Advertisement

Services and payer mix of Black-serving hospitals and related severe maternal morbidity

Published:March 30, 2021DOI:https://doi.org/10.1016/j.ajog.2021.03.034

      Background

      Black-serving hospitals are associated with increased maternal risk. However, prior administrative data research on maternal disparities has generally included limited hospital factors. More detailed evaluation of hospital factors related to obstetric outcomes may be important in understanding disparities.

      Objective

      To examine detailed characteristics of Black-serving hospitals and how these characteristics are associated with risk for severe maternal morbidity (SMM).

      Methods

      This serial cross-sectional study linked the 2010-2011 Nationwide Inpatient Sample and the 2013 American Hospital Association Annual Survey Databases. Delivery hospitalizations occurring to women 15-54 years of age were identified. The proportions of non-Hispanic Black patients within a hospital was categorized into quartiles, and hospital factors such as specialized medical, surgical and safety-net services as well as payer mix were compared across these quartiles. A series of models was performed evaluating risk for SMM with Black-serving hospital quartile as the primary exposure. Log linear regression models with a Poisson distribution (and robust variance) were performed with unadjusted and adjusted risk ratios (aRR) with 95% confidence intervals (CIs) as measures of effect.

      Results

      Overall 965,202 deliveries from 430 hospitals met inclusion criteria and were included in the analysis. By quartile, non-Hispanic Black patients accounted for 1.3%, 5.4%, 13.4%, and 33.8% of patients. Many services were significantly less common in the lowest compared to the highest Black-serving hospital quartile including cardiac intensive care (48.9% versus 74.5%), neonatal intensive care (28.9% versus 64.9%), pediatric intensive care (20.0% versus 45.7%), pediatric cardiology (29.6% versus 44.7%), and HIV/AIDS services (36.3% versus 71.3%) (p≤0.01 for all). Indigent care clinics, crisis prevention, and enabling services (p≤0.01 for all) were more common at Black-serving hospitals as was Medicaid payer. Following adjustments for detailed hospital factors, the lowest Black serving hospital quartile carried the lowest risk for SMM. However, SMM risks were similar across the 2nd (aRR 1.31, 95% CI 1.08, 1.59), 3rd (aRR 1.27, 95% 1.05, 1.55), and 4th (aRR 1.29, 95% CI 1.07, 1.55) quartiles.

      Conclusion

      Black-serving hospitals were more likely to provide a range of specialized medical, surgical, and safety-net services and to have a higher Medicaid burden. Payer mix and unmeasured confounding may account for some of the maternal risk associated with Black-serving hospitals.

      Key words

      Introduction

      Racial disparities account for a substantial proportion of overall obstetric morbidity and mortality with non-Hispanic Black women at higher risk for adverse outcomes compared to other racial and ethnic groups.
      ACOG Committee Opinion No. 649: racial and ethnic disparities in obstetrics and gynecology.
      • Louis J.M.
      • Menard M.K.
      • Gee R.E.
      Racial and ethnic disparities in maternal morbidity and mortality.
      • Grobman W.A.
      • Bailit J.L.
      • Rice M.M.
      • et al.
      Racial and ethnic disparities in maternal morbidity and obstetric care.
      • Leonard S.A.
      • Main E.K.
      • Scott K.A.
      • Profit J.
      • Carmichael S.L.
      Racial and ethnic disparities in severe maternal morbidity prevalence and trends.
      • Creanga A.A.
      • Bateman B.T.
      • Kuklina E.V.
      • Callaghan W.M.
      Racial and ethnic disparities in severe maternal morbidity: a multistate analysis, 2008-2010.
      • Admon L.K.
      • Winkelman T.N.A.
      • Zivin K.
      • Terplan M.
      • Mhyre J.M.
      • Dalton V.K.
      Racial and ethnic disparities in the incidence of severe maternal morbidity in the United States, 2012-2015.
      • Bryant A.S.
      • Worjoloh A.
      • Caughey A.B.
      • Washington A.E.
      Racial/ethnic disparities in obstetric outcomes and care: prevalence and determinants.
      Prior studies across medical specialties have demonstrated that site of care may be an important factor in disparities; hospitals with large proportions of Black patients have higher risk for adverse outcomes including major morbidity and death.
      • Lucas F.L.
      • Stukel T.A.
      • Morris A.M.
      • Siewers A.E.
      • Birkmeyer J.D.
      Race and surgical mortality in the United States.
      • Haider A.H.
      • Ong’uti S.
      • Efron D.T.
      • et al.
      Association between hospitals caring for a disproportionately high percentage of minority trauma patients and increased mortality: a nationwide analysis of 434 hospitals.
      • Hasnain-Wynia R.
      • Baker D.W.
      • Nerenz D.
      • et al.
      Disparities in health care are driven by where minority patients seek care: examination of the hospital quality alliance measures.
      • Ly D.P.
      • Lopez L.
      • Isaac T.
      • Jha A.K.
      How do black-serving hospitals perform on patient safety indicators? Implications for national public reporting and pay-for-performance.
      • Li Y.
      • Yin J.
      • Cai X.
      • Temkin-Greener J.
      • Mukamel D.B.
      Association of race and sites of care with pressure ulcers in high-risk nursing home residents.
      Observational research in obstetrics has demonstrated that delivering at a Black-serving hospital is associated with increased severe maternal morbidity (SMM) with Black women who deliver at such hospitals at highest risk.
      • Howell E.A.
      • Egorova N.
      • Balbierz A.
      • Zeitlin J.
      • Hebert P.L.
      Black-white differences in severe maternal morbidity and site of care.
      • Creanga A.A.
      • Bateman B.T.
      • Mhyre J.M.
      • Kuklina E.
      • Shilkrut A.
      • Callaghan W.M.
      Performance of racial and ethnic minority-serving hospitals on delivery-related indicators.
      • Howell E.A.
      • Egorova N.N.
      • Balbierz A.
      • Zeitlin J.
      • Hebert P.L.
      Site of delivery contribution to black-white severe maternal morbidity disparity.
      Determining the cause of differential obstetric outcomes in Black-serving hospitals is an important goal in the overall effort to reduce disparities.
      • Howell E.A.
      • Egorova N.
      • Balbierz A.
      • Zeitlin J.
      • Hebert P.L.
      Black-white differences in severe maternal morbidity and site of care.
      Prior research has not clarified to what degree differentials in outcomes are due to incomplete case mix adjustment, hospital characteristics, or other structural factors. Prior administrative data research on maternal disparities has generally included limited hospital factors.
      • Howell E.A.
      • Egorova N.
      • Balbierz A.
      • Zeitlin J.
      • Hebert P.L.
      Black-white differences in severe maternal morbidity and site of care.

       Why was this study conducted?

      This study aimed to analyze detailed characteristics of Black-serving hospitals and how these characteristics are associated with risk for severe maternal morbidity.

       Key findings

      Black-serving hospitals were more likely to provide a range of specialized medical and surgical services and to have a less favorable payer mix.

       What does this add to what is known?

      Payer mix and unmeasured confounders may account for some of the maternal risk associated with Black-serving hospitals.
      Given that more detailed evaluation of hospital factors related to obstetric outcomes may be important in understanding disparities, the purpose of this study was to analyze detailed characteristics of Black-serving hospitals and how these characteristics are associated with maternal outcomes.

