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Marilyn Tseng, PhD, Karin Yeatts, PhD, Robert Millikan, DVM, MPH, PhD and Beth Newman, PhD

Marilyn Tseng is with the Division of Population Science, Fox Chase Cancer Center, Philadelphia, Pa. Karin Yeatts and Robert Millikan are with the Department of Epidemiology, University of North Carolina, Chapel Hill. Beth Newman is with the Queensland University of Technology, Kelvin Grove, Queensland, Australia.

Correspondence: Requests for reprints should be sent to Marilyn Tseng, PhD, Division of Population Science, Fox Chase Cancer Center, 7701 Burholme Ave, Philadelphia, PA 19111 (e-mail: m_tseng{at}fccc.edu).

	ABSTRACT

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Objectives. This study examined whether area-level characteristics are associated with individual smoking behavior among women.

Methods. Analyses included 648 women enrolled as control patients in the Carolina Breast Cancer Study (1993–1996). Smoking and covariate information was obtained from interviews. Area-level characteristics included census block-group education level, poverty, unemployment, car–home ownership, crowding, and, for 431 women, city-level crime rates.

Results. In multivariate logistic regression models, no area characteristics were clearly associated with a history of smoking. Among those who had ever smoked, continued smoking was associated with living in low-education areas (odds ratio [OR] = 1.7, 95% confidence interval [CI] = 1.0, 2.9), highunemployment areas (OR = 1.7, 95% CI = 1.0, 2.8), and high-crime areas (OR = 1.6, 95% CI = 0.8, 3.2).

Conclusions. The present findings are consistent with a growing literature suggesting that area-level social and economic disadvantage influences individual smoking behavior.

	INTRODUCTION

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Despite a decline in smoking since the 1960s,¹ smoking prevalence rates among US women remain substantial.² Factors recognized as influencing smoking behavior include socioeconomic position,^1–3 stress,^4–6 parental and peer influence,^7,8 and community norms.^9,10 Moreover, among 4 recently conducted studies in Europe on determinants of smoking,^11–14 3 of the studies suggest that area-level socioeconomic characteristics influence smoking.^11–13

We examined the association between area-level characteristics and individual smoking behavior in a sample of North Carolina women. We conducted separate analyses to distinguish between correlates of smoking initiation and of continuing to smoke. Given possible links between environment, stress, and smoking behavior,^15–17 we focused on area-level characteristics that may serve as stressors,^18–20 including socioeconomic disadvantage,^11–13,21 crowding,^22,23 and high crime rates.^15,18

	METHODS

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Our study sample was derived from the control population of the Carolina Breast Cancer Study, a case–control study conducted in North Carolina from 1993 to 1996. Selection of control patients, identified from North Carolina Division of Motor Vehicles and US Health Care Financing Administration records, followed a modified randomized recruitment strategy²⁴ to achieve age and race frequency matching with case patients. Of 1245 control patients who were contacted and found to be eligible, 790 (63%) were interviewed²⁵; 773 of those completing interviews were White or African American.

Interviews included questions on cigarette use, alcohol consumption during 3 age periods (<25, 25–49, 50 years), marital status, and education. We defined ever smoking as having smoked at least 100 cigarettes, ever drinking as having consumed any alcohol during any of the 3 age periods, and recent drinking as having consumed any alcohol in the current age interval.²⁶ Each woman's residence at the time of the interview was "geocoded" and linked to 1990 census block-group data. Census variables of interest included education level, poverty, unemployment, home and vehicle ownership, and crowding. Of 773 controls, 654 were geocoded, representing 479 different block groups.

Among 433 women from 18 different locations, residential information was also successfully linked to 1996 crime data from the Uniform Crime Reporting (UCR) system,²⁷ available for cities and towns with populations of 10 000 or more. Crime rate was calculated as the number of UCR Crime Index–based offenses (murder and nonnegligent manslaughter, forcible rape, robbery, aggravated assault, burglary, larceny–theft, and motor vehicle theft) divided by the population of each city or town.

We compared ever and never smokers to examine predictors of smoking initiation, and we compared current and former smokers to examine predictors of continued smoking. Analyses included 648 women not missing any covariate data. We constructed separate logistic regression models for each area-level variable, while adjusting for age (years) and race (Black or White).

More fully adjusted models included individual-level education (less than high school, high school, some college, or college), marital status (single/widowed/divorced or married), and either a history of drinking or recent drinking to better match the time frame for either a history of smoking or continued smoking. Education (25% vs >25% of residents with less than a high school education) and poverty (20% vs >20% of residents with household income below the poverty level) measures were dichotomized via cutpoints recommended in the literature.²¹ Unemployment rates (3.7% vs >3.7%) and crime rates (9.4% vs >9.4%) were dichotomized at the median.

