities by Level of Educational Attainment and Birth Cohort in the US. Am J Prev Med. In Press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Jeon J, Cao P, Fleischer NL, et al. Birth Cohort-Specific Smoking Patterns by Family Income in the US. Am J Prev Med. In Press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Ho JY, Elo IT. The Contribution of Smoking to Black-White Differences in U.S. Mortality. Demography. 2013;50(2):545–568. doi: 10.1007/s13524-012-0159-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Lariscy JT, Hummer RA, Hayward MD. Hispanic older adult mortality in the United States: new estimates and an assessment of factors shaping the Hispanic paradox. Demography. 2015;52(1):1–14. doi: 10.1007/s13524-014-0357-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Lariscy JT, Hummer RA, Rogers RG. Cigarette Smoking and All-Cause and Cause-Specific Adult Mortality in the United States. Demography. 2018;55(5):1855–1885. doi: 10.1007/s13524-018-0707-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Fenelon A, Preston SH. Estimating smoking-attributable mortality in the United States. Demography. 2012;49(3):797–818. doi: 10.1007/s13524-012-0108-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Holford TR, Levy Phd DT, Meza R. Comparison of Smoking History Patterns Among African American and White Cohorts in the United States Born 1890 to 1990. Nicotine Tob Res. 2016;18 Suppl 1((Suppl 1)):S16–29. doi: 10.1093/ntr/ntv274 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Ho JY, Fenelon A. The Contribution of Smoking to Educational Gradients in U.S. Life Expectancy. J Health Soc Behav. 2015;56(3):307–322. doi: 10.1177/0022146515592731 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Tobacco-Related Mortality | CDC. Accessed April 3, 2022. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/health_effects/tobacco_related_mortality/index.htm
  • 15.Pampel FC. The Persistence of Educational Disparities in Smoking. Soc Probl. 2009;56(3):526–542. doi: 10.1525/SP.2009.56.3.526 [DOI] [Google Scholar]
  • 16.Maralani V Understanding the links between education and smoking. Soc Sci Res. 2014;48:20–34. doi: 10.1016/j.ssresearch.2014.05.007 [DOI] [PubMed] [Google Scholar]
  • 17.Zhu BP, Giovino GA, Mowery PD, Eriksen MP. The relationship between cigarette smoking and education revisited: implications for categorizing persons’ educational status. Am J Public Health. 1996;86(11):1582–1589. doi: 10.2105/AJPH.86.11.1582 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Rostron BL, Lynn BCD, Chang CM, Ren C, Salazar E, Ambrose BK. The contribution of smoking-attributable mortality to differences in mortality and life expectancy among US African-American and white adults, 2000–2019. Demogr Res. 2022;46:905–918. doi: 10.4054/DEMRES.2022.46.31 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Inoue-Choi M, Christensen CH, Rostron BL, et al. Dose-Response Association of Low-Intensity and Nondaily Smoking With Mortality in the United States. JAMA Netw Open. 2020;3(6):e206436. doi: 10.1001/jamanetworkopen.2020.6436 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Moolgavkar SH, Holford TR, Levy DT, et al. Impact of reduced tobacco smoking on lung cancer mortality in the United States during 1975–2000. J Natl Cancer Inst. 2012;104(7):541–548. doi: 10.1093/jnci/djs136 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Tam J, Levy DT, Jeon J, et al. Projecting the effects of tobacco control policies in the USA through microsimulation: a study protocol. BMJ Open. 2018;8(3):e019169. doi: 10.1136/bmjopen-2017-019169 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Jeon J, Holford TR, Levy DT, et al. Smoking and Lung Cancer Mortality in the United States