Introduction
The purpose of this study is to estimate the national occurrence of deliveries in women with hearing loss and to compare their birth outcomes to women without hearing loss.
Methods
This study examined the 2008–2011 Nationwide Inpatient Sample of the Healthcare Cost and Utilization Project in 2015 to compare birth outcomes in women with hearing loss and without. Birth outcomes included preterm birth and low birth weight. Multivariate regression analyses compared birth outcomes between women with and without hearing loss, controlling for maternal age, racial and ethnic identity, type of health insurance, comorbidity, region of hospital, location and teaching status of the hospital, ownership of the hospital, and median household income for mother’s ZIP code.
Results
Of an estimated 17.9 million deliveries, 10,462 occurred in women with hearing loss. In adjusted regression analyses controlling for demographic characteristics, women with hearing loss were significantly more likely than those without hearing loss to have preterm birth (OR=1.28, 95% CI=1.08, 1.52, p<0.001) and low birth weight (OR=1.43, 95% CI=1.09, 1.90, p<0.05).
Conclusions
This study provides a first examination of the pregnancy outcomes among women with hearing loss in the U.S. This analysis demonstrates significant disparities in birth outcomes between women with and without hearing loss. Understanding and addressing the causes of these disparities is critical to improving pregnancy outcomes among women with hearing loss.
Introduction
Approximately 1% of people in the U.S. aged 18–44 years have hearing loss (HL).
1
HL can be categorized based on type (i.e., sensorineural, conductive, and mixed); severity (e.g., mild to profound); configuration (e.g., pattern of HL across frequencies); and age of onset.National Health Interview Survey. QuickStats: percentage of adults* who reported being deaf or having a lot of trouble hearing without a hearing aid, by sex and age group—United States, 2003. www.cdc.gov/mmwr/preview/mmwrhtml/mm5425a5.htm. Accessed December 1, 2015.
2
, 3
HL is a marginalizing and disabling condition, resulting in various adverse social and health outcomes.4
Unfortunately, individuals who are deaf and hard of hearing are at extremely high risk for significant health disparities and social marginalization.5
, 6
, 7
, 8
, 9
, 10
, 11
Barriers in language, communication, and culture, along with a general mistrust of the medical community, result in social and healthcare marginalization for many individuals with HL.12
, 13
, 14
, Stern CSA. DeafDOC.org: health education for the deaf and hard of hearing community, interpreters, and healthcare professionals. http://deafdoc.org. Published 2014. Accessed August 30, 2014.
15
, 16
The marginalization in health care and society results from cumulative effects of communication and language barriers that reduce the population’s opportunities to benefit from mass media, healthcare messages, healthcare communication, and incidental learning opportunities.17
, 18
Healthcare providers rarely receive training on how to effectively communicate and care for individuals with HL, resulting in poor communication, increased provider frustrations, and changes in healthcare delivery.9
, 19
, 20
Individuals with HL can also vary with their communication preferences and cultural affiliations. For example, deaf American Sign Language users identify themselves as a linguistic minority community, with their own unique language and culture.8
, 21
, 22
, 23
They view their HL as a cultural identity, hence their usage of a capitalized “Deaf” versus individuals who consider themselves “hard of hearing” or “deaf.”24
Hard of hearing or deaf are more likely than deaf American Sign Language users to prefer communication in English (e.g., lip reading) and the use of hearing augmentation (e.g., hearing aids).9
Significant health disparities and health knowledge gaps have been demonstrated in this population across a myriad health conditions.
6
, 25
, 26
, 27
, 28
, 29
, 30
, 31
People with HL generally have lower income and experience social isolation and poorer physical and mental health.32
More importantly, with the advent of childhood immunizations and improved management of early childhood infections, the etiologies of HL for young populations are more likely to be attributable to congenital factors, receipt of care in a neonatal intensive care unit, and genetic etiologies.33
These sociodemographic, behavioral, and biological differences are consistent with potential risk factors for preterm and low birth weight in the general literature.American Speech-Language-Hearing Association. Causes of hearing loss in children. www.asha.org/public/hearing/Causes-of-Hearing-Loss-in-Children. Accessed April 15, 2016.
