Neonatal Outcomes of Mothers with a Disability

Hilary K Brown; Simon Chen; Astrid Guttmann; Susan M Havercamp; Susan Parish; Joel G Ray; Simone N Vigod; Lesley A Tarasoff; Yona Lunsky

doi:10.1542/peds.2021-055318

. Author manuscript; available in PMC: 2023 Sep 1.

Published in final edited form as: Pediatrics. 2022 Sep 1;150(3):e2021055318. doi: 10.1542/peds.2021-055318

Neonatal Outcomes of Mothers with a Disability

Hilary K Brown ^a,^b,^c,^d, Simon Chen ^d, Astrid Guttmann ^b,^d,^e,^f,^g, Susan M Havercamp ^h, Susan Parish ⁱ, Joel G Ray ^b,^d,^j, Simone N Vigod ^b,^c,^d,^k, Lesley A Tarasoff ^a,^l, Yona Lunsky ^b,^k,^l

^aDepartment of Health & Society, University of Toronto Scarborough, Toronto, Ontario, Canada;

^bDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada;

^cWomen’s College Research Institute, Women’s College Hospital, Toronto, Ontario, Canada;

^dICES, Toronto, Ontario, Canada;

^eHospital for Sick Children, Toronto, Ontario, Canada;

^fEdwin HS Leong Centre for Healthy Children, University of Toronto, Toronto, Ontario, Canada;

^gDepartment of Pediatrics, University of Toronto, Toronto, Ontario, Canada;

^hCenter for Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, Ohio, United States;

ⁱCollege of Health Professions, Virginia Commonwealth University, Richmond, Virginia, United States;

^jLi Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Ontario, Canada;

^kDepartment of Psychiatry, University of Toronto, Toronto, Ontario, Canada;

^lAzrieli Adult Neurodevelopmental Centre, Centre for Addiction & Mental Health, Toronto, Ontario, Canada.

^✉

Address correspondence to: Dr. Hilary K. Brown, Department of Health & Society, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada, M1C 1A5 (Fax: +1 416 287-7283; Tel: +1 416 208-2239; hk.brown@utoronto.ca)

CONTRIBUTORS’ STATEMENT

Dr. Brown undertook the conception and design of the study, the analysis and interpretation of the data, and drafting of the manuscript.

Mr. Chen contributed to the design of the study, undertook the analysis of the data, and critically revised the manuscript for important intellectual content.

Drs. Guttmann, Havercamp, Parish, Ray, Vigod, and Tarasoff contributed to the design of the study and interpretation of the data and critically revised the manuscript for important intellectual content.

Dr. Lunsky contributed to the conception and design of the study and the interpretation of the data and critically revised the manuscript for important intellectual content.

All of the authors approve the final version to be published and agree to be accountable for all aspects of the work.

PMCID: PMC9694113 NIHMSID: NIHMS1851961 PMID: 35934737

Abstract

Objectives:

To assess the risk of neonatal complications among women with a physical, sensory, and/or intellectual/developmental disability.

Methods:

This population-based cohort study comprised all hospital singleton livebirths in Ontario, Canada from 2003–2018. Newborns of women with a physical (N=144,187), sensory (N=44,988), intellectual/developmental (N=2,207), or ≥2 disabilities (N=8,823) were each compared to 1,593,354 newborns of women without a disability. Outcomes were preterm birth <37 and <34 weeks, small for gestational age birthweight (SGA), large for gestational age birthweight, neonatal morbidity and mortality <28 days after birth, neonatal abstinence syndrome (NAS), and neonatal intensive care unit (NICU) admission. Relative risks (aRR) were adjusted for maternal age, parity, income quintile, rurality, immigrant status, preconception physical and mental health, and prenatal care adequacy.

Results:

Risks for neonatal complications were elevated among newborns of women with disabilities compared to those without disabilities. aRRs were especially high for newborns of women with an intellectual/developmental disability, including preterm birth <37 weeks (1.37, 95% CI 1.19–1.58), SGA (1.37, 1.24–1.59), neonatal morbidity (1.42, 1.27–1.60), NAS (1.53, 1.12–2.08), and NICU admission (1.53, 1.40–1.67). The same was seen for newborns of women with ≥2 disabilities, including preterm birth <37 weeks (1.48, 1.39–1.59), SGA (1.13, 1.07–1.20), neonatal morbidity (1.28, 1.20–1.36), NAS (1.87, 1.57–2.23), and NICU admission (1.35, 1.29–1.42).

Conclusions:

There is mild to moderate elevated risk for complications among newborns of women with an intellectual/developmental disability, and those with multiple disabilities. These women may need adapted and enhanced preconception and prenatal care, and their newborns may require extra support after birth.

Table of contents summary:

Newborns of women with disabilities have elevated risk for neonatal complications, demonstrating the need for customized preconception and prenatal care, and tailored, family-centered newborn care.

INTRODUCTION

Advances in obstetric and newborn care have improved neonatal outcomes in industrialized countries.^1,2 Yet, disparities in rates of preterm birth, small for gestational age, and neonatal morbidity and mortality persist.^3,4 Such disparities are a public health challenge because of the links between adverse neonatal outcomes and long-term risks of neurodevelopmental impairment and chronic disease,^5,6 and the resulting psychosocial and economic burden on families and the health care system. To address population risk factors and improve health care quality, it is imperative to improve identification of groups with disproportionate risk for adverse outcomes. There is longstanding concern about the relationship between social and structural determinants of health, and in particular, poverty and systemic racism, and adverse neonatal outcomes.^3,4 However, little research and clinical attention has been paid to mothers with disabilities, who similarly experience social and structural barriers to care and other supports.

About 12% of reproductive-aged women experience a physical, sensory, or intellectual/developmental disability.⁷ These disabilities affect mobility, flexibility, dexterity, vision, hearing, cognition, and/or social skills.⁷ Over the last two decades, fertility rates among women with disabilities have increased,^8,9 driven by medical advances and greater enforcement of the reproductive rights of people with disabilities. Several studies have shown women with disabilities are at elevated risk for pregnancy and delivery complications, including gestational diabetes mellitus, gestational hypertension, and caesarean delivery,¹⁰ paralleled by preconception socioeconomic, physical health, and mental health disparities,^11–17 as well as inequitable access to prenatal care.^18,19 Few studies have examined newborn outcomes in women with disabilities; existing studies largely focused on preterm birth and low birth weight.²⁰ Accordingly, there is an urgent need for a more comprehensive understanding of neonatal outcomes among women with disparate disabilities, which would inform development of effective care for mothers and newborns. We thus investigated neonatal complications among women with a physical, sensory, and/or intellectual/developmental disability, compared to women without a disability.

METHODS

Study setting and study design

We performed a population-based cohort study in Ontario. Ontario is the largest province in Canada, with a population of 14.7 million residents and 140,000 births per year.²¹ Ontario has a universal health care system, wherein all primary and acute care, including care of mothers and newborns, is delivered at no direct cost. We accessed and analyzed linked health administrative datasets at ICES (Toronto, Ontario), an independent, non-profit organization that holds medical and sociodemographic data derived from Ontario residents’ health care encounters. We used the MOMBABY dataset, derived from hospital discharge abstracts, to identify maternal-newborn records for all hospital births, which represent > 98% of births in Ontario.²¹ We also used ICES data to identify all hospitalizations, emergency department visits, outpatient physician visits, immigration status, and other sociodemographic characteristics (Table 1). Datasets were linked using a unique encoded identifier. ICES datasets are accurate and complete for primary hospital diagnoses, recorded using International Classification of Diseases and Related Health Problems 10^th revision or Diagnostic and Statistical Manual of Mental Disorders 4^th edition codes; outpatient physician diagnoses, recorded using billing claims codes; and sociodemographic data.²² The use of data for this study was authorized under section 45 of Ontario’s Personal Health Information Protection Act, which does not require review by a Research Ethics Board.

