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Enregistrement W3201173330 · doi:10.1111/1471-0528.16935

Pregnancy and the risk of severe coronavirus disease 2019 infection: methodological challenges and research recommendations

2021· article· en· W3201173330 sur OpenAlexaff
D A Savitz, Angela M. Bengtson, Erica Hardy, Deshayne B. Fell

Notice bibliographique

RevueBJOG An International Journal of Obstetrics & Gynaecology · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 Impact on Reproduction
Établissements canadiensChildren's Hospital of Eastern OntarioUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésPregnancyCoronavirus disease 2019 (COVID-19)CoronavirusDiseaseSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicMedicine2019-20 coronavirus outbreakCoronavirus InfectionsVirologyIntensive care medicineInfectious disease (medical specialty)Internal medicineBiologyOutbreak

Résumé

récupéré en direct d'OpenAlex

Optimal prevention and treatment of infectious diseases requires identifying segments of the population at elevated risk of developing severe disease who would benefit from heightened efforts to prevent exposure or to use personal protective equipment. These are the groups that would have high priority for vaccine access and warrant outreach efforts to encourage vaccine use. Elevated burden of disease could, in theory, result from some combination of a greater prevalence of infection with a typical distribution of disease severity or from a typical prevalence of infection with a greater risk of severe disease. Many infectious diseases, including coronavirus disease 2019 (COVID-19), have a wide spectrum of severity; however, the primary public health concern is severe manifestations that can lead to serious morbidity or death. Pregnant women are often considered a potential high-risk group for identifying, preventing and treating infectious diseases. An elevated risk of severe illness and mortality among pregnant women was identified for pandemic 2009/10 influenza1 and as data accrue, the same has been reported recently with regard to COVID-19.2 With some infectious diseases, risk is primarily to the fetus (e.g. teratogenic viruses like rubella or vertically transmitted viruses like HIV) and protecting fetuses from exposure to the infectious agent is the goal, irrespective of maternal illness. Conversely, other infectious diseases (e.g. influenza) increase the risk of serious maternal illness, which may also result in harm to the fetus through other pathways. Both immunological and physiological adaptations occur in pregnancy that can predispose pregnant women to increased susceptibility to infection, or severity of disease if infected.3, 4 Immunological modulation in pregnancy, including a shift from cell-mediated to humoral-mediated immunity, which is required to protect the fetus from rejection, may increase susceptibility to certain infections or to more severe manifestations of disease. There are also physiological alterations in the cardiovascular and respiratory systems in pregnancy, beginning early after implantation and continuing throughout gestation. These adaptations, such as increased heart rate, blood volume and oxygen consumption, as well as decreased functional residual capacity of the lungs, are necessary to meet the increased maternal and fetal metabolic demands and ensure adequate uteroplacental circulation, but they can enhance vulnerability to severe respiratory or cardiovascular disease, particularly in later gestation when the physiological demands of pregnancy are greatest. In this commentary, we address the methodological challenges encountered in examining the impact of pregnancy on severity of COVID-19 infection and offer strategies to more accurately assess the risk of severe COVID-19 among pregnant women. Clearer information on this issue has direct policy relevance in assigning pregnant women as a priority group for vaccination.5 Given the increasingly clear evidence that severe COVID-19 infection has a detrimental effect on maternal and neonatal morbidity,6 the question of whether pregnancy itself affects risk of severe COVID-19 infection has become increasingly important. For epidemiologists, the question is whether pregnant women who develop severe infectious disease would not have done so, had they not been pregnant. As always with counterfactual contrasts, we cannot observe the same individuals in both the pregnant and non-pregnant state to directly answer the question, and there are a number of ways in which comparison of the risk in pregnant and non-pregnant women is susceptible to bias. Epidemiological studies typically rely on ‘detected disease’, not actually on the ‘occurrence of disease’. Pregnancy may influence infectious disease detection through enhanced clinical scrutiny associated with women’s greater health awareness, regular contact with healthcare providers through prenatal care, and increased surveillance for health problems during prenatal care. If pregnancy increases care-seeking behaviour or contact with clinicians that leads to identification of disease that would not otherwise have been detected, it will appear that pregnant women are at increased risk of infectious diseases. A non-pregnant woman with mild or moderate respiratory symptoms may not seek medical care given the inconvenience of scheduling and planning a visit to a healthcare provider. In contrast, the vigilance associated with pregnancy, ease of reaching out to their prenatal care provider and access to health insurance while pregnant could alter the threshold for action, making pregnant women more likely to be screened, tested or diagnosed. In the case of COVID-19, there is a lower clinical threshold