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Supplementary File SUPPLEMENTARY FILE Methods S1. Modification of “pregnancies-at-risk approach” The modification of pregnancies-at-risk approach has been previously applied in time-series analysis of birth outcomes. 1,2 The aim of this is to properly account for the variations of the baseline risk of the population at risk. It consists of the inclusion of an outcome-specific offset with the weekly number of on-going gestations which would be susceptible to deliver prematurely (between 22 and 36 gestational weeks) or at risk of having an early-term baby (with 37 or 38 gestational weeks). A weekly correcting factor of the offset was also introduced as an explanatory variable, with the aim of properly correcting for the temporal variation of the baseline probability of giving birth according to the mean gestational age of the pregnancies at risk included in the offset. The weekly number of pregnancies at risk for preterm birth began to decline gradually 22 weeks before the end of the study period (December 31 st 2012). It was due to the fact that December 31 st 2012 was the last birthdate considered for inclusion in the study, so those on-going gestations giving birth after this date were not considered for the offset of at-risk pregnancies for the previous weeks coinciding with study period. For example, a full-term birth (42 gestational weeks) born in January 1 st 2013 would not account for the offset during the preceding 20 weeks (because it would be included when it reached the 22 gestational week). To avoid the potential bias due to this fact, we decided to exclude from the analysis the last 22 weeks of the year 2012. 1. Vicedo-Cabrera AM, Iñíguez C, Barona C, Ballester F. Exposure to elevated temperatures and risk of preterm birth in Valencia, Spain. Environ Res 2014;134C:210–7. 2. Vicedo-Cabrera AM, Olsson D, Forsberg B. Exposure to seasonal temperatures during the last month of gestation and the risk of preterm birth in Stockholm. Int J Environ Res Public Health 2015;12(4):3962–78. 1 Methods S2. Sensitivity analysis of the before-and-after study We firstly obtained the overall change in risk including in the meta-analysis the estimations of the cantons excluded from the main analysis (i.e. Neuchatel, Geneva and Ticino). The annual mean value of tobacco taxes was included in the time-series models as a linear term, with the aim of differentiating between the change in risk coinciding with the ban introduction and the effect due to the increase in tobacco taxes. The annual tobacco taxes per cigarette pack applied in Switzerland (from 2007 to 2012) was obtained from Federal Department of Finance (Eidgenössisches Finanzdepartement). We also assessed the potential post-ban gradual changes in risk, by adding an interaction term between the time elapsed under the smoking ban and the indicator variable for time under smoking ban. And, in case this gradual change was non-linear, we distinguished between the first 12 months of the ban and the remaining time under the ban. In addition, we introduced a natural spline function of calendar time, to explore potential non-linearities of the long-term trend in the risk of both outcomes. The number of degrees of freedom (df) was selected (choosing from 1 to 12) according to the quasi-Akaike criteria in each canton-specific model. And finally, we estimated the impact on full term birth rates (39-42 complete gestational weeks) as a “sensitivity outcome”, since we should not expect an effect of the introduction of the law in this case. Methods S3. Principal component analysis (PCA) The PCA was performed on several cantonal specific indicators including percentage of foreign population, urban population, unemployment, gross domestic product (GDP), educational level (percentage of population with university degree or similar, or tertiary education degree), population density, family size, status index (a score representing the occupational status and earnings of the population), and different health profiles of the population derived as an average of the last three Swiss Health Surveys (2002, 2007 and 2012) for prevalence of obesity, arterial hypertension (AHT), percentage of population reporting a good health status, elevated daily consumption of alcohol, and a frequent physical activity. Correlation between indicators was assessed through Spearman rank 2 correlation coefficients and a varimax rotation was applied after the PCA. Three components were extracted explaining 85% of the overall variance. One was highly correlated with the socioeconomic (SES) indicators (foreign population, urban population, GDP, educational level, population density, family size, and status index). The other two components were correlated with the health indicators and the unemployment