COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study

Raffetti, Elena; Bolton, Thomas; Nolan, John; Zuccolo, Luisa; Denholm, Rachel; Smith, Gordon; Akbari, Ashley; Harron, Katie; Curry, Gwenetta; Allara, Elias; Lawlor, Deborah A.; Caputo, Massimo; Abbasizanjani, Hoda; Chico, Tim; Wood, Angela M.

doi:10.1016/j.lanepe.2024.101037

Journal article 16 views 2 downloads

COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study

Elena Raffetti

, Thomas Bolton, John Nolan, Luisa Zuccolo, Rachel Denholm, Gordon Smith, Ashley Akbari

, Katie Harron

, Gwenetta Curry, Elias Allara, Deborah A. Lawlor, Massimo Caputo, Hoda Abbasizanjani

, Tim Chico, Angela M. Wood

The Lancet Regional Health - Europe, Volume: 45, Start page: 101037

Swansea University Authors: Ashley Akbari , Hoda Abbasizanjani

Check full text

DOI (Published version): 10.1016/j.lanepe.2024.101037

Abstract

BackgroundThe extent to which COVID-19 diagnosis and vaccination during pregnancy are associated with risks of common and rare adverse pregnancy outcomes remains uncertain. We compared the incidence of adverse pregnancy outcomes in women with and without COVID-19 diagnosis and vaccination during pre...

Full description

Published in:	The Lancet Regional Health - Europe
ISSN:	2666-7762
Published:	Elsevier BV 2024
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa67906
Tags:	Add Tag No Tags, Be the first to tag this record!