Table 1.

Details of health administrative data sources.

Data source	Construct	Coding structure	Inception
Canadian Institute for Health Information Discharge Abstract Database	Hospital admissions, including those for maternal-newborn records (MOMBABY)	Canadian Coding Standards for the International Classification of Diseases and Related Health Problems codes for diagnoses	1988
Immigration, Refugees, and Citizenship Canada Permanent Residents Database	Immigrant / refugee status	N/A	1985
National Ambulatory Care Reporting System	Emergency department visits	Canadian Coding Standards for the International Classification of Diseases and Related Health Problems codes for diagnoses and Canadian Classification of Health Interventions codes for procedures	2000
Ontario Health Insurance Database	Outpatient physician visits	Physician billing codes	1991
Ontario Mental Health Reporting System	Psychiatric hospital admissions	Diagnostic and Statistical Manual of Mental Disorders	2005
Registered Persons Database	Sociodemographic data, via linkage with Census	N/A	1991

Open in a new tab

Study population

We identified all singleton livebirths that were conceived between April 1, 2003 and March 31, 2018 to 15- to 49-year-old women. Maternal disability status was determined using definitions of physical, sensory, and intellectual/developmental disabilities developed from published algorithms for measuring disability in health administrative data.^23–25 Details have been reported previously.²⁶ Briefly, maternal physical (i.e., congenital anomaly, musculoskeletal disorder, neurological disorder, or permanent injury), sensory (i.e., hearing or vision loss), intellectual/developmental (i.e., autism spectrum disorder, chromosomal or autosomal anomalies resulting in intellectual disability, or developmental disability such as fetal alcohol spectrum disorder), and multiple disabilities (diagnoses in ≥ 2 of these categories) were defined as being present if a relevant diagnostic code was recorded in ≥ 2 physician visits or ≥ 1 emergency department visits or hospitalizations between database inception and the pregnancy’s conception date. The estimated date of conception was back-calculated from the index birth date minus the gestational weeks at birth.²⁷ Women without any recognized disability were the comparator.

Outcomes

Outcomes were indicators of birth timing, growth, and neonatal morbidity and mortality defined by the Canadian Perinatal Surveillance System,²⁸ namely, preterm birth at < 37 and < 34 weeks’ gestation; small for gestational age (birthweight < 10^th percentile for gestational age);²⁹ large for gestational age (> 90^th percentile);²⁹ neonatal morbidity (brachial plexus injury / palsy, convulsions of the newborn, grade III/IV intraventricular hemorrhage / periventricular leukomalacia, hypoxic ischemic encephalopathy, neonatal sepsis, persistent fetal circulation / neonatal hypertension, or respiratory distress syndrome) < 28 days; neonatal abstinence syndrome; neonatal intensive care unit (NICU) admission; and neonatal mortality at < 28 days.

Covariates

Using Misra et al.’s integrated perinatal health framework,³⁰ we measured factors related to the social determinants of health, preconception health, and health care access that reflect disparities between women with and without disabilities.^11–17 Maternal age and parity were derived from the MOMBABY dataset. Neighbourhood income quintile was measured by linking residential postal codes with Census area-level income data. Rural residence was measured using the Rurality Index of Ontario, which is based on community characteristics such as travel time to referral centers.³¹ Immigrant/refugee status was determined from the Immigrants, Refugees, and Citizenship Canada Permanent Residents Database. Chronic medical conditions were measured using the Johns Hopkins Adjusted Clinical Groups (ACG)^® System v. 10 collapsed ambulatory diagnostic groups, which classifies non-disability conditions as stable or unstable based on acute health care patterns.³² Mental illness (i.e., psychotic, mood/anxiety, or other disorders) and substance use disorders were ascertained based on ≥ 2 physician visits or ≥ 1 emergency department visits or hospital admissions less than two years before conception. The Revised Graduated Prenatal Care Utilization Index³³ measured prenatal care adequacy based on the timing of initiation of care and number of visits, and served as an indicator of health care access. In a subsample of births in 2007–2018 with data linkable to a clinical birth registry (the Better Outcomes Registry & Network), we measured smoking in pregnancy. Finally, we measured pregnancy complications (i.e., gestational diabetes, gestational hypertension, preeclampsia/eclampsia, venous thromboembolism, or severe maternal morbidity)³⁴ and delivery mode.

Analyses

We used frequencies and percentages to describe women with physical, sensory, intellectual/developmental, and multiple disabilities and computed standardized differences to compare them to women without disabilities.³⁵

We employed modified Poisson regression³⁶ to directly estimate a relative risk (RR) and 95% confidence interval (CI) for each neonatal outcome, comparing each disability group to women without disabilities. We used generalized estimating equations to account for inclusion of multiple newborns born to the same mother during the study period.³⁷ We derived minimally adjusted models including maternal age, parity, neighbourhood income quintile, rural residence, and immigrant status, and models including these variables and further adjusting for stable and unstable chronic conditions, mental illness, substance use disorders, and prenatal care adequacy.

We carried out several additional analyses. In a subsample of births in 2007–2018, we further adjusted models for maternal smoking. Since pregnancy and delivery complications may also affect neonatal outcomes, and are more common in women with disabilities,¹⁰ we also stratified models according to (1) the presence of pregnancy complications and (2) delivery mode. Finally, we examined neonatal outcomes according to disability subtype.

All analyses used SAS version 9.4.

RESULTS

From 2003 to 2018, there were 144,187 newborns delivered to women with a physical disability, 44,988 to women with a sensory disability, 2,207 to women with an intellectual/developmental disability, 8,823 to women with multiple disabilities, and 1,593,354 to women without any recognized disability. Compared to women without disabilities, women with intellectual/developmental disabilities were younger and more likely to live in neighbourhoods in the lowest income quintile. Women with physical and multiple disabilities were more likely to have stable chronic conditions, and those with physical, intellectual/developmental, and multiple disabilities were more likely to have unstable chronic conditions. All four disability groups were more likely to have mental illness and to smoke, and women with intellectual/developmental and multiple disabilities were more likely to have substance use disorders (Table 2).

Table 2.

Characteristics of 15- to 49-year-old women with a physical, sensory, intellectual/developmental, or multiple disabilities, and those without a disability, who had a singleton livebirth in Ontario, Canada, 2003–2018 (N, %).