34
, 35
Despite these disparities in the health of people with HL and their reduced access to health care, to date there are no population-based studies about pregnancy experiences and outcomes among women with HL. A recent study on pregnancy among women with different disabilities found that women with HL were equally likely to be pregnant compared to other women with and without disabilities.
36
Another study37
on Deaf women’s experiences with prenatal care found that Deaf women were less satisfied with their prenatal care and were more likely to have fewer prenatal visits than hearing women.Further research is clearly warranted to document the pregnancy experiences and outcomes of U.S. women with HL. To address some of these research gaps, this study used a nationally representative data set to (1) investigate the number of deliveries occurring in women with HL and (2) compare the percentage of deliveries complicated by adverse birth outcomes in U.S. women with and without HL. Given the elevated risk of poor health among people with HL and their reduced healthcare access,
18
, 38
the investigators hypothesized that the birth outcomes of deliveries among women with HL would be worse than those of other women.Methods
Data Sample
The data for this study were derived from the Nationwide Inpatient Sample (NIS) of the Health Care and Cost Utilization Project (HCUP), the largest all-payer, publicly available U.S. inpatient healthcare database. It contains data on approximately 8 million hospital stays each year from about 1,000 hospitals sampled to approximate a 20% stratified sample of U.S. community hospitals. The sample of hospitals was drawn from 46 states and were divided into 60 strata based on geographic region, ownership, location, teaching status, and bed size. Detailed information on the design of the survey is available elsewhere.
39
Agency for Healthcare Research and Quality. Introduction to the HCUP Nationwide Inpatient Sample (NIS) 2011. www.hcup-us.ahrq.gov/db/nation/nis/NIS_Introduction_2011.jsp#sampling. Published November 2015. Accessed December 12, 2015.
The NIS contains >100 clinical and nonclinical data elements for each hospital stay, including primary and secondary diagnoses and procedures, admission and discharge status, patient demographic characteristics, hospital characteristics, expected payer, total charges, and length of stay.
40
The NIS does not include unique patient identifiers; thus the unit of analysis is hospital discharge. However, each delivery is associated with only one pregnancy; any woman who delivered more than once in a single calendar year was counted twice. Nevertheless, this situation is uncommon because short interpregnancy intervals that result in women giving birth twice within a 12-month period are relatively rare.41
All delivery-related hospitalizations were included in the analysis. Delivery hospitalizations were identified using the ICD-9-CM 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 HL were identified from ICD-9-CM codes (Table 1). The control group was identified as any delivery hospitalization among women without HL. Owing to the small number of cases of deliveries in women with HL, the study combined data from 2008 to 2011 to increase the sample size, and hence the power of analysis.
Table 1Classification of Hearing Loss
| Hearing loss | ICD-9 codes |
|---|---|
| Conductive hearing loss | 389.0 |
| Sensorineural hearing loss | 389.1 |
| Mixed conductive and sensorineural hearing loss | 389.2 |
| Deaf nonspeaking, not elsewhere classifiable | 389.7 |
| Other specified forms of hearing loss | 389.8 |
| Unspecified hearing loss | 389.9 |
a Excludes: mixed conductive and sensorineural hearing loss (389.20–389.22).
b Excludes: abnormal auditory perception (388.40–388.44).
Measures
The main dependent variables included the following birth outcomes: (1) preterm birth identified using ICD-9-CM code 644.2, 644.20, 644.21, 765.0, or 765.1 and (2) low birth weight (656.5, 656.50, 656.51, 656.53). The main independent variable was the HL status of women with delivery-related hospitalization.
Model covariates included maternal age; racial and ethnic identity (non-Hispanic white, non-Hispanic black, Hispanic, non-Hispanic other); type of health insurance (private, public, uninsured); comorbidity (having one or more of the comorbidities identified by the Agency for Health Care Research and Quality using standard methods by Elixhauser et al.
