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Tob Control 2007;16:i75-i80 doi:10.1136/tc.2006.019745
  • SUPPLEMENT

Do state characteristics matter? State level factors related to tobacco cessation quitlines

  1. Paula A Keller1,
  2. Kalsea J Koss2,
  3. Timothy B Baker3,
  4. Linda A Bailey4,
  5. Michael C Fiore5
  1. 1
    University of Wisconsin Center for Tobacco Research and Intervention, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
  2. 2
    University of Wisconsin Center for Tobacco Research and Intervention, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA (currently at the University of Notre Dame, South Bend, IN, USA)
  3. 3
    University of Wisconsin Center for Tobacco Research and Intervention, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
  4. 4
    North American Quitline Consortium, Phoenix, AZ, USA
  5. 5
    University of Wisconsin Center for Tobacco Research and Intervention, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
  1. Paula A Keller, MPH, University of Wisconsin Center for Tobacco Research and Intervention, University of Wisconsin School of Medicine and Public Health 1930 Monroe Street, Suite 200, Madison, WI 53711, USA; pak{at}ctri.medicine.wisc.edu
  • Received 21 December 2006
  • Accepted 18 April 2007

Abstract

Background: Quitline services are an effective population-wide tobacco cessation strategy adopted widely in the United States as part of state comprehensive tobacco control efforts. Despite widespread evidence supporting quitlines’ effectiveness, many states lack sufficient financial resources to adequately fund and promote this service. Efforts to augment state tobacco control efforts might be fostered by greater knowledge of state level factors associated with the funding and implementation of those efforts.

Methods: We analysed data from the 2004 North American Quitline Consortium survey and from publicly available sources to identify state level factors related to quitline implementation and funding. Factors included in the analyses were state demographic characteristics, tobacco use variables, state tobacco control spending, and economic and political climate variables. Univariate and multivariate regression analyses were conducted.

Results: The best fitting multivariate model that significantly predicted the presence or absence of a state quitline included only cigarette excise tax rate (p = 0.020). In terms of funding levels, states with high rates of cigarette consumption (p = 0.047) and with higher per capita expenditures for tobacco control programmes (p = 0 .0.004) were most likely to spend more on per capita operations budget for quitlines.

Conclusion: State level factors appear to play a part in whether states had established quitlines by mid-2004 and the amount of per capita quitline funding.

Footnotes

  • Competing interest statement: PAK, KJK, and LAB have no financial conflicts of interest. TBB has served as a principal investigator or co-principal investigator for research clinical trials for several pharmaceutical companies, including Glaxo-Wellcome, Pfizer, and Nabi; he does not take honoraria or payments/compensation for services rendered from such corporate entities. Over the past five years (ending in December 2005), MCF has received honoraria for lectures and consulting fees from Pfizer and GlaxoSmithKline; over the past five years, he has served as an investigator on research studies at the University of Wisconsin (UW) that were funded wholly or in part by Pfizer, GlaxoSmithKline, Sanofi-Aventis, and Nabi. In 1998, the UW appointed MCF to a named chair, made possible by an unrestricted gift to UW from GlaxoWellcome.

  • * We took the following steps to transform predictor variables to correct distributional problems: (1) Population data were skewed, so the data were transformed to the square root of total state population to address distributional problems. Exploratory analyses were conducted to examine the effects of remaining outliers in the population variable. No major changes were found, so outliers remained in the dataset to maintain data. (2) Outliers above or below three standard deviations of the mean in the consumption variable were rescaled, maintaining rank order, to address distributional problems. (3) Per capita tobacco control expenditures were skewed. An inverse transformation did not remove the distributional problem so a median split was conducted.

  • Abbreviations:
    CDC
    Centers for Disease Control and Prevention
    MSA
    Master Settlement Agreement

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