8: No. 6, November 2011
Jenine K. Harris, PhD; Caroline Geremakis, MPH; Sarah Moreland-Russell, MPH; Bobbi J. Carothers, PhD; Barbara Kariuki, MPH; Sarah C. Shelton, MPH; Matthew Kuhlenbeck, MHA
Suggested citation for this article: Harris JK, Geremakis C, Moreland-Russell S, Carothers BJ, Kariuki B, Shelton SC, Kuhlenbeck M. Demographic and geographic differences in exposure to secondhand smoke in Missouri workplaces, 2007-2008. Prev Chronic Dis
http://www.cdc.gov/pcd/issues/2011/nov/10_0197.htm. Accessed [date].
African Americans, Hispanics, service and blue-collar workers, and residents of rural areas are among those facing higher rates of workplace secondhand smoke exposure in states without smokefree workplace
laws. Consequently, these groups also experience more negative health effects resulting from secondhand smoke exposure. The objective of this study was to examine disparities in workplace secondhand smoke exposure in a state without a comprehensive statewide smokefree workplace
and to use this information in considering a statewide law.
We developed a logistic multilevel model by using data from a 2007-2008 county-level study to account for individual and county-level differences in workplace secondhand smoke exposure. We included sex, age, race, annual income, education level, smoking status, and rural or urban residence as predictors of workplace secondhand smoke exposure.
Factors significantly associated with increased exposure to workplace secondhand smoke were male sex, lower education levels, lower income, living in a small rural or isolated area, and current smoking. For example, although the overall rate of workplace exposure in Missouri is 11.5%, our model predicts that among young white men with low incomes and limited education living in small rural areas, 40%
of nonsmokers and 56% of smokers may be exposed to secondhand smoke at work.
Significant disparities exist in workplace secondhand smoke exposure across Missouri.
A statewide smokefree workplace law would protect all citizens from workplace secondhand smoke exposure.
Back to top
Progress has been achieved in the United States during the past 20 years in establishing smokefree environments in homes, workplaces, and public places such as restaurants and bars. Approximately 70% of workers in the United States are now protected by a smokefree workplace policy (1), and the proportion of nonsmokers nationally with detectable cotinine levels (a biomarker for secondhand smoke exposure) has been halved from 88% to 43%, meeting the Healthy People
2010 objective in this area (2,3).
Despite this success, significant disparities in secondhand smoke exposure persist. African Americans are more heavily exposed
to secondhand smoke than whites and Mexican Americans (4). People with lower incomes are more heavily exposed than those with higher incomes (4-7). Certain categories of workers, including blue collar, service, and hospitality workers, are substantially less likely to be protected by a smokefree workplace law and are substantially more likely to be exposed to secondhand smoke on the
job than white collar and professional workers (8-11). Nonsmoking men, younger adults, and those living in a county without any smokefree workplace restrictions are substantially more likely to be exposed to secondhand smoke (12,13) than nonsmoking women, older adults, and
people living in counties with some workplace smoking restrictions. In addition to being exposed more often to secondhand smoke, these groups are also disproportionately burdened by the poor health outcomes
associated with tobacco use and secondhand smoke exposure (14).
Exposure to secondhand smoke can be decreased by implementing comprehensive state and local smokefree workplace
laws (2,12,15-17). Comprehensive statewide laws protect the entire state, including people who have traditionally faced disproportionate exposure to secondhand smoke and health disparities related to tobacco
use (18). These laws can be especially beneficial for disadvantaged groups, leading to a reduction in disparities (19,20).
Statewide laws have also demonstrated effectiveness in reducing the negative health consequences of secondhand smoke exposure (21). For example, following enactment of New York’s statewide smokefree workplace
law in 2003, by
2004 the number of myocardial infarctions in New York decreased by 7.9%, and the number of strokes decreased by 11% (21,22).