first_indexed	2024-11-05T12:45:02Z
last_indexed	2024-11-05T12:45:02Z
id	cronfa67906
recordtype	SURis
fullrecord	<?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>67906</id><entry>2024-10-04</entry><title>COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study</title><swanseaauthors><author><sid>aa1b025ec0243f708bb5eb0a93d6fb52</sid><ORCID>0000-0003-0814-0801</ORCID><firstname>Ashley</firstname><surname>Akbari</surname><name>Ashley Akbari</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>93dd7e747f3118a99566c68592a3ddcc</sid><ORCID>0000-0002-9575-4758</ORCID><firstname>Hoda</firstname><surname>Abbasizanjani</surname><name>Hoda Abbasizanjani</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-10-04</date><deptcode>MEDS</deptcode><abstract>BackgroundThe extent to which COVID-19 diagnosis and vaccination during pregnancy are associated with risks of common and rare adverse pregnancy outcomes remains uncertain. We compared the incidence of adverse pregnancy outcomes in women with and without COVID-19 diagnosis and vaccination during pregnancy.MethodsWe studied population-scale linked electronic health records for women with singleton pregnancies in England and Wales from 1 August 2019 to 31 December 2021. This time period was divided at 8th December 2020 into pre-vaccination and vaccination roll-out eras. We calculated adjusted hazard ratios (HRs) for common and rare pregnancy outcomes according to the time since COVID-19 diagnosis and vaccination and by pregnancy trimester and COVID-19 variant.FindingsAmongst 865,654 pregnant women, we recorded 60,134 (7%) COVID-19 diagnoses and 182,120 (21%) adverse pregnancy outcomes. COVID-19 diagnosis was associated with a higher risk of gestational diabetes (adjusted HR 1.22, 95% CI 1.18–1.26), gestational hypertension (1.16, 1.10–1.22), pre-eclampsia (1.20, 1.12–1.28), preterm birth (1.63, 1.57–1.69, and 1.68, 1.61–1.75 for spontaneous preterm), very preterm birth (2.04, 1.86–2.23), small for gestational age (1.12, 1.07–1.18), thrombotic venous events (1.85, 1.56–2.20) and stillbirth (only within 14-days since COVID-19 diagnosis, 3.39, 2.23–5.15). HRs were more pronounced in the pre-vaccination era, within 14-days since COVID-19 diagnosis, when COVID-19 diagnosis occurred in the 3rd trimester, and in the original variant era. There was no evidence to suggest COVID-19 vaccination during pregnancy was associated with a higher risk of adverse pregnancy outcomes. Instead, dose 1 of COVID-19 vaccine was associated with lower risks of preterm birth (0.90, 0.86–0.95), very preterm birth (0.84, 0.76–0.94), small for gestational age (0.93, 0.88–0.99), and stillbirth (0.67, 0.49–0.92).InterpretationPregnant women with a COVID-19 diagnosis have higher risks of adverse pregnancy outcomes. These findings support recommendations towards high-priority vaccination against COVID-19 in pregnant women.</abstract><type>Journal Article</type><journal>The Lancet Regional Health - Europe</journal><volume>45</volume><journalNumber/><paginationStart>101037</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2666-7762</issnPrint><issnElectronic/><keywords>Adverse pregnancy outcomes; COVID-19; Vaccination; Trimester; COVID-19 variants</keywords><publishedDay>1</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-10-01</publishedDate><doi>10.1016/j.lanepe.2024.101037</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>This work is carried out with the support of the BHF Data Science Centre led by HDR UK. This study makes use of de-identified data held in NHS England's Secure Data Environment service for England and the SAIL Databank, and made available via the BHF Data Science Centre's CVD-COVID-UK/COVID-IMPACT consortium. This work uses data provided by patients and collected by the NHS as part of their care and support. We would also like to acknowledge all data providers who make health relevant data available for research. This study makes use of anonymised data held in the Secure Anonymised Information Linkage (SAIL) Databank. This work uses data provided by patients and collected by the NHS as part of their care and support. We would also like to acknowledge all data providers who make anonymised data available for research. We wish to acknowledge the collaborative partnership that enabled acquisition and access to the de-identified data, which led to this output. The collaboration was led by the Swansea University Health Data Research UK team under the direction of the Welsh Government Technical Advisory Cell (TAC) and includes the following groups and organisations: the SAIL Databank, Administrative Data Research (ADR) Wales, Digital Health and Care Wales (DHCW), Public Health Wales, NHS Shared Services Partnership (NWSSP) and the Welsh Ambulance Service Trust (WAST). All research conducted has been completed under the permission and approval of the SAIL independent Information Governance Review Panel (IGRP) project number 0911. The British Heart Foundation Data Science Centre (grant No SP/19/3/34678, awarded to Health Data Research (HDR) UK) funded co-development (with NHS England) of the Secure Data Environment service for England, provision of linked datasets, data access, user software licences, computational usage, and data management and wrangling support, with additional contributions from the HDR UK Data and Connectivity component of the UK Government Chief Scientific Adviser's National Core Studies programme to coordinate national COVID-19 priority research. This work was supported by the COVID-19 Longitudinal Health and Wellbeing National Core