42
); region of hospital (Northeast, Midwest, South, West); location and teaching status of the hospital (urban teaching, urban non-teaching, rural), ownership of the hospital (public, private); and median household income for mother’s ZIP code (first quartile, $1–$38,999; second quartile, $39,000–$47,999; third quartile, $48,000–$62,999; fourth quartile, ≥$63,000).Statistical Analysis
National estimates of the sample’s sociodemographic characteristics were compared for women with HL and without HL. Unadjusted hospitalizations with adverse birth outcomes were calculated for each group. Chi-square analysis was applied to test the difference in rates in each group. Logistic regression analyses were performed for each dichotomous dependent variable. The model was similar to what has been estimated in previous studies on pregnancy and delivery outcomes.
43
, 44
, 45
This study used HL as the main independent variable and adjusted for race/ethnicity, maternal age, household income, health insurance status, commorbidity, region, and hospital location, teaching status, and ownership.Logistic regression coefficients and 95% CI were estimated as AORs. Hospital discharge weights were applied to the sample data for all bivariate and multivariate statistical analyses. All estimation procedures were corrected for complex design of the NIS. All analyses were performed in 2015 using Stata, version 14 MP.
This analysis was approved by the Brandeis University IRB.
Results
There were 3,266,577 delivery-associated hospitalizations identified through the HCUP 2008–2011 data. Of these, 1,902 delivery hospitalizations were among women with HL, and 3,264,675 were among women without HL. The national estimates, after application of the sample weights, made up 17,967,659 delivery-associated hospitalizations, including 10,462 among women with HL and 17,957,197 among women without HL. Women with HL were more likely to be white and Hispanic and less likely to be from other racial/ethnic groups than other women (Table 2). Women with HL who delivered were less likely to be aged <18 years and more likely to be aged >34 years compared with other women. Medicare and Medicaid were the most common payers for delivery hospitalizations among women with HL, but private insurance was most common among women without HL. Nearly one in seven of women with HL had their deliveries paid for by Medicare, which was significantly higher than Medicare coverage for women without HL (13.3% vs 0.6%). Women with HL were almost twice more likely to have one or more comorbidities and were also more likely to be admitted to urban teaching hospitals.
Table 2Description of the Sample of Women With and Without Hearing Loss, NIS, HCUP, 2008–2011
| Characteristic | Women without HL, n (%) (n=3,264,675) | Women with HL, n (%) (n=1,902) | Test statistics (F test) |
|---|---|---|---|
| Racial and ethnic identity | 5.02*** | ||
| Non-Hispanic white | 1,467,699 (45.0) | 937 (49.4) | |
| Non-Hispanic black | 391,909 (12.1) | 257 (13.4) | |
| Hispanic | 640,596 (19.5) | 322 (16.8) | |
| Non-Hispanic other | 304,255 (9.3) | 135 (7.2) | |
| Missing | 460,216 (14.1) | 251 (13.2) | |
| Age group (age in years at admission) | 8.89*** | ||
| <24 | 1,088,774 (33.4) | 579 (30.3) | |
| 25–34 | 1,701,490 (52.2) | 987 (51.9) | |
| >34 | 471,537 (14.5) | 335 (17.8) | |
| Insurance payer type | 743.60*** | ||
| Medicare | 20,035 (0.6) | 253 (13.4) | |
| Medicaid | 1,412,676 (43.3) | 810 (42.6) | |
| Private insurance | 1,629,797 (50.1) | 744 (39.3) | |
| Uninsured | 197,286 (6.1) | 92 (4.9) | |