From 2015 to 2065: A Comparative Modeling Approach. Ann Intern Med. 2018;169(10):684–693. doi: 10.7326/M18-1250 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Rosenberg MA, Feuer EJ, Yu B, et al. Chapter 3: Cohort life tables by smoking status, removing lung cancer as a cause of death. Risk Anal. 2012;32 Suppl 1(Suppl 1). doi: 10.1111/J.1539-6924.2011.01662.X [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Thun MJ, Carter BD, Feskanich D, et al. 50-year trends in smoking-related mortality in the United States. N Engl J Med. 2013;368(4):351–364. doi: 10.1056/NEJMSA1211127 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.NCHS Data Linkage - Mortality Data. Accessed April 3, 2022. https://www.cdc.gov/nchs/data-linkage/mortality.htm?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fnchs%2Fdata_access%2Fdata_linkage%2Fmortality.htm
  • 26.Blewett L, Rivera Drew J, King M, Williams K, Ponte N, Convey P. PUMS Health Surveys: National Health Interview Survey, Version 7.2 [dataset]. Minneapolis, MN: IPUMS, 2022. Accessed December 4, 2022. 10.18128/D070.V7.2 [DOI] [Google Scholar]
  • 27.National Center for Health Statistics. The Linkage of National Center for Health Statistics Survey Data to the National Death Index — 2019 Linked Mortality File (LMF): Linkage Methodology and Analytic Considerations, June 2022. Accessed October 16, 2022. https://www.cdc.gov/nchs/data/datalinkage/2019NDI-Linkage-Methods-and-Analytic-Considerations-508.pdf
  • 28.Herd N, Borland R, Hyland A. Predictors of smoking relapse by duration of abstinence: findings from the International Tobacco Control (ITC) Four Country Survey. Addiction. 2009;104(12):2088–2099. doi: 10.1111/j.1360-0443.2009.02732.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Hughes JR, Peters EN, Naud S. Relapse to smoking after 1 year of abstinence: a meta-analysis. Addict Behav. 2008;33(12):1516–1520. doi: 10.1016/j.addbeh.2008.05.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Krall EA, Garvey AJ, Garcia RI. Smoking relapse after 2 years of abstinence: findings from the VA Normative Aging Study. Nicotine Tob Res. 2002;4(1):95–100. doi: 10.1080/14622200110098428 [DOI] [PubMed] [Google Scholar]
  • 31.Kalkhoran S, Kruse GR, Chang Y, Rigotti NA. Smoking-Cessation Efforts by US Adult Smokers with Medical Comorbidities. Am J Med. 2018;131(3):318.e1–318.e8. doi: 10.1016/J.AMJMED.2017.09.025 [DOI] [PubMed] [Google Scholar]
  • 32.Fry JS, Lee PN, Forey BA, Coombs KJ. How rapidly does the excess risk of lung cancer decline following quitting smoking? A quantitative review using the negative exponential model. Regul Toxicol Pharmacol. 2013;67(1):13–26. doi: 10.1016/J.YRTPH.2013.06.001 [DOI] [PubMed] [Google Scholar]
  • 33.Knoke JD, Burns DM, Thun MJ. The change in excess risk of lung cancer attributable to smoking following smoking cessation: an examination of different analytic approaches using CPS-I data. Cancer Causes Control. 2008;19(2):207–219. doi: 10.1007/S10552-007-9086-5 [DOI] [PubMed] [Google Scholar]
  • 34.Thomson B, Emberson J, Lacey B, et al. Association Between Smoking, Smoking Cessation, and Mortality by Race, Ethnicity, and Sex Among US Adults. JAMA Netw Open. 2022;5(10):e2231480. doi: 10.1001/JAMANETWORKOPEN.2022.31480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Current Cigarette Smoking Among Adults in the United States | CDC. Accessed September 22, 2022. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/cig_smoking/index.htm
  • 36.Huisman M, Kunst AE, Mackenbach JP. Inequalities in the prevalence of smoking in the European Union: comparing education and income. Prev Med (Baltim). 2005;40(6):756–764. doi: 10.1016/J.YPMED.2004.09.022 [DOI] [PubMed] [Google Scholar]
