1 Cancer Prevention and Control Program, University of California, San Diego, Cancer Center, La Jolla, CA.
2 Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD.
3 Information Management Services, Silver Spring, MD.
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ABSTRACT |
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outcome assessment (health care); public health; smoking; tobacco
Abbreviations: CPS, Current Population Survey; IOI, initial outcomes index
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INTRODUCTION |
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Comprehensive tobacco control programs include resources (both monetary and human) to employ a wide variety of efforts aimed at influencing the social environment. These efforts include 1) working to educate the public (via paid mass media programs, public service announcements, or media advocacy), 2) encouraging enactment of new legislation or policies (to increase tobacco taxes, create smoke-free workplaces, or regulate tobacco advertising), 3) fostering smoking cessation (by increasing awareness of and access to existing programs or developing new cessation methods or programs), and 4) advocating for increased enforcement of existing laws (including youth access laws or laws restricting smoking). The immediate results of these tobacco control efforts are what we have labeled "initial outcomes." These outcomes can be quantified and can serve as early indicators of tobacco control efforts (8). Examples of initial outcomes include greater population exposure to antitobacco messages or news coverage, increased tobacco taxes, stronger laws for regulating smoking in public places, more smokers receiving smoking cessation assistance, increased compliance checks, and assessment of penalties to prevent illegal tobacco sales to minors or for violations of workplace smoking restrictions. Initial outcomes can be viewed as the first steps along a continuum of change that ultimately should result in reduced tobacco use by the general population. To rank states according to these initial outcomes requires uniform measures for each state, some assurance that these outcomes can influence smoking behavior, and some variability in the measures among states.
Appropriate measures currently are not available for all of these initial outcomes. However, all states include tobacco excise taxes in the retail price paid by the consumer, and each year the tobacco industry uniformly reports the price of cigarettes in each state to the Federal Trade Commission. The average price per pack differs considerably among states, and there is ample evidence that price affects smoking behavior (9, 10
). Community concern about the harmful effects of secondhand tobacco smoke has led to local and statewide ordinances restricting smoking in public places as well as in the workplace (11
, 12
). Adoption of workplace smoking restrictions has not been uniform across all states (11
, 12
), but when these restrictions are in effect, they have been shown to modify smoking behavior (13
18
). Concern about the health consequences of secondhand smoke also can lead to considerable pressure against exposure to smoke in one's personal environment (19
). One form of this pressure is imposition of home smoking restrictions, and complete home smoking bans have also been associated with smoking behavior (20
). State-level measures of the proportion of the population working or living under smoking bans are available from the tobacco use supplement to the Current Population Surveys (CPS) of 1992 and 1993. These measures vary considerably. As standard measures become available for other initial outcomes, they may also prove to be candidates for inclusion in the index. For the present study, we focused on three initial outcomes: cigarette price, workplace smoking bans, and home smoking bans.
We ranked the 50 US states and the District of Columbia (referred to in this paper as the 51 states) according to each of these three initial outcomes; in this paper, we report the correlation of each outcome with common measures of smoking behavior: per capita cigarette consumption and smoking prevalence. We also created z scores for the initial outcomes, summed each of the standardized variables to form an index, and correlated the resulting initial outcomes index (IOI) with the smoking behavior measures. Finally, we divided the 51 states into three groups determined by IOI tertiles and examined the group mean smoking prevalence and trends in per capita cigarette consumption.
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MATERIALS AND METHODS |
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Because the public-use data files for these three surveys include weighting variables for all respondents (n = 291,054) and for self-respondents (n = 237,733), population estimates could be determined. The weights are computed to be consistent with 1990 US Census totals based on state population and are representative of the US population by gender, age, and race/ethnicity distributions. Persons for whom data were missing (<1 percent) for smoking status were omitted from the analysis.
Cigarette price.
Each November, The Tobacco Institute publishes the weighted average price per pack of cigarettes for each US state and the District of Columbia (23). The price analyzed includes state and federal (but not local) excise taxes and state (but not local) sales tax. The weighting reflects the brand mix, whether the cigarettes were purchased by the pack or the carton, and the type of store in which the cigarettes were purchased. However, the weights are national rather than state specific. We did not attempt to adjust cigarette price by a cost-of-living indicator for each state because there is no agreed-upon method for accomplishing such an adjustment.
Cigarette sales.
