critique of a paper

Racial and ethnic disparities in birth outcomes and labour and delivery-related charges among women with intellectual and developmental disabilities

I. Akobirshoev,1 M. Mitra,1 S. L. Parish,2 T. A. Moore Simas,3 R. Dembo1

& C. N. Ncube2

1 Lurie Institute for Disability Policy, Heller School for Social Policy and Management, Brandeis University, Waltham, MA, USA 2 Bouvé College of Health Science, Northeastern University, Boston, MA, USA 3 Departments of Obstetrics & Gynecology, Pediatrics, Psychiatry and Quantitative Health Sciences, University of Massachusetts Medical School/UMass Memorial Health Care, Worcester, MA, USA

Abstract

Background Women with intellectual and developmental disabilities (IDD) in the USA are bearing children at increasing rates. However, very little is known whether racial and ethnic disparities in birth outcomes and labour and delivery-related charges exist in this population. This study investigated racial and ethnic disparities in birth outcomes and labour and delivery-related charges among women with IDD. Methods The study employed secondary analysis of the 2004–2011Healthcare Cost and Utilization Project National Inpatient Sample, the largest all-payer, publicly available US inpatient healthcare database. Hierarchical mixed-effect logistic and linear regression models were used to compare the study outcomes. Results We identified 2110 delivery-associated hospitalisations among women with IDD including 1275 among non-Hispanic White women, 527 among non-Hispanic Black women and 308 among Hispanic women. We found significant disparities in stillbirth among non-Hispanic Black and Hispanic women

with IDD compared with their non-Hispanic White peers [odds ratio = 2.50, 95% confidence interval (CI): 1.16–5.28, P < 0.01 and odds ratio = 2.53, 95% CI: 1.08–5.92, P < 0.01, respectively]. There were no racial and ethnic disparities in caesarean delivery, preterm birth and small-for-gestational-age neonates among women with IDD. The average labour and delivery-related charges for non-Hispanic Black and Hispanic Women with IDD ($18 889 and $22 481, respectively) exceeded those for non-Hispanic White women with IDD ($14 886) by $4003 and $7595 or by 27% and 51%, respectively. The significant racial and ethnic differences in charges persisted even after controlling for a range of individual-level and institutional-level characteristics and were 6% (ln(β) = 0.06, 95% CI: 0.01–0.11, P < 0.05) and 9% (ln(β) = 0.09, 95% CI: 0.03–0.14, P < 0.01) higher for non-Hispanic Black and Hispanic Women with IDD compared with non-Hispanic White women with IDD. Conclusions Our findings highlight the need for an integrated approach to the delivery of comprehensive perinatal services for racial and ethnic minority women with IDD to reduce their risk of having a stillbirth. Additionally, further research is needed to examine the causes of racial and ethnic disparities in

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Correspondence: Dr. Ilhom Akobirshoev, Lurie Institute for

Disability Policy, Heller School for Social Policy and Management,

Brandeis University, 415 South Street, Waltham, MA 02453, USA

(e-mail: [email protected]).

Journal of Intellectual Disability Research doi: 10.1111/jir.12577

VOLUME 63 PART 4 pp 313–326 APRIL 2019

© 2018 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and

John Wiley & Sons Ltd

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hospital charges for labour and delivery admission among women with IDD and ascertain whether price discrimination exists based on patients’ racial or ethnic identities.

Keywords caesarean delivery, intellectual and developmental disabilities, preterm birth, small for- gestational-age neonates, stillbirth hospital charges

It has been well documented that racial and ethnic minorities in the USA, particularly non-Hispanic Black and Hispanic individuals, experience worse health outcomes and fare worse on most measures of healthcare access, utilisation, quality, outcomes and costs. (Institute of Medicine 2002; Benjamins 2012; Agency for Healthcare Research and Quality 2017a, 2017b). The disparities in perinatal care and outcomes between women of different racial and ethnic backgrounds have also received attention in the health services literature. For example, compared with non-Hispanic White women (hereafter ‘White’), non-Hispanic Black (hereafter ‘Black’) and Hispanic women are less likely to have access to routine and specialised prenatal care services (Gavin et al. 2004; Kuo et al. 2008). The prenatal care services that women of colour receive are also less likely to be adequate (Russo et al. 2006; Cox et al. 2011; Roberts and Nuru-Jeter 2011). Further, Black and Hispanic women are more likely to have adverse birth outcomes, such as low birth weight, small-for- gestational-age neonates, preterm birth, stillbirth and infant death compared with their White counterparts (Dominguez 2008; Zhang et al. 2013; Rosenstein et al. 2014; Parish et al. 2015). Recent research also shows that, in general, the hospital charges/costs associated with racial and ethnic minority patients generally exceed those of Whites (Chumney et al. 2006; Hanchate et al. 2009; Poulin 2016; Poulin et al. 2016). The differences in hospital charges/costs were primarily attributed to Black and Hispanic patients having more frequent, more complicated and costlier hospitalisations compared with White patients.

Alongside the growing body of evidence regarding racial and ethnic health disparities, there is an emergent body of research on health disparities between people with and without intellectual and developmental disabilities (IDD). Researchers have noted that individuals with IDD tend to have lower income, which is often associated with less access to

health care, the receipt of poorer quality services, as well as worse health outcomes (Erickson & von Schrader 2011; Magana et al. 2016). Women with IDD are at greater risk for pregnancy complications and adverse birth outcomes compared with women without IDD (McConnell et al. 2008; Hoglund et al. 2012; Mitra et al. 2015; Parish et al. 2015; Brown, Cobigo, Lunsky, Dennis, & Vigod, 2016; Brown, Cobigo, Lunsky, & Vigod, 2017; Brown et al. 2016; Akobirshoev et al. 2017).

