Pregnancy complications and maternal birth outcomes in women with intellectual and developmental disabilities in Wisconsin Medicaid.

BACKGROUND: Women with intellectual and developmental disabilities (IDD) may face greater risk for poor pregnancy outcomes. Our objective was to examine risk of maternal pregnancy complications and birth outcomes in women with IDD compared to women without IDD in Wisconsin Medicaid, from 2007-2016.
METHODS: Data were from the Big Data for Little Kids project, a data linkage that creates an administrative data based cohort of mothers and children in Wisconsin. Women with ≥1 IDD claim the year before delivery were classified as having IDD. Common pregnancy complications and maternal birth outcomes were identified from the birth record. We calculated risk ratios (RR) using log-linear regression clustered by mother. We examined outcomes grouped by IDD-type and explored interaction by race.
RESULTS: Of 177,691 women with live births, 1,032 (0.58%) had an IDD claim. Of 274,865 deliveries, 1,757 were to mothers with IDD (0.64%). Women with IDD were at greater risk for gestational diabetes (RR: 1.28, 95% CI: 1.0, 1.6), gestational hypertension (RR: 1.22, 95% CI: 1.0, 1.5), and caesarean delivery (RR 1.32, 95% CI: 1.2, 1.4) compared to other women. Adjustment for demographic covariates did not change estimates. Women with intellectual disability were at highest risk of gestational hypertension. Black women with IDD were at higher risk of gestational hypertension than expected under a multiplicative model.
CONCLUSIONS: Women with IDD have increased risk of pregnancy complications and adverse outcomes in Wisconsin Medicaid. Results were robust to adjustment. Unique patterns by IDD types and Black race warrant further exploration.

Introduction

Intellectual and developmental disabilities (IDD)—which present before 18 years of age and are defined by limitations in adaptive, cognitive, and social functioning [1]—affect approximately 2.1 million adults [2]. As larger cohorts of children with IDD age into adulthood [3], health and service needs will change from mainly pediatric services to specialized adult care [4]. Clinicians and health care systems will face new challenges in serving a population at risk for disparities in health outcomes.

A specific area illustrative of health disparities in IDD is poor perinatal outcomes for women with IDD. Like women without IDD, women with IDD have the right to achieve their sexual health goals, including the choice to engage in sexual relationships and the choice to become a parent. However, historically and in recent decades women with IDD have been denied reproductive rights through forced sterilization, institutionalization, and stigma [5, 6]. Although attempts have been made to repudiate past injustices [5], individuals with IDD face discrimination and healthcare inequity that place them at greater risk for adverse pregnancy outcomes [7, 8]. The stigma surrounding pregnancy and the lack of adequate reproductive and sexual healthcare [9, 10] makes an at-risk population of women with IDD especially vulnerable during pregnancy.

A recent meta-analysis identified 10 studies of pregnancy outcomes for women with IDD suggesting that women with IDD are more likely than peers to experience complications [11]. Yet, studies of pregnancy in women with IDD are often confounded by socioeconomic status because a full population comparison groups does not mirror the lower socioeconomic profile of the IDD population [11]. Medicaid, a federal-state partnership to provide low- or no-cost health care, nursing home care, and personal care services to low-income individuals in the US, is a source of data that may allow for assessment of more like IDD and non-IDD groups. Further, little is known about pregnancy outcomes among specific IDD types (e.g. cerebral palsy, intellectual disability) and more work is needed, as there may be different associations with outcomes based on phenotypic and genetic presentation of the IDD [12]. Adults of color with disabilities often face compounded disparity that leads to excess poor health [13, 14], but literature is lacking surrounding pregnancy in women of color with IDD [15].

Our objective was to replicate and expand upon past findings focused on pregnancy complications and maternal birth outcomes in women with IDD. We used linked administrative data from the Big Data for Little Kids (BD4LK) project from 2007–2016 to characterize and compare pregnancy complications and maternal birth outcomes in 1,032 women with IDD and 176,665 women without IDD. Our focus is on relatively common complications that we can assess in birth records. We explored differences by IDD type and race. We hypothesized that women with IDD would be at higher risk for gestational hypertension, gestational diabetes, and caesarean delivery compared to women without IDD, with the highest risk for complications in women with intellectual disability. Additionally, we hypothesized that Black women with IDD would have higher risk of gestational diabetes and gestational hypertension compared to the expected independent risks associated with being Black and having IDD.

Materials and methods

Big Data for Little Kids

We used data from BD4LK, a longitudinal cohort of all Wisconsin birth records for in-state resident deliveries resulting in a live birth from 2007–2016 linked to administrative data sources, including Wisconsin Medicaid claims and encounters (hereafter ‘claims’). BD4LK uses deterministic matching to link birth records to a Medicaid demographic base file using a mother’s full name and birth date. BD4LK then links the file to claims using the Medicaid-specific person identifying number. In BD4LK, Medicaid claims from one year pre-delivery through delivery are available. BD4LK spans two versions of the birth record: the 1989 (2007–2010 records) and 2003 (2011–2016 records) Revision of the US Standard Certificate of the Live Birth. Shared variables were harmonized, although variables unique to the 2003 Revision are only available on 2011–2016 records. Further BD4LK methods can be found elsewhere [16-18].

Our sample consisted of all live births to women who had at least one Medicaid-paid delivery in Wisconsin during 2007–2016. Among 666,375 unique birth records in BD4LK, we linked 284,496 deliveries (42.6%) to women with Medicaid claims. We excluded records with imperfect linkages (e.g. missing maternal or child identifier; N = 1,825: <0.3%) and records of children born to women whose only observed Medicaid-paid delivery occurred in 2013, as data from Medicaid were unavailable for 2013 (N = 7,806: 1.2%). This yielded a final analytic sample of 274,865 birth records (41.2% of all BD4LK birth records) among 177,697 women.