Missouri does not have a comprehensive statewide smokefree workplace law and ranks 50th (of the 50 states and the District of Columbia) in the percentage of indoor employees exposed to secondhand smoke — 12% compared
with 7.3% nationwide (23). The objectives of this study were to 1) identify the
characteristics of Missourians most affected by secondhand smoke exposure in the
workplace, and 2) describe geographic variation in exposure to secondhand smoke
statewide. A secondary objective was to examine the support for
a statewide smokefree
Back to top
We conducted a descriptive cross-sectional study by using survey data collected in the 2007 Missouri County-Level Study (CLS). The Missouri Department of Health and Senior Services and the Missouri Foundation for Health conducted the CLS
to determine county-level prevalence of behavioral risk factors, chronic
diseases, and preventive practices among adults (www.health.mo.gov/data/cls/). Survey administration followed Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor
Surveillance System (BRFSS) methods; the survey consisted of items from the BRFSS and CDC Adult Tobacco Survey. Data were collected
from February 2007 through April 2008 via telephone interviews of adults aged 18
years or older. The response rate was 60.3% based on response rate formula 2 (RR2) from the American Association of Public Opinion Research (24).
There were 49,513 completed interviews; no information was collected on nonrespondents. Of the 49,513 participants, 30,398 indicated that they were currently employed for wages or self-employed; 23,923 indicated that they worked indoors most of the time, and 23,820 (99.6%) of indoor workers responded to the question assessing secondhand smoke in the workplace. We conducted analyses on these 23,820 participants. Data were weighted to be representative of the Missouri adult noninstitutionalized population based on distributions of age, sex, race, and county of residence from the 2000 census.
We selected the following individual-level characteristics to study: sex, age, race, annual income, education level, and smoking status.
A review of studies on the reliability and validity of BRFSS measures found age,
sex, race/ethnicity, education, income, and smoking status to have high
reliability and moderate to high validity (25). Although occupation has been identified as a factor in secondhand smoke exposure, it was not assessed in the CLS. Age was measured in years.
Race was identified by 4 categories: non-Hispanic white, non-Hispanic black, Hispanic, or other. Annual income was measured in 6 increments, starting with less than $15,000 as the lowest category
or more as the highest category. Income was treated as continuous in the models. Education was categorized into less than high school
graduate, high school graduate or General Educational Development certificate, some college, or college graduate. Smoking status was collapsed from current, former, and never smoker status into current smoker and nonsmoker. The county-level predictor was rurality, and was determined by the Rural-Urban Continuum Codes (RUCC) (www.ers.usda.gov/briefing/rurality/ruralurbcon/),
which we classified into 4 categories (urban, large rural, small rural, or
Dependent variables were workplace secondhand smoke exposure and support for
a smokefree workplace law. The workplace secondhand smoke exposure variable was
measured by determining how many participants answered yes to both of the
questions “While working at your job, are you indoors most of the time?” and “As
far as you know, in the past 7 days, has anyone smoked in your work area?”
Support for a smokefree workplace law was measured by response to the question
“Some cities and towns are considering laws that would make workplaces smokefree
by prohibiting smoking in all indoor workplaces, including restaurants, bars and
casinos. Would you support such a law in your community?” Even though these
questions are specific to support of smokefree workplaces
laws at the local
level, support for local laws can increase support and demand for a statewide
law by increasing awareness, demonstrating the ease of implementation, and
changing social norms (23,26). Perceptions of the health effects of secondhand
smoke was measured by response to the item, “Do you think that breathing smoke from other people’s cigarettes is: 1-Very harmful to one’s health; 2-Somewhat harmful to one’s health; 3-Not very harmful to one’s health; and 4-Not harmful at all to one’s health.” The variable was collapsed into a binary variable where
yes represented responses 1 and 2 and no represented responses 3 and 4.