  • 37.Curtin F, Morabia A, Bernstein M. Smoking behavior in a Swiss urban population: the role of gender and education. Prev Med (Baltim). 1997;26(5 Pt 1):658–663. doi: 10.1006/PMED.1997.0187 [DOI] [PubMed] [Google Scholar]
  • 38.Giskes K, Kunst AE, Benach J, et al. Trends in smoking behaviour between 1985 and 2000 in nine European countries by education. J Epidemiol Community Health (1978). 2005;59(5):395–401. doi: 10.1136/JECH.2004.025684 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Tomioka K, Kurumatani N, Saeki K. The Association Between Education and Smoking Prevalence, Independent of Occupation: A Nationally Representative Survey in Japan. J Epidemiol. 2020;30(3):136–142. doi: 10.2188/JEA.JE20180195 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Escobedo LG, Peddicord JP. Smoking prevalence in US birth cohorts: the influence of gender and education. Am J Public Health. 1996;86(2):231–236. doi: 10.2105/ajph.86.2.231 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Singh GK, Jemal A. Socioeconomic and Racial/Ethnic Disparities in Cancer Mortality, Incidence, and Survival in the United States, 1950–2014: Over Six Decades of Changing Patterns and Widening Inequalities. J Environ Public Health. 2017;2017:2819372. doi: 10.1155/2017/2819372 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Businelle MS, Kendzor DE, Reitzel LR, et al. Mechanisms linking socioeconomic status to smoking cessation: a structural equation modeling approach. Health Psychol. 2010;29(3):262–273. doi: 10.1037/A0019285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Lewer D, McKee M, Gasparrini A, Reeves A, de Oliveira C. Socioeconomic position and mortality risk of smoking: evidence from the English Longitudinal Study of Ageing (ELSA). Eur J Public Health. 2017;27(6):1068–1073. doi: 10.1093/eurpub/ckx059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Benjamins MR, Silva A, Saiyed NS, de Maio FG. Comparison of All-Cause Mortality Rates and Inequities Between Black and White Populations Across the 30 Most Populous US Cities. JAMA Netw Open. 2021;4(1). doi: 10.1001/JAMANETWORKOPEN.2020.32086 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Haines MR. Ethnic Differences in Demographic Behavior in the United States Has There Been Convergence? Hist Methods. 2010;36(4):157–195. doi: 10.1080/01615440309604818 [DOI] [Google Scholar]
  • 46.Harper S, MacLehose RF, Kaufman JS. Trends in the black-white life expectancy gap among US states, 1990–2009. Health Aff (Millwood). 2014;33(8):1375–1382. doi: 10.1377/HLTHAFF.2013.1273 [DOI] [PubMed] [Google Scholar]
  • 47.Cunningham TJ, Croft JB, Liu Y, Lu H, Eke PI, Giles WH. Vital Signs: Racial Disparities in Age-Specific Mortality Among Blacks or African Americans - United States, 1999–2015. MMWR Morb Mortal Wkly Rep. 2017;66(17):444–456. doi: 10.15585/MMWR.MM6617E1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Issabakhsh M, Meza R, Li Y, Yuan Z, Sanchez-Romero LM, Levy DT. Public health impact of a US menthol cigarette ban on the non-Hispanic black population: a simulation study. Tob Control. Published online 2022. doi: 10.1136/TOBACCOCONTROL-2022-057298 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Mendez D, Le TTT. Consequences of a match made in hell: the harm caused by menthol smoking to the African American population over 1980–2018. Tob Control. Published online September 16, 2021:tobaccocontrol-2021–056748. doi: 10.1136/TOBACCOCONTROL-2021-056748 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Monograph 22 A Socioecological Approach to Addressing Tobacco-Related Health Disparities. In: NIH Publication No. 17-CA-8035A. U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute; 2017. Accessed January 31, 2022. http://cancercontrol.cancer.gov/brp/tcrb/monographs [Google Scholar]