| Comorbidity a Comorbidity variable is generated using the AHRQ comorbidity software.64 Patients are considered to have comorbidity if their discharge records show that they have one more of the 29 types of patient comorbidities identified by AHRQ using standard methods by Elixhauser.38 AHRQ, Agency for Healthcare Research and Quality; HCUP, Healthcare Cost and Utilization Project; HL, hearing loss; NIS, nationwide inpatient sample. | 232.5*** | ||
| One or more comorbidities | 778,800 (23.8) | 761 (40.0) | |
| No comorbidity | 2,485,875 (76.2) | 1,141 (60.0) | |
| Region of hospital | 1.36 | ||
| Northeast | 505,269 (15.9) | 296 (16.2) | |
| Midwest | 698,971 (21.6) | 418 (22.8) | |
| South | 1,238,350 (37.9) | 663 (34.5) | |
| West | 822,085 (24.7) | 525 (27.3) | |
| Hospital location and teaching status | 12.72*** | ||
| Rural | 364,844 (11.5) | 172 (9.2) | |
| Urban non-teaching | 1,362,573 (42.0) | 680 (35.7) | |
| Urban teaching | 1,494,352 (46.5) | 1,033 (55.1) | |
| Control/ownership of hospital | 6.36* | ||
| Government, non-federal (public) | 458,367 (13.9) | 283 (14.7) | |
| Private, not-for-profit (voluntary) | 2,343,123 (72.9) | 1,435 (76.4) | |
| Private, investor-owned (proprietary) | 420,279 (13.3) | 167 (8.9) | |
| Median household income for patient’s ZIP code | 2.01 | ||
| 1st quartile: $1–$38,999 | 856,850 (26.8) | 552 (29.6) | |
| 2nd quartile: $39,000–$47,999 | 815,225 (25.5) | 449 (24.1) | |
| 3rd quartile: $48,000–$62,999 | 802,771 (25.0) | 473 (25.1) | |
| 4th quartile: ≥$63,000 | 729,487 (22.7) | 398 (21.3) | |
| Age (in years), M (SE) | 27.6 (0.07) | 28.5 (0.16) | 27.18*** |
Note: Percentages are weighted; significant differences are presented for weighted sample. Boldface indicates statistical significance (*p<0.05; **p<0.01; ***p<0.001).
a Comorbidity variable is generated using the AHRQ comorbidity software.
64
Patients are considered to have comorbidity if their discharge records show that they have one more of the 29 types of patient comorbidities identified by AHRQ using standard methods by Elixhauser.McCarthy J. Deaf people and social security. http://libguides.gallaudet.edu/content.php?pid=119476&sid=4748939. Published March 2014. Accessed June 3, 2015.
38
AHRQ, Agency for Healthcare Research and Quality; HCUP, Healthcare Cost and Utilization Project; HL, hearing loss; NIS, nationwide inpatient sample.Table 3 reports the unadjusted, weighted comparison in adverse birth outcomes between women with and without HL. Women with HL had higher rates of both preterm birth and low birth weight compared with other women. After controlling for model covariates, women with HL were significantly more likely to have both preterm birth (OR=1.28, 95% CI=1.08, 1.52, p<0.001) and low birth weight (OR=1.43, 95% CI=1.09, 1.90, p<0.05) (Table 4).
Table 3Unadjusted, Weighted Birth Outcomes for Women With and Without Hearing Loss, NIS, HCUP, 2008–2011
| Outcomes | Women without HL, n (%) (n=3,264,675) | Women with HL, n (%) (n=1,902) | Test statistics (F test) |
|---|---|---|---|
| Birth outcomes | |||
| Preterm birth | 233,974 (7.20) | 189 (9.97) | 19.71*** |
| Low birth weight | 71,289 (2.18) | 60 (3.13) | 7.54*** |
Note: Percentages are weighted; significant differences are presented for weighted sample. Boldface indicates statistical significance (*p<0.05; **p<0.01; ***p<0.001).
HCUP, Healthcare Cost and Utilization Project; HL, hearing loss; NIS, Nationwide Inpatient Sample.
Table 4Logistic Regression Analysis for Hearing Loss Status and Other Relevant Variables Predicting Birth Outcomes—U.S.
