PREDICTORS OF PRENATAL SCREENING IN A LOW-SCREENING POPULATION
Notice bibliographique
Résumé
Introduction New prenatal screening technologies have made it possible for a simple blood test to assist in the identification of several conditions in the fetus early in pregnancy. Given more information about the health status of the fetus, women are able to make decisions about whether to continue or terminate pregnancies affected by congenital anomalies. The reasons for choosing to screen or not to screen are as complex as each individual, but also depend on cultural, programmatic and system-level factors. As such, rates of uptake of prenatal screening vary widely by geographic location, and correlate to client and physician characteristics and value systems. The Saskatchewan Maternal Serum Screening (MSS) programme was implemented in 2001 offering the ‘triple marker screen’, but uptake has been consistently low when compared with other jurisdictions. For instance, in 2006, screening uptake was 63% in Ontario, 60% in Manitoba, 49% in British Columbia and 25% in Saskatchewan. Studies from other countries have shown that personal characteristics, such as maternal age, religious background, ethnicity and geographic location all predict MSS use to some extent. However, it is not known if similar factors influence the decision-making capacity of pregnant women in Canada. There is the perception by some Saskatchewan physicians that aboriginal women in the province accept MSS less often than non-aboriginal women. This is the first study in Canada to conduct a detailed exploration of the predictors of MSS uptake, while controlling for other covariates. Objectives To explore variations in utilisation of prenatal screening according to mothers' geographic location, ethnicity and age. Methods A 3-year subset (2002–2004) of a larger population-based, prospective, cohort study was used for the current analysis. Data were drawn from multiple sources, including Saskatchewan's administrative databases (hospital and physician billing), vital statistics, a provincial laboratory MSS data file, and cytogenetic laboratory data from the Saskatoon and Regina Qu'Appelle Health Regions. Pregnancies resulting in a live or stillbirth, or those that were terminated for congenital anomaly, were included in the current analysis, while elective medical or spontaneous abortions were excluded. χ2 Analysis was used to determine which variables to include in the multivariate model. Using Hosmer and Lemeshow's model building strategy, binary logistic regression was used to explore differences between women who had MSS and those who did not. Results A total of 35 515 pregnancies were included in analysis. Over the 3-year period, MSS uptake in Saskatchewan increased slightly from 21.2% to 25.8% of all births. Health region of residence, age group, registered Indian status, and year of MSS test were all significant predictors (p<0.001) for MSS. Significant interaction effects were found between age group and health region of residence (p<0.001), age group and registered Indian status (p=0.02), and health region of residence and registered Indian status (p=0.004). Conclusions The likelihood that women will have MSS increases with age; a gradient observed also for First Nations and rural women. However, screening rates were much lower in First Nations (compared with non-First Nations), and rural women (compared with urban) for all age groups considered. Interestingly, First Nations women living in an urban health region were more likely to screen than First Nations women living in a rural health region. Lower screening rates in First Nations and rural populations may reflect differences in personal value systems and/or in the way prenatal care is accessed and delivered. Differential uptake across populations is important to understand, because in some cases, MSS precipitates the termination of pregnancies affected by a congenital anomaly; a practice that has the potential to lower congenital anomaly prevalence and the infant mortality rate differentially across groups.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
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 ».