Research on the intersection of race, ethnicity and disability is also emerging. Recent studies (Peterson- Besse et al. 2014) have demonstrated that people who have a disability and are members of racial and ethnic minority groups face greater barriers in healthcare access and receive lower quality services than either White individuals with disabilities or people of colour without disabilities. For instance, Magaña, Seltzer, & Krauss (2008) found that Hispanic adults with intellectual disabilities were more likely to experience significant challenges in accessing health care compared with their White peers. Other studies (Shafi et al. 2007; Peterson-Besse et al. 2014) have demonstrated that, in general, Black and Hispanic adults with disabilities were less likely than their White counterparts with disabilities to have access to adequate healthcare services. Recent research (Magana et al. 2016) on health disparities among adults with IDD also found that Black and Hispanic adults were more likely than their White peers to have overall poor physical and mental health as well as other chronic health conditions.

This study aims to contribute to the dearth of literature on the intersection of race, ethnicity and disability as they relate to birth outcomes and labour and delivery-related charges among women with IDD. We used nationally representative data to answer two research questions: (1) Are there racial and ethnic disparities in adverse birth outcomes, including in caesarean delivery, preterm birth, small- for-gestational-age neonates and stillbirth among women with IDD after adjusting for sociodemographic, clinical and hospital characteristics? And (2) are there racial and ethnic disparities in labour and delivery-related charges? We hypothesised that the birth outcomes of Black and Hispanic women with IDD would be worse than their White counterparts with IDD. In addition, we hypothesised that racial and ethnic minority status

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independently will not be associated with increased labour and delivery-related charges.

Methods

Data

The study employed secondary analysis of the 2004– 2011Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS), the largest all-payer, publicly available US inpatient healthcare database. This dataset contains information on approximately eight million hospital stays each year from about 1000 hospitals. This approach yields approximately a 20% stratified sample of US. community hospitals. The sample of hospitals was drawn from 37 to 46 states (depending on the survey year) and is divided into 60 strata based on geographic region, ownership, location, teaching status and size. Detailed information on the design of the survey is available elsewhere (Agency for Healthcare Research and Quality, November 2015). The HCUP-NIS contains more than 100 clinical and nonclinical data elements for each hospital stay, including primary diagnosis and up to 24 secondary diagnoses as well as up to 14 procedure coded using International Classification of Diseases and Related Health Problems 9th Revision (ICD- 9-CM) (Centers for Disease Control and Prevention, November 2015). Records include admission and discharge status, patients’ sociodemographic characteristics (e.g. sex, age, race, health insurance, income), clinical characteristics (Elixhauser co- morbidities) (Elixhauser et al. 1998), hospital characteristics (e.g. bed size and region of hospital), length of hospital stay, number of diagnoses, number of procedures and hospital charges. The HCUP-NIS does not include unique patient identifiers, so the unit of analysis is the hospitalisation and not the woman. However, each delivery is associated with only one pregnancy; any woman who delivered more than once during the 2004–2011 period was counted each time she delivered.

Sample

Only women with IDD with delivery-related hospitalisations who were White, Black and Hispanic were included in the analysis. Delivery-related hospitalisations were identified using the ICD-9 codes 640.0–676.9, where the fifth digit is 1

(delivered, with or without mention of antepartum condition) or 2 (delivered, with mention of postpartum complication) or ICD-9-CM 650 (normal delivery). Women with IDD were identified from ICD-9-CM codes (see Table 1 for complete listing) (Mitra et al. 2015; Akobirshoev et al. 2017). Because of the relatively small number of deliveries to women with IDD, we combined data from eight years (2004–2011) to increase the sample size, hence the statistical power of the analyses.

Measures

Dependent variables

The main dependent variables are the following: (1) caesarean delivery, identified using ICD-9 codes

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Table 1 Classification of intellectual and developmental disability

Intellectual and developmental disabilities ICD-9 codes

Mild intellectual disability 317 Moderate intellectual disability 318.0 Severe intellectual disability 318.1 Profound intellectual disability 318.2 Unspecified intellectual disability 319 Fragile X syndrome 759.83 Prader–Willi syndrome 759.81 Down syndrome 758.0 Rett syndrome 330.8 Lesch–Nyhan syndrome 277.2 Cri du chat 758.31 Autistic disorder 299.0, 299.00, 299.01 Childhood disintegrative disorder 299.1, 299.10, 299.11 Other specified pervasive developmental disorder

299.8, 299.80, 299.81

Unspecified pervasive developmental disorder

299.9, 299.90, 299.91

Tuberous sclerosis 759.5 Fetal alcohol syndrome 760.71 Cerebral palsy athetoid 333.71 Cerebral palsy diplegic 343.0 Cerebral palsy hemiplegic 343.1 Cerebral palsy quadriplegic 343.2 Cerebral palsy monoplegic 343.3 Other cerebral palsy 343.4 Infantile cerebral palsy 343.8 Cerebral palsy spastic 343.9 Cerebral palsy spastic non-congenital non-infantile

344.89

ICD-9, International Classification of Diseases, Ninth Revision.