Variable^a	Physical disability only (N=144,187)	Sensory disability only (N=44,988)	Intellectual/developmental disability only (N=2,207)	Multiple disabilities (N=8,823)	No disability (N=1,593,354)
Age, years
15–19	6,132 (4.3)	2,880 (6.4)^a	361 (16.4)^a	524 (5.9)	66,945 (4.2)
20–24	19,700 (13.7)	7,616 (16.9)^a	639 (29.0)^a	1,642 (18.6)	214,688 (13.5)
25–29	40,940 (28.4)	12,796 (28.4)	540 (24.5)^a	2,447 (27.7)	479,076 (30.1)
30–34	48,117 (33.4)	13,205 (29.4)^a	399 (18.1)^a	2,457 (27.8)	545,561 (34.2)
35–39	24,157 (16.8)	6,917 (15.4)	212 (9.6)^a	1,392 (15.8)	244,470 (15.3)
40–44	4,876 (3.4)	1,471 (3.3)	50–55^*	340 (3.9)	40,807 (2.6)
45–49	265 (0.2)	103 (0.2)	0–5^*	21 (0.2)	1,807 (0.1)
Multiparous	83,978 (58.2)	24,474 (54.4)	1,245 (56.4)	4,930 (55.9)	909,992 (57.1)
Neighbourhood income quintile (Q)
Q1 (lowest)	30,815 (21.4)	9,977 (22.2)	830 (37.6)^a	2,280 (25.8)	351,228 (22.0)
Q2	28,717 (19.9)	9,101 (20.2)	482 (21.8)	1,784 (20.2)	320,726 (20.1)
Q3	29,495 (20.5)	9,197 (20.4)	393 (17.8)	1,747 (19.8)	326,203 (20.5)
Q4	30,418 (21.1)	9,331 (20.7)	265 (12.0)^a	1,676 (19.0)	328,279 (20.6)
Q5 (highest)	24,132 (16.7)	7,243 (16.1)	220 (10.0)^a	1,306 (14.8)	260,871 (16.4)
Missing	610 (0.4)	139 (0.3)	17 (0.8)	30 (0.3)	6,047 (0.4)
Region of residence
Rural	8,230 (5.7)	2,002 (4.5)	112 (5.1)	497 (5.6)	66,671 (4.2)
Urban	133,784 (92.8)	42,473 (94.4)	2,036 (92.3)	8,204 (93.0)	1,506,957 (94.6)
Missing	2,173 (1.5)	513 (1.1)	59 (2.7)	122 (1.4)	19,726 (1.2)
Immigrant/refugee	16,899 (11.7)^a	6,704 (14.9)^a	154 (7.0)^a	769 (8.7)^a	420,564 (26.4)
Stable chronic conditions	39,708 (27.5)^a	11,645 (25.9)	550 (24.9)	2,842 (32.2)^a	364,775 (22.9)
Unstable chronic conditions	23,363 (16.2)^a	6,558 (14.6)	336 (15.2)^a	1,980 (22.4)^a	179,780 (11.3)
Diabetes mellitus	3,606 (2.5)	1,344 (3.0)	61 (2.8)^a	452 (5.1)^a	24,123 (1.5)
Chronic hypertension	5,214 (3.6)	1,363 (3.0)	37 (1.7)	450 (5.1)^a	35,775 (2.2)
Cardiovascular disease	257 (0.2)	42 (0.1)	0–5^*	37 (0.4)	790 (0.0)
Mental illness	28,300 (19.6)^a	7,793 (17.3)^a	831 (37.7)^a	2,360 (26.7)^a	200,987 (12.6)
Substance use disorder	2,818 (2.0)	546 (1.2)	141 (6.4)^a	301 (3.4)^a	14,184 (0.9)
Smoking in pregnancy^b	17,955 (12.5)^a	5,177 (11.5)^a	538 (24.4)^a	1,426 (16.2)^a	128,178 (8.0)
Inadequate prenatal care	14,421 (10.0)	4,764 (10.6)	225 (10.2)	827 (9.4)	163,542 (10.3)
Any pregnancy complication^c	18,055 (12.5)	5,322 (11.8)	237 (10.7)	1,313 (14.9)^a	170,457 (10.7)
Gestational diabetes	6,137 (4.3)	2,008 (4.5)	67 (3.0)	388 (4.4)	74,463 (4.7)
Gestational hypertension	2,447 (1.7)	727 (1.6)	23 (1.0)	152 (1.7)	22,733 (1.4)
Preeclampsia/eclampsia	7,263 (5.0)	2,145 (4.8)	98 (4.4)	519 (5.9)a	61,054 (3.8)
Venous thromboembolism	1,868 (1.3)	425 (0.9)	20 (0.9)	182 (2.1)a	10,578 (0.7)
Severe maternal morbidity	3,369 (2.3)	914 (2.0)	64 (2.9)	306 (3.5)	26,665 (1.7)
Caesarean delivery	44,035 (30.5)	13,153 (29.2)	595 (27.0)	2,994 (33.9)a	436,381 (27.4)

Open in a new tab

Values suppressed to protect patient privacy due to small cell counts < 6.

Standardized difference > 0.10, comparing women within each respective disability group to women without a disability.

Analyses limited to births in 2007–2018, linkable with the Better Outcomes Registry & Network, a clinical birth registry.

Pregnancy complications included a composite of gestational diabetes, gestational hypertension, preeclampsia/eclampsia, venous thromboembolism, or severe maternal morbidity.

With few exceptions, newborns of women with disabilities, compared to those of women without disabilities, had higher rates of preterm birth at < 37 weeks (7.3%−9.9% across disability groups, vs. 6.2% in newborns of women without disabilities), preterm birth at < 34 weeks (1.8%−2.6%, vs. 1.5%), small for gestational age (11.7%−17.0%, vs. 12.3%), and large for gestational age (7.7%−9.6%, vs. 8.1%). In the fully adjusted models, newborns of women with disabilities remained more likely to be born preterm at < 37 and < 34 weeks’ gestation. All disability groups, except newborns of women with physical disabilities, were more likely to be small for gestational age, and all, except newborns of women with intellectual/developmental disabilities, were more likely to be large for gestational age. For all outcomes, newborns of women with intellectual/developmental and multiple disabilities had the highest risks (aRRs ranging from 1.37 to 1.53, and 1.13 to 1.58, respectively) (Table 3).

Table 3.

Risk of adverse neonatal outcomes related to birth timing and fetal growth in women with a disability compared to women without any recognized disability.

Disability type	Number (%) with outcome	Unadjusted RR (95% CI)	Adjusted RR (95% CI)^a	Adjusted RR (95% CI)^b
Preterm birth <37 weeks
No disability (N=1,593,354)	98,686 (6.2)	1.00 (Referent)	1.00 (Referent)	1.00 (Referent)
Physical only (N=144,187)	10,996 (7.6)	1.22 (1.20–1.25)	1.21 (1.19–1.24)	1.18 (1.16–1.20)
Sensory only (N=44,988)	3,291 (7.3)	1.18 (1.14–1.22)	1.17 (1.13–1.21)	1.15 (1.11–1.19)
Intellectual/developmental only (N=2,207)	204 (9.2)	1.45 (1.25–1.67)	1.49 (1.29–1.72)	1.37 (1.19–1.58)
Multiple (N=8,823)	876 (9.9)	1.59 (1.48–1.70)	1.57 (1.47–1.68)	1.48 (1.39–1.59)
Preterm birth <34 weeks
No disability (N=1,593,354)	24,529 (1.5)	1.00 (Referent)	1.00 (Referent)	1.00 (Referent)
Physical only (N=144,187)	2,704 (1.9)	1.21 (1.16–1.26)	1.22 (1.17–1.27)	1.19 (1.14–1.24)
Sensory only (N=44,988)	820 (1.8)	1.18 (1.10–1.27)	1.17 (1.09–1.26)	1.16 (1.08–1.25)
Intellectual/developmental only (N=2,207)	56 (2.5)	1.60 (1.19–2.16)	1.68 (1.24–2.26)	1.53 (1.13–2.06)
Multiple (N=8,823)	227 (2.6)	1.67 (1.46–1.91)	1.66 (1.45–1.91)	1.58 (1.38–1.81)
Small for gestational age birthweight
No disability (N=1,593,354)	19,6053 (12.3)	1.00 (Referent)	1.00 (Referent)	1.00 (Referent)
Physical only (N=144,187)	16,810 (11.7)	0.94 (0.93–0.96)	1.02 (1.00–1.03)	1.01 (0.99–1.02)
Sensory only (N=44,988)	5,675 (12.6)	1.02 (0.99–1.04)	1.06 (1.03–1.08)	1.05 (1.03–1.08)
Intellectual/developmental only (N=2,207)	375 (17.0)	1.37 (1.24–1.51)	1.42 (1.28–1.57)	1.37 (1.24–1.51)
Multiple (N=8,823)	1,206 (13.7)	1.08 (1.02–1.15)	1.15 (1.09–1.22)	1.13 (1.07–1.20)
Large for gestational age birthweight
No disability (N=1,593,354)	12,8513 (8.1)	1.00 (Referent)	1.00 (Referent)	1.00 (Referent)
Physical only (N=144,187)	13,072 (9.1)	1.14 (1.11–1.16)	1.05 (1.03–1.07)	1.03 (1.01–1.05)
Sensory only (N=44,988)	3,897 (8.7)	1.09 (1.05–1.13)	1.05 (1.01–1.08)	1.03 (1.00–1.07)
Intellectual/developmental only (N=2,207)	169 (7.7)	0.99 (0.85–1.15)	0.91 (0.78–1.07)	0.91 (0.77–1.06)
Multiple (N=8,823)	842 (9.6)	1.21 (1.13–1.30)	1.12 (1.05–1.20)	1.09 (1.01–1.17)

