Factors that influence vaccination decision-making among pregnant women: A systematic review and meta-analysis.

BACKGROUND: The most important factor influencing maternal vaccination uptake is healthcare professional (HCP) recommendation. However, where data are available, one-third of pregnant women remain unvaccinated despite receiving a recommendation. Therefore, it is essential to understand the significance of other factors and distinguish between vaccines administered routinely and during outbreaks. This is the first systematic review and meta-analysis (PROSPERO: CRD 42019118299) to examine the strength of the relationships between identified factors and maternal vaccination uptake. METHODS: We searched MEDLINE, Embase Classic & Embase, PsycINFO, CINAHL Plus, Web of Science, IBSS, LILACS, AfricaWideInfo, IMEMR, and Global Health databases for studies reporting factors that influence maternal vaccination. We used random-effects models to calculate pooled odds ratios (OR) of being vaccinated by vaccine type. FINDINGS: We screened 17,236 articles and identified 120 studies from 30 countries for inclusion. Of these, 49 studies were eligible for meta-analysis. The odds of receiving a pertussis or influenza vaccination were ten to twelve-times higher among pregnant women who received a recommendation from HCPs. During the 2009 influenza pandemic an HCP recommendation increased the odds of antenatal H1N1 vaccine uptake six times (OR 6.76, 95% CI 3.12-14.64, I2 = 92.00%). Believing there was potential for vaccine-induced harm had a negative influence on seasonal (OR 0.22, 95% CI 0.11-0.44 I2 = 84.00%) and pandemic influenza vaccine uptake (OR 0.16, 95% CI 0.09-0.29, I2 = 89.48%), reducing the odds of being vaccinated five-fold. Combined with our qualitative analysis the relationship between the belief in substantial disease risk and maternal seasonal and pandemic influenza vaccination uptake was limited. CONCLUSIONS: The effect of an HCP recommendation during an outbreak, whilst still powerful, may be muted by other factors. This requires further research, particularly when vaccines are novel. Public health campaigns which centre on the protectiveness and safety of a maternal vaccine rather than disease threat alone may prove beneficial

Categorizing Vaccine Confidence With a Transformer-Based Machine Learning Model: Analysis of Nuances of Vaccine Sentiment in Twitter Discourse.

BACKGROUND: Social media has become an established platform for individuals to discuss and debate various subjects, including vaccination. With growing conversations on the web and less than desired maternal vaccination uptake rates, these conversations could provide useful insights to inform future interventions. However, owing to the volume of web-based posts, manual annotation and analysis are difficult and time consuming. Automated processes for this type of analysis, such as natural language processing, have faced challenges in extracting complex stances such as attitudes toward vaccination from large amounts of text. OBJECTIVE: The aim of this study is to build upon recent advances in transposer-based machine learning methods and test whether transformer-based machine learning could be used as a tool to assess the stance expressed in social media posts toward vaccination during pregnancy. METHODS: A total of 16,604 tweets posted between November 1, 2018, and April 30, 2019, were selected using keyword searches related to maternal vaccination. After excluding irrelevant tweets, the remaining tweets were coded by 3 individual researchers into the categories Promotional, Discouraging, Ambiguous, and Neutral or No Stance. After creating a final data set of 2722 unique tweets, multiple machine learning techniques were trained on a part of this data set and then tested and compared with the human annotators. RESULTS: We found the accuracy of the machine learning techniques to be 81.8% (F score=0.78) compared with the agreed score among the 3 annotators. For comparison, the accuracies of the individual annotators compared with the final score were 83.3%, 77.9%, and 77.5%. CONCLUSIONS: This study demonstrates that we are able to achieve close to the same accuracy in categorizing tweets using our machine learning models as could be expected from a single human coder. The potential to use this automated process, which is reliable and accurate, could free valuable time and resources for conducting this analysis, in addition to informing potentially effective and necessary interventions

