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Investigating the relation between placement of Quit antismoking advertisements and number of telephone calls to Quitline: a semiparametric modelling approach
  1. Bircan Erbas1,
  2. Quang Bui2,
  3. Richard Huggins2,
  4. Todd Harper3,
  5. Victoria White3
  1. 1Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Population Health, University of Melbourne, Victoria, Australia
  2. 2Centre for Mathematics and its Applications, Australian National University, Canberra, Australia
  3. 3Cancer Council Victoria, Australia
  1. Correspondence to:
 Dr B Erbas
 Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Level 2, 723 Swanston Street, Carlton 3053, Victoria, Australia; b.erbas{at}unimelb.edu.au

Abstract

Study objectives: Quitline—an antismoking advertising and a telephone helpline service—is an effective public health intervention strategy for tobacco control. The objective of this short report is to model the relation between placement of antismoking advertisements and calls to Quitline on a given day.

Methods/design: Data on daily Quitline antismoking advertisements, television target audience rating points (TARPS), and calls to Quitline Victoria were studied for the period 1 August 2000 and 31 July 2001. The outcome—calls to Quitline—is a count and thus assumed to follow a Poisson distribution. Generalised partial linear models were used to model the logarithm of mean daily calls as a non-parametric function of time and a linear parametric function of the day of week, number of advertisements, and TARPS.

Main results: Peak calls to Quitline Victoria occurred during Monday to Wednesday with around three times as many calls compared with Sunday. Both placement of Quitline advertisements (p<0.001) and an increase in TARPS (p<0.001) on a given day significantly increased the number of calls made to Quitline Victoria. The model adequately captured fluctuations in call volume and diagnostics showed no model inadequacy.

Conclusions: In this short report the emphasis is on modelling the parametric components—day of week, placement of advertisements, and TARPS on call volume. The dynamics of the underlying time trend in call volume is captured in a non-parametric component. Future analysis of hourly data would provide additional information to assess different media buying strategies that might increase call volume.

  • smoking cessation
  • semiparametric models
  • mass media advertising
  • telephone helpline

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Footnotes

  • Funding: none.

  • Conflicts of interest: none declared.

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