Analytical design

This study was conducted using a quasi-experimental design to examine trends in search queries around interventions. Interrupted time series analysis allows the comparison of outcome measures before and after the implementation of an intervention [29,30]. Therefore, this method can be used to analyse the effects of the introduction of new tobacco control policies by using RSV data. Time series analyses have been previously used to measure the impact of tobacco control policies on smoking prevalence in Australia [31]. RSV data were made available by Google Trends from 2004 onwards. Dutch RSV data for the query ‘quit smoking’ (‘stoppen met roken’) were retrieved for the 2004 to 2013 period on a weekly scale. Based on the availability of Google Trends data, three Dutch smoking cessation interventions could be included: the smoking ban of 2008, the reimbursement of SCS in 2011 and the reintroduction of the reimbursement of 2013 (Table 1). In January 2004, a national smoke-free legislation at the workplace was implemented. Since no pre-intervention data were available, the effect of this legislation could not be analysed. Therefore, January 2004 was excluded from the analysis. In total, data from 517 weekly time points were retrieved, with the highest RSV observed in the middle of 2008 (Fig 1). Sensitivity analysis showed that our results were robust with exclusion of the first 6 months of 2004 (instead of January only).

Dutch RSV (A), Dutch seasonally adjusted RSV (B) and as ARIMA (1,0,1) model (C).

We added the Dutch speaking part of Belgium as the control group, since Belgium is the country that is most comparable to the Netherlands in terms of geography, language, history and culture. Moreover, Belgium and the Netherlands have a similar smoking prevalence [18]. We used the same Dutch term on ‘quit smoking’ to obtain Belgian RSV data. The Belgian RSV ‘quit smoking’ data comprised a large number of missing data points until mid-2006. Therefore the period of January 2004 to August 2006 was excluded from the Belgian analysis. In total, data from 387 weekly time points were retrieved, with the highest RSV observed in the beginning of 2007 (Fig 2).

Belgian RSV (A), Belgian RSV seasonally adjusted (B) and as ARIMA (1,0,2) Model (C).