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Abstract
Background
Pregnant women are at increased risk from influenza, yet maternal influenza vaccination
levels remain suboptimal.
Aim
To estimate associations between sociodemographic and health characteristics and seasonal
influenza vaccination uptake among pregnant women, and to understand trends over time
to inform interventions to improve vaccine coverage.
Design and setting
Retrospective cohort study using linked electronic health records of women in North
West London with a pregnancy overlapping an influenza season between September 2010
and February 2020.
Method
A multivariable mixed-effects logistic regression model was used to identify associations
between characteristics of interest and the primary outcome of influenza vaccination.
Results
In total, 451 954 pregnancies, among 260 744 women, were included. In 85 376 (18.9%)
pregnancies women were vaccinated against seasonal influenza. Uptake increased from
8.4% in 2010/11 to 26.4% in 2017/18, dropping again to 21.1% in 2019/20. Uptake was
lowest among women aged 15–19 years (11.9%; reference category) or ≥40 years (15.2%;
odds ratio [OR] 1.17, 95% confidence interval [CI] = 1.10 to 1.24); of Black (14.1%;
OR 0.55, 95% CI = 0.53 to 0.57) or unknown ethnicity (9.9%; OR 0.42, 95% CI = 0.39
to 0.46); who lived in more deprived areas (OR least versus most deprived [reference
category] 1.16, 95% CI = 1.11 to 1.21); or with no known risk factors for severe influenza.
Conclusion
Seasonal influenza vaccine uptake in pregnant women increased in the decade before
the COVID-19 pandemic, but remained suboptimal. Targeted approaches are recommended
to reducing inequalities in access to vaccination and should focus on women of Black
ethnicity, younger and older women, and women living in deprived areas.
Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
Background Concerns have been raised regarding a potential surge of COVID-19 in pregnancy, secondary to rising numbers of COVID-19 in the community, easing of societal restrictions, and vaccine hesitancy. Even though COVID-19 vaccination is now offered to all pregnant women in the UK, there are limited data on its uptake and safety. Objectives and study design : This was a cohort study of pregnant women who gave birth at St George’s University Hospitals NHS Foundation Trust, London, UK, between March 1 st and July 4 th 2021. The primary outcome was uptake of COVID-19 vaccination and its determinants. The secondary outcomes were perinatal safety outcomes. Data were collected on COVID-19 vaccination uptake, vaccination type, gestational age at vaccination, as well as maternal characteristics including age, parity, ethnicity, index of multiple deprivation score and co-morbidities. Further data were collected on perinatal outcomes including stillbirth (fetal death ≥24 weeks’ gestation), preterm birth, fetal/congenital abnormalities and intrapartum complications. Pregnant women who received the vaccine were compared with a matched cohort of propensity balanced pregnant women to compare outcomes. Effect magnitudes of vaccination on perinatal outcomes were reported as mean differences or odds ratios with 95% confidence intervals. Factors associated with antenatal vaccination were assessed with logistic regression analysis. Results Data were available for 1328 pregnant women of whom 141 received at least one dose of vaccine before giving birth and 1187 women who did not; 85.8% of those vaccinated received their vaccine in the third trimester and 14.2% in the second trimester. Of those vaccinated, 128 (90.8%) received an mRNA vaccine and 13 (9.2%) a viral vector vaccine. There was evidence of reduced vaccine uptake in younger women (P=0.002), those with high levels of deprivation (i.e., fifth quintile of Index of Multiple Deprivation, P=0.008) and women of Afro-Caribbean or Asian ethnicity, compared to Caucasian ethnicity (P<0.001). Women with pre-pregnancy diabetes had increased vaccine uptake (P=0.008). In the multivariable model adjusting for variables that had a significant effect according to the univariable analysis, fifth deprivation quintile (most deprived) was significantly associated with lower antenatal vaccine uptake (adjusted OR 0.09, 95% CI 0.02–0.39, P=0.002), while pre-pregnancy diabetes was significantly associated with higher antenatal vaccine uptake (adjusted OR 11.1, 95% CI 2.01–81.6, P=0.008). In a propensity score matched cohort, compared with non-vaccinated pregnant women, 133 women who received at least one dose of the COVID-19 vaccine in pregnancy (vs. those unvaccinated) had similar rates of adverse pregnancy outcomes (P>0.05 for all): stillbirth (0.0% vs 0.3%), fetal abnormalities (2.2% vs 2.7%), intrapartum pyrexia (3.7% vs 1.5%), postpartum hemorrhage (9.8% vs 9.5%), cesarean section (30.8% vs. 30.6%), small for gestational age (12.0% vs 15.8%), maternal high dependency unit or intensive care admission (6.0% vs 3.5%) or neonatal intensive care unit admission (5.3% vs 5.4%). Mixed-effects Cox regression showed that vaccination was not significantly associated with birth <40 weeks’ gestation (hazard ratio 0.93, 95% CI 0.71–1.23, P=0.630). Conclusions Of pregnant women eligible for COVID-19 vaccination, less than one third accepted COVID-19 vaccination during pregnancy and they experienced similar pregnancy outcomes. There was lower uptake among younger women, non-white ethnicity, and lower socioeconomic background. This study contributes to the body of evidence that having COVID-19 vaccination in pregnancy does not alter perinatal outcomes. Clear communication to improve awareness among pregnant women and healthcare professionals on vaccine safety is needed, alongside strategies to address vaccine hesitancy. This includes post-vaccination surveillance to gather further data on pregnancy outcomes, particularly after first trimester vaccination, as well as long-term infant follow-up.
Role: Professor of paediatrics and child public health
Sonia Saxena:
ORCID: http://orcid.org/0000-0003-3787-2083
Role: Professor of primary care
Azeem Majeed:
ORCID: http://orcid.org/0000-0002-2357-9858
Role: Professor of primary care and public health
Paul Aylin:
ORCID: http://orcid.org/0000-0003-4589-1743
Role: Professor of epidemiology and public health
Journal
Journal ID (nlm-ta): Br J Gen Pract
Journal ID (iso-abbrev): Br J Gen Pract
Journal ID (hwp): bjgp
Journal ID (publisher-id): bjgp
Title:
The British Journal of General Practice
Publisher:
Royal College of General Practitioners
ISSN
(Print):
0960-1643
ISSN
(Electronic):
1478-5242
Publication date Collection:
February
2023
Publication date
(Electronic):
24
January
2023
Publication date PMC-release: 24
January
2023
Volume: 73
Issue: 727
Pages: e148-e155
Affiliations
Department of Primary Care and Public Health, Imperial College London,
London.
Department of Primary Care and Public Health, Imperial College London,
London.
Department of Primary Care and Public Health, Imperial College London,
London.
Department of Primary Care and Public Health, Imperial College London,
London.
Department of Primary Care and Public Health, Imperial College London,
London.
Department of Primary Care and Public Health, Imperial College London,
London.
Department of Primary Care and Public Health, Imperial College London,
London.
Department of Primary Care and Public Health, Imperial College London,
London.
Department of Primary Care and Public Health, Imperial College London,
London.
Department of Primary Care and Public Health, Imperial College London,
London.
Author notes
Address for correspondence Thomas Woodcock, Department of Primary Care and Public Health, Imperial College London,
Room 328, Reynolds Building, St Dunstan’s Road, London W6 8RP, UK. Email:
thomas.woodcock99@
123456imperial.ac.uk
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