Results Of 104 249 live births, 31 295 (30%) were exposed to pH1N1 influenza vaccination in utero. No significant associations were found with upper or lower respiratory infections, otitis media, any infectious diseases, neoplasms, sensory disorders, urgent and inpatient health services use, pediatric complex chronic conditions, or mortality. A weak association was observed between prenatal pH1N1 vaccination and increased risk of asthma (adjusted hazard ratio 1.05, 95% confidence interval 1.02 to 1.09) and decreased rates of gastrointestinal infections (adjusted incidence rate ratio 0.94, 0.91 to 0.98). These results were unchanged in sensitivity analyses accounting for any potential differential healthcare seeking behavior or access between exposure groups.

Despite the strong evidence of benefit to mothers and newborns, uptake of influenza vaccination during pregnancy has been low, even when recommended and funded. 32 33 Concern about safety is a commonly cited reason for pregnant women not being immunized, 34 35 36 37 and this can also affect healthcare providers’ willingness to recommend influenza vaccination to their pregnant patients. 37 Although substantial evidence now supports the safety of maternal influenza immunization with respect to birth outcomes (for example, preterm birth, congenital anomalies), 38 39 40 41 42 43 few studies have assessed pediatric health outcomes beyond the first six months of life. 44 45 46 47 48 49 The lack of information on longer term health outcomes in children following influenza vaccination during pregnancy may be a potential barrier to achieving higher uptake and has been identified as an evidence gap for maternal immunization policy globally. 50 51 In this study, we evaluated the relation between 2009 pandemic H1N1 (pH1N1) influenza vaccination during pregnancy and pediatric health outcomes in the first five years of life.

Pregnant women are considered to be at high risk for serious illness due to influenza related mortality and morbidity documented during influenza pandemics and seasonal epidemics. 1 2 3 4 5 6 7 8 9 10 11 12 13 In the United States and Canada, policies advising all pregnant women to be immunized against influenza have been in place for many years. 14 15 16 17 18 Similar policies now exist in other countries, 19 20 many of which were implemented in response to the 2009 H1N1 influenza pandemic. 20 In addition to directly protecting pregnant women, vaccine derived maternal antibodies cross the placenta and confer passive immunity to infants during the first months of life. 21 22 23 24 25 26 As infants under 6 months of age have the highest burden of morbidity and mortality associated with pediatric influenza, 27 28 29 but influenza vaccines are not licensed for use in this group, 30 immunization during pregnancy is an important strategy for protecting young infants from influenza infection. 31

Methods

Study design, data sources, and study population We did a population based retrospective cohort study of infants born to residents of Ontario, Canada from 2 November 2009 through 31 October 2010. We used the Better Outcomes Registry & Network (BORN) Ontario birth registry (https://www.bornontario.ca/en/about-born/) to identify the study population. This province-wide registry contains maternal-newborn records for all hospital births of at least 500 g or at least 20 weeks of gestational age. In addition to routine sociodemographic and clinical data, information on receipt of the monovalent 2009 pH1N1 influenza vaccine during pregnancy was captured as part of enhanced surveillance during the 2009 influenza pandemic. We linked this one year birth cohort with health administrative databases to ascertain pediatric health outcomes over a five year follow-up period. All databases were linked using unique encoded identifiers and analyzed at ICES (https://www.ices.on.ca/). The administrative databases included the Canadian Institute for Health Information’s Discharge Abstract Database (hospital admissions) and the National Ambulatory Care Reporting System (emergency department visits), each containing clinical diagnoses made during healthcare encounters, coded using the Canadian adaptation of the international classification of diseases, 10th revision (ICD-10-CA) system, as well as the Ontario Asthma dataset, which is derived from the administrative databases. We additionally linked with the Ontario Cancer Registry to identify cases of pediatric cancer and with the Registered Persons Database to derive follow-up time for each child and to ascertain childhood mortality. Further description of the data sources and linkage methodology is provided in supplementary methods 1. We excluded infants whose birth registry record could not be linked to the administrative databases, those born to women who were not continuously eligible to receive healthcare in Ontario during pregnancy, those with records with data quality problems (for example, duplicates, invalid date of birth), and those not eligible for publicly funded provincial healthcare at birth. We also excluded infants whose records were missing information on 2009 pH1N1 influenza vaccination during pregnancy or who died on their date of birth.