People with missing data for any of the independent variables were not included in the final model. We used HLM 6.08 for Windows (Scientific Software International, Inc, Lincolnwood, Illinois) for multilevel modeling and SPSS 17.0.1 (SPSS, Inc, Chicago, Illinois) for all other analyses.
We employed bivariate statistics to describe the sample and examine differences in workplace secondhand smoke exposure related to geographic and demographic characteristics. To determine whether a multilevel modeling strategy was appropriate, we used 2 strategies to test the heterogeneity of proportions, that is, does workplace secondhand smoke exposure vary significantly by county? First, we calculated an intraclass correlation coefficient (ICC [ρ]) specifically developed for binary
outcomes (27) between workplace secondhand smoke exposure and county of residence. Second, we conducted a χ2 test of county and workplace secondhand smoke exposure (28). In addition to examining the significance of the χ2 statistic, we examined the standardized residuals to determine any significant differences. Standardized residuals
with a magnitude greater than 2 are major contributors to significant χ2
results; they indicate that the observed frequency was
much higher than expected, and standardized residuals less than
−2 indicate that the observed frequency was much lower than expected.
Although we found a low ICC value (ρworkplace = 0.014), the χ2 analyses found significant differences across Missouri counties for exposure to secondhand smoke in the workplace (χ2(114) = 334.4; P < .001). Because of this heterogeneity of proportions, we used multilevel modeling to examine secondhand smoke exposure in Missouri, accounting for individual and county-level differences. Specifically, we developed multilevel logistic
regression random intercepts models consisting of the following: 1) null model, 2) individual-level characteristics (model 1), and 3)
model 1 plus county-level characteristics (model 2). We assessed measures of model significance (χ2)
and model fit (Akaike Information Criterion [AIC], deviance) and compared models by using the likelihood ratio (LR) test.
AIC and deviance are used with nested models to quantify the lack of fit in 1
model relative to the other; lower AIC values represent better fitting models
with less lack of fit. LR tests are used with nested logistic models to determine which model is a better fit. The LR statistic is the difference between the measures of deviance (lack of fit)
of 2 models,
and it follows a χ2 distribution with degrees of freedom equal to the difference between the parameters in the 2 models (29). A significant LR test indicates that the larger model is a better fit. As a demonstration, we calculated the probability of secondhand smoke exposure in specific groups of Missourians by substituting values representing the groups into our final model.
Finally, to determine the feasibility of adopting and implementing a statewide smokefree workplace
law in Missouri, we conducted bivariate analyses to examine support for a workplace
law across demographic and geographic groups. Where there were significant differences, we examined standardized residuals to determine which groups were significantly different in terms of their support for
a smokefree law.
Back to top
Workplace secondhand smoke exposure
Of the 23,820 indoor workers who responded to the question assessing secondhand smoke in the workplace, 2,740
respondents (11.5%) reported being exposed to secondhand smoke at work in the last week. Extrapolated to more than 2 million Missourians employed in indoor settings, this
finding indicates that approximately 236,000 Missourians are exposed to secondhand smoke in their workplaces.
Demographic characteristics of those exposed and not exposed to secondhand smoke
at work are shown in Table 1,
and a few of the notable differences are demonstrated in Figure 1. Among those exposed to secondhand smoke in the workplace,
60.9% were nonsmokers, and 88.9% believed
secondhand smoke exposure was harmful to health; however, 62.0% declared that they were not in favor of a local smokefree workplace
law. Close to half (41.2%) of those not in favor were current smokers
(data not shown). There was a significant association between smoking status and support for a local workplace
law among those who were exposed (χ2(1) = 419.7; P < .001); 13.7% of current smokers and 53.7% of nonsmokers supported a workplace
for a local workplace law was high (60.3%) among people who had not been exposed to secondhand smoke at work.
Figure 1. Percentage of men and women employed indoors and exposed to workplace secondhand smoke, Missouri County-Level Study, 2007-2008. Abbreviation: GED, General Educational Development certificate. Rurality was determined by using the Rural-Urban Continuum codes (www.ers.usda.gov/briefing/rurality/ruralurbcon/).