BD4LK’s first wave pulled claims that were bounded by 2007–2012, and the second wave pulled claims bounded by 2013–2016. As such, BD4LK does not have access to a full year of predelivery claims for women delivering in 2007, 2013, and early 2014. We generated a subsample excluding those years (N = 195,691: 71.2% of full analytic sample) for sensitivity analyses. Since we included all births to women who had any one delivery covered by Medicaid, a woman could have had a non-Medicaid covered delivery. Therefore, we ran a sensitivity analysis restricting to Medicaid covered deliveries only (N = 254,957: 92.8% of full analytic sample).

Identifying IDD

We assessed maternal IDD using an algorithm that examined all International Classification of Disease 9 and 10 codes in available Medicaid claims. Women had differing amounts of time enrolled in Medicaid, but based on consistency in IDD claims over time [3] and ability to identify IDD during delivery-related hospitalizations [19], we believe the data were adequate for assessing IDD. Women with IDD qualify for Medicaid either by receiving a Social Security Disability Determination and/or by meeting income and asset requirements. IDD codes were identified from previous literature [20-22] (S1 Table). Women had to have one claim of IDD to be in the IDD group; we ran a sensitivity analysis using two claims and two claims on two different days. We grouped conditions into IDD-types: intellectual disability, genetic or chromosomal anomalies (‘genetic conditions’), autism spectrum disorder (“autism”), and cerebral palsy. Women could have more than one IDD-type.

Pregnancy complications and maternal birth outcomes

Data for maternal pregnancy complications and maternal birth outcomes were identified a priori from the birth record, specifically gestational diabetes, gestational hypertension, and common delivery complications (induced labor, precipitous labor [birth after <3 hours of regular contractions], prolonged labor [>20 hours for first time mother, >14 hours otherwise], caesarean-section, and maternal hospital transfer). We examined receipt of health services: timing of entry into prenatal care, use of Special Supplemental Nutrition Program for Women Infants and Children during pregnancy (WIC; 2011–2016 birth records only- N = 157226). We assessed pre-pregnancy BMI (2011–2016 birth records only—N = 157,226) as a potential risk factor.

Demographic variables

Maternal demographic characteristics at delivery were available from the birth record. We examined maternal race (white, Black, Asian, other), ethnicity (Hispanic, non-Hispanic), marital status (married, single), education (< high school, some high school, completed high school, ≥ some college), nativity, geographic classification of the birth county (based on National Center for Health Statistics 2013 Urban Rural Classification Scheme [23]), number of live births during the ten-year period (1, 2, ≥3), and total parity (first, second, third, or fourth or subsequent delivery). We categorized maternal age at childbirth in years (≤18, 19–24, 25–34, 35–39, ≥40). For women with multiple observed deliveries in the 10-year period, we present demographic characteristics from a randomly selected birth.

Statistical analyses

We calculated descriptive statistics for women with and without IDD and by IDD-type. All analyses were restricted to live births. For each outcome we ran a multi-level log-linear regression calculating unadjusted risk ratios (uRR) and 95% confidence intervals (95% CI) comparing women with and without IDD and then by IDD-types. Outcomes with multiple levels (i.e. BMI) were dichotomized. We clustered by mother and used an exchangeable correlation structure to account for women having more than one birth. For prevalent outcomes (>5%) we calculated adjust risk ratios (aRR) models by adding covariates for maternal race, ethnicity, age, nativity, parity, and geographic classification of birth county to the previously described model. Lastly, we assessed race by IDD interaction on the risk ratio scale by adding a race term (white or Black, irrespective of ethnicity) and a race by IDD interaction term to the unadjusted models. Statistical significance was set at an alpha level of 0.1 for interaction analyses. We ran analyses using SAS version 9.4 (SAS Institute, Cary, NC). The data received for this secondary analysis were anonymized. The BD4LK project received a waiver of informed consent. This study was approved by the University of Wisconsin-Madison Institutional Review Board.

Results

Of 177,691 women in our full sample, 1,032 (0.58%) had a Medicaid claim for IDD; those women delivered 1,757 children (0.64% of all births). Of the 1,032 women with IDD, 435 had claims for a genetic condition, 330 had claims for intellectual disability, 100 had claims for autism, 170 had claims for cerebral palsy, and 31 had claims for an ‘other’ IDD.

Demographic characteristics

Our sample was predominantly of white race (Table 1). Approximately 25% of women with IDD reported some college or greater compared to 40% of women without IDD. The percentage of women with IDD who were foreign born (4.9%) or Hispanic (10.6%) was less than women without IDD (10.1% foreign born and 14.5% Hispanic). Categorized, mean, and median age at birth were similar for those with and without IDD.

Table 1

Demographic characteristics of women with a live Medicaid covered birth in Wisconsin, 2007–2016; by intellectual and developmental disability status.

