Does the model of comprehensive smoke-free ban matter?


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Luke Clancy - Mark Ward
Tobacco Free Research Institute
TFRI Ireland

ICO-WHO Symposium 2012

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  • Source: Smoke free Partnership:
  • % reduction is the key here.Range % change refers to the variation in reduction/increase in PM2.5 for the venues included in each country. Significance level should be clear.R is a measure of effect size. – indicates a decrease from T1 to T2. The closer to -1 the value the greater the decrease. So Ireland and Scotland the greatest decrease in average PM2.5 with the smallest decrease observed in Italy. Also note: Due to the non-normal distribution of the measurements, non-parametric tests were used. A Wilcoxon signed rank test, which is the non-parametric equivalent of the parametric paired sample T-test, was used where both pre and post smoke-free legislation measurements were recorded at the same venue. In the case of Italy a Mann-Whitney U Test was used as not all the same venues were included in pre and post-ban data collection. The change in Turkey was not significant p=0.347. The report has a full explanation of why this is the case.
  • Different monitors used and different conversion factors mean that PM2.5 levels themselves are not directly comparable. For this reason we compared the % change in each country
  • A final OLS multiple regression analysis was performed with percentage change in PM2.5 concentrations the outcome of interest and the independent variables consisting of those shown earlier to be significant predictors and also the season in which pre and post-ban measurements were taken, the lag time between implementation of smoke-free and measurement. The level of legislation (partial or comprehensive) and the level of enforcement were also included in the initial solution. Due to there being fourteen independent variables it was not feasible to use the ‘stepwise’ method which requires a ratio of 40:1 (cases: IV’s) so the ‘enter’ method was used instead (Tabachnick and Fidell, 2007). Screening of the solution indicated that there was not enough statistical power to include all of the independent variables desired and so a final model with sufficient power was designed and contained the following six independent variables: (1)prevalence rate among men, (2)female legislators, senior officials and managers, (3) the life expectancy of men, and (4)enforcement, (5) the level of legislation and (6) the lag time between implementation of smoke-free legislation and data collection. Using the ‘stepwise’ method, the final mode included only the enforcement level and the smoking prevalence rate among men prior to implementation of smoke-free. The model explained 22% (22.4% adjusted) of the variance in percentage change in PM2.5 concentration which was significantly greater than zero [F(2, 268)=37.69, p<.001]. As can be seen in Table 19 lower smoking prevalence among men and stronger enforcement of smoke-free legislation was associated with higher percentage changes (reduction) in PM2.5 concentrations. The level of enforcement made a stronger contribution to the final model.
  • Does the model of comprehensive smoke-free ban matter?