      Materials and Methods

       Data source

      The National (Nationwide) Inpatient Sample (NIS) from the Agency for Healthcare Research and Quality for the years 2010 to 2011 was used for this retrospective repeat cross sectional study.
      • Klebanoff M.A.
      • Snowden J.M.
      Historical (retrospective) cohort studies and other epidemiologic study designs in perinatal research.
      The NIS is one of the largest publicly available, all-payer inpatient databases in the United States, and is comprised of a sample of approximately 20% of hospitalizations in the US.
      Agency for Healthcare Research and Quality
      Overview of the National (Nationwide) inpatient sample (NIS).
      The NIS includes both academic and community hospitals, as well as general and specialty-specific hospitals. Data after 2011 was not included in this analysis as the sampling approach for the NIS changed in 2012; full data from individual hospitals is not available from 2012 on because of changes in the sampling structure of the NIS.
      • Houchens R.
      • Ross D.
      • Elixhauser A.
      • Jiang J.
      Nationwide inpatient sample (NIS) redesign final report. HCUP Methods Series Report # 2014-04 ONLINE. 2014.
      Hospitals from the NIS were linked to the 2013 American Hospital Association (AHA) Annual Survey. The AHA Annual Survey contains detailed hospital information including facility characteristics, utilization, finance, hospital staffing, medical services offered, ownership, and management structure.
      American Hospital Association
      AHA data and insights: data collection methods.
      These hospital characteristics are not reported in the NIS. The NIS includes limited data on characteristics including hospital bed volume (small, medium, and large), rurality (rural, urban), geographic location (Northeast, Midwest, South, or West), and teaching status (teaching, non-teaching). The AHA Annual Survey has been linked to other databases and used for health services and outcomes research in obstetrics and a wide range of other specialties.
      • Kozhimannil K.B.
      • Hung P.
      • Henning-Smith C.
      • Casey M.M.
      • Prasad S.
      Association Between loss of hospital-based obstetric services and birth outcomes in rural counties in the United States.
      • Zhu J.
      • Dy S.M.
      • Wenzel J.
      • Wu A.W.
      Association of magnet status and nurse staffing with improvements in patient experience with hospital care, 2008-2015.
      • Walker D.M.
      • Mora A.M.
      • Hogan T.H.
      • Diana M.L.
      • McAlearney A.S.
      Assessing trends in hospital system structures From 2008 to 2015.
      • Talutis S.D.
      • Chen Q.
      • Wang N.
      • Rosen A.K.
      Comparison of risk-standardized readmission rates of surgical patients at safety-net and non-safety-net Hospitals Using Agency for Healthcare Research and quality and American Hospital Association data.

       Study population

      Delivery hospitalizations in the NIS of women age 15 to 54 years were identified using the International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) codes v27.x and 650 as these codes identify over 95% of delivery hospitalizations.
      • Kuklina E.V.
      • Whiteman M.K.
      • Hillis S.D.
      • et al.
      An enhanced method for identifying obstetric deliveries: implications for estimating maternal morbidity.
      Maternal race was analyzed by the following categories within the NIS: non-Hispanic White, non-Hispanic Black, Hispanic, Asian or Pacific Islander, other (including Native American), and unknown. Four quartiles with roughly equal numbers of deliveries were created based on the hospital-level proportion of non-Hispanic Black patients undergoing delivery hospitalization. For both 2010 and 2011, three states did not contribute data on race (Minnesota, Washington, and West Virginia in 2010 and Minnesota, North Dakota, and West Virginia in 2011). Hospitals in these states for these years were excluded. Hospitals in the NIS without identifiers allowing linkage to the AHA Annual Survey were excluded. Finally, hospitals performing <500 deliveries per year were also excluded.
      Underlying comorbidity for individual patients was determined using an obstetric comorbidity index. This index provides weighted scores for comorbidity for individual patients based on the presence of specific diagnosis codes and demographic factors present in administrative data. Higher scores are associated with increased risk for severe morbidity. In the initial study validating the comorbidity index in a general obstetric population, patients with the lowest score of 0 had a 0.68% risk of severe morbidity whereas a score of >10 was associated with a risk of severe morbidity of 10.9%.
      • Bateman B.T.
      • Mhyre J.M.
      • Hernandez-Diaz S.
      • et al.
      Development of a comorbidity index for use in obstetric patients.
      This comorbidity index was subsequently validated in an external population.
      • Metcalfe A.
      • Lix L.M.
      • Johnson J.A.
      • et al.
      Validation of an obstetric comorbidity index in an external population.
      We categorized women based on comorbidity index scores: 0 (lowest risk), 1, 2, and ≥3 (highest).

       Study objectives and outcomes

       Primary objective

      The primary objective of this study was to describe detailed characteristics of Black-serving hospitals in the United States. We aimed to determine how Black-serving hospitals differed from other hospitals in three ways: (i) whether Black-serving hospitals provided more specialized medical and surgical services, (ii) whether Black-serving hospitals provided more safety-net services, and (iii) whether Black-serving hospitals had a higher Medicaid burden. We evaluated these characteristics based on two hypotheses; we hypothesized that: (i) Black-serving hospitals were more likely perform a range of specialized, referral medical and surgical care, and (ii) Black-serving hospitals were more likely to care for patient populations enrolled in Medicaid and requiring safety-net services.
      Based on data from the AHA Annual Survey, we determined whether hospitals provided the following specialized medical and surgical services: medical, surgical, cardiac, pediatric, and neonatal intensive care, psychiatric care including emergency services, cardiac services including interventional cardiology, hemodialysis, HIV and AIDS care, oncology services, transplant surgery, radiology services, organ transplant services, genetic testing and counseling, and other medical services. The AHA Annual Survey was also used to determine if hospitals provided the following safety-net services: crisis prevention services, enabling services, enrollment services, and indigent care clinics. Hospital ownership structure (government, not-for-profit non-government, for profit) was also determined based on AHA Annual Survey data.
      Using data from the NIS, Medicaid burden was analyzed for each hospital. To determine Medicaid burden, the hospital-level proportion of delivery hospitalizations with Medicaid as the payer was determined. Hospitals were grouped into four approximately equal quartiles in terms of delivery volume based on the proportion of Medicaid deliveries. A second measure of Medicaid burden (not restricted to obstetric delivery hospitalizations) was additionally calculated evaluating all hospitalizations.

       Secondary objective

      The secondary objective of this study was to determine the relationship between Black-serving hospitals and risk for severe maternal morbidity (SMM) accounting for detailed hospital factors and patient-level comorbidities. We hypothesized that some of the maternal risk associated with Black-serving hospitals could be accounted for by Black-serving hospitals (i) being referral centers for specialized medical and surgical care, (ii) having higher Medicaid burdens, and (iii) having patient populations more likely to require safety-net services.
      SMM was defined by criteria from the Centers for Disease Control and Prevention (CDC) that includes ICD-9-CM diagnosis and procedure codes for 21 indicators that represent potentially life-threatening illnesses and indicators of organ failure. For this analysis, we evaluated SMM excluding transfusion from the composite as transfusion, compared to other diagnoses in the composite, is much less likely to lead to long-term disability or death. The remaining 20 indicators were included in the analysis: acute myocardial infarction, aneurysm, acute renal failure, adult respiratory distress syndrome, amniotic fluid embolism, cardiac arrest, conversion of cardiac rhythm, disseminated intravascular coagulation, eclampsia, heart failure or arrest during surgery or procedure, hysterectomy, puerperal cerebrovascular disorders, pulmonary edema or acute heart failure, severe anesthesia complications, sepsis, shock, sickle cell disease with crisis, air and thrombotic embolism, temporary tracheostomy, and ventilation.