	Women with intellectual and developmental disability		Women without intellectual and developmental disabilities
	N = 1032		N = 176665
	N	%	N	%
Maternal race
White	739	71.6	129724	73.4
Black	228	22.1	32558	18.5
Asian	44	4.3	10279	5.8
Other	21	2.0	4098	2.3
Hispanic ethnicity
Hispanic	109	10.6	25663	14.5
Non-Hispanic	923	89.4	150996	85.5
Foreign born
Yes	51	4.9	17823	10.1
No	981	95.1	158832	89.9
Maternal education
<High school	268	26.3	36820	21.0
Completed high school	496	48.6	71609	40.8
Some college	206	20.2	53246	30.3
≥ Completed college	50	4.9	13833	7.9
Missing	12	-	1151
Marital status
Married	284	27.5	57950	32.8
Not Married	748	72.5	118704	67.2
Missing			19
Number of births, 2007–2016
1	560	54.3	106647	60.3
2	293	28.4	50113	28.4
3+	179	17.3	19899	11.3
	By births
	N = 1757		N = 273108
	N	%	N	%
Maternal age at childbirth
≤18	68	3.8	8919	3.3
19–24	772	43.9	118667	43.5
25–29	486	27.7	81042	29.7
30–34	264	15.0	43932	16.1
35–39	124	7.0	16847	6.1
≥40	44	2.5	3691	1.4
Mean age, SD	26.5	6.0	26.3	5.7
Median age, IQR	26.0	8.3	25.6	8.1
Year of birth
2007	180	10.2	28375	10.4
2008	243	13.8	29032	10.6
2009	222	12.6	30153	11.0
2010	192	10.9	29242	10.7
2011	195	11.1	28983	10.6
2012	193	11.0	28840	10.6
2013	136	7.7	25242	9.2
2014	149	8.5	25092	9.2
2015	127	7.2	24328	8.9
2016	120	6.8	23821	8.7
Parity
First born	557	31.7	93882	34.4
Second born	497	28.3	81326	29.8
Third born	334	19.0	51084	18,7
Fourth born or later	369	21.0	46717	17.1
Missing			99
Plural delivery
Yes	61	3.5	7409	2.7
No	1696	96.5	265699	97.3
Preterm birth (gestational age <37 weeks)
Yes	261	14.9	25037	9.2
No	1488	85.1	247358	90.8
Missing	-		713
Gestational age in weeks (SD)	38.1	2.6	38.6	2.0
County size where child was borna
Large central metro	546	31.1	78540	28.8
Large fringe metro	135	7.7	25553	9.4
Medium metro	247	14.1	39540	14.5
Small metro	448	25.5	70671	25.9
Micropolitan	195	11.1	32149	11.8
Non-core	186	10.6	26655	9.8

SD: standard deviation; IQR: interquartile range.

a If childbirth county was missing maternal residential county was used.

Cells with sample sizes <10 are suppressed.

By IDD-type (S2 Table), 33.8% of women with intellectual disability were Black and the percent of Black women in the other IDD-types was between 13% and 17%. Educational attainment was lower among women with intellectual disability (6.5% had ≥ some college education) compared of those with genetic conditions (36.2%), cerebral palsy (24.7%), and autism (27.0%).

Occurrence of pregnancy complications and other maternal outcomes

Gestational diabetes and gestational hypertension were slightly more common in women with IDD compared to women without IDD (Table 2). Women with IDD were more likely to be transferred to another medical facility during labor compared to women without IDD. Caesarean delivery was more common in women with IDD compared to women without IDD. Results were robust and did not meaningfully change when using two-claims for IDD, excluding births in 2007, 2013, and 2014, or restricting to just Medicaid covered deliveries (S3 and S4 Tables).

Table 2

Occurrence and risk ratios of maternal pregnancy complications and maternal birth outcomes for all births comparing women with and without intellectual and developmental disabilities in Wisconsin Medicaid, 2007–2016.

	Pregnancies of women with Intellectual and developmental disabilities	Pregnancies of women without intellectual and developmental disabilities	Unadjusted risk ratioa
	N = 1757	N = 273108
	%	%	RR	95% CI
Service use
Trimester prenatal care began
1	72.8	73.4	0.99	0.9, 1.0
2	21.4	21.6
3	4.2	4.1
None	1.6	0.8	1.83	1.2, 2.8
Missing (N)	54	8069
WICc during pregnancy
Yes	79.6	66.6	1.20	1.1, 1.3
No	20.4	33.4
Missing (N)	25	3139
Pregnancy complications and maternal birth outcomes
Prepregnancy BMIb
Underweightc	4.6	3.1	1.67	1.2, 2.3
Normal weight	30.9	37.2
Overweight	24.5	26.0
Obesed	40.1	33.7	1.22	1.1, 1.3
Missing (N)	22	3363
Ever smoke during pregnancy
Yes	29.9	26.6	1.09	1.0, 1.2
No	70.1	73.4
Missing (N)	16	1255
Gestational diabetes
Yes	7.0	5.5	1.28	1.0, 1.6
No	93.0	94.5
Gestational hypertension
Yes	6.0	5.0	1.22	1.0, 1.5
No	94.0	95.0
Complications of delivery
Maternal transfer
Yes	1.5	0.8	1.86	1.2, 2.8
No	98.5	99.2
Missing (N)	-	313
Prolonged labor
Yes	1.1	1.1	0.95	0.6, 1.5
No	98.9	98.9
Missing (N)	-	300
Precipitous labor
Yes	4.8	4.2	1.17	0.9, 1.4
No	95.2	95.8
Missing (N)	-	300
Induced labor
Yes	28.4	27.5	1.02	0.9, 1.1
No	71.6	72.5
Missing (N)	-	225
Caesarean delivery
Yes	26.3	20.8	1.30	1.2, 1.4
No	73.7	79.2
Missing (N)	101	12135

CI: confidence interval; WIC: Special Supplemental Nutrition Program for Women Infants and Children; BMI: body mass index.

a Multilevel regression clustered by mother.

b Data only available for birth years 2011–2016.

c Comparison group is normal weight.

Bold indicates statistical significance at an alpha = 0.05 level.

Cells with values <10 are suppressed.