[A tabular version of this figure is also
Exposure to secondhand smoke varied in counties across the state. A few of the counties with lower than expected secondhand smoke exposure
(standardized residuals less than −2) have communities that enacted comprehensive smokefree workplace
ordinances shortly before the CLS data were collected (Figure 2). All of the counties with lower than expected secondhand smoke exposure had high population densities and were clustered around the 3 largest metropolitan areas, Kansas City, Columbia, and Saint Louis.
Counties with higher than expected secondhand smoke exposure rates were scattered throughout the state, but were particularly concentrated in the southeast boot-heel region, which is mostly rural.
Figure 2. Population density per square mile by county (2000 US Census), exposure to workplace secondhand smoke (2007-2008 Missouri County-Level Study [CLS]), and location of communities that enacted comprehensive smokefree ordinances just before collection of CLS data. Plus and minus signs indicate counties with
much higher or lower than expected workplace secondhand smoke exposure. See the Methods section for more details of this analysis.
[A text description of this figure is also
All 3 models demonstrated statistical significance. A comparison of fit indices (deviance and AIC) indicated that the model including individual and county predictors was the best fit. LR tests comparing model 1 to the null model and comparing model 2 to model 1 yielded significant results. Given the measures of fit and significant LR test (χ2(3) = 18.0; P = .001), model 2 was adopted as the final model
(Table 2). Age, sex, income, education,
smoking status, and rurality were all predictors of
the probability of secondhand smoke exposure. Race, however, was not. Living in
a small rural or isolated area significantly increased the likelihood of
exposure to secondhand smoke at work compared with living in an urban setting.
However, living in a large rural area was not significantly different than
living in an urban setting. Increased age and increased income were associated
with a reduced likelihood of secondhand smoke exposure in the workplace. Male
sex, lower levels of education, and current smoking were significantly
associated with increased secondhand smoke exposure.
The more rural the county a person lived in and the less education they had, the
more likely they were to be exposed to
secondhand smoke at work (Figure 3).
Figure 3. The probability of secondhand smoke exposure at work among white male nonsmokers in Missouri, by education and rurality (average
age, 41.7 y; median annual income, $35,000-$49,999). Probabilities are based on the model presented in Table 2. Area of residence (rurality) was determined by using the Rural-Urban Continuum codes (www.ers.usda.gov/ briefing/rurality/ruralurbcon/). Abbreviation: GED, General Educational Development certificate.
[A tabular version of this figure is also
Overall exposure to secondhand smoke at work across Missouri is 11.5%. However, among young white men with low incomes and limited education living in small rural areas, 40% of nonsmokers and 56% of smokers may be exposed to secondhand smoke at work.
On the basis of our model, the highest exposure category is smokers who were
young black men making less than $15,000 a year with less than a high school
education and living in isolated areas. This group has a 66% chance of being
exposed to secondhand smoke in the workplace. This same group with nonsmoking
status would have a 50% chance of exposure based on our model.
Support for a smokefree workplace law
Among employed Missourians, there were significant differences in the proportion of
respondents supporting a smokefree workplace law by sex (χ2(1) = 113.3; P < .001), education level (χ2(3) = 528.2; P < .001), income category (χ2(5) = 78.3; P < .001), race (χ2(3) = 41.5; P < .001), exposure to secondhand smoke at work (χ2(1) = 468.9; P < .001), and smoking status
(χ2(1) = 3,426.2; P < .001). There were no significant differences (P > .05) in support for a local smokefree
law by age or rurality. Overall, 58% of Missourians support a local smokefree workplace
law, regardless of exposure and smoking status.
Residual analyses indicated that, although our multilevel model showed that the following groups were more likely to be exposed to secondhand smoke, they were also significantly less likely than expected
(standardized residuals less than −2) to support a local smokefree workplace
law: men, smokers, people making less than $75,000 per year, and people with less than a college education.
