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Obstetric Anesthesiology: Original Clinical Research Report

Increased Perinatal Morbidity and Mortality Among Asian American and Pacific Islander Women in the United States

Siddiqui, Maryam MD*; Minhaj, Mohammed MD, MBA; Mueller, Ariel MA; Tung, Avery MD, FCCM; Scavone, Barbara MD; Rana, Sarosh MD, MPH*; Shahul, Sajid MD, MPH

Author Information
doi: 10.1213/ANE.0000000000001778

Abstract

Currently, nearly half of all births in the United States are to women who self-identify as a racial or ethnic minority.1 Asian American/Pacific Islander (AAPI) women have the second highest fertility rate among all races and are the fastest-growing group in the United States with a 46% increase in population between 2000 and 2010.2,3 Racial disparities in obstetric care have been recognized by the American College of Obstetricians and Gynecologists as a crucial area of investigation and may meaningfully affect maternal/fetal health among AAPIs.4

Existing data in smaller studies suggest differences in maternal morbidities between AAPI and Caucasian women. When compared with Caucasian women, AAPI parturients have higher rates of gestational diabetes, peripartum hemorrhage, perineal lacerations, and infection.5–8 Delayed childbearing and advanced maternal age (>35 years of age) are also more common among AAPI women compared with Caucasian parturients and may increase maternal morbidity/mortality.2,9

Although these aspects of AAPI obstetric care have been previously reported, no prior large nationwide epidemiological studies have examined the relationship between maternal comorbidities and maternal morbidity and mortality among AAPI parturients.2,4–6,10 We hypothesized that the higher risk of maternal mortality observed in AAPI when compared with Caucasian women is explained by differences in demographic characteristics and the burden of comorbidity between these 2 populations. To test our hypothesis, we analyzed data from the National Inpatient Sample (NIS), a large administrative database, and compared AAPI and Caucasian women with respect to comorbidities, severe maternal conditions, peripartum complications, and mortality.

METHODS

Data Source and Study Population

We performed a retrospective cohort analysis using the NIS from 2002 to 2013. The NIS database provides discharge billing data on approximately 8 million inpatient stays annually and is the largest inpatient database in the United States. The Healthcare Cost Utilization Project uses a stratified framework (based on ownership/control, bed size, teaching status, urban/rural location, and region) to sample 20% of all participating hospitals (N = 4924 in 2013) and weights the sample to produce data estimates for 95% of all inpatient hospitalizations in the United States. Weights are required to produce national estimates of descriptive statistics like sums, means, and standard errors.11 The NIS data set includes demographic information, comorbidities, principal and secondary diagnoses and procedures, inpatient mortality, and disposition.

The NIS data set is validated both internally and externally every year, and it is in excellent agreement with Medicare Provider Analysis and Review data from the Centers for Medicare and Medicaid Services.11 Because the NIS has no patient identifiers, the Institutional Review Board at the University of Chicago declared this study exempt from review.

Using a previously validated method, we identified a cohort of women who were hospitalized for delivery using International Classification of Diseases, Ninth Revision codes.12 We further restricted our analysis to only discharges coded as “Caucasian” or “Asian or Pacific Islander.” Race and ethnicity data definitions are standardized in the NIS to create a uniform set of categories based on established rules for race and ethnicity reporting for federal statistics. These include separate categories for Hispanic and 5 non-Hispanic racial groups (Caucasian, African American, Asian or Pacific Islander, Native American, and other).13 Data on race and ethnicity in NIS are based on self-report/self-identification. Asian or Pacific Islanders are defined as people originally from the “Far East, Southeast Asia, the Indian subcontinent, or the Pacific Islands” and are not further subdivided. For the purposes of this analysis, we limited the age range to 15 to 44 years.

Missing Data

Race was missing in 19.64% of patient admissions. We used 3 approaches to account for these missing data. We first performed the analysis on patients with complete case information in our cohort. Second, to assess the effect of missing data on our analysis, we created 5 complete data sets using multiple imputation and generated missing values using a weighted sequential hot deck method that does not place any restrictions on missing data patterns. Pooled values were then utilized to calculate race-specific outcomes. This multiple imputation approach is recommended by Health Care Utilization Project to overcome inherent biases.14 Third, in another validated method for analyzing missing race data in the NIS, we used a reweighted estimating equation analysis. In brief, reweighted estimating equations is a technique whereby the survey weight is adjusted by the inverse of the probability of the observed missing covariate.15,16

