Self-Reported Medication Use Across Racial and Rural or Urban Subgroups of People Who Are Pregnant in the United States: Decentralized App-Based Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Maternal health outcomes have been underresearched due to people who are pregnant being underrepresented or excluded from studies based on their status as a vulnerable study population. Based on the available evidence, Black people who are pregnant have dramatically higher maternal morbidity and mortality rates compared to other racial and ethnic groups. However, insights into prenatal care-including the use of medications, immunizations, and prenatal vitamins-are not well understood for pregnant populations, particularly those that are underrepresented in biomedical research. Medication use has been particularly understudied in people who are pregnant; even though it has been shown that up to 95% of people who are pregnant take at least 1 or more medications. Understanding gaps in use could help identify ways to reduce maternal disparities and optimize maternal health outcomes. OBJECTIVE: We aimed to characterize and compare the use of prenatal vitamins, immunizations, and commonly used over-the-counter and prescription medications among people who are pregnant, those self-identifying as Black versus non-Black, and those living in rural versus urban regions in the United States. METHODS: We conducted a prospective, decentralized study of 4130 pregnant study participants who answered survey questionnaires using a mobile research app that was only available on iOS (Apple Inc) devices. All people who were pregnant, living in the United States, and comfortable with reading and writing in English were eligible. The study was conducted in a decentralized fashion with the use of a research app to facilitate enrollment using an eConsent and self-reported data collection. RESULTS: Within the study population, the use of prenatal vitamins, antiemetics, antidepressants, and pain medication varied significantly among different subpopulations underrepresented in biomedical research. Black participants reported significantly lower frequencies of prenatal vitamin use compared to non-Black participants (P<.001). The frequency of participants who were currently receiving treatment for anxiety and depression was also lower among Black and rural groups compared to their non-Black and urban counterparts, respectively. There was significantly lower use of antidepressants (P=.002) and antiemetics (P=.02) among Black compared to non-Black participants. While prenatal vitamin use was lower among participants in rural areas, the difference between rural and urban groups did not reach statistical significance (P=.08). There were no significant differences in vaccine uptake for influenza or tetanus-diphtheria-pertussis (TDaP) across race, ethnicity, rural, or urban status. CONCLUSIONS: A prospective, decentralized app-based study demonstrated significantly lower use of prenatal vitamins, antiemetics, and antidepressants among Black pregnant participants. Additionally, significantly fewer Black and rural participants reported receiving treatment for anxiety and depression during pregnancy. Future research dedicated to identifying the root mechanisms of these differences can help improve maternal health outcomes, specifically for diverse communities.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».