Respondents exposed to secondhand smoke in the workplace were also significantly less likely than expected to support local smokefree workplace
laws. Conversely, many of those groups who were already protected (ie,
women, college graduates, people with annual incomes of more than $75,000,
people not exposed to secondhand smoke at work, and nonsmokers) were more likely
than expected to indicate support for a local smokefree workplace
laws. Race was not a significant predictor of secondhand smoke exposure in the multilevel model; however, there were a few notable differences in the bivariate
analyses. Specifically, Hispanic and other race Missourians were more likely than expected to support local smokefree workplace
Back to top
The objectives of this study were to identify the demographic and geographic characteristics of Missourians exposed to secondhand smoke in the workplace. In addition, we sought to determine support for smokefree workplace
laws among Missouri residents. Consistent with other studies of secondhand smoke exposure (4-13), we found in Missouri that exposure was significantly more likely for men, younger adults, those with less education, lower income people, current smokers, and residents of
rural or isolated geographic areas. Although significant differences in exposure were shown by race in the bivariate
analyses and have been identified in previous studies, race was not a
significant predictor in the multivariate model. This may be due to the
geographic distribution of race in Missouri; 94.6% of non-Hispanic black
Missourians live in urban areas, where we found significantly lower levels of
exposure to secondhand smoke. Finally, most Missourians support
laws; significantly higher levels of local law support were seen among nonsmokers, women, college graduates, those with high incomes, and people not exposed to secondhand smoke at work.
CDC considers people of low socioeconomic status and African American, Hispanic/Latino, Asian American/Pacific Islander/Native Hawaiian, American Indian/Alaska Native, and lesbian/gay/bisexual/transgender populations as facing tobacco-related disparities. The largest limitation of this study is the exclusion of the last 3 of these populations. In addition,
the sample of Missourians analyzed here did not include residents with wireless telephone numbers only (30) or no telephone service at all (31).
Recent studies have shown some demographic and health-related differences
between people who use only a cellular telephone and those who use a land-based
telephone line. People not reachable during the calling hours may represent important segments of the workforce, such as the service industry who are exposed to secondhand smoke.
Because the location of the participant’s employment was not collected, we were
also unable to account for the existence of local smokefree
laws in our
analysis. However, given the very small number of cities (n = 4) that had
already adopted comprehensive smokefree workplace laws at the time of data
collection, we do not think that this omission affected our results. Although the data were weighted to be representative of the general population, the distribution of income was skewed toward higher income levels,
limiting generalizability. However, because exposure to secondhand smoke is more common in lower income levels, we believe this skew may have precipitated an underestimation of the number of Missourians exposed to secondhand smoke at work.
Although comprehensive smokefree workplaces policies adopted in some Missouri communities appear to have decreased secondhand smoke exposure on a local level, many areas in Missouri have been reluctant to adopt comprehensive smokefree workplace
laws, allowing disparities to persist throughout the state. For example, St. Louis County implemented a smokefree workplace
law on January 2, 2011 (32), but the law is not comprehensive. Exemptions leave those who work in small bars,
casinos, hotels and motels, and several other venues at risk for workplace secondhand smoke exposure. Statewide comprehensive
law is the only method that will ensure that those most at risk in Missouri are protected (33-35) and, on
the basis of prior research, will reduce the overall rate of workplace secondhand smoke exposure by more than half (19). This reduction would provide an additional 133,000 Missourians with smokefree workplaces. Given the evidence related to exposure rates and disparities
and the support for a smokefree law among Missourians, it may be a good time for policy advocates to push for a statewide comprehensive smokefree workplace
Back to top
Funding for this project was provided in part by the Missouri Foundation for Health. The Missouri Foundation for Health is a philanthropic organization whose vision is to improve the health of the people in the communities it serves.