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669.7 and 763.4 and procedure codes 74, 74.1, 74.2, 74.4 and 74.9; (2) preterm birth1 identified using ICD-9 codes 644.2, 644.20, 644.21, 765.0 and 765.1; (3) poor fetal growth or small-for-gestational-age neonates identified using ICD-9 codes 656.5, 656.50, 656.51 and 656.53; (4) stillbirth identified using ICD- 9 codes 656.4, 656.40, 656.41, 656.43, 768.0, 768.1, V27.1, V27.3 and V27.4; and (5) labour and delivery- related charges, which were derived for each individual hospitalisation related to labour and delivery admission. The total labour and delivery- related charges were adjusted for inflation using the medical care services component of the consumer price index data released by the US government (U.S. Bureau of Labor Statistics n.d.) and reported in 2011 dollars.

Independent variable

Race and ethnicity were grouped into a single variable with the following mutually exclusive categories: non- Hispanic White (‘White’), non-Hispanic Black (‘Black’) and Hispanic of any race.

Covariates

Model covariates for adverse birth outcomes were based on previous research (Cabacungan et al. 2012; Mitra et al. 2015; Parish et al. 2015; Brown et al. 2017; Akobirshoev et al. 2017; Campbell et al. 2017; Inoue et al. 2017;), availability in our data and included sociodemographic, clinical and hospital characteristics. Sociodemographic characteristics included maternal age (≤19, 20–34 and 35+), type of health insurance (Medicare, Medicaid, private and uninsured), median household income for patients’ zip code ($1–$38 999, $39 000–$47 999, $48 000– $62 999 and ≥$63 000). Clinical characteristics included Elixhauser co-morbidities (having 1 or more of the co-morbidities identified by the Agency for Health Care Research and Quality using standard methods developed by Elixhauser (1998). Hospital characteristics included location (urban vs. rural), teaching status (teaching vs. non-teaching), bed size (small: 1–49 beds; medium: 50–99 beds; and large: ≥100 beds) and region of the hospital (Northeast, Midwest, South and West). Hospital bed size is based

on the number of hospital beds and is specific to the region of the USA, the urban–rural designation of the hospital and the teaching status of the facility (Agency for Healthcare Research and Quality, September 2008). Finally, owing to the use of combined 2004– 2011 HCUP-NIS dataset, birth year was also modelled to control for the effect of unobserved time- variant confounders.

The model covariates for the labour and delivery- related charges as a continuous variable were also informed by previous research (Hsia et al. 2014; Singh et al. 2015; Poulin et al. 2016). In addition to the covariates mentioned previously, models for labour and delivery-related charges included the variables related to the mode of delivery and birth outcomes (caesarean delivery, preterm birth, small-for- gestational-age neonates and stillbirth), as well as the length of stay, number of diagnoses and number of procedures.

Analysis

Unadjusted racial and ethnic differences in sociodemographic, clinical characteristics, adverse birth outcomes and labour and delivery-related charges were compared within the sample. Frequencies and proportions were reported for categorical variables, with Chi-square test used to test significance (Table 2). Means, standard deviations and medians were reported for continuous variables; significance was tested using t-tests when normally distributed and Wilcoxon–Mann–Whitney (Moses 2005) test when not normally distributed (Table 2). All estimations in the descriptive analysis were corrected for the complex survey design of the HCUP-NIS.

With the individual deliveries to women with IDD as the unit of analyses, we used hierarchical mixed- effects models that account for hierarchical nature of the HCUP-NIS data. In this study, patients (level 1) were clustered within hospitals (level 2). Hierarchical mixed-effects logistic regression models were used for each dichotomous dependent variable (caesarean delivery, preterm birth, small-for-gestational-age neonates and stillbirth), and a hierarchical mixed- effects linear regression model was used for the continuous dependent variable (labour and delivery- related charges), adjusting for model covariates. As the hospital charges were right-skewed, we

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1 Birth of an infant before 37 weeks of pregnancy (Source: World

Health Organization).

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Table 2 Sociodemographic, clinical characteristics, hospital characteristics, birth outcomes and labour and delivery charges of the study population, n = 2110

Characteristics

White women with IDD

Black women with IDD

Hispanic women with IDD

Statistical difference

N % N % N % F statistics

Total # of patients, unweighted 1275 60.4 527 25.0 308 14.6 Total # of patients, weighted 6229 60.4 2575 25.0 1505 14.6 Sociodemographics Maternal age at admission —†

<25 581 45.6 298 56.5 144 46.8 25–34 553 43.4 191 36.2 119 38.6 35+ 141 11.1 38 7.2 45 14.6

Insurance payer type —†,‡

Private insurance 369 29.0 53 10.1 44 14.3 Medicare 209 16.4 91 17.3 37 12.0 Medicaid 652 51.2 370 70.3 214 69.5 Uninsured 44 3.5 12 2.3 13 4.2

Median household income for patient’s zip code —†,‡

$1–$38 999 419 33.6 301 58.3 156 52.0 $39 000–$47 999 374 30.0 111 21.5 62 20.7 $48 000–$62 999 266 21.3 65 12.6 55 18.3 $63 000 + 188 15.1 39 7.6 27 9.0

Mean SD Mean SD Mean SD t-test Maternal age at admission 26.12 0.17 24.6 0.26 26.5 0.38 —†