Attitudes of pregnant women and healthcare professionals towards clinical trials and routine implementation of antenatal vaccination against respiratory syncytial virus : a multicenter questionnaire study

Introduction: Respiratory syncytial virus (RSV) is a common cause of infant hospitalization and mortality. With multiple vaccines in development, we aimed to determine: (1) the awareness of RSV among pregnant women and healthcare professionals (HCPs), and (2) attitudes toward clinical trials and routine implementation of antenatal RSV vaccination.Methods: Separate questionnaires for pregnant women and HCPs were distributed within 4 hospitals in South England (July 2017–January 2018).Results: Responses from 314 pregnant women and 204 HCPs (18% obstetricians, 75% midwives, 7% unknown) were analyzed. Most pregnant women (88%) and midwives (66%) had no/very little awareness of RSV, unlike obstetricians (14%). Among pregnant women, 29% and 75% would likely accept RSV vaccination as part of a trial, or if routinely recommended, respectively. Younger women (16–24 years), those of 21–30 weeks’ gestation, and with experience of RSV were significantly more likely to participate in trials [odds ratio (OR): 1.42 (1.72–9.86); OR: 2.29 (1.22–4.31); OR: 9.07 (1.62–50.86), respectively]. White-British women and those of 21–30 weeks’ gestation were more likely to accept routinely recommended vaccination [OR: 2.16 (1.07–4.13); OR: 2.10 (1.07–4.13)]. Obstetricians were more likely than midwives to support clinical trials [92% vs. 68%, OR: 2.50 (1.01–6.16)] and routine RSV vaccination [89% vs. 79%, OR: 4.08 (1.53–9.81)], as were those with prior knowledge of RSV, and who deemed it serious.Conclusions: RSV awareness is low among pregnant women and midwives. Education will be required to support successful implementation of routine antenatal vaccination. Research is needed to understand reasons for vaccine hesitancy among pregnant women and HCPs, particularly midwives.<br/

Single-cell multi-omics analysis of the immune response in COVID-19

Peer reviewedPublisher PD

Local and systemic responses to SARS-CoV-2 infection in children and adults.

It is not fully understood why COVID-19 is typically milder in children1-3. Here, to examine the differences between children and adults in their response to SARS-CoV-2 infection, we analysed paediatric and adult patients with COVID-19 as well as healthy control individuals (total n = 93) using single-cell multi-omic profiling of matched nasal, tracheal, bronchial and blood samples. In the airways of healthy paediatric individuals, we observed cells that were already in an interferon-activated state, which after SARS-CoV-2 infection was further induced especially in airway immune cells. We postulate that higher paediatric innate interferon responses restrict viral replication and disease progression. The systemic response in children was characterized by increases in naive lymphocytes and a depletion of natural killer cells, whereas, in adults, cytotoxic T cells and interferon-stimulated subpopulations were significantly increased. We provide evidence that dendritic cells initiate interferon signalling in early infection, and identify epithelial cell states associated with COVID-19 and age. Our matching nasal and blood data show a strong interferon response in the airways with the induction of systemic interferon-stimulated populations, which were substantially reduced in paediatric patients. Together, we provide several mechanisms that explain the milder clinical syndrome observed in children

Single-cell multi-omics analysis of the immune response in COVID-19

Funder: Lister Institute of Preventive Medicine; doi: https://doi.org/10.13039/501100001255Funder: University College London, Birkbeck MRC Doctoral Training ProgrammeFunder: The Jikei University School of MedicineFunder: Action Medical Research (GN2779)Funder: NIHR Clinical Lectureship (CL-2017-01-004)Funder: NIHR (ACF-2018-01-004) and the BMA FoundationFunder: Chan Zuckerberg Initiative (grant 2017-174169) and from Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194)Funder: UKRI Innovation/Rutherford Fund Fellowship allocated by the MRC and the UK Regenerative Medicine Platform (MR/5005579/1 to M.Z.N.). M.Z.N. and K.B.M. have been funded by the Rosetrees Trust (M944)Funder: Barbour FoundationFunder: ERC Consolidator and EU MRG-Grammar awardsFunder: Versus Arthritis Cure Challenge Research Grant (21777), and an NIHR Research Professorship (RP-2017-08-ST2-002)Funder: European Molecular Biology Laboratory (EMBL)Abstract: Analysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy

Categorizing Vaccine Confidence With a Transformer-Based Machine Learning Model: Analysis of Nuances of Vaccine Sentiment in Twitter Discourse

BackgroundSocial media has become an established platform for individuals to discuss and debate various subjects, including vaccination. With growing conversations on the web and less than desired maternal vaccination uptake rates, these conversations could provide useful insights to inform future interventions. However, owing to the volume of web-based posts, manual annotation and analysis are difficult and time consuming. Automated processes for this type of analysis, such as natural language processing, have faced challenges in extracting complex stances such as attitudes toward vaccination from large amounts of text. ObjectiveThe aim of this study is to build upon recent advances in transposer-based machine learning methods and test whether transformer-based machine learning could be used as a tool to assess the stance expressed in social media posts toward vaccination during pregnancy. MethodsA total of 16,604 tweets posted between November 1, 2018, and April 30, 2019, were selected using keyword searches related to maternal vaccination. After excluding irrelevant tweets, the remaining tweets were coded by 3 individual researchers into the categories Promotional, Discouraging, Ambiguous, and Neutral or No Stance. After creating a final data set of 2722 unique tweets, multiple machine learning techniques were trained on a part of this data set and then tested and compared with the human annotators. ResultsWe found the accuracy of the machine learning techniques to be 81.8% (F score=0.78) compared with the agreed score among the 3 annotators. For comparison, the accuracies of the individual annotators compared with the final score were 83.3%, 77.9%, and 77.5%. ConclusionsThis study demonstrates that we are able to achieve close to the same accuracy in categorizing tweets using our machine learning models as could be expected from a single human coder. The potential to use this automated process, which is reliable and accurate, could free valuable time and resources for conducting this analysis, in addition to informing potentially effective and necessary interventions

Delayed healthcare seeking and prolonged illness in healthcare workers during the COVID-19 pandemic: a single-centre observational study

Objectives To describe a cohort of self-isolating healthcare workers (HCWs) with presumed COVID-19.Design A cross-sectional, single-centre study.Setting A large, teaching hospital based in Central London with tertiary infection services.Participants 236 HCWs completed a survey distributed by internal staff email bulletin. 167 were women and 65 men.Measures Information on symptomatology, exposures and health-seeking behaviour were collected from participants by self-report.Results The 236 respondents reported illness compatible with COVID-19 and there was an increase in illness reporting during March 2020 Diagnostic swabs were not routinely performed. Cough (n=179, 75.8%), fever (n=138, 58.5%), breathlessness (n=84, 35.6%) were reported. Anosmia was reported in 42.2%. Fever generally settled within 1 week (n=110/138, 88%). Several respondents remained at home and did not seek formal medical attention despite reporting severe breathlessness and measuring hypoxia (n=5/9, 55.6%). 2 patients required hospital admission but recovered following oxygen therapy. 84 respondents (41.2%) required greater than the obligated 7 days off work and 9 required greater than 3 weeks off.Conclusion There was a significant increase in staff reporting illness compatible with possible COVID-19 during March 2020. Subsequent serology studies at the same hospital study site have confirmed sero-positivity for COVID-19 up to 45% by the end of April 2020 in frontline HCWs. The study revealed a concerning lack of healthcare seeking in respondents with significant red flag symptoms (severe breathlessness, hypoxia). This study also highlighted anosmia as a key symptom of COVID-19 early in the pandemic, prior to this symptom being more widely recognised as a feature of COVID-19