| Variables | Preterm birth, OR (95% CI) (n=2,708,127) | Low birth weight, OR (95% CI) (n=2,708,127) |
|---|---|---|
| Hearing status | ||
| Women without HL (ref) | 1 (1) | 1 (1) |
| Women with HL | 1.28** (1.08, 1.52) | 1.43* (1.09, 1.90) |
| Age (in years) | ||
| <24 | 1 (1) | 1 (1) |
| 25–34 | 0.96*** (0.95, 0.97) | 0.74*** (0.72, 0.76) |
| >34 | 1.15*** (1.13, 1.18) | 0.81*** (0.78, 0.84) |
| Race and ethnicity | ||
| Non-Hispanic white (ref) | 1 (1) | 1 (1) |
| Non-Hispanic black | 1.31*** (1.26, 1.37) | 1.27*** (1.20, 1.34) |
| Hispanic | 0.91*** (0.86, 0.97) | 0.65*** (0.60, 0.71) |
| Non-Hispanic other | 0.97 (0.93, 1.02) | 1.10*** (1.04, 1.17) |
| Type of insurance coverage | ||
| Private insurance (ref) | 1 (1) | 1 (1) |
| Public insurance | 1.12*** (1.09, 1.14) | 1.09** (1.04, 1.13) |
| Uninsured | 1.18*** (1.11, 1.26) | 0.96 (0.88, 1.04) |
| Comorbidity a Comorbidity variable is generated using the AHRQ comorbidity software.64 Patients are considered to have comorbidity if their discharge records show that they have one more of the 29 types of patient comorbidities identified by AHRQ using standard methods by Elixhauser.38 AHRQ, Agency for Healthcare Research and Quality; HCUP, Healthcare Cost and Utilization Project; HL, hearing loss. | ||
| No comorbidity | 1 (1) | 1 (1) |
| One or more comorbidities | 1.58*** (1.53, 1.63) | 1.40*** (1.35, 1.46) |
| Region of hospital | ||
| Northeast (ref) | 1 (1) | 1 (1) |
| Midwest | 1.02 (0.94, 1.11) | 0.99 (0.88, 1.11) |
| South | 1.18*** (1.09, 1.28) | 1.11 (0.99, 1.25) |
| West | 1.15*** (1.06, 1.25) | 0.99 (0.88, 1.12) |
| Hospital location and teaching status | ||
| Urban teaching (ref) | 1 (1) | 1 (1) |
| Rural | 0.61*** (0.55, 0.67) | 0.76*** (0.67, 0.85) |
| Urban non-teaching | 0.74*** (0.69, 0.79) | 0.83*** (0.76, 0.91) |
| Ownership of hospital | ||
| Private (ref) | 1 (1) | 1 (1) |
| Public | 1.05 (0.93, 1.19) | 0.95 (0.79, 1.14) |
| Median household income for patient’s ZIP code | ||
| 4th quartile: ≥$63,000 (ref) | 1 (1) | 1 (1) |
| 1st quartile: $1–$38,999 | 1.17*** (1.11, 1.22) | 0.99 (0.91, 1.07) |
| 2nd quartile: $39,000–$47,999 | 1.08*** (1.04, 1.12) | 1.01 (0.95, 1.09) |
| 3rd quartile: $48,000–$62,999 | 1.03 (1.00, 1.06) | 0.98 (0.93, 1.04) |
| Year | ||
| Calendar year=2008 (ref) | 1 (1) | 1 (1) |
| Calendar year=2009 | 0.94 (0.88, 1.00) | 0.99 (0.91, 1.08) |
| Calendar year=2010 | 0.98 (0.92, 1.04) | 1.21*** (1.11, 1.33) |
| Calendar year=2011 | 0.88*** (0.82, 0.93) | 1.20*** (1.10, 1.31) |
Source: HCUP 2008–2011.
Note: Boldface indicates statistical significance (*p<0.05; **p<0.01; ***p<0.001).
a Comorbidity variable is generated using the AHRQ comorbidity software.
64
Patients are considered to have comorbidity if their discharge records show that they have one more of the 29 types of patient comorbidities identified by AHRQ using standard methods by Elixhauser.McCarthy J. Deaf people and social security. http://libguides.gallaudet.edu/content.php?pid=119476&sid=4748939. Published March 2014. Accessed June 3, 2015.
38
AHRQ, Agency for Healthcare Research and Quality; HCUP, Healthcare Cost and Utilization Project; HL, hearing loss.Discussion
To the investigators’ knowledge, this is the first population-based examination of birth outcomes among U.S. women with HL. This study demonstrates that HL is associated with elevated risk of selected adverse birth outcomes. Controlling for socioeconomic and demographic characteristics and comorbidity, HL is significantly associated with preterm delivery and low birth weight infants. Although measuring the impact of the poor birth outcomes among women with HL is beyond the scope of this study, prematurity is generally associated with significant long-term consequences for the infant as well as significant costs to the family and society.