The Tobacco Institute also compiles data and reports on tax payments from all packs of cigarettes removed from wholesale warehouses to retail outlets within each state (24). The reporting unit is the number of cigarette packs on which taxes were paid in any given month. The present analysis included data from February 1983 through March 1997. While consumption estimates obtained from this source are subject to monthly and seasonal variations that are business related rather than reflect variation in consumption patterns, these data are gathered uniformly across states and are the usual source for estimates of national per capita cigarette consumption. This data source is used by both the US Surgeon General's Office for its reports on smoking and health and by the US Department of Agriculture (25
).
Measures of smoking behavior
Smoking prevalence.
CPS respondents were asked, "Have you (or the person responded for) smoked at least 100 cigarettes in your (his or her) entire life?" Those with "yes" responses were considered ever smokers and were asked a second question: "Do you (or the person responded for) now smoke cigarettes every day, some days, or not at all?"
We computed the percentage of current smokers (defined as those who smoked at least 100 cigarettes in their lifetime and indicated at the time of the survey that they smoked either every day or some days) among adults aged >24 years and also considered the percentage of current smokers among young people aged 1524 years. Both self- and proxy reports of smoking status were included in the determination of smoking prevalence.
Per capita cigarette consumption.
Changes in total cigarette sales within a state may not be related to changes in smoking behavior if the population of that state has changed. We estimated per capita consumption for a given state in any given year by using US Bureau of the Census estimates for the state population aged 18 years. We followed the Surgeon General's age criterion of 18 years (9
) because only about 1 percent of the cigarettes sold in the United States are smoked by people aged
18 years (26
). We used decade US Census population data computed as of April 1, 1980 and 1990 and supplemental within-decade estimates computed as of July 1 of each year (27
, 28
). To obtain monthly estimates of state populations, we interpolated from regression lines fitted to the yearly census data.
Our per capita consumption estimates were based on the total number of packs removed from wholesale warehouses in a given month divided by the population estimate for that month. Since retail outlets appear to stock up on cigarettes in the last month of both the fiscal and calendar years, we partially removed this source of variation by considering bimonthly averages (e.g., December/January, February/March). To further distinguish the underlying or deseasonalized trends, we applied the SABL procedure (available in the statistical package S-Plus) to the bimonthly data (29, 30
). As a point estimate of per capita cigarette consumption for 19921993, we considered the mean per capita consumption for the 10-month period from August/September 1992 through April/May 1993, a period that encompassed the CPS tobacco use supplements.
Measures of tobacco control initial outcomes
Cigarette price.
We used the mean of the weighted average price of cigarettes for November 1992 and November 1993 to adjust for price fluctuations during the period corresponding to the interval during which CPS data were collected. During this period, 16 states increased their tobacco excise taxes (1365 cents), and the federal excise tax on cigarettes increased by 4 cents on January 1, 1993. However, in most states, the weighted average per-pack price of cigarettes was 515 cents lower in November 1993 than it was in November 1992 as a result of a tobacco industry price cut for premium brands of cigarettes (31).
Workplace smoking bans.
CPS tobacco use supplement self-respondents were asked, "Which of these best describes the area in which you work most of the time?" A response indicating that the person worked indoors and outside the home and was not self-employed led to a further question, "Does your place of work have an official policy that restricts smoking in any way?" Those who answered "yes" were then asked, "Which of these best describes your place of work's smoking policy for work areas?" and "Which of these best describes your place of work's smoking policy for indoor public or common areas?" Those who reported that smoking was "not allowed" (one of the response choices) in any work areas and was "not allowed" in any public areas were considered to be covered by a workplace smoking ban. State workplace smoking ban status was calculated as the percentage of self-respondent ever smokers aged 15 years who were eligible to be asked the questions regarding a workplace smoking ban. Percentage of ever smokers was used because smoking bans would be more likely to influence current and former smokers than never smokers. The only possible behavioral effect on never smokers would be to prevent smoking uptake in some young people (aged 1524 years) by making it difficult for them to establish nicotine tolerance. Current smokers may modify their smoking behavior in response to bans, and former smokers may be less likely to relapse if they work where they cannot smoke. Recent former smokers may also have quit in response to smoking bans.
Home smoking bans.
CPS tobacco use supplement self-respondents were also asked about the existence of smoking restrictions in their homes: "Which statement best describes the rules about smoking in your home?" We considered a home smoking ban to be in place if the respondent indicated that "no one is allowed to smoke anywhere." As defined for workplace smoking bans, state home smoking ban status was taken as the percentage of self-respondent ever smokers aged 15 years that reported a home smoking ban. Since all household persons aged
15 years responded to the full tobacco use supplement, discrepant reporting was possible about the existence of a home smoking ban. Although agreement concerning the existence of a home smoking ban was high, it was not perfect.
