Nicotine Use and DSM-IV Nicotine Dependence in the United States, 2001–2002 and 2012–2013
Publication: American Journal of Psychiatry
Abstract
Objective:
Nationally representative data on changes in 12-month prevalences of nicotine use, DSM-IV nicotine dependence, and DSM-IV nicotine dependence among users were analyzed to test the “hardening hypothesis,” which proposes that declines in nicotine use resulting from population-level control measures leave a growing proportion of highly dependent users.
Methods:
Data were derived from two nationally representative surveys of U.S. adults: the 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, N=43,093) and the 2012–2013 NESARC-III (N=36,309). Weighted estimates of nicotine use, DSM-IV nicotine dependence, and an approximation of the Fagerström Test for Nicotine Dependence were compared for the 2001–2002 NESARC and 2012–2013 NESARC-III among the overall population and among nicotine users. Adjusted risk differences were obtained from logistic regression analyses using the predicted marginal approach.
Results:
Between the 2001–2002 and 2012–2013 surveys, rates of 12-month nicotine use declined slightly (from 27.7% to 26.9%), but increased slightly but significantly when adjusted for sociodemographic characteristics (adjusted risk difference=1.4%). Larger significant increases were seen in 12-month nicotine dependence (adjusted risk difference=2.6%) and nicotine dependence among users (adjusted risk difference=6.4%). With few exceptions, increases in nicotine use, nicotine dependence, and nicotine dependence among users were statistically significant across most sociodemographic subgroups. Notable increases were seen among men; middle and older age groups; whites, blacks, and Hispanics; and the socioeconomically disadvantaged.
Conclusions:
Smaller increases in 12-month nicotine use relative to larger increases in 12-month nicotine dependence and nicotine dependence among users suggests that increases in nicotine dependence between the 2001–2002 and 2012–2013 surveys are findings that support the hardening hypothesis. Vulnerable subgroups of the population in terms of hardening were identified who would benefit from targeted nicotine dependence intervention programs to help them in overcoming dependence and quitting nicotine use.
Tobacco use is the leading cause of preventable morbidity and mortality among adults in the United States and worldwide (1–3). Tobacco use causes 480,000 (i.e., 1 in 5) deaths per year in the United States (4). Tobacco use has been associated with numerous health consequences, including coronary heart disease, stroke, chronic obstructive pulmonary disease, lung and other cancers, and increased risk of preterm delivery and low birth weight (1). Tobacco use can result in nicotine dependence, which is highly comorbid with alcohol and drug use disorders as well as mood, anxiety, and personality disorders (5, 6). Tobacco use also imposes enormous economic costs in the United States, recently estimated at $193 billion per year (7).
Prevalence of cigarette use in the U.S. general population has decreased markedly since the 1960s, but several trend studies have shown smaller declines in more recent decades, suggesting that large population improvements in cigarette use have lessened (8–10). Although tracking changes in cigarette use is common, less is known about changes in overall tobacco product (i.e., nicotine) use or nicotine dependence or other proxy measures used to characterize severely dependent users (11). The “hardening hypothesis” posits that as tobacco use declines, less dependent users will quit, leaving a growing proportion of severely dependent users who may be less likely to quit, resulting in a leveling off of declines in smoking (12, 13).
Studies examining the hardening hypothesis have generally focused on proxy measures of nicotine dependence such as increased quit attempts, decreased ability to abstain, successful abstention, number of cigarettes per day, and time to first cigarette within 30 minutes of awakening or a combination of these measures. Results from these studies, conducted largely outside the United States between 2009 and 2015, are mixed, with some supporting (14, 15) and others rejecting the hardening hypothesis (16–19). Other studies used cotinine levels as a proxy measure of nicotine dependence and found that levels had not changed between 1988 and 2012 (20).
DSM-IV (21) measures of nicotine dependence have been used in only two national surveys, not to assess changes in nicotine dependence over time, but rather across each cohort (22, 23). Both studies found that tobacco users were becoming more dependent over time. No published U.S. population survey has assessed changes over time using alternative measures of dependence (24), such as the Nicotine Dependence Syndrome Scale (25) or the Fagerström Test for Nicotine Dependence (26).
This study fills a major gap in the literature by providing information on changes in 12-month nicotine use, DSM-IV nicotine dependence, and nicotine dependence among 12-month nicotine users among major population subgroups to examine the hardening hypothesis. Small increases or decreases or stability in the rates of nicotine use with larger increases in the rates of nicotine dependence among nicotine users over time would support the hardening hypothesis. Changes over time were examined using two large U.S. representative surveys: the 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) (27) and the 2012–2013 NESARC-III (28).
Methods
Source of Data
The NESARC-III is a nationally representative, face-to-face interview survey of 36,309 U.S. adults (age 18 or older) residing in households and selected group quarters (28), with respondents selected through multistage probability sampling. The data were collected from April 2012 to June 2013. Primary sampling units were counties or groups of contiguous counties, secondary sampling units were groups of U.S. Census–defined blocks, and tertiary sampling units were households, within which eligible adult respondents were selected. Black, Asian/Pacific Islander, and Hispanic individuals were oversampled. The household response rate was 72.0%, the person-level response rate was 84.0%, and the overall response rate was 60.0%, comparable with other current U.S. national surveys (29, 30). Data were adjusted for oversampling and nonresponse and weighted to represent the U.S. civilian population based on the 2012 American Community Survey (31). Weighting adjustment compensated for nonresponse. Protocol and written informed consent procedures were approved by the National Institutes of Health and Westat institutional review boards.
The NESARC is a nationally representative face-to-face interview survey of 43,093 U.S. adults, described elsewhere in detail (27). The data were collected from April 2001 to June 2002. The target population was the U.S. adult population (age 18 or older) residing in households and selected group quarters. Primary sampling units consisted of counties or county equivalents from which eligible adults were selected, with black and Hispanic individuals and young adults oversampled. The sampling frame response rate was 98.5%, the household response rate was 88.5%, and the person response rate was 93.0%, yielding an overall survey response rate of 81.0%. Data were adjusted for oversampling and nonresponse and were weighted to represent the civilian U.S. population based on the 2000 Decennial Census (32). The survey protocol, including written informed consent procedures, received full ethical review and approval from the U.S. Census Bureau and the U.S. Office of Management and Budget.