Study, which is funded by the Medical Research Council (MC_PC_20059) and by the CONVALSCENCE study, which is funded by NIHR (COV-LT-0009). This work was supported by the Con-COV team funded by the Medical Research Council (grant number: MR/V028367/1). This work was supported by Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-9006) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. This research has been supported by the ADR Wales programme of work. ADR Wales, part of the ADR UK investment, unites research expertise from Swansea University Medical School and WISERD (Wales Institute of Social and Economic Research and Data) at Cardiff University with analysts from Welsh Government. ADR UK is funded by the Economic and Social Research Council (ESRC), part of UK Research and Innovation. This research was supported by ESRC funding, including Administrative Data Research Wales (ES/W012227/1). This research was supported by the National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312) and the British Heart Foundation (RG/18/13/33946: RG/F/23/110103). Consortium partner organisations funded the time of contributing data analysts, biostatisticians, epidemiologists, and clinicians. This research was supported by the National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). ER was supported by HDR-UK2022.0173; ‘Developing capacity and capability to undertake UK-wide studies on >65 M people using COVID-19 as an exemplar (COALESCE)’ and by The Swedish Research Council for Health, Working Life and Welfare (Forte 2022-00882) and The Swedish Research Council (VR 2023-01982). LZ's contribution to this study is supported by Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058), with additional support by The Alan Turing Institute via ‘Towards Turing 2.0’ EPSRC Grant Funding and by the Health Data Science Centre, Human Technopole, Milan (Italy). RD's contribution to this study is supported by NIHR Bristol Biomedical Research and Health Data Research UK South-West. DAL's contribution to this study is supported by the British Heart Foundation (CH/F/20/90003 and AA/18/1/34219) and the UK Medical Research Council (MC_UU_00032/05). AMW is supported by the BHF Data Science Centre (HDRUK2023.0239) and as an NIHR Research Professor (NIHR303137). Cambridge BHF Centre of Research Excellence (RE/18/1/34212), BHF Chair Award (CH/12/2/29428) and by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. AMW conducted this research whilst part of the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No 116074 and whilst supported by the BHF-Turing Cardiovascular Data Science Award (BCDSA∖100005).</funders><projectreference/><lastEdited>2024-11-05T12:47:51.5767614</lastEdited><Created>2024-10-04T08:23:23.9207217</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Elena</firstname><surname>Raffetti</surname><orcid>0000-0001-8742-3986</orcid><order>1</order></author><author><firstname>Thomas</firstname><surname>Bolton</surname><order>2</order></author><author><firstname>John</firstname><surname>Nolan</surname><order>3</order></author><author><firstname>Luisa</firstname><surname>Zuccolo</surname><order>4</order></author><author><firstname>Rachel</firstname><surname>Denholm</surname><order>5</order></author><author><firstname>Gordon</firstname><surname>Smith</surname><order>6</order></author><author><firstname>Ashley</firstname><surname>Akbari</surname><orcid>0000-0003-0814-0801</orcid><order>7</order></author><author><firstname>Katie</firstname><surname>Harron</surname><orcid>0000-0002-3418-2856</orcid><order>8</order></author><author><firstname>Gwenetta</firstname><surname>Curry</surname><order>9</order></author><author><firstname>Elias</firstname><surname>Allara</surname><order>10</order></author><author><firstname>Deborah A.</firstname><surname>Lawlor</surname><order>11</order></author><author><firstname>Massimo</firstname><surname>Caputo</surname><order>12</order></author><author><firstname>Hoda</firstname><surname>Abbasizanjani</surname><orcid>0000-0002-9575-4758</orcid><order>13</order></author><author><firstname>Tim</firstname><surname>Chico</surname><order>14</order></author><author><firstname>Angela M.</firstname><surname>Wood</surname><order>15</order></author></authors><documents><document><filename>67906__32848__a2162e160b304072b2fd95089b7b2593.pdf</filename><originalFilename>67906.VoR.pdf</originalFilename><uploaded>2024-11-05T12:45:47.8146359</uploaded><type>Output</type><contentLength>1136905</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright © 2024 The Authors. This is an open access article under the CC BY license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling	v2 67906 2024-10-04 COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 93dd7e747f3118a99566c68592a3ddcc 0000-0002-9575-4758 Hoda Abbasizanjani Hoda Abbasizanjani true false 2024-10-04 MEDS BackgroundThe extent to which COVID-19 diagnosis and vaccination during pregnancy are associated with risks of common and rare adverse pregnancy outcomes remains uncertain. We compared the incidence of adverse pregnancy outcomes in women with and without COVID-19 diagnosis and vaccination during pregnancy.MethodsWe studied population-scale linked electronic health records for women with singleton pregnancies in England and Wales from 1 August 2019 to 31 December 2021. This time period was divided at 8th December 2020 into pre-vaccination and vaccination roll-out eras. We calculated adjusted hazard ratios (HRs) for common and rare pregnancy outcomes according to the time since COVID-19 diagnosis and vaccination and by pregnancy