Clinical characteristics Co-morbidity¶

One or more co-morbidities 947 74.3 360 68.3 198 64.3 —†,‡

Mode of delivery Caesarean delivery 607 47.6 247 46.9 150 48.7

Mean SD Mean SD Mean SD WMW¶

Length of hospital stay 3.3 0.1 4.6 0.3 4.0 0.3 —†

Total number of diagnoses 7.7 0.1 8.5 0.2 7.4 0.2 —†,‡

Total number of procedures 2.2 0.04 2.3 0.06 2.3 0.08 Median IQR Median IQR Median IQR

Length of hospital stay 3 2 3 2 3 2 Total number of diagnoses 7 3 8 4 7 4 Total number of procedures 2 2 2 2 2 2 Hospital characteristics Location of hospital —†,‡

Rural 252 19.9 32 6.1 13 4.2 Urban 1,013 80.1 489 93.9 293 95.8

Teaching status of the hospital —†,‡

Non-teaching 682 53.9 142 27.3 130 42.5 Teaching 583 46.1 379 72.7 176 57.5

Hospital bed size —‡

Large 173 13.7 50 9.6 32 10.5 Medium 319 25.2 131 25.1 56 18.3 Small 773 61.1 340 65.3 218 71.2

Region of hospital —†,‡

Northeast 307 24.1 124 23.5 63 20.5 Midwest 268 21.0 75 14.2 15 4.9 South 493 38.7 298 56.5 110 35.7 West 207 16.2 30 5.7 120 39.0

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transformed them to log form, which allowed us to interpret the log form regression coefficients (ln(β)) as per cent change. Also, given the already- complicated estimation procedure and the Agency for Health Care Research and Quality recommendation (Houchens and Steiner 2007), we did not use the hospital-level discharge weights in the multilevel regression analyses. All analyses were performed using STATA 14 MP (StataCorp 2015).

This study was approved by the authors’ university institutional review board.

Results

There were 2110 delivery-associated hospitalisations between 2004 and 2011 among women with IDD. Of these, 1275 delivery hospitalisations were for White women with IDD, 527 for Black women with IDD and 308 for Hispanic women with IDD. After application of the sample weights, there were an estimated 10 308 delivery-associated hospitalisations of women with IDD, including 6228 delivery hospitalisations of White women with IDD, 2575 of

Black women with IDD and 1505 of Hispanic women with IDD during the 2004–2011 study period.

In Table 2, we show bivariate, unadjusted contrasts of sociodemographic, clinical characteristics, hospital characteristics, birth outcomes and labour and delivery-related charges by race and ethnicity within the study sample of women with IDD. Hispanic women with IDD, compared with their White peers, were more likely to be older, from lower-income households, have public health insurance (Medicaid and Medicare) and were more likely to give birth in urban hospitals, teaching hospitals, in hospitals with fewer beds and in hospitals in the West region. Black women with IDD, compared with White women with IDD, were more likely to be younger, from lower- income households, have public health insurance and were more likely to give birth in urban hospitals, teaching hospitals and in hospitals of the South region. Compared to White women with IDD, Hispanic and Black Women with IDD were less likely to have one or more co-morbidities. Nearly half of all women with IDD (48%) had a caesarean delivery. Black women with IDD were more likely to have a

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Table 2. (Continued)

Characteristics

White women with IDD

Black women with IDD

Hispanic women with IDD

Statistical difference

N % N % N % F statistics

Birth outcomes Preterm birth 167 13.1 98 18.6 52 16.9 —†

Small for gestational age 73 5.7 25 4.7 <11§ 3.2 Stillbirth 21 1.6 15 2.8 <11 3.2

Hospital charges Mean SD Mean SD Mean SD WMW††

Labour and delivery charges $14 886 $470 $18 889 $901 $22 481 $2183 —†,‡

Median IQR‡‡ Median IQR Median IQR Labour and delivery charges $11 255 $9232 $12 810 $13 006 $15 385 $12 657

Data source: Healthcare Cost and Utilization Project Nationwide Inpatient Sample, 2007–2011 IDD, intellectual and developmental disability; IQR, interquartile range; SD, standard deviation. †Statistically significant difference at P < 0.05 between White and Black. ‡Statistically significant difference at P < 0.05 between White and Hispanic. ¶Co-morbidity variable is generated using the AHRQ co-morbidity software (Elixhauser et al., 1998). §To maintain confidentiality, cells with <11 cases for non-missing outcomes cannot be reported. (Agency for Healthcare Research and Quality 2017a, 2017b). ††Two-sample Wilcoxon–Mann–Whitney (WMW) test. ‡‡The IQR is the 75th percentile minus the 25th percentile. Patients are considered to have co-morbidity if their discharge records show that they have one more of the 29 types of patient co-morbidities identified by Agency for Health Care Research and Quality using the standard method by Elixhauser et al. (1998).

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longer hospital stay and a greater number of diagnoses compared with White women with IDD. While Hispanic women had fewer diagnoses compared with their White peers, the length of hospital stay between them was not significantly different. Compared with their White peers, a higher proportion of Black and Hispanic women with IDD had a preterm birth (13% vs. 19% and 13% vs. 17, respectively). The proportion of Black and Hispanic women with IDD who had a stillbirth was almost twice as high as compared with their White peers (2.8% vs. 1.6% and 3.2% vs. 1.6%, respectively). However, a smaller proportion of Black and Hispanic women with IDD had small-for-gestational-age neonates than their White peers (4.7% vs. 5.7% and 3.2% vs. 5.7%, respectively).

