Abstract
Background
To compare risk of neonatal morbidities between women with and without documented disability and to evaluate mediation of these associations by pre-term birth and caesarean delivery.
Methods
Using data from the Consortium on Safe Labor (2002–2008; n = 223 385), we evaluated risk of 22 neonatal outcomes among singleton deliveries using ICD-9 codes to define physical (n = 1733), sensory (n = 250) and intellectual disability (n = 91). Adjusted relative risk (aRR) was estimated for each outcome among each category of disability, and among women with any disability using Poisson regression models with robust variance. Causal mediation methods evaluated pre-term birth and caesarean delivery as mediators.
Results
Compared with no disability, neonates of women with any disability had higher risk of nearly all neonatal outcomes, including pre-term birth (aRR = 1.77; 95% CI 1.62–1.94), small for gestational age (SGA) (aRR = 1.25; CI 1.11–1.41), neonatal intensive care unit (NICU) admission (aRR = 1.70; CI 1.54–1.87), seizures (aRR = 2.81; CI 1.54–5.14), cardiomyopathy (aRR = 4.92; CI 1.15–20.95), respiratory morbidities (aRR ranged from 1.33–2.08) and death (aRR = 2.31; CI 1.38–3.87). Women with disabilities were more likely to have a maternal indication for pre-term delivery, including pre-pregnancy diabetes (aRR = 3.80; CI 2.84–5.08), chronic hypertension (aRR = 1.46; CI 0.95–2.25) and severe pre-eclampsia/eclampsia (aRR = 1.47; CI 1.19–1.81). Increased risk varied but was generally consistent across all disability categories. Most outcomes were partially mediated by pre-term birth, except SGA, and heightened risk remained for NICU admissions, respiratory distress syndrome, anaemia and a composite of any adverse outcome (aRR = 1.21; CI 1.10–1.32).
Conclusion
Neonates of women with disabilities were at higher risk of a broad range of adverse neonatal outcomes, including death. Risks were not fully explained by pre-term birth.
Keywords: Disability, pregnancy, neonatal outcomes, pre-term birth
Key Messages.
Neonates of women with disabilities have higher risk for a range of adverse outcomes, including death.
Women with disabilities were more likely to have a pre-term birth that was medically indicated for maternal reasons such as hypertensive disorders of pregnancy and diabetes.
Much of the risk of adverse neonatal outcomes is mediated through pre-term birth, though pre-term birth does not fully explain increased risk of small for gestational age, neonatal intensive care unit admissions, anaemia and respiratory distress syndrome.
Altogether, findings support the need for systemic changes to promote pre-conception and prenatal health for women with disabilities to improve neonatal health outcomes.
Introduction
Women with disabilities are at elevated risk of pregnancy complications and adverse perinatal outcomes1 but less is known about the risk of adverse outcomes among their neonates. Neonates of women with disabilities have been found to be at increased risk of pre-term birth and low birthweight2 but little is known about other neonatal outcomes. Given women with disabilities’ higher risk for adverse perinatal outcomes, it is plausible that the neonates of women with disabilities may also be at higher risk for a range of poor outcomes. Furthermore, women with disabilities are at higher risk for caesarean delivery, which is also an independent risk factor for neonatal respiratory morbidity.3 Given that the neonatal period is a critical time for long-term child development, understanding the risk of neonatal morbidity may help identify targeted areas of intervention to improve long-term health outcomes.
Compounding the lack of information on neonatal outcomes beyond pre-term birth and low birthweight is the lack of availability of high-quality studies. For example, only half of the studies identified in a recent systematic review (17/31) controlled for confounding variables and only a third adjusted analyses for co-morbidities or lifestyle behaviours that may contribute to both functional limitations affecting disability status and adverse neonatal outcomes.2 Additionally, existing studies primarily evaluate outcomes among women with one type of disability (i.e. intellectual, sensory or physical) and findings may not apply across all types of disabilities.2,4 Though studies of individual disability type are important to highlight mechanistic pathways, more evidence is needed on multiple disabilities to clarify the scope of the disparities experienced by pregnant women with any disability. Thus, quality, comprehensive analyses of neonatal outcomes are critical to achieving a better understanding of optimal health outcomes among women with physical, intellectual/developmental and sensory disabilities and their neonates, consistently with an ongoing call for research from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.5
In this study, we utilized data from a large pregnancy study of electronic medical records to identify the risk of a comprehensive set of neonatal outcomes among women with physical, sensory and intellectual/developmental disabilities. We also evaluated indications for pre-term birth given the higher rates of these births among women with disabilities. We conducted mediation analyses to determine what proportion of neonatal morbidities could be attributed to pre-term birth and caesarean delivery.
Methods
Sample
The Consortium on Safe Labor (CSL) was a large, retrospective study of pregnant women with extensive medical chart abstraction of reproductive history and pregnancy information, including provider diagnoses and International Classification of Diseases-9 (ICD-9) codes, described in detail elsewhere.6 Data were abstracted from hospital delivery admissions and discharge summaries for all births (n = 228 668) that occurred between 2002 and 2008 at 12 US clinical sites across 19 hospitals and mapped to pre-defined categories (Supplementary Table S1, available as Supplementary data at IJE online). Our analytic sample consisted of all singleton births with information on neonatal outcomes (n = 223 385). Of note, though we refer to ‘women’ throughout the manuscript to reflect how data were collected in the CSL, we acknowledge that our sample may include individuals who do not identify with this gender or terminology.
Neonatal outcomes
Neonatal outcomes were defined using information from the medical records and supplemented with ICD-9 data, including birthweight, gestational age at delivery from the best obstetrical estimate, level of resuscitation in delivery room requiring continuous positive airway pressure (CPAP) or higher, neonatal intensive care unit (NICU) admission, respiratory distress syndrome, apnoea, sepsis, anaemia, transient tachypnea, infective pneumonia, asphyxia, intracerebral haemorrhage, seizure, cardiomyopathy, periventricular or intraventricular haemorrhage, necrotizing enterocolitis, aspiration, retinopathy of prematurity (ROP), intrauterine fetal death, congenital anomalies and neonatal death as previously described.7 We defined pre-term birth as delivery at <37 weeks of gestation as well as categorizing as extremely (<28 weeks), very (28–31 weeks) and moderate to late pre-term (32–36 weeks). Indications for pre-term birth were coded as spontaneous [including pre-term premature rupture of membranes (PPROM)], medically indicated (including hypertensive disorders of pregnancy, any diabetes, placental abruption or previa, chorioamnionitis, fever at admission, history of maternal or fetal indication, fetal macrosomia, congenital or chromosomal anomaly, intrauterine fetal death, any other maternal or fetal indication) and no indication, based on previous definitions.8 One site did not report indications for pre-term birth and was excluded from all analyses evaluating pre-term birth indications (n = 566). Small for gestational age (SGA) and large for gestational age were defined as birthweight <10th or >90th percentile for gestational age using the Duryea birthweight reference.9 Because many neonatal outcomes were rare events, we additionally constructed a composite measure that included all outcomes except birthweight, size for gestational age, pre-term birth and congenital anomalies.
