Child Opportunity, Race and Ethnicity, and Language in Congenital Cardiac Surgery Outcomes

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We evaluated the association between surgical outcomes and a composite child neighborhood opportunity index (COI), race and ethnicity, and language barriers among children undergoing congenital heart surgery. Methods Utilizing the Society of Thoracic Surgeons database, we conducted a single-center retrospective cohort study of patients who underwent congenital cardiac surgery from 2010 to 2023. Patients were classified by quintile COI scores based on their reported address. Outcomes included in-hospital mortality, length of stay (LOS), and major complications. Adjusted analyses were performed using generalized linear mixed models. Discussion Among 1,568 patients, 51.2% were non-Hispanic White and 64.1% had public insurance. 57.4% lived in very low or low COI neighborhoods. Race, ethnicity, primary language, and insurance payer were significantly associated with COI (p < .0001). After adjustment, COI was not associated with mortality, LOS, or complications. Compared with non-Hispanic White patients, non-Hispanic Black patients had higher odds of in-hospital mortality (OR 2.19, 95% CI 1.07–5.30) and longer LOS (β = 0.25, 95% CI 0.09–0.42). Patients with non-English-speaking caregivers had longer LOS (β = 0.30, 95% CI 0.10–0.51). Conclusion Neighborhood opportunity was not independently associated with surgical outcomes. However, disparities by race and ethnicity and language persisted, suggesting that COI may not fully capture structural racism or communication barriers in pediatric cardiac care. congenital heart disease child opportunity index race and ethnicity language barriers health disparities Figures Figure 1 Figure 2 Figure 3 Introduction Children undergoing surgery for congenital heart disease (CHD) experience variable post-operative outcomes, including mortality, complications, and hospital length of stay (LOS) [ 1 , 2 ]. Growing evidence highlights the role of social determinants of health (SDOH), such as neighborhood environment, socioeconomic status, and healthcare access, as key contributors to these disparities [ 3 – 5 ]. The Child Opportunity Index 2.0 (COI) is a geospatially derived, multidimensional metric that summarizes neighborhood opportunity across educational, environmental, and socioeconomic domains [ 6 ]. Prior studies report a graded association between lower COI and adverse CHD outcomes, including increased mortality, LOS, and healthcare utilization [ 3 , 4 , 7 – 9 ]. These data underscore the influence of a child’s residential context on their peri-operative risk. However, persistent racial and ethnic disparities remain after accounting for COI, clinical risk, and insurance status [ 4 , 10 ]. Black and Hispanic children are more likely to undergo cardiac surgery at lower-volume hospitals and face delays in diagnosis and follow-up, reflecting structural inequities and emphasizing the need for more granular modeling approaches [ 10 – 12 ]. Language barriers represent another understudied but potentially critical axis of disparity. Limited English proficiency (LEP) is associated with adverse outcomes in pediatric populations, yet its contribution to CHD surgical outcomes, particularly when considered alongside COI and race and ethnicity, remains underexplored [ 13 – 15 ]. Few studies have examined the associations between congenital heart surgery outcomes and child opportunity, race and ethnicity, and caregiver language status. Prior research often isolates these factors, particularly focusing on neighborhood metrics like the COI, while omitting race and ethnicity and preferred language from statistical models. To address how structural and communication-related disparities shape outcomes in children with CHD, we evaluated the combined and separate associations of COI, race and ethnicity, and language with postoperative mortality, complications, and LOS in a large single-center cohort. Methods Study Design and Patient Population This study utilized data from the Society of Thoracic Surgeons (STS) Congenital Heart Database in a single-center, retrospective cohort [ 16 ]. We included all patients who underwent congenital cardiac surgery from January 1, 2010, to April 30, 2023, at our institution, excluding patent ductus arteriosus (PDA) ligation when the patient was less than 2.5 kg, and excluding pacemaker procedures when the patient was less than 30 days of age at the surgery date. Patients with missing data on study outcomes and/or demographics, or addresses that could not be mapped to a COI score, were excluded