Nicotine Use and DSM-IV Nicotine Dependence
The Alcohol Use Disorder and Associated Disabilities Interview Schedule–DSM-IV Version (AUDADIS-IV) (33) used in the NESARC and the AUDADIS-5 (34) used in the NESARC-III assessed 12-month nicotine use with identical questions. Respondents were classified as lifetime nicotine users if they ever smoked at least 100 cigarettes or 50 cigars or used snuff or dipping or chewing tobacco at least 20 times. Among lifetime users, 12-month nicotine users were respondents who used any of those nicotine products in the past 12 months. Rates of 12-month nicotine use were assessed for the total NESARC samples.
Consistent with DSM-IV, a nicotine dependence diagnosis required that at least three of seven dependence criteria were met in the year preceding the survey, with the generic DSM-IV dependence criteria modified to assess nicotine dependence, following DSM-IV guidelines (21, p. 243). The “using nicotine to relieve or avoid withdrawal symptoms” criterion was operationalized using four symptom items: use of nicotine upon waking, use of nicotine after being in a situation in which use was restricted, use of nicotine to avoid nicotine withdrawal symptoms, and waking up in the middle of the night to use nicotine. The “giving up activities in favor of nicotine use” criterion was assessed by two items: giving up or cutting down on activities that were important, such as associating with friends or relatives or attending social activities, because nicotine use was not permitted at the activity; and giving up or cutting down on activities that you were interested in or gave you pleasure because nicotine use was not permitted at the activity. The “great deal of time spent using nicotine” criterion was assessed by chain-smoking. The “using nicotine more than intended” criterion was operationalized as having a period when nicotine was used more than intended. Nicotine dependence was assessed in the aggregate for any tobacco product, including cigarettes, cigars, pipes, chewing tobacco, and snuff. Rates of 12-month nicotine dependence were assessed for the total NESARC samples, whereas rates of 12-month nicotine dependence among 12-month users were, by definition, conditional on the number of users. The test-retest reliability of nicotine use measures was good to excellent (kappa values, 0.60, 0.92) and 12-month DSM-IV nicotine dependence diagnoses were good (kappa=0.63) (35). The validity of DSM-IV nicotine dependence diagnoses was good to excellent (36).
Approximate Fagerström Test for Nicotine Dependence
As the NESARC and NESARC-III focused on DSM diagnoses, the Fagerström Test for Nicotine Dependence (FTND) (37) was not included, but similar questions were available to define a 12-month FTND-like measure. One question was asked and scored as in the FTND: “How many cigarettes per day do you smoke?”, with 0 points for ≤10, 1 point for 11–20, 2 points for 21–30, and 3 points for ≥31. Instead of “How soon after you wake up do you smoke your first cigarette,” the item “Often use tobacco just after/shortly after getting up in morning” was used, with a positive response scored as 2 points (corresponding to 6–30 minutes), based on association between that question and “Use tobacco or nicotine within 30 minutes of waking up” in the NESARC-III (Wald χ2=736.1, p≤0.0001). “Find yourself using tobacco just after being in a situation where tobacco use was prohibited” was used instead of “Find it difficult to keep from using tobacco in places where prohibited” and “Wake up in the middle of the night to use tobacco” instead of “Use tobacco or nicotine more frequently during first hours of waking up,” with each positive response scored as 1 point; these pairs of items were associated in the NESARC-III (Wald χ2=470.0, p≤0.0001, and Wald χ2=394.5, p≤0.0001, respectively). Two questions not included in either the NESARC or the NESARC-III were excluded from the measure (“Which cigarette would you hate most to give up: first in the morning, any other” and “Do you smoke when you are so ill that you are in bed most of the day: yes, no”). For ease of analysis and for the greatest comparability with DSM-IV nicotine dependence, a binary approximate measure of Fagerström dependence was defined as 3 or more points of the possible 7.
Statistical Analysis
Weighted cross-tabulations produced estimates of 12-month nicotine use, DSM-IV nicotine dependence, and approximate Fagerström dependence, overall and among 12-month nicotine users. Risk differences adjusted for all sociodemographic characteristics assessed associations between survey periods (2001–2002 compared with 2012–2013) and the risk for each of these nicotine outcomes. Because nicotine use, nicotine dependence, and approximate Fagerström dependence vary by age, sex, race/ethnicity, marital status, socioeconomic status, and geographic characteristics (region and urbanicity), multivariable analyses were controlled for these respondent characteristics. Adjusted risk differences were obtained from logistic regression analyses using the predicted marginal approach (38). The independent variable of interest was survey period effect, with the 2001–2002 survey serving as reference. Separate adjusted regression models using the average predicted marginal approach tested whether the adjusted risk differences significantly varied across different levels of each sociodemographic variable (additive interactions) (39). All statistical analyses were performed using SUDAAN (40) to accommodate the complex sample designs of the surveys.
Results
Nicotine Use
Although the 12-month prevalences of nicotine use declined from 27.7% in the 2001–2002 survey to 26.9% in the 2012–2013 survey, when adjusted for sociodemographic characteristics, the adjusted risk difference (1.4%) denoted a small but significant increase in these estimates (Table 1). Increases in 12-month nicotine use were also seen among men (adjusted risk difference=1.3%), women (adjusted risk difference=1.4%), 30- to 44-year-olds (adjusted risk difference=2.8%), those age 65 or older (adjusted risk difference=1.2%), whites (adjusted risk difference=1.3%), blacks (adjusted risk difference=2.5%), the previously married or never married (adjusted risk differences, 1.8% and 1.67%, respectively), those with high school or less education (adjusted risk differences, 4.0% and 2.6%, respectively), those with incomes less than $35,000 or less than $20,000 (adjusted risk differences, 5.0% and 1.9%, respectively), and those residing in urban areas and in the Midwest and South (adjusted risk differences, 1.3%, 1.8%, and 1.7%, respectively).