Adjusted risk ratios comparing women with IDD to the Medicaid sample

Adjustment attenuated the association between WIC (aRR: 1.11; 95% CI: 1.0, 1.2) or pre-pregnancy obesity (aRR: 1.14; 95% CI: 1.0, 1.3) and IDD (Fig 1). The associations between gestational diabetes (aRR: 1.37, 95% CI:1.1, 1.7) or gestational hypertension (aRR: 1.30, 1.0, 1.7) and IDD were strengthened after adjustment. There was no meaningful change in estimate for caesarean delivery after adjustment (aRR: 1.33, 1.2, 1.5). In a post-hoc analysis, we restricted to nulliparous women with singleton, vertex, term births and found women with IDD had 1.20 times the risk of caesarean delivery compared to women without IDD (95% CI: 0.9, 1.5). Stratifying by each of the restrictions, caesarean delivery risk did not differ for women with and without IDD by levels of term birth, nulliparous birth, or singleton birth. Women with IDD had significantly higher risk of caesarean delivery compared to women without IDD if the infant was not breach and if the mother had no previous caesarean deliveries (S5 Table).

Fig 1

Unadjusted and adjusted risk ratios for prevalent pregnancy complications and maternal birth outcomes for all births comparing women with and without intellectual and developmental disabilities in Wisconsin Medicaid, 2007–2016.

Multilevel regression clustered by mother. Adjusted for maternal age, race, ethnicity, foreign born mother, geographic classification of birth county size, parity, marriage, year. Obesity and WIC Data only available for birth years 2011–2016.

Results by IDD-type

Women with intellectual disability were less likely to receive prenatal care in the first trimester compared to women without any IDD (Table 3). Women with a genetic condition had a higher risk of gestational diabetes and women with intellectual disability had a higher risk of gestational hypertension compared to women without IDD. Women with intellectual disability, genetic conditions, and cerebral were at higher risk of caesarean delivery compared to women without IDD.

Table 3

Occurrence and unadjusted risk ratios of pregnancy complications and maternal birth outcomes for all births to women with intellectual and developmental disability types compared to women without intellectual and developmental disabilities, 2007–2016.

	Live births to women by intellectual and developmental disability type
	Intellectual disability			Genetic condition			Cerebral palsy			Autism
	N = 552			N = 777			N = 279			N = 156
	%	RRa	95% CI	%	RRa	95% CI	%	RRa	95% CI	%	RRa	95% CI
Service use
Trimester prenatal care began
1	65.4	0.89	0.8, 1.0	76.7	1.04	0.9,1.1	77.8	1.06	1.0, 1.1	74.3	1.02	0.9, 1.1
2	25.7			20.7			17.2			14.5
3	6.0			1.9			4.0			9.2
None	3.0			-
Missing (N)	15			27
WICb during pregnancy
Yes	86.2	1.31	1.2, 1.4	74.2	1.11	1.0, 1.2	79,7	1.25	1.1, 1.4	79.8	1.20	1.1, 1.3
No	13.4			25.8			16.1			20.2
Pregnancy Complications and maternal birth outcomes
Prepregnancy BMIb
Underweight	6.7			4.1
Normal weight	28.5			27.9			40.6			37.4
Overweight	22,7			27.4			24.6			28.6
Obesec	42.7	1.38	1.2, 1.6	40.7	1.22	1.1, 1.4	34.8	0.98	0.7, 1.3	34.1	1.06	0.8, 1.4
Ever smoke during pregnancy
Yes	31.6	1.11	0.9. 1.3	26.9	1.00	0.9, 1.2	28.8	1.03	0.8, 1.3	35.3	1.27	0.9, 1.6
No	68.5			73.1			71.2			64.7
Gestational diabetes
Yes	6.7	1.20	0.8, 1.7	8.4	1.54	1.2, 2.0	5.4	0.96	0.5, 1.8	6.4	1.22	0.6, 2.3
No	93.3			91.6			94.6			93.6
Gestational hypertension
Yes	8.7	1.71	1.3, 2.3	4.6	1.00	0.7, 1.3	3.9	0.73	0.4, 1.4	7.7	1.60	0.9, 2.8
No	91.3			95.4			96.1			92.3
Complications of delivery
Induced labor
Yes	26.7	0.96	0.8, 1.1	31.6	1.13	1.0, 1.3	21.9	0.81	0.6, 1.0	28.6	1.03	0.8, 1.4
No	73.3			68.4			78.1			71.4
Caesarean delivery
Yes	23.8	1.25	1.1, 1.5	29.7	1.29	1.1, 1.5	35.7	1.63	1.3, 2.0	24.3	1.16	0.8, 1.6
No	76.2			70.3			64.3			75.7
Missing	40			42			21

CI: confidence interval; WIC: Special Supplemental Nutrition Program for Women Infants and Children; BMI: body mass index, RR: risk ratio.

a Multilevel regression clustered by mother comparing intellectual and developmental disability type to women without intellectual and developmental disability.

b Data only available for birth years 2011–2016.

c Comparison group is normal weight.

Bold indicates statistical significance at an alpha = 0.05 level.

Cells with values <10 are suppressed.

Interaction by race

When assessing differences by race and IDD (Table 4), results indicated that Black women with or without IDD were less likely than white women with or without IDD to receive first trimester prenatal care, ever smoke during pregnancy, or have gestational diabetes. Black women were more likely to be on WIC or have gestational hypertension, with additional excess risk for women with IDD; the race by IDD interaction term was statistically significant for gestational hypertension (P for interaction = 0.07) and WIC utilization (P = 0.02) indicating Black women with IDD were at higher risk than would expected based on risk in Black women and women with IDD independently.

Table 4

Occurrence and unadjusted risk ratios for pregnancy complications and maternal birth outcomes comparing births to white women with and without intellectual and developmental disabilities and black women with and without intellectual and developmental disabilities in Wisconsin Medicaid, 2007–2016.