from the analyses. Surgical procedures were identified based on procedural codes within the STS database to ensure standardized case definitions [ 16 ]. Relevant preoperative, operative, and postoperative data were extracted from the database, and all data were verified against institutional records for completeness and accuracy. Johns Hopkins All Children’s Hospital Institutional Review Board approved the study with a waiver of informed consent. Study methods adhered to the STS database licensing agreement, institutional data usage policies, and ethical guidelines. Study methods adhered to the STS database licensing agreement, institutional data usage policies, and ethical guidelines. Childhood Opportunity Level To assess COI, the primary study exposure, each patient was assigned a COI score based on their residential address at the time of surgery. COI scores are published for each census tract in a publicly available database [ 6 ]. COI scores were categorized into quintiles, ranging from "very low" (quintile 1) to "very high" (quintile 5) as previously published. 5 We combined the "high" (quintile 4) and "very high" (quintile 5) COI categories into a single group, referred to as "high/very high COI," to allow for meaningful analysis given sample size limitations. Outcomes, Exposures, and Covariates The primary study outcome was hospital mortality, with hospital LOS (measured in days) and major postoperative complications as secondary outcomes. Mortality was defined as any death occurring prior to discharge following the surgical intervention. LOS was measured as the total number of days from admission to discharge for the surgical hospitalization, including time spent in the intensive care unit (ICU) and on the acute care floor. Major postoperative complications were defined per STS guidelines, including renal failure requiring dialysis, permanent neurologic deficit, pacemaker placement, paralyzed diaphragm, mechanical circulatory support, cardiac arrest, and unplanned reintervention [ 17 ]. Unplanned reinterventions encompassed any unscheduled surgical or catheter-based procedures required to address acute complications or residual lesions. Covariates included demographic, clinical, and procedural variables that are independently known to influence outcomes and/or could potentially confound the association between study outcomes and COI. These included race and ethnicity, patient’s or their legal guardians’ preferred language, age, sex, prematurity, comorbidities, and surgical complexity, as quantified by the STS Congenital Heart Surgery Mortality Category (STAT score) [ 17 ]. Race and ethnicity were extracted from the medical record as self-reported by patient families and categorized as non-Hispanic White, non-Hispanic Black, Hispanic, Asian, or Other/Multiracial. The preferred language was documented based on whether the patient or family endorsed English or another language as their language of choice [ 15 ]. Statistical Analysis Demographic and clinical characteristics were summarized for the total cohort and stratified by COI quintile. Continuous variables were described using medians and interquartile ranges (IQRs), while categorical variables were described using frequencies and percentages. Comparisons across COI quintiles were performed with Kruskal-Wallis tests for continuous variables and Chi-square or Fisher’s exact tests for categorical variables. Generalized multivariable linear mixed models were used to assess associations between COI and outcomes. A binomial distribution was used for the mortality and major complications outcomes, while a log-normal distribution was used for LOS. In addition to COI as the primary study exposure, each model included the following covariates: race and ethnicity (reference category: Non-Hispanic White/other), patient sex, preferred patient language (reference category: primary language other than English), insurance payer (reference category: commercial), presence of medical comorbidities (reference category: no comorbidities), STAT category (reference category: STAT 1–3), and age. Models were fitted with a random patient intercept to account for patients with multiple surgeries. Collinearity was assessed with variance inflation factors (VIF), where an acceptable VIF was considered as < 5. Adjusted odds ratios (ORs), Beta coefficients (β), and corresponding 95% confidence intervals (CIs) were calculated. A complete case analysis was performed; therefore, no missing data were imputed. All analyses were performed with Stata/SE Version 17.1 (College Station, TX). A two-sided P -value < 0.05 was considered statistically