Maternal disability
Maternal disability was defined using ICD-9 codes that were linked to each delivery to identify individuals with diagnoses leading to likely functional limitations and disability in the physical, intellectual/developmental and sensory domains according to an existing algorithm (Supplementary Table S2, available as Supplementary data at IJE online).10 Of note, the algorithm classified diagnoses as only one type of disability so some conditions typically associated with both physical and intellectual/developmental disability (e.g. cerebral palsy) were only coded in one category. Though some women had multiple codes in the same disability category, none had codes that would classify them into more than one category. We additionally created a binary variable that grouped women with physical, intellectual/developmental or sensory disabilities into one ‘any’ disability category. Although we recognize that each type of disability category likely has a different mechanistic association with neonatal outcomes, we hypothesize that the barriers faced by women with any type of disability may contribute to disparities in neonatal outcomes. All other women in the CSL were considered to have no documented disability.
Covariates
Covariates were selected based on their associations with disability and neonatal outcomes based on a directed acyclic graph (DAG; Supplementary Figure S1, available as Supplementary data at IJE online). Covariates included maternal age (years), race (non-Hispanic White, Black, Hispanic, Asian/Pacific Islander, Other or multi-racial), parity (primiparous vs multiparous), marital status (married or unmarried), insurance type (private, public/self-pay, other/unknown), pre-pregnancy body mass index (BMI; kg/m2), maternal history of chronic disease (including asthma, depression or anxiety, HIV, hypertension, pre-gestational diabetes and renal, heart or thyroid disease), smoking (yes or no) and alcohol use during pregnancy (yes or no). Additionally, we examined birth by caesarean delivery (yes or no) and median NICU stay (days). We did not consider calendar year of delivery to be a potential confounder because 86% of all births in the CSL occurred between 2005 and 2007 (Supplementary Table S1, available as Supplementary data at IJE online).
Analysis
We compared women across disability categories on all covariates listed previously using chi-squared tests for categorical variables, and t-tests and Kruskal–Wallis tests for continuous variables. Additionally, we compared individual medical indications for pre-term birth between women with any disability and no disability using chi-squared tests. To estimate the risk of adverse neonatal outcomes, we fit modified Poisson regression models with robust variance and generalized estimating equations to account for repeat pregnancies in the CSL (∼7%).11 For all outcomes, we estimated adjusted relative risk (aRR) and 95% CI, comparing women with any disability and each category of disability to women with no documented disability. For some covariates, we considered that relationships with disability may be bidirectional such that the covariate could either lie on the intermediary pathway between disability and neonatal outcomes or act as a confounder by contributing to or exacerbating existing disability. To account for the role of some covariates as potential mediators, we completed main analyses with two set of models. In fully adjusted models, analyses were adjusted for maternal age, race, parity, marital status, insurance type, clinical site, pre-pregnancy BMI, smoking and alcohol use during pregnancy, and history of any chronic disease. In minimally adjusted models, we removed potential mediators, BMI, smoking and alcohol use, and history of chronic disease.
Using causal mediation methods,12 we tested for mediation of pre-term birth and caesarean delivery in the association between any maternal disability and all neonatal outcomes. We excluded ROP, as it only occurs among pre-term neonates and is not associated with caesarean delivery. We calculated percent mediated, total effect and the natural direct effect for associations between physical, intellectual/developmental, sensory, as well as any disability and each neonatal outcome. The total effect may be interpreted as the main association between disability and the neonatal outcome without including the mediator, whereas the natural direct effect can be interpreted as the association between disability and the neonatal outcome when taking the mediator into consideration, i.e. it is the association that is not due to mediation. All analyses were considered exploratory and completed using SAS v9.4 (Cary, NC) and R v4.1.0 (R Core Team, 2021).
Results
A higher proportion of women with disabilities were non-Hispanic White (62.4 vs 49.3%), unmarried (41.4 vs 38.0%), had a history of chronic disease (35.7 vs 17.7%) and smoked during pregnancy (11.1 vs 6.6%) (Table 1). Disability groups did not differ in terms of parity or alcohol use during pregnancy. The neonates of women with any disability had shorter mean gestational age than women with no recorded disability (37.6 vs 38.6 weeks) and longer median NICU length of stay (11 vs 7 days). The most prevalent congenital anomaly was patent ductus arteriosus (PDA) followed by ventricular septal defects (VSD), which both occurred in similar proportions among women with and without disabilities (1.8% and 1.3%, respectively for PDA and 1.2% and 1.0% for VSD).
Table 1.
