Air pollution and pregnancy: A long history of rising exposure
Notice bibliographique
Résumé
Humans have been exposed to air pollution for millennia, from both natural (volcanoes and wildfires) and man-made (heating, lighting, cooking and manufacturing) sources. By the Middle Ages air pollution was already recognised as a health problem in England. Burning coal rather than wood was prohibited in London in 1273, having been declared ‘prejudicial to health’, and King Edward I of England supported this ban with a royal proclamation in 1306 (www.air-quality.org.uk). However, this measure was difficult to implement. Severe pollution episodes or ‘smogs’ (resulting from the combination of smoke, fog and sulphur dioxide (SO2) emissions) were recorded from as early as the 17th century. The energy to power the industrial revolution required a vast consumption of fossil fuel. The resulting smoke from factories and railways rapidly became the dominant source of air pollution in the towns of the UK. Smogs or ‘pea-soupers’ were a regular feature of life in Victorian London, and recurred throughout the 20th century. From the late 19th century, spreading industrialisation has accelerated the emission of air pollutants worldwide. An additional related risk, first recognised by Canadian physicist Gilbert Plass in the1950s, and now well established, was the link between the increased use of fossil fuels and the rising levels of carbon dioxide (CO2) in the atmosphere, leading to ‘global warming’ (Plass, Scientific American 1959;201:41–7). Much of the epidemiological, clinical and basic science research into the effects of air pollution on human health has focused on the development of respiratory diseases, particularly asthma (Bharadwaj et al., Am J Respir Crit Care Med 2016;194:1475–82). However, in recent decades, there has been a growing awareness of the association between maternal exposure to air pollution during pregnancy and the risks of preterm birth, placental abruption and stillbirth. In a small study comparing carbon monoxide (CO) levels in maternal and fetal blood between groups of smokers and non-smokers, Young and Pugh found no significant difference. They concluded that maternal exposure to CO during pregnancy is not the main factor in the association between smoking and low birthweight. They also found that CO levels were higher in both groups than in non-smoking male laboratory workers, indicating potential environmental exposure to CO from sources other than smoking (Young & Pugh, J Obstet Gynaecol Br Commonw 1963;70:681–4) (Figure 1). The challenge of establishing the individual contribution of exposure to a particular environmental factor is illustrated in an article by Baird (Br J Obstet Gynaecol 1980;87:1057–84). He describes the many confounding socio-economic factors that have specific effects on pregnancy outcome, such as unemployment, poverty and health during childhood, and that are impossible to separate. Following implementation of version 2 of the Saving Babies Lives Care Bundle (SBLCBv2) and to comply with the recommendations of the Maternity Incentive Scheme, all UK National Health Service (NHS) trusts are now required to assess maternal CO levels throughout pregnancy. The identification of high CO levels may perhaps motivate pregnant women to quit smoking, service their gas appliances and instal CO alarms at home, but tackling the wider causes of air pollution will require more concerted action from all of us and from our governments. The authors declare that they have no conflicts of interest.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».