significant. Results Demographics The study cohort included 1,568 patients who underwent congenital heart surgery. Over half of the cohort (57.4%) resided in very low or low COI areas, 22.6% lived in moderate opportunity areas, and the remaining ~ 20% were from high or very high opportunity neighborhoods. The majority of patients self-identified as non-Hispanic White (51.2%), followed by 20.0% Hispanic, 13.3% non-Hispanic Black, 6.7% non-Hispanic of other races (excluding White or Black), and 8.8% with unknown/missing race and ethnicity. Most patients (64.1%) were insured through public programs. The primary caregiver language was English for the majority of families (~ 88%), with Spanish (~ 10%) being the most common non-English language [Table 1 ]. Table 1 Demographic and clinical characteristics by Childhood Opportunity Index (at first surgery) Childhood Opportunity Index (COI) Characteristic Total (n = 1568) Very Low (n = 490) Low (n = 410) Moderate (n = 354) High/Very High (n = 314) P-value Race, % (n) < .0001 Non-Hispanic White 803 (51.21) 173 (35.31) 206 (50.24) 203 (57.34) 221 (70.38) Non-Hispanic Black 208 (13.27) 119 (24.29) 46 (11.22) 29 (8.19) 14 (4.46) Hispanic (of any race) 314 (20.03) 122 (24.90) 92 (22.44) 66 (18.64) 34 (10.83) Non-Hispanic (other race besides White/Black) 105 (6.70) 27 (5.51) 28 (6.83) 24 (6.78) 26 (8.28) Unknown/missing 138 (8.80) 49 (10.0) 38 (9.27) 32 (9.04) 19 (6.05) Gender, % (n) 0.11 Female 705 (44.96) 239 (48.78) 184 (44.88) 156 (44.07) 126 (40.13) Male 863 (55.04) 251 (51.22) 226 (55.12) 198 (55.93) 188 (59.87) Primary Language, % (n) < .0001 English 1387 (88.46) 401 (81.84) 364 (88.78) 321 (90.68) 301 (95.86) Spanish 158 (10.08) 83 (16.94) 40 (9.76) 25 (7.06) 10 (3.18) Other 23 (1.47) 6 (1.22) 6 (1.46) 8 (2.26) 3 (0.96) Insurance, % (n) < .0001 Non-Medicaid 563 (35.91) 73 (14.90) 127 (30.98) 158 (44.63) 205 (65.29) Medicaid 1005 (64.09) 417 (85.10) 283 (69.02) 196 (55.37) 109 (34.71) Preterm birth, % (n) 0.21 Yes 289 (18.43) 78 (15.92) 83 (20.24) 74 (20.90) 55 (17.20) No 1267 (80.80) 409 (83.47) 325 (79.27) 279 (78.81) 254 (80.89) Unknown/missing 12 (0.77) 3 (0.61) 2 (0.49) 1 (0.28) 6 (1.91) Antenatal diagnosis of congenital heart defects, % (n) 0.97 Yes 582 (37.12) 179 (36.53) 155 (37.80) 131 (37.26) 117 (37.26) No 957 (61.03) 303 (61.84) 246 (60.00) 219 (61.86) 189 (60.32) Unknown/missing 29 (1.85) 8 (1.63) 9 (2.20) 4 (1.13) 8 (2.55) Any comorbidity, % (n) 0.94 Yes 884 (56.38) 272 (55.51) 230 (56.10) 201 (56.78) 181 (57.64) No 684 (43.62) 218 (44.49) 180 (43.90) 153 (43.22) 133 (42.36) Age in days at time of first surgery 0.13 Median (IQR) 227 (74 to 1555) 200.5 (60 to 1299) 252 (78 to 1671) 235 (78 to 1564) 294.5 (86 to 1938) n 1567 490 410 353 314 COI in units 0.0001 Median (IQR) 34 (16 to 56.5) 11 (6 to 15) 30 (26 to 34) 50 (46 to 55) 71 (65 to 78) n 1568 490 410 354 314 Unadjusted Analyses In Table 1 , the unadjusted analyses revealed that patient race and ethnicity, primary caregiver language, and insurance payer were associated with COI (P-value < 0.0001 for all). Non-Hispanic Black patients and those with non-English-speaking caregivers were disproportionately represented in the lowest opportunity neighborhoods (very low COI/low COI: 79% and 75%), whereas non-Hispanic White patients were more likely to reside in higher opportunity areas (high COI/very high COI: 53%). Similarly, patients with public insurance were more likely to live in low-opportunity neighborhoods compared to those with private insurance (70% vs 36%). As shown in Table 2 , a lower COI was associated with an increase in length of stay, while mortality and major complication rates did not differ significantly across COI categories [Table 2 ]. Table 2 Surgical characteristics and outcomes by Childhood Opportunity Index (surgical encounter level) Childhood Opportunity Index Total (n = 1871) Very Low (n = 581) Low (n = 501) Moderate (n = 430) High/Very High (n = 359) P-value Outcomes Mortality at 30 days post-procedure, % (n) 0.44 Alive 1830 (97.81) 563 (96.90) 493 (98.40) 422 (98.14) 352 (98.05) Dead 37 (1.98) 16 (2.75) 8 (1.60) 6 (1.40) 7 (1.95) Unknown/missing 4 (0.21) 2 (0.34) 0 (0.0) 2 (0.46) 0 (0.0) Mortality at hospital discharge, % (n) 0.29 Alive 1814 (96.95) 558 (96.04) 491 (98.00) 418 (97.21) 347 (96.66) Dead 57 (3.05) 23 (3.96) 10 (2.00) 12 (2.79) 12 (3.34) Major complications, including death, % (n) 0.14 Yes 166 (8.87) 62 (10.67) 44 (8.78) 40 (9.30) 20 (5.57) No 888 (47.46) 288 (49.57) 240 (47.90) 190 (44.19) 170 (47.35) Unknown/missing 817 (43.67) 231 (39.76) 217 (43.31) 200 (46.51) 169 (47.08) Major complications, % (n) 0.21 Yes 107 (5.72) 37 (6.37) 22 (4.39) 31 (7.21) 17 (4.74) No 1764 (94.28) 544 (93.63) 479 (95.61) 399 (92.79) 342 (95.26) Reoperation within admission, % (n) 0.2 Planned reoperation 22 (1.18) 6 (1.03) 8 (1.60) 7 (1.63) 1 (0.28) Unplanned reoperation 3 (0.16) 0 (0.0) 2 (0.40) 1 (0.23) 0 (0.0) No reoperation 1846 (98.66) 575 (98.97) 491 (98.0) 422 (98.14) 358 (99.72) Length of stay in days 0.01 Median (IQR) 9 (5 to 28) 11 (5 to 33) 9 (5 to 29) 9 (4 to 31) 8 (4 to 23) n 1871 581 501 430 359 Characteristics STAT Category, % (n) 0.25 1 to 3 1644 (87.87) 508 (87.44) 445 (88.82) 368 (85.58) 323 (89.97) 4 to 5 227 (12.13) 73 (12.56) 56 (11.18) 62 (14.42) 36 (10.03) Multivariable Analysis COI Disparities and Study Outcomes After adjustment for patient demographics, clinical risk factors, insurance type, race and ethnicity, and caregiver primary language, there was no association observed between COI and in-hospital mortality, major complications, or LOS [Figures 1-3]. Legend: In figures 1-3, variables included in the model included Childhood Opportunity Index or COI (ref: very low), race/ethnicity (ref: Non-Hispanic White), preferred patient language (ref: primary language English), presence of medical comorbidities (ref: no comorbidities), Congenital Heart Surgery Mortality Category or STAT category (ref: STAT 1-3), insurance payer (ref: commercial), patient sex, and age. Racial and Ethnic Disparities and Study Outcomes Patient race and ethnicity were strongly associated with outcomes even after controlling for COI and other covariates. Non-Hispanic black patients had an increased odds of mortality compared to non-Hispanic white patients with an adjusted odds ratio of 2.2 (95% CI 1.07-5.3) [Figure 1]. Non-Hispanic Black patients also experienced longer hospitalizations (25% increase in mean LOS, 95% CI 0.09–0.42; Figure 3). No statistically significant differences in mortality or LOS were observed for other racial/ethnic groups (e.g., Hispanic vs. non-Hispanic White) in the adjusted analysis. Adjusted major complication rates were similar across racial/ethnic categories [Figure 2]. Language Disparities in Outcomes Among patients whose caregivers primarily spoke a non-English language, the mean LOS was significantly prolonged (30% increase in mean LOS; 95% CI: 0.098–0.51) [Figure 3]. There were no differences in mortality or major complications based on language. Discussion In this single-center study of children undergoing congenital heart surgery, neighborhood opportunity, as measured by COI, was not independently associated with in-hospital mortality, major postoperative complications, or LOS after adjustment for race and ethnicity, caregiver language, insurance status, surgical complexity, and other clinical covariates. Non-Hispanic Black patients in our cohort experienced significantly higher in-hospital mortality and longer hospital stays compared to non-Hispanic White peers, even after adjusting for COI, insurance, and clinical risk. Patients with caregivers who spoke a non-English primary language also had significantly longer LOS. The lack of association between COI and outcomes diverges from prior multi-institutional studies, which reported a graded relationship between lower COI and adverse outcomes following congenital heart operations [3,4,7,8,18]. However, our results align with more recent analyses, which also found no significant association between COI and short-term morbidity in infants with single-ventricle physiology [9]. A possible explanation for this discrepancy lies in model specification. Many previous studies did not include race and ethnicity as variables on the grounds that observed disparities by race and ethnicity were mediated by COI, due to the strong association between race and ethnicity and COI quintiles, rather than an independent sociopolitical exposure. Additionally, few studies incorporated caregiver language as a proxy for institutional or interpersonal bias [11]. Not including race and ethnicity as explanatory predictors can mask the impact of systemic racism on outcomes. While COI captures spatially anchored disadvantage, it may obscure within-tract or zip code heterogeneity, fail to reflect real-time socioeconomic shifts, and overlook institutional bias at the point of care [13,19]. When these additional axes of inequity are omitted, variance attributable to structural racism may be misattributed to neighborhood-level metrics such as COI. In contrast, our modeling strategy included race and language as covariates, thereby allowing us to isolate their independent effects. Other studies also cautioned against overreliance on geographic proxies for individual or institutional