RACIAL AND INSURANCE DISPARITIES IN HOSPITAL REFERRAL AND MORTALITY FOR CHILDREN UNDERGOING CONGENITAL HEART SURGERY. Titus Chan

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1 RACIAL AND INSURANCE DISPARITIES IN HOSPITAL REFERRAL AND MORTALITY FOR CHILDREN UNDERGOING CONGENITAL HEART SURGERY by Titus Chan A thesis submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Master of Science In in Clinical Investigation School of Medicine The University of Utah May 2009

2 Copyright Titus Chan 2009 All Rights Reserved

3 THE UNIVERSITY OF UTAH GRADUATE SCHOOL SUPERVISORY COMMITTEE APPROVAL of a thesis submitted by Titus Chan This thesis has been read by each member of the following supervisory committee and by majority vote has been found to be satisfactory. Chair: Susan Bratton Gregory Stoddard

4 THE UNIVERSITY OF UTAH GRADUATE SCHOOL FINAL READING APPROVAL To the Graduate Council of the University of Utah: I have read the thesis of Titus Chan in its final form and have found that (1) its format, citations, and bibliographic style are consistent and acceptable; (2) its illustrative materials including figures, tables, and charts are in place and (3) the fmal manuscript is satisfactory to the supervisory committee and is ready submission to The Graduate School. Date Susan Bratton Chair: Supervisory Committee Approved for the Major Department Donald McClain ChairlDean Approved for the Graduate Council David S. Chapman Dean of The Graduate School

5 ABSTRACT Racial and insurance disparities have been documented in survival following congenital heart surgery. However, many studies used limited state or regional data. This study examines the impact of race and insurance status by applying a comprehensive risk-stratification system to two of the most recent national pediatric hospitalization databases. All admissions that fit a Risk Adjustment for Congenital Heart Surgery-l Surgery-1 (RACHS-1) category from the Kids' Inpatient Database (KID) 2003 and 2006 were examined. Patient demographics and hospital characteristics were compared between survivors and nonsurvivors as well as between insurance groups. Logistic regression models examining hospital mortality, referral to high mortality hospitals, and emergent admissions were constructed. Children with Medicaid insurance had increased odds of mortality in 2003 (Odds Ratio 1.6, 95% Confidence Interval ). In 2006, nonwhite children had higher odds of mortality (Odds Ratio 1.4, 95% Confidence Interval ). When examining low mortality hospitals in 2006, where greater than 85% of children received care, neither race nor insurance status were independently associated with hospital mortality. However, in both years, children with Medicaid insurance and nonwhite children were more likely to be emergently admitted to hospital for a cardiac surgical procedure. Hospital mortality disparities based on insurance status and race appear to

6 be improving. However, insurance and race continue to playa a role in access to timely pediatric cardiac services. v

7 CONTENTS ABSTRACT iv LIST OF TABLES vii INTRODUCTION METHODS Patient Level Variables... 2 Hospital Level Variables Data Analysis Data Analysis 5 RESULTS RESULTS 7 Univariate Analysis..., Univariate Multivariate Analysis... > DISCUSSION Multivariate..... Analysis DISCUSSION CONCLUSIONS REFERENCES CONCLUSIONS REFERENCES 25

8 LIST OF TABLES Table Page 1. Selected Characteristics of Survivors and Nonsurvivors Selected Demographic and Clinical Characteristics and Insurance Type Selected Demographic and Clinical Characteristics and Insurance Type Final Multivariable Logistic Regression Model for Factors Associated with Hospital Mortality (2003 & 2006) Multivariable Logistic Regression for Factors Associated with an Operation at a High Mortality Hospital (2003 & 2006) Multivariable Logistic Regression Examining Factors Associated with Emergent Admission (2003 & 2006)... 18

