COVID-19 Vaccination in Pregnancy: The Impact of Multimorbidity and Smoking Status on Vaccine Hesitancy, a Cohort Study of 25,111 Women in Wales, UK

BackgroundMultimorbidity and pregnancy are two risk factors for more severe outcomes after a SARS-CoV-2 infection, thus vaccination uptake is important for pregnant women living with multimorbidity. This study aimed to examine the impact of multimorbidity, smoking status, and demographics (age, ethnic group, area of deprivation) on vaccine hesitancy among pregnant women in Wales using electronic health records (EHR) linkage. MethodsThis cohort study utilised routinely collected, individual-level, anonymised population-scale linked data within the Secure Anonymised Information Linkage (SAIL) Databank. Pregnant women were identified from 13th April 2021 to 31st December 2021. Survival analysis was utilised to examine and compare the length of time to vaccination uptake in pregnancy by multimorbidity and smoking status, as well as depression, diabetes, asthma, and cardiovascular conditions independently. Variation in uptake by; multimorbidity, smoking status, and demographics was examined jointly and separately for the independent conditions using hazard ratios (HR) from the Cox regression model. A bootstrapping internal validation was conducted to assess the performance of the models. ResultsWithin the population cohort, 8,203 (32.7%) received at least one dose of the COVID-19 vaccine during pregnancy, with 8,572 (34.1%) remaining unvaccinated throughout the follow-up period, and 8,336 (33.2%) receiving the vaccine postpartum. Women aged 30 years or older were more likely to have the vaccine in pregnancy. Those who had depression were slightly but significantly more likely to have the vaccine compared to those without depression (HR = 1.08, 95% CI 1.03 to 1.14, p = 0.02). Women living with multimorbidity (> 1 health condition) were 1.12 times more likely to have the vaccine compared to those living without multimorbidity (HR = 1.12, 95% CI 1.04 to 1.19, p = 0.001). Vaccine uptakes were significantly lower among both current smokers and former smokers compared to never smokers (HR = 0.87, 95% CI 0.81 to 0.94, p < 0.001 and HR = 0.92, 95% CI 0.85 to 0.98, p = 0.015 respectively). Uptake was also lower among those living in the most deprived areas compared to those living in the most affluent areas (HR = 0.89, 95% CI 0.83 to 0.96, p = 0.002). The validated model had similar performance and revealed that multimorbidity, smoking status, age, and deprivation level together have a significant impact on vaccine hesitancy (p < 0.05 for all). ConclusionYounger women, living without multimorbidity (zero or only one health condition), current and former smokers, and those living in the more deprived areas are less likely to have the vaccine, thus, a targeted approach to vaccinations may be required for these groups. Women living with multimorbidity are slightly but significantly less likely to be hesitant about COVID-19 vaccination when pregnant.

The Incidence of Immune Mediated Inflammatory Diseases Following COVID-19: a Matched Cohort Study in UK Primary Care