trimester and COVID-19 variant.FindingsAmongst 865,654 pregnant women, we recorded 60,134 (7%) COVID-19 diagnoses and 182,120 (21%) adverse pregnancy outcomes. COVID-19 diagnosis was associated with a higher risk of gestational diabetes (adjusted HR 1.22, 95% CI 1.18–1.26), gestational hypertension (1.16, 1.10–1.22), pre-eclampsia (1.20, 1.12–1.28), preterm birth (1.63, 1.57–1.69, and 1.68, 1.61–1.75 for spontaneous preterm), very preterm birth (2.04, 1.86–2.23), small for gestational age (1.12, 1.07–1.18), thrombotic venous events (1.85, 1.56–2.20) and stillbirth (only within 14-days since COVID-19 diagnosis, 3.39, 2.23–5.15). HRs were more pronounced in the pre-vaccination era, within 14-days since COVID-19 diagnosis, when COVID-19 diagnosis occurred in the 3rd trimester, and in the original variant era. There was no evidence to suggest COVID-19 vaccination during pregnancy was associated with a higher risk of adverse pregnancy outcomes. Instead, dose 1 of COVID-19 vaccine was associated with lower risks of preterm birth (0.90, 0.86–0.95), very preterm birth (0.84, 0.76–0.94), small for gestational age (0.93, 0.88–0.99), and stillbirth (0.67, 0.49–0.92).InterpretationPregnant women with a COVID-19 diagnosis have higher risks of adverse pregnancy outcomes. These findings support recommendations towards high-priority vaccination against COVID-19 in pregnant women. Journal Article The Lancet Regional Health - Europe 45 101037 Elsevier BV 2666-7762 Adverse pregnancy outcomes; COVID-19; Vaccination; Trimester; COVID-19 variants 1 10 2024 2024-10-01 10.1016/j.lanepe.2024.101037 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Another institution paid the OA fee This work is carried out with the support of the BHF Data Science Centre led by HDR UK. This study makes use of de-identified data held in NHS England's Secure Data Environment service for England and the SAIL Databank, and made available via the BHF Data Science Centre's CVD-COVID-UK/COVID-IMPACT consortium. This work uses data provided by patients and collected by the NHS as part of their care and support. We would also like to acknowledge all data providers who make health relevant data available for research. This study makes use of anonymised data held in the Secure Anonymised Information Linkage (SAIL) Databank. This work uses data provided by patients and collected by the NHS as part of their care and support. We would also like to acknowledge all data providers who make anonymised data available for research. We wish to acknowledge the collaborative partnership that enabled acquisition and access to the de-identified data, which led to this output. The collaboration was led by the Swansea University Health Data Research UK team under the direction of the Welsh Government Technical Advisory Cell (TAC) and includes the following groups and organisations: the SAIL Databank, Administrative Data Research (ADR) Wales, Digital Health and Care Wales (DHCW), Public Health Wales, NHS Shared Services Partnership (NWSSP) and the Welsh Ambulance Service Trust (WAST). All research conducted has been completed under the permission and approval of the SAIL independent Information Governance Review Panel (IGRP) project number 0911. The British Heart Foundation Data Science Centre (grant No SP/19/3/34678, awarded to Health Data Research (HDR) UK) funded co-development (with NHS England) of the Secure Data Environment service for England, provision of linked datasets, data access, user software licences, computational usage, and data management and wrangling support, with additional contributions from the HDR UK Data and Connectivity component of the UK Government Chief Scientific Adviser's National Core Studies programme to coordinate national COVID-19 priority research. This work was supported by the COVID-19 Longitudinal Health and Wellbeing National Core Study, which is funded by the Medical Research Council (MC_PC_20059) and by the CONVALSCENCE study, which is funded by NIHR (COV-LT-0009). This work was supported by the Con-COV team funded by the Medical Research Council (grant number: MR/V028367/1). This work was supported by Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-9006) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. This research has been supported by the ADR Wales programme of work. ADR Wales, part of the ADR UK investment, unites research expertise from Swansea University Medical School and WISERD (Wales Institute of Social and Economic Research and Data) at Cardiff University with analysts from Welsh Government. ADR UK is funded by the Economic and Social Research Council (ESRC), part of UK Research and Innovation. This research was supported by ESRC funding, including Administrative Data Research Wales (ES/W012227/1). This research was supported by the National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312) and the British Heart Foundation (RG/18/13/33946: RG/F/23/110103). Consortium partner organisations funded the time of contributing data analysts, biostatisticians, epidemiologists, and clinicians. This research was supported by the National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). ER was supported by HDR-UK2022.0173; ‘Developing capacity and capability to undertake UK-wide studies on >65 M people using COVID-19 as an exemplar (COALESCE)’ and by The Swedish Research Council for Health, Working Life and Welfare (Forte 2022-00882) and The Swedish Research Council (VR 2023-01982). LZ's contribution to this study is supported by Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058), with additional