9 INTRODUCTION In contrast to adult heart disease, the diagnosis and treatment of congenital heart disease depends on highly specialized and predominantly urban pediatric services. Since such services are relatively scarce, access to high quality pediatric cardiac care can be used to assess health disparities. Previous studies show that both race and insurance status impact age at referral to a pediatric cardiologist l as well as age at surgical repair? repair. Furthermore, both insurance and racial status are associated with referral to hospitals with higher mortality rates. 3,' 4 However, of greatest concern are mortality differences associated with race and insurance for children undergoing congenital heart surgery. Previous studies describe the effect of races, 5,6 6 and insurance 4, 4 ' 7, 7 ' 8 on hospital mortality. However, most studies were limited to a subset of surgical procedures 4, 4,7 7, ' 8 or geographical regions. 2 "-4 4 This study utilizes the comprehensive Risk Adjustment for Congenital Heart Surgery-l Surgery-1 (RACHS-I) (RACHS-1) as well as the Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID) 2003 and 2006, to perform a riskadjusted analysis regarding the influence of race and insurance status on hospital mortality following congenital heart surgery. risk-

10 METHODS This is a retrospective cohort study using the 2003 and 2006 KID. The KID was created by the Agency for Healthcare Research and Quality (AHRQ) as part of the HCUR HCUP. The 2003 KID is a database of unidentified pediatric hospitalization records from 36 states and contains over 2.9 million pediatric discharges, while the 2006 KID contains data from 38 states and has over 3.1 million discharges. Both databases include 10% of uncomplicated hospital births and 80% of complicated in-hospital births and pediatric admissions. The KID contains three separate datasets in each year which contain demographic and administrative data for each discharge, indicators of co-morbidities, and hospital level information such as location, size and type. Patient Level Variables The target population was all children who underwent congenital cardiac surgical procedures in 2003 and Each KID record contains up to 15 diagnosis codes and 15 procedure codes based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9). (lcd-9). Children (18 years of age or younger) were initially selected if they had a diagnosis code consistent with congenital heart disease and an associated procedure. Children with heart disease who underwent diagnostic or interventional catheterization procedures without evidence of a surgical procedure were excluded.

11 3 Using a computerized algorithm that analyzes combinations of procedure and diagnosis codes, a Risk Adjustment for Congenital Heart Surgery-19 9 (RACHS-l) (RACHS-1) category was assigned for all cases with qualifying diagnoses and procedures. The RACHS-1 RACHS-l is a method of in-hospital mortality risk-adjustment that accounts for surgical complexity, age and medical co-morbidities. Increasing RACHS-l RACHS-1 categories correspond with increasingly complex surgical procedures and risk of hospital mortality. For patients with multiple procedures, the highest RACHS-1 RACHS-l category was assigned. The RACHS-l RACHS-1 method also utilizes prematurity, age and the presence of other structural, non-cardiac defects (such as cleft palate and intestinal malformations) as risk factors for mortality. Prematurity and noncardiac structural defects were included as dichotomous variables. Per the RACHS-1 RACHS-l algorithm, age at admission was divided into three groups: less than 30 days, one month to one year and older than one year. The socioeconomic status of each patient was examined indirectly using variables for race, insurance and income quartile of the child's residence. Insurance was divided into three categories: private insurance which included private managed care, Medicaid which included both fee-for-service and Medicaid managed care and "other" forms of payment which included self-pay, Medicare, and charity payments. Race was analyzed initially by HCUP categories consisting of white, black, Hispanic, Asian or Pacific Islander, Native American and other. Because of relatively smaller numbers, Asian/Pacific Islander, Native American and other categories were combined into an "other" group for use in the regression models. Additionally, because of heterogeneous effects within individual racial groups, race was also divided into white and nonwhite for

12 4 some of the analysis. The KID contains a quartile classification of the median income for households in each patient's ZIP code. Patient residency in a ZIP code with a median income in the bottom quartile of the national population was classified as living in a low income area. Other indirect measures of access to care such as admission day (weekend or weekday), and elective versus emergent admission were also assessed. The KID Severity dataset was used to analyze co-morbidities. Co-morbidities are assigned by ARHQ using the ARHQ co-morbidity software, diagnoses and diagnosisrelated groups (DRG) codes. 10 Co-morbidities were recorded as absent or present and included congestive heart failure, coagulopathy, electrolyte abnormalities, neurologic disorders and hypothyroidism. A single dichotomous variable denoting "any" organ dysfunction was constructed using all the co-morbidity variables associated with death in the univariate analysis for each study year. diagnosis- Hospital Level Variables Hospital surgical volume was computed by totaling the number of RACHS-I RACHS-1 procedures for each patient in the individual hospital for both 2003 and A standardized mortality rate (SMR) for hospitals that performed more than 10 procedures per year was computed, adjusting for surgical complexity based on RACHS-l RACHS-1 caseload and using the mortality average for the entire dataset as a baseline. Dichotomous variables were constructed to differentiate high volume and high mortality hospitals from low volume and low mortality hospitals. To determine groupings for both hospital volume and SMR, k-means cluster analysis was used to find natural groupings in the data. I 11 I K K-means cluster analysis assigns individual records into prespecified groups that