46
, 47
, 48
The findings of this study call for a systematic examination of the pregnancy experiences, complications, costs, quality of care, and outcomes of women with HL. In addition, given the results of this study, further research is warranted in understanding these disparities in birth outcomes between women with and without HL.Given the cross-sectional nature of this study, it is difficult to ascertain the causal factors associated with poor birth outcomes among women with HL. There are several potential factors that may contribute to the study findings. First, HL represents a significant source of communication barrier in the healthcare setting.
18
, 49
Poor health communication adversely affects a variety of health-related outcomes, especially health behaviors, treatment adherence, and patient satisfaction.50
, 51
, 52
, 53
, 54
A 2006 qualitative study found that Deaf women were less satisfied with their patient–provider communication and prenatal care than hearing women. Deaf women were also more likely to have shorter breastfeeding duration if they did not have language access through interpreters and sign language–using providers, and access to technologies.55
Effective communication between healthcare providers and patients during the prenatal period is especially critical. Prenatal counseling is largely focused on patient education, including encouraging healthy behaviors and reducing risk factors to improve the chances of an optimal pregnancy outcome.Second, sexual health knowledge among individuals with HL is lower than what is seen in the general population.
27
, 28
, 29
, 30
, 31
, 56
It is less clear if pregnancy-related knowledge is also reduced among women with HL. Individuals with HL struggle with inadequate health literacy and access to health information, including incidental learning opportunities.17
Though health literacy has been demonstrated to correlate with health knowledge in other studies, no known studies have examined how this may affect women’s health, especially prenatal and perinatal health. Lower health knowledge and inadequate health literacy are associated with poorer health access, delayed medical care seeking, higher mistrust of the healthcare system, and poorer health outcomes, and may be a potential source of higher rates of adverse birth outcomes among women with HL.57
In addition, women with HL represent a vulnerable population. This is a population that struggles with a rate of interpersonal violence nearly double that of the general population.
58
, 59
Studies demonstrate that women are at high risk for interpersonal violence during their pregnancy.60
Women who have experienced interpersonal violence victimization have higher rates of sexually transmitted infections and poorer pregnancy-related outcomes, including low birth weight and preterm babies.61
Psychosocial stress is reported higher among women with disabilities62
than the general population. This is a potential concern given the role of psychosocial stress on adverse pregnancy outcomes, including low birth weights and preterm deliveries.46
Additionally, women with HL are more likely to have public insurance, including Medicare. This is not a surprising finding. Blanchfield and colleagues63
analyzed data from multiple national data sets and found that individuals with HL were significantly more likely to be publicly insured. Individuals with HL can qualify for both the Medicare and Social Security Disability Insurance on the basis of their hearing disability, especially if their disability prevents successful employment.64
McCarthy J. Deaf people and social security. http://libguides.gallaudet.edu/content.php?pid=119476&sid=4748939. Published March 2014. Accessed June 3, 2015.
There may be additional biological factors that may explain some of the perinatal outcomes among women with HL. Genetic factors are increasingly recognized as important determinants of preterm delivery, but the magnitude of effect and the degree to which they contribute to differences in preterm risk across populations remain largely unknown.
35
This is relevant given that the majority of congenital HL is likely due to genetic polymorphisms, with the majority due to autosomal recessive HL,2
, 65
yet little is known about how these are phenotypically expressed and if any obstetric and fetal risk factors are associated with them.Given these prior studies, and the findings from this study, longitudinal studies are needed to examine the pathways through which women with HL experience poor birth outcomes. A recent perinatal health framework developed by Mitra et al.