Back to top
Corresponding Author: Jenine K. Harris, PhD, George Warren Brown School of Social Work, Washington University in St. Louis, Campus Box 1196, One Brookings Dr, Saint Louis, MO 63130. Telephone: 314-935-3939. E-mail:
email@example.com. Dr Harris is also affiliated with the Center for Tobacco Policy Research, Washington University in St. Louis, Saint Louis, Missouri.
Author Affiliations: Caroline Geremakis, School of Public Health, Saint Louis University, Saint Louis, Missouri; Sarah Moreland-Russell, Bobbi J. Carothers, Sarah C. Shelton, Center for Tobacco Policy Research, Washington University in St. Louis, Saint Louis, Missouri; Barbara Kariuki, School of Medicine, Saint Louis University, Saint Louis, Missouri; Matthew Kuhlenbeck, Missouri Foundation for Health, Saint Louis, Missouri.
Back to top
- American Nonsmokers’ Rights Foundation. Summary of 100% smokefree
state laws and population protected by 100% US smokefree laws. 2010. http://www.no-smoke.org/pdf/SummaryUSPopList.pdf. Accessed April 30, 2010.
- The health consequences of involuntary exposure to tobacco smoke: a report of the Surgeon General. Atlanta (GA):
US Department of Health and Human Services, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2006.
- Erickson MP, Cerak R.
The diffusion and impact of clean indoor air laws. Annu Rev Public Health 2008;29:171-85.
- Wilson SE, Kahn RS, Khoury J, Lanphear BP.
Racial differences in exposure to environmental tobacco smoke among children. Environ Health Perspect 2005;113(3):362-7.
- Centers for Disease Control and Prevention.
signs: nonsmokers’ exposure to secondhand smoke — United States, 1999-2008. MMWR Morb Mortal Wkly Rep 2010;59(35):1141-6.
- Centers for Disease Control and Prevention.
Cigarette smoking among adults — United States, 2007
[published correction appears in MMWR Morb Mortal Wkly Rep
2008;57(47):1281]. MMWR Morb Mortal Wkly Rep 2008;57(45):1221-6.
- Pirkle JL, Bernert JT, Caudill SP, Sosnoff CS, Pechacek TF.
Trends in the exposure of nonsmokers in the U.S. population to secondhand smoke: 1988-2002. Environ Health Perspect 2006;114(6):853-8.
- Gerlach KK, Shopland DR, Hartman AM, Gibson JT, Pechacek TF.
Workplace smoking policies in the United States: results from a national survey of more than 100,000 workers. Tob Control 1997;6(3):199-206.
- Barbeau EM, Krieger N, Soobader M-J.
Working class matters: socioeconomic disadvantage, race/ethnicity, gender,
and smoking in NHIS 2000 [published correction appears in Am J Public
Health 2004;94(8):1295]. Am J Public Health 2004;94(2):269-78.
- Shopland DR, Anderson CM, Burns DM, Gerlach KK.
Disparities in smoke-free workplace policies among food service workers. J Occup Environ Med 2004;46(4):347-56.
- Patt DA, Shopland DR, Anderson CM, Burns DM, Dunnington J, Gritz ER.
Measuring progress to protect workers from job-related secondhand smoke in Texas. Tex Med 2005;101(12):50-6.
- Pickett MS, Schober SE, Brody DJ, Curtin LR, Giovino GA.
Smoke-free laws and secondhand smoke exposure in US non-smoking adults, 1999-2002. Tob Control 2006;15(4):302-7.
- Gilpin EA, Farkas AJ, Emery SL, Ake CF, Pierce JP.
Clean indoor air: advances in California, 1990-1999. Am J Public Health 2002;92(5):785-91.
- Ebbert JO, Croghan IT, Schroeder DR, Murawski J, Hurt RD.
Association between respiratory tract diseases and secondhand smoke exposure among never smoking flight attendants: a cross-sectional survey. Environ Health 2007;6:28.