There were significant racial and ethnic disparities in labour and delivery-related charges. The average labour and delivery-related charges for Black and Hispanic women with IDD ($18 889 and $22 481, respectively) exceeded those for White women with IDD ($14 886) by $4003 and $7595 or by 27% and 51%, respectively. Median labour and delivery-related charges for Black and Hispanic women with IDD ($12 810 and $15 385, respectively) exceeded those for White women with IDD ($11 255) by $1555 and $4130, or by 14% and 37%, respectively.

In Table 3, we present the hierarchical mixed- effects logistic regression results that address our first research question, namely, whether there are racial and ethnic disparities in adverse birth outcomes among women with IDD after adjusting for sociodemographic, clinical and hospital characteristics. Compared with White women with IDD, Black and Hispanic women with IDD had more than two times higher odds of having stillbirths [odds ratio = 2.50, 95% confidence interval [CI]: 1.16–5.28, P < 0.01 and OR = 2.53, 95% CI: 1.08–5.92, P < 0.01, respectively]. We found no differences in the odds of having a caesarean delivery, preterm birth and having small-for-gestational-age neonates between Black and White women with IDD. Similarly, we found no differences in the odds of having caesarean delivery and preterm birth neonates between Hispanic and White women with IDD. However, we found that Hispanic women with IDD were less likely to have small-for-gestational-age neonates (odds ratio = 0.48, 95% CI: 0.24–0.98, P < 0.01) compared with their White peers with IDD.

In Table 4, we present the hierarchical mixed- effects linear regression results that address our second research question on whether there were racial and ethnic disparities in labour and delivery-related charges, after adjusting for all available covariates. Compared with White women with IDD, Black and Hispanic women with IDD had 6% (ln(β) = 0.06, 95% CI: 0.01–0.11, P < 0.05) and 9% (ln(β) = 0.09, 95% CI: 0.03–0.14, P < 0.01, respectively) higher labour and delivery-related charges.

Discussion

To our knowledge, this is the first nationally representative investigation of racial and ethnic disparities in the birth outcomes and labour and delivery-related charges among women with IDD in the USA. Our findings indicated mixed results.

We found marked racial and ethnic disparities in stillbirths within the population of women with IDD. The proportion of Black and Hispanic women with IDD who had a stillbirth was almost twice as high as compared with their White peers. Even after adjusting for sociodemographic, clinical and hospital characteristics, Black and Hispanic women with IDD were more likely to have a stillbirth than their White counterparts with IDD. These findings mirror results from previous research on racial disparities in birth outcomes among women in the general population (Stillbirth Collaborative Research Network Writing, Group 2011; Faiz et al. 2012; Brisendine et al. 2017; Crawford et al. 2017; Mutambudzi et al. 2017). However, unlike findings from previous research (Willinger et al. 2009; Stillbirth Collaborative Research Network Writing, Group 2011), which did not find significant disparities in stillbirth based on Hispanic ethnicity in the general obstetric population, we found significant disparities in stillbirth between Hispanic women with IDD compared with their White peers with IDD. Previous research (Peterson- Besse et al. 2014) has demonstrated that people who have a disability and are also members of racial/ethnic minority groups, in general, face greater barriers in healthcare access and receive lower quality services than either White women with disabilities or women of colour without disabilities. For instance, Magaña, Seltzer, & Kraus (2008) found that Hispanic adults with IDD and their caregivers were more likely to experience a range of challenges in accessing health

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Table 3 Association between race/ethnicity and birth outcomes among women with intellectual and developmental disabilities, n = 473 hospitals and 2110 patients

Variables

Caesarean delivery Preterm birth Small for gestational age Stillbirth

OR 95% CI OR 95% CI OR 95% CI OR 95% CI

Race and ethnicity White (referent) 1 1 1 1 1 1 1 1 Black 1.12 0.89–1.42 1.19 0.87–1.62 0.66 0.40–1.10 2.50** 1.16–5.38 Hispanic 1.07 0.80–1.42 1.02 0.70–1.49 0.48** 0.24-0.98 2.53** 1.08–5.92

Age group <24 (referent) 1 1 1 1 1 1 1 1 25–34 1.25** 1.03–1.53 0.83 0.63–1.10 0.78 0.50–1.21 1.13 0.58–2.22 >34 1.72*** 1.24–2.37 1.23 0.80–1.89 0.54 0.22–1.31 1.13 0.42–3.01

Type of insurance coverage Private insurance (referent) 1 1 1 1 1 1 1 1 Medicare 0.96 0.71–1.31 1.34 0.86–2.09 2.06* 1.00–4.24 1.12 0.50–2.55 Medicaid 0.80* 0.62–1.03 1.30 0.89–1.89 1.66 0.88–3.14 0.38** 0.17–0.85 Uninsured 1.04 0.60–1.81 1.44 0.69–3.02 1.38 0.38–5.04 0.00 0.00

Median household income $63 000 + (referent) 1 1 1 1 1 1 1 1 $1–$38 999 0.92 0.73–1.16 0.87 0.63–1.19 0.67 0.40–1.14 1.35 0.64–2.84 $39 000–$47 999 0.97 0.75–1.26 0.86 0.60–1.24 1.10 0.65–1.87 0.75 0.28–1.98 $48 000–$62 999 0.90 0.65–1.24 0.73 0.46–1.16 0.64 0.29–1.44 1.31 0.49–3.50

Co-morbidity‡

No co-morbidity 1 1 1 1 1 1 1 1 1 or more co-morbidities 2.05*** 1.67–2.53 1.57*** 1.17–2.12 0.92 0.60–1.43 0.51** 0.27–0.94