62
identified a set of individual and mediating factors that may impact maternal and birth outcomes for women with physical disabilities. Mediating factors, for example, include provider knowledge and attitudes toward pregnancy, family support, and psychosocial factors such as stressful life events. Although these factors are not identifiable in the HCUP data, this framework may be also applicable to women with HL. Given the earlier studies on patient–provider communication, potential biological factors, interpersonal violence, and health knowledge and health literacy among people with HL and the general dissatisfaction of people with HL with their health care, these mediating factors could potentially explain the poor birth outcomes found in this study.Limitations
The study limitations warrant consideration. First, some NIS data may have been miscoded. Second, the unit of analysis was hospitalization rather than the individual patient; therefore, any woman who delivered more than once in a single calendar year was counted twice. However, this situation is not likely to be common because short interpregnancy intervals resulting in U.S. women giving birth within one 12-month period are rare;
41
thus, few women give birth twice in 1 calendar year. Further, this unit of analysis precludes identification of complications that were treated in the hospital prior to delivery. Third, the estimates are conservatively biased because some women with HL were probably not assigned the ICD-9 code for their HL.66
Therefore, the weighted estimate of 10,462 deliveries for the 2008–2011 period occurring to women with HL should not be interpreted as an incidence statistic. Yet, the smaller-than-anticipated number may be reflective of not-well-understood providers’ coding behaviors. For example, healthcare providers may only be prompted to add ICD-9 HL codes when the HL resulted in significant communication barriers during the hospital visit. Undercoding and underdiagnosis of HL is a common issue across different types of healthcare visits.67
Patients may be reluctant to disclose their HL owing to fear of discrimination, perceptions of disability, and vanity.68
These factors likely resulted in low rates of ICD-9 codes for HL. Fourth, the level of detail provided by the NIS was limited; future research could explore the relationships between early prenatal care and pregnancy and health outcomes. Fifth, these data do not permit longitudinal analyses, which could facilitate understanding the health outcomes of women and their children over time. Sixth, the NIS does not permit linkage between the hospital records of the infant and the mother, so it was not possible to analyze infant outcomes other than those reported here (which are part of the maternal discharge summary). Additionally, household income included in this paper is based on the median household income for mother’s ZIP code. Patient-level household income data are not included in the NIS. Additionally, owing to HCUP data restrictions, the study was unable to account for marital status, a potent variable that has unique impacts on both low birth weight and preterm birth.69
Another potential limitation is out-of-hospital deliveries. Although recent studies70
, 71
, 72
have shown an increase in the trend of out-of-hospital deliveries, only 1.5% of all deliveries in the U.S. occurred out of hospital. Notably, women who have lower birth risks and fewer or no comorbidities are among those who have given birth outside hospitals. Though there are no exact statistics on out-of-hospital deliveries among women with HL, the investigators believe that given higher comorbidity rates among women with HL, the rate of out-of-hospital deliveries among women with HL would be lower than those of the general obstetric population. Finally, missing data of the race variable was another limitation. In the combined 2008–2011 HCUP data set, values for about 13% of the race variable were missing. Most of the data were missing for the early cross-section years of the 2008–2011 HCUP data set, including 20% in 2008, 15% in 2009, 11% in 2010, and 9% in 2011. Missing values for race were considered missing at random and analyses were performed using observations that did not contain missing data for the race variable. Additionally, the study tested the regression results with the full sample by excluding the race variable from the model and did not find significant bias in the ORs.This study has important strengths. The NIS provides high-quality, nationally representative data and therefore permits this study to draw inferences about the entire U.S. population of women with HL who have given birth. Despite potential omitted variable bias, the sample is less constrained by selection bias or sampling bias arising from convenience samples derived from a single hospital, service provider organization, or single geographic region.
Conclusions
This study provides a first examination of birth outcomes among U.S. women with HL. Results highlight the need for further research using longitudinal data and direct measures of HL to understand the causes of disparities in birth outcomes, and to examine the perinatal care experiences and potential barriers to perinatal care experienced by women with HL. Efforts to develop clinical interventions and maternal and child health programs are also needed to improve these adverse outcomes among women with HL.
Acknowledgments
Support for this study was provided by the Lurie Institute for Disability Policy at the Heller School for Social Policy and Management at Brandeis University and by the National Institute on Disability, Independent Living, and Rehabilitation Research’s Advanced Rehabilitation Research Training Program on Health and Functioning of People with Disabilities, grant # 90AR5024-01-00.
Dr. Mitra takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design by Mitra and McKee. Drafting and revision of manuscript by Mitra, McKee, Akobirshoev, Iezzoni. Statistical analysis by Akobirshoev.
No financial disclosures were reported by the authors of this paper.
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Published online: September 26, 2016
This activity is available for CME credit. See page A3 for information.Identification
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© 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
