Methods

THE SIMSMOKE MODEL

The clean air module is part of a larger computer simulation model of tobacco control policies, known asSimSmoke. That model predicts future prevalence rates and smoking attributable deaths across the US population and by age, sex, and racial/ethnic group.

Discussed at greater length elsewhere,6 7 SimSmoke begins with the number of smokers, never smokers, and six categories of ex-smokers (< 1, 1–2, 3–5, 6–10, 11–15, > 15 years) distinguished by age, sex, and racial/ethnic group in the baseline year 1993. As each cohort moves through time, the population model allows for births and deaths, and the smoking model allows for initiation, cessation, and relapse. A first order discrete Markov process is employed.

Smokers are defined as individuals who have smoked at least 100 cigarettes in their lifetime and are currently smoking. Individuals are classified as never smokers from birth until they initiate smoking or die. Since initiation generally occurs before the age of 25 years,2 initiation is modelled to occur until age 25. Initiation rates at a particular age are measured as the change in prevalence rate between those smoking at one age and those smoking at the previous age. This method insures that the number of smokers equals the number of actual smokers at age 25, rather than relying on separate, unstable measures of initiation and cessation for those under age 25.

Ex-smokers are defined as those who have smoked at least 100 cigarettes, and are not currently smoking. The rate at which smokers become ex-smokers depends on the first year quit rate (net of relapse). The first year average quit rate is calculated by the quit rate model with adjusters for the age, sex, and racial/ethnic groups.8 Smokers may relapse in future years.

The source of data on prevalence, initiation, and cessation rates by demographic group for those ages 15 and above is the 1992/3 tobacco supplement of the Current Population Survey,9 and for those below age 15, the 1993 Teenage Attitudes and Practices Survey.10 Relapse rates are based on various sources.11-14 Population data are from the 1993 Census of Population, fertility rates are from the US Census vital rate inputs tables, and mortality rates are from the 1993 multiple cause-of-death file.15

Based on prevalence and cessation rates and on risks of smokers and ex-smokers relative to non-smokers, deaths attributable to smoking are predicted. Excess risk caused by smoking is calculated as the difference between death rates of a smoking category and the death rate of never smokers (where death rates are standardised to preserve the overall population death rate). Excess risk is multiplied by the population of the relevant demographic/smoking group projected for a particular year to obtain smoking attributable deaths. Deaths are distinguished by age, sex, and smoking groups, based on the Cancer Prevention Study II.16

ASSUMPTIONS AND STRUCTURE OF THE CLEAN AIR MODULE

In developing the clean air module, simplifications and estimates were developed with our expert advisers based on: (1) policy relevance, (2) empirical support, and (3) tractability.

Focus on clean air laws

Because SimSmoke is a public policy model, we consider the passage of laws requiring smoking bans. Instead of passing laws, public action may be taken to exhort businesses or homes to impose their own smoking bans voluntarily. We do not consider these public policies, because they are not commonly employed by government or empirically studied.

Laws implemented at the state level

Since SimSmoke uses US prevalence rates, we examine the policy effects at a national level. We assume, however, that policies are implemented by individual states, which commonly implement most clean air laws and are the subject of most empirical studies.3 4

Categories of clean air laws

The module includes four categories of clean air laws: workplaces, restaurants, schools, and other public places. Worksites are designated as a separate entity because smokers spend many waking hours at their places of employment. Although the number of hours an individual may spend in a restaurant per day or week is small, eating and smoking are often linked, and these laws typically generate much media attention. School clean air policies are included because they reduce opportunities to smoke and affect norms when they are actively publicised and enforced. In the model, they influence those below the age of 18 (for example, college bans are not considered).

“Other public places” denotes public places besides restaurants and schools, and includes shopping malls, retail stores (including grocery stores), enclosed arenas, and public transit.17 18Restrictions in government buildings, hospitals and childcare centres are subsumed under workplace laws, and elevators, prisons, and hotels/motels were considered to have limited exposure. Although “other public places” are typically less controversial than restaurant bans, they further limit the places in which a smoker can smoke. In addition, they are often the first laws that states implement. They are classified as a single group rather than as separate sites for simplicity, and because there was little basis to distinguish their effects.

The effect of laws is likely to depend on how broadly they apply. We assume that the laws ban all smoking in the designated area, except for workplace laws. The partial workplace ban disallows smoking in work areas, but allows smoking in some designated common areas.

Publicity and enforcing the law

Efforts to pass clean air laws are generally accompanied by media publicity and community organisation. Media publicity after the law is enacted may be important in informing and getting business and other establishments to comply with the law and may shape social norms. Active enforcement may also increase the perception that smoking is not condoned and may be necessary to insure compliance with the laws in less politically mobilised communities.3 Empirical studies suggest that enforcement and publicity have an impact,19-22 but the magnitude could not be ascertained from studies.4 Publicity and enforcement are considered part of the policy.

