Abstract Background Estimates of influenza vaccination coverage during pregnancy vary, but studies in the US prior to the 2020-21 influenza season show increasing coverage over time. However, there are limited data on influenza vaccination coverage among pregnant people during the COVID-19 pandemic. Methods Within the Vaccine Safety Datalink, we examined influenza vaccination coverage among people ages 18-49 years identified as pregnant with a live birth using a validated algorithm. For primary analyses covering the 2016-17 through 2021-22 influenza seasons, we captured all influenza vaccination between July 1 and March 31 of each season, irrespective of the timing of administration relative to pregnancy (i.e., prior to, during, or after pregnancy), and assessed crude coverage; demographic and clinical characteristics associated with vaccination; and vaccination patterns by calendar month of pregnancy start. Secondary analyses included crude coverage estimates for the 2022-23 season, using data through January 2023, stratified by race and ethnicity. Results In primary analyses, among cohorts of pregnant people ranging from 128,267 to 139,927 each season, crude annual influenza vaccination coverage ranged from a high of 71% (2019-20) to a low of 56% (2021-22). In each of the six seasons, coverage was lowest among 18-24-year-olds (Figure) and among non-Hispanic Black pregnant people. The 2021-22 season had the lowest coverage estimates across all age and race and ethnicity groups. Coverage differed based on the calendar month of pregnancy onset, with the lowest coverage observed among pregnancies starting in January-March during each influenza season. In secondary analyses, overall coverage as of January 2023 was 47.9%, a decrease of 7.7 percentage points from January 2022 estimates. Conclusion Influenza vaccination coverage among all pregnant people increased from the 2016-17 influenza season through the 2019-20 influenza season, then decreased to the lowest level in the 2021-22 season. Interim estimates for the 2022-23 season declined further. The decreases seen in recent seasons, likely due in part to the COVID-19 pandemic, were consistent across all characteristics examined, and highlight the need for continued efforts to improve influenza vaccination coverage in pregnant people. Disclosures Edward Belongia, MD, Seqirus: Grant/Research Support Lisa Jackson, MD, MPH, Pfizer: Grant/Research Support Nicola P. Klein, MD, PhD, GlaxoSmithKline: Grant/Research Support|Merck: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi Pasteur: Grant/Research Support
<p dir="ltr">Objective</p><p dir="ltr"><a href="" target="_blank">We investigated the association between historic redlining and risk of </a>gestational diabetes (GDM), and if this relationship is mediated by maternal obesity and area-level deprivation.</p><p dir="ltr">Research design and methods</p><p dir="ltr">This retrospective study included 86,834 singleton pregnancies from Kaiser Permanente Southern California’s (KPSC) health records (2008–2018). <a href="" target="_blank">Redlining was assessed using digitized </a>Home Owners' Loan Corporation (HOLC) maps, with patient’s residential addresses geocoded and assigned HOLC grades (A,B,C,D) based on their geographic location within HOLC-graded zones. For GDM cases, exposure was assigned based on address at diagnosis date; for non-cases, it was assigned based on address during the 24th–28th gestational week. Health records were combined with area deprivation index (ADI) from 2011–2015 census data. Mixed-effect logistic regression models assessed associations between redlining and GDM, with mediation by BMI and ADI evaluated using inverse odds ratio weighting. <a href="" target="_blank">Models were adjusted for maternal age, education, race and ethnicity, neighborhood level income, and smoking status.</a></p><p dir="ltr">Results</p><p dir="ltr"><a href="" target="_blank">Among the 10,134 (11.67%) GDM cases</a>, we found increased risk of GDM in B ("Still desirable," adjusted odds ratio [aOR] 1.20, 95% confidence interval [CI] 0.99-1.44), C ("Definitely declining," aOR 1.22, 95% CI 1.02-1.47), and D ("Hazardous, i.e., redlined," aOR 1.30, 95% CI 1.08-1.57) graded neighborhoods compared to the "Best" graded zone. Pre-pregnancy BMI and ADI mediated 44.2%, and 64.5% of the increased GDM risk among mothers in redlined areas.</p><p dir="ltr">Conclusions</p><p dir="ltr"><a href="" target="_blank">Historic redlining is associated with an increased risk of GDM, mediated by maternal obesity and neighborhood deprivation. Future research is needed to explore the complex pathways linking redlining to pregnancy outcomes.</a></p>
As prenatal vaccinations continue to be given more frequently, it is important to assess long-term safety events. We investigated the association between prenatal influenza vaccination or infection and autism spectrum disorder (ASD) in offspring.A retrospective cohort study of mother-child pairs with deliveries between 1 January 2011 and 31 December 2014 at Kaiser Permanente Southern California was performed. Children aged >1 year were followed through 31 December 2018. Maternal influenza vaccination or infection during pregnancy was obtained from electronic health records. ASD was defined by International Classification of Diseases, Ninth or Tenth Revisions, Clinical Modification, codes after age 1 year. Cox proportional hazard models estimated the crude and inverse probability of treatment weighted (IPTW) hazard ratios (HR) for the association between maternal influenza vaccination or infection and ASD.There were 84 739 mother-child pairs included in the final analytic sample. Of the 46 257 women vaccinated, 32.4% were vaccinated during the first trimester, 41.8% during the second trimester, and 25.8% during the third trimester. ASD was diagnosed in 1930 (2.3%) children. The IPTW analyses showed no association between prenatal influenza vaccination or infection and ASD in offspring (HR, 1.04; 95% confidence interval [CI], .95-1.13; HR, 1.12; 95% CI, .66-1.89, respectively).Prenatal influenza vaccination or infection was not associated with ASD risk in offspring. The findings support recommendations to vaccinate pregnant women to protect themselves and their infants, both of whom are vulnerable to severe morbidity following infection.
Abstract Data collected from a validation substudy permit calculation of a bias-adjusted estimate of effect that is expected to equal the estimate that would have been observed had the gold standard measurement been available for the entire study population. In this paper, we develop and apply a framework for adaptive validation to determine when sufficient validation data have been collected to yield a bias-adjusted effect estimate with a prespecified level of precision. Prespecified levels of precision are decided a priori by the investigator, based on the precision of the conventional estimate and allowing for wider confidence intervals that would still be substantively meaningful. We further present an applied example of the use of this method to address exposure misclassification in a study of transmasculine/transfeminine youth and self-harm. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue, with emphasis on the precision of the bias-adjusted estimate. This method can be applied within the context of any parent epidemiologic study design in which validation data will be collected and modified to meet alternative criteria given specific study or validation study objectives.
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