exposure. Notably, race and ethnicity were independently associated with outcomes, with non-Hispanic Black patients in our cohort experiencing significantly higher in-hospital mortality and longer hospital stays compared to non-Hispanic White peers, independent of clinical risk as measured by comorbidities and STAT scores. This persistent disparity aligns with other studies and underscores that race continues to act as a proxy for exposure to systemic inequities, not as a biologic risk factor [20-23]. When using race as an independent variable, it is vital to remember that race is a social construct and that disparities seen based on race are due to societal factors and do not represent immutable biological factors. Patients with caregivers who spoke a non-English primary language had significantly longer LOS, consistent with prior research linking LEP to communication breakdowns, delayed discharge planning, and reduced engagement with ancillary services [12-14]. This association persisted even after adjusting for socioeconomic and clinical covariates, suggesting that language discordance represents a distinct barrier to timely recovery. Although few studies have directly examined the role of LEP in congenital heart surgery, broader pediatric literature supports the association of LEP with adverse outcomes, including hospital readmission and missed follow-up [12,14,24]. The implications of these findings are twofold. First, the lack of independent association between COI and outcomes in either univariate or fully adjusted models highlights that neighborhood-level indices alone, in this cohort, may be insufficient to account for structural vulnerability. Second, the observed disparities by race and ethnicity and caregiver language underscore the need to retain these variables when evaluating health disparities. Importantly, this is not to reinforce biological essentialism, but to quantify the effects of structural racism and communication barriers when direct measures of discrimination are unavailable [10,11]. Our findings reinforce growing calls to move beyond composite indices in health equity research and adopt more granular, theory-informed approaches that directly measure institutional discrimination, communication barriers, and differential treatment patterns [11,25]. While neighborhood opportunity remains a meaningful contextual factor, it does not fully capture the lived experience of structural racism or language discordance within the healthcare system, or in fact, the lived experience of direct racism. Interventions focused on COI factors remain important; however, disparities within each COI quintile are likely to continue. Our research supports the need to broaden efforts to address how race and language directly impact outcomes. To address these inequities, future work should evaluate system-level interventions such as structured bias mitigation training, language-concordant care teams, and culturally tailored navigation programs. In addition, hospital-level characteristics (e.g., interpreter staffing, diversity of care teams, and equity-focused protocols) should be incorporated into future predictive models to better understand how institutional factors mediate disparate outcomes [26,27]. By systematically addressing these factors, it may be possible to significantly reduce inequities in post-operative outcomes for CHD patients and move closer toward achieving health equity in congenital cardiac care, resulting in improved outcomes for all. Limitations This study has several limitations. As a single-center retrospective cohort study, our findings may not be generalizable to other institutions, especially those with different patient demographics or resource profiles [28]. Although we adjusted for several clinical and sociodemographic factors, unmeasured confounders may persist, including detailed care process variables or clinician-level biases. Language proficiency was inferred from caregiver-reported preferred language rather than validated assessments of fluency, and our dataset lacked granular information on interpreter use or care navigation services. Finally, while our modeling approach aimed to disentangle the effects of race, language, and COI, these exposures remain intertwined through historical and structural processes that may be incompletely captured by available variables [29]. Conclusion In this single-center cohort, neighborhood opportunity, as measured by COI, was not independently associated with postoperative mortality, complications, or length of stay following congenital heart surgery. Non-Hispanic Black race and caregiver non-English language