	White				Black
	IDD	No-IDD	RR		IDD	RR		No-IDD	RR
	N = 1220	N = 196684	IDD white vs. No-IDD white		N = 394	IDD black vs. No-IDD white		N = 52414	No-IDD black vs. No-IDD white		Interaction P value
	%	%	RRa	95% CI	%	RRa	95% CI	%	RRa	95% CI
Service use
Trimester prenatal care began
1	77.7	76.7	1.01	0.9, 1.0	65.2	0.85	0.8, 0.9	66.5	0.87	0.8, 0.9	0.5
2	18.7	19.2			24.9			25.7
3	3.6	3.5			5.1			5.8
None		0.6			4.8			2.0
Missing (N)	41	4988			-			2458
WICb during pregnancy
Yes	77.3	62.2	1.24	1.2, 1.3	82.6	1.34	1.2, 1.4	78.3	1.26	1.2, 1.3	<0.001
No	22.7	37.8			17.4			27.7
Missing (N)	15	2164			-			608
Pregnancy complications and maternal birth outcomes
Prepregnancy BMIb
Underweight	4.4	3.1			6.1			3.1
Normal weight	30.1	37.9			31.1			32.9
Overweight	23.8	25.9			25.5			25
Obese	41.6	33.1	1.29	1.2, 1.4	37.2	1.16	0.9, 1.4	39	1.18	1.1, 1.2	0.02
Missing (N)	11	2927			-			666
Ever smoke during pregnancy
Yes	33.4	29.8	1.07	0.9, 1.2	22.6	0.78	0.6, 0.9	19.1	0.62	0.6, 0,7	0.2
No	66.6	70.2			77.4			80.9
Missing (N)		816			-			255
Gestational diabetes
Yes	7.1	5.5	1.30	1.0, 1.6	4.2	0.71	0.4, 1.3	4.1	0.75	0.7, 0.8	0.3
No	92.9	94.5			95.9			95.9
Gestational hypertension
Yes	4.5	4.7	1.02	0.8, 1.3	10.5	2.24	1.5, 3.1	7.2	1.51	1.4, 1.6	0.07
No	95.5	95.3			89.5			92.9
Complications of delivery
Induced delivery
Yes	29.6	28.4	1.03	0.9, 1.1	27.2	0.96	0.8, 1.1	27.2	0.97	0.9, 1.0	0.8
No	70.4	71.6			72.3			72.8
Missing (N)		140			-			58
Caesarean delivery
Yes	26.9	21.5	1.30	1.2, 1.5	25.1	1.32	1.1, 1.6	20.2	0.96	0.9, 1.0	0.6
No	73.1	78.5			74.9			79.8
Missing	65	9192			11			2318

IDD: intellectual and developmental disabilities; CI: confidence interval; WIC: Special Supplemental Nutrition Program for Women Infants and Children; BMI: body mass index; RR: risk ratio.

a Multilevel regression clustered by mother, with race term and race by intellectual and developmental disability interaction term; referent group is white women without IDD.

b Data only available for birth years 2011–2016.

c Comparison group is normal weight.

Bold indicates statistical significance at an alpha = 0.1 level.

Cells with values <10 are suppressed.

Discussion

Women with IDD face health inequity and disparity that may impact pregnancy outcomes. In this study, we replicated past findings that identify increased risk of poor maternal birth outcomes for pregnant women with IDD in the Wisconsin Medicaid system. We expanded upon previous findings, assessing complications and maternal birth outcomes by IDD-type and exploring statistical interaction between race and IDD.

Our findings using birth records are in line with past research on occurrence of outcomes. Using data from the 2010 US Nationwide Inpatient Sample, Parish et al. [20] found that 7.0% of pregnant women with IDD had gestational diabetes and 49.0% had caesarean deliveries. Occurrence of caesarean delivery was higher than in our sample which may be an effect of temporal trends related to decreasing national rates of caesarean delivery [24], or may be a result of the lower rates of caesarean delivery in Wisconsin [25]. Mitra et al. [26, 27] used data from the Massachusetts Pregnancy to Early Life Longitudinal Data system and found similar occurrence of gestational diabetes (5.8%), gestational hypertension (5.8%), and caesarean delivery (36.0%) in women with IDD. Our results were also in line with Darney et al’s [28] work in a retrospective cohort of all live births in California from 2000–2010 (gestational diabetes: 8.1%; caesarean delivery: 38.0%). In the Ontario health system from 2002–2010, Brown et al. [29] found lower rates of gestational diabetes (3.2%) and gestational hypertension (1.2%) compared to our findings with similar rates of caesarean delivery (27.0%). With these consistent results, obstetricians should be aware of the high risk of maternal morbidity when treating pregnant women with IDD and researchers need to continue to investigate mechanistic pathways and IDD-specific interventions and preventive programs.

When comparing women with IDD to a non-IDD sample, our estimates align with the recent meta-analysis by Tarasoff et al. [11] For gestational diabetes, our uRR of 1.28 was larger than the meta-analytic unadjusted pooled odds ratio of 1.10 (assuming odds ratio approaches RR) yet was within the range of other studies (odds ratio range: 0.77–1.71). Our uRR for gestational hypertension (1.22) was smaller than the meta-analytic estimate (odds ratio 1.77) but within previous studies’ range (0.63–2.49). Lastly, Tarasoff et al. [11] estimated unadjusted and adjusted odds ratios for caesarean delivery finding an unadjusted and adjusted pooled odds ratio of 1.29 and 1.46 respectively. The pooled odds ratio for caesarean delivery (1.46) was higher than our estimate (aRR 1.29), possibly due to the odds ratio overestimating the risk ratio given the high prevalence of caesarean delivery. We expected variation compared to past work because of differing demographics and methods, including the relatively low rates of caesarean delivery in Wisconsin [30]; however, the direction and magnitude of effects are similar to past work and provide further evidence of increased risk for maternal pregnancy complications for women with IDD. Promisingly, we found no difference in uptake of prenatal care services, which is in contrast to the disparity in reproductive health services often seen for women with IDD [9]. In this analysis, we identified outcomes a priori based on existing literature; in the future we aim to link more data and use Big Data methodology, such as machine learning, to explore novel risk factors and interactions for pregnancy outcomes for women with IDD.