| NESARC (N=11,118) | NESARC-III (N=9,957) | ||||||
|---|---|---|---|---|---|---|---|
| Characteristic | % | SE | % | SE | Adjusted Risk Differenceb (%) | Adjusted Risk Difference pb | Adjusted Additive Interaction pb |
| Total | 27.7 | 0.56 | 26.9 | 0.44 | 1.4 | 0.0045 | n/a |
| Sex | |||||||
| Male | 33.9 | 0.71 | 32.8 | 0.57 | 1.3 | 0.0457 | 0.8811 |
| Female | 22.0 | 0.54 | 21.5 | 0.49 | 1.4 | 0.0113 | Ref |
| Age (years) | |||||||
| 18–29 | 32.7 | 0.92 | 31.0 | 0.68 | 0.1 | 0.9572 | 0.2483 |
| 30–44 | 31.2 | 0.89 | 31.8 | 0.75 | 2.8 | 0.0019 | 0.1335 |
| 45–64 | 27.9 | 0.67 | 27.8 | 0.69 | 1.2 | 0.1287 | 0.9839 |
| ≥65 | 13.7 | 0.48 | 13.1 | 0.55 | 1.2 | 0.0314 | Ref |
| Race/ethnicity | |||||||
| White | 29.7 | 0.46 | 29.3 | 0.61 | 1.3 | 0.0267 | Ref |
| Black | 24.8 | 0.84 | 27.2 | 0.63 | 2.5 | 0.0043 | 0.2323 |
| Native American | 41.5 | 2.21 | 37.3 | 2.67 | –1.9 | 0.5533 | 0.3127 |
| Asian/Pacific Islander | 15.3 | 1.37 | 15.2 | 0.94 | 1.3 | 0.4963 | 0.9999 |
| Hispanic | 19.9 | 1.25 | 19.5 | 0.61 | 1.0 | 0.3928 | 0.7690 |
| Marital status | |||||||
| Married or cohabiting | 25.2 | 0.59 | 23.5 | 0.60 | 1.2 | 0.0778 | Ref |
| Widowed, divorced, or separated | 32.1 | 0.82 | 32.1 | 0.69 | 1.8 | 0.0297 | 0.5228 |
| Never married | 31.2 | 0.83 | 31.2 | 0.56 | 1.6 | 0.0288 | 0.6344 |
| Education | |||||||
| Less than high school | 32.5 | 1.41 | 35.8 | 1.11 | 4.0 | 0.0024 | 0.0044 |
| High school | 32.7 | 0.67 | 34.2 | 0.60 | 2.6 | 0.0007 | 0.0087 |
| Some college or higher | 23.6 | 0.49 | 22.0 | 0.42 | 0.2 | 0.7412 | Ref |
| Family income ($) | |||||||
| 0–19,999 | 30.1 | 0.85 | 35.9 | 0.76 | 5.0 | <0.0001 | <0.0001 |
| 20,000–34,999 | 31.1 | 0.83 | 30.8 | 0.74 | 1.9 | 0.0400 | 0.0059 |
| 35,000–69,999 | 28.0 | 0.69 | 26.3 | 0.62 | 0.9 | 0.2384 | 0.0153 |
| ≥70,000 | 21.9 | 0.62 | 18.6 | 0.62 | –1.6 | 0.0618 | Ref |
| Urbanicity | |||||||
| Urban | 26.1 | 0.60 | 25.5 | 0.45 | 1.3 | 0.0166 | 0.8522 |
| Rural | 34.2 | 0.72 | 32.3 | 1.26 | 1.5 | 0.1183 | Ref |
| Region | |||||||
| Northeast | 25.7 | 1.23 | 24.2 | 0.83 | 0.7 | 0.5603 | 0.8503 |
| Midwest | 31.0 | 0.86 | 30.5 | 0.85 | 1.8 | 0.0300 | 0.5672 |
| South | 29.3 | 0.71 | 29.2 | 0.91 | 1.7 | 0.0400 | 0.6268 |
| West | 23.2 | 1.16 | 22.2 | 0.66 | 1.0 | 0.3773 | Ref |
a
NESARC=National Epidemiologic Survey on Alcohol and Related Conditions; Ref=reference.
b
Adjusted for all other covariates.
In analyses testing whether changes in prevalence across surveys differed by sociodemographic strata (adjusted additive interaction p values), adjusted risk differences in nicotine use were significantly greater among those with high school or less education (4.0% and 2.6%, respectively) relative to those with some college or higher education (0.2%), and among those with incomes less than $69,999 compared with those of $70,000 or more.
Nicotine Dependence
The prevalence of nicotine dependence increased between the 2001–2002 (12.8%) and 2012–2013 (14.0%) surveys (Table 2). Significant increases were seen among nearly all sociodemographic subgroups, except 18- to 29-year-olds, Native Americans, Asians/Pacific Islanders, and respondents with incomes of $70,000 of more. The adjusted risk difference of nicotine dependence was greater among men (3.4%) than women (1.9%); among 30- to 44-year-olds (3.5%) than those age 65 or older (1.8%); among the previously married (3.7%) compared with married respondents (2.2%); among those with high school or lower education (5.5% and 4.3%, respectively) compared with those with some college or higher education (1.1%); and among those in lower income groups (2.4%−5.6%) relative to those with incomes of $70,000 or more (0.1%).