ObjectiveTo assess whether there is an association between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) infection and the incidence of immune mediated inflammatory diseases (IMIDs). DesignMatched cohort study. SettingPrimary care electronic health record data from the Clinical Practice Research Datalink Aurum database. ParticipantsThe exposed cohort included 458,147 adults aged 18 years and older with a confirmed SARS CoV-2 infection by reverse transcriptase polymerase chain reaction (RT-PCR) or lateral flow antigen test, and no prior diagnosis of IMIDs. They were matched on age, sex, and general practice to 1,818,929 adults in the unexposed cohort with no diagnosis of confirmed or suspected SARS CoV-2 infection and no prior diagnosis of IMIDs. Main Outcome MeasuresThe primary outcome measure was a composite of the incidence of any of the following IMIDs: 1. autoimmune thyroiditis, 2. coeliac disease, 3. inflammatory bowel disease (IBD), 4. myasthenia gravis, 5. pernicious anaemia, 6. psoriasis, 7. rheumatoid arthritis (RA), 8. Sjogrens syndrome, 9. systemic lupus erythematosus (SLE), 10. type 1 diabetes mellitus (T1DM), and 11. vitiligo. The secondary outcomes were the incidence of each of these conditions separately. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI) for the primary and secondary outcomes comparing the exposed to the unexposed cohorts, and adjusting for age, sex, ethnic group, smoking status, body mass index, relevant infections, and medications. Results537 patients (0.11%) in the exposed cohort developed an IMID during the follow-up period over 0.29 person years, giving a crude incidence rate of 3.54 per 1000 person years. This was compared 1723 patients (0.09%) over 0.29 person years in the unexposed cohort, with an incidence rate of 2.82 per 1000 person years. Patients in the exposed cohort had a 22% relative increased risk of developing an IMID, compared to the unexposed cohort (aHR 1.22, 95% CI 1.10 to 1.34). The incidence of three IMIDs were statistically significantly associated with SARS CoV-2 infection. These were T1DM (aHR 1.56, 95% CI 1.09 to 2.23), IBD (1.52, 1.23 to 1.88), and psoriasis (1.23, 1.05 to 1.42). ConclusionsSARS CoV-2 was associated with an increased incidence of IMIDs including T1DM, IBD and psoriasis. Further research is needed to replicate these findings in other populations and to measure autoantibody profiles in cohorts of individuals with COVID-19, including Long COVID and matched controls. Summary Box What is already known on this topicO_LIA subsection of the population who tested positive for SARS CoV-2 is suffering from post-Covid-19 condition or long COVID. C_LIO_LIPreliminary findings, such as case reports of post-COVID-19 IMIDs, increased autoantibodies in COVID-19 patients, and molecular mimicry of the SARS-CoV-2 virus have given rise to the theory that long COVID may be due in part to a deranged immune response. C_LI What this study addsO_LICOVID-19 exposure was associated with a 22% relative increase in the risk of developing certain IMIDs, including type 1 diabetes mellitus, inflammatory bowel disease, and psoriasis. C_LIO_LIThese findings provide further support to the hypothesis that a subgroup of Long Covid may be caused by immune mediated mechanisms. C_LI

Therapies for Long COVID in non-hospitalised individuals - from symptoms, patient-reported outcomes, and immunology to targeted therapies (The TLC Study): Study protocol

IntroductionIndividuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4-12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies. Methods and analysisA cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink (CPRD) and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability, and patient reported outcome measures. Data will be collected monthly for one year. Statistical clustering methods will be used to identify distinct Long COVID symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear sub-study which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy. We will review existing evidence on interventions for post-viral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulated evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation. Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group. Ethics and disseminationEthical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). The study is registered on the ISRCTN Registry (1567490). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers. Article SummaryO_ST_ABSStrengths and limitations of the studyC_ST_ABSO_LIThe study will generate a nationally representative cohort of individuals with Long COVID recruited from primary care. C_LIO_LIWe will recruit controls matched on a wide range of demographic and clinical factors to assess differences in symptoms between people with Long COVID and similar individuals without a history of COVID-19. C_LIO_LIWe will use a newly developed electronic patient reported outcome measure (Symptom Burden Questionnaire) for Long COVID to comprehensively assess a wide range of symptoms highlighted by existing literature, patients, and clinicians. C_LIO_LIImmunological, proteomic, genetic, and wearable data captured in the study will allow deep phenotyping of Long COVID syndromes to help better target therapies. C_LIO_LIA limitation is that a significant proportion of non-hospitalised individuals affected by COVID-19 in the first wave of the pandemic will lack confirmatory testing and will be excluded from recruitment to the study. C_LI

