ABSTRACT
ObjectiveInfluenza poses a serious health risk to pregnant women and their babies. Despite this risk, influenza vaccine uptake in pregnant women in the UK is less than 50%. Little is known about how COVID-19 affects pregnant women, but its management may affect attitudes and behaviours towards vaccination in pregnancy. The study objectives were to establish attitudes and knowledge of pregnant women towards influenza disease and influenza vaccination and to compare these to attitudes and knowledge about COVID-19 and COVID-19 vaccination. DesignA cross-sectional survey was conducted using an online questionnaire distributed through local advertisement and social media outlets. Information was sought on attitudes and knowledge of influenza and COVID-19 and their respective vaccines. Participants and settingPregnant women residing in Liverpool City Region, UK ResultsOf the 237 respondents, 73.8% reported receiving an influenza vaccine. Over half (56.5%) perceived themselves to be at risk from influenza, 70.5% believed that if they got influenza, their baby would get ill, and 64.6% believed getting influenza could hurt their baby, 60.3% believed that the influenza vaccine would prevent their baby from getting ill, and 70.8% believed it would protect their baby. Only 32.9% of respondents stated they would receive the COVID-19 vaccine if it were available to them. However, 80.2% stated they would receive a COVID-19 vaccine if they were not pregnant. Most of the women stated that they would accept a vaccine if recommended to them by healthcare professionals. ConclusionsAcceptance of the influenza and COVID-19 vaccines during pregnancy seems to be more related to the safety of the baby rather than the mother. Women perceived their child to be more at risk than themselves. Information about influenza and COVID-19 vaccine safety as well as healthcare provider recommendations play an important role in vaccine uptake in pregnant women. Strengths and limitations of this studyO_LIThe study provides information on how a pandemic affects vaccine attitudes and behaviours during pregnancy. C_LIO_LIThe study compares and contrasts attitudes and behaviours towards influenza and COVID-19 vaccines. C_LIO_LIThe study provides new information relating to barriers to COVID-19 vaccine acceptance and provides insights into mechanisms for improving uptake. C_LIO_LIThe sample size is small and self-selected which might lead to an over-representation of women likely to accept or have strong opinions on vaccinations. C_LIO_LIResponses to the questions on vaccine status are self-reported, not provided from healthcare records. C_LI
ABSTRACT
BackgroundCOVID-19 pandemic has developed rapidly and the ability to stratify the most vulnerable patients is vital. However, routinely used severity scoring systems are often low on diagnosis, even in non-survivors. Therefore, clinical prediction models for mortality are urgently required. MethodsWe developed and internally validated a multivariable logistic regression model to predict inpatient mortality in COVID-19 positive patients using data collected retrospectively from Tongji Hospital, Wuhan (299 patients). External validation was conducted using a retrospective cohort from Jinyintan Hospital, Wuhan (145 patients). Nine variables commonly measured in these acute settings were considered for model development, including age, biomarkers and comorbidities. Backwards stepwise selection and bootstrap resampling were used for model development and internal validation. We assessed discrimination via the C statistic, and calibration using calibration-in-the-large, calibration slopes and plots. FindingsThe final model included age, lymphocyte count, lactate dehydrogenase and SpO2 as independent predictors of mortality. Discrimination of the model was excellent in both internal (c=0{middle dot}89) and external (c=0{middle dot}98) validation. Internal calibration was excellent (calibration slope=1). External validation showed some over-prediction of risk in low-risk individuals and under-prediction of risk in high-risk individuals prior to recalibration. Recalibration of the intercept and slope led to excellent performance of the model in independent data. InterpretationCOVID-19 is a new disease and behaves differently from common critical illnesses. This study provides a new prediction model to identify patients with lethal COVID-19. Its practical reliance on commonly available parameters should improve usage of limited healthcare resources and patient survival rate. FundingThis study was supported by following funding: Key Research and Development Plan of Jiangsu Province (BE2018743 and BE2019749), National Institute for Health Research (NIHR) (PDF-2018-11-ST2-006), British Heart Foundation (BHF) (PG/16/65/32313) and Liverpool University Hospitals NHS Foundation Trust in UK. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSince the outbreak of COVID-19, there has been a pressing need for development of a prognostic tool that is easy for clinicians to use. Recently, a Lancet publication showed that in a cohort of 191 patients with COVID-19, age, SOFA score and D-dimer measurements were associated with mortality. No other publication involving prognostic factors or models has been identified to date. Added value of this studyIn our cohorts of 444 patients from two hospitals, SOFA scores were low in the majority of patients on admission. The relevance of D-dimer could not be verified, as it is not included in routine laboratory tests. In this study, we have established a multivariable clinical prediction model using a development cohort of 299 patients from one hospital. After backwards selection, four variables, including age, lymphocyte count, lactate dehydrogenase and SpO2 remained in the model to predict mortality. This has been validated internally and externally with a cohort of 145 patients from a different hospital. Discrimination of the model was excellent in both internal (c=0{middle dot}89) and external (c=0{middle dot}98) validation. Calibration plots showed excellent agreement between predicted and observed probabilities of mortality after recalibration of the model to account for underlying differences in the risk profile of the datasets. This demonstrated that the model is able to make reliable predictions in patients from different hospitals. In addition, these variables agree with pathological mechanisms and the model is easy to use in all types of clinical settings. Implication of all the available evidenceAfter further external validation in different countries the model will enable better risk stratification and more targeted management of patients with COVID-19. With the nomogram, this model that is based on readily available parameters can help clinicians to stratify COVID-19 patients on diagnosis to use limited healthcare resources effectively and improve patient outcome.