       Statistical analysis

      For the primary objective, hospital characteristics including specialized medical and surgical services, safety-net services, and Medicaid burden were compared between Black-serving hospital quartiles using the chi-squared tests or Fisher’s exact tests as appropriate with the Bonferroni correction applied for multiple comparisons.
      For the secondary objective of determining the relationship between Black-serving hospitals and SMM, we first analyzed unadjusted risk between individual hospital and patient characteristics and risk for SMM. Then we performed three sequential adjusted models for risk for SMM with each model including additional data on hospital characteristics. For the first adjusted model for SMM, we included the following factors: (i) underlying patient comorbidity, (ii) Black-serving hospital quartile, (iii) obstetric Medicaid burden quartile, and (iv) maternal race and ethnicity.
      For the second adjusted model for SMM, we then added hospital factors from the NIS (hospital bed volume, rurality, geographic location, and teaching status) and the AHA Annual Survey: (i) medical and surgical intensive care, (ii) neonatal intensive care, (iii) trauma center, (iv) genetic testing and counseling, (v) HIV/AIDS services, and (vi) transplant services. Hospital safety-net factors from the AHA Annual Survey included hospital ownership and the following: (i) crisis prevention services, (ii) enabling services, (iii) enrollment services, and (iv) indigent care clinics. These factors were chosen to reflect a broad range of services. However, not all hospital characteristics were included in this model because of concerns related to collinearity.
      For the third adjusted model, principal component analysis was used to address potential concerns about collinearity. Principal component analysis is a method that reduces the dimensionality of the data while preserving most of the variation in the dataset.
      • Jolliffe I.T.
      • Cadima J.
      Principal component analysis: a review and recent developments.
      This approach identifies a list of principal components that maximizes the variation from variables of interest (37 hospital factors in the current analysis). Principal components are independent orthogonal linear combinations of the individual variables and are listed in decreasing order of proportion of explained variance. The first component accounts for as much variance as possible in the data. The next component will account for as much of the leftover variance as it can, given the assumption that it is uncorrelated with the previous components. The eigenvalue-one criterion was used to determine the number of components retained in the analysis where eigenvalues of components (the amount of variance that is accounted for by a given component) are 1.00 or greater. The hospital variables and component factors pattern are presented after varimax rotation that tend to maximize the variance of a column of the factor pattern matrix. The rotated factor pattern (Supplemental Figure 1) identifies hospital variables that demonstrate high loading for a given component and determines what these variables have in common. The cut-off we used for loading a hospital variable to a factor is an absolute value of component loadings greater than 0.4. In subsequent analysis, identified principal components can be used as covariates in outcome regression models as was the case in this study in which principal components were analyzed together with patient-level comorbidity and health insurance status. For all four models for SMM, log linear regression models with a Poisson distribution based on the robust variance estimation method were performed with unadjusted (RR) and adjusted risk ratios (aRR) with 95% CIs as measures of effects. Results additionally accounting for hospital clustering with broader confidence intervals are presented. Given the de-identified nature of the data this study was deemed exempt by the Columbia University Institutional Review Board. All analyses were performed with SAS 9.4 (SAS Institute, Cary, NC).