Statistical analysis
For each of the three index factorscigarette price per pack, workplace smoking bans among ever smokers, and home smoking bans among ever smokerswe computed z scores for each of the 51 states. Next, we summed the z scores for the three factors to form a tobacco control IOI and computed a Cronbach's alpha coefficient (32). The IOI was then correlated (Pearson's) with adult and youth smoking prevalence and the point estimate of per capita cigarette consumption. Correlation coefficients were also computed for the index component variable z scores among themselves and with prevalence and the point estimate of per capita cigarette consumption.
Finally, we classified the states into three groups based on tertiles (n = 17 each) of the IOI. Mean smoking prevalence and per capita cigarette consumption (from the point estimates for each state) among the three groups of 17 states were compared by using analysis of variance with correction for multiple comparisons (two-tailed Bonferroni's method). As described above, we also plotted the bimonthly raw per capita data from February/March 1984 to February/March 1997 for each tertile group along with the deseasonalized trends.
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RESULTS |
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Table 1 shows the correlations among the index factors' z scores that comprise the IOI and for each factor with adult smoking prevalence, youth smoking prevalence, and the point estimate of per capita cigarette consumption. The relative level of ever smokers in each state who were indoor workers with workplace smoking bans was highly correlated with the relative level of ever smokers with home smoking bans. The correlations of these factors with cigarette price were not as strong. All three index factors showed a significant relation to the outcome measures of adult smoking prevalence and per capita cigarette consumption. However, the relations were weaker for youth smoking prevalence, and only the correlation of home smoking bans with youth smoking prevalence was statistically significant. Of note is that price correlated highly with per capita cigarette consumption but less strongly with adult smoking prevalence.
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In the highest tertile group, per capita cigarette consumption ranged from a low of 6.3 packs per month in Hawaii to a high of 12.7 packs per month in Vermont, with a mean of 9.1. In the middle tertile group, per capita consumption ranged from a low of 7.9 packs per month in New Mexico to a high of 16.2 packs per month in New Hampshire, for an overall mean of 10.5. Finally, in the lowest tertile group, consumption ranged from 9.3 packs per month in Montana to 18.0 packs per month in Kentucky, for a mean of 12.2. The mean per capita consumption for the highest tertile group was statistically significantly different from the two lowest tertile groups (overall analysis of variance, followed by pairwise comparisons, p < 0.05).
Figure 5 shows the bimonthly raw per capita cigarette consumption data and the SABL deseasonalized trends for the three groups of states from February/March 1983 to February/March 1997. Before 1991, per capita cigarette consumption in the tertile groups seemed to be different, but the downward trends were roughly parallel. Between 1991 and 1994, the downward trends for the highest and middle groups continued, but the downward trend for the lowest IOI group seemed to diminish. After 1994, the trend for the lowest tertile group indicated an increase in consumption, the middle tertile group no longer showed a downward trend, and the trend for the highest group was still downward.
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DISCUSSION |
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There were significant correlations among the IOI components, which is to be expected, since most current tobacco control programs use a shotgun approach. The multiple interventions may exhibit some degree of synergy. For instance, a smoker who works where smoking is banned may be more likely to agree to a smoking ban at home. When additional standard and widely available measures for initial outcomes become available, it is likely that they will correlate with these components and among themselves. It will be important to keep the index parsimonious and add only those variables that have been demonstrated to be related to smoking behavior and that vary considerably among the states. Other possible additional factors include assessment of actual state or local antitobacco legislation and a measure of exposure to antitobacco-related media (8).
Although there were significant and strong correlations between both the IOI components and the overall index with per capita cigarette consumption and adult (aged 25 years) smoking prevalence, only home smoking bans were correlated with smoking prevalence for young people aged 1524-years. The youth smoking prevalence estimates were less precise than the adult ones because of small state-level sample sizes. Also, proxy reports of smoking status for younger people may be more likely to be in error, because more people in this age group are occasional smokers (33
). Both of these factors may hinder detection of any underlying relation that might exist between prevalence and workplace smoking bans or cigarette price. The fact that only about 16 percent of the younger people in our study were indoor workers further limited the possibility of detecting a relation. The lack of a significant relation between price and youth smoking prevalence is more difficult to explain. Most evidence of a price effect on youth smoking comes from econometric analyses of cross-sectional data (34
36
). A number of research projects are currently investigating this issue further, some in the setting of multistate longitudinal studies. Perhaps it will be essential that initial outcomes more directly related to youth smoking, such as success in restricting access or tobacco advertising and promotions, will be required to develop a useful index for the younger population.
