Few studies have examined associations of geographically proximal cigarette prices with within-person changes in smoking outcomes or assessed interactions between cigarette prices and smoking bans.
We linked neighborhood cigarette prices (inflation-adjusted) at chain supermarkets and drug stores and bar/restaurant smoking ban policies to cohort participants (632 smokers from the Multi-Ethnic Study of Atherosclerosis, 2001–2012, baseline mean age 58 years) using geocoded retailer and participant addresses. We used fixed-effects models to investigate associations of within-person changes in price and ban exposures with within-person changes in five smoking outcomes: current smoking, heavy (≥10 cigarettes) smoking, cessation, relapse, and intensity (average number of cigarettes smoked per day, natural log transformed). We assessed intensity associations among all smokers, and heavy (≥10 cigarettes per day) and light (<10) baseline smokers. Finally, we tested interactions between cigarette price and bans.
A $1 increase in price was associated with a 3% reduction in risk of current smoking (adjusted risk ratio [aRR]: 0.97; 95% confidence interval [CI] = 0.93, 1.0), a 7% reduction in risk of heavy smoking (aRR: 0.93; CI = 0.87, 0.99), a 20% increase in risk of smoking cessation (aRR: 1.2; CI = 0.99, 1.4), and a 35% reduction in the average number of cigarettes smoked per day by heavy baseline smokers (ratio of geometric means: 0.65; CI = 0.45, 0.93). We found no association between smoking bans and outcomes, and no evidence that price effects were modified by the presence of bans.
Results underscore the importance of local prices, but not hospitality smoking bans, in influencing older adults’ smoking behaviors.
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From the aSchool of Public Health, Drexel University, Philadelphia, PA; bSchool of Economics, Drexel University, Philadelphia, PA; cSchool of Public Health, Columbia University, New York, NY; and dSchool of Public Health, University of Washington, Seattle, WA.
Submitted 3 December 2016; accepted 3 August 2017.
The authors report no conflicts of interest.
Description of the process by which someone else could obtain the data and computing code required to replicate the results: MESA data are available to scientific colleagues (http://www.mesanhlbi.org); however, participant geocodes are not publically available and access to the pricing dataset would require buying data anew from the vendor. Computer code may be available to scientific colleagues who are attempting replication.
This research was partially supported by P60 MD002249-05 (from US Department of Health and Human Services, National Institutes of Health [NIH], and National Institute of Minority Health and Health Disparities) and R01 HL071759 (NIH, National Heart, Lung, and Blood Institute [NIH NHLBI]). Funding for the MESA parent study came from NIH NHLBI contracts: HHSN268201500003I, N01-HC-95159 through 95169, and from grants UL1-TR-000040 and UL1-TR-001079 (NIH, National Center for Research Resources).
The authors take sole responsibility for all data analyses, interpretation, and views expressed in this article. Any errors in the manuscript are the sole responsibility of the authors, not of Information Resources Inc. (IRI, who supplied the pricing dataset).
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
Correspondence: Amy H. Auchincloss, Department of Epidemiology and Biostatistics, School of Public Health, Drexel University, 3215 Market Street, 5th floor, Philadelphia, PA 19104. E-mail: firstname.lastname@example.org.