Vehicle ownership (<75% vs 75% of occupied housing units with a vehicle), home ownership (<50% vs 50% of occupied housing units owned vs rented), crowding (0% vs >0% of occupied housing units with an average of more than 1 person per room), and urban–rural status (50% vs >50% of residents in an urban area) were dichotomized with cutpoints that could be applied to other samples while ensuring adequate numbers in each comparison group for these analyses. Finally, because of known differences in smoking patterns by race,¹ we examined joint effects of area-level characteristics and individual-level race.

	RESULTS

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The mean age in our sample (n = 648) was 52 years (range: 21–75 years), and 42% of the respondents were African American. In logistic regression analyses (Table 1), a history of smoking was associated with being White and having a history of drinking, whereas associations with other individual- and area-level characteristics were close to the null. Continued smoking was initially associated with being Black, being single, having less education, living in a low-education or highunemployment block group, and living in a high-crime city. After adjustment for individual-level characteristics, estimates for area-level education, unemployment, and crime persisted but were marginally or nonsignificant. Notably, adjustment for block-group variables reduced the odds ratio for race from 2.0 (95% confidence interval [CI] = 1.2, 3.3) to 1.3 (95% CI = 0.8, 2.4) and increased the odds ratio for alcohol consumption from 1.3 (95% CI = 0.8, 2.4) to 2.0 (95% CI = 1.1, 3.6).

	DISCUSSION

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We found, as have others,^11–13 that some area-level characteristics may be associated with individual smoking behavior, particularly continued smoking among ever smokers. Our findings are also consistent with previous results indicating that Blacks are less likely than Whites to have ever smoked but that, if they have a history of smoking, they are more likely to continue smoking.¹ In less educated areas, however, White women were as likely to continue smoking as Black women. Factors that promote smoking cessation among White women in more advantaged neighborhoods, or that encourage continued smoking among disadvantaged White women and Black women regardless of neighborhood, have yet to be elucidated but may involve differences in social support, sense of empowerment, or experiences with racism or classism.

Several limitations merit discussion. Our power to detect statistically significant associations was limited, especially in the analyses comparing current and former smokers. Also, our findings are based on respondents' residence at the time of the interview. Area-level effects could have been overestimated if former smokers were more likely to have moved to better educated areas or if areas in which continuing smokers resided were more likely to deteriorate over time.

Conversely, comparisons of women who were and were not geocoded indicate that smokers and less educated women were underrepresented. If these women were more likely to live in disadvantaged neighborhoods, our effect estimates are probably underestimates. Using other individual-level socioeconomic indicators rather than or in addition to education would be unlikely to change our results, in that education has been more strongly and consistently associated with cigarette smoking than has income, occupation, or a composite of all 3 measures.³

In our data, the association between continued smoking and living in a high-crime city was suggestive but not statistically significant after adjustment for individual-level characteristics. However, use of city-level crime data as a proxy for immediate residential exposure may have attenuated estimates. Crowding was not associated with either a history of smoking or continued smoking, but an individual-level rather than area-level measure of household crowding might have been more appropriate in relation to individual-level smoking.

We hypothesized that area-level characteristics could affect smoking by serving as a source of stressors to local residents.^16,28 The actual mechanisms by which area-level characteristics can influence individual smoking behaviors, however, are not easily specified and could also involve cultural norms,^9,10 advertising,^29–31 and enforcement of smoking regulations.^32,33 These factors, not measured in our study, may also have confounded effect estimates.

Future research will require integrating a wider variety of factors at multiple levels into a comprehensive theoretical framework and considering them simultaneously in statistical analyses. Such research may offer insight into why the once widespread practice of smoking is now concentrated in the lower socioeconomic subset of the population.³⁴ Individual-level characteristics such as education level are important, but the concentration of smoking into specific subpopulations may also result from a failure to uniformly create environments that promote smoking cessation. While differences in tobacco regulations, cigarette availability, and advertising contribute to this nonuniformity, so might different social and economic conditions not directly related to smoking behavior.

	Acknowledgments

 
The protocol for the Carolina Breast Cancer Study was reviewed and approved by the Institutional Review Board of the University of North Carolina at Chapel Hill.

We thank Marilyn Knowles and Jessica Tse for their technical support in preparing the data, Dr Clarice Weinberg for her input during statistical analysis of the data, and Drs Alison Evans and Eric Ross for their suggestions on a draft of the paper.

	Footnotes

 
M. Tseng and K. Yeatts conceptualized the research, conducted the data analysis, and interpreted findings, with substantial input from R. Millikan and B. Newman. R. Millikan and B. Newman designed and directed the Carolina Breast Cancer Study. M. Tseng wrote the manuscript, but all coauthors contributed to revising and editing drafts of the paper.

Peer Reviewed

Accepted for publication December 6, 2000.

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