13 5 are clustered around group centers and then computes the mean for each group. This process is repeated until all individual records remain in the same group for two iterations. To find the appropriate number. of clusters, the cluster analysis with the largest Calillksi Calihksi and Harabasz pseudo-f value was used. After performing the cluster analysis, the upper cluster for hospital volume was used to denote "high" volume hospitals with all other hospitals being "low" volume hospitals. Similarly, the upper cluster for hospital mortality was used to denote "high" mortality hospitals in Since there was a substantial decrease in overall mortality between 2003 and 2006, 'high" mortality hospitals in 2006 were determined by taking the hospitals with the highest SMR that accounted for a proportionate percentage of patients as compared to 2003 (roughly 13%). Data Analysis Demographics, medical and surgical covariates, hospital characteristics, and socioeconomic variables were compared between survivors and non-survivors and between insurance groups. The chi-square test or the Fisher test was used for dichotomous and categorical data. The Student t-test and analysis of variance tests were used to compare normally distributed continuous data, whereas the Wilcoxon-Mann Wilcoxon-Mann- Whitney test and the Kruskal-Wallis analysis of variance were used for comparison of skewed continuous data. Data are presented as median values with the interquartile range (IQR; 25th and 75 th quartiles). A multivariate logistic regression model examining factors associated with hospital mortality was developed. A p-value ofless than 0.05 in the univariate analysis was considered statistically significant and was used as criteria for consideration in the

14 6 multivariate model. An iterative, forward selection process was used, with the criterion for inclusion being a p-value < If variables were found to change the point estimate of other covariates by more than 10%, they were included in the model as potential confounders. A separate logistic regression model was constructed for each year and covariates included in one year were kept in the final model for both years to allow for comparison between the two study periods. To account for nested clustering of patient outcomes within hospitals, a random effects logistic regression model was utilized. In order to examine differences between "high" and "low" mortality hospitals, the same regression models were analyzed while stratifying on hospital mortality status. A second logistic model examining referral to a "high" mortality hospital was also constructed for each year. The covariates used in the regression model for hospital mortality were used in this second model to determine if variables associated with mortality were also associated with referral to "high" mortality hospitals. A third logistic model examining factors associated with emergent admission for a surgical procedur~ procedure was constructed using the same covariates from the previous two models. To assess goodness-of-fit, a c-statistic was computed for the logistic regression models. The sample size in both years provided approximately 80% power to detect an absolute 1% difference, using a two-sided alpha=0.05 comparison. Stata 10 software (Stata Corp LP, College Statio, TX) was used for all analyses. In compliance with the data usage agreement for HCUP, all cells in tables with less than 10 cases were grouped with larger cells. For this reason, RACHS-1 RACHS-l categories 1 and 2 were grouped together, and categories 5 and 6 were grouped together.

15 RESULTS In 2003, 12,168 procedures had an assignable RACHS-l RACHS-1 category based on ICD-9 procedure codes. The overall surgical mortality rate was 4.0% in In 2006, 13,434 procedures could be assigned a RACHS-1 RACHS-l category. In 2006, the overall mortality rate was 2.9%. In both years, procedures of increasing surgical complexity were significantly associated with mortality. Using cluster analysis, hospitals were considered "high" volume hospitals if their volume was greater than 623 cases per year in 2003 and 591 cases per year in The "high" volume hospitals accounted for 20.4% and 22.1% % of all surgical admissions in 2003 and 2006, respectively and had a significantly lower rate of mortality compared to "low" volume hospitals. In a similar manner, "high" mortality hospitals (SMR > 6.3% in 2003 and> > 4.4% in 2006) accounted for 14.4% and 13.3% of all hospital admissions in 2003 and 2006, respectively. Univariate Analysis Selected demographic and clinical data divided by study year and survival status are summarized in Table 1. In both years, mortality was not associated with sex, but was significantly associated with a younger age. When examining race, the largest portion of children undergoing cardiac procedures were white children, making up 53.9% of all cases in 2003 and 52.8% of all cases in Hispanic and black children were the next largest ethnic/racial groups. Likewise, children with private insurance made up the