Medical and demographic characteristics of women and their neonates by disability status, Consortium on Safe Labor, 2002–2008 (n = 223 385)
| Disability category |
||||||
|---|---|---|---|---|---|---|
| Total | No disability | Anya | Physical | Intellectual | Sensory | |
| (n = 221 252) | (n = 2142) | (n = 1733) | (n = 91) | (n = 250) | ||
| Gestational age at delivery [mean (SD)]b | 38.6 (2.4) | 38.6 (2.3) | 37.6 (3.0) | 37.8 (2.9) | 36.7 (3.8) | 37.0 (3.7) |
| Birthweight (g) [mean (SD)]b | 3244 (607) | 3246 (605) | 3066 (718) | 3095 (693) | 2798 (843) | 2965 (808) |
| NICU admission [n (%)] | 27 230 (12.2) | 26 793 (12.1) | 437 (21.1) | 330 (19.0) | 30 (33.0) | 77 (30.8) |
| NICU length of stay (days) [median (IQR)]b | 7 (3–19) | 7 (3–19) | 11 (3.8–24) | 9 (3–21) | 21 (9–46) | 16.5 (7–33) |
| Maternal age (years) [mean (SD)]b | 27.6 (6.2) | 27.6 (6.2) | 28.8 (6.4) | 29.0 (6.3) | 25.2 (7.1) | 29.0 (6.5) |
| Maternal race [n (%)]b | ||||||
| White/Non-Hispanic | 110 443 (49.4) | 109 149 (49.3) | 1294 (62.4) | 1124 (64.9) | 43 (49.4) | 127 (50.8) |
| Black/Non-Hispanic | 50 235 (22.5) | 49 841 (22.5) | 394 (19.0) | 309 (17.8) | 25 (27.5) | 60 (24.0) |
| Hispanic | 39 039 (17.5) | 38 811 (17.5) | 228 (11.0) | 178 (10.3) | 13 (14.3) | 37 (14.8) |
| Asian/Pacific Islander | 9206 (4.1) | 9164 (4.1) | 42 (2.0) | 34 (2.0) | – | – |
| Other or multi-racial | 14 461 (6.5) | 14 345 (6.5) | 116 (5.6) | 88 (5.1) | – | 19 (7.6) |
| Parity [n (%)] | ||||||
| Nulliparous | 89 030 (39.9) | 88 178 (39.8) | 852 (41.1) | 700 (40.4) | 51 (56.0) | 101 (40.4) |
| Parous (at least one child) | 134 355 (60.2) | 133 133 (60.2) | 1222 (58.9) | 1033 (59.6) | 40 (44.0) | 149 (59.6) |
| Marital status [n (%)]b | ||||||
| Married | 131 175 (58.7) | 130 039 (58.8) | 1136 (54.8) | 981 (56.6) | 25 (27.5) | 130 (52.0) |
| Not married | 84 995 (38.1) | 84 136 (38.0) | 859 (41.4) | 690 (39.8) | 63 (69.2) | 106 (42.4) |
| Unknown | 7215 (3.2) | 7136 (3.2) | 79 (3.8) | 62 (3.6) | – | 14 (5.6) |
| Insurance type [n (%)]b | ||||||
| Private | 124 913 (55.9) | 123 795 (55.9) | 1118 (53.9) | 990 (57.1) | 21 (23.1) | 107 (42.8) |
| Public/self-pay | 74 810 (33.5) | 74 004 (33.4) | 806 (38.9) | 620 (35.8) | 58 (63.7) | 128 (51.2) |
| Other/unknown | 23 662 (10.6) | 23 512 (10.6) | 150 (7.2) | 123 (7.1) | 12 (13.2) | 15 (6.0) |
| Pre-pregnancy BMI (kg/m2) [mean (SD)]b | 25.4 (6.2) | 25.4 (6.2) | 26.3 (6.9) | 26.4 (7.0) | 25.6 (6.0) | 25.9 (6.7) |
| Maternal history of chronic disease [n (%)]b,c | 39 795 (17.8) | 39 054 (17.7) | 741 (35.7) | 603 (34.8) | 35 (38.5) | 103 (41.2) |
| Smoking during pregnancy [n (%)]b | 14 930 (6.7) | 14 700 (6.6) | 230 (11.1) | 196 (11.3) | 11 (12.1) | 23 (9.2) |
| Alcohol use during pregnancy [n (%)] | 4090 (1.8) | 4051 (1.8) | 39 (1.9) | 31 (1.8) | – | – |
Any disability combines all women with physical, intellectual/developmental and sensory disabilities.
Indicates statistically significant difference (P < 0.05) between women with any vs no disability using chi-squared tests for categorical variable, t-tests for all continuous variables except NICU length of stay (Kruskal–Wallis tests to compare medians).
Chronic disease includes history of asthma, depression or anxiety, HIV, hypertension, renal disease, heart disease, pre-gestational diabetes or thyroid disease.
Cells with counts of <10 censored with dashed line.
BMI, body mass index; IQR, interquartile range; NICU, neonatal intensive care unit.
In fully adjusted models, compared with women with no recorded disability, neonates of women with any disability were 1.34-fold more likely to be delivered by caesarean compared with vaginal delivery and 1.77-fold more likely to deliver pre-term at <37 weeks with an almost 2-fold elevated risk of all pre-term birth categories, extremely, very and moderate to late pre-term (Table 2). Furthermore, they were more likely to be of low birthweight <2500 g (aRR = 1.68; 95% CI 1.51, 1.88) and SGA (aRR = 1.25; CI 1.11, 1.41) and less likely to be macrosomic (birthweight ≥ 4000 g).
Table 2.
Riska of neonatal outcomes comparing categories of disability, Consortium on Safe Labor, 2002–2008 (n = 223 385)
| No disability (n = 221 311) |
Any disability (n = 2142) |
Physical disability (n = 1733) |
Intellectual disability (n = 91) |
Sensory disability (n = 250) |
||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Outcome | n (%) | aRR (95% CI) | n (%) | aRR (95% CI) | n (%) | aRR (95% CI) | n (%) | aRR (95% CI) | n (%) | aRR (95% CI) |
| Birthweight (g) | ||||||||||
| <2500 | 20 499 (9.3) | Ref. | 342 (16.5) | 1.68 (1.51, 1.88) | 258 (14.9) | 1.56 (1.38, 1.77) | 25 (27.5) | 2.34 (1.58, 3.47) | 59 (23.6) | 2.19 (1.69, 2.83) |
| 2500–3999 | 184 592 (83.4) | Ref. | 1618 (78.0) | 0.95 (0.90, 1.00) | 1378 (79.5) | 0.97 (0.92, 1.02) | 61 (67.0) | 0.82 (0.64, 1.05) | 179 (71.6) | 0.88 (0.76, 1.02) |
| ≥4000 | 16 220 (7.3) | Ref. | 114 (5.5) | 0.68 (0.57, 0.82) | 97 (5.6) | 0.68 (0.56, 0.83) | 5 (5.5) | 0.85 (0.35, 2.03) | 12 (4.8) | 0.64 (0.37, 1.13) |
| Size for gestational age | ||||||||||
| SGA | 24 493 (11.1) | Ref. | 272 (13.1) | 1.25 (1.11, 1.41) | 222 (12.8) | 1.24 (1.09, 1.42) | 19 (20.9) | 1.58 (1.01, 2.48) | 31 (12.4) | 1.13 (0.79, 1.61) |