Open in a new tab

Bolded text identifies statistically significant RRs.

Model 1 adjusts for maternal age, parity, neighbourhood income quintile, rural residence, and immigrant status.

Model 2 further adjusts for stable chronic conditions, unstable chronic conditions, mental illness, substance use disorders, and prenatal care adequacy.

Likewise, newborns of women with disabilities, compared to those of women without disabilities, had higher rates of neonatal morbidity (8.9%−12.1%, vs. 7.7%), neonatal abstinence syndrome (0.62%−2.7%, vs. 0.48%), NICU admission (13.2%−20.6%, vs. 11.7%), and neonatal mortality (0.22%−0.29%, vs. 0.19%). In fully adjusted models, all disability groups remained at increased risk for neonatal morbidity, and all, except newborns of women with sensory disabilities, were at increased risk for neonatal abstinence syndrome. All disability groups were at increased risk for NICU admission. Risks for neonatal mortality were elevated but were non-significant, likely due to small numbers for this rare but serious outcome. Again, for all outcomes, newborns of women with intellectual/developmental and multiple disabilities had the highest risks (aRRs 1.27 to 1.53, and 1.28 to 1.87, respectively) (Table 4).

Table 4.

Risk of adverse neonatal outcomes related to morbidity and mortality, in women with a disability, compared to women without any recognized disability.

Disability type	Number (%) with outcome	Unadjusted RR (95% CI)	Adjusted RR (95% CI)^a	Adjusted RR (95% CI)^b
Neonatal morbidity
No disability (N=1,593,354)	123,396 (7.7)	1.00 (Referent)	1.00 (Referent)	1.00 (Referent)
Physical only (N=144,187)	13,103 (9.1)	1.17 (1.15–1.19)	1.16 (1.13–1.18)	1.12 (1.10–1.14)
Sensory only (N=44,988)	3,991 (8.9)	1.15 (1.11–1.18)	1.13 (1.09–1.16)	1.11 (1.07–1.14)
Intellectual/developmental only (N=2,207)	267 (12.1)	1.55 (1.38–1.74)	1.54 (1.37–1.73)	1.42 (1.27–1.60)
Multiple (N=8,823)	957 (10.8)	1.40 (1.32–1.49)	1.36 (1.28–1.45)	1.28 (1.20–1.36)
Neonatal abstinence syndrome
No disability (N=1,593,354)	7,756 (0.48)	1.00 (Referent)	1.00 (Referent)	1.00 (Referent)
Physical only (N=144,187)	1,926 (1.3)	2.98 (2.81–3.16)	2.45 (2.31–2.60)	1.78 (1.68–1.89)
Sensory only (N=44,988)	281 (0.62)	1.33 (1.16–1.53)	1.06 (0.92–1.22)	0.99 (0.86–1.14)
Intellectual/developmental only (N=2,207)	60 (2.7)	6.00 (4.50–8.01)	3.54 (2.68–4.69)	1.53 (1.12–2.08)
Multiple (N=8,823)	185 (2.1)	4.73 (4.01–5.57)	3.47 (2.94–4.10)	1.87 (1.57–2.23)
NICU admission
No disability (N=1,593,354)	186,742 (11.7)	1.00 (Referent)	1.00 (Referent)	1.00 (Referent)
Physical only (N=144,187)	20,233 (14.0)	1.19 (1.18–1.21)	1.19 (1.18–1.21)	1.14 (1.12–1.16)
Sensory only (N=44,988)	5,931 (13.2)	1.12 (1.10–1.15)	1.12 (1.09–1.15)	1.09 (1.06–1.12)
Intellectual/developmental only (N=2,207)	455 (20.6)	1.71 (1.57–1.87)	1.73 (1.58–1.89)	1.53 (1.40–1.67)
Multiple (N=8,823)	1,584 (18.0)	1.52 (1.44–1.59)	1.49 (1.42–1.56)	1.35 (1.29–1.42)
Neonatal mortality
No disability (N=1,593,354)	3,065 (0.19)	1.00 (Referent)	1.00 (Referent)	1.00 (Referent)
Physical only (N=144,187)	318 (0.22)	1.14 (1.02–1.28)	1.15 (1.02–1.29)	1.12 (0.99–1.26)
Sensory only (N=44,988)	100 (0.22)	1.15 (0.94–1.41)	1.17 (0.96–1.44)	1.15 (0.94–1.41)
Intellectual/developmental only (N=2,207)	6 (0.27)	1.37 (0.60–3.10)	1.38 (0.61–3.13)	1.27 (0.56–2.89)
Multiple (N=8,823)	26 (0.29)	1.53 (1.03–2.29)	1.55 (1.04–2.32)	1.46 (0.98–2.18)

Open in a new tab

Bolded text identifies statistically significant RRs.

Model 1 adjusts for maternal age, parity, neighbourhood income quintile, rural residence, and immigrant status.

Model 2 further adjusts for stable chronic conditions, unstable chronic conditions, mental illness, substance use disorders, and prenatal care adequacy.

In additional analyses, risks were slightly reduced when further adjusting for maternal smoking (Table S1 and Table S2). Some risks were attenuated among mothers who experienced pregnancy complications (Table S3, Table S4); findings were largely consistent whether newborns were delivered by caesarean or vaginal delivery (Table S5, Table S6). Risks were elevated across all specific disability subtypes (Table S7, Table S8).

DISCUSSION

In this large, population-based study, we found that newborns of women with disabilities were at increased risk for a range of relatively rare neonatal complications. Elevations in risk were mild to moderate across all disability groups compared to women without a disability, and were highest for newborns of women with intellectual/developmental and multiple disabilities. Risks largely remained elevated after adjustment; after stratification by the presence of delivery mode, and, to a lesser extent, pregnancy complications; and in analyses by specific subtype of disability. These findings demonstrate that, to support the best possible outcomes for all newborns, women with disabilities may benefit from enhanced preconception and prenatal care, followed by family-centered supports after birth, that are tailored to their unique needs.