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care

ObjectivesExisting UK prognostic models for patients admitted to hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death, intensive therapy unit (ITU) admission) in UK secondary care; and externally validate the existing 4C score. DesignCandidate predictors included demographic variables, symptoms, physiological measures, imaging, laboratory tests. Final models used logistic regression with stepwise selection. SettingModel development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset. ParticipantsPatients with COVID-19 admitted to UHB January-August 2020 were included. Main outcome measuresDeath and ITU admission within 28 days of admission. Results1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating curve (AUROC) for mortality was 0.791 (95%CI 0.761-0.822) in UHB and 0.767 (95%CI 0.754-0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95%CI 0.883-0.929) in UHB and 0.811 (95%CI 0.795-0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the 4C score in the UHB dataset was 0.754 (95%CI 0.721-0.786). ConclusionsThe novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and outperformed the existing 4C score. The models can be integrated into electronic medical records systems to calculate each individual patients probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated. Article SummaryO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LIWe developed novel prognostic models predicting mortality and ITU admission within 28 days of admission for patients hospitalised with COVID-19, using a large routinely collected dataset gathered at admission with a wide range of possible predictors (demographic variables, symptoms, physiological measures, imaging, laboratory test results). C_LIO_LIThese novel models showed good discrimination and calibration in both derivation and external validation cohorts, and outperformed the existing ISARIC model and 4C score in the derivation dataset. We found that addition of comorbidities to the set of candidate predictors included in model derivation did not improve model performance. C_LIO_LIIf integrated into hospital electronic medical records systems, the model algorithms will provide a predicted probability of mortality or ITU admission for each patient based on their individual data at, or close to, the time of admission, which will support clinicians decision making with regard to appropriate patient care pathways and triage. This information might also assist clinicians in explaining complex prognostic assessments and decisions to patients and their relatives. C_LIO_LIA limitation of the study was that in the external validation cohort we were unable to examine all of the predictors included in the original full UHB model due to only a reduced set of candidate predictors being available in CovidCollab. Nevertheless, the reduced model performed well and the results suggest it may be applicable in a wide range of datasets where only a reduced set of predictor variables is available. C_LIO_LIFurthermore, it was not possible to carry out stratified analysis by ethnicity as the UHB dataset contained too few patients in most of the strata, and no ethnicity data was available in the CovidCollab dataset. C_LI

Renin-angiotensin system inhibitors and susceptibility to COVID-19 in patients with hypertension: a propensity score-matched cohort study in primary care

Introduction A significant proportion of patients with Coronavirus Disease-19 (COVID-19) have hypertension and are treated with renin-angiotensin system (RAS) inhibitors, namely angiotensin-converting enzyme I inhibitors (ACE inhibitors) or angiotensin II type-1 receptor blockers (ARBs). These medications have been postulated to influence susceptibility to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The objective of this study was to assess a possible association between prescription of RAS inhibitors and the incidence of COVID-19 and all-cause mortality. Methods We conducted a propensity-score matched cohort study to assess the incidence of COVID-19 among patients with hypertension who were prescribed ACE inhibitors or ARBs compared to patients treated with calcium channel blockers (CCBs) in a large UK-based primary care database (The Health Improvement Network). We estimated crude incidence rates for confirmed/suspected COVID-19 among those prescribed ACE inhibitors, ARBs and CCBs. We used a Cox proportional hazards model to produce adjusted hazard ratios for COVID-19 comparing patients prescribed ACE inhibitors or ARBs to those prescribed CCBs. We further assessed all-cause mortality as a secondary outcome and a composite of accidents, trauma or fractures as a negative control outcome to assess for residual confounding. Results In the propensity score matched analysis, 83 of 18,895 users (0.44%) of ACE inhibitors developed COVID-19 over 8,923 person-years, an incidence rate of 9.3 per 1000 person-years. 85 of 18,895 (0.45%) users of CCBs developed COVID-19 over 8,932 person-years, an incidence rate of 9.5 per 1000 person-years. The adjusted hazard ratio for suspected/confirmed COVID-19 for users of ACE inhibitors compared to CCBs was 0.92 (95% CI 0.68 to 1.26). 79 out of 10,623 users (0.74%) of ARBs developed COVID-19 over 5010 person-years, an incidence rate of 15.8 per 1000 person-years, compared to 11.6 per 1000 person-years among users of CCBs. The adjusted hazard ratio for suspected/confirmed COVID-19 for users of ARBs compared to CCBs was 1.38 (95% CI 0.98 to 1.95). There were no significant associations between use of ACE inhibitors or ARBs and all-cause mortality, compared to use of CCBs. We found no evidence of significant residual confounding with the negative control analysis. Conclusion Current use of ACE inhibitors was not associated with the risk of suspected or confirmed COVID-19 whereas use of ARBs was associated with a statistically non-significant 38% relative increase in risk compared to use of CCBs. However, no significant associations were observed between prescription of either ACE inhibitors or ARBs and all-cause mortality during the peak of the pandemic.