We saw minimal changes when statistically adjusting for demographic differences. Our use of a Medicaid sample reference group was selected to reduce and account for much of the confounding due to socio-economic differences seen in previous studies. With additional data, we will have the statistical power to adjust our IDD-subtype analyses and better explore specific mediators and causal pathways from IDD to outcome.

With the heterogeneity of IDD, it was important to assess outcomes in by IDD type. Women with intellectual disability were less likely to receive prenatal care in the first trimester compared to women without IDD. The lack of adequate reproductive and sexual health care [9] may delay recognition of pregnancy and start of services. In addition, women with intellectual disability may face increased barriers when navigating the health care service system [31], especially for pregnancy care [9]. The increased risk for caesarean delivery for women with cerebral palsy may be due to chronic pain, pelvic abnormalities, spasticity, and other physical disabilities [12]. Because of the approximate 4:1 male to female sex ratio in autism [32], and temporal cohort effects [3], we saw relatively fewer births to women on the autism spectrum. While we present what we believe to be the first population-based estimates of pregnancy outcomes specific to autistic women, these data will be bolstered as more women on the spectrum age into adulthood and start families.

We found some differences in risk of pregnancy complications between Black and white women with IDD, with Black women having increased risks of obesity and gestational hypertension and white women having increased risks of smoking during pregnancy and gestational diabetes. It is possible that pre-pregnancy diabetes and hypertension may influence this finding and will need further exploration. For other outcomes, such as caesarean delivery and prenatal care, we found little evidence of excess risk for being both black and having IDD. Based on our results in pregnancies covered by Wisconsin Medicaid, much of the racial disparity in complications experienced by white and Black women with IDD is not different from racial disparity from white and Black women without IDD.

Determination of both maternal pregnancy outcomes and IDD could be impacted by misclassification. Our reliance on birth records to quantify outcomes may lead to an underestimation of morbidity, especially for gestational hypertension and diabetes, and maternal smoking [33, 34]. Ultimately, our estimates were consistent with past studies that used other data sources for maternal outcomes [26, 27]. Because of the demographic similarities between the IDD and non-IDD group in our sample and the minimal effect of statistical adjustment, differential misclassification biasing RRs was unlikely. Our findings need to be replicated with other data sources such as maternal self-report, electronic health records, and claims. With additional data sources we can better describe and understand prenatal care and perinatal management in women with IDD. IDD was identified using up to one year of maternal Medicaid claims as available in BD4LK; we are not able to determine IDD status in women on Medicaid who did not have claims for IDD. It would have been preferable to have a longer period to assess claims or additional data sources. However, our results were consistent when using more restrictive IDD claim criteria. Our sample included women who received Medicaid and does not represent the full population of women with IDD. We cannot make inferences on women with IDD on private insurance who may have different socioeconomic profiles. IDD often co-occur [35], which was not evident in our claims; our categorization by IDD-type may not have captured the true overlap of some conditions and additional data are needed to better capture IDD phenotype. Our results are conditioned on live birth and do not account for pregnancy losses. Results may not be generalizable to other state Medicaid systems due to the demographic distribution and state-specific policies of Wisconsin.

Conclusions

Pregnant women with IDD and a live birth in Wisconsin Medicaid were at greater risk of gestational diabetes, gestational hypertension, and caesarean delivery compared to pregnant women without IDD. Our findings are in line with past studies and highlight the importance of proper accounting for socioeconomic status and exploring IDD-type and race. Results support the need for increased research and attention to maternal pregnancy complications and adverse birth outcomes for women with IDD. Further work is needed to deduce biologic and social mechanisms for the presentation of complications.

International classification of disease 9 and 10 codes used to identify intellectual and developmental disabilities.

(DOCX)

Click here for additional data file.

Demographic characteristics of mothers with a live Medicaid covered birth in Wisconsin, 2007–2016; by Intellectual and developmental disability type.

(DOCX)

Click here for additional data file.

Demographic characteristics of mothers with live birth in Wisconsin 2007–2016 comparing intellectual and developmental disability identification criteria in Medicaid claims one-year pre pregnancy.

(DOCX)

Click here for additional data file.

Occurrence and risk ratios of maternal pregnancy complications and adverse outcomes for all births to mothers with intellectual and developmental disabilities compared to the full Wisconsin Medicaid sample of mothers, 2007–2016 with sensitivity analyses for IDD claim count and excluding years.

(DOCX)

Click here for additional data file.

Analysis of caesarean delivery in women with and without intellectual and developmental disabilities in Wisconsin Medicaid, 2007–2016.

(DOCX)

Click here for additional data file.

Birth outcomes affecting infants of mothers with intellectual and developmental disabilities.

(PDF)

Click here for additional data file.

17 Aug 2020

PONE-D-20-17994

Pregnancy complications and maternal birth outcomes in women with intellectual and developmental disabilities in Wisconsin Medicaid

PLOS ONE

Dear Dr. Rubenstein,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR: Very interesting article about an important subject with a big sample size. The article needs a major revision and the authors should add many obstetrical information about these specific pregnant women to interest the reader.

==============================

Please submit your revised manuscript by Oct 01 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Guillaume Ducarme, MD, MSc, PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In ethics statement in the manuscript and in the online submission form, please provide additional information about the database used in your retrospective study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have their data used in research, please include this information.