      Results

      Of 1,559,523 delivery hospitalizations occurring at 699 hospitals to women age 15 to 54 in 2010-2011, 965,202 deliveries from 430 hospitals met inclusion criteria. Across the four quartiles of Black-serving hospitals, non-Hispanic Black patients accounted for 1.3%, 5.4%, 13.4%, and 33.8% of patients respectively (Table 1).
      Table 1Patient demographics according to Black-serving hospital quartiles
      DemographicsBlack-serving hospital quartileP value
      First (lowest), n (%)Second, n (%)Third, n (%)Fourth (highest), n (%)
      Race
       Non-Hispanic White140,549 (57.9)137,187 (56.9)112,767 (47.4)91,715 (37.6)<.001
       Non-Hispanic Black3051 (1.3)12,923 (5.4)31,942 (13.4)82,415 (33.8)
       Hispanic60,888 (25.1)57,241 (23.8)60,661 (25.5)45,498 (18.7)
       Asian18,737 (7.7)13,611 (5.6)14,463 (6.1)8614 (3.5)
       Other9271 (3.8)13,930 (5.8)10,217 (4.3)12,998 (5.3)
       Unknown10,241 (4.2)6065 (2.5)7700 (3.2)2518 (1.0)
      Obstetrical comorbidity index
       0150,249 (61.9)145,783 (60.5)142,311 (59.9)141,396 (58.0)<.001
       162,293 (25.7)62,897 (26.1)63,098 (26.5)67,926 (27.9)
       223,039 (9.5)24,167 (10.0)24,208 (10.2)25,262 (10.4)
       ≥37156 (2.9)8110 (3.4)8133 (3.4)9174 (3.8)
      Bed size<.001
       Small31,545 (13.0)23,982 (10.0)21,589 (9.1)20,001 (8.2)
       Medium71,552 (29.5)63,112 (26.2)42,060 (17.7)63,663 (26.1)
       Large139,640 (57.5)153,863 (63.9)174,101 (73.2)160,094 (65.7)
      Teaching hospitals50,618 (20.9)124,144 (51.5)131,434 (55.3)163,110 (66.9)<.001
      Urban area215,760 (88.9)231,911 (96.2)230,476 (96.9)230,072 (94.4)<.001
      Region<.001
       Northeast30,177 (12.4)59,306 (24.6)86,337 (36.3)52,877 (21.7)
       South5,607 (2.3)36,402 (15.1)77,372 (32.5)167,345 (68.7)
       West175,836 (72.4)113,130 (47.0)56,113 (23.6)5,967 (2.4)
       Midwest31,117 (12.8)32,119 (13.3)17,928 (7.5)17,569 (7.2)
      Obstetrical Medicaid burden<.001
       First quartile (lowest)86,296 (35.6)68,409 (28.4)60,642 (25.5)27,437 (11.3)
       Second quartile62,526 (25.8)60,574 (25.1)69,242 (29.1)47,563 (19.5)
       Third quartile43,332 (17.9)60,020 (24.9)53,744 (22.6)82,359 (33.8)
       Fourth quartile (highest)50,583 (20.8)51,954 (21.6)54,122 (22.8)86,399 (35.4)
      Ona et al. Services and payer mix of Black-serving hospitals and related severe maternal morbidity. Am J Obstet Gynecol 2021.
      Black-serving hospitals were more likely to provide a range of specialized medical and surgical services (Table 2). Cardiac intensive care was present in 48.9% of hospitals in the lowest Black-serving quartile compared to 74.5% of hospitals in the highest Black serving quartile, neonatal intensive care in 28.9% versus 64.9%, pediatric intensive care in 20.0% versus 45.7%, psychiatric care in 49.6% versus 69.1%, pediatric cardiology in 29.6% versus 44.7%, genetic testing and counseling in 35.6% versus 58.5%, and HIV/AIDS services in 36.3% versus 71.3% (p≤0.01 for all). Black-serving hospitals were also more likely to perform higher risk deliveries, with patient comorbidity increasing significantly by Black-serving quartile (p<0.01). While statistical comparisons for some medical and surgical services were non-significant, there were no services that were more likely in the lowest compared to the highest Black-serving hospital quartile.
      Table 2Medical, surgical and safety-net services according to Black-serving hospital quartile
      ServicesBlack-serving hospital quartileP value
      First (lowest) (n=135)Second (n=103)Third (n=98)Fourth (highest) (n=94)
      Medical and surgical services (% of hospitals)
       Medical-surgical intensive care82.285.492.990.4.07
       Cardiac intensive care48.955.362.274.5<.01
      Indicates statistical significance.
       Neonatal intermediate care50.460.266.370.2.01
       Neonatal intensive care28.939.846.964.9<.01
      Indicates statistical significance.
       Pediatric intensive care20.031.139.845.7<.01
      Indicates statistical significance.
       Psychiatric care49.659.268.469.1<.01
       Adult cardiology services79.382.589.886.2.16
       Pediatric cardiology services29.643.749.044.7.01
       Adult interventional cardiac catheterization62.276.784.779.8<.01
      Indicates statistical significance.
       Adult cardiac surgery54.865.068.472.3.03
       Genetic testing and counseling35.649.557.158.5<.01
      Indicates statistical significance.
       Hemodialysis74.886.487.886.2.02
       Certified trauma center48.247.650.061.7.15
       HIV and AIDS services36.353.463.371.3<.01
      Indicates statistical significance.
       Oncology services80.782.586.786.2.56
       Neurology services73.382.585.783.0.08
       Magnetic resonance imaging88.988.391.889.4.86
       Ultrasound radiology services88.188.391.890.4.79
       Image-guided radiation therapy52.658.366.366.0.10
       Proton beam therapy48.961.258.263.8.10
       Transplant services—bone marrow17.825.226.533.0.07
       Transplant services—heart17.020.419.427.7.26
       Transplant services—kidney21.526.226.534.0.21
       Transplant services—liver15.623.316.329.8.04
       Transplant services—lung9.618.412.216.0.22
      Safety-net services (% of hospitals)P value
       Indigent care clinic34.152.462.264.9<.01
      Indicates statistical significance.
       Enrollment services77.079.686.780.9.32
       Crisis prevention43.050.562.261.7.01
       Enabling services38.550.554.161.7<.01
       Social work services87.489.387.889.4.95
      OwnershipP value
       Investor14.815.510.219.1<.01
       Nonprofit, nongovernment77.077.778.657.5
       Government8.26.811.223.4
      Obstetrical Medicaid burden (% of hospitals)P value
       First quartile (lowest)25.222.316.37.4<.01
      Indicates statistical significance.
       Second quartile28.128.226.516.0
       Third quartile23.727.225.531.9
       Fourth quartile (highest)23.022.331.644.7
      All hospitalization Medicaid burden (% of hospitals)P value
       First quartile (lowest)27.429.117.412.8<.01
      Indicates statistical significance.
       Second quartile30.434.036.722.3
       Third quartile23.020.423.527.7
       Fourth quartile (highest)19.316.522.537.2
      Bonferroni correction was performed for multiple comparisons. The statistically significant level was set at α/n=0.05/32=0.001563.
      Ona et al. Services and payer mix of Black-serving hospitals and related severe maternal morbidity. Am J Obstet Gynecol 2021.
      a Indicates statistical significance.
      Evaluating safety-net services, Black-serving hospitals were also more likely to have indigent care clinics, crisis prevention, and enabling services (p≤0.01 for all). Black-serving hospitals had higher Medicaid burdens; 76% of hospitals in the highest Black-serving quartile had an above average obstetric Medicaid burden (<0.01). In addition to obstetric Medicaid burden, Black-serving hospitals also had higher all-hospitalization Medicaid burden and were more likely to be government owned (p<0.01 for both).
      In the unadjusted model for SMM, Black-serving hospital quartile was associated with increased risk for SMM with risk higher in the 2nd quartile (RR 1.40, 95% CI 1.30, 1.51), 3rd quartile (RR 1.35, 95% CI 1.26, 1.46), and 4th quartile (RR 1.57, 95% CI 1.46, 1.69) compared to the 1st quartile (Table 3). The presence of medical and surgical services such as medical surgical intensive care (RR 1.57, 95% CI 1.42, 1.73), neonatal intensive care (RR 1.20, 95% CI 1.14, 1.26), a trauma center (RR 1.44, 95% CI 1.37, 1.52), and HIV/AIDS services (RR 1.24, 95% CI 1.18, 1.31) were associated with increased risk for SMM. Government compared to investor ownership (RR 1.46, 95% CI 1.31, 1.62) and safety-net services such as enrollment services (RR 1.44, 95% CI 1.33, 1.56) were also associated with increased risk. Other significant risk factors included non-Hispanic Black race (RR 1.62, 95% CI 1.52-1.73), higher comorbidity index score, higher Medicaid burden compared to the lowest Medicaid burden quartile, larger compared to smaller bed volume, teaching versus non-teaching status (RR 1.37, 95% CI 1.30-1.44), and urban versus rural location (RR 1.61, 95% CI 1.41, 1.83).
      Table 3Adjusted and unadjusted model for severe maternal morbidity excluding transfusion
      CharacteristicsUnadjusted RR (95% CI)First adjusted model, aRR (95% CI)Second adjusted model, aRR (95% CI)
      Race
       Non-Hispanic WhiteRefRefRef
       Non-Hispanic Black1.62 (1.52–1.73)
      Statistically significant with a P value of <.01
      1.48 (1.38–1.59)
      Statistically significant with a P value of <.01
      1.48 (1.38–1.59)
      Statistically significant with a P value of <.01
       Hispanic1.02 (0.96–1.09)1.11 (1.03–1.18)
      Statistically significant with a P value of <.05
      1.11 (1.04–1.19)
      Statistically significant with a P value of <.05
       Asian0.83 (0.67–1.03)0.93 (0.75–1.16)0.86 (0.69–1.08)
       Other1.26 (1.13–1.41)
      Statistically significant with a P value of <.01