| NESARC (N=4,962) | NESARC-III (N=5,071) | ||||||
|---|---|---|---|---|---|---|---|
| Characteristic | % | SE | % | SE | Adjusted Risk Differenceb (%) | Adjusted Risk Difference pb | Adjusted Additive Interaction pb |
| Total | 12.8 | 0.39 | 14.0 | 0.33 | 2.6 | <0.0001 | n/a |
| Sex | |||||||
| Male | 14.1 | 0.47 | 16.1 | 0.43 | 3.4 | <0.0001 | 0.0127 |
| Female | 11.5 | 0.40 | 12.1 | 0.38 | 1.9 | <0.0001 | Ref |
| Age (years) | |||||||
| 18–29 | 16.5 | 0.66 | 16.7 | 0.60 | 1.4 | 0.0838 | 0.5727 |
| 30–44 | 14.9 | 0.64 | 16.5 | 0.57 | 3.5 | <0.0001 | 0.0459 |
| 45–64 | 12.6 | 0.49 | 14.7 | 0.51 | 3.0 | <0.0001 | 0.0748 |
| ≥65 | 4.0 | 0.25 | 5.6 | 0.31 | 1.8 | <0.0001 | Ref |
| Race/ethnicity | |||||||
| White | 14.3 | 0.35 | 15.7 | 0.41 | 2.7 | <0.0001 | Ref |
| Black | 10.3 | 0.51 | 13.6 | 0.58 | 3.0 | <0.0001 | 0.6725 |
| Native American | 23.1 | 2.01 | 23.8 | 3.10 | 1.6 | 0.6184 | 0.7324 |
| Asian/Pacific Islander | 6.4 | 0.69 | 6.7 | 0.66 | 1.2 | 0.2645 | 0.2091 |
| Hispanic | 6.3 | 0.65 | 8.6 | 0.49 | 2.3 | 0.0005 | 0.6284 |
| Marital status | |||||||
| Married or cohabiting | 11.1 | 0.41 | 11.5 | 0.40 | 2.2 | <0.0001 | Ref |
| Widowed, divorced, or separated | 15.7 | 0.61 | 17.8 | 0.54 | 3.7 | <0.0001 | 0.0462 |
| Never married | 15.2 | 0.60 | 17.2 | 0.53 | 2.7 | <0.0001 | 0.4312 |
| Education | |||||||
| Less than high school | 15.2 | 0.94 | 19.8 | 0.90 | 5.5 | <0.0001 | 0.0001 |
| High school | 15.3 | 0.54 | 18.8 | 0.62 | 4.3 | <0.0001 | 0.0001 |
| Some college or higher | 10.7 | 0.35 | 10.8 | 0.30 | 1.1 | 0.0113 | Ref |
| Family income ($) | |||||||
| 0–19,999 | 14.9 | 0.62 | 20.9 | 0.60 | 5.6 | <0.0001 | <0.0001 |
| 20,000–34,999 | 14.7 | 0.60 | 15.9 | 0.56 | 2.8 | 0.0002 | 0.0046 |
| 35,000–69,999 | 12.8 | 0.50 | 13.6 | 0.39 | 2.4 | <0.0001 | 0.0018 |
| ≥70,000 | 9.0 | 0.40 | 8.1 | 0.45 | 0.1 | 0.9288 | Ref |
| Urbanicity | |||||||
| Urban | 12.0 | 0.44 | 13.1 | 0.34 | 2.4 | <0.0001 | 0.3088 |
| Rural | 16.0 | 0.46 | 17.3 | 0.80 | 3.2 | <0.0001 | Ref |
| Region | |||||||
| Northeast | 11.5 | 0.70 | 13.0 | 0.62 | 3.0 | 0.0002 | 0.9299 |
| Midwest | 15.7 | 0.78 | 16.0 | 0.62 | 1.7 | 0.0155 | 0.3266 |
| South | 13.0 | 0.52 | 14.7 | 0.66 | 2.8 | <0.0001 | 0.9469 |
| West | 10.5 | 0.87 | 11.8 | 0.56 | 2.9 | 0.0017 | Ref |
a
NESARC=National Epidemiologic Survey on Alcohol and Related Conditions; Ref=reference.
b
Adjusted for all other covariates.
DSM-IV Nicotine Dependence Among Users
Between the 2001–2002 and 2012–2013 surveys, the prevalence of 12-month nicotine dependence among 12-month nicotine users increased from 46.1% to 52.0% (Table 3). In the adjusted analyses, increases were seen in nearly all sociodemographic subgroups except Native Americans, Asians/Pacific Islanders, respondents with incomes of $70,000 or more, and respondents residing in the Midwest. The adjusted risk difference of nicotine dependence among users was greater among men (8.0%) than women (4.6%); among Hispanics (12.5%) than whites (5.6%); among respondents with high school or less education (8.8% and 9.2%, respectively) than those with some college or higher education (4.0%); and among the lowest income groups (9.1%) compared with those with incomes of $70,000 or more (3.8%). The adjusted risk difference was lower among all three younger age groups (4.2%−7.6%) than the oldest age group (14.0%) and among those residing in the Midwest (2.6%) than in the West (9.2%).
| NESARC (N=4,962) | NESARC-III (N=5,071) | ||||||
|---|---|---|---|---|---|---|---|
| Characteristic | % | SE | % | SE | Adjusted Risk Differenceb (%) | Adjusted Risk Difference pb | Adjusted Additive Interaction pb |
| Total | 46.1 | 0.71 | 52.0 | 0.77 | 6.6 | <0.0001 | n/a |
| Sex | |||||||
| Male | 41.8 | 0.89 | 49.0 | 0.95 | 8.0 | <0.0001 | 0.0487 |
| Female | 52.3 | 0.93 | 56.3 | 1.05 | 4.6 | 0.0007 | Ref |
| Age (years) | |||||||
| 18–29 | 50.4 | 1.23 | 54.0 | 1.26 | 4.2 | 0.0141 | 0.0008 |
| 30–44 | 47.6 | 1.14 | 52.1 | 1.22 | 5.5 | 0.0009 | 0.0032 |
| 45–64 | 45.2 | 1.13 | 52.9 | 1.12 | 7.6 | <0.0001 | 0.0176 |
| ≥65 | 29.6 | 1.63 | 42.6 | 1.80 | 14.0 | <0.0001 | Ref |
| Race/ethnicity | |||||||
| White | 48.0 | 0.74 | 53.5 | 0.89 | 5.6 | <0.0001 | Ref |
| Black | 41.7 | 1.28 | 50.1 | 1.63 | 8.2 | 0.0001 | 0.2521 |
| Native American | 55.8 | 3.36 | 63.9 | 5.34 | 6.5 | 0.2982 | 0.8945 |
| Asian/Pacific Islander | 41.6 | 3.59 | 44.3 | 3.67 | 5.1 | 0.2971 | 0.9228 |
| Hispanic | 31.6 | 1.85 | 44.0 | 1.74 | 12.5 | <0.0001 | 0.0087 |
| Marital status | |||||||
| Married or cohabiting | 44.1 | 0.97 | 48.7 | 0.96 | 5.7 | <0.0001 | Ref |
| Widowed, divorced, or separated | 49.1 | 1.20 | 55.6 | 1.28 | 7.8 | <0.0001 | 0.2605 |
| Never married | 48.5 | 1.25 | 55.1 | 1.30 | 7.5 | <0.0001 | 0.3651 |
| Education | . | ||||||
| Less than high school | 46.7 | 1.45 | 55.2 | 1.63 | 8.8 | <0.0001 | 0.0363 |
| High school | 46.8 | 1.10 | 55.0 | 1.39 | 9.2 | <0.0001 | 0.0136 |
| Some college or higher | 45.4 | 0.98 | 48.9 | 0.94 | 4.0 | 0.0033 | Ref |
| Family income ($) | |||||||
| 0–19,999 | 49.5 | 1.14 | 58.4 | 1.17 | 9.1 | <0.0001 | 0.0241 |
| 20,000–34,999 | 47.3 | 1.21 | 51.8 | 1.18 | 5.2 | 0.0018 | 0.6055 |
| 35,000–69,999 | 45.7 | 1.13 | 51.7 | 1.16 | 7.2 | <0.0001 | 0.1860 |
| ≥70,000 | 41.0 | 1.33 | 43.7 | 1.68 | 3.8 | 0.0695 | Ref |
| Urbanicity | |||||||
| Urban | 45.9 | 0.87 | 51.5 | 0.83 | 6.2 | <0.0001 | 0.5364 |
| Rural | 46.7 | 1.19 | 53.6 | 1.61 | 7.6 | 0.0001 | Ref |
| Region | |||||||
| Northeast | 44.7 | 1.33 | 53.6 | 1.76 | 9.8 | <0.0001 | 0.8429 |
| Midwest | 50.6 | 1.48 | 52.4 | 1.40 | 2.6 | 0.1710 | 0.0206 |