| AGA | 174 736 (79.0) | Ref. | 1572 (75.8) | 0.97 (0.92, 1.02) | 1323 (76.3) | 0.98 (0.93, 1.03) | 63 (69.2) | 0.89 (0.69, 1.14) | 186 (74.4) | 0.95 (0.83, 1.10) |
| LGA | 22 082 (10.0) | Ref. | 230 (11.1) | 0.97 (0.85, 1.10) | 188 (10.9) | 0.93 (0.81, 1.08) | 9 (9.9) | 1.07 (0.55, 2.05) | 33 (13.2) | 1.18 (0.84, 1.66) |
| Pre-term birth (any) <37 weeks | 25 647 (11.6) | Ref. | 483 (23.3) | 1.77 (1.62, 1.94) | 366 (21.1) | 1.64 (1.48, 1.82) | 30 (33.0) | 2.23 (1.56, 3.20) | 87 (34.8) | 2.41 (1.95, 2.98) |
| Extremely pre-term (<28 weeks) | 2319 (1.1) | Ref. | 47 (2.3) | 2.01 (1.51, 2.70) | 34 (2.0) | 1.82 (1.29, 2.56) | 4 (4.4) | 3.11 (1.17, 8.31) | 9 (3.6) | 2.66 (1.38, 5.12) |
| Very pre-term (28–31 weeks) | 3080 (1.4) | Ref. | 77 (3.8) | 2.41 (1.92, 3.03) | 50 (2.9) | 1.92 (1.45, 2.54) | 6 (6.9) | 3.79 (1.70, 8.46) | 21 (8.7) | 4.84 (3.15, 7.44) |
| Moderate to late pre-term (32–36 weeks) | 20 248 (9.4) | Ref. | 359 (18.4) | 1.73 (1.55, 1.92) | 282 (17.1) | 1.63 (1.44, 1.83) | 20 (24.7) | 2.14 (1.38, 3.32) | 57 (25.9) | 2.27 (1.74, 2.95) |
| Pre-term birth indicationb | ||||||||||
| Spontaneousc | 9982 (40.1) | Ref. | 144 (30.6) | 0.79 (0.67, 0.93) | 112 (31.3) | 0.81 (0.67, 0.98) | 11 (40.7) | 1.03 (0.57, 1.85) | 21 (24.7) | 0.63 (0.41, 0.97) |
| Medically indicatedd | 10 744 (43.2) | Ref. | 257 (54.7) | 1.18 (1.04, 1.33) | 186 (52.0) | 1.12 (0.97, 1.30) | 13 (48.2) | 1.05 (0.61, 1.80) | 58 (68.2) | 1.41 (1.09, 1.84) |
| Unknown indication | 4154 (16.7) | Ref. | 69 (14.7) | 0.99 (0.78, 1.25) | 60 (16.8) | 1.09 (0.84, 1.41) | 3 (11.1) | 0.82 (0.26, 2.54) | 6 (7.1) | 0.54 (0.24, 1.20) |
| Caesarean delivery | 61 653 (27.9) | Ref. | 905 (43.6) | 1.34 (1.26, 1.43) | 741 (42.8) | 1.33 (1.23, 1.43) | 42 (46.2) | 1.49 (1.10, 2.01) | 122 (48.8) | 1.39 (1.17, 1.67) |
| Level of resuscitation in delivery room CPAP or higher | 3711 (1.7) | Ref. | 71 (3.4) | 1.32 (1.04, 1.68) | 53 (3.1) | 1.15 (0.87, 1.51) | 6 (6.6) | 3.19 (1.43, 7.12) | 12 (4.8) | 2.09 (1.18, 3.68) |
| NICU admissions | 26 793 (12.1) | Ref. | 437 (21.1) | 1.70 (1.54, 1.87) | 330 (19.0) | 1.59 (1.42, 1.77) | 30 (33.0) | 2.28 (1.59, 3.26) | 77 (30.8) | 2.12 (1.69, 2.66) |
| Respiratory distress syndrome | 7181 (3.2) | Ref. | 147 (7.1) | 2.02 (1.72, 2.39) | 104 (6.0) | 1.78 (1.47, 2.17) | 11 (12.1) | 2.88 (1.60, 5.21) | 32 (12.8) | 3.03 (2.14, 4.28) |
| Apnoea | 4617 (2.1) | Ref. | 101 (4.9) | 1.86 (1.52, 2.27) | 78 (4.5) | 1.79 (1.43, 2.24) | 7 (7.7) | 2.46 (1.17, 5.17) | 16 (6.4) | 2.03 (1.24, 3.32) |
| Sepsis | 6121 (2.8) | Ref. | 103 (5.0) | 1.77 (1.46, 2.16) | 75 (4.3) | 1.62 (1.29, 2.04) | 10 (11.0) | 2.78 (1.50, 5.19) | 18 (7.2) | 2.19 (1.38, 3.49) |
| Anaemia | 4120 (1.9) | Ref. | 83 (4.0) | 2.08 (1.67, 2.59) | 57 (3.3) | 1.83 (1.41, 2.38) | 6 (6.6) | 2.56 (1.15, 5.71) | 20 (8.0) | 3.10 (2.00, 4.82) |
| Transient tachypnea | 7894 (3.6) | Ref. | 112 (5.4) | 1.33 (1.11, 1.61) | 87 (5.0) | 1.26 (1.02, 1.56) | 6 (6.6) | 1.51 (0.68, 3.36) | 19 (7.6) | 1.71 (1.08, 2.72) |
| Infective pneumonia | 1296 (0.6) | Ref. | 19 (0.9) | 1.41 (0.89, 2.22) | 11 (0.6) | 0.99 (0.54, 1.79) | 1 (1.1) | 1.63 (0.23, 11.61) | 7 (2.8) | 4.06 (1.93, 8.55) |
| Asphyxia | 583 (0.3) | Ref. | 7 (0.3) | 1.31 (0.62, 2.76) | 6 (0.4) | 1.42 (0.63, 3.18) | 1 (1.1) | 3.12 (0.44, 22.24) | 0 | 0 |
| Intracerebral haemorrhage | 564 (0.3) | Ref. | 7 (0.3) | 1.56 (0.74, 3.30) | 4 (0.2) | 1.16 (0.43, 3.10) | 1 (1.1) | 3.11 (0.44, 22.22) | 2 (0.8) | 2.86 (0.71, 11.49) |
| Seizure | 456 (0.2) | Ref. | 11 (0.5) | 2.81 (1.54, 5.14) | 6 (0.4) | 1.94 (0.86, 4.37) | 4 (4.4) | 16.60 (6.17, 44.67) | 1 (0.4) | 1.70 (0.24, 12.11) |
| Cardiomyopathy | 208 (0.1) | Ref. | 2 (0.1) | 4.92 (1.15, 20.95) | 1 (0.1) | N/Ae | 0 | 0 | 1 (0.4) | N/Ae |
| Periventricular or intraventricular haemorrhage | 1282 (0.6) | Ref. | 29 (1.4) | 2.16 (1.49, 3.12) | 22 (1.3) | 2.07 (1.35, 3.16) | 2 (2.2) | 2.55 (0.64, 10.24) | 5 (2.0) | 2.45 (1.02, 5.90) |
| Necrotizing enterocolitis | 439 (0.2) | Ref. | 13 (0.6) | 3.50 (2.00, 6.10) | 8 (0.5) | 2.75 (1.36, 5.57) | 5 (5.5) | 20.45 (8.42, 49.69) | 0 | 0 |
| Aspiration | 1129 (0.5) | Ref. | 15 (0.7) | 1.45 (0.87, 2.43) | 12 (0.7) | 1.41 (0.80, 2.50) | 2 (2.2) | 3.86 (0.96, 15.47) | 1 (0.4) | 0.78 (0.11, 5.52) |
| Retinopathy of prematurity | 984 (0.4) | Ref. | 24 (1.2) | 2.35 (1.57, 3.54) | 14 (0.8) | 1.81 (1.07, 3.08) | 2 (2.2) | 3.01 (0.75, 12.07) | 8 (3.2) | 4.44 (2.21, 8.92) |
| Intrauterine fetal death (stillbirth) | 986 (0.5) | Ref. | 6 (0.3) | 0.67 (0.30, 1.49) | 5 (0.3) | 0.67 (0.28, 1.61) | 1 (1.1) | 2.18 (0.31, 15.53) | 0 | 0 |
| Neonatal death | 716 (0.3) | Ref. | 15 (0.7) | 2.31 (1.38, 3.87) | 7 (0.4) | 1.40 (0.66, 2.95) | 6 (6.6) | 14.51 (6.47, 32.57) | 2 (0.8) | 1.87 (0.47, 7.50) |
| Congenital anomaly | 15 334 (6.9) | Ref. | 209 (10.1) | 1.39 (1.21, 1.59) | 158 (9.1) | 1.28 (1.09, 1.49) | 18 (19.8) | 2.54 (1.60, 4.03) | 33 (13.2) | 1.67 (1.19, 2.36) |
| Combined neonatal compositef | 30 369 (13.7) | Ref. | 499 (24.1) | 1.59 (1.45, 1.74) | 384 (22.2) | 1.49 (1.34, 1.64) | 32 (35.2) | 2.14 (1.51, 3.03) | 83 (33.2) | 2.03 (1.63, 2.52) |
All analyses adjusted for maternal age, race, parity, marital status, insurance type, pre-pregnancy BMI, smoking and alcohol use during pregnancy, clinical site and history of any chronic disease.