3.We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: REVIEW: PONE-D-20-17994

Thanks to the editorial board to give me the opportunity to review this interesting works. The resuts of this study confirm well-known issues for women with Intellectual and Developmental Disabilities, nevertheless with a “big data” approach their provide us a confirmation of obstetrical outcomes in this population.

Minors Comments:

Methods

1- Could authors clarify the datas mining in this “Big data” study step by step. This could be helpful for further studies in the same methodology and for external validity / reproducibility of this study.

2- Authors assess obstetrical outcomes and c-section more particularly. This outcome is very difficult to assess because of numbers of biais leading to c-section. Could you precise, in the according to the numbers of patient and variables why a propensity score was not choice before adjustment ? This method is more and more recommended in big data analysis currently.

Results

1- In this big data analysis we can be surpize by the little numbers of missing values, How can authors explain that. Please discuss this in discussion.

2- Authors compared population characteristics in the table 1 without precision on the significance of comparisons done. Please test and add p-value in table 1, and report in the section results.

Discussion:

1- For non American readers I think it’s could be useful to precise what is medicare “a government insurance program for persons of all ages whose income and resources are insufficient to pay for health care”, in order to better precise the characteristic of this sample. Could the results observed in this singular sample of population be extrapolate in general population according to this insurance selection ? Please debates this.

2- The discussion of the results is welle done wizth an epidemiological point of view. An obstetrical vision in the discussion could be useful, because obstetricians could be interest by the result of this study, So discussion about indication of c-section in this context is necessary. Indeed indication of c-section in the context of IDD could be done for obstetrical reason but also for organization reason, or obstetrician convenience considering the difficulties to manage such patient in labor ward for example.

Reviewer #2: The authors reported perinatal outcome of pregnancies in women with intellectual and developmental disabilities (IDD). Data were extracted from the Big Data for Little Kids project in Wisconsin Medicaid from 2007-2016. The authors showed that women with IDD have increased risk of pregnancy complications and adverse outcomes in Wisconsin Medicaid (greater risk for gestational, gestational hypertension, and caesarean delivery).

The subject is very interesting and the manuscript is very well-written. The sample size is very important (1032 women with IDD compared to 176665 women without IDD) and results seem robust to adjustment.

But, some problems decrease the interest and the authors should make modifications and add some major information about obstetric and perinatal management of these women.

No information about the gestational age at birth.

“Delivery complications”: Did the authors mean postpartum hemorrhage? Severe perineal tears? Operative vaginal delivery? Episiotomy?.... Many perinatal information lack in the text.

What is “precipitous labor”? Did they mean preterm labor?

Cesarean section: the authors should add some major information about the indications (failed labor progression, fetal distress…).

Same comment about the women who required nduced labor: why? Indications? Techniques? Results?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: David Desseauve

Reviewer #2: Yes: Guillaume DUCARME

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

16 Sep 2020

Maternal IDD outcomes response to reviewers

We would like to thank the editors and reviewers for the positive and constructive review for our paper assessing maternal outcomes for women with intellectual and developmental disabilities. In addressing your concerns, we believe that we have a stronger, more robust paper. Additionally, we have taken the steps to meet Journal requirements. We have responded to comments and provide the page and line numbers for changes. In the text, changes are tracked

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

We have made changes to meet the style requirements of PLOS ONE, including bolding the abstract

2. In ethics statement in the manuscript and in the online submission form, please provide additional information about the database used in your retrospective study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have their data used in research, please include this information.

We have added information on the waived consent and anonymization on page 7 line 20.

3.We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

We have added the following paragraph to the cover letter:

The data from this secondary data analysis are from the Big Data for Little Kids projects, which access proprietary Wisconsin state administrative data. As part of the larger study, the data manager has access to protected health information. While this study did not have access to those data, there is not yet a mechanism or agreement to share de-identified data with people outside the research team. Further, the larger projects Data Use Agreement with the Wisconsin Department of Health Services explicitly states no data can be shared publicly. The body governing the data is the University of Wisconsin Institute for Research on Poverty and for further inquiries you can contact Steve Cook (steven.cook@wisc.edu), IRP data sharing coordinator.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

Please see above explanation as to why we cannot share anonymized data.

Reviewer #1: REVIEW: PONE-D-20-17994

Thanks to the editorial board to give me the opportunity to review this interesting works. The resuts of this study confirm well-known issues for women with Intellectual and Developmental Disabilities, nevertheless with a “big data” approach their provide us a confirmation of obstetrical outcomes in this population.

We thank the reviewer for their time and effort in providing us feedback. While there is existing literature on this topic, we believe that more is needed, and our study adds data from a different health system and geographic area.

Minors Comments:

Methods

1- Could authors clarify the datas mining in this “Big data” study step by step. This could be helpful for further studies in the same methodology and for external validity / reproducibility of this study.

This study did not use any typical ‘data mining’ techniques. We developed an algorithm to identify IDD based on previous work and then examined a priori chosen outcomes. We have added that explanation on page 6 line 10. We have added a future direction of using big data techniques, e.g. machine learning, in the discussion (page 19 line 16).

2- Authors assess obstetrical outcomes and c-section more particularly. This outcome is very difficult to assess because of numbers of biais leading to c-section. Could you precise, in the according to the numbers of patient and variables why a propensity score was not choice before adjustment ? This method is more and more recommended in big data analysis currently.

While there may be differences in who gets C-sections, the goal of our study was to describe occurrence in women with IDD and compare to women without IDD. We believe that C-section is accurately defined in birth records (see Northam and Knapp 2006 doi: 10.1111/j.1552-6909.2006.00016.x) but acknowledge that some outcomes are not as reliable in the birth record.

