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Enregistrement W4307468194 · doi:10.1001/jamanetworkopen.2022.39264

Analysis of State Medicaid Expansion and Access to Timely Prenatal Care Among Women Who Were Immigrant vs US Born

2022· article· en· W4307468194 sur OpenAlexaff
Teresa Janević, Ellerie Weber, Frances M. Howell, Morgan Steelman, Mahima Krishnamoorthi, Ashley Fox

Notice bibliographique

RevueJAMA Network Open · 2022
Typearticle
Langueen
DomainePsychology
ThématiqueMigration, Health and Trauma
Établissements canadiensWomen's Health Research Institute
Organismes subventionnairesnon disponible
Mots-clésMedicaidPrenatal careMedicineImmigrationDemographyEthnic groupPoverty levelPovertyPregnancyGerontologyHealth careEnvironmental healthPopulationGeographyPolitical science

Résumé

récupéré en direct d'OpenAlex

Importance: Disparities exist in access to timely prenatal care between immigrant women and US-born women. Exclusions from Medicaid eligibility based on immigration status may exacerbate disparities. Objective: To examine changes in timely prenatal care by nativity after Medicaid expansion. Design, Setting, and Participants: A cross-sectional difference-in-differences (DID) and triple-difference analysis of 22 042 624 singleton births from January 1, 2011, to December 31, 2019, in 31 states was conducted using US natality data. Data analysis was performed from February 1, 2021, to August 24, 2022. Exposures: Within 16 states that expanded Medicaid in 2014, the rate of timely prenatal care by nativity in years after expansion was compared with the rate in the years before expansion. Similar comparisons were conducted in 15 states that did not expand Medicaid and tested across expansion vs nonexpansion states. Main Outcomes and Measures: Timely prenatal care was categorized as prenatal care initiated in the first trimester. Individual-level covariates included age, parity, race and ethnicity, and educational level. State-level time-varying covariates included unemployment, poverty, and Immigrant Climate Index. Results: A total of 5 390 814 women preexpansion and 6 544 992 women postexpansion were included. At baseline in expansion states, among immigrant women, 413 479 (27.3%) were Asian, 110 829 (7.3%) were Black, 752 176 (49.6%) were Hispanic, and 238 746 (15.8%) were White. Among US-born women, 96 807 (2.5%) were Asian, 470 128 (12.1%) were Black, 699 776 (18.1%) were Hispanic, and 2 608 873 (67.3%) were White. Prenatal care was timely in 75.9% of immigrant women vs 79.9% of those who were US born in expansion states at baseline. After Medicaid expansion, the immigrant vs US-born disparity in timely prenatal care was similar to the preexpansion level (DID, -0.91; 95% CI, -1.91 to 0.09). Stratifying by race and ethnicity showed an increase in the Asian vs White disparity after expansion, with 1.53 per 100 fewer immigrant women than those who were US born accessing timely prenatal care (95% CI, -2.31 to -0.75), and in the Hispanic vs White disparity (DID, -1.18 per 100; 95% CI, -2.07 to -0.30). These differences were more pronounced among women with a high school education or less (DID for Asian women, -2.98; 95% CI, -4.45 to -1.51; DID for Hispanic women, -1.47; 95% CI, -2.48 to -0.46). Compared with nonexpansion states, differences in DID estimates were found among Hispanic women with a high school education or less (triple-difference, -1.86 per 100 additional women in expansion states who would not receive timely prenatal care; 95% CI, -3.31 to -0.42). Conclusions and Relevance: The findings of this study suggest that exclusions from Medicaid eligibility based on immigration status may be associated with increased health care disparities among some immigrant groups. This finding has relevance to current policy debates regarding Medicaid coverage during and outside of pregnancy.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,188
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,015
Tête enseignante GPT0,320
Écart entre enseignants0,304 · 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

Citations23
Publié2022
Routes d'admission1
Résumé présentoui

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