Excludes 566 women from one site who did not provide any indications for pre-term birth.
Includes spontaneous pre-term birth and pre-term premature rupture of membranes (PROM).
Includes gestational hypertension, pre-eclampsia/eclampsia, chronic hypertension, pre-gestational and gestational diabetes, placental abruption, placenta previa, chorioamnionitis, fever at admission, history of maternal indication, other maternal indication, macrosomia, congenital or chromosomal anomaly, history of fetal indication, other fetal indication, stillbirth.
Models do not converge to provide estimates.
Includes all neonatal outcomes listed above except birthweight, size for gestational age, pre-term birth, caesarean delivery and congenital anomaly.
AGA, appropriate for gestational age; aRR, adjusted relative risk; CPAP, continuous positive airway pressure; LGA, large for gestational age; SGA, small for gestational age.
Compared with women with no recorded disability, neonates of women with any disability had higher risk of almost all adverse outcomes (Table 2) including requiring resuscitation in the delivery room with CPAP or higher, NICU admission, respiratory distress syndrome, apnoea, sepsis, anaemia, transient tachypnea, seizure, cardiomyopathy, periventricular or intraventricular haemorrhage, necrotizing enterocolitis, ROP, congenital anomaly and neonatal death (aRR = 2.31; CI 1.38, 3.87). When combining these outcomes in a composite measure, neonates of women with disability had nearly 60% higher risk (aRR = 1.59; CI 1.45, 1.74) of any adverse outcome compared with neonates of women with no recorded disability. Patterns of elevated risk were similar across individual categories of disability. In minimally adjusted models, risk ratios were generally higher for all outcomes, although the difference in effect estimates was slight and did not appreciably change the interpretation of results (Supplementary Table S3, available as Supplementary data at IJE online). For example, after minimal adjustment for women with any disability, aRR of pre-term delivery was 1.87 (CI 1.70, 2.04) vs 1.77 (CI 1.62, 1.94) after full adjustment.
Women with disability were less likely to have a spontaneous pre-term birth at <37 weeks (aRR = 0.79; CI 0.67, 0.93) and were 18% (1.04, 1.33) more likely to have a medically indicated pre-term delivery. When evaluating individual medical indications (Supplementary Table S4, available as Supplementary data at IJE online), compared with women with no disability, a higher percentage of women with disabilities were delivered prior to 37 weeks due to severe pre-eclampsia/eclampsia (19.5 vs 12.3%), chronic hypertension (4.6 vs 2.6%), pre-pregnancy diabetes (11.3 vs 3.0%), other maternal indications (25.3 vs 12.8%) and congenital or chromosomal anomalies (1.9 vs 0.9%).
Results of mediation analyses (Table 3) indicated that caesarean delivery did not mediate the association between disability and any neonatal outcome. Pre-term birth was not a mediating factor for the association between maternal disability and neonatal SGA, and the direct effect of any maternal disability remained significant for NICU admission, respiratory distress syndrome and anaemia, although point estimates were attenuated, suggesting partial mediation. The direct effect of disability was in the same direction but with loss of precision for other individual neonatal outcomes, although disability remained associated with the composite of neonatal outcomes (aRR = 1.21; CI 1.10, 1.32) after mediation. Pre-term birth partially mediated (63.8%) this association. Results of mediation analyses conducted in minimally adjusted models were similar to those of the main analyses (Supplementary Table S5, available as Supplementary data at IJE online). According to ad hoc power analyses (Supplementary Table S6, available as Supplementary data at IJE online), we had adequate power to detect mediation by pre-term birth for individual disability categories and most outcomes. Mediation results for individual categories of disability can be viewed in Supplementary Table S7 (available as Supplementary data at IJE online) but should be interpreted with caution given the small cell sizes observed for some outcomes.
Table 3.