As for propensity scores, we believe that with our dataset standard statistical adjustment is a strong approach to address confounding and allows us to use the full Medicaid sample. Further, we saw little change with statistical adjustment using standard regression and would expect to see minor differences using a different method like propensity scores.

Results

1- In this big data analysis we can be surpize by the little numbers of missing values, How can authors explain that. Please discuss this in discussion.

We have added to the discussion on page 21 line 13 discussing how we are missing data on women with IDD who do not have claims for IDD. That would not appear in the tables but is still missing. The birth record data has very little missingness, which is a credit to the reporting system.

2- Authors compared population characteristics in the table 1 without precision on the significance of comparisons done. Please test and add p-value in table 1, and report in the section results.

We do not believe that testing for P values in table 1 is appropriate here. Since P values are an effect of sample size, we may see differences that are not clinically meaningful. Additionally, we used a conceptual framework to determine what to adjust for rather than statistical tests, so the P values would not inform our research. See the STROBE guideline for reporting observational studies for our reasoning in not including P values in table 1. (doi:10.1371/journal.pmed. 0040297, page 1643).

Discussion:

1- For non American readers I think it’s could be useful to precise what is medicare “a government insurance program for persons of all ages whose income and resources are insufficient to pay for health care”, in order to better precise the characteristic of this sample. Could the results observed in this singular sample of population be extrapolate in general population according to this insurance selection ? Please debates this.

That is a terrific point. We have added a description of Medicaid in the introduction (page 3 line 24). We have also added to the discussion about how results may not be representative of a non-Medicaid sample (page 21 line 6).

2- The discussion of the results is welle done wizth an epidemiological point of view. An obstetrical vision in the discussion could be useful, because obstetricians could be interest by the result of this study, So discussion about indication of c-section in this context is necessary. Indeed indication of c-section in the context of IDD could be done for obstetrical reason but also for organization reason, or obstetrician convenience considering the difficulties to manage such patient in labor ward for example.

We attempted to further examine c-section by doing a post-hoc analysis restricting to singleton, term, vertex birth. While not exactly the obstetrical reasons you mention, it does give us some insight into why this happens. We have added more information and supplement with this analysis (supplement 5, page 12, line 20).

We have added a sentence on the importance of the results to obstetricians (page 18 line 20). We do not want to overstate the implications or the actions to be taken from this study, but we acknowledge that with such high rates, obgyn’s should be aware of the increased risk of poor outcomes in women with IDD.

Reviewer #2: The authors reported perinatal outcome of pregnancies in women with intellectual and developmental disabilities (IDD). Data were extracted from the Big Data for Little Kids project in Wisconsin Medicaid from 2007-2016. The authors showed that women with IDD have increased risk of pregnancy complications and adverse outcomes in Wisconsin Medicaid (greater risk for gestational, gestational hypertension, and caesarean delivery).

The subject is very interesting and the manuscript is very well-written. The sample size is very important (1032 women with IDD compared to 176665 women without IDD) and results seem robust to adjustment.

Thank you for your time and effort in reviewing our paper. We aimed to address your comments and concerns and believe that the manuscript is stronger having done so.

1. But, some problems decrease the interest and the authors should make modifications and add some major information about obstetric and perinatal management of these women.

No information about the gestational age at birth.

We have added gestational age to table 1. We have a separate analysis assessing infant outcomes which fully examines preterm birth, but we understand your point about it missing here. Our information on perinatal management is limited to prenatal care, which we present. As for perinatal management, we have added our lack of information as a limitation (page 21 line 6)

2.“Delivery complications”: Did the authors mean postpartum hemorrhage? Severe perineal tears? Operative vaginal delivery? Episiotomy?.... Many perinatal information lack in the text.

Thank you for pointing out that “delivery complications” is too ambiguous of a term. We have noted in the abstract that they are ‘common complications.’ Since we only had ~1000 women with IDD and we could not present outcomes with <10 occurrences (due to our data use agreement with the Department of Health Services), some of the rarer outcomes cannot be presented. We have added in our objective paragraph that we focused on common outcomes that we could assess in birth records (page 4 line 8) and made the same note when we talk about delivery complications (page 6 line 16)

3. What is “precipitous labor”? Did they mean preterm labor?

We have added the definition of precipitous and prolonged labor (page 6 line 16)

Preciptious labor is delivery after <3 hours of contractions, prolonged labor is >20 hours for first time mother or >14 hours for second time or greater mother.

Cesarean section: the authors should add some major information about the indications (failed labor 4. progression, fetal distress…). Same comment about the women who required nduced labor: why? Indications? Techniques? Results?

Because of our reliance on birth records we were not able to pinpoint the indication of cesarean delivery or induction. For cesarean delivery, we did a post hoc analysis to see if effects were different between term, vertex, singleton births compared to other births. Risk was higher for preterm, breach, and multiple births. We have added tables of this analysis to the supplement 5 (page 12 line 20). In future work we hope to add electronic health records to get more detail on these conditions.

Submitted filename: PONE_response.docx

Click here for additional data file.

13 Oct 2020

Pregnancy complications and maternal birth outcomes in women with intellectual and developmental disabilities in Wisconsin Medicaid

PONE-D-20-17994R1

Dear Dr. Rubenstein,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Guillaume Ducarme, MD, MSc, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thanks to the authors for the revision.

Major concern were solved by the authors and this paper could be accepted

Reviewer #2: All comments have been addressed. The revised manuscript is stronger after modifications.

The authors have strongly modified the manuscript to increase the interest for physicians, and, specifically, for obstetricians.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Guillaume DUCARME

15 Oct 2020

PONE-D-20-17994R1

Pregnancy complications and maternal birth outcomes in women with intellectual and developmental disabilities in Wisconsin Medicaid

Dear Dr. Rubenstein:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Guillaume Ducarme

Academic Editor

PLOS ONE