A growing number of studies, mostly from the US, have examined neonatal outcomes in women with disabilities.²⁰ A recent meta-analysis of these studies showed higher risk of preterm birth in newborns of women with sensory (pooled unadjusted odds ratio [pOR] 1.37, 95% CI 1.27–1.48; 7 studies) and intellectual/developmental disabilities (pOR 1.76, 95% CI 1.59–1.96; 12 studies), compared to newborns of women without disabilities.²⁰ There was also elevated risk of low birthweight in newborns of women with physical (pOR 1.81, 95% CI 1.47–2.23; 3 studies), sensory (pOR 1.36, 95% CI 1.18–1.57; 4 studies), and intellectual/developmental disabilities (pOR 1.98, 95% CI 1.50–2.61; 5 studies).²⁰ However, most of these studies were rated as having low or moderate quality, with lack of control for confounding being the most common limitation. Moreover, few studies examined other important newborn outcomes such as neonatal morbidity and mortality.²⁰ The current study therefore adds to the literature by examining risks of a broad range of neonatal outcomes, overall and after adjusting for pre-existing maternal disparities related to the social determinants of health, preconception health, and prenatal care adequacy, and after stratifying by pregnancy and delivery-related factors.

It is understood that women with disabilities are disproportionately impacted by socioeconomic disparities, physical and mental health conditions, and health behaviours such as smoking,^11–17,38 and often have difficulty accessing prenatal care.^18,19 They are also at elevated risk for pregnancy complications and caesarean delivery.^10,26 In the general population, these factors are known predictors of neonatal complications.^39,40 Observed risks of adverse outcomes in newborns of women with disabilities were partly, but not completely, explained by adjustment for pre-pregnancy social and health disparities and smoking in pregnancy, and stratification by pregnancy complications and caesarean delivery. This finding is an important indicator of the multifaceted nature of the observed risks, given that women with disabilities experience many known risk factors for neonatal complications.^11–17,38 Other unmeasured factors could also partly explain our results. At the provider/system level, women with disabilities encounter barriers to timely, coordinated, and high-quality care, including physically inaccessible care environments; communication that does not address sensory or cognitive needs; inadequate provider knowledge about disability; negative provider attitudes about disability, pregnancy, and parenting; and fragmentation of health and social services.^41–43 At the individual level, factors such as medication use⁴⁴ could also contribute to risks for specific outcomes such as preterm birth, small for gestational age, and neonatal abstinence syndrome; however, medication use could not be measured. Future studies should examine how these factors contribute to neonatal complications in this population to improve understanding of risk attributable to modifiable provider/system-level and individual-level risk factors, and to generate targets for support.

While most neonatal complications are rare, our findings have important implications for clinical practice. Many of the outcomes we examined, including preterm birth and small for gestational age, are preventable through better access to preconception and prenatal care.^45,46 Our data suggest women with disabilities could benefit from improved access to high-quality health care before and during pregnancy that is adapted to their needs. While women with disabilities in our cohort had similar rates of prenatal care access as those without disabilities, we had no indicators of the quality or appropriateness of that care. However, prior research has shown gaps in these areas.^41–43 Given high rates of chronic disease, mental illness, and smoking among these women,^14–17 prenatal care should emphasize chronic condition management and health promotion using accessible approaches that meet the needs of women with disabilities. Higher rates of preterm birth, neonatal morbidity, and NICU admission in newborns of women with disabilities show a need to consider the physical, communication, and other disability accommodation needs of mothers with a newborn in the NICU, as well as additional practical and emotional supports for mothers in the immediate newborn period. Post-discharge, family-centered approaches to newborn care should consider barriers new mothers with disabilities may encounter, including physical inaccessibility of physician offices; hearing, vision, or cognitive-related communication barriers; cognitive difficulties related to memory and organizational skills; as well as difficulties navigating other community supports such as breastfeeding clinics.^41,42 Since obstetric supports for women in Canada drop off after 6 weeks postpartum, such family-centered approaches are critical for new mothers with disabilities who are at risk for postpartum complications themselves.^47,48 Finally, the structural barriers experienced by women with disabilities^18,19 make it imperative for clinicians to address the social determinants of health, including poverty. All of these efforts will require robust training and resources for obstetricians, pediatricians, and other health care providers to understand the unique needs of women with disabilities.

Study strengths include the use of a large, population-based cohort, and the ability to adjust for factors related to the social determinants of health, preconception health, smoking, and health care access that prior studies have not considered.²⁰ Several limitations must be noted. We used diagnoses to measure disability; this approach reflects a medical model of disability, and does not consider functional limitations or how the environment influences women’s lived experience of disability.⁴⁹ This approach, while well-established in prior research,^9,17,23–26 likely conservatively biased the findings in that some women who had disabilities may not have been accurately classified—for example, if their disability was undiagnosed, or their diagnosis was not recorded. The disability groups examined represent diverse populations whose experiences may differ. Consistent with prior research,²⁶ we conducted analyses by disability subtype, and showed findings were largely consistent in newborns of women with different disabilities within larger categories used in our main analyses. However, some heterogeneity in outcomes could exist at an even more granular level of specific disability diagnosis. We examined several outcomes, thus increasing the risk of type 1 error. However, the focus of our discussion is on the magnitude of the effects rather than their statistical significance. Unmeasured confounding may partly explain our results. For example, we had no individual-level data on socioeconomic status, nor did we have data on race/ethnicity.⁵⁰ The dual impact of experiences of ableism and racism on neonatal outcomes is an important are for future research. We also had no information on pathway variables that might explain the relation between maternal disability and neonatal complications, including medication use.⁴⁴ Further, our measures of prenatal care timing and number of visits could not assess care quality, including negative provider attitudes, lack of provider knowledge, and other factors that might impact quality.^18,19 We also could not measure additional supports women might have received outside the health care system because of their disability. Despite these limitations, this is one of the largest and most comprehensive analyses of neonatal outcomes in women with disabilities to date.²⁰

CONCLUSION

This population-based cohort study found a mild to moderate elevated risk of complications in newborns of women with disabilities, with the highest risks observed in those with an intellectual/developmental disability, or with multiple disabilities. While most of the observed outcomes are rare, the findings indicate a need for improved health care to prevent them. Women with disabilities may benefit from customized preconception and prenatal care, followed by tailored, family-centered supports after birth to reduce their risks. Birth outcomes and the neonatal period are important predictors of lifelong health and development,^5,6 indicating improved preconception, prenatal, and neonatal care are critical for this under-served population.

Supplementary Material

NIHMS1851961-supplement-Supplementary_Material.pdf^{(147.2KB, pdf)}

What’s known on this subject:

Prior research suggests that newborns of women with disabilities are at elevated risk for preterm birth and low birthweight. Factors that might explain that relation were not accounted for therein, nor were neonatal morbidity and mortality assessed.

What this study adds:

There is mild to moderate elevated risk for neonatal complications among newborns of women with disabilities compared to newborns of women without disabilities. Women with disabilities may need customized preconception and prenatal care, and tailored, family-centered supports for their newborns.

ACKNOWLEDGMENTS

Parts of this material are based on data and/or information compiled and provided by the Canadian Institute for Health Information (CIHI) and Immigration, Refugees and Citizenship Canada (IRCC) current to March 31, 2018. However, the analyses, conclusions, opinions and statements expressed in the material are those of the author(s), and not necessarily those of CIHI or IRCC. This Study is based in part on data provided by Better Outcomes Registry and Network (“BORN”), part of the Children’s Hospital of Eastern Ontario. The interpretation and conclusions contained herein do not necessarily represent those of BORN Ontario.