Impact on smoking initiation and cessation

Prior studies of smoking restrictions4 have found the greatest effects on the amount smoked per smoker within six months, with some tendency for these effects to dissipate over time. Reductions in prevalence rates and increases in quit rates become more pronounced over time.4

Like other policy modules in SimSmoke, the effects of each clean air law are developed in terms of the percentage change in prevalence rates from their initial rate. The effects are assumed to occur directly through the prevalence rate, and indirectly through initiation and quit rates. For a particular demographic group, we apply the same percentage effect to each of these rates. The effect on the prevalence rate is applied in equal increments (one third of the total effect) to each of the first three years in which the law is implemented. This represents the immediate cessation that occurs in response to the laws. Decreases in the average amount smoked by those who continue smoking are expected to increase cessation further each year that a law is in effect.23 Increases in the one year quit rate each year that the law is in effect lead to further reductions in prevalence after the first three years. Reductions in prevalence during the first three years in which the policy is in effect are sustained through decreases in the initiation rate.

The structure of the model is shown in fig 1. The number of smokers is decreased directly through reductions in prevalence rates, and indirectly through higher cessation rates and lower initiation rates. As the number of smokers declines, fewer deaths are attributable to smoking.

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Figure 1

Effects of new smoking restrictions on smoking behaviour and health of smokers.

Effects of policies are independent

Knowledge about the interactive effects of other tobacco control policies, such as media policies and taxes, with clean air laws is limited.4 There may be a synergistic relationship of different policies on social norms and/or smoking behaviours. Alternatively, if other policies (for example, taxes) already reduce smoking opportunities, clean air laws may have less impact. In accordance with other policy modules, we make the neutral assumption that the effects of policies are independent of each other—that is, the effects of clean air laws on prevalence rates are unaffected by other policies in place.

Prior studies also do not explicitly consider interactive effects between each type of clean air law.4 While there may be synergistic effects of the laws on social norms, a restaurant law alone may have a greater effect on norms than its incremental impact with a worksite ban already in place. We assume that the effects of different clean air laws are independent.

Health impacts limited to smokers

Considerable evidence links exposure to smoke with health and even deaths to non-smokers, and this evidence provides a strong justification for the passage of clean air laws. The current version ofSimSmoke, however, is limited to considering only the deaths of smokers. The effects of clean air laws on non-smokers will be considered separately when deaths from environmental tobacco smoke are incorporated intoSimSmoke.

MAGNITUDE OF EFFECTS

In comparing an extensive law (including worksite, restaurant and other public places) to a minimal clean air law, Emontet al 24 and Ohsfeldtet al 25 found 12–14% reductions in prevalence rates. Chaloupka and Saffer,26Wasserman et al,27 and Yurekli and Zhang28 found 4–20% reductions in per capita consumption, which includes changes in quantity smoked by continuing smokers and prevalence, and thus serves as an upper bound. Based on these studies, Levy and Friend4 estimate a 10% reduction in prevalence rates, with a range of 6–14%, as a result of extensive laws within three years after implementation

Prior literature provides limited evidence on the effects of separate clean air laws, except worksite laws. To distinguish the effects, we considered how laws affect social norms and smoking opportunities.

Workplace laws

Ohsfeldt et al 25 found that 60–70% of the effects of clean air laws were attributable to worksite laws. Using the 10% reduction for extensive laws, a 6–7% reduction in prevalence rates was attributable to worksite laws. Both individual worksite and population based studies indicated that workplace restrictions implemented by private entities reduced prevalence rates by 10–20%, with most population based studies finding effects closer to 20%.4 Since only 63% of the adult population worked in 199329 and 67% of workers worked indoors,30 the effects would be applied to 42% (67% of 63%) of the adult population. Thus, the 10–20% reductions found in studies of workers would imply a 4–8% reduction for the overall adult smoking population. These results are consistent with the 6–7% estimate from Ohsfeldt et al.25 We estimate a total reduction of a 7% in the average prevalence rate for the adult population.

A less than a complete ban provides the worker greater opportunity to smoke at work. Farrelly et al 31and other studies4 have found that partial bans had about half or less than half the effect of a complete ban. In addition, partial bans may not be readily enforceable. We estimate that partial bans have one third the impact of a full ban (that is, a 2% reduction in prevalence rates).