16 8 8 Table 1: Selected Characteristics of Survivors and Nonsurvivors Characteristic Survivors Nonsurvivors Survivors Nonsurvivors (2003) (N=11,683) (2003) (N=485) (2006) (n=l3,048) (n=13,048) (2006) (n=386) Age in days, Median (1QR)* (lqr)* 243 ( ) ( ") 18(0-183) 207 ( ) 8.5 (0-183) Sex Female Male Missing 5234 (96.2%) 6,414(95.9%) 35 (100%) 207 (3.8%) 278 (4.2%) 0(0%) 7,114(97.1%) 5,908 (97.1%) 26 (100%) 210(2.9%) 176(2.9%) 0(0%) Race/Ethnicityt Nonhispanic White Black Hispanic White Asian/Pacific AsianlPacific Islander Native American Other 4,563 (96.6%) 912(95.6%) 1,973 (95.6%) 297 (96.4%) 50 (94.3%) 614 (94.0%) 159 (3.4%) 42 (4.4%) 91 (4.4%) 11 (3.6%) 3 (5.7%) 39 (6.0%) 5,243 (97.6%) 1,105 (95.7%) 2,302 (97.2%) 336 (98.0%) 73 (97.3%) 817(95.8%) 127 (2.4%) 49 (4.3%) 65 (2.8%) 7 (2.0%) 2 (2.7%) 36 (4.2%) Insurance Private Medicaid Other Missing 6,021 (96.6%) 4,455 (95.1%) 1,188 (96.3%) 19(100%) 212(3.4%) 228 (4.9%) 45 (3.7%) 0(0%) 6,122 (97.6%) 5,670 (96.6%) 1,251 (97.2%) 5 (83.3%) 151 (2.4%) 198 (3.4%) 36 (2.8%) 1 (16.7%) Elective Admission Elective Nonelective Missing 7,583 (98.2%) 4,053 (92.1%) 47 (95.9%) 135(1.8%) 348 (7.9%) 2(4.1%) 8,216(99.1%) 4,695 (93.9%) 137 (99.3%) 78 (0.9%) 307 (6.1%) 1 (0.7%) Length of Stay Median (lqr) (IQR) 6(4-13) 6 16 (6-38) 7(4-15) 22 (9-56) Neighborhood Affluence Non-Low Income Area Low Income Area Missing 8532 (96.2%) 2,739 (95.3%) 412 (95.8%) 332 (3.8%) 135 (4.7%) 18(4.2%) 9243 (97.2%) 3,442 (97.0%) 363 (96.8%) 269 (2.8%) 105 (3.0%) 12 (3.2)% Hospital Type Nonpediatric Pediatric 6,116(95.6%) 5,567 (96.5%) 282 (4.4%) 203 (3.5%) 7,115(96.9%) 5,933 (97.4%) 226 (3.1%) 160 (2.6%) Hospital Volume Low High 9,278 (95.7%) 2,405 (97.0%) 412 (4.3%) 73 (3.0%) 10,155 (97.0%) 2,893 (97.7%) 317(3.0%) 69 (2.3%) Hospital Standardized Mortality Low High Missing 9,960 (96.6%) 1,601 (92.7%) 122 (95.3%) 352 (3.4%) 127 (7.3%) 6 (4.7%) 11,095 (97.6%) 1,630(93.8%) 323 (98.8%) 274 (2.4%) 108 (6.2%) 108 (1.2%) RACHS-l RACHS-1 Categories; J 1& &6 6,041 (98.6%) 3,990 (95.7%) 1,341 (93.1%) 311 (72.7%) 88(1.4%) 180(4.3%) 100 (6.9%) 117(27.3%) 6,180 (99.0%) 4,928 (97.2%) 1,537(94.2%) 403 (81.9%) 62(1.0%) 141 (2.8%) 94 (5.8%) 89(18.1%)

