3. What did SESL aim to do?
SESL aimed to provide evidence to support implementation of
comprehensive Smokefree policy across the European Union in
accordance 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. 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 for
any observed reduction in SHS or are other factors important.
5. Overarching Objective
Examine the relationship between the
models of Smokefree legislation
and the
level of exposure to secondhand smoke
in different European countries
to provide the evidence base to enable the legislators to
make Smokefree laws which give maximal protection to
the population
6. Countries included (8)
Ireland – C – April 2004
Italy – C – January 2005
Spain – P – January 2006 – C – January 2011
Scotland – C – March 2006
France – P – January 2007 – C – January 2008
Portugal – P – January 2008
Greece – P – July 2009 – C – January 2010
Turkey – C – July 2009
7.
8. Table 1: SHS data collection
Monitor Duration Conversion Date pre Date post
factor
Aerocet Jan-Dec 2007 Mar-Nov 2008
France Missing
Met One 531 8.2
Sidepak Feb 2006 – Jan April-2010
Greece 30 mins+ 0.32
AM510 2009
Aerocet Oct-2003 Oct-2004
Ireland 3hrs+
Met One 531 None Mar-2004 Mar-2005
x=(y+21.01)/4.01 Nov-Dec 2004 Mar-April 2005
DustTrak
Italy 20 mins x=(y+9.1)/ 2.66 & Nov-Dec
8520
2005
SidePak April 2009 July 2010
Portugal 30 mins 0.51
AM510
SidePak n/a Oct-Dec 2007
Spain Missing 0.51
AM510
April 2009
SidePak
Turkey 30 mins 0.23 (Pre) Sept-2010
AM510
Nov-Dec 2009)
SidePak Jan-March March-May
Scotland 30 mins 0.295
AM510 2007 2007
9. Table 2: Summary of SHS measurements
Overall Range Sig Test R
% % change
change
63% (-)99% - (+)124 z(112)= -8.364, p < 0.001 -0.54
France
40% (-)99% - (+)66% z(14)= -2.291, p < 0.05 -0.43
Greece
82% (-)100% - (-)13 z(42)= -5.648, p < 0.001 -0.62
Ireland
70% n/a U(61) = 267.0, p < 0.01 -0.36
Italy
41% (-)81% - (+)67% z(12)= -2.51, p < 0.05 -0.51
Portugal
35% (-)93% - (+)251% z(12)= -0.941, p = 0.347 n/a
Turkey
92% (-)99% - (-)12% z(53)= -6.334, p < 0.001 -0.61
Scotland
10. Comparison of PM2.5 concentrations before and after implementation of
smoke-free legislation
11. Table 3: Ecological factors
Correlation with p-value r2
change in PM2.5
Female participation .460(n=278) p<0.001 0.21
GDP per capita .432 (n=278) p<0.001 0.19
Minimum hourly wage .404 (n=245) p<0.001 0.16
GINI coefficient -.364(n=278) p<0.001 0.13
Health expenditure as
.358(n=278) p<0.001 0.13
% of GDP
Corruption -.300(n=278) p<0.001 0.09
Trust public institutions .252 (n=278) p<0.001 0.06
Life expectancy – Men .244(n=278) p<0.001 0.06
Men – Smoking -.478 (n=278) p<0.001 0.23
Prevalence
Women – Smoking n/a(n=278) p=0.051 n/a
Prevalence
Overall – Smoking -.171 (n=278) p<0.01 0.03
Prevalence
12. 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=.220
enforcement Adjusted
R2=.214
Smoking -1.56* -.224
prevalence
among men
Intercept = 98.82* *p<.001
13. Key Messages
Comprehensive Smoke-free laws work.
Partial smoke-free laws do not work as evidenced by their failure to
reduce SHS significantly in the hospitality sector in Greece, Portugal
and Spain.
Developments in Greece and Spain have seen stronger smoke-free
laws put in place and these moves represent an important affirmation
of comprehensive laws.
Any law, regardless of scope must be actively enforced in order to
have the desired impact.
There is a need to continue surveillance in all countries.
In particular Greece and Turkey seem to need particular attention .
15. Acknowledgements
SHS data provided by
Prof Nazmi Bilir (Turkey)
Prof José Alberto Gomes Precioso, Dr Jose Luis Castro, Dr Ana Catarina
Samorinha ,(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)
16. Funding Acknowledgement
SESL was funded by the a Pfizer
Tobacco Control and Policy Micro-Grant project
% 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.