Location of hospital Rural (referent) 1 1 1 1 1 1 1 1 Urban 0.89 0.65–1.20 1.55* 0.97–2.47 0.99 0.54–1.83 0.98 0.36–2.65

Teaching status of hospital Non-teaching (referent) Teaching 0.86 0.70–1.07 1.37** 1.03–1.82 1.20 0.77–1.88 0.82 0.43–1.60

Hospital bed size Small (referent) 1 1 1 1 1 1 1 1 Medium 0.95 0.68–1.33 0.78 0.49–1.27 0.92 0.43–1.97 0.88 0.29–2.71 Large 1.05 0.78–1.42 1.33 0.88–2.01 1.32 0.68–2.57 1.20 0.45–3.21

Region of hospital Northeast (referent) 1 1 1 1 1 1 1 1 Midwest 1.03 0.75–1.39 1.05 0.67–1.63 0.71 0.37–1.39 1.86 0.74–4.71 South 1.29** 1.00–1.65 1.38* 0.98–1.95 0.95 0.57–1.58 0.85 0.36–2.02 West 1.22 0.89–1.68 1.74*** 1.14–2.64 0.81 0.41–1.62 1.43 0.54–3.81

Birth year 2004 (referent) 1 1 1 1 1 1 1 1 2005 1.54** 1.02–2.32 0.84 0.49–1.43 0.59 0.22–1.54 1.45 0.37–5.60 2006 1.62** 1.09–2.41 0.78 0.46–1.32 1.11 0.50–2.47 1.38 0.36–5.32 2007 1.76*** 1.19–2.61 1.10 0.67–1.80 0.93 0.41–2.14 1.66 0.47–5.87 2008 1.36 0.93–1.99 0.66 0.40–1.11 0.97 0.44–2.13 1.49 0.42–5.25 2009 2.08*** 1.43–3.03 0.86 0.53–1.39 0.77 0.35–1.72 1.39 0.39–4.90 2010 1.74*** 1.19–2.54 0.70 0.43–1.16 0.83 0.37–1.86 1.66 0.49–5.70 2011 1.37* 0.94–2.00 0.70 0.42–1.15 0.94 0.43–2.05 0.37 0.07–2.09

*P < 0.10; **P < 0.05; ***P < 0.01. Data source: Healthcare Cost and Utilization Project Nationwide Inpatient Sample, 2007–2011 CI, confidence interval; OR, odds ratios. Co-morbidity variable is generated using the Agency for Health Care Research and Quality co-morbidity software (Elixhauser et al. 1998). Patients are considered to have co-morbidity if their discharge records show that they have one or more of the 29 types of patient co-morbidities identified by Agency for Health Care Research and Quality using the standard method by Elixhauser et al. (1998).

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care compared with their White peers, including lack of knowledge of the health system, being unsatisfied with services, not having services available in the area, lack of transportation, high costs of services and language barriers. Other studies (Shafi et al. 2007; Peterson-Besse et al. 2014) have demonstrated that, in general, when Black, Hispanic and White adults with other types of disabilities had the same level of health insurance coverage, education or income, Black and Hispanic adults with disabilities were less likely than their White counterparts with disabilities to have access to adequate healthcare services. Further, recent research on health disparities among adults with IDD (Magana et al. 2016) also found that Black and Hispanic adults with IDD were more likely than their White peers with IDD to be in fair or poor health and to have fair or poor mental health. The authors also found that Hispanic adults with IDD were also more likely to be obese and have diabetes compared

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Table 4 Regression analysis for race, ethnicity and other relevant variables predicting labour and delivery charges of women with

intellectual and developmental disabilities, n = 2043

Variables

Hospitalisation charges

ln(β) 95% CI

Race and ethnicity White (referent) 1 1 Black 0.06** 0.01–0.11 Hispanic 0.09*** 0.03–0.14

Age group <24 (referent) 1 1 25–34 �0.02 �0.06 to 0.02 >34 �0.00 �0.06 to 0.06

Type of insurance coverage Private insurance (referent) 1 1 Medicare �0.02 �0.08 to 0.04 Medicaid �0.03 �0.08 to 0.02 Uninsured �0.02 �0.12 to 0.09

Median household income $63 000 + (referent) 1 1 $1–$38 999 �0.03 �0.07 to 0.02 $39 000–$47 999 0.03 �0.02 to 0.08 $48 000–$62 999 0.09*** 0.02–0.16

Co-morbidity†

No co-morbidity 1 1 1 or more co-morbidities 0.06*** 0.02–0.10

Method of delivery Vaginal (referent) 1 1 Caesarean delivery 0.44*** 0.40–0.47

Preterm birth No (referent) 1 1 Yes 0.12*** 0.07–0.17

Small-for-gestational-age neonates No (referent) 1 1 Yes 0.09** 0.02–0.17

Having a stillbirth No (referent) 1 1 Yes 0.13** 0.02–0.25

Length of hospital stay 0.06*** 0.05–0.06 Number of diagnoses on this record* 0.03*** 0.02–0.03 Number of procedures on this record*

0.06*** 0.04–0.07

Location of hospital Rural (referent) 1 1 Urban �0.08* �0.18 to 0.01

Teaching status of the hospital Non-teaching (referent) 1 1 Teaching 0.02 �0.05 to 0.08

Hospital bed size Small (referent) 1 1 Medium �0.05 �0.15 to 0.05 Large 0.02 �0.08 to 0.11

