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Article Supplemental Information SUPPLEMENTAL METHODS Geographic Data for Birth and Residence Location Hospital were <30 km apart and were ’ grouped together as #6007; because Inclusion and Exclusion Criteria Fredericia Municipality s population Until December 31, 2006, Denmark density was approximately twice that was divided into 270 districts. On of Kolding Municipality, we assigned This cohort study included all live January 1, 2007, its government #6007 to Fredericia Municipality. births in Denmark from 2004 to consolidated those districts into 98 2012, identified by using the MBR. municipalities, and allocated each Specifying Dates for We included children in the study municipality to one of 5 regions. To Experiencing February ’ “ ” cohort even if their MBR record did compare geographic data over the not list a father s 10-digit personal entire study period, we harmonized registration number. Out of 568397 data according to the 98-municipality To allocate proper age intervals “ ” total birth records in the MBR, we data structure. We then used national for birth season cohorts as they excluded 6, 668 children (1.2%) from census data taken on January 1, experienced February as depicted the final study cohort who met at 2012 (available from Statistics in Fig 2 B and C in the main article, least one of the following criteria: Denmark, http:// www. StatBank. dk/ we set boundaries for age intervals ’ bev22), to classify each municipality by birth season cohort on the 15th of 1. the 10-digit personal registration ’ number in the MBR for the child or with regard to population density the season s first month to the 15th mother could not be linked to the (number of residents per square of the season s last month (Fig 2). For Danish Civil Registration System, kilometer). example, children born in the spring (green, March to May) experienced which contains information29 on We assigned geographic location ’ February from 8.5 to 11.5 months, vital status’ and migration ; for municipality and region for the because children born on May 15 child s birth (by using data from 2. the child s sex or date of birth was were 8.5 months old on February 1, the MBR) and their residence after inconsistent across registries; and children born on March 15 were birth (by using the Danish Civil 3. a record for a redeemed ’ 11.5 months old on February 28. ’ Registration System). Home births prescription drug preceded a were classified by the mother s Supplemental Methods for ’ child s date of birth; or residence (by using the Danish Civil Interrupted Time Series Analysis 4. the place of childbirth or Registration System) on the child s residence in Denmark could not be date of birth. For hospital births, we ⚬ ascertained, as follows: obtained –a historical list of hospitals The near-coincident IRF bulletin from the Health Care Classification the child had no record of being 51 53 and 7-valent pneumococcal born in a Danish hospital or at System (http:// www. medinfo. conjugate vaccination (PCV7) ⚬ home in Denmark; dk/ sks/ brows. php and ftp:// filer. sst. catch-up program were assigned dk/ filer/ sks/ data/ skscomplete/ ) and ’ to the same interruption date, May the child had no residence conducted online searches to identify 1, 2006, to allow a 1-month lag for recorded in the Danish Civil each hospital s municipality and bulletin dissemination. Thus, the Registration System by at least region and classified each hospital as 4 interruption points in our final ⚬ ’ their second day of life; university-affiliated or not. analysis occurred at weeks 122, 182, 315, and 352 of the 468-week study the mother s residence was not There were 5 hospital codes in recorded in the Danish Civil period. ’ the historical list that pertained to Registration System on the groups of neighboring hospitals, Risk measurements corresponded ⚬ ’ child s date of birth; or and we assigned each of those codes to distinct cohorts of infants so that ’ the mother s residence on the to the most densely populated there was no risk carryover from ’ ’ child s date of birth did not municipality included in that the same infants being counted in match the child s residence on code s coverage area. For example, multiple birth weeks. Therefore, their second day of life. Fredericia Hospital and Kolding because error terms for each birth PEDIATRICS Volume 140, number 3, September 2017 1 Kinlaw et al https://doi.org/10.1542/peds.2017-0441 September 2017 Trends in Antibiotic Use by Birth Season and Birth Year 3 140 Pediatrics 2017 ROUGH GALLEY PROOF ” w week cohort33 are theoretically after an interruption date were of the IRF bulletin and the PCV7 unrelated, our primary analysis did coded as 0 if the birth week catch-up program and (2) the not account for serial autocorrelation