17 9 Table 1 continued Characteristic Survivors Nonsurvivors Survivors Nonsurvivors (2003) (N= 11,683) (2003) (N=485) (2006) (n=13,048) (2006) (n=386) Any Organ Dysfunction Absent 9,401 (97.2%) (2.8%) 9,862 (98.3%) 170(1.7%) Present 2,282 (91.4%) 214(8.6%) 3,186 (93.6%) 216(6.4%) Prematurity Term 11,458 (96.4%) 429 (3.6%) 12,705 (97.4%) 342 (2.6%) Premature 225 (80.1%) 56(19.9%) 343 (88.6%) 44(11.4%) Other Defects Absent 11,302 (96.1%) 453 (3.9%) 12,439 (97.2%) 360 (2.8%) Present 381 (92.3%) 32 (7.8%) 609 (95.9%) 26 (4.1%) * Interquartile Range fin tin 2003, 10 states did not report race and thus, in 21.4% of all records, race is not reported leaving 6.7% of records missing race. In 2006, 9 states do not report race and thus 18.4% of all patient records do not record race, leaving 6.0% missing race demographics. J Risk Adjustment for Congenital Heart Surgery-1 trisk Adjustment for Congenital Heart Surgery-I

18 10 majority of patients in both years, while Medicaid was the second largest insurer. Children with "other" forms of payment accounted for roughly 10% of all cases in both years. Nonwhite children had a higher rate of mortality compared to white children (2003: 4.6% vs. 3.4%, P p < 0.01; 2006: 3.3% vs. 2.4%, p < 0.01). Children with Medicaid also had a higher mortality rate compared to children with private insurance in both years. No significant difference in mortality was noted when comparing children with "other" payment types to children with either private insurance or Medicaid. Emergent admissions, weekend admissions, prematurity, and the presence of any organ dysfunction were all significantly associated with mortality. The presence of other structural, noncardiac defects was associated with mortality in 2003, but not in Admission to a non-pediatric hospital in 2003 was significantly associated with mortality, although this association was not found in Selected demographic and clinical characteristics stratified by insurance are presented in Table 2 and 3. In both 2003 and 2006, there was an overall difference in age at admission between all insurance groups with Medicaid patients being significantly younger and "other" payer patients being older. There was a significantly higher proportion of nonwhite patients with Medicaid and "other" forms of insurance compared to children with private insurance in both years. In both years, children with Medicaid insurance were significantly more likely to be admitted emergently. There was no consistent trend for prematurity across insurance groups and study years. However, children with Medicaid insurance were significantly more likely to have other noncardiac non-

19 11 11 Table 2: Selected Demographic and Clinical Characteristics and Insurance Type 2003 Characteristic Private (N=6233) Medicaid (N=4683) Other (N=1233) Age in days, Median 235 ( ) 183 ( ) 405 ( ) (lqr) (IQR)** Sex Male 3,447 (55.3%) 2,561 (54.7%) 675 (54.7%) Race/Ethnicity Nonhispanic White Black Hispanic White Asian/Pacific Islander Native American Other 3,073 (69.2%) 270 (6.1%) 586(13.2%) 182 (4.1%) 18(0.4%) 313(7.1%) 1,278 (37.1%) 616(17.9%) 1,182 (34.3%) 85 (2.5%) 21 (0.6%) 267 (7.7%) 359 (42.2%) 68 (8.0%) 296 (34.8%) 41 (4.8%) 14(1.7%) 73 (8.6%) Emergent Admission 2,085 (33.5%) 1,988 (42.5%) 327 (26.5%) Length of Stay Median 6, (4-12) 7, (4-17) 6, (4-13) (IQR) (lqr) Low Income Area 821 (13.2%) 1782 (38.1%) 262 (21.3%) Pediatric Hospital 3,111 (49.9%) 2,113(45.1%) 545 (44.2%) High Hospital Volume 1,458 (23.4%) 843 (18.0%) 177(14.4%) High Mortality Hospital 747 (12.0%) 670 (14.3%) 311 (25.2%) RACHS-1 categoriest J 1& & (50.6%) 2,158 (34.6%) 708 (11.4%) 215(3.5%) 2333 (49.8%) 1,584 (33.8%) 594 (12.7%) 172 (3.7%) 633 (51.3%) 421 (34.1%) 139(11.3%) 40 (3.2%) Organ Dysfunction 1,209(19.4%) 1,043 (22.3%) 242 (19.6%) Prematurity 141 (2.3%) 116(2.5%) 24 (2.0%) Other Defects 173 (2.8%) 195 (4.2%) 45 (3.7%) * Interquartile Range {Risk trisk Adjustment for Congenital Heart Surgery-1