Funding/support:

This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under award # 5R01HD092326. This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program to Dr. Hilary K. Brown. The analyses, conclusions, opinions and statements expressed herein are solely those of the authors and do not reflect those of the funding; no endorsement is intended or should be inferred.

Role of the funder/sponsor:

The funding source had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Abbreviations:

CI: confidence interval
NICU: neonatal intensive care unit
pOR: pooled unadjusted OR
RR: relative risk

Footnotes

Conflict of interest disclosures: None

REFERENCES

1.Zeitlin J, Mortensen L, Cuttini M, et al. Declines in stillbirth and neonatal mortality rates in Europe between 2004 and 2010: results from the Euro-Peristat project. J Epidemiol Community Health. 2016;70(6):609–615. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Lorch SA. A decade of improvement in neonatal intensive care: how do we continue the momentum? JAMA Pediatrics. 2017;171(3):e164395–e164395. [DOI] [PubMed] [Google Scholar]
3.Gage TB, Fang F, O’Neill E, DiRienzo G. Maternal education, birth weight, and infant mortality in the United States. Demography. 2013;50(2):615–635. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Howell EA, Zeitlin J, Hebert P, Balbierz A, Egorova N. Paradoxical trends and racial differences in obstetric quality and neonatal and maternal mortality. Obstetrics Gynecol. 2013;121(6):1201. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Huang J, Zhu T, Qu Y, Mu D. Prenatal, perinatal and neonatal risk factors for intellectual disability: a systemic review and meta-analysis. PloS One. 2016;11(4). [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Kajantie E, Osmond C, Barker DJ, Eriksson JG. Preterm birth—a risk factor for type 2 diabetes? the Helsinki Birth cohort study. Diabetes Care. 2010;33(12):2623–2625. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Statistics Canada. Canadian Survey on Disability – Data Tables. Catalogue no. 89 654X-No. 001. Ottawa, ON: Statistics Canada; 2013. [Google Scholar]
8.Horner-Johnson W, Biel FM, Darney BG, Caughey AB. Time trends in births and cesarean deliveries among women with disabilities. Disabil Health J. 2017;10(3):376–381. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Brown HK, Chen S, Guttmann A, et al. Rates of recognized pregnancy in women with disabilities in Ontario, Canada. Am J Obstet Gynecol. 2020;222(2):189–192. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.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(1):27. e21–27. e32. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Parish SL, Rose RA, Andrews ME. Income poverty and material hardship among US women with disabilities. Social Serv Rev. 2009;83(1):33–52. [Google Scholar]
12.Hoffman DW, Livermore GA. The house next door: A comparison of residences by disability status using new measures in the American Housing Survey. Am Housing Survey. 2012;14(1):5–33. [Google Scholar]
13.Huang J, Guo B, Kim Y. Food insecurity and disability: Do economic resources matter? Soc Sci Res. 2010;39(1):111–124. [Google Scholar]
14.Mitra M, Clements KM, Zhang J, Smith LD. Disparities in adverse preconception risk factors between women with and without disabilities. Mat Child Health J. 2016;20(3):507–515. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Kim M, Kim H-J, Hong S, Fredriksen-Goldsen KI. Health disparities among childrearing women with disabilities. Mat Child Health J. 2013;17(7):1260–1268. [DOI] [PubMed] [Google Scholar]
16.Wisdom JP, McGee MG, Horner-Johnson W, Michael YL, Adams E, Berlin M. Health disparities between women with and without disabilities: a review of the research. Social Work Public Health. 2010;25(3–4):368–386. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Tarasoff L, Lunsky Y, Chen S, Guttmann A, Havercamp S, Parish S, Vigod S, Carty A, Brown HK. Preconception health characteristics of women with disabilities in Ontario: A population-based, cross-sectional study. J Women’s Health. 2020;29(12):1564–1575. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Horner-Johnson W, Biel FM, Caughey AB, Darney BG. Differences in prenatal care by presence and type of maternal disability. Am J Prev Med. 2019;56(3):376–382. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Nishat F, Lunsky Y, Tarasoff LA, Brown HK. Prenatal care adequacy among women with disabilities: A population-based study. Am J Prev Med. 2021;doi: 10.1016/j.amepre.2021.05.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Tarasoff L, Murtaza F, Salaeva D, Carty A, Hamilton A, Brown HK. Health of newborns and infants born to women with disabilities: A meta-analysis. Pediatrics. 2020;146(6):e20201635. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Statistics Canada. Live births and fetal deaths (stillbirths) by place of birth (hospital and non-hospital). Ottawa: Statistics Canada; 2019. [Google Scholar]
22.Williams JI, Young WA. Summary of studies on the quality of health care administrative databases in Canada. Ottawa, ON: Canadian Medical Association;1996. [Google Scholar]
23.Darney BG, Biel FM, Quigley BP, Caughey AB, Horner-Johnson W. Primary cesarean delivery patterns among women with physical, sensory, or intellectual disabilities. Women’s Health Issues. 2017;27(3):336–344. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Biel F, Darney B, Caughey A, Horner-Johnson W. Medical indications for primary caesarean delivery in women with and without disabilities. J Maternal-Fetal Neonata Medicine. 2020;33(20):3391–3398. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Lin E, Balogh R, Cobigo V, Ouellette‐Kuntz H, Wilton AS, Lunsky Y. Using administrative health data to identify individuals with intellectual and developmental disabilities: a comparison of algorithms. J Intellect Disabil Res. 2013;57(5):462–477. [DOI] [PubMed] [Google Scholar]
26.Brown HK, Ray JG, Chen S, Guttmann A, Havercamp S, Parish S, Vigod SN, Tarasoff L, Lunsky Y. Association of pre-existing disability and severe maternal morbidity or mortality in Ontario, Canada. JAMA Ntwrk Open. 2021;4(1):e2034993. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.You JJ, Alter DA, Stukel TA, et al. Proliferation of prenatal ultrasonography. CMAJ. 2010;182(2):143–151. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Irvine B, Dzakpasu S, Leon J. Perinatal health indicators 2013: a surveillance report by the Public Health Agency of Canada’s Perinatal Surveillance System. Health Promot Chron Dis Prev Can. 2015;35(1):23. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Ray JG, Sgro M, Mamdani MM, Glazier RH, Bocking A, Hilliard R, et al. Birth weight curves tailored to maternal world region. J Obstet Gynaecol Can 2012;34:159–71. [DOI] [PubMed] [Google Scholar]
30.Misra DP, Guyer B, Allston A. Integrated perinatal health framework: A multiple determinants model with a life span approach. Am J Prev Med. 2003;25(1):65–75. [DOI] [PubMed] [Google Scholar]
31.Kralj B. Measuring “rurality” for purposes of health-care planning: an empirical measure for Ontario. Ont Med Rev. 2000;67(9):33–52. [Google Scholar]
32.Health JHBSoP. The Johns Hopkins CGH system: Technical reference guide version 9.0. 2009. http://acg.jhsph.org/public-docs/ACGv9.0TechRefGuide.pdf Accessed August 27, 2015.
33.Alexander GR, Kotelchuck M. Quantifying the adequacy of prenatal care: a comparison of indices. Public Health Rep. 1996;111(5):408. [PMC free article] [PubMed] [Google Scholar]
34.Ray JG, Park AL, Dzakpasu S, et al. Prevalence of severe maternal morbidity and factors associated with maternal mortality in Ontario, Canada. JAMA Netw Open. 2018;1(7):e184571–e184571. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Comm Stat Sim Comp. 2009;38(6):1228–1234. [Google Scholar]
36.Zou G A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–706. [DOI] [PubMed] [Google Scholar]
37.Zou G, Donner A. Extension of the modified Poisson regression model to prospective studies with correlated binary data. Stat Methods Med Res. 2013;22(6):661–670. [DOI] [PubMed] [Google Scholar]
38.Mitra M, Lu E, Diop H. Smoking among pregnant women with disabilities. Women’s Health Issues. 2012;22(2):233–239. [DOI] [PubMed] [Google Scholar]
39.Tucker CM, Berrien K, Menard MK, et al. Predicting preterm birth among women screened by North Carolina’s Pregnancy medical Home Program. Mat Child Health J. 2015;19:2438–2452. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.De Graaf J, Steegers EAP, Bonsel GJ. Inequalities in perinatal and maternal health. Current Opinion Obstet Gynecol. 2013;25(2):98–108. [DOI] [PubMed] [Google Scholar]
41.Heideveld-Gerritsen M, van Vulpen M, Hollander M, Maatman SO, Ockhuijsen H, van den Hoogen A. Maternity care experiences of women with physical disabilities: A systematic review. Midwifery 2021;10:102938. [DOI] [PubMed] [Google Scholar]
42.Powell R, Andrews E, Ayers K. Becoming a disabled parent: Eliminating access barriers to health care before, during, and after pregnancy. Tulane Law Rev. 2021;forthcoming. [Google Scholar]
43.Khan M, Brown HK, Lunsky Y, Welsh K, Proulx L, Havercamp S, Tarasoff L. A socio-ecological approach to understanding the perinatal care experiences of people with intellectual and developmental disabilities in Ontario, Canada. Women’s Health Issues. 2021; doi: 10.1016/j.whi.2021.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Hong Y, Geraci M, Turk MA, Love BL, McDermott SW. Opioid prescription patterns for adults with longstanding disability and inflammatory conditions compared to other users, using a national representative sample. Arch Phys Med Rehabil. 2019;100(1):86–94. [DOI] [PubMed] [Google Scholar]
45.Shapiro-Mendoza CK, Barfield WD, Henderson Z, et al. CDC Grand Rounds: Public health strategies to prevent preterm birth. MMWR. 2016;65(12):826–831. [DOI] [PubMed] [Google Scholar]
46.Medley N, Vogel JP, Care A, Alfirevic Z. Interventions during pregnancy to prevent preterm birth: An overview of Cochrane systematic reviews. Cochrane Database Syst Rev. 2018;11(11):CD012505. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Mitra M, Parish SL, Akobirshoev I, Rosenthal E, Simas TAM. Postpartum hospital utilization among Massachusetts women with intellectual and developmental disabilities: a retrospective cohort study. Mat Child Health J. 2018;22(10):1492–1501. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Brown HK, Cobigo V, Lunsky Y, Vigod S. Postpartum acute care utilization among women with intellectual and developmental disabilities. J Women’s Health. 2017;26(4):329–337. [DOI] [PubMed] [Google Scholar]
49.Brown HK, Carty A, Havercamp S, Parish S, Lunsky Y. Algorithms to identify reproductive-aged women with physical and sensory disabilities in administrative health data: A systematic review. Disabil Health J. 2020;13(3):100909. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Courtney-Long EA, Carroll DD, Zhang QC. Prevalence of disability and disability type among adults—United States, 2013. MMWR. 2015;64(29):777–783. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material