were each independently associated with poorer outcomes, including increased in-hospital mortality and prolonged hospitalizations, even after adjusting for clinical and socioeconomic risk factors. These findings suggest that while SDOH metrics such as COI provide important context, they may not fully capture the impact of structural racism and communication barriers within healthcare delivery systems. A multifaceted approach that includes institutional accountability, culturally responsive care models, and bias-mitigation strategies is essential to closing persistent equity gaps in congenital cardiac surgery outcomes [30]. Future work should prioritize prospective, multi-center evaluations of targeted interventions aimed at improving equity in both perioperative care and long-term outcomes. Declarations Funding: This research received no specific grant from any funding agency, commercial entity, or not-for-profit organization. Competing Interests: The authors declare no competing interests. Ethics Approval: Approved by the Johns Hopkins All Children’s Hospital Institutional Review Board with waiver of informed consent. Consent to Participate: Not applicable. Consent for Publication: All authors consent to publication. Availability of Data and Materials: Data available upon reasonable request from the corresponding author. Code Availability: Not applicable. Authors’ Contributions: Drs. Coca and Ng conceptualized and designed the study, collected and managed data, drafted the manuscript, and critically revised it. Dr. Fierstein and Mr. Manipadam conducted analyses and revised the manuscript. Drs. 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J Am Heart Assoc 8:e010342. https://doi.org/10.1161/JAHA.118.010342 Funkhouser T, Michalski T, Luna B et al (2024) Impact of neighborhood context on outcomes in children with congenital heart disease. J Cardiovasc Dev Dis 11(2):67. https://doi.org/10.3390/jcdd11020067 Crook S, Rattan R, Razzaghi H et al (2024) Impact of social determinants of health on congenital heart surgery outcomes: Multi-institutional insights. J Am Coll Cardiol. https://doi.org/10.1016/j.jacc.2024.03.430 Acevedo-Garcia D, McArdle N, Hardy EF et al (2014) The Child Opportunity Index: Improving collaboration between community and health systems to reduce disparities. Am J Public Health 104(9):1616–1623. https://doi.org/10.2105/AJPH.2014.301969 Jacobs JP, Mayer JE, Mavroudis C et al (2014) Databases for assessing the outcomes of the treatment of patients with congenital and pediatric cardiac disease. Cardiol Young 24(1):104–113. https://doi.org/10.1017/S1047951114002121 Peyvandi S, Baer RJ, Moon-Grady AJ et al (2018) Socioeconomic status is associated with adverse preoperative factors in neonates with congenital heart disease. Pediatr Res 84(1):36–43. https://doi.org/10.1038/s41390-018-0001-4 Johnson TJ, Rohan A, Ostfeld BM et al (2022) Health equity and antiracism in pediatric cardiology: Why it matters and how to lead change. Pediatrics 150(4):e2022057191. https://doi.org/10.1542/peds.2022-057191 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2026 Read the published version in Pediatric Cardiology → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 30 Nov, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 06 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 03 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8022074","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545738272,"identity":"b67a1a11-3fdc-4f74-b02e-4a0ac665bc5e","order_by":0,"name":"Kenneth Coca","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACCQh1AEJ94DnAwIYQxaclAaKFcQbJWph5oNbh1SLZ3nvswccfdxj425sffraRuRPNx8B88DYPHi3SPOfSDWckPGOQOHPMWDqH51luGwNbsjU+LXISOWbSPAmHGQwkchiAWg4DtfAARfBpkX9jJv0HooX5twVYC/83vFqkJYBmMkC0sEkzQGxhw6tFsifHTLIn7TAP0C9mlj0gLcxsxpZz8GiROH7GTOKHzWE5YIg9vvGz53DufGDQ3XiDRwsMQFzC2AMkmIlQjgR+kKZ8FIyCUTAKRgYAAJ1iRkyO/DXXAAAAAElFTkSuQmCC","orcid":"","institution":"Johns Hopkins All Children’s Heart Institute, Johns Hopkins All Children’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Coca","suffix":""},{"id":545738273,"identity":"68e65346-d69c-4293-af9d-9ddffd8b71a3","order_by":1,"name":"Jamie Fierstein","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jamie","middleName":"","lastName":"Fierstein","suffix":""},{"id":545738274,"identity":"7b475711-86a6-486d-8e41-35453b579175","order_by":2,"name":"Joseph Manipadam","email":"","orcid":"","institution":"Johns Hopkins All Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Manipadam","suffix":""},{"id":545738275,"identity":"a1cb6132-66df-4423-81c1-0e2419700a28","order_by":3,"name":"Vera Ignjatovic","email":"","orcid":"","institution":"Johns Hopkins All Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Vera","middleName":"","lastName":"Ignjatovic","suffix":""},{"id":545738276,"identity":"510cd9a6-566e-44af-a2c3-d43473ccf868","order_by":4,"name":"Joyce Johnson","email":"","orcid":"","institution":"Johns Hopkins All Children’s Heart Institute, Johns Hopkins All Children’s 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1","display":"","copyAsset":false,"role":"figure","size":17174,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) from multivariable analysis of mortality at discharge.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8022074/v1/0c951bae336edaf974e791b7.png"},{"id":96252149,"identity":"e0c1e377-b20a-4eaf-99cb-62f9997c7db0","added_by":"auto","created_at":"2025-11-19 07:40:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17348,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) from multivariable analysis of post-operative major complication.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8022074/v1/b29ced7b0ea95281186bec27.png"},{"id":96251145,"identity":"1dcd0915-4580-471b-9b73-8fcb6cc22a8c","added_by":"auto","created_at":"2025-11-19 07:39:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16873,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of adjusted difference in mean length of stay (95% CI).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8022074/v1/8d467ddb6675bbe5b1de1e66.png"},{"id":101152433,"identity":"3cfdc44f-bfa2-4300-9907-b2475c170cfd","added_by":"auto","created_at":"2026-01-26 16:11:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1075459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8022074/v1/73daf2f3-1e13-4b12-a890-19bed127f955.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Child Opportunity, Race and Ethnicity, and Language in Congenital Cardiac Surgery Outcomes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChildren undergoing surgery for congenital heart disease (CHD) experience variable post-operative outcomes, including mortality, complications, and hospital length of stay (LOS) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Growing evidence highlights the role of social determinants of health (SDOH), such as neighborhood environment, socioeconomic status, and healthcare access, as key contributors to these disparities [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Child Opportunity Index 2.0 (COI) is a geospatially derived, multidimensional metric that summarizes neighborhood opportunity across educational, environmental, and socioeconomic domains [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prior studies report a graded association between lower COI and adverse CHD outcomes, including increased mortality, LOS, and healthcare utilization [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These data underscore the influence of a child\u0026rsquo;s residential context on their peri-operative risk. However, persistent racial and ethnic disparities remain after accounting for COI, clinical risk, and insurance status [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Black and Hispanic children are more likely to undergo cardiac surgery at lower-volume hospitals and face delays in diagnosis and follow-up, reflecting structural inequities and emphasizing the need for more granular modeling approaches [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLanguage barriers represent another understudied but potentially critical axis of disparity. Limited English proficiency (LEP) is associated with adverse outcomes in pediatric populations, yet its contribution to CHD surgical outcomes, particularly when considered alongside COI and race and ethnicity, remains underexplored [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFew studies have examined the associations between congenital heart surgery outcomes and child opportunity, race and ethnicity, and caregiver language status. Prior research often isolates these factors, particularly focusing on neighborhood metrics like the COI, while omitting race and ethnicity and preferred language from statistical models. To address how structural and communication-related disparities shape outcomes in children with CHD, we evaluated the combined and separate associations of COI, race and ethnicity, and language with postoperative mortality, complications, and LOS in a large single-center cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Patient Population\u003c/h2\u003e\u003cp\u003eThis study utilized data from the Society of Thoracic Surgeons (STS) Congenital Heart Database in a single-center, retrospective