Region of hospital

Table 4. (Continued)

Variables

Hospitalisation charges

ln(β) 95% CI

Northeast (referent) 1 1 Midwest 0.02 �0.08 to 0.12 South �0.15*** �0.23 to �0.07 West 0.14*** 0.04–0.24

Birth year 2004 (referent) 1 1 2005 �0.01 �0.09 to 0.07 2006 �0.09** �0.17 to �0.01 2007 �0.03 �0.11 to 0.04 2008 �0.03 �0.11 to 0.04 2009 �0.03 �0.11 to 0.04 2010 �0.07* �0.15 to 0.01 2011 �0.05 �0.12 to 0.03

*P < 0.10; **P < 0.05; ***P < 0.01. Data source: Healthcare Cost and Utilization Project Nationwide Inpatient Sample, 2007–2011 CI, confidence interval; ln(β), log coefficients. Co-morbidity variable is generated using the Agency for Health Care Re- search and Quality co-morbidity software (Elixhauser et al. 1998). Patients are considered to have co-morbidity if their discharge records show that they have one more of the 29 types of patient co-morbidities identified by Agency for Health Care Research and Quality using the standard method by Elixhauser et al. (1998).

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with their White peers with IDD. It is likely that healthcare professionals, including obstetricians and midwives who provide prenatal care to women with IDD in general, and women of colour with IDD lack awareness of their elevated potential for adverse pregnancy outcomes. Further, healthcare professionals might lack training on how to personalise prenatal care for women with IDD and motivate patients with IDD to adhere to medical advice. For example, one study on satisfaction with prenatal care among women with a physical disability (Mitra et al. 2017) found that providers lacked training and education regarding the prenatal care needs of women with physical disabilities and how their disability can impact their pregnancy. It is likely that persistent racial and ethnic healthcare disparities coupled with lack of awareness and training among healthcare professionals (Gavin et al. 2004; Cox et al. 2011) could contribute to increased stillbirth risk among Black and Hispanic women with IDD. Given that our sample consisted only of women with IDD, we were unable to examine whether there is a differential effect of maternal IDD on stillbirths by race or ethnicity. Future research using data with the general obstetric population needs to examine whether this difference exists.

We found no support for the hypothesis regarding racial and ethnic disparities in caesarean delivery among women with IDDbut an overall rate higher than the general obstetric population. Nearly half of all women with IDD (48% in White, 47% in Black and 49% in Hispanic women) had a caesarean delivery during the study period – substantially higher proportions compared with those of the general obstetric population (31.3%), as described byOsterman and Martin (2013). Our findings are consistent with previous research (Darney et al. 2017; Hoglund et al. 2012; Parish et al. 2015) suggesting that women with IDD are generally at higher risk for caesarean delivery than other women. Parish et al. 2015 who analysed HCUP data found that 49% of women with IDD had caesarean delivery compared with only 33% among women without IDD. Another study from Sweden (Hoglund et al. 2012) also found that 25% of women with IDD had caesarean deliveries versus 18% among women without IDD. Previous studies attributed the high proportion of caesarean delivery among women with IDD, compared with the general obstetric popu- lation, in part, to higher prevalence of pre-pregnancy

health conditions or pregnancy-related complications in this population, including hypertension, pre- eclampsia, obesity, smoking during pregnancy, diabe- tes or gestation diabetes (Parish et al. 2015; Brown, Lunsky, Wilton, Cobigo, & Vigod, 2016; Akobirshoev et al. 2017; Darney et al. 2017; Horner-Johnson et al. 2017).

Although considerably higher proportion of Black and Hispanic women with IDD had premature delivery (19% and 17%, respectively) than White women with IDD (13%), after adjusting for sociodemographic, clinical and hospital characteristics, these differences were not significant. Nevertheless, our findings support findings from previous research that women with IDD overall have significantly higher premature delivery than women without IDD within the general obstetric population. One study (Parish et al. 2015) that also used HCUP data found that 13% of women with IDD delivered prematurely compared with only 8% of women without IDD. Racial and ethnic disparities in preterm birth prevalence have persisted for decades (Kessel et al. 1988). Various factors from the individual level (such as family history) to the healthcare domain (e.g. obstetric management of pregnancy related to labour induction) are potential causes of these disparities (Institute of Medicine 2007). The patient and hospital characteristics adjusted for in this study capture aspects of these potential causes thus statistically accounting for the racial and ethnic differences in preterm delivery in this population of women with IDD.

We found no difference in the odds of having small- for-gestational-age neonates between Black and White women with IDD, after adjusting for sociodemographic, clinical and hospital characteristics. Hispanic women with IDD had significantly reduced odds of having small-for- gestational-age neonates compared with their White peers with IDD. This finding is consistent with previous research that termed this phenomenon the ‘Latino epidemiologic paradox’ when a lower likelihood of low birth weight or small-for-gestational- age neonates occur among Hispanic women, despite their low socio-economic status (Fuentes-Afflick et al. 1999). Our findings are also consistent with previous studies in that, in general, substantially higher proportion of women with IDD have small-for- gestational-age (4.7% for Black, 3.2% for Hispanic

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and 5.7% for White) compared with women without IDD from the general obstetric population. For example, Parish et al. (2015) used the 2010 HCUP data and found that 4% of women with IDD had small-for-gestational-age neonates versus only 2% of women without IDD from the general obstetric population. In the general obstetric population, the prevalence of small-for-gestational age neonates is more than twice as high among Black women as that observed among White women (Alexander et al. 1999). While the causes of the disparity are believed to be pathological, they are yet to be elucidated (Kramer et al. 2006).