| South | 44.2 | 1.14 | 50.5 | 1.43 | 6.5 | 0.0002 | 0.3134 |
| West | 45.1 | 1.90 | 53.4 | 1.25 | 9.2 | <0.0001 | Ref |
a
NESARC=National Epidemiologic Survey on Alcohol and Related Conditions; Ref=reference.
b
Adjusted for all other covariates.
Approximate Fagerström Dependence
Overall, similar to DSM-IV nicotine dependence, the prevalence of approximate Fagerström dependence increased (adjusted risk difference=1.8%), with increases among many sociodemographic subgroups, except 18- to 29-year-olds, Native Americans, Asians/Pacific Islanders, respondents with incomes of $70,000 or more, those with some college or higher education, and those living in the Northeast or the West. Similar to DSM-IV nicotine dependence, the adjusted risk difference of approximate Fagerström dependence was greater among those with high school or lower education (3.3% and 4.2%, respectively) compared with those with some college or higher education (0.6%) and among those in lower income groups (1.5%−4.7%) relative to those with incomes of $70,000 or more (−0.5%). Among 12-month nicotine users, similar to DSM-IV nicotine dependence, the prevalence of approximate Fagerström dependence increased (adjusted risk difference=3.1%), with increases among men, individuals age 65 or older, whites, Hispanics, married, never married, high school education, lowest and middle income groups, urban location, and the South region. Different from DSM-IV nicotine dependence, adjusted risk differences for approximate Fagerström dependence did not differ significantly by sociodemographic characteristics. See Tables S1 and S2 in the online supplement for details.
Discussion
The overall unadjusted 12-month prevalence of nicotine use showed a modest decline over the period between the 2001–2002 NESARC and the 2012–2013 NESARC-III, consistent with declines documented in the National Survey on Drug Use and Health, which found that rates of use of “any tobacco product” declined from 32.2% to 26.9% between 2002 and 2012 (41). However, with adjustment for sociodemographic characteristics, the 12-month rates of nicotine use significantly increased between the 2001–2002 and 2012–2013 surveys. These results are primarily due to increases in nicotine use among population subgroups, especially those disadvantaged in terms of education and income. These findings underscore the importance of adjustment in studies examining changes over time in nicotine use. Larger increases were seen in 12-month nicotine dependence and nicotine dependence among 12-month nicotine users. The modest increase in 12-month nicotine use seen in this study is consistent with slight increases or the slowing in the decline of rates in current cigarette use among adults in the United States during the same time period (8, 10, 14, 15) and the prevalences of cigarette or nicotine use in the 2001–2002 and 2012–2013 periods seen in other national surveys (8, 10, 14, 15, 41, 42). The absence of studies on DSM-IV-defined nicotine dependence preclude further comparisons with previous studies.
The smaller increase in 12-month nicotine use relative to the much larger increases in 12-month nicotine dependence among 12-month users seen in this study suggests that increases in nicotine dependence are largely the result of increases in nicotine dependence among nicotine users and not increases in the prevalence of nicotine use. Taken together, these results support the hardening hypothesis. These findings are at variance with some (16–18, 43, 44) but not all (14, 16) studies examining changes over time using proxy measures of nicotine dependence (e.g., cigarettes/day, time to first cigarette, and quit attempts). Sensitivity analyses showing similar increases in the approximate Fagerström dependence measure in our sample supports the validity of the finding for DSM-IV nicotine dependence. Although tobacco control measures may encourage less dependent individuals to quit, such efforts may not be effective among individuals who are already nicotine dependent (23). Since tobacco control measures have had a significant effect on reducing nicotine consumption, future studies on the hardening hypothesis should use measures of nicotine dependence that do not rely heavily on nicotine consumption (11, 45, 46).
In line with previous research (8–10), men had greater prevalences of nicotine use than women during both survey periods. Further, adjusted risk differences in nicotine use, nicotine dependence, and nicotine dependence among users increased among both men and women, but changes in adjusted risk differences for nicotine dependence and nicotine dependence among users over the survey period were greater among men than women. In previous studies using consumption-based measures of nicotine dependence, severely dependent women smokers were more likely to have seen a health care provider and to have received cessation advice from a provider (47). Health care provider visits have been shown to increase the probability of making a cessation attempt and achieving a successful cessation attempt (48–50). Severely dependent female smokers were also less likely than severely dependent male smokers to be exposed to smoking restrictions at home and work (47). Despite greater increases in nicotine dependence and nicotine dependence among users among men, women experience unique nicotine-related conditions associated with pregnancy, prenatal outcomes, oral contraceptive use, cervical cancer, and osteoporosis (7). Further research examining gender-specific outcomes in nicotine dependence treatment is warranted.
