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Identifying Pregnant Women With Disabilities and Maternal and Newborn Outcomes

2025· article· en· W4408891589 on OpenAlexaff
Alka Dev, Willi Horner‐Johnson, Andrew Schaefer, Cecilia Ganduglia‐Cazaban, Thérèse A. Stukel, David C. Goodman, JoAnna K. Leyenaar

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Minority Health and Health Disparities
KeywordsMedicinePregnancyMedicaidPopulationPoisson regressionDiagnosis codeGestational ageObstetricsPediatricsHealth careEnvironmental health

Abstract

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Importance: Pregnant women with disabilities are at higher risk of poor pregnancy and birth outcomes. Different methods for identifying disability may affect estimates of health disparities in this population. Objective: To compare pregnancy and birth outcomes among pregnant women using different ways of identifying maternal disability. Design, Setting, and Participants: Retrospective cohort study of linked vital records and maternal and newborn claims for Medicaid-insured live births from January 2010 to December 2014 in Texas. Data analysis was conducted from October 2023 to May 2024. Exposure: Births grouped into 5 maternal cohorts: no identified disability, disability benefits enrollment only, disability diagnostic code only, both benefits enrollment and a diagnostic code, and either disability benefits or a diagnostic code. Main Outcomes and Measures: Mode of delivery (cesarean) and severe maternal morbidity (SMM) were identified from maternal claims. Low birthweight (LBW), preterm birth (PTB), and small for gestational age (SGA) were identified from birth certificates. Modified Poisson regression with robust variance estimators was used to estimate adjusted risk ratios (aRRs) for the association of each of the 5 outcomes with disability group status. Results: Among 921 218 births (mean [SD] maternal age at birth, 25.1 [5.7] years), 895 201 (97.2%) were to mothers with no disability, 6160 (0.7%) were to mothers enrolled in disability benefits only, 17 742 (1.9%) were to mothers with a disability diagnostic code only, 2115 (0.2%) were to mothers with both benefits enrollment and a disability code, and 26 017 (2.8%) were to mothers meeting either disability definition. Compared with those without disabilities, those with only disability diagnostic codes had the highest rates for cesarean delivery (306 589 births [34.3%] vs 7658 births [43.2%]), LBW (750 058 births [8.4%] vs 869 births [14.2%]), and PTB (92 807 births [10.4%] vs 977 births [15.9%]). Compared with those with no disability, the adjusted relative risks were highest in the diagnostic codes only group for cesarean delivery (aRR, 1.22; 95% CI, 1.20-1.24), LBW (aRR, 1.77, 95% CI, 1.71-1.84), and PTB (aRR, 1.68; 95% CI, 1.62-1.74). The risk for SMM (aRR, 4.82; 95% CI, 3.96-5.86) and SGA (aRR, 1.43; 95% CI, 1.24-1.66) were highest in those with both benefits enrollment and a disability code. Conclusions and relevance: In this cohort study, disability was associated with adverse outcomes, regardless of definition. However, the burden of disparities was dependent on how disability was defined, suggesting that the assessment of disability-associated health risks should consider how disability is conceptualized.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.347
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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