The effects of worksite laws may vary by demographic group, because of different reactions to worksite laws or variations in labour participation rates. Based on labour force participation rates,32 prevalence rates inSimSmoke, and results from Farrellyet al,31 we multiplied the effect of workplace laws by a factor of 1.06 for whites, Asians, and others and by a factor of 0.65 for African Americans and Hispanics. Farrely et al 31 do not find differences in reactions by males and females; female participation rates are on average 80% that of males and females. We scaled the effect on males by a factor of 1.10 and the effect on females by a factor of 0.91. Conflicting effects by age group were found by Farrellyet al 31 and by Ohsfeldtet al.25 Correcting only for differences in labour participation rates, we scaled the effects of those ages 18–19 by a factor of 0.9, those ages 20–25 by 1.05, those ages 26–35 by 1.11, those ages 36–49 by 1.10, those ages 50–59 by 0.90, those ages 60–79 by 0.3, and those ages 80–89 by 0%. We estimated that smokers below 18 years of age do not reduce their prevalence rates except through reductions in initiation rates. They are much less likely to work a significant number of hours, but initiation rates are affected through norms and the reduced availability through parents and older peer smokers.

Restaurant and other public places laws

Based on the estimates that a comprehensive clean air law has a 10% effect and a worksite law alone has a 7% effect, a 3% impact is implied for both restaurants and for other places. The smaller effects are consistent with expectations that smokers generally have much less exposure to these places than worksites. Most of the impact of restaurants and other places is expected to occur through changes in social norms. A restaurant ban sends a message that smoking is not socially acceptable in public. The publicity that often accompanies the passage of these laws is also likely to further impact norms. Because laws pertaining to other places tend to be less controversial to implement, they are expected to have a smaller effect than restaurant bans. A 2% maximal effect was estimated for restaurant laws and 1% for other places combined. Because of a lack of evidence or theoretical justification, the effect of public place bans is not distinguished by demographic group.

School laws

While some studies4 33 found that school policies reduced youth prevalence rates, the magnitude of effects could not be ascertained.4 Since all schools had at least classrooms bans through federal and state laws by 1993, prohibitions on smoking in school grounds are expected to have little impact in reducing opportunities to smoke, especially in the absence of enforcement and penalties for violating the law. Strict school bans might, however, carry a strong message to students that smoking is not acceptable, particularly if the law is backed by enforcement. A complete school ban is estimated to have a 1% effect on youth prevalence.

EFFECTS OF ALREADY EXISTING LAWS AND PRIVATE POLICIES

New policies in the model are implemented in the year 2000 and their effects are predicted through to 2040. We consider how the effect of new laws depend on the existence of existing laws and smoking restrictions voluntarily imposed by firms.3 The effect of existing laws and private policies are tracked in the model from 1993 through 2000. As shown in fig 2, the laws are differentiated by type and stringency, and are aggregated over the states weighted by the population of smokers in the states. Private work restrictions in the state are distinguished by their stringency relative to the laws in the state.

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Figure 2

Impact of existing clear air laws and work restrictions.

Using data from the Centers for Disease Control and Prevention (CDC),17 18 34 states with “no smoking allowed (100% smoke free)” were counted as 100% of the effect, with “no smoking allowed or designated smoking areas allowed if separately ventilated” as a 50% effect, and with “designated smoking areas required or allowed” as a 25% effect. Because data were not available from the CDC before 1995, we relied on the date of implementation and American Lung Association (ALA)35 data for 1993 and 1994. Local laws are from National Cancer Institute (NCI) and are weighted by the local population.36 37 For school policies, data were only available through the ALA.35 A state index was created for each type of law and each year. BecauseSimSmoke is a national model, the state indices for each type of law were weighted by the state smoking population for each year.38

The state representative 1993 and 1996 Current Population Survey tobacco use supplements provided data on the percentage of workers in firms already with private workplace restrictions.39 They classified worksites as smoke free, strong (smoking is not allowed in work areas, but in some public areas) and moderate (some or all work areas and some or all public areas). We weighted the latter two categories by 0.5 and 0.25, respectively, and summed over the three categories. For 1999, data were obtained from the CDC.40We interpolated between years and assumed the same rates in 2000 as in 1999. For non-worksite laws, we assumed no prior effects of privately imposed restrictions, since much of their effect is through changes in norms as a result of passing the law.

Worksite laws are likely to have less effect on worksites that already have restrictions than those that do not, but may increase anti-smoking sentiment and make it more difficult for workers to switch jobs to work in a firm that allows smoking. We assumed that a new law boosts the effect of a private worksite policy already in place by 33%. Further adjustments incorporated changes in labour participation rates, which increased by 6% between 1993 and 2000. We assume that effects of a law are randomly distributed over existing firms independent of their worksite practices. The final index for the worksite laws was obtained by multiplying (1 − state laws index) by (1 − private worksite ban index) and by the labour participation index.