A noteworthy finding of this study is the new evidence of racial and ethnic disparities in labour and delivery-related charges among women with IDD. Namely, contrary to our second study hypothesis, in the bivariate unadjusted analysis, charges for Black and Hispanic women with IDD were 27% and 51% higher compared with White women with IDD. Even more remarkably, the racial and ethnic disparities in delivery and labour-related charges remained robust and significant even after adjusting for maternal sociodemographic characteristics, clinical characteristics, hospital characteristics and the clustered nature of the data. Thus, among women with IDD who gave birth to a child during the study period, Black race and Hispanic ethnicity, independently predicted, accordingly, 6% and 9% higher labour and delivery-related charges. Given that the racial and ethnic differences in labour and delivery-related charges from our bivariate unadjusted analyses were reduced from 27% to 6% and from 51% to 9%, we speculate that together the select covariates from our multilevel regression model explained approximately 78% and 82% of the differences in labour and delivery-related charges. Previous studies have shown that market-level characteristics, such as patient flow, number of hospitals, Herfindahl– Hirschman index,2 wage index and the per cent of people in the county who are uninsured or living in poverty, also influence hospital charges (Wong et al. 2005; Mutter et al. 2008; Ginsburg 2010; Hsia et al. 2014; McAuliffe 2015). However, owing to the HCUP data restrictions, we could not control for

market-level characteristics in this study. Additionally, racial and ethnic differences in hospital charges might be confounded by clinical complexity during labour and delivery. For example, Black and Hispanic women with IDD might have more complex diagnoses and intensive procedures during labour and delivery, requiring costlier monitoring. These factors may, in turn, be reflected in higher charges. The relevance of market-level characteristics or clinical complexity notwithstanding, our findings suggest that Black and Hispanic women with IDD are charged on average 6% and 9% more than non-Hispanic White women with IDD, respectively, all else being equal. Future research using longitudinal data needs to further define the determinants of higher labour and delivery-related charges in racial and ethnic minority women with IDD as well as in the general population.

Limitations

The study limitations warrant consideration. First, some women with IDD who gave birth may not have been coded as having IDD, because labour and delivery were the focus of the hospitalisation and not the women’s IDD. Additionally, obstetric care is a bundled payment for reimbursement; thus, obstetric providers may not be adequately motivated to select more codes and spend more time if it does not appreciably affect remuneration. As such, the final analytical sample may represent an undercount of deliveries by women with IDD. Second, about 20% of the race/ethnicity variables were missing in the combined 2004–2011 HCUP-NIS data, primarily because some states restrict the availability of information about patients’ race and ethnicity, especially in the earlier years of the HCUP. Because race/ethnicity was the main independent variable and used as a criterion for the analytical sample, we did not impute the missing values. Therefore, all observations with missing values for race and ethnicity were excluded from the analyses. Despite the lack of reporting of race and ethnicity in the early years of HCUP, our final sample derived from the combined 2004–2011 HCUP data represented 42 states. Third, owing to HCUP data restrictions, we could not account for hospital or market-level characteristics (i.e. Herfindahl–Hirschman index, wage index, per cent uninsured in the county and per cent below the poverty line in the county) that are

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2 Herfindahl–Hirschman index is a measure of the size of firms in

relation to the industry and an indicator of the amount of

competition among them.

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associated with hospital charges (Wong et al. 2005; Mutter et al. 2008; Ginsburg 2010; Hsia et al. 2014). There is wide variation in hospital charges and prices across the USA, whereby hospitals in less competitive healthcare markets tend to charge higher prices than hospitals in more competitive healthcare markets (Melnick et al. 1992; Gaynor and Vogt 2003; Ginsburg 2010). Fourth, causality cannot be established due to the cross-sectional nature of these data. Fifth, given that we combined the 2004–2011 HCUP-NIS data, it is possible that one woman has multiple delivery hospitalisations during 2004–2011. Unfortunately, in our data, there is no way to know this. Finally, there is the potential problem of omitted variable bias. For example, one likely confounder could be provider’s explicit or implicit bias, which was not available in the HCUP data.

Despite these limitations, this study is the first to investigate racial and ethnic disparities in birth outcomes and labour and delivery charges among women with IDD. Similar to findings from previous research among the general obstetric population, this study found that there are significant racial and ethnic disparities in stillbirth and labour and delivery-related charges among women with IDD. However, unlike in general obstetric population, we found no racial or ethnic disparities in caesarean delivery, preterm birth and small-for-gestational-age neonates within the population of women with IDD.

Conclusions

Our findings highlight the need for an integrated approach to the delivery of comprehensive perinatal services for racial and ethnic minority women with IDD to reduce their risk of having a stillbirth. Additionally, further research is needed to understand the causes of racial and ethnic disparities in hospital charges for labour and delivery admission among women with IDD and ascertain whether price discrimination exists based on patients’ racial or ethnic identities.

Source of funding

Funding support for this research was provided by grant no. 1R01HD082105-01 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development.

Conflict of Interest

The authors report no conflict of interest.

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Accepted 16 November 2018

326 Journal of Intellectual Disability Research VOLUME 63 PART 4 APRIL 2019

I. Akobirshoev et al. • Racial and ethnic disparities in birth outcomes and delivery charges

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