Exposure and outcome measurement The exposure of interest was receipt of the monovalent 2009 pH1N1 influenza vaccine during pregnancy, ascertained from database specific codes in the birth registry. We classified infants born to mothers with documented 2009 pH1N1 influenza immunization during pregnancy as exposed to vaccine and those whose mothers were not immunized against 2009 pH1N1 influenza during pregnancy as unexposed. The pandemic influenza vaccination campaign in Ontario started on 26 October 2009. In Canada, two pandemic vaccines were produced (both by GlaxoSmithKline Biologicals)—an unadjuvanted pH1N1 influenza vaccine, specifically intended for use in pregnant women, and an AS03 adjuvanted product (Arepanrix) produced for the general population. The second one was not contraindicated in pregnancy if the unadjuvanted product was unavailable and the risk of influenza was deemed to be high.5253 A consensus list of standardized case definitions has been developed for monitoring obstetric and neonatal outcomes following immunization during pregnancy.545556 In the absence of such guidance for later pediatric health outcomes and the limited research on this topic, we pre-specified three groups of childhood morbidity outcomes for our evaluation. Firstly, we were primarily interested in immune related outcomes (infectious and atopic diseases), as the developing fetal immune system is thought to be sensitive to influences such as maternal immunization.575859 To assess safety, we included two non-immune related morbidity outcomes that have been used in other safety studies among pregnant women (neoplasms, sensory disorders)60 and two non-specific morbidity outcomes (urgent and inpatient health services use, pediatric complex chronic conditions). Where possible, we used clinical registries and standardized validated algorithms to identify outcomes. When no relevant registry or established algorithm was available, we measured outcomes by using diagnostic codes from emergency department visits and hospital admissions, but not from outpatient primary care visits, in an effort to limit the analysis to cases with better measurement in the available databases, as well as more serious clinical implications. Subsequent to developing our original study protocol, we included childhood mortality up to the age of 5 years as an additional outcome. Infectious outcomes included upper and lower respiratory tract infections, gastrointestinal infections, otitis media, and a composite of these four categories of infections. Sensory disorders included vision and hearing loss combined. We searched for ICD-10-CA diagnostic codes for each of these outcomes in primary or secondary diagnostic code field positions of the hospital admission and emergency department databases. Diagnoses of asthma were ascertained from the Ontario Asthma dataset, which uses a validated algorithm (sensitivity 89%, specificity 72%) to identify cases of asthma from health administrative databases.61 Children in our cohort who were in the Ontario Asthma dataset but coded with a diagnosis of asthma before 6 months of age were not classified as asthma cases unless a diagnostic code for asthma could be found in the hospital admission or emergency department databases later during follow-up.62 Confirmed diagnoses of pediatric cancer came from the Ontario Cancer Registry. We modified an existing algorithm to identify children with a complex chronic condition (expected to last more than 12 months and need specialty care, likely including hospital admission in a tertiary care center).63 We ascertained child mortality from the Registered Persons Database. See supplementary tables A and B for a list of diagnostic codes used to identify all morbidity outcomes.

Statistical analyses We described the characteristics of the study population by using frequencies for categorical variables and medians (interquartile ranges) for continuous variables. We used standardized differences to assess the balance of baseline covariates between the two exposure groups, with an absolute standardized difference below 0.10 considered indicative of a well balanced covariate.64 We used weights derived from propensity scores to adjust for confounding in our study. We developed a logistic regression model to calculate a propensity score for each infant, representing the predicted probability of 2009 pH1N1 influenza immunization during pregnancy. Before running the propensity score models, we used multiple imputation to correct for missing values for covariates—6.9% of records had missing information for one or more covariates that we intended to include in the propensity score; the percentage of missing data for any one of the individual variables was less than 1% for most (rural residence, public health unit region, parity, fifths of neighborhood income) and was highest for maternal smoking during pregnancy (3.9%). We included the following pre-selected covariates from the birth registry in the propensity score models, which were developed using each of the 10 multiple imputation datasets: maternal age, parity, maternal smoking, season of conception, antenatal care provider, maternal pre-existing medical comorbidity, obstetric complications, use of antenatal steroids, multifetal gestation, fifth of neighborhood income, rural residence, and public health unit region. Subsequently, we developed inverse probability of treatment weights (IPTWs), whereby the value for each vaccine exposed infant was the inverse of the propensity score and the value for each unexposed infant was the inverse of 1 minus the propensity score.65 To stabilize any extreme weights, we standardized to the entire study population by multiplying by the marginal propensity score. Follow-up began on the date of birth and continued until the child either became ineligible for healthcare in Ontario (owing to emigration or death) or reached 5 years of age. However, for time-to-event outcomes (asthma, neoplasms, sensory disorders, mortality), the end of follow-up was the event date for those experiencing the outcome. We used Cox proportional hazards models (asthma, neoplasms, sensory disorders, mortality), negative binomial models (infectious disease outcomes, urgent and inpatient health services use), and log binomial models (pediatric complex chronic conditions) to estimate unadjusted and adjusted hazard ratios, incidence rate ratios, and risk ratios, respectively, with 95% confidence intervals. To generate adjusted results, we ran weighted models for each outcome by using the stabilized IPTWs generated from each of the 10 multiple imputation datasets. We then statistically combined the resulting β parameters and standard errors to produce a single adjusted point estimate and 95% confidence interval. For the Cox models, we found the proportional hazards assumption for the exposure variable to be fulfilled on the basis of examination of Schoenfeld residual plots and Wald tests for interaction between exposure status and time. In sensitivity analyses, we examined those outcomes for which the 95% confidence interval around the adjusted point estimated excluded the null value. Firstly, we restricted the study population to infants with at least two well baby visits or primary immunization visits in the first year of life to ensure that the child was accessing the healthcare system (see supplementary methods 2). We also repeated our main analyses with additional adjustment for the number of maternal outpatient visits within six months before the index pregnancy and the number of non-obstetric hospital admissions within two years before the start of the index pregnancy to account for any differences in healthcare seeking or access. For the asthma outcome, we also further adjusted for maternal asthma status. We recalculated the confidence intervals after applying a Bonferroni correction to account for multiple comparisons (n=10 pre-specified morbidity outcomes) and explored a negative control outcome (rate of all cause injuries) to assess possible rival explanations.66 Finally, we did several additional analyses to characterize the effect of excluding records with missing information on pH1N1 vaccination. We used SAS version 9.4 for all analyses.