We considered using different definitions of tobacco control initial outcomes in the index. The amount of new state excise taxes adopted between 1989 and early 1993 would have been a fairly direct measure of an initial outcome from tobacco control activity. However, the price data we used included both federal and state excise taxes and sales taxes. Furthermore, since the tobacco companies can manipulate the price of cigarettes (31), tax increases may not always be reflected in cigarette price. The CPS data provided a reasonably accurate estimation of workplace smoking bans based on workers' self-report of the types of smoking restrictions at their workplace. In the future, information from the National Cancer Institute's State Cancer Legislative Database (12
) could also be included; this database would provide information concerning the type of state legislation restricting workplace smoking in effect in each state.
Interestingly, as in previous analyses (37), current smokers who worked indoors were less likely to report workplace smoking bans (36 percent) than were former or never smokers (both 49 percent). We repeated the entire analysis by considering all indoor workers, regardless of smoking status, and just current smokers (rather than ever smokers), and the results were only slightly different. Another potential definitional problem was an inconsistency in the report of home smoking bans. Reanalyzing the data (counting a report by an ever smoker of a home smoking ban only if all other adults in the home also reported the ban) did not improve the correlation of this variable with smoking prevalence. Because both workplace and home smoking bans might be considered an indication of societal norms rather than a direct influence on smoking behavior, home smoking bans were also analyzed for the entire population, not just for ever smokers. Since there was a very high correlation (r = 0.96) between the two measures, the results changed only slightly. Finally, we examined the percentage of ever smokers in each state with workplace and home smoking bans computed only for those aged
25 years, which would correspond to the adult population considered in the present study; again, the results changed very little.
As one measure of smoking behavior, we used the number of packs of cigarettes removed from wholesale to retail outlets as a surrogate measure of per capita cigarette consumption. However, some cigarettes sold are probably never smoked, and cigarettes that smokers acquire from other than retail outlets (e.g., military bases, Indian reservations, state or national cross-border smuggling) are not included. Econometric models could potentially account for the impact of these and other factors (e.g., differences in state demographic composition) on consumption (38), and such refinements might improve the correlation of per capita consumption with the IOI. When the mean self-reported daily cigarette consumption for smokers included in the CPS was used instead of the sales data as a measure of cigarette consumption in each state, the correlation with the IOI was somewhat lower (r = 0.64, p < 0.0001). As expected, reported daily cigarette consumption and per capita consumption computed from the sales data were substantially correlated (r = 0.74, p < 0.0001). We also were concerned that smoking prevalence may vary among states more because of demographic factors than because of tobacco control activity. However, a partial correlation analysis that adjusted for the state population percentages of females, African Americans, and Hispanics and for median age changed the results only slightly.
Despite these potential limitations, the correlations between the tobacco control IOI and the adult smoking prevalence and per capita cigarette consumption are significant, fairly strong, and robust with respect to variations in definition and analysis. The observed trends in per capita consumption (figure 5) support the hypothesis that those states that have more tobacco control initial outcomes are reducing consumption more than states with lower levels of these outcomes. It is interesting that tobacco-growing states (e.g., Kentucky, Tennessee, North Carolina, South Carolina, and West Virginia) were all included in the lowest tertile group, which suggests that if there is significant economic dependence on tobacco, there also may be more resistance to efforts to reduce tobacco use.
Whether the IOI will be associated with changes in state-level adult smoking prevalence must await further data and analyses. However, our findings that price was related more strongly to per capita consumption than to adult smoking prevalence and that the correlation of the IOI with consumption was marginally greater than it was with prevalence suggest that the relation of the IOI to declines in prevalence may be less strong than for consumption. Smokers may take many years to quit successfully (39) but respond in the short term to tobacco control interventions by reducing their consumption (40
).
Ranking of tobacco control activity by the IOI should, over time, prove useful in determining whether statewide tobacco control efforts are producing initial outcomes that should eventually lead to reduced smoking. It is expected that states with the relatively highest IOI scores will eventually see the greatest declines in smoking. Furthermore, low scores may provide an incentive for some states to adopt new tobacco control measures.
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ACKNOWLEDGMENTS |
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The authors acknowledge Dr. Barry I. Graubard, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, for helpful methodological discussions.
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NOTES |
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REFERENCES |
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