variable, respectively, and they were summarized in a composite index representing the health profile of the population. Details on the definition and sources of aggregated canton-specific indicators are presented in Table S1 of this Appendix. Descriptive results of the different indicators are shown in Table S2, and the correlation between them in Table S3. Methods S4. Correction to avoid the fixed-cohort bias To avoid the fixed cohort bias, we excluded births with conception date earlier than 26 weeks before the study start (January 1 st 2007, or the specific date in Vaud, Valais and Geneva), or later than 42 weeks before the study end (31 st December 2012), following the procedure of Strand et al. 2011. That is, we defined the inclusion of the pregnancies in analyses by date of conception, rather than the date of birth. The bias occurs in retrospective cohorts which include all births occurring within a fixed start and end date, which means shorter pregnancies are missed at the start of the study, and longer pregnancies are missed at the end. Strand LB, Barnett AG, Tong S. Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes. BMC Med Res Methodol 2011;11:49. Methods S5. Information on statistical software All statistical analyses were performed with R software (lm4, metafor, survival packages). 3 Figure S1. Graphical scheme of the dates when smoking ban were introduced, and the pre/post-ban period by type of law applied in each canton. “Federal Law”: cantons following the basic federal legislation on smoke-free environments in public places; “Federal Law + restrictions”: cantons which applied additional restrictions to the Federal smoking ban in terms of occupational exposure, allowing smoking only in dedicated rooms of limited size. Aargau (AG); Appenzell Innerrhoden (AI); Appenzell Ausserrhoden (AR); Bern (BE); Basel-Landschaft (BL); Basel-Stadt (BS); Fribourg (FR); Glarus (GL); Graubunden (GR); Jura (JU); Luzern (LU); Nidwalden (NW); Obwalden (OW); St. Gallen (SG); Schaffhausen (SH); Solothurn (SO); Schwyz (SZ); Thurgau (TG); Uri (UR); Vaud (VD); Valais (VS); Zug (ZG); Zurich (ZH). 4 N born between 2007 -2012 : 478,703 births N with GA (22 - 42 weeks), Swiss residents & singleton births: 446,492 births BEFORE-AND-AFTER STUDY: TIME-TO-EVENT STUDY: N with GA (22 - 42 weeks) & N with GA (27 - 42 weeks) & adjusted by excluding TI GE NE: 400,784 births fixed-cohort bias correction: 413,516 births Figure S2 . Selection of the study population according to the inclusion criteria defined for each sub- study. N: number; GA: gestational age; TI: Ticino, GE: Geneva; NE: Neuchâtel. 5 Figure S3. Hazard ratios (HR, 95% confidence interval [CI]) of preterm (26-36 gestational weeks) and early-term births (37-38 gestational weeks) for a 10%-increase in the proportion of pregnancy time under the smoking ban (whole pregnancy) per categories of contextual factors of the maternal canton of residence. Type of smoking ban: Fed. Law: cantons which followed only the Federal Smoking Ban, Fed. Law + restr: cantons which applied additional restrictions to the Federal Smoking Ban. Categories of smoking prevalence (adult population (general) and child-bearing-aged women (CBA-women)), and number of establishments of the 3 rd sector (proxy of hospitality venues) defined as below or above the median value among all cantons. Categories of socioeconomic status (SES) and health status based on terciles of the corresponding scores derived from the principal components analysis (SES/Health status: lower /better(1 st tercile), intermediate (2 nd tercile), higher /worse(3 rd tercile). 6 Table S1. Description of the socioeconomic and health related variables collected at cantonal level. TEMPORAL AVAILABLE GEO AGGREG. GROUP VARIABLE DATA ORIGIN CHARACTERISTICS AGGREG. PERIOD LEVEL LEVEL SES – urbanity status Foreign population Federal Office of Statistics % of foreign population. 2007 to 2012 Annual Cantonal Urban population Federal Office of Statistics % of urban population. 2007 to 2012 Annual Cantonal Gross domestic product Federal Office of Statistics CHF per inhabitant 2008 to 2011 Annual Cantonal Unemployment Federal Office of Statistics % of unemployed population (registered unemployed people/active population). 2007 to 2012 Annual Cantonal Distribution of the population by percentage in each level (Without post-compulsory, Educational level Federal Office of Statistics 2010 to 2012 Annual Cantonal secondary, tertiary level) in Swiss permanent pop >25years Status Index* Federal Office of Statistics Composite index representing the occupational status and earnings of the population. 2000 Annual Cantonal Population density Federal Office of Statistics Number of inhabitants per km2 of surface (total or productive) 2008 to
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