for testing pregnant women and, in many settings, universal COVID-19 screening practices upon admission to the hospital for labour and delivery would result in significant surveillance bias.7 Extensive testing among pregnant women will result in a higher overall rate of detected COVID-19 disease, particularly milder or subclinical infections. The response of a clinician to a report of infectious disease symptoms may range from telephone contact with recommendations for managing symptoms to an office visit or hospital admission for close monitoring. The apparent risk of ‘severe disease’, as defined by indicators of enhanced clinical management or hospital admission, may be increased for pregnant women even if the underlying symptoms are the same as those among non-pregnant women. Once engaged in clinical care, the likelihood of performing a diagnostic test may be greater for pregnant women and, so may elevate the frequency of case ascertainment. For instance, to the extent that a non-specific respiratory disease is the clinically assigned diagnosis in non-pregnant women versus laboratory-confirmed COVID-19 in pregnant women, the risk of COVID-19 would appear to be elevated among pregnant women only because the likelihood of having been tested and subsequently diagnosed with COVID-19 has been increased through clinical decisions. Even upon engaging with the healthcare system, pregnant women may be preferentially admitted to the hospital or provided with other forms of enhanced care. The risk factor profile for severe infectious disease among pregnant women may differ from that among non-pregnant women. Pregnancy is a marker in many cases of having a partner, being of sufficiently good health to conceive and either choosing to conceive (which may indicate economic stability) or having an unintended pregnancy (which may indicate lack of access to contraception or low relationship power). Once pregnancy is recognised, there are myriad behavioural changes commonly undertaken to enhance the health of the fetus, such as alterations in tobacco and alcohol use, changes in diet and physical activity and modifications in day-to-day activities such as work and socialising that may affect risk of acquiring infections and/or severity of infection-related illness. Although it could be argued that pregnancy is the cause of this cascade of changes that affect risk of severe infectious disease, they are not a result of the pregnancy per se. Available data suggest that, compared with non-pregnant women, pregnant women are less likely to report fever, muscle aches and myalgia symptoms associated with COVID-19, but may be more likely to receive medical interventions related to severe COVID-19 infection.2, 8 The most recently published update of the meta-analysis from Allotey et al.9 (https://www.bmj.com/content/bmj/370/bmj.m3320.full.pdf) indicates that ‘Compared with non-pregnant women of reproductive age with COVID-19, the odds of admission to the intensive care unit (odds ratio 2.13, 95% confidence interval 1.53–2.95; seven studies, 601 108 women) and need for invasive ventilation (2.59, 2.28–2.94; six studies, 601 044 women) and extracorporeal membrane oxygenation (2.02, 1.22–3.34; two studies, 461 936 women) were higher in pregnant and recently pregnant women.’ In contrast, for all-cause mortality, the odds ratio was 0.96 (95% CI 0.79–1.18) based on 601 122 women. In the most recent analysis of US surveillance data from the Centers for Disease Control, symptomatic pregnant women had higher all-cause mortality compared with symptomatic non-pregnant women with COVID-192 (1.5 versus 1.2 per 1000 cases; relative risk 1.7; 95% CI 1.2–2.4) leaving the question of excess mortality associated with pregnancy unresolved. Because each of the methodological considerations described above has the potential to affect the results of studies of pregnancy and COVID-19 severity, efforts to synthesise the literature need to take these factors into account. For aggregating results, only studies that are similar to one another on these key characteristics should be combined using some simple categories: The basis for testing would ideally be identified as universal, symptom-based or uncertain/mixed. Severity of infection should be subdivided into asymptomatic, mild or severe. Specific health outcomes would be considered and grouped into those that are and are not likely to be affected by pregnancy-driven clinical decisions otherwise independent of health status. Finally, the extent to which covariates are fully addressed as potential confounders could be classified as minimal (routine sociodemographic factors) and extensive (including more detailed indicators such as body mass index and healthcare access). As the number of studies grows there should be more opportunity to effectively examine the impact of these considerations on the pattern of results and by doing so, more accurately assess the causal impact of pregnancy on COVID-19 severity. None declared. Completed disclosure of interests form available to view online as supporting information. All authors wrote sections of the manuscript draft and edited the full draft manuscript. Not applicable. None. None. There are no data to share concerning this commentary. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,045
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,697
Score d'incertitude au seuil0,963

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,045
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,287
Tête enseignante GPT0,488
Écart entre enseignants0,201 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2021
Routes d'admission1
Résumé présentoui

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