NIHMS1851961-supplement-Supplementary_Material.pdf^{(147.2KB, pdf)}

[R1] 1.Zeitlin J, Mortensen L, Cuttini M, et al. Declines in stillbirth and neonatal mortality rates in Europe between 2004 and 2010: results from the Euro-Peristat project. J Epidemiol Community Health. 2016;70(6):609–615. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Lorch SA. A decade of improvement in neonatal intensive care: how do we continue the momentum? JAMA Pediatrics. 2017;171(3):e164395–e164395. [DOI] [PubMed] [Google Scholar]

[R3] 3.Gage TB, Fang F, O’Neill E, DiRienzo G. Maternal education, birth weight, and infant mortality in the United States. Demography. 2013;50(2):615–635. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Howell EA, Zeitlin J, Hebert P, Balbierz A, Egorova N. Paradoxical trends and racial differences in obstetric quality and neonatal and maternal mortality. Obstetrics Gynecol. 2013;121(6):1201. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R5] 5.Huang J, Zhu T, Qu Y, Mu D. Prenatal, perinatal and neonatal risk factors for intellectual disability: a systemic review and meta-analysis. PloS One. 2016;11(4). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Kajantie E, Osmond C, Barker DJ, Eriksson JG. Preterm birth—a risk factor for type 2 diabetes? the Helsinki Birth cohort study. Diabetes Care. 2010;33(12):2623–2625. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Statistics Canada. Canadian Survey on Disability – Data Tables. Catalogue no. 89 654X-No. 001. Ottawa, ON: Statistics Canada; 2013. [Google Scholar]

[R8] 8.Horner-Johnson W, Biel FM, Darney BG, Caughey AB. Time trends in births and cesarean deliveries among women with disabilities. Disabil Health J. 2017;10(3):376–381. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Brown HK, Chen S, Guttmann A, et al. Rates of recognized pregnancy in women with disabilities in Ontario, Canada. Am J Obstet Gynecol. 2020;222(2):189–192. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.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(1):27. e21–27. e32. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Parish SL, Rose RA, Andrews ME. Income poverty and material hardship among US women with disabilities. Social Serv Rev. 2009;83(1):33–52. [Google Scholar]

[R12] 12.Hoffman DW, Livermore GA. The house next door: A comparison of residences by disability status using new measures in the American Housing Survey. Am Housing Survey. 2012;14(1):5–33. [Google Scholar]

[R13] 13.Huang J, Guo B, Kim Y. Food insecurity and disability: Do economic resources matter? Soc Sci Res. 2010;39(1):111–124. [Google Scholar]

[R14] 14.Mitra M, Clements KM, Zhang J, Smith LD. Disparities in adverse preconception risk factors between women with and without disabilities. Mat Child Health J. 2016;20(3):507–515. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Kim M, Kim H-J, Hong S, Fredriksen-Goldsen KI. Health disparities among childrearing women with disabilities. Mat Child Health J. 2013;17(7):1260–1268. [DOI] [PubMed] [Google Scholar]

[R16] 16.Wisdom JP, McGee MG, Horner-Johnson W, Michael YL, Adams E, Berlin M. Health disparities between women with and without disabilities: a review of the research. Social Work Public Health. 2010;25(3–4):368–386. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Tarasoff L, Lunsky Y, Chen S, Guttmann A, Havercamp S, Parish S, Vigod S, Carty A, Brown HK. Preconception health characteristics of women with disabilities in Ontario: A population-based, cross-sectional study. J Women’s Health. 2020;29(12):1564–1575. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Horner-Johnson W, Biel FM, Caughey AB, Darney BG. Differences in prenatal care by presence and type of maternal disability. Am J Prev Med. 2019;56(3):376–382. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Nishat F, Lunsky Y, Tarasoff LA, Brown HK. Prenatal care adequacy among women with disabilities: A population-based study. Am J Prev Med. 2021;doi: 10.1016/j.amepre.2021.05.037. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Tarasoff L, Murtaza F, Salaeva D, Carty A, Hamilton A, Brown HK. Health of newborns and infants born to women with disabilities: A meta-analysis. Pediatrics. 2020;146(6):e20201635. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Statistics Canada. Live births and fetal deaths (stillbirths) by place of birth (hospital and non-hospital). Ottawa: Statistics Canada; 2019. [Google Scholar]