Results by age group suggest that nicotine dependence emerges in later years of nicotine use and may be persistent despite the awareness of or actual health concerns accompanying older age. Reasons for the greater increases among older individuals of nicotine dependence and nicotine dependence among users may include social and historical influences of smoking initiation, social isolation, perceived benefits of smoking (e.g., as a preventative for depression), and unrealistic optimism (11, 51–53). Research is needed on the characteristics and motivations of nicotine use and nicotine dependence among older adults, who are especially vulnerable to nicotine-related physical disabilities, that can help inform age-specific interventions.
Previous studies on the hardening hypothesis using consumption-based measures of nicotine dependence have not examined race/ethnicity differences in detail, although a few studies report that white race is correlated with persistent nicotine use (11, 54, 55). Although reasons for increases in nicotine dependence and nicotine dependence among users among whites, blacks, and Hispanics are unclear, further study on the social, cultural, behavioral, and genetic factors influencing nicotine use and the development of nicotine dependence among race/ethnicity groups is warranted, with special attention to access to treatment as a key factor.
Across the surveys, increases in nicotine use, nicotine dependence, and nicotine dependence among users were especially notable among individuals with lower education and income, and changes over time in these nicotine outcomes were greater among those with lower socioeconomic status relative to those with greater incomes and education. These findings are consistent with other studies that have characterized lower socioeconomic status as a characteristic of dependent or severely dependent smokers (16, 56–58) and other research showing a decline in severely dependent smoking among higher-socioeconomic-status smokers but not among lower-socioeconomic-status smokers (59). These socioeconomic status disparities in nicotine use and nicotine dependence may be attributed to social disadvantage, shared social environments with other nicotine users, and fewer limitations on nicotine use inside and outside the home (59). It is also possible that less educated individuals have lower access to information on the health consequences of nicotine use and to treatment services, or may be less responsive to health promotion (60, 61). A greater understanding of increases in nicotine use and nicotine dependence among individuals of lower socioeconomic status is needed, especially in light of the disproportionate prevalence of nicotine-related morbidity and mortality among in this population (60).
This study has several limitations. Nicotine use was assessed by self-report, and future national surveys should include nicotine biomarkers. Social desirability bias may have contributed to underreporting, and thus our study may underestimate nicotine outcomes. The NESARC-III response rate was acceptable and was similar to other concurrent national surveys (60.1%) but was lower than that of the NESARC (81.0%). However, weighting facilitated comparisons between the surveys (62). The employers of the NESARC and NESARC-III were different—the U.S. Census Bureau and Westat, respectively. However, both surveys were presented to respondents as voluntary, and both were conducted under the auspices of the federal government. In addition, respondents in the NESARC-III received a modest payment for their time and effort, whereas those in the NESARC were not paid. While such changes in survey methodology are unlikely to have influenced participants’ response propensity in answering questions about tobacco and nicotine use (i.e., the primary study outcomes), this (and other limitations) is a reminder that replication in other studies is warranted. The NESARC and NESARC-III provided assessments at two time points, and potential fluctuations during the intervening years cannot be determined. The NESARC-III but not the NESARC collected DNA for genetic analyses. However, saliva samples were collected after questionnaire administration, minimizing any differential impact on survey estimates presented here. Nicotine dependence was not ascertained for each nicotine-containing product, so product-specific analysis could not be conducted. The FTND was not included in either the NESARC or the NESARC-III data sets, but an approximate measure based on four of the six Fagerström questions was created, and it included the two questions often considered the “core” Fagerström questions (63). Limitations are balanced by the numerous strengths of the NESARC and NESARC-III, including large sample sizes, rigorous methodology, reliable measures of nicotine use and nicotine dependence, and non-consumption-based measurement of nicotine dependence.
In summary, the results of this study support the hardening hypothesis. Because of this hardening, the remaining nicotine users may be less likely to quit because of dependence, and the allocation of nicotine intervention services may need to be reconsidered. Some individuals appear to be unlikely to quit with public health efforts (e.g., increased price, elimination of advertising, protections against secondhand smoke), so evidence-based treatment interventions should supplement these public health activities. Comprehensive population-based and treatment-oriented interventions hold great promise in reducing nicotine use and nicotine dependence as a devasting cause of preventable morbidity and mortality. This study identified notable increases in nicotine dependence and nicotine dependence among users in men, middle-aged and older individuals, whites, blacks, Hispanics, and the socioeconomically disadvantaged. These subgroups would benefit most from targeted nicotine dependence intervention programs to help them overcome their dependence and quit nicotine use.
Acknowledgments
The authors thank S. Patricia Chou, Ph.D., W. June Ruan, M.S., Boji Huang, M.D., Ph.D., Bradley T. Kerridge, Ph.D., Tulshi D. Saha, Ph.D., Amy Fan, Ph.D., and Haitao Zhang, Ph.D., for their assistance in the analyses of data and insights into the complex NESARC surveys.
Footnote
The views and opinions expressed in this article are those of the authors and should not be construed to represent the views of NIDA, NIH, or the U.S. Department of Health and Human Services.
Supplementary Material
File (appi.ajp.2020.19090900.ds001.pdf)
- View/Download
- 168.70 KB
References
1.
Department of Health and Human Services: The Health Consequences of Smoking: 50 Years of Progress. Washington, DC, Department of Health and Human Services, 2014
2.
World Health Organization: Tobacco Fact Sheet. Geneva, World Health Organization, 2014
3.
Ng M, Freeman MK, Fleming TD, et al: Smoking prevalence and cigarette consumption in 187 countries, 1980–2012. JAMA 2014; 311:183–192
4.
Centers for Disease Control and Prevention: Health Effects of Smoking. Atlanta, Centers for Disease Control and Prevention, 2017
5.
Chou SP, Goldstein RB, Smith SM, et al: The epidemiology of DSM-5 nicotine use disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions–III. J Clin Psychiatry 2016; 77:1404–1412
6.
Grant BF, Hasin DS, Chou SP, et al: Nicotine dependence and psychiatric disorders in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry 2004; 61:1107–1115
7.
Ekpu VU, Brown AK: The economic impact of smoking and of reducing smoking prevalence: review of evidence. Tob Use Insights 2015; 8:1–35
8.
Agaku IT, King BA, Dube SR; Centers for Disease Control and Prevention (CDC): Current cigarette smoking among adults: United States, 2005–2012. MMWR Morb Mortal Wkly Rep 2014; 63:29–34
9.
Hu SS, Neff L, Agaku IT, et al: Tobacco product use among adults: United States, 2013–2014. MMWR Morb Mortal Wkly Rep 2016; 65:685–691
10.
Jamal A, Homa DM, O’Connor E, et al: Current cigarette smoking among adults: United States, 2005–2014. MMWR Morb Mortal Wkly Rep 2015; 64:1233–1240
11.
Strong DR, Pearson J, Ehlke S, et al: Indicators of dependence for different types of tobacco product users: descriptive findings from wave 1 (2013–2014) of the Population Assessment of Tobacco and Health (PATH) study. Drug Alcohol Depend 2017; 178:257–266
12.
Burns DM, Warner KE, Hughes JR: Smokers who have not quit: is cessation more difficult and should we change our strategies, in Those Who Continue to Smoke: Is Achieving Abstinence Harder and Do We Need to Change Our Interventions? (Smoking and Tobacco Control Monograph 15, NIH Publication 03-5370). Bethesda, Md, US Department of Human Services, National Institutes of Health, National Cancer Institute, 2003
13.
Costa ML, Cohen JE, Chaiton MO, et al: “Hardcore” definitions and their application to a population-based sample of smokers. Nicotine Tob Res 2010; 12:860–864
14.
Docherty G, McNeill A, Gartner C, et al: Did hardening occur among smokers in England from 2000 to 2010? Addiction 2014; 109:147–154
15.
Goodwin RD, Wall MM, Gbedemah M, et al: Trends in cigarette consumption and time to first cigarette on awakening from 2002 to 2015 in the USA: new insights into the ongoing tobacco epidemic. Tob Control 2018; 27:379–384
16.
Azagba S: Hardcore smoking among continuing smokers in Canada, 2004–2012. Cancer Causes Control 2015; 26:57–63
17.
Bommelé J, Nagelhout GE, Kleinjan M, et al: Prevalence of hardcore smoking in the Netherlands between 2001 and 2012: a test of the hardening hypothesis. BMC Public Health 2016; 16:754
18.
Edwards R, Tu D, Newcombe R, et al: Achieving the tobacco endgame: evidence on the hardening hypothesis from repeated cross-sectional studies in New Zealand, 2008–2014. Tob Control 2017; 26:399–405
19.
Fernández E, Lugo A, Clancy L, et al: Smoking dependence in 18 European countries: hard to maintain the hardening hypothesis. Prev Med 2015; 81:314–319
20.
Jarvis MJ, Giovino GA, O’Connor RJ, et al: Variation in nicotine intake among US cigarette smokers during the past 25 years: evidence from NHANES surveys. Nicotine Tob Res 2014; 16:1620–1628
21.
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 4th ed. Washington, DC, American Psychiatric Association, 1994
22.
Breslau N, Johnson EO, Hiripi E, et al: Nicotine dependence in the United States: prevalence, trends, and smoking persistence. Arch Gen Psychiatry 2001; 58:810–816
23.
Goodwin RD, Keyes KM, Hasin DS: Changes in cigarette use and nicotine dependence in the United States: evidence from the 2001–2002 wave of the National Epidemiologic Survey on Alcoholism and Related Conditions. Am J Public Health 2009; 99:1471–1477
24.
Hughes JR, Brandon TH: A softer view of hardening. Nicotine Tob Res 2003; 5:961–962
25.
Shiffman S, Waters A, Hickcox M: The Nicotine Dependence Syndrome Scale: a multidimensional measure of nicotine dependence. Nicotine Tob Res 2004; 6:327–348
26.
Fagerström KO, Kunze M, Schoberberger R, et al: Nicotine dependence versus smoking prevalence: comparisons among countries and categories of smokers. Tob Control 1996; 5:52–56
27.
Grant BF, Moore TC, Shepard J, et al: Source and Accuracy Statement: Wave I National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Bethesda, Md, National Institute on Alcohol Abuse and Alcoholism, 2003
28.
Grant BF, Amsbary M, Chu A, et al: Source and Accuracy Statement: National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III). Rockville, Md, National Institute on Alcohol Abuse and Alcoholism, 2014
29.
Adams PF, Kirzinger WK, Martinez M: Summary health statistics for the US population: National Health Interview Survey, 2012. Vital Health Stat 10, December 2013, no 259:1–95
30.
Substance Abuse and Mental Health Services Administration: Results From the 2012 National Survey on Drug Use and Health: Summary of National Findings, Appendix B: Statistical Methods and Measurement. Rockville, Md, Substance Abuse and Mental Health Services Administration, 2012
31.
US Census Bureau: American Community Survey, 2012. Suitland, Md, US Census Bureau, 2013
32.
US Census Bureau: Decennial Census, 2000. Suitland, Md, US Census Bureau, 2000
33.
Grant BF, Goldstein RB, Chou SP, et al: The Alcohol Use Disorder and Associated Disabilities Interview Schedule–DSM-IV Version. Bethesda, Md, National Institute on Alcohol Abuse and Alcoholism, 2001
34.
Grant BF, Goldstein RB, Chou SP, et al: The Alcohol Use Disorder and Associated Disabilities Interview Schedule–Diagnostic and Statistical Manual of Mental Disorders, 5th ed (AUDADIS-5). Rockville, Md, National Institute on Alcohol Abuse and Alcoholism, 2011
35.
Grant BF, Dawson DA, Stinson FS, et al: The Alcohol Use Disorder and Associated Disabilities Interview Schedule–IV (AUDADIS-IV): reliability of alcohol consumption, tobacco use, family history of depression, and psychiatric diagnostic modules in a general population sample. Drug Alcohol Depend 2003; 71:7–16
36.
Grant BF, Goldstein RB, Smith SM, et al: The Alcohol Use Disorder and Associated Disabilities Interview Schedule–5 (AUDADIS-5): reliability of substance use and psychiatric disorder modules in a general population sample. Drug Alcohol Depend 2015; 148:27–33
37.
Heatherton TF, Kozlowski LT, Frecker RC, et al: The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. Br J Addict 1991; 86:1119–1127
38.
Bieler GS, Brown GG, Williams RL, et al: Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data. Am J Epidemiol 2010; 171:618–623
39.
Vanderweele T: Explanation in Causal Inference: Methods for Mediational and Interaction. Oxford, UK, Oxford University Press, 2015
40.
SUDAAN: (computer program), Version 11. Research Triangle Park, NC, Research Triangle Institute, 2012
41.
Substance Abuse and Mental Health Services Administration: Cigarette and Nicotine Use Detailed Tables, 2001–2013. Rockville, Md, Substance Abuse and Mental Health Services Administration, 2014
42.
Kasza KA, Ambrose BK, Conway KP, et al: Tobacco-product use by adults and youths in the United States in 2013 and 2014. N Engl J Med 2017; 376:342–353
43.
Lund M, Lund KE, Kvaavik E: Hardcore smokers in Norway, 1996–2009. Nicotine Tob Res 2011; 13:1132–1139
44.
Kulik MC, Glantz SA: The smoking population in the USA and EU is softening not hardening. Tob Control 2016; 25:470–475
45.
Hughes JR: The hardening hypothesis: is the ability to quit decreasing due to increasing nicotine dependence? A review and commentary. Drug Alcohol Depend 2011; 117:111–117
46.
Piper ME, McCarthy DE, Bolt DM, et al: Assessing dimensions of nicotine dependence: an evaluation of the Nicotine Dependence Syndrome Scale (NDSS) and the Wisconsin Inventory of Smoking Dependence Motives (WISDM). Nicotine Tob Res 2008; 10:1009–1020
47.
Augustson EM, Barzani D, Rutten LJ, et al: Gender differences among hardcore smokers: an analysis of the tobacco use supplement of the current population survey. J Womens Health (Larchmt) 2008; 17:1167–1173
48.
Kottke TE, Battista RN, DeFriese GH, et al: Attributes of successful smoking cessation interventions in medical practice: a meta-analysis of 39 controlled trials. JAMA 1988; 259:2883–2889
49.
Ockene JK, Kristeller J, Pbert L, et al: The physician-delivered smoking intervention project: can short-term interventions produce long-term effects for a general outpatient population? Health Psychol 1994; 13:278–281
50.
Silagy C, Stead LF: Physician advice for smoking cessation. Cochrane Database Syst Rev 2001; (2):CD000165
51.
Dillard AJ, McCaul KD, Klein WM: Unrealistic optimism in smokers: implications for smoking myth endorsement and self-protective motivation. J Health Commun 2006; 11(suppl 1):93–102
52.
Kerr S, Watson H, Tolson D, et al: Smoking after the age of 65 years: a qualitative exploration of older current and former smokers’ views on smoking, stopping smoking, and smoking cessation resources and services. Health Soc Care Community 2006; 14:572–582
53.
Sepinwall D, Borrelli B: Older, medically ill smokers are concerned about weight gain after quitting smoking. Addict Behav 2004; 29:1809–1819
54.
Emery S, Gilpin EA, Ake C, et al: Characterizing and identifying “hard-core” smokers: implications for further reducing smoking prevalence. Am J Public Health 2000; 90:387–394
55.
Sorg A, Xu J, Doppalapudi SB, et al: Hardcore smokers in a challenging tobacco control environment: the case of Missouri. Tob Control 2011; 20:388–390
56.
Darville A, Hahn EJ: Hardcore smokers: what do we know? Addict Behav 2014; 39:1706–1712
57.
Pennanen M, Broms U, Korhonen T, et al: Smoking, nicotine dependence, and nicotine intake by socio-economic status and marital status. Addict Behav 2014; 39:1145–1151
58.
Schnoll RA, Goren A, Annunziata K, et al: The prevalence, predictors, and associated health outcomes of high nicotine dependence using three measures among US smokers. Addiction 2013; 108:1989–2000
59.
Clare P, Bradford D, Courtney RJ, et al: The relationship between socioeconomic status and “hardcore” smoking over time: greater accumulation of hardened smokers in low-SES than high-SES smokers. Tob Control 2014; 23(e2):e133–e138
60.
Hiscock R, Bauld L, Amos A, et al: Socioeconomic status and smoking: a review. Ann N Y Acad Sci 2012; 1248:107–123
61.
Benjamin-Garner R, Oakes JM, Meischke H, et al: Sociodemographic differences in exposure to health information. Ethn Dis 2002; 12:124–134
62.
Grant BF, Goldstein RB, Saha TD, et al: Epidemiology of DSM-5 alcohol use disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions–III. JAMA Psychiatry 2015; 72:757–766
63.
Heatherton TF, Kozlowski LT, Frecker RC, et al: Measuring the heaviness of smoking: using self-reported time to the first cigarette of the day and number of cigarettes smoked per day. Br J Addict 1989; 84:791–799
Information & Authors
Information
Published In
History
Received: 2 September 2019
Revision received: 25 March 2020
Accepted: 27 April 2020
Published online: 14 August 2020
Published in print: November 01, 2020
Keywords
Authors
Competing Interests
Dr. Compton is a stockholder in General Electric, 3M Companies, and Pfizer. The other authors report no financial relationships with commercial interests.
Funding Information
The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and the NESARC-III were funded by the National Institute on Alcohol Abuse and Alcoholism, with supplemental support from NIDA.
Metrics & Citations
Metrics
Citations
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
View Options
View options
PDF/EPUB
View PDF/EPUBGet Access
Login options
Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.
Personal login Institutional Login Open Athens loginNot a subscriber?
PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.
Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).