      1.24 (1.11–1.39)
      Statistically significant with a P value of <.05
      1.24 (1.11–1.39)
      Statistically significant with a P value of <.05
       Unknown0.78 (0.65–0.94)
      Statistically significant with a P value of <.05
      0.87 (0.72–1.04)0.82 (0.68–0.98)
      Statistically significant with a P value of <.05
      Obstetrical comorbidity index
       0RefRefRef
       12.41 (2.26–2.56)
      Statistically significant with a P value of <.01
      2.40 (2.25–2.56)
      Statistically significant with a P value of <.01
      2.37 (2.23–2.53)
      Statistically significant with a P value of <.01
       23.74 (3.48–4.03)
      Statistically significant with a P value of <.01
      3.77 (3.50–4.06)
      Statistically significant with a P value of <.01
      3.70 (3.44–3.99)
      Statistically significant with a P value of <.01
       ≥313.35 (12.45–14.32)
      Statistically significant with a P value of <.01
      13.25 (12.35–14.22)
      Statistically significant with a P value of <.01
      12.94 (12.05–13.89)
      Statistically significant with a P value of <.01
      Black-serving hospital quartile
       First quartile (lowest)RefRefRef
       Second quartile1.40 (1.30–1.51)
      Statistically significant with a P value of <.01
      1.30 (1.20–1.40)
      Statistically significant with a P value of <.01
      1.23 (1.14–1.34)
      Statistically significant with a P value of <.01
       Third quartile1.35 (1.26–1.46)
      Statistically significant with a P value of <.01
      1.19 (1.10–1.29)
      Statistically significant with a P value of <.01
      1.12 (1.02–1.23)
      Statistically significant with a P value of <.05
       Fourth quartile (highest)1.57 (1.46–1.69)
      Statistically significant with a P value of <.01
      1.21 (1.11–1.31)
      Statistically significant with a P value of <.01
      1.05 (0.94–1.18)
      Obstetrical Medicaid burden
       First quartile (lowest)RefRefRef
       Second quartile1.34 (1.25–1.44)
      Statistically significant with a P value of <.01
      1.38 (1.29–1.49)
      Statistically significant with a P value of <.01
      1.32 (1.22–1.43)
      Statistically significant with a P value of <.01
       Third quartile1.25 (1.16–1.34)
      Statistically significant with a P value of <.01
      1.27 (1.18–1.37)
      Statistically significant with a P value of <.01
      1.15 (1.06–1.25)
      Statistically significant with a P value of <.05
       Fourth quartile (highest)1.20 (1.11–1.29)
      Statistically significant with a P value of <.01
      1.23 (1.14–1.33)
      Statistically significant with a P value of <.01
      1.22 (1.12–1.33)
      Statistically significant with a P value of <.01
      Bed size
       SmallRefn/aRef
       Medium1.40 (1.26–1.55)
      Statistically significant with a P value of <.01
      n/a1.28 (1.15–1.43)
      Statistically significant with a P value of <.01
       Large1.41 (1.28–1.55)
      Statistically significant with a P value of <.01
      n/a1.26 (1.13–1.39)
      Statistically significant with a P value of <.01
      Teaching hospital1.37 (1.30–1.44)
      Statistically significant with a P value of <.01
      n/a1.11 (1.04–1.19)
      Statistically significant with a P value of <.05
      Urban area1.61 (1.41–1.83)
      Statistically significant with a P value of <.01
      n/a1.43 (1.24–1.65)
      Statistically significant with a P value of <.01
      Region
       NortheastRefn/aRef
       South1.14 (1.07–1.22)
      Statistically significant with a P value of <.01
      n/a1.02 (0.92–1.13)
       West0.87 (0.81–0.93)
      Statistically significant with a P value of <.01
      n/a1.18 (1.09–1.28)
      Statistically significant with a P value of <.01
       Midwest0.94 (0.85–1.03)n/a1.05 (0.96–1.14)
      Medical-surgical intensive care1.57 (1.42–1.73)
      Statistically significant with a P value of <.01
      n/a1.13 (0.98–1.31)
      Neonatal intensive care1.20 (1.14–1.26)
      Statistically significant with a P value of <.01
      n/a0.99 (0.93–1.05)
      Trauma center1.44 (1.37–1.52)
      Statistically significant with a P value of <.01
      n/a1.40 (1.31–1.51)
      Statistically significant with a P value of <.01
      Genetic testing and counseling1.29 (1.23–1.37)
      Statistically significant with a P value of <.01
      n/a0.96 (0.89–1.04)
      HIV and AIDS services1.24 (1.18–1.31)
      Statistically significant with a P value of <.01
      n/a1.04 (0.97–1.11)
      Transplant services
      Services include at least 1 of the following types of transplants: marrow, heart, lung, kidney, or liver transplant.
      1.18 (1.13–1.24)
      Statistically significant with a P value of <.01
      n/a0.95 (0.89–1.01)
      Indigent care clinic1.17 (1.11–1.23)
      Statistically significant with a P value of <.01
      n/a0.90 (0.84–0.96)
      Statistically significant with a P value of <.05
      Enrollment services1.44 (1.33–1.56)
      Statistically significant with a P value of <.01
      n/a1.14 (1.01–1.28)
      Statistically significant with a P value of <.05
      Crisis prevention1.01 (0.96–1.06)n/a0.83 (0.78–0.89)
      Statistically significant with a P value of <.01
      Enabling services1.20 (1.14–1.26)
      Statistically significant with a P value of <.01
      n/a0.98 (0.91–1.05)
      Ownership
       InvestorRefn/aRef
       Nonprofit, nongovernment1.23 (1.13–1.34)
      Statistically significant with a P value of <.01
      n/a1.12 (0.99–1.26)
       Government1.46 (1.31–1.62)
      Statistically significant with a P value of <.01
      n/a1.11 (1.00–1.22)
      Statistically significant with a P value of <.05
      The first adjusted model includes a limited number of factors that were included in the table. The second adjusted model includes the additional hospital factors listed in the table. The models above account for hospital-level clustering.
      aRR, adjusted risk ratio; CI, confidence interval; n/a, not applicable; RR, unadjusted risk ratio.
      Ona et al. Services and payer mix of Black-serving hospitals and related severe maternal morbidity. Am J Obstet Gynecol 2021.
      a Statistically significant with a P value of <.01
      b Statistically significant with a P value of <.05
      c Services include at least 1 of the following types of transplants: marrow, heart, lung, kidney, or liver transplant.
      In the first adjusted model accounting for Black-serving hospital quartile, maternal race, obstetric comorbidity index, and obstetric Medicaid burden, these risk factors generally retained their statistical significance compared to the unadjusted model (Table 3). Increased risk associated with delivering in the 4th (highest) compared to 1st (lowest) Black-serving hospital quartile was attenuated but still present (aRR 1.21, 95% 1.11-1.31). Estimates for increased risk associated with delivery in the 2nd quartile (aRR 1.30, 95% CI 1.20, 1.40) and 3rd quartile (aRR 1.19, 95% CI 1.10, 1.29) of Black-serving hospitals compared to the 1st quartile were similar to the unadjusted analysis. Risk estimates between the 2nd, 3rd and 4th quartiles did not differ significantly.
      In the second adjusted model including a number of hospital characteristics from the NIS and the AHA survey, maternal race, comorbidity index score, and obstetric Medicaid burden again retained significance. Larger hospital bed volume, being a teaching hospital, urban compared to rural location, the presence of a trauma center, and enrollment services were all associated with increased risk for SMM. Compared to the unadjusted model, the effect of delivering in the 3rd versus 1st quartile Black-serving hospital quartile was attenuated (aRR 1.12, 95% CI 1.02, 1.23). The risk of delivering in the 4th compared to the 1st Black-serving hospital quartile was not significantly different (aRR 1.05, 95% CI 0.94, 1.18). Delivering in the 2nd versus 1st Black-serving hospital quartile was associated with increased risk (aRR 1.23, 95% CI 1.14, 1.34). When models were repeated accounting for clustering, confidence intervals were broader (Supplemental Table 1).
      In the third adjusted analysis utilizing principal component analysis, 7 variables were constructed accounting for 69.6% of the total variance (Supplemental Figure 1). All of the hospital level variables were incorporated in these principal components with the exception of psychiatric care, HIV/AIDS services, and pediatric cardiology services. In the adjusted principal component analysis models additionally accounting for comorbidity index score and payer, risks associated with the 2nd, 3rd, and 4th quartiles of Black-serving hospitals were similar (aRR 1.31, 95% 1.08- 1.50; aRR 1.27, 95% CI 1.05-1.55; aRR 1.29, 95% CI 1.07-1.55, respectively) (Table 4).
      Table 4Principal component and inverse probability of treatment weighting analysis of the effect of Black-serving hospital quartile on severe morbidity excluding transfusion
      Black-serving hospital quartilePrincipal component analysis

      Adjusted risk ratio with 95% CI
      IPTW analysis

      Adjusted risk ratio with 95% CI
       First quartile (lowest)RefRef
       Second quartile1.31 (1.08–1.59)
      Statistically significant with a P value of <.05
      1.44 (1.31–1.60)
      Statistically significant with a P value of <.01.
       Third quartile1.27 (1.05–1.55)
      Statistically significant with a P value of <.05
      1.34 (1.21–1.49)
      Statistically significant with a P value of <.01.
       Fourth quartile (highest)1.29 (1.07–1.55)
      Statistically significant with a P value of <.05
      1.50 (1.36–1.66)
      Statistically significant with a P value of <.01.
      Principal component analysis model included 7 principal components and an adjustment for comorbidity index score and payer.
      CI, confidence interval; IPTW, inverse probability of treatment weighting.
      Ona et al. Services and payer mix of Black-serving hospitals and related severe maternal morbidity. Am J Obstet Gynecol 2021.
      a Statistically significant with a P value of <.05
      b Statistically significant with a P value of <.01.

      Discussion

       Main findings

      This cross-sectional analysis found that Black-serving hospitals were more likely to provide a range of specialized medical and surgical services, to provide safety-net services, and to have a significant Medicaid burden. Medical and surgical services, safety-net services, and Medicaid burden in turn were all associated with SMM. Analyzing Black-serving hospital quartile and risk for SMM in adjusted analyses, this study found that risk in the lowest Black-serving quartile was generally lower than other quartiles. However, risk between the 2nd, 3rd, and 4th quartiles generally did not differ significantly despite large differences in the proportion of non-Hispanic Black patients.

       Clinical implications

      These findings may have implications for maternal outcomes and disparities research. First, this analysis supports the possibility that unmeasured patient-level confounding may account for some increased risk for adverse maternal outcomes in Black-serving hospitals. It is likely that hospitals with a broad array of specialized medical and surgical services attract a patient population with higher underlying comorbidity even if these services are not directly related to obstetric care. In the unadjusted model, specialized medical and surgical services were generally associated with increased risk for SMM and they were more likely to be associated with Black-serving hospitals. Health services research in other areas of medicine has demonstrated on a hospital level that more specialized services may be associated with increased risk, even if the services are not directly clinically related to a particular outcome.
      • Wang H.E.
      • Donnelly J.P.
      • Shapiro N.I.
      • Hohmann S.F.
      • Levitan E.B.
      Hospital variations in severe sepsis mortality.
      ,
      • Volk M.L.
      • Sakr A.
      • De Vera M.
      Cardiovascular complications After liver transplant: a shifting clinical presentation.
      Specialized hospital services may be a ‘marker’ for incomplete case-mix adjustment, which is a concern with administrative data research as secondary diagnosis codes not associated with reimbursement may be under-ascertained.
      • Nimptsch U.
      Disease-specific trends of comorbidity coding and implications for risk adjustment in hospital administrative data.
      Second, Black-serving hospitals were more likely to provide safety-net services and have a higher Medicaid burden. These findings support the possibility that Black-serving hospitals not only care for a higher-risk referral population, but do so at lower reimbursement rates with attendant implications for staffing and resources. Third, given that small differences in risk for SMM were noted between the 2nd, 3rd, and 4th quartiles despite large differences in maternal race and ethnicity supports that there may be unmeasured hospital-level factors that account for outcomes. More granular data including the presence of safety protocols, obstetric care staffing, and quality assurance procedures may further explain variation.

       Strengths and limitations

      There are several limitations that are important to consider in interpreting the findings of this study. First, while the NIS is a nationally representative sample, exclusions in this study including multiple states not providing data on race mean that the patient population was not nationally representative. If risk factor or outcome differentials are present in included versus excluded hospitals, the results of this study may be biased. Second, while the study did include a large population, these findings do not preclude the possibility that Black-serving hospitals in specific geographic regions may account for significantly more risk related to adverse outcomes after adjustment.
      • Howell E.A.
      • Egorova N.N.
      • Balbierz A.
      • Zeitlin J.
      • Hebert P.L.
      Site of delivery contribution to black-white severe maternal morbidity disparity.
      Third, cross sectional data precludes the more robust case-mix adjustment that could occur with longitudinal data. Fourth, administrative data is used primarily for billing and there are concerns for both under-ascertainment and misclassification for both exposures and outcomes. Fifth, we linked the 2013 AHA survey to the 2010 and 2011 NIS and it is possible there may have been some hospital-level changes in facilities that occurred between 2011 and 2013. Finally, it is of note that the NIS data used is almost ten years old and may not reflect current clinical risk estimates. These models could be repeated using State Inpatient Databases with more contemporary data.
      • Easter S.R.
      • Robinson J.N.
      • Menard M.K.
      • et al.
      Potential effects of regionalized maternity care on U.S. Hospitals.
      Strengths of this study include the large population of hospitals and patients, the ability to analyze a number of medical and surgical and safety-net services, and that a number of approaches to adjusted analysis yielded relatively consistent results.

       Conclusion

      In conclusion, this study found that Black-serving hospitals were more likely to provide a range of specialized medical, surgical, and safety-net services and to have a higher Medicaid burden. These findings build on prior studies of Black-serving hospitals
      • Howell E.A.
      • Egorova N.
      • Balbierz A.
      • Zeitlin J.
      • Hebert P.L.
      Black-white differences in severe maternal morbidity and site of care.
      by performing further investigations including more detailed hospital-level factors. This study supports the hypotheses that payer mix and unmeasured confounding may account for some of the maternal risk associated with Black-serving hospitals and that the proportion of non-Hispanic Black patients delivering at a hospital may not be a robust risk factor for severe morbidity in adjusted analyses. These findings further support that enhancing resources at Medicaid-heavy, referral hospitals caring for disproportionately higher risk and non-Hispanic Black populations may be important in reducing maternal disparities and improving overall maternal care.

      Appendix

      Figure thumbnail fx1
      Supplemental FigureRotated factor pattern in principal component analysis for SMM
      The varimax rotation was applied to maximize the variance of a column of the factor pattern matrix. Printed component loadings values are multiplied by 100 and rounded to the nearest integer. The cutoff we used for loading a hospital variable to a factor is the absolute value of component loadings greater than 0.4 denoted by an asterisk.
      SMM, severe maternal morbidity.
      Ona et al. Services and payer mix of Black-serving hospitals and related severe maternal morbidity. Am J Obstet Gynecol 2021.
      Supplemental TableModels for severe morbidity excluding transfusion accounting for hospital clustering
      CharacteristicsUnadjusted RR (95% CI)First adjusted model, aRR (95% CI)Second adjusted model, aRR (95% CI)
      Race
       Non-Hispanic WhiteRefRefRef
       Non-Hispanic Black1.62 (1.44–1.83)
      Statistically significant with a P value of <.01
      1.48 (1.31–1.67)
      Statistically significant with a P value of <.01
      1.48 (1.31–1.67)
      Statistically significant with a P value of <.01
       Hispanic1.02 (0.90–1.16)1.11 (0.99–1.24)1.11 (1.00–1.23)
      Statistically significant with a P value of <.05
       Asian0.83 (0.61–1.13)0.93 (0.70–1.23)0.86 (0.66–1.13)
       Other1.26 (1.06–1.50)
      Statistically significant with a P value of <.05
      1.24 (1.06–1.44)
      Statistically significant with a P value of <.05
      1.24 (1.08–1.44)
      Statistically significant with a P value of <.05
       Unknown0.78 (0.62–0.98)
      Statistically significant with a P value of <.05
      0.87 (0.70–1.07)0.82 (0.67–1.01)
      Obstetrical comorbidity index
       0RefRefRef
       12.41 (2.20–2.63)
      Statistically significant with a P value of <.01
      2.40 (2.19–2.63)
      Statistically significant with a P value of <.01
      2.37 (2.17–2.59)
      Statistically significant with a P value of <.01
       23.74 (3.39–4.13)
      Statistically significant with a P value of <.01
      3.77 (3.41–4.16)
      Statistically significant with a P value of <.01
      3.70 (3.35–4.09)
      Statistically significant with a P value of <.01
       ≥313.35 (11.87–15.02)
      Statistically significant with a P value of <.01
      13.25 (11.74–14.96)
      Statistically significant with a P value of <.01
      12.94 (11.47–14.60)
      Statistically significant with a P value of <.01
      Black-serving hospital quartile
       First quartile (lowest)RefRefRef
       Second quartile1.40 (1.14–1.73)
      Statistically significant with a P value of <.05
      1.30 (1.06–1.58)
      Statistically significant with a P value of <.05
      1.23 (1.03–1.48)
      Statistically significant with a P value of <.05
       Third quartile1.35 (1.08–1.70)
      Statistically significant with a P value of <.05
      1.19 (0.96–1.49)1.12 (0.87–1.45)
       Fourth quartile (highest)1.57 (1.31–1.90)
      Statistically significant with a P value of <.01
      1.21 (0.99–1.46)1.05 (0.82–1.36)
      Obstetrical Medicaid burden
       First quartile (lowest)RefRefRef
       Second quartile1.34 (1.06–1.69)
      Statistically significant with a P value of <.05
      1.38 (1.11–1.71)
      Statistically significant with a P value of <.05
      1.32 (1.10–1.60)
      Statistically significant with a P value of <.05
       Third quartile1.25 (1.04–1.50)
      Statistically significant with a P value of <.05
      1.27 (1.08–1.50)
      Statistically significant with a P value of <.05
      1.15 (0.97–1.37)
       Fourth quartile (highest)1.20 (1.01–1.41)
      Statistically significant with a P value of <.05
      1.23 (1.05–1.43)
      Statistically significant with a P value of <.05
      1.22 (1.04–1.44)
      Statistically significant with a P value of <.05
      Bed size
       SmallRefn/aRef
       Medium1.40 (1.10–1.78)
      Statistically significant with a P value of <.05
      n/a1.28 (1.01–1.62)
      Statistically significant with a P value of <.05
       Large1.41 (1.15–1.71)
      Statistically significant with a P value of <.05
      n/a1.26 (1.04–1.52)
      Statistically significant with a P value of <.05
      Teaching status1.37 (1.17–1.60)
      Statistically significant with a P value of <.01
      n/a1.11 (0.88–1.41)
      Urban1.61 (1.35–1.91)
      Statistically significant with a P value of <.01
      n/a1.43 (1.13–1.80)
      Statistically significant with a P value of <.05
      Region
       NortheastRefn/aRef
       South1.14 (0.90–1.44)n/a1.18 (0.88–1.60)
       West0.87 (0.70–1.08)n/a1.05 (0.85–1.29)
       Midwest0.94 (0.72–1.22)n/a1.02 (0.81–1.27)
      Medical surgical intensive care1.57 (1.32–1.86)
      Statistically significant with a P value of <.01
      n/a1.13 (0.90–1.43)
      Neonatal intensive care1.20 (1.02–1.40)
      Statistically significant with a P value of <.05
      n/a0.99 (0.83–1.18)
      Trauma center1.44 (1.25–1.67)
      Statistically significant with a P value of <.01
      n/a1.40 (1.15–1.71)
      Statistically significant with a P value of <.05
      Genetic testing and counseling1.29 (1.10–1.52)
      Statistically significant with a P value of <.05
      n/a0.96 (0.75–1.22)
      HIV and AIDS services1.24 (1.07–1.45)
      Statistically significant with a P value of <.05
      n/a1.04 (0.84–1.28)
      Transplant services
      Services include at least 1 of the following types of transplants: marrow, heart, lung, kidney, or liver transplant.
      1.18 (1.01–1.39)
      Statistically significant with a P value of <.05
      n/a0.95 (0.78–1.16)
      Indigent care clinic1.17 (1.00–1.37)n/a0.90 (0.74–1.09)
      Enrollment services (ENR)1.44 (1.25–1.65)
      Statistically significant with a P value of <.01
      n/a1.14 (0.93–1.40)
      Crisis prevention (CPREV)1.01 (0.84–1.20)n/a0.83 (0.61–1.13)
      Enabling services (ENB)1.20 (1.02–1.40)
      Statistically significant with a P value of <.05
      n/a0.98 (0.80–1.20)
      Ownership
       InvestorRefn/aRef
       Nonprofit, nongovernment1.23 (1.04–1.46)
      Statistically significant with a P value of <.05
      n/a1.12 (0.87–1.43)
       Government1.46 (1.16–1.84)
      Statistically significant with a P value of <.05
      n/a1.11 (0.89–1.38)
      The first adjusted model includes the limited factors including in the table. The second adjusted model includes the additional hospital factors listed in the table.
      aRR, adjusted risk ratio; CI, confidence interval; RR, unadjusted risk ratio; SMM, severe maternal morbidity.
      Ona et al. Services and payer mix of Black-serving hospitals and related severe maternal morbidity. Am J Obstet Gynecol 2021.
      a Statistically significant with a P value of <.01
      b Statistically significant with a P value of <.05
      c Services include at least 1 of the following types of transplants: marrow, heart, lung, kidney, or liver transplant.

      References

      1. ACOG Committee Opinion No. 649: racial and ethnic disparities in obstetrics and gynecology.
        Obstet Gynecol. 2015; 126: e130-e134
        • Louis J.M.
        • Menard M.K.
        • Gee R.E.
        Racial and ethnic disparities in maternal morbidity and mortality.
        Obstet Gynecol. 2015; 125: 690-694
        • Grobman W.A.
        • Bailit J.L.
        • Rice M.M.
        • et al.
        Racial and ethnic disparities in maternal morbidity and obstetric care.
        Obstet Gynecol. 2015; 125: 1460-1467
        • Leonard S.A.
        • Main E.K.
        • Scott K.A.
        • Profit J.
        • Carmichael S.L.
        Racial and ethnic disparities in severe maternal morbidity prevalence and trends.
        Ann Epidemiol. 2019; 33: 30-36
        • Creanga A.A.
        • Bateman B.T.
        • Kuklina E.V.
        • Callaghan W.M.
        Racial and ethnic disparities in severe maternal morbidity: a multistate analysis, 2008-2010.
        Am J Obstet Gynecol. 2014; 210: 435.e1-435.e8
        • Admon L.K.
        • Winkelman T.N.A.
        • Zivin K.
        • Terplan M.
        • Mhyre J.M.
        • Dalton V.K.
        Racial and ethnic disparities in the incidence of severe maternal morbidity in the United States, 2012-2015.
        Obstet Gynecol. 2018; 132: 1158-1166
        • Bryant A.S.
        • Worjoloh A.
        • Caughey A.B.
        • Washington A.E.
        Racial/ethnic disparities in obstetric outcomes and care: prevalence and determinants.
        Am J Obstet Gynecol. 2010; 202: 335-343
        • Ly D.P.
        • Lopez L.
        • Isaac T.
        • Jha A.K.
        How do black-serving hospitals perform on patient safety indicators? Implications for national public reporting and pay-for-performance.
        Med Care. 2010; 48: 1133-1137
        • Li Y.
        • Yin J.
        • Cai X.
        • Temkin-Greener J.
        • Mukamel D.B.
        Association of race and sites of care with pressure ulcers in high-risk nursing home residents.
        JAMA. 2011; 306: 179-186
        • Lucas F.L.
        • Stukel T.A.
        • Morris A.M.
        • Siewers A.E.
        • Birkmeyer J.D.
        Race and surgical mortality in the United States.
        Ann Surg. 2006; 243: 281-286
        • Haider A.H.
        • Ong’uti S.
        • Efron D.T.
        • et al.
        Association between hospitals caring for a disproportionately high percentage of minority trauma patients and increased mortality: a nationwide analysis of 434 hospitals.
        Arch Surg. 2012; 147: 63-70
        • Hasnain-Wynia R.
        • Baker D.W.
        • Nerenz D.
        • et al.
        Disparities in health care are driven by where minority patients seek care: examination of the hospital quality alliance measures.
        Arch Intern Med. 2007; 167: 1233-1239
        • Howell E.A.
        • Egorova N.
        • Balbierz A.
        • Zeitlin J.
        • Hebert P.L.
        Black-white differences in severe maternal morbidity and site of care.
        Am J Obstet Gynecol. 2016; 214: 122.e1-122.e7
        • Creanga A.A.
        • Bateman B.T.
        • Mhyre J.M.
        • Kuklina E.
        • Shilkrut A.
        • Callaghan W.M.
        Performance of racial and ethnic minority-serving hospitals on delivery-related indicators.
        Am J Obstet Gynecol. 2014; 211: 647.e1-647.e16
        • Howell E.A.
        • Egorova N.N.
        • Balbierz A.
        • Zeitlin J.
        • Hebert P.L.
        Site of delivery contribution to black-white severe maternal morbidity disparity.
        Am J Obstet Gynecol. 2016; 215: 143-152
        • Klebanoff M.A.
        • Snowden J.M.
        Historical (retrospective) cohort studies and other epidemiologic study designs in perinatal research.
        Am J Obstet Gynecol. 2018; 219: 447-450
        • Agency for Healthcare Research and Quality
        Overview of the National (Nationwide) inpatient sample (NIS).
        (Available at:)
        https://www.hcup-us.ahrq.gov/nisoverview.jsp
        Date: 2018
        Date accessed: September 17, 2019
        • Houchens R.
        • Ross D.
        • Elixhauser A.
        • Jiang J.
        Nationwide inpatient sample (NIS) redesign final report. HCUP Methods Series Report # 2014-04 ONLINE. 2014.
        (Available at:) (Accessed February 1, 2021)
        • American Hospital Association
        AHA data and insights: data collection methods.
        (Available at:)
        • Kozhimannil K.B.
        • Hung P.
        • Henning-Smith C.
        • Casey M.M.
        • Prasad S.
        Association Between loss of hospital-based obstetric services and birth outcomes in rural counties in the United States.
        JAMA. 2018; 319: 1239-1247
        • Zhu J.
        • Dy S.M.
        • Wenzel J.
        • Wu A.W.
        Association of magnet status and nurse staffing with improvements in patient experience with hospital care, 2008-2015.
        Med Care. 2018; 56: 111-120
        • Walker D.M.
        • Mora A.M.
        • Hogan T.H.
        • Diana M.L.
        • McAlearney A.S.
        Assessing trends in hospital system structures From 2008 to 2015.
        Med Care. 2018; 56: 831-839
        • Talutis S.D.
        • Chen Q.
        • Wang N.
        • Rosen A.K.
        Comparison of risk-standardized readmission rates of surgical patients at safety-net and non-safety-net Hospitals Using Agency for Healthcare Research and quality and American Hospital Association data.
        JAMA Surg. 2019; 154: 391-400
        • Kuklina E.V.
        • Whiteman M.K.
        • Hillis S.D.
        • et al.
        An enhanced method for identifying obstetric deliveries: implications for estimating maternal morbidity.
        Matern Child Health J. 2008; 12: 469-477
        • Bateman B.T.
        • Mhyre J.M.
        • Hernandez-Diaz S.
        • et al.
        Development of a comorbidity index for use in obstetric patients.
        Obstet Gynecol. 2013; 122: 957-965
        • Metcalfe A.
        • Lix L.M.
        • Johnson J.A.
        • et al.
        Validation of an obstetric comorbidity index in an external population.
        BJOG. 2015; 122: 1748-1755
        • Jolliffe I.T.
        • Cadima J.
        Principal component analysis: a review and recent developments.
        Philos Trans A Math Phys Eng Sci. 2016; 374: 20150202
        • Wang H.E.
        • Donnelly J.P.
        • Shapiro N.I.
        • Hohmann S.F.
        • Levitan E.B.
        Hospital variations in severe sepsis mortality.
        Am J Med Qual. 2015; 30: 328-336
        • Volk M.L.
        • Sakr A.
        • De Vera M.
        Cardiovascular complications After liver transplant: a shifting clinical presentation.
        Liver Transpl. 2018; 24: 1331-1332
        • Nimptsch U.
        Disease-specific trends of comorbidity coding and implications for risk adjustment in hospital administrative data.
        Health Serv Res. 2016; 51: 981-1001
        • Easter S.R.
        • Robinson J.N.
        • Menard M.K.
        • et al.
        Potential effects of regionalized maternity care on U.S. Hospitals.
        Obstet Gynecol. 2019; 134: 545-552