[R22] 22.Williams JI, Young WA. Summary of studies on the quality of health care administrative databases in Canada. Ottawa, ON: Canadian Medical Association;1996. [Google Scholar]

[R23] 23.Darney BG, Biel FM, Quigley BP, Caughey AB, Horner-Johnson W. Primary cesarean delivery patterns among women with physical, sensory, or intellectual disabilities. Women’s Health Issues. 2017;27(3):336–344. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Biel F, Darney B, Caughey A, Horner-Johnson W. Medical indications for primary caesarean delivery in women with and without disabilities. J Maternal-Fetal Neonata Medicine. 2020;33(20):3391–3398. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Lin E, Balogh R, Cobigo V, Ouellette‐Kuntz H, Wilton AS, Lunsky Y. Using administrative health data to identify individuals with intellectual and developmental disabilities: a comparison of algorithms. J Intellect Disabil Res. 2013;57(5):462–477. [DOI] [PubMed] [Google Scholar]

[R26] 26.Brown HK, Ray JG, Chen S, Guttmann A, Havercamp S, Parish S, Vigod SN, Tarasoff L, Lunsky Y. Association of pre-existing disability and severe maternal morbidity or mortality in Ontario, Canada. JAMA Ntwrk Open. 2021;4(1):e2034993. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.You JJ, Alter DA, Stukel TA, et al. Proliferation of prenatal ultrasonography. CMAJ. 2010;182(2):143–151. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Irvine B, Dzakpasu S, Leon J. Perinatal health indicators 2013: a surveillance report by the Public Health Agency of Canada’s Perinatal Surveillance System. Health Promot Chron Dis Prev Can. 2015;35(1):23. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Ray JG, Sgro M, Mamdani MM, Glazier RH, Bocking A, Hilliard R, et al. Birth weight curves tailored to maternal world region. J Obstet Gynaecol Can 2012;34:159–71. [DOI] [PubMed] [Google Scholar]

[R30] 30.Misra DP, Guyer B, Allston A. Integrated perinatal health framework: A multiple determinants model with a life span approach. Am J Prev Med. 2003;25(1):65–75. [DOI] [PubMed] [Google Scholar]

[R31] 31.Kralj B. Measuring “rurality” for purposes of health-care planning: an empirical measure for Ontario. Ont Med Rev. 2000;67(9):33–52. [Google Scholar]

[R32] 32.Health JHBSoP. The Johns Hopkins CGH system: Technical reference guide version 9.0. 2009. http://acg.jhsph.org/public-docs/ACGv9.0TechRefGuide.pdf Accessed August 27, 2015.

[R33] 33.Alexander GR, Kotelchuck M. Quantifying the adequacy of prenatal care: a comparison of indices. Public Health Rep. 1996;111(5):408. [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Ray JG, Park AL, Dzakpasu S, et al. Prevalence of severe maternal morbidity and factors associated with maternal mortality in Ontario, Canada. JAMA Netw Open. 2018;1(7):e184571–e184571. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Comm Stat Sim Comp. 2009;38(6):1228–1234. [Google Scholar]

[R36] 36.Zou G A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–706. [DOI] [PubMed] [Google Scholar]

[R37] 37.Zou G, Donner A. Extension of the modified Poisson regression model to prospective studies with correlated binary data. Stat Methods Med Res. 2013;22(6):661–670. [DOI] [PubMed] [Google Scholar]

[R38] 38.Mitra M, Lu E, Diop H. Smoking among pregnant women with disabilities. Women’s Health Issues. 2012;22(2):233–239. [DOI] [PubMed] [Google Scholar]

[R39] 39.Tucker CM, Berrien K, Menard MK, et al. Predicting preterm birth among women screened by North Carolina’s Pregnancy medical Home Program. Mat Child Health J. 2015;19:2438–2452. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.De Graaf J, Steegers EAP, Bonsel GJ. Inequalities in perinatal and maternal health. Current Opinion Obstet Gynecol. 2013;25(2):98–108. [DOI] [PubMed] [Google Scholar]

[R41] 41.Heideveld-Gerritsen M, van Vulpen M, Hollander M, Maatman SO, Ockhuijsen H, van den Hoogen A. Maternity care experiences of women with physical disabilities: A systematic review. Midwifery 2021;10:102938. [DOI] [PubMed] [Google Scholar]

[R42] 42.Powell R, Andrews E, Ayers K. Becoming a disabled parent: Eliminating access barriers to health care before, during, and after pregnancy. Tulane Law Rev. 2021;forthcoming. [Google Scholar]

[R43] 43.Khan M, Brown HK, Lunsky Y, Welsh K, Proulx L, Havercamp S, Tarasoff L. A socio-ecological approach to understanding the perinatal care experiences of people with intellectual and developmental disabilities in Ontario, Canada. Women’s Health Issues. 2021; doi: 10.1016/j.whi.2021.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 44.Hong Y, Geraci M, Turk MA, Love BL, McDermott SW. Opioid prescription patterns for adults with longstanding disability and inflammatory conditions compared to other users, using a national representative sample. Arch Phys Med Rehabil. 2019;100(1):86–94. [DOI] [PubMed] [Google Scholar]

[R45] 45.Shapiro-Mendoza CK, Barfield WD, Henderson Z, et al. CDC Grand Rounds: Public health strategies to prevent preterm birth. MMWR. 2016;65(12):826–831. [DOI] [PubMed] [Google Scholar]

[R46] 46.Medley N, Vogel JP, Care A, Alfirevic Z. Interventions during pregnancy to prevent preterm birth: An overview of Cochrane systematic reviews. Cochrane Database Syst Rev. 2018;11(11):CD012505. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Mitra M, Parish SL, Akobirshoev I, Rosenthal E, Simas TAM. Postpartum hospital utilization among Massachusetts women with intellectual and developmental disabilities: a retrospective cohort study. Mat Child Health J. 2018;22(10):1492–1501. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Brown HK, Cobigo V, Lunsky Y, Vigod S. Postpartum acute care utilization among women with intellectual and developmental disabilities. J Women’s Health. 2017;26(4):329–337. [DOI] [PubMed] [Google Scholar]

[R49] 49.Brown HK, Carty A, Havercamp S, Parish S, Lunsky Y. Algorithms to identify reproductive-aged women with physical and sensory disabilities in administrative health data: A systematic review. Disabil Health J. 2020;13(3):100909. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 50.Courtney-Long EA, Carroll DD, Zhang QC. Prevalence of disability and disability type among adults—United States, 2013. MMWR. 2015;64(29):777–783. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Neonatal Outcomes of Mothers with a Disability

Hilary K Brown, PhD

Simon Chen, MPH

Astrid Guttmann, MDCM MSc FRCPC

Susan M Havercamp, PhD

Susan Parish, PhD

Joel G Ray, MD MSc FRCPC

Simone N Vigod, MD MSc FRCPC

Lesley A Tarasoff, PhD

Yona Lunsky, PhD C.Psych

Abstract

Objectives:

Methods:

Results:

Conclusions:

Table of contents summary:

INTRODUCTION

METHODS

Study setting and study design

Table 1.

Study population

Outcomes

Covariates

Analyses

RESULTS

Table 2.

Table 3.

Table 4.

DISCUSSION

CONCLUSION

Supplementary Material

What’s known on this subject:

What this study adds:

ACKNOWLEDGMENTS

Funding/support:

Role of the funder/sponsor:

Abbreviations:

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases