Analysis of uptake, effectiveness and safety of COVID-19 vaccinations in pregnancy using the QResearch database: research protocol and statistical analysis plan (preprint)

Background The COVID-19 pandemic has affected millions of people globally with major health, social and economic consequences, prompting development of vaccines for use in the general population. However, vaccination uptake is lower in some groups, including in pregnant women, because of concerns regarding vaccine safety. There is evidence of increased risk of adverse pregnancy and neonatal outcomes associated with SARS-CoV-2 infection, but fear of vaccine-associated adverse events on the baby both in short and longer term is one of the main drivers of low uptake for this group. Other vaccines commonly used in pregnancy include influenza and pertussis. These both have reportedly higher uptake compared with COVID-19 vaccination, which may be because they are perceived to be safer. In this study, we will undertake an independent evaluation of the uptake, effectiveness and safety of COVID-19 vaccinations in pregnant women using the QResearch primary care database in England. Objectives A. To determine COVID-19 vaccine uptake in pregnant women compared to uptake of influenza and pertussis vaccinations. B. To estimate COVID-19 vaccine effectiveness in pregnant women by evaluating the risk of severe COVID-19 outcomes following vaccination. C. To assess the safety of COVID-19 vaccination in pregnancy by evaluating the risks of adverse pregnancy and perinatal outcomes and adverse events of special interest for vaccine safety after COVID-19 vaccination compared with influenza and pertussis vaccinations. Methods This population-based study uses the QResearch database of primary health care records, linked to individual-level data on hospital admissions, mortality, COVID-19 vaccination, SARS-CoV-2 testing data and congenital anomalies. We will include women aged 16 to 49 years with at least one pregnancy during the study period of 30th December 2020 to the latest date available. Babies born during the study period will be identified and linked to the mothers record, where possible. We will describe vaccine uptake in pregnant women by trimester and population subgroups defined by demographics and other characteristics. Cox proportional hazards multivariable regression will be used to identify factors associated with vaccine uptake. The effectiveness of COVID-19 vaccines in pregnant women will be assessed using time varying Royston-Palmar regression analyses to determine unadjusted and adjusted hazard ratios for the occurrence of severe COVID-19 outcomes after each vaccine dose compared with unvaccinated individuals. For the safety analysis, we will we use logistic regression analyses to determine unadjusted and adjusted odds ratios for the occurrence of maternal (e.g. miscarriage, ectopic pregnancy and gestational diabetes) and perinatal outcomes (e.g. stillbirth, small for gestational age and congenital anomalies) by vaccination status compared to unvaccinated individuals. For the adverse events of special interest for vaccine safety (e.g. venous thromboembolism, myocarditis and Guillain Barre syndrome), we will use time varying Royston-Palmar regression analyses to determine unadjusted and adjusted hazard ratios for the occurrence of each outcome by vaccination status to unvaccinated individuals. Ethics and dissemination QResearch is a Research Ethics Approved Research Database with ongoing approval from the East Midlands Multi-Centre Research Ethics Committee (Ref: 18/EM/0400). This study was approved by the QResearch Scientific Committee on 9th June 2022. This research protocol has been developed with support from a patient and public involvement panel, who will continue to provide input throughout the duration of the study. Research findings will be submitted to pre-print servers such as MedRxIv, academic publication and disseminated more broadly through media releases and community groups and conference presentations.

Temporal sequencing in multimorbidity using population-scale linked data for 1.7 million individuals with 20-year follow-up (preprint)

Multimorbidity is defined as the coexistence of two or more chronic health conditions in an individual. The objective of this study was to examine how diseases in a cluster of physical-mental health multimorbidity with a high all-cause mortality (psychosis, diabetes, and congestive heart failure) develop and coexist over time, and to assess the associated impact of different temporal sequences on mortality. Population-scale, individual-level, anonymised, linked, demographic, administrative and electronic health record data were modelled using multi-state models for 1,675,585 individuals over a 20-year period (2000–2019). Cox regression models were used to estimate baseline hazards for transitions between states, adjusted for gender, age, and area-level deprivation. Our findings suggest that the order of disease acquisition in physical-mental health multimorbidity had an important impact and complex relationship on patient mortality. Individuals developing diabetes, psychosis, and congestive heart failure, in that order, had an increased all-cause mortality rate compared to the development of the same conditions in a different order, resulting in the highest loss in expectation of life of 13 years at age 50 compared to the general population. Congestive heart failure as a single condition and in combination with psychosis had an equally high loss in expectation of life. Identification and therapeutic targets for psychosis and congestive heart failure may be beneficial within 5 years following an initial diagnosis of diabetes. The use of multi-state models offers a flexible framework to assess temporal sequences of diseases and associated patient outcomes, and allows identification of potential risk factors, screening opportunities, and therapeutic targets in multimorbidity.