Study Outcomes

The primary outcome was inpatient mortality during the same delivery hospitalization. We sequentially adjusted for potential mediators of mortality by incorporating known risk factors for maternal mortality during delivery including age, mode of delivery, surrogates for socioeconomic status (median household income per zip code of residence) and insurance status (Medicare, Medicaid, private insurance, self-pay, no charge, and other), and postpartum hemorrhage in our model. To adjust for presence and severity of comorbid conditions, we used the Bateman Comorbidity Index (BCI) that has been specifically validated in obstetric patients to predict severe maternal morbidity (SMM) or mortality.17,18 Variables included in the BCI include severe preeclampsia/eclampsia, pulmonary hypertension, multiple gestation, placenta previa, and previous cesarean delivery. These variables are then weighted based on their likelihood of contributing to acute maternal end-organ injury or death. For all analyses, the BCI was used as an ordinal variable based on the total weighted BCI comorbidities value. To identify comorbidities, we used a comprehensive set of validated measures developed for use with the NIS.19 To identify clinically meaningful differences between the 2 groups, we have utilized the Agency for Healthcare Research and Quality definition for meaningful differences that is based on 2 criteria. First, the difference between the 2 groups must be statistically significant with P < .05 on a 2-tailed test. Second, the relative difference between the comparison group and the reference group must be at least 10%.

Statistical Analysis

Analyses were performed using SAS 9.3 (SAS Institute, Cary, NC) and SUDAAN 11.1 (Research Triangle Institute, Research Triangle Park, NC). For all analyses, weighted estimates were used to adjust for design effects of the sampling. Categorical variables were presented as frequencies or number per 1000 maternal stays and compared using the χ2 test or the χ2 test for trend. An unadjusted clustered regression model was used to estimate the association between race and mortality. Given the possibility for differences between groups at baseline, we then fit sequential multivariable models when assessing the relationship between race and in-hospital mortality, adjusting for age, surrogates for socioeconomic status (median household income per zip code of residence) and insurance status (Medicare, Medicaid, private insurance, self-pay, no charge, and other), BCI, mode of delivery, and postpartum hemorrhage. To account for in-hospital clustering, we used generalized estimating equations with robust variance estimates and exchangeable correlation. The quasi-likelihood information criterion (QIC) was calculated for each sequential model as a measure of goodness-of-model fit. The model with the lowest QIC was deemed the best fitting model. This model was used for all interpretations. We assessed multicollinearity by calculating a variance inflation factor for race with all other covariates. Variance inflation factor for race was 1.02 suggesting no collinearity issues in the analyses. All tests were 2-sided and values of <.05 were considered statistically significant.

RESULTS

Demographics and Patient Characteristics

Table 1.
Table 1.:
Unadjusted NIS Parturient Characteristics at Presentation Between Caucasian and AAPI Women

We identified 21,898,501 weighted patient discharges for delivery coded as either Caucasian (91.16%) or AAPI women (8.84%) in the NIS database. There were a total of 49,216,117 births in the United States during the study period. In the study period, out-of-hospital births accounted for only 1% to 1.5% of all births in the United States.20 Demographics and hospital characteristics of our study cohort are presented in Table 1. AAPI women presenting for delivery were more likely than Caucasian women to be older, be privately insured or self-pay, and live in an area with a higher median household income. Median household income in Caucasian women was evenly distributed across quartiles, while more than 70% of AAPI women had household incomes above the 50th percentile with nearly half of AAPI women in the top quartile.

Patient Comorbidities

Table 2.
Table 2.:
Unadjusted Patient Comorbidities per 1000 Patients

The incidences of maternal comorbidities of pregnancy are shown in Table 2. When compared with Caucasian women, AAPI women were less likely to have higher BCI scores but had significantly higher rates of pulmonary hypertension, sickle cell disease, and placenta previa. Previous validation work on the BCI has revealed that a BCI ≥ 7 is associated with a higher risk of adverse events.18,21 Among the subset of women with BCI ≥ 7, we found a significantly higher mean BCI score (7.63 in AAPI versus 7.53 in Caucasians; P < .001). When compared with Caucasian women, AAPI women had lower rates of severe preeclampsia, mild preeclampsia, gestational hypertension, preexisting hypertension, congenital heart disease, cardiac valvular disease, drug or alcohol abuse, and previous cesarean delivery. The incidence of preexisting diabetes mellitus and systemic lupus erythematosus did not differ between AAPI and Caucasian women.

Postpartum Hemorrhage

Table 3.
Table 3.:
Unadjusted Delivery Outcomes per 1000 Patients

AAPI women were more likely to have a placenta previa but less likely to have a previous cesarean delivery or multiple gestation (Table 2). When compared with Caucasian women, AAPI women had increased rates of postpartum hemorrhage (Table 3). Among patients experiencing postpartum hemorrhage, AAPI women also had higher rates of third and fourth degree lacerations, and they were more likely to receive blood products and have a peripartum hysterectomy. Rates of uterine atony and abruption were comparable between groups.