  • 51.Tam J, Jeon J, Thrasher JF, et al. Estimated Prevalence of Smoking and Smoking-Attributable Mortality Associated With Graphic Health Warnings on Cigarette Packages in the US From 2022 to 2100. JAMA Health Forum. 2021;2(9):e212852. doi: 10.1001/JAMAHEALTHFORUM.2021.2852 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Apelberg BJ, Feirman SP, Salazar E, et al. Potential Public Health Effects of Reducing Nicotine Levels in Cigarettes in the United States. N Engl J Med. 2018;378(18):1725–1733. doi: 10.1056/NEJMsr1714617 [DOI] [PubMed] [Google Scholar]
  • 53.Le TT, Mendez D. An estimation of the harm of menthol cigarettes in the United States from 1980 to 2018. Tob Control. 2021;0:1–5. doi: 10.1136/tobaccocontrol-2020-056256 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Jeon J, Meza R, Krapcho M, Clarke LD, Byrne J, Levy DT. Chapter 5: Actual and counterfactual smoking prevalence rates in the U.S. population via microsimulation. Risk Anal. 2012;32 Suppl 1(Suppl 1):S51–68. doi: 10.1111/j.1539-6924.2011.01775.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Holford TR, Meza R, Warner KE, et al. Tobacco control and the reduction in smoking-related premature deaths in the United States, 1964–2012. JAMA. 2014;311(2):164–171. Accessed December 27, 2021. https://pubmed.ncbi.nlm.nih.gov/24399555/ [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Meza R, Jeon J, Toumazis I, et al. Evaluation of the Benefits and Harms of Lung Cancer Screening with Low-Dose Computed Tomography: Modeling Study for the US Preventive Services Task Force. JAMA. 2021;325(10):988–997. doi: 10.1001/jama.2021.1077 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Cao P, Jeon J, Levy DT, et al. Potential Impact of Cessation Interventions at the Point of Lung Cancer Screening on Lung Cancer and Overall Mortality in the United States. J Thorac Oncol. 2020;15(7):1160–1169. doi: 10.1016/j.jtho.2020.02.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Meza R, Cao P, Jeon J, et al. Impact of Joint Lung Cancer Screening and Cessation Interventions Under the New Recommendations of the U.S. Preventive Services Task Force. J Thorac Oncol. 2022;17(1):160–166. doi: 10.1016/j.jtho.2021.09.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.1998 Surgeon General’s Report—Tobacco Use Among U.S. Racial/Ethnic Minority Groups | Smoking & Tobacco Use | CDC. Accessed September 22, 2022. https://www.cdc.gov/tobacco/sgr/1998/index.htm
  • 60.FDA on Track to Take Actions to Address Tobacco-Related Health Disparities | FDA. Accessed September 22, 2022. https://www.fda.gov/news-events/fda-voices/fda-track-take-actions-address-tobacco-related-health-disparities
  • 61.Christensen CH, Rostron B, Cosgrove C, et al. Association of Cigarette, Cigar, and Pipe Use With Mortality Risk in the US Population. JAMA Intern Med. 2018;178(4):469–476. doi: 10.1001/jamainternmed.2017.8625 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Levy DT, Tam J, Sanchez-Romero LM, et al. Public health implications of vaping in the USA: the smoking and vaping simulation model. Popul Health Metr. 2021;19(1):19. doi: 10.1186/s12963-021-00250-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Escobedo LG, Remington PL. Birth cohort analysis of prevalence of cigarette smoking among Hispanics in the United States. JAMA. 1989;261(1):66–69. doi: 10.1001/jama.1989.03420010076036 [DOI] [PubMed] [Google Scholar]
  • 64.Escobedo L, Remington P, Anda R. Long-term secular trends in initiation of cigarette smoking among Hispanics in the United States. Public Health Rep. 1989;104(6):583–587. [PMC free article] [PubMed] [Google Scholar]

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