Results of mediation by pre-term birth (<37 weeks’ gestation) and caesarean delivery for associations between any disability and neonatal outcomes, 2002–2008 (n = 2142)
| Outcome | Percentage mediated by pre-term birth | Total effect aRR (without pre-term birth) | Natural direct effect aRR (with pre-term birth) | Percentage mediated by caesarean | Total effect aRR (without caesarean) | Natural direct effect aRR (with caesarean) |
|---|---|---|---|---|---|---|
| Birthweight <2500 g | 84.2 | 1.70 (1.48, 1.91) | 1.11 (0.99, 1.23) | 6.4 | 1.80 (1.54, 2.06) | 1.75 (1.50, 2.00) |
| SGA | 0.92 | 1.25 (1.10, 1.40) | 1.25 (1.10, 1.40) | 1.5 | 1.29 (1.09, 1.49) | 1.29 (1.08, 1.49) |
| NICU admissions | 58.2 | 1.74 (1.55, 1.92) | 1.31 (1.18, 1.43) | 8.5 | 1.68 (1.46, 1.90) | 1.62 (1.41, 1.84) |
| Respiratory distress syndrome | 74.5 | 2.14 (1.75, 2.52) | 1.29 (1.08, 1.50) | 12.4 | 1.85 (1.42, 2.28) | 1.74 (1.34, 2.15) |
| Sepsis | 68.6 | 1.74 (1.39, 2.10) | 1.23 (0.99, 1.48) | 9.0 | 1.63 (1.19, 2.08) | 1.58 (1.15, 2.00) |
| Anaemia | 70.2 | 2.15 (1.66, 2.64) | 1.34 (1.05, 1.64) | 8.8 | 2.48 (1.77, 3.19) | 2.35 (1.68, 3.02) |
| Transient tachypnea | 66.2 | 1.31 (1.06, 1.57) | 1.11 (0.90, 1.32) | 12.5 | 1.47 (1.12, 1.83) | 1.42 (1.08, 1.75) |
| Infective pneumonia | 76.2 | 1.50 (0.82, 2.19) | 1.12 (0.61, 1.63) | 9.0 | 1.49 (0.56, 2.42) | 1.44 (0.54, 2.35) |
| Asphyxia | 96.9 | 1.30 (0.33, 2.27) | 1.01 (0.25, 1.76) | 30.3 | 1.24 (0.01, 2.46) | 1.16 (0.01, 2.31) |
| Intracerebral haemorrhage | 121.2a | 1.40 (0.35, 2.46) | 0.91 (0.23, 1.60) | 8.9 | 1.79 (0.20, 3.37) | 1.71 (0.19, 3.24) |
| Seizure | 40.6 | 2.74 (1.08, 4.39) | 2.03 (0.80, 3.26) | 11.4 | 1.77 (0.01, 3.53) | 1.68 (0.01, 3.35) |
| Periventricular or intraventricular haemorrhage | 74.5 | 2.27 (1.41, 3.13) | 1.33 (0.83, 1.82) | 9.8 | 2.18 (1.02, 3.35) | 2.07 (0.96, 3.17) |
| Necrotizing enterocolitis | 58.6 | 3.60 (1.57, 5.63) | 2.08 (0.92, 3.24) | 6.9 | 2.78 (0.52, 5.05) | 2.66 (0.49, 4.82) |
| Aspiration | 5.3 | 1.46 (0.71, 2.20) | 1.43 (0.70, 2.16) | 1.6 | 1.45 (0.49, 2.41) | 1.44 (0.49, 2.40) |
| Neonatal death | 67.5 | 2.29 (1.10, 3.48) | 1.42 (0.69, 2.15) | 12.8 | 1.85 (035, 3.35) | 1.74 (0.33, 3.15) |
| Combined neonatal compositeb | 63.8 | 1.59 (1.43, 1.74) | 1.21 (1.10, 1.32) | 7.8 | 1.55 (1.36, 1.74) | 1.50 (1.32, 1.68) |
Percent mediated >100% occurs when the exposure’s association with the mediator leads to decreased risk of the outcome, but the association between the exposure and the outcome through any other pathways (not the mediator) increases the risk of the outcome.
Includes all neonatal outcomes except birthweight, size for gestational age, pre-term birth, caesarean and congenital anomaly; composite outcomes for pre-term birth also exclude retinopathy of prematurity, as this outcome only occurs among neonates delivered pre-term.
aRR, adjusted relative risk; NICU, neonatal intensive care unit; SGA, small for gestational age.
Discussion
Women with physical, intellectual/developmental and sensory disabilities had increased risk of a broad range of adverse neonatal outcomes compared with women with no recorded disability. Though caesarean delivery is an independent risk factor for many adverse neonatal outcomes3 and caesarean delivery occurred more frequently among women with disabilities in our sample, it did not mediate the association between maternal disability and any outcome. Many outcomes could be partially explained by an increased risk of pre-term birth, although risk of NICU admission, respiratory distress, anaemia and a composite of rare neonatal outcomes remained elevated. Risk of extremely (<28 weeks) and very pre-term (28–31 weeks) birth was also increased for women with disabilities compared with women without. Pre-term birth did not account for the increased risk of SGA among women with any disability, suggesting there may be other mechanisms such as fetal growth restriction driving the association between disability and SGA. Our study is the first to examine indications for pre-term birth, finding that women with disabilities were less likely to have a spontaneous pre-term birth and more likely to have one that was medically indicated, including for pre-pregnancy diabetes and hypertension, severe pre-eclampsia or eclampsia. Fetal medical indications did not differ between women with and without disability, except for an elevated risk of indication due to congenital or chromosomal anomaly.
Our results are consistent with a 2020 systematic review and meta-analysis that identified elevated risk for pre-term birth and low birthweight.2 Tarasoff et al. reported elevated risk of SGA, although they could only pool results for intellectual and developmental disabilities due to heterogeneity in other studies. For all outcomes, most investigations focused on women with intellectual/developmental or sensory disabilities only, with too few studies conducted among women with any disability or physical disabilities to pool results in these groups. Our findings on the increased risk of other neonatal outcomes among women with disability are novel. Pre-term birth is among the leading causes of infant mortality and an independent risk factor for most other adverse neonatal outcomes, although it did not mediate all associations in our study.13 Considering the impact of early-life exposure on health trajectories,14 it is critical to evaluate the risk of other neonatal outcomes and whether they occur independent of pre-term birth to better understand risk of long-term morbidity. For example, neonatal sepsis is associated with increased risk of early death and neurodevelopmental delay15 and respiratory distress syndrome is the leading cause of morbidity in infants and children,16 with increased risk of chronic lung disease even among term neonates.17
On a positive note, our findings provide evidence that a large proportion of neonatal morbidity could be eliminated by reducing pre-term birth in women with disabilities. Although women with disabilities were more likely to be induced for medical reasons than women with no documented disability, medical indications were primarily driven by maternal factors such as pre-pregnancy chronic conditions and severe pre-eclampsia/eclampsia but not fetal factors such as macrosomia or stillbirth. In combination with elevated risk observed for SGA, which could be an indicator of fetal growth restriction,18 these findings about maternal risk factors highlight several points of intervention that could potentially improve neonatal outcomes.
Specifically, adverse neonatal outcomes in this sample could be linked to general poor pre-conception health and barriers to care among women with disabilities. Although the CSL did not collect the number of prenatal care visits, this notion is supported by our findings that the causes of pre-term birth in this sample were more likely to be maternal factors, some of which were conditions present prior to pregnancy (i.e. pre-pregnancy diabetes and hypertension). Even the greater proportion of pre-term delivery due to congenital anomalies could be attributed to poor pre-conception health, as women with disabilities had higher rates of diabetes and maternal hyperglycemia may be considered teratogenic.19 Furthermore, untreated diabetes is associated with an increased risk for pre-eclampsia and pregnancy-related hypertensive disorders.20 Perhaps women with disabilities had suboptimal diabetes control.