Severe Maternal Morbidity and Peripartum Procedures

Table 4.
Table 4.:
Unadjusted Severe Maternal Morbidity Indicators and Race per 1000 Patients

The CDC defines SMM as the most severe complications of pregnancy that indicates a potentially life-threatening maternal condition or complication.22 By this definition, AAPI women experienced higher unadjusted rates of SMM including acute renal failure, acute respiratory distress syndrome, pulmonary edema, sepsis, and shock (Table 4). They were also more likely to require blood transfusions, peripartum hysterectomy, and mechanical ventilation.

Adjusted Inpatient Mortality, Mediators, and Severe Maternal Morbidity

Overall, AAPI women had an increased unadjusted risk of inpatient maternal mortality compared with Caucasian women (7.7 vs 5.5 per 100,000 maternal delivery stays). Table 5 depicts the results of the nested models. The unadjusted odds ratio (OR) associated with mortality was 1.64 (95% confidence interval [CI]: 1.09–2.47; P = .02) for AAPI women as compared with Caucasian women. Each sequential model reported an improved QIC as compared with the one that preceded it. Despite this, only a small change was observed when adjusting for age group (model 1) and mode of delivery (model 4).

Table 5.
Table 5.:
Unadjusted and Adjusted Associations Between Race and Complications for Parturients

In the final model (model 5) after adjustment for age, median household income, payer status, BCI, mode of delivery (vaginal or cesarean delivery), and postpartum hemorrhage, the OR for in-hospital mortality remained higher for AAPI parturients (OR 1.72, 95% CI: 1.14–2.59). Results from the full model can be observed in Supplemental Digital Content 1 (Supplemental Appendix 1, http://links.lww.com/AA/B588). Differences in mortality were unchanged after multiple imputation for missing data (OR 1.40, 95% CI: 1.09–1.80) or after the reweighting estimating equation for missing data analyses (OR 1.63, 95% CI: 1.09–2.45).

DISCUSSION

Although AAPI women are more likely to live in areas with a higher socioeconomic status, to carry private insurance, and to enter pregnancy free of serious comorbidities influencing obstetric outcomes, they experience higher rates of SMM, acute end-organ damage, and mortality during hospitalization for delivery. The differences we observed are consistent with previously reported smaller US regional study data that reflect increased morbidity (specifically increased hemorrhage, severe laceration, and infection rates) among AAPI compared with Caucasian women.6,8 Data from the United Kingdom also indicate a similar increased risk of SMM among women of East Indian descent despite adjustment for socioeconomic status, smoking status, body mass index, age, and utilization of prenatal care.23,24 Our findings extend previously reported results by using a larger (national) database and finding a higher incidence of inpatient mortality among AAPI women when compared with Caucasian women even after adjustment for established correlates of SMM.25,26

The mechanisms linking AAPI race to worse maternal outcomes are unclear. AAPI women were more likely to present with advanced maternal age, a known risk factor for adverse pregnancy outcomes.27 However, even when we controlled for age, differences in mortality between AAPI and Caucasian women persisted.

Although higher income and private payer status are thought to be protective, adjusting for correlates of socioeconomic status actually increased the odds of mortality for AAPI. Despite higher markers of socioeconomic status, many AAPI women have social and cultural characteristics that affect health outcomes. Those with limited linguistic proficiency may be particularly vulnerable, given the association with low health literacy.28 The wide variety of primary languages spoken, the use of family members as interpreters, limitations in cultural competency among providers, and differing norms for self-advocacy may also compromise communication, health care utilization, and relationships with care providers among the AAPI population.28

Despite adjusting for BCI, we found an increase in both the magnitude and significance of the relationship between AAPI parturients and in-hospital mortality. AAPI women are less likely to have BCI ≥ 2, so once that is taken into account, the increase in the relationship between AAPI and death is magnified.

Cesarean delivery is a well-recognized independent risk factor for maternal morbidity and mortality as compared with vaginal delivery.29–32 Data from retrospective cohort studies, prospective trials, and systemic reviews demonstrate increased risk of SMMs and mortality in cesarean delivery whether it is planned or unplanned, prelabor or laboring, term or preterm, or in younger or older women.29–32 In our study, AAPI women had similar rates of cesarean delivery as compared with Caucasian women. Further adjusting for mode of delivery did not significantly alter the estimated odds of mortality.