occurred before the interruption constancy over time of their effects of error terms. and as time (in weeks) since on antibiotic use. In particular, the the interruption for birth weeks IRF bulletin was less current for Overall annual risks for each year ≤ occurring afterward. The coefficients children born after January 2007 were obtained by taking the mean ofα primary interest were: because they were 3 months old in each year of week-level predicted when the bulletin was published. risks (Fig 3). To group children , mean risk for births occurring β β This sensitivity analysis introduced into birth week cohorts for the immediately before January 2004; a new coefficient ( 1a), which interrupted time series analysis, we 0, baseline linear trend for risk – represented the change in trend defined 52 weeks in the year on the before interruptions (January 1, after January 1, 2007. After this basis of 7-day increments. Exceptions β ’ 2004 April 30, 2006); new interruption, we hypothesized to the 7-day definition for the week 1, change in trend for risk after the that (1) the bulletin s effect on the of birth variable were as follows: (1) β interruption on May 1, 2006; trend would be attenuated because February 29 was always grouped into 2 children born after January 2007 week 9 so that every 4 years there , change in trend for risk after the β would have been no more than 3 were 8 days in week #9; and (2) interruption on July 1, 2007; – months old when the bulletin was week #52 always contained 8 days 3 , change in trend for risk after the published, before the vast majority (24 31 December). interruption on January 19, 2010; of infants require consideration for β ’ We hypothesized that each and their first antibiotic treatment, and interruption would have a gradual 4, change in trend for risk after the (2) the catch-up program s effect on effect over time on the trend of interruption on October 1, 2010. the trend would be amplified because β^ β^ antibiotic prescribing among infants. Trend estimates were equal to increasing numbers of children were Therefore, we did not include baseline trend estimate ( 0 ) plus enrolled over time. Given that we can parameters in our model that would estimates for all trend changes at only observe the mixture of these 2 measure discontinuity (ie, changes33, in 54 interruptions preceding the segment effects, parsing the original second level) between adjacent segments. of interest; eg, for the third^ segment^ ^ segment into 2 separate segments β β β was intended to illuminate how their To control confounding by (July 1, 2007, until January 19, 2010) 0 1 2 co-occurrence drove time-varying seasonality in our segmented the estimate was equal to + + . changes in antibiotic use. linear regression model, we usedπi SUPPLEMENTAL INTERRUPTED a transformed35 cosineπi periodic TIME SERIES SENSITIVITY Sensitivity Analyses 2, 3, and 4 function i with terms for sin(2 /52 ANALYSES radians) and cos(2i /52 radians), in which denotes the week of birth 36, 37 In the second and third sensitivity during the year, = {1, 2, ..., 52}. We conducted 4 sensitivity analyses analyses, we controlled for Given the full segmented regression of the time series study to assess the seasonality by using a vector of 51 model,Rw α β time after Jan w effects of the number of interruptions birth week indicator variables, by β time after May w we enumerated (analysis 1), how we using the first week of the year as = + 0 ( 1 2004) β time after Jul w controlled for seasonality (analyses the referent (sensitivity analysis + 1 ( 1 2006) β time after Jan w 2 and 3), and how we accounted 2) and by using the cosine function + 2 ( 1 2007) β time after Oct w for potential serial autocorrelation in a 2-stage weighted maximum + 3 ( 19 2010) β πi of error terms (analysis 4). Each likelihood estimation approach + 4 ( 1 2010) β πi sensitivity analysis is described in analogous to a weighted least squares + 5 (sin (2 /52)) 36 detail below, and results from these approach (sensitivity analysis 3). + 6 (cos(2 /52)) analyses are shown in Supplemental Rw in which the dependent variable, Fig 4 and Supplemental Tables 8 and 9. In the fourth sensitivity analysis, we , was the 1-year risk of at least 1 Sensitivity Analysis 1 conducted the primary analysis and redeemedw antibiotic prescriptionw previous 3 sensitivity analyses by during infancyi for children born in using autoregressive parameters to week of the study period, = {1, In the first sensitivity analysis, we evaluate our assumption that there 2, ..., 468}, and denotes the (seasonal) added an interruption on January 1, was no serial autocorrelation of error i “ week of birth during the year, 2007, to relax previous assumptions terms across birth week cohorts. We = {1, 2, ..., 52}.

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