Systemic corticosteroids show no benefit in severe and critical COVID-19 patients in Wuhan, China: A retrospective cohort study

BackgroundSystemic corticosteroids are recommended by some treatment guidelines and used in severe and critical COVID-19 patients, though evidence supporting such use is limited. MethodsFrom December 26, 2019 to March 15, 2020, 1514 severe and 249 critical hospitalized COVID-19 patients were collected from two medical centers in Wuhan, China. We performed multivariable Cox models, Cox model with time-varying exposure and propensity score analysis (both inverse-probability-of-treatment-weighting (IPTW) and propensity score matching (PSM)) to estimate the association of corticosteroid use with the risk of in-hospital mortality among severe and critical cases. ResultsCorticosteroids were administered in 531 (35.1%) severe and 159 (63.9%) critical patients. Compared to no corticosteroid use group, systemic corticosteroid use showed no benefit in reducing in-hospital mortality in both severe cases (HR=1.77, 95% CI: 1.08-2.89, p=0.023), and critical cases (HR=2.07, 95% CI: 1.08-3.98, p=0.028). In the time-varying Cox analysis that with time varying exposure, systemic corticosteroid use still showed no benefit in either population (for severe patients, HR=2.83, 95% CI: 1.72-4.64, p<0.001; for critical patients, HR=3.02, 95% CI: 1.59-5.73, p=0.001). Baseline characteristics were matched after IPTW and PSM analysis. For severe COVID-19 patients at admission, corticosteroid use was not associated with improved outcome in either the IPTW analysis. For critical COVID-19 patients at admission, results were consistent with former analysis that corticosteroid use did not reduce in-hospital mortality. ConclusionsCorticosteroid use showed no benefit in reducing in-hospital mortality for severe or critical cases. The routine use of systemic corticosteroids among severe and critical COVID-19 patients was not recommended.

Ethnicity and risk of death in patients hospitalised for COVID-19 infection: an observational cohort study in an urban catchment area

BackgroundStudies suggest that certain Black and Asian Minority Ethnic groups experience poorer outcomes from COVID-19 but these studies have not provided insight into potential reasons for this. We hypothesised that outcomes would be poorer for those of South Asian ethnicity hospitalised from a confirmed SARS-CoV-2 infection, once confounding factors, health seeking behaviours and community demographics were considered and that this might reflect a more aggressive disease course in these patients. MethodsPatients with confirmed SARS-CoV-2 infection requiring admission to University Hospitals Birmingham NHS Foundation Trust(UHB) in Birmingham UK between 10th March 2020-17th April 2020 were included. Standardised Admission Ratio(SAR) and Standardised Mortality Ratio(SMR) were calculated using observed COVID-19 admissions/deaths and 2011 census data. Hazard Ratio (aHR) for mortality was estimated using Cox proportional hazard model adjusting and propensity score matching. ResultsAll patients admitted to UHB with COVID-19 during the study period were included (2217 in total). Fifty-eight percent were male, 69.5% White and the majority (80.2%) had co-morbidities. Eighteen and a half percent were of South Asian ethnicity, and these patients were more likely to be younger, have no co-morbidities but twice the prevalence of diabetes than White patients. SAR and SMR suggested more admissions and deaths in South Asian patients than would be predicted and they were more likely to present with severe disease despite no delay in presentation since symptom onset. South Asian ethnicity was associated with an increased risk of death; both by Cox regression (Hazard Ratio 1.4 (95%CI 1.2-1.8) after adjusting for age, sex, deprivation and comorbidities and by propensity score matching, matching for the same factors but categorising ethnicity into South Asian or not (Hazard ratio 1.3 (1.0-1.6)). ConclusionsThose of South Asian ethnicity appear at risk of worse COVID-19 outcomes, further studies need to establish the underlying mechanistic pathways.