20 12 Table 3: Selected Demographic and Clinical Characteristics and Insurance Type 2006 Characteristic Private (n=6273) Medicaid (n=5868) Other (n=1287) Age in days, Median 224 ( ) 183 (84-913) 495 ( ) (IQR)* Sex Male 3,460 (55.2%) 3,172 (54.l (54.1%) 691 (53.7%) (53.7%o) Race/Ethnicity Nonhispanic White Black Hispanic White Asian/Pacific AsianlPacific Islander Native American Other 3329 (69.6%) 308 (6.4%) 534 (11.2%) 188 (3.9%) 17(0.4%) 406 (8.5%) 1666 (37.3%) 757(17.0%) 1513 (33.9%) 124 (2.8%) 47(1.1%) 356 (8.0%) 373 (40.8%) 89 (9.7%>) (9.7%) 320 (35.0%) 31 (3.4%) 11 (1.2%) 91 (10.0%) Emergent Admission 2,121 (33.8%) 2,516(42.9%) 363 (28.2%) Length of Stay Median 6, (4-14) 8, (4-19) 6, (4-13) (IQR) Low Income Area 951 (15.2%) 2257 (38.5%) 339 (26.3%) Pediatric Hospital 2929 (46.7%) 2585 (44.1%) 579 (45.0%) High Hospital Volume 1648 (26.3%) 1114(19.0%) 196(15.2%) High Mortality Hospital 759(12.1%)) (12.l %» 832 (14.2%) 147(11.4%) RACHS-1 categories~ RACHS-1 categories} 1& & (46.6%) 2370 (37.8%) 783 (12.5%) 195 (3.1%) 2684 (45.7%) 2228 (38.0%) 701 (12.0%) 255 (4.4%) 629 (48.9%) 470 (36.5%) 146(11.3%) 42 (3.3%) Organ Dysfunction 1,503 (24.0%) 1,599 (27.3%) 300 (23.3%) Prematurity 173 (2.8%) 194 (3.3%) 20(1.6%) Other Defects 268 (4.3%) 317(5.4%) 49 (3.8%) * Interquartile Range {Risk ~Risk Adjustment for Congenital Heart Surgery-1

21 13 structural defects and organ dysfunction of any type compared to t6 children with private insurance in both years. Referral to pediatric, "high" volume volulll~ and "high" mortality hospitals based on insurance type was examined. In both years, Medicaid insured children were significantly less likely to be admitted to a pediatric hospital than children with private insurance. Additionally, children with Medicaid insurance and "other" insurance were significantly less likely to be admitted to "high" volume hospitals. Finally, children with Medicaid insurance were significantly more likely to be admitted to "high" mortality hospitals in both years compared to children with private insurance. Multivariate Analysis A logistic regression for each study year was constructed examining covariates associated with in-hospital mortality (Table 4). In both years, increasing RACHS-1 categories, young age, prematurity and any organ dysfunction were significantly associated with mortality. Additionally, increasing hospital volume was found to be a protective factor in both years. In 2003, Medicaid insurance was significantly associated with mortality, adjusting for surgical complexity, prematurity, hospital volume, race and emergent admissions (Odds Ratio (OR) 1.6,95% Confidence Interval (CI) ). Race was included in the model in 2003 as a possible confounder. In 2006, insurance type ceased to be significantly associated with mortality. Race remained in the 2006 regression model as black race was found to be associated with increased mortality (OR 1.7, 95% CI ); however, no other racial group was independently associated with risk of mortality. When race was divided into a dichotomous variable of white and

22 14 Table 4: Final Multivariable Logistic Regression Model Examining Factors Associated with Hospital Mortality (2003 & 2006) Variable Odds 95% Confidence Odds 95% Confidence Ratio. Interval Ratio Interval (2003) (2006) RACHS-l RACHS-1 Category! Category} 1& Referent 1.0 Referent & Age Older than 1 year 1.0 Referent 1.0 Referent 31 days to 12 months Less than 31 days Hospital Volume ( cases/year) Prematurity Insurance Private 1.0 Referent 1.0 Referent Medicaid Other Race/Ethnicity RacelEthnicity Nonhispanic White 1.0 Referent 1.0 Referent Black Hispanic White Other Emergency Any Organ Dysfunction }Risk trisk Adjustment for Congenital Heart Surgery-l Surgery-1 C-statistic (for logistic regression model without random effects) = 0.84 (2003) & 0.87 (2006)