It has been well documented that women with disabilities have poorer pre-conception health than women without disabilities.21 Specifically, women with disabilities have more risk factors for poor health outcomes, including more socio-economic disadvantage, higher rates of chronic disease, more experiences of assault, higher use of potentially teratogenic medications to manage their conditions (e.g. anti-epileptics), greater substance use and generally poorer pre-conception health across indicators.21–23 Although information on family income was not available in the medical records, we found that indicators of lower SES (e.g. a higher proportion using public insurance and being unmarried) occurred in higher percentages among women with disabilities in the sample. Furthermore, they are less likely to receive preventive care in general,24–26 which should be considered in combination with a recent survey of practising US physicians that found that only 40% of physicians were very confident in their ability to provide quality care to patients with disabilities.27 This survey also found that longer-serving physicians and physicians in private practice had lower odds of reporting that they strongly welcome patients with disabilities into their practices.27 When combining this potential lack of health equity with increased barriers to receiving comprehensive sexual and reproductive health education,28–30 women with disabilities enter pregnancy in a less healthy state and have more unplanned and unintended pregnancies than their non-disabled peers.24 As a direct example of this combination of factors in our sample, the higher risk of congenital anomalies and greater proportion of pre-term delivery due to congenital anomalies could be a direct reflection of poorer pre-conception health or barriers that delayed prenatal care.
Interventions to address these risk factors could include some focus on individual-level behavioural interventions to reduce pre-pregnancy diabetes and hypertension; and low-dose aspirin regimens introduced early in pregnancy to prevent severe pre-eclampsia, pre-term birth and fetal growth restriction may moderately reduce adverse neonatal outcomes among women with disabilities.31,32 However, to be more effective at a population level, there is a strong need for systemic changes, such as: (i) improving health equity to ensure women with disabilities can access and feel comfortable accessing preventive healthcare; (ii) improving the socio-economic situation of women with disabilities to improve long-term child outcomes;33 (iii) including women with disabilities in sexual and reproductive health interventions to reduce unintended pregnancies; (iv) ensuring accessible health education materials that help women using medications to understand any potential harmful effects in the event of pregnancy; and (v) a general overhaul of the healthcare provider education system to ensure competency and confidence in working with women with disabilities.
One strength of our study is the use of a large pregnancy cohort with comprehensive information on a variety of neonatal outcomes and indications for pre-term birth—an advantage over hospital administrative data that are usually not as comprehensive. Additionally, we had a large enough sample with adequate power to test for mediation by pre-term birth, which allowed us to describe the risk of neonatal outcomes independently of pre-term birth among women with any disability and across disability categories. We were also able to adjust for many behavioural and lifestyle variables that could confound the association between maternal disability and adverse neonatal outcomes. Although our data are from 2002–2008, we believe that our results are generalizable to a more modern cohort. Obstetric practice remained relatively stable during the study period, with rates of caesarean delivery at the midpoint of the study remaining consistent with the rate of 31.8% reported in 2020.34,35
The primary limitation of our study is our reliance on ICD-9 codes to identify disability, although it was based on an existing algorithm.10 Use of diagnostic codes cannot provide information on the level of functional limitation women experienced and by restricting our definition to these codes, we may have misclassified some women with significant limitations on daily activities who did not have a formal diagnosis of a condition linked to disability. Additionally, these codes are only used for conditions actively addressed in the healthcare setting, further limited to interactions during labour and delivery in the CSL. This timing of code collection may have implications for associations with covariates, as factors such as BMI, alcohol and tobacco use, and chronic conditions may exacerbate disabling conditions, thus making them more likely to be identified in a healthcare encounter. Although these conditions may also theoretically mediate associations between disability and adverse neonatal outcomes, the consistency in effect estimates between main analyses and minimally adjusted models that excluded potential mediators support the robustness of our findings. Another limitation of the use of an algorithm is that multiple categorizations of disability may have been under-represented, which is why we did not find women with more than one category of disability in our sample. However, we do not believe this would change the interpretation of our findings considering the consistency of magnitude and direction of estimates across disability categories. Furthermore, we expect this misclassification would only bias our findings towards the null, meaning that effect sizes may have been larger with more accurate classification of disability status. Additionally, although we had power to detect mediation in associations between physical, intellectual/developmental and sensory disabilities and most adverse neonatal outcomes, results of those analyses should be interpreted with caution given the small sample size for some combinations. Particularly of note, the intellectual/developmental disabilities category included only 91 women and although effect estimates were high, confidence intervals were often wide, again suggesting caution in interpretation of results for associations between intellectual/developmental disability and some rare neonatal outcomes. Although we did not account for multiple testing in our exploratory analyses, finding significant results at the P = 0.05 level in 85 of 126 tests indicates that our findings occurred well beyond chance, as we would expect to see only 7 significant tests by chance at a false-positive rate of 0.05.
Conclusion
Neonates of women with disabilities were at higher risk of a broad range of adverse neonatal outcomes, including death. Much of this risk could be eliminated by targeting maternal factors that lead to pre-term birth, although these interventions may have the most benefit if aimed towards improving systems of healthcare and social disadvantage among women with disabilities.
Ethics approval
The procedures of the CSL were approved by institutional review boards at the National Institutes of Health, US on 9 September 2008 (IRB #2007–0656). This secondary analysis of existing de-identified data was exempt from human patients’ review.
Supplementary Material
Contributor Information
Jessica L Gleason, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Jagteshwar Grewal, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Zhen Chen, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Alison N Cernich, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Katherine L Grantz, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Data availability
All data, study forms and protocols are publicly available through the Eunice Kennedy Shriver National Institute of Child Health and Human Development Data and Specimen Hub (DASH) (https://dash.nichd.nih.gov/).
Supplementary data
Supplementary data are available at IJE online.
Author contributions
J.L.G. helped conceptualize the project and completed data analysis in consultation with Z.C. J.L.G. drafted and revised all sections of the manuscript. A.N.C. provided subject-matter expertise and consultation on the data. J.G. helped revise the manuscript and provided methodological input. K.L.G. conceptualized and supervised all aspects of the project. All authors reviewed and helped revise the final manuscript.
Funding
This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD; contract #HHSN267200603425C).
Conflict of interest
None declared.
References
- 1. Tarasoff LA, Ravindran S, Malik H, Salaeva D, Brown HK.. Maternal disability and risk for pregnancy, delivery, and postpartum complications: a systematic review and meta-analysis. Am J Obstet Gynecol 2020;222:27.e1–e32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Tarasoff LA, Murtaza F, Carty A, Salaeva D, Hamilton AD, Brown HK.. Health of newborns and infants born to women with disabilities: a meta-analysis. Pediatrics 2020;146:e20201635. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Huennekens K, Oot A, Lantos E, Yee LM, Feinglass J.. Using electronic health record and administrative data to analyze maternal and neonatal delivery complications. Jt Comm J Qual Patient Saf 2020;46:623–30. [DOI] [PubMed] [Google Scholar]
- 4. Mitra M, McKee MM, Akobirshoev I, Ritter GA, Valentine AM.. Pregnancy and neonatal outcomes among deaf or hard of hearing women: results from nationally representative data. Womens Health Issues 2021;31:470–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. National Institutes of Health. Pregnancy in Women with Disabilities (R01). 2011. https://grants.nih.gov/grants/guide/pa-files/PAR-11-258.html (16 June 2021, date last accessed).