Given higher rates of postpartum hemorrhage in AAPI women, we sought to account for this recognized cause of increased maternal mortality. Although a sizable decrease in the effect size is seen by adding this covariate, it does not entirely account for the difference we observed. Consistent with previous data, we found a higher incidence of postpartum hemorrhage among AAPI women when compared with Caucasian women, despite a lower incidence of traditional risk factors such as severe preeclampsia and multiple gestation and no difference in the incidence of abruption.33–36 Uterine atony has been previously described as a risk factor for postpartum hemorrhage.37 However, AAPI women had similar rates of atony compared with Caucasian women. Another possibility, supported by our data and others’, is that AAPI women are more likely to have third and fourth degree lacerations during delivery, increasing the likelihood of postpartum hemorrhage. Such a difference may be practice related, because women delivering in Asian countries have lower rates of severe perineal trauma than do AAPI women delivering in the United States, suggesting that differences in US obstetric practices may account for some of the outcome differences observed.6,8,38

Postpartum hemorrhage is the most common cause of maternal mortality worldwide but is often underrecognized. Delays in treatment can lead to adverse outcomes.39 The concurrent presence of uterine atony and blood loss from severe perineal lacerations may lead clinicians to underestimate actual blood loss, delay prompt treatment, and worsen outcomes. Patterns of SMM further support a link between hemorrhage and outcome because postpartum hemorrhage and peripartum mortality are associated with increased rates of hemorrhage and sepsis, which progress to end-organ damage marked by pulmonary edema, acute respiratory distress syndrome, mechanical ventilation, renal failure, shock, blood transfusion, and hysterectomy. Furthermore, total blood volume (TBV) varies based on height, weight, and sex. Standard calculations using the Nadler formula can overestimate TBV in Asian patients.40 AAPI women typically have a smaller body mass and therefore a lower TBV than Caucasian women.

Biological variations in the vascular system, effect of obesity, higher body fat percentage at lower BMIs, and effect of glycemic status on endothelial dysfunction may all affect disparities in cerebrovascular and cardiovascular outcomes in AAPI patients.41–43 Asians have lower thrombogenic coagulation profiles as compared with Caucasians, which may be detrimental for hemostasis after surgery or delivery.44,45 Genetic variations in β2-adrenoreceptors known to affect uterine contractility and progress during active labor could also affect differences in uterine atony rates or severity.46 Taken together, these factors may contribute to differences reported in this study since pregnancy is considered as a maternal stress test and may unmask subclinical disease.47 Disparities in pain management between AAPI and Caucasian patients have been previously described and may also have an effect.48–50 Because pain is a presenting symptom related to obstetric emergencies (eg, headache associated with severe preeclampsia, abruption, uterine rupture), inaccurate assessment of pain can delay the recognition of these emergencies and possibly lead to worsened outcomes.

Our study has several limitations. Because the NIS is an administrative database and all patient data were deidentified, we were unable to review patient-specific data. Identifying causal links between race, comorbidities, and outcomes is not possible. In addition, the AAPI classification used in the NIS database includes Indian, Chinese, Filipino, Korean, Japanese, and Vietnamese women and Pacific Islanders (including Guamanians and Samoans) and differences in obstetric outcomes among these subgroups are known to exist.3,51 More recently, the NIH and the federal government have disaggregated Asian Americans from Pacific Islanders or Native Hawaiian patients to account for the heterogeneity in these groups; however, the NIS database does not make this distinction.52 Finally, the timing of diagnoses cannot be determined from NIS data. Diagnoses such as cardiogenic shock and coagulopathy may reflect either comorbidities or complications of pregnancy, making identification of risk factors difficult. We did not account for deaths during a readmission within the 42 days after delivery or out-of-hospital deaths in our study.

In summary, we identified an increased risk for maternal mortality during hospitalization for delivery among AAPI women when compared with Caucasian women in the NIS database from 2002 to 2013. This difference was independent of maternal comorbidities, pregnancy complications, severe perinatal morbidities, advanced maternal age, and mode of delivery, and it was present despite a higher socioeconomic status. Higher rates of postpartum hemorrhage as well as unidentified patient, provider, or systems issues may contribute to these disparities. The expected growth of the AAPI population over the coming decades makes identifying modifiable causes of maternal outcome disparities a public health priority.53

DISCLOSURES

Name: Maryam Siddiqui, MD.

Contribution: This author helped conceive and design the study, analyze and interpret the data, and draft the manuscript.

Name: Mohammed Minhaj, MD, MBA.

Contribution: This author helped conceive and design the study, analyze and interpret the data, and draft the manuscript.

Name: Ariel Mueller, MA.

Contribution: This author helped analyze and interpret the data, and draft the manuscript.

Name: Avery Tung, MD, FCCM.

Contribution: This author helped analyze and interpret the data, and draft the manuscript.

Name: Barbara Scavone, MD.

Contribution: This author helped analyze and interpret the data, and draft the manuscript.

Name: Sarosh Rana MD, MPH.

Contribution: This author helped conceive and design the study, analyze and interpret the data, and draft the manuscript.

Name: Sajid Shahul, MD, MPH.

Contribution: This author helped conceive and design the study, analyze and interpret the data, and draft the manuscript.

This manuscript was handled by: Jill M. Mhyre, MD.

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