support by The Alan Turing Institute via ‘Towards Turing 2.0’ EPSRC Grant Funding and by the Health Data Science Centre, Human Technopole, Milan (Italy). RD's contribution to this study is supported by NIHR Bristol Biomedical Research and Health Data Research UK South-West. DAL's contribution to this study is supported by the British Heart Foundation (CH/F/20/90003 and AA/18/1/34219) and the UK Medical Research Council (MC_UU_00032/05). AMW is supported by the BHF Data Science Centre (HDRUK2023.0239) and as an NIHR Research Professor (NIHR303137). Cambridge BHF Centre of Research Excellence (RE/18/1/34212), BHF Chair Award (CH/12/2/29428) and by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. AMW conducted this research whilst part of the BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No 116074 and whilst supported by the BHF-Turing Cardiovascular Data Science Award (BCDSA∖100005). 2024-11-05T12:47:51.5767614 2024-10-04T08:23:23.9207217 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Elena Raffetti 0000-0001-8742-3986 1 Thomas Bolton 2 John Nolan 3 Luisa Zuccolo 4 Rachel Denholm 5 Gordon Smith 6 Ashley Akbari 0000-0003-0814-0801 7 Katie Harron 0000-0002-3418-2856 8 Gwenetta Curry 9 Elias Allara 10 Deborah A. Lawlor 11 Massimo Caputo 12 Hoda Abbasizanjani 0000-0002-9575-4758 13 Tim Chico 14 Angela M. Wood 15 67906__32848__a2162e160b304072b2fd95089b7b2593.pdf 67906.VoR.pdf 2024-11-05T12:45:47.8146359 Output 1136905 application/pdf Version of Record true Copyright © 2024 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title	COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study
spellingShingle	COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study Ashley Akbari Hoda Abbasizanjani
title_short	COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study
title_full	COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study
title_fullStr	COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study
title_full_unstemmed	COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study
title_sort	COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study
author_id_str_mv	aa1b025ec0243f708bb5eb0a93d6fb52 93dd7e747f3118a99566c68592a3ddcc
author_id_fullname_str_mv	aa1b025ec0243f708bb5eb0a93d6fb52_*_Ashley Akbari 93dd7e747f3118a99566c68592a3ddcc_*_Hoda Abbasizanjani
author	Ashley Akbari Hoda Abbasizanjani
author2	Elena Raffetti Thomas Bolton John Nolan Luisa Zuccolo Rachel Denholm Gordon Smith Ashley Akbari Katie Harron Gwenetta Curry Elias Allara Deborah A. Lawlor Massimo Caputo Hoda Abbasizanjani Tim Chico Angela M. Wood
format	Journal article
container_title	The Lancet Regional Health - Europe
container_volume	45
container_start_page	101037
publishDate	2024
institution	Swansea University
issn	2666-7762
doi_str_mv	10.1016/j.lanepe.2024.101037
publisher	Elsevier BV
college_str	Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id	facultyofmedicinehealthandlifesciences
hierarchy_top_title	Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id	facultyofmedicinehealthandlifesciences
hierarchy_parent_title	Faculty of Medicine, Health and Life Sciences
department_str	Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str	1
active_str	0
description	BackgroundThe extent to which COVID-19 diagnosis and vaccination during pregnancy are associated with risks of common and rare adverse pregnancy outcomes remains uncertain. We compared the incidence of adverse pregnancy outcomes in women with and without COVID-19 diagnosis and vaccination during pregnancy.MethodsWe studied population-scale linked electronic health records for women with singleton pregnancies in England and Wales from 1 August 2019 to 31 December 2021. This time period was divided at 8th December 2020 into pre-vaccination and vaccination roll-out eras. We calculated adjusted hazard ratios (HRs) for common and rare pregnancy outcomes according to the time since COVID-19 diagnosis and vaccination and by pregnancy trimester and COVID-19 variant.FindingsAmongst 865,654 pregnant women, we recorded 60,134 (7%) COVID-19 diagnoses and 182,120 (21%) adverse pregnancy outcomes. COVID-19 diagnosis was associated with a higher risk of gestational diabetes (adjusted HR 1.22, 95% CI 1.18–1.26), gestational hypertension (1.16, 1.10–1.22), pre-eclampsia (1.20, 1.12–1.28), preterm birth (1.63, 1.57–1.69, and 1.68, 1.61–1.75 for spontaneous preterm), very preterm birth (2.04, 1.86–2.23), small for gestational age (1.12, 1.07–1.18), thrombotic venous events (1.85, 1.56–2.20) and stillbirth (only within 14-days since COVID-19 diagnosis, 3.39, 2.23–5.15). HRs were more pronounced in the pre-vaccination era, within 14-days since COVID-19 diagnosis, when COVID-19 diagnosis occurred in the 3rd trimester, and in the original variant era. There was no evidence to suggest COVID-19 vaccination during pregnancy was associated with a higher risk of adverse pregnancy outcomes. Instead, dose 1 of COVID-19 vaccine was associated with lower risks of preterm birth (0.90, 0.86–0.95), very preterm birth (0.84, 0.76–0.94), small for gestational age (0.93, 0.88–0.99), and stillbirth (0.67, 0.49–0.92).InterpretationPregnant women with a COVID-19 diagnosis have higher risks of adverse pregnancy outcomes. These findings support recommendations towards high-priority vaccination against COVID-19 in pregnant women.
published_date	2024-10-01T12:47:50Z
_version_	1814886739224821760
score	11.036684

COVID-19 diagnosis, vaccination during pregnancy, and adverse pregnancy outcomes of 865,654 women in England and Wales: a population-based cohort study

Similar Items