- 6. Zhang J, Troendle J, Reddy UM. et al. Contemporary cesarean delivery practice in the United States. Am J Obstet Gynecol 2010;203:326 e1–e10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Mannisto T, Mendola P, Reddy U, Laughon SK.. Neonatal outcomes and birth weight in pregnancies complicated by maternal thyroid disease. Am J Epidemiol 2013;178:731–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Laughon SK, Reddy UM, Sun L, Zhang J.. Precursors for late preterm birth in singleton gestations. Obstet Gynecol 2010;116:1047–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Duryea EL, Hawkins JS, McIntire DD, Casey BM, Leveno KJ.. A revised birth weight reference for the United States. Obstet Gynecol 2014;124:16–22. [DOI] [PubMed] [Google Scholar]
- 10. Darney BG, Biel FM, Quigley BP, Caughey AB, Horner-Johnson W.. Primary cesarean delivery patterns among women with physical, sensory, or intellectual disabilities. Womens Health Issues 2017;27:336–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol 2004;159:702–06. [DOI] [PubMed] [Google Scholar]
- 12. Valeri L, Vanderweele TJ.. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods 2013;18:137–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Ely DM, Driscoll AK.. Infant mortality in the United States, 2017: data from the period linked birth/infant death file. Natl Vital Stat Rep 2019;68:1–20. [PubMed] [Google Scholar]
- 14. Barker DJ. The developmental origins of adult disease. J Am Coll Nutr 2004;23:588S–95S. [DOI] [PubMed] [Google Scholar]
- 15. Bakhuizen SE, de Haan TR, Teune MJ. et al. Meta-analysis shows that infants who have suffered neonatal sepsis face an increased risk of mortality and severe complications. Acta Paediatr 2014;103:1211–18. [DOI] [PubMed] [Google Scholar]
- 16. Hermansen CL, Mahajan A.. Newborn respiratory distress. Am Fam Physician 2015;92:994–1002. [PubMed] [Google Scholar]
- 17. Chowdhury N, Giles BL, Dell SD.. Full-term neonatal respiratory distress and chronic lung disease. Pediatr Ann 2019;48:e175–81. [DOI] [PubMed] [Google Scholar]
- 18. McCowan LM, Figueras F, Anderson NH.. Evidence-based national guidelines for the management of suspected fetal growth restriction: comparison, consensus, and controversy. Am J Obstet Gynecol 2018;218:S855–68. [DOI] [PubMed] [Google Scholar]
- 19. Ornoy A, Reece EA, Pavlinkova G, Kappen C, Miller RK.. Effect of maternal diabetes on the embryo, fetus, and children: congenital anomalies, genetic and epigenetic changes and developmental outcomes. Birth Defects Res C Embryo Today 2015;105:53–72. [DOI] [PubMed] [Google Scholar]
- 20. Negrato CA, Jovanovic L, Tambascia MA. et al. Association between insulin resistance, glucose intolerance, and hypertension in pregnancy. Metab Syndr Relat Disord 2009;7:53–59. [DOI] [PubMed] [Google Scholar]
- 21. Tarasoff LA, Lunsky Y, Chen S. et al. Preconception health characteristics of women with disabilities in Ontario: a population-based, cross-sectional study. J Womens Health (Larchmt) 2020;29:1564–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Mitra M, Clements KM, Zhang J, Smith LD.. Disparities in adverse preconception risk factors between women with and without disabilities. Matern Child Health J 2016;20:507–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Mitra M, Lu E, Diop H.. Smoking among pregnant women with disabilities. Womens Health Issues 2012;22:e233–39. [DOI] [PubMed] [Google Scholar]
- 24. Horner-Johnson W, Dissanayake M, Wu JP, Caughey AB, Darney BG.. Pregnancy intendedness by maternal disability status and type in the United States. Perspect Sex Reprod Health 2020;52:31–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Iezzoni LI, Yu J, Wint AJ, Smeltzer SC, Ecker JL.. General health, health conditions, and current pregnancy among U.S. women with and without chronic physical disabilities. Disabil Health J 2014;7:181–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Kim M, Kim HJ, Hong S, Fredriksen-Goldsen KI.. Health disparities among childrearing women with disabilities. Matern Child Health J 2013;17:1260–68. [DOI] [PubMed] [Google Scholar]
- 27. Iezzoni LI, Rao SR, Ressalam J. et al. Physicians’ perceptions of people with disability and their health care. Health Aff (Millwood) 2021;40:297–306. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Treacy AC, Taylor SS, Abernathy TV.. Sexual health education for individuals with disabilities: a call to action. Am J Sex Educ 2017;13:65–93. [Google Scholar]
- 29. Abells D, Kirkham YA, Ornstein MP.. Review of gynecologic and reproductive care for women with developmental disabilities. Curr Opin Obstet Gynecol 2016;28:350–58. [DOI] [PubMed] [Google Scholar]
- 30. Crabb C, Owen R, Heller T.. Female medicaid enrollees with disabilities and discussions with health care providers about contraception/family planning and sexually transmitted infections. Sex Disabil 2020;38:299–312. [Google Scholar]
- 31. Xu TT, Zhou F, Deng CY, Huang GQ, Li JK, Wang XD.. Low-dose aspirin for preventing preeclampsia and its complications: a meta-analysis. J Clin Hypertens (Greenwich) 2015;17:567–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Roberge S, Nicolaides K, Demers S, Hyett J, Chaillet N, Bujold E.. The role of aspirin dose on the prevention of preeclampsia and fetal growth restriction: systematic review and meta-analysis. Am J Obstet Gynecol 2017;216:110–20.e6. [DOI] [PubMed] [Google Scholar]
- 33. McConnell D, Hahn L, Growing up with parents with disabilities. In: Hupp S, Jewell J (eds). The Encyclopedia of Child and Adolescent Development. John Wiley & Sons; 2022, pp. 1–12. [Google Scholar]
- 34. Menacker F, Hamilton BE.. Recent Trends in Cesarean Delivery in the United States. Hyattsville, MD: National Center for Health Statistics, 2010. [PubMed] [Google Scholar]
- 35. Osterman M, Hamilton B, Martin JA, Driscoll AK, Valenzuela CP.. Births: final data for 2020. Natl Vital Stat Rep 2021;70:1–50. [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data, study forms and protocols are publicly available through the Eunice Kennedy Shriver National Institute of Child Health and Human Development Data and Specimen Hub (DASH) (https://dash.nichd.nih.gov/).