    1. 1. Does the model of comprehensive smoke-free ban matter? Mark Ward Luke Clancy ICO-WHO Tobacco Control Symposium Barcelona 5 July 2012
    2. 2. SESLSecondhand smoke Exposure and Smoke-free Legislation
    3. 3. What did SESL aim to do?SESL aimed to provide evidence to support implementation ofcomprehensive Smokefree policy across the European Union inaccordance with Article 8 of the WHO Framework Convention on Tobacco Control The clearest and most rapid support in driving advocacy for Smokefree and Article 8 of the WHO FCTC is to examine the exposure outcome from different models of Smokefree law
    4. 4. Hypothesis (1) Comprehensive laws are the only laws to deliver the full potential of reduction of exposure to second hand smoke. (2) Is the comprehensive nature of the law the only reason forany observed reduction in SHS or are other factors important.
    5. 5. Overarching Objective Examine the relationship between the models of Smokefree legislation and the level of exposure to secondhand smoke in different European countriesto provide the evidence base to enable the legislators to make Smokefree laws which give maximal protection to the population
    6. 6. Countries included (8)Ireland – C – April 2004Italy – C – January 2005Spain – P – January 2006 – C – January 2011Scotland – C – March 2006France – P – January 2007 – C – January 2008Portugal – P – January 2008Greece – P – July 2009 – C – January 2010Turkey – C – July 2009
    7. 7. Table 1: SHS data collection Monitor Duration Conversion Date pre Date post factor Aerocet Jan-Dec 2007 Mar-Nov 2008France Missing Met One 531 8.2 Sidepak Feb 2006 – Jan April-2010Greece 30 mins+ 0.32 AM510 2009 Aerocet Oct-2003 Oct-2004Ireland 3hrs+ Met One 531 None Mar-2004 Mar-2005 x=(y+21.01)/4.01 Nov-Dec 2004 Mar-April 2005 DustTrakItaly 20 mins x=(y+9.1)/ 2.66 & Nov-Dec 8520 2005 SidePak April 2009 July 2010Portugal 30 mins 0.51 AM510 SidePak n/a Oct-Dec 2007Spain Missing 0.51 AM510 April 2009 SidePakTurkey 30 mins 0.23 (Pre) Sept-2010 AM510 Nov-Dec 2009) SidePak Jan-March March-MayScotland 30 mins 0.295 AM510 2007 2007
    8. 8. Table 2: Summary of SHS measurements Overall Range Sig Test R % % change change 63% (-)99% - (+)124 z(112)= -8.364, p < 0.001 -0.54France 40% (-)99% - (+)66% z(14)= -2.291, p < 0.05 -0.43Greece 82% (-)100% - (-)13 z(42)= -5.648, p < 0.001 -0.62Ireland 70% n/a U(61) = 267.0, p < 0.01 -0.36Italy 41% (-)81% - (+)67% z(12)= -2.51, p < 0.05 -0.51Portugal 35% (-)93% - (+)251% z(12)= -0.941, p = 0.347 n/aTurkey 92% (-)99% - (-)12% z(53)= -6.334, p < 0.001 -0.61Scotland
    9. 9. Comparison of PM2.5 concentrations before and after implementation of smoke-free legislation
    10. 10. Table 3: Ecological factors Correlation with p-value r2 change in PM2.5Female participation .460(n=278) p<0.001 0.21GDP per capita .432 (n=278) p<0.001 0.19Minimum hourly wage .404 (n=245) p<0.001 0.16GINI coefficient -.364(n=278) p<0.001 0.13Health expenditure as .358(n=278) p<0.001 0.13% of GDPCorruption -.300(n=278) p<0.001 0.09Trust public institutions .252 (n=278) p<0.001 0.06Life expectancy – Men .244(n=278) p<0.001 0.06Men – Smoking -.478 (n=278) p<0.001 0.23PrevalenceWomen – Smoking n/a(n=278) p=0.051 n/aPrevalenceOverall – Smoking -.171 (n=278) p<0.01 0.03Prevalence
    11. 11. Table 4: Regression analysis of country characteristics, level of legislation and percentage reduction in PM2.5 B β Final R=.469*Level of 28.26* .309 R2=.220enforcement Adjusted R2=.214Smoking -1.56* -.224prevalenceamong men Intercept = 98.82* *p<.001
    12. 12. Key Messages Comprehensive Smoke-free laws work. Partial smoke-free laws do not work as evidenced by their failure toreduce SHS significantly in the hospitality sector in Greece, Portugaland Spain. Developments in Greece and Spain have seen stronger smoke-freelaws put in place and these moves represent an important affirmationof comprehensive laws. Any law, regardless of scope must be actively enforced in order tohave the desired impact. There is a need to continue surveillance in all countries. In particular Greece and Turkey seem to need particular attention .
    13. 13. Fully Comprehensive V ‘Fully’ Comprehensive ?Ire + ScoIta + Fra
    14. 14. Acknowledgements SHS data provided byProf Nazmi Bilir (Turkey)Prof José Alberto Gomes Precioso, Dr Jose Luis Castro, Dr Ana CatarinaSamorinha ,(Portugal)Prof Bertrand Dautzenberg (France)Dr Francesco Forastiere, Dr Pasquale Valente and Dr Giuseppe Gorini(Italy)Professor Pat Goodman and Ms. Marie Mccaffrey (Ireland)Dr Maria José Lopez (Spain)Dr Sean Semple (Scotland)Dr Constantine Vardavas and Prof Panagiotas Behrakis (Greece)
    15. 15. Funding Acknowledgement SESL was funded by the a PfizerTobacco Control and Policy Micro-Grant project