cohort [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. We included all patients who underwent congenital cardiac surgery from January 1, 2010, to April 30, 2023, at our institution, excluding patent ductus arteriosus (PDA) ligation when the patient was less than 2.5 kg, and excluding pacemaker procedures when the patient was less than 30 days of age at the surgery date. Patients with missing data on study outcomes and/or demographics, or addresses that could not be mapped to a COI score, were excluded from the analyses. Surgical procedures were identified based on procedural codes within the STS database to ensure standardized case definitions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Relevant preoperative, operative, and postoperative data were extracted from the database, and all data were verified against institutional records for completeness and accuracy. Johns Hopkins All Children\u0026rsquo;s Hospital Institutional Review Board approved the study with a waiver of informed consent. Study methods adhered to the STS database licensing agreement, institutional data usage policies, and ethical guidelines. Study methods adhered to the STS database licensing agreement, institutional data usage policies, and ethical guidelines.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eChildhood Opportunity Level\u003c/h3\u003e\n\u003cp\u003eTo assess COI, the primary study exposure, each patient was assigned a COI score based on their residential address at the time of surgery. COI scores are published for each census tract in a publicly available database [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. COI scores were categorized into quintiles, ranging from \"very low\" (quintile 1) to \"very high\" (quintile 5) as previously published.\u003csup\u003e5\u003c/sup\u003e We combined the \"high\" (quintile 4) and \"very high\" (quintile 5) COI categories into a single group, referred to as \"high/very high COI,\" to allow for meaningful analysis given sample size limitations.\u003c/p\u003e\n\u003ch3\u003eOutcomes, Exposures, and Covariates\u003c/h3\u003e\n\u003cp\u003eThe primary study outcome was hospital mortality, with hospital LOS (measured in days) and major postoperative complications as secondary outcomes. Mortality was defined as any death occurring prior to discharge following the surgical intervention. LOS was measured as the total number of days from admission to discharge for the surgical hospitalization, including time spent in the intensive care unit (ICU) and on the acute care floor. Major postoperative complications were defined per STS guidelines, including renal failure requiring dialysis, permanent neurologic deficit, pacemaker placement, paralyzed diaphragm, mechanical circulatory support, cardiac arrest, and unplanned reintervention [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Unplanned reinterventions encompassed any unscheduled surgical or catheter-based procedures required to address acute complications or residual lesions.\u003c/p\u003e\u003cp\u003eCovariates included demographic, clinical, and procedural variables that are independently known to influence outcomes and/or could potentially confound the association between study outcomes and COI. These included race and ethnicity, patient\u0026rsquo;s or their legal guardians\u0026rsquo; preferred language, age, sex, prematurity, comorbidities, and surgical complexity, as quantified by the STS Congenital Heart Surgery Mortality Category (STAT score) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Race and ethnicity were extracted from the medical record as self-reported by patient families and categorized as non-Hispanic White, non-Hispanic Black, Hispanic, Asian, or Other/Multiracial. The preferred language was documented based on whether the patient or family endorsed English or another language as their language of choice [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDemographic and clinical characteristics were summarized for the total cohort and stratified by COI quintile. Continuous variables were described using medians and interquartile ranges (IQRs), while categorical variables were described using frequencies and percentages. Comparisons across COI quintiles were performed with Kruskal-Wallis tests for continuous variables and Chi-square or Fisher\u0026rsquo;s exact tests for categorical variables.\u003c/p\u003e\u003cp\u003eGeneralized multivariable linear mixed models were used to assess associations between COI and outcomes. A binomial distribution was used for the mortality and major complications outcomes, while a log-normal distribution was used for