- Wisotzky M, Albuquerque M, Pechacek TF, Park BZ.
Tobacco Control Program: focusing on policy to broaden impact. Public Health Rep 2004;119(3):303-10.
- Brownson RC, Haire-Joshu D, Luke DA.
Shaping the context of health: a review of environmental and policy approaches in the prevention of chronic diseases. Annu Rev Public Health 2006;27:341-70.
- Giovino GA, Biener L, Hartman AM, Marcus SE, Schooley MW, Pechacek TF, Vallone D.
Monitoring the tobacco use epidemic I. Overview: optimizing measurement to facilitate change. Prev Med 2009;48(1 Suppl):S4-10.
- Dinno A, Glantz S.
Tobacco control policies are egalitarian: a vulnerabilities perspective on clean indoor air laws, cigarette prices, and tobacco use disparities. Soc Sci Med 2009;68(8):1439-47.
- Shopland DR, Gerlach KK, Burns DM, Hartman AM, Gibson JT.
State-specific trends in smoke-free workplace policy coverage. The Current Population Survey Tobacco Use Supplement 1993 to 1999. J Occup
Environ Med 2001;43(8):680-6.
- Thomas S, Fayter D, Misso K, Ogilvie D, Petticrew M, Sowden A, et al.
Population tobacco control interventions and their effects on social inequalities in smoking: systematic review. Tob Control 2008;17(4):230-7.
- Meyers DG, Neuberger JS, He J.
Cardiovascular effect of bans on smoking in public places: a systematic review and meta-analysis. J Am Coll Cardiol 2009;54(14):1249-55.
- Juster HR, Loomis BR, Hinman TM, Farrelly MC, Hyland A, Bauer UE,
Declines in hospital admissions for acute myocardial infarction in New York
State after implementation of a comprehensive smoking ban. Am J Public
- Tobacco control state highlights, 2010. Centers for Disease Control and Prevention; 2010. http://www.cdc.gov/tobacco/data_statistics/state_data/ state_highlights/2010/states/missouri/index.htm. Accessed January 19, 2011.
definitions — final dispositions of case codes and outcome rates for surveys.The
American Association for Public Opinion Research.
StandardDefinitions/StandardDefinitions2011.pdf. Accessed July 18, 2011.
- Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA.
Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Soz Praventivmed 2001;46 Suppl
- Key outcome indicators for evaluating comprehensive tobacco control programs. Atlanta (GA): Centers for Disease Control and Prevention; 2005.
- Ridout MS, Demétrio CG, Firth D.
correlation for binary data. Biometrics 1999;55(1):137-48.
- Snijders TAB, Bosker RJ. Multilevel analysis: an introduction to basic and advanced multilevel modeling. London (GB): SagePublications; 2000.
- Luke DA. Multilevel modeling. Thousand Oaks (CA): Sage Publications; 2004.
- Blumberg SJ, Luke JV. Coverage bias in traditional telephone surveys of low-income and young adults. Public Opin Q 2007;71(5):734-49.
- Tucker C, Brick JM, Meekins B. Household telephone service and usage patterns in the United States in 2004: implications for telephone samples. Public Opin Q 2007;71(1):3-22.
- Saint Louis County Department of Health. Saint Louis County becomes smoke-free.
http://www.stlouisco.com/HealthandWellness/IndoorCleanAirCode. Accessed July
- Bonnie RJ, Stratton K, Wallace RB, editors. Ending the tobacco problem: a blueprint for the nation. Washington (DC): The National Academies Press; 2007.
- US Department of Health and Human Services. Healthy people 2010: understanding and improving health. 2nd edition. Washington (DC): US Government Printing Office; 2000.
- Best practices user guide: pursuing health equity — state and community interventions. Atlanta (GA): US Department of Health and Human Services,
Centers for Disease Control and Prevention. Forthcoming.
Back to top