23 15 nonwhite, nonwhite status continued to be significantly associated with hospital mortality in 2006 (OR 1.4, 95% CI ). Because California datasets comprised a large proportion, if not all, of the data in the two tw9 studies that show an increased risk of mortality for children with Medicaid,3, 3 ' 12 we constructed a logistic model for hospital mortality that excluded California patients. In this constricted model, the effect of Medicaid insurance on mortality was increased (OR 1.8; 95% CI ). A second logistic regression model was constructed examining covariates associated with referral to a "high" mortality hospital (Table 5). In both years, increasing hospital volume was inversely associated with being a "high" mortality hospital while surgical complexity did not affect referral. In 2003, children with Medicaid and "other" insurance had higher odds of being referred to a "high" mortality hospital compared to children with private insurance (Medicaid: OR 1.2, 95% CI ; "other": OR 2.1, 95% CI ). Race had a heterogeneous effect on referral to "high" mortality hospitals; however, nonwhite children had increased odds of referral to "high" mortality hospitals when compared to white children (OR 1.3, 95% CI ). In 2006, insurance type was not associated with referral. Similar to 2003, in 2006, race had a heterogeneous effect on referral to "high" mortality hospitals. However, in contrast to 2003, nonwhite children had decreased odds of referral to "high" mortality hospitals (OR 0.62, 95% CI ). The logistic regression models for hospital mortality were repeated for each year while stratifying based on hospital mortality status ("high" versus "low"). In 2003, at "low" mortality hospitals, Medicaid insurance was independently associated with

24 16 Table 5: Multivariable Logistic Regression for Factors Associated with an Operation at a High Mortality Hospital (2003 & 2006) Variable Odds Ratio 95% Confidence Odds Ratio 95% Confidence {2003} (2003) Interval (2006} (2006) Interval Age Older than 1 year 1.0 Referent 1.0 Referent 31 days to months Less than 31 days Insurance Private 1.0 Referent 1.0 Referent Medicaid Other RacelEthnicity Race/Ethnicity Nonhispanic White 1.0 Referent 1.0 Referent Black Hispanic White Other Organ D~sfunction Dysfunction Model adjusted for surgical complexity (Risk Adjustment for Congenital Heart Surgery- 1), hospital volume, prematurity, and admission type (emergent vs. elective) C-statistic = 0.84 (2003) & 0.79 (2006)

25 17 mortality (OR % CI ) while race continued to be a confounder. At "high" mortality institutions, nonwhite race was associated with mortality (OR % CI ) while insurance was not. In 2006, at_"low" mortality hospitals, neither race nor insurance were associated with mortality. At "high" mortality hospitals, insurance was not associated with mortality while nonwhite race was (OR % CI ). In both hospital types in 2003 and 2006, increasing surgical complexity, younger age, and any organ dysfunction were associated with hospital mortality. A final regression model examining factors associated with emergent admissions was examined (Table 6). Covariates used in the previous models were retained. In 2003, children with Medicaid were significantly more likely to be admitted emergently (OR 1.4, 95% CI ) as were nonwhite children (OR 1.3, 95% CI ). In 2006, children with Medicaid insurance (OR 1.4, 95% CI ) and nonwhite children (OR 1.2, 95% CI ) had higher odds of being emergently admitted.

26 18 Table 6: Multivariable Logistic Regression Examining Factors Associated with Emergent Admission (2003 & 2006) Variable Odds Ratio 95% Odds 95% (2003) Confidence Ratio Confidence Interval (2006) Interval RACHS-1 Categoryt Category} 1& Referent 1.0 Referent & Age Older than 1 year 1.0 Referent 1.0 Referent 31 days to 12 months Less than days Insurance Private 1.0 Referent 1.0 Referent Medicaid Other Nonwhite Race Any Organ Dysfunction }Risk trisk Adjustment for Congenital Heart Surgery-1 Model adjusted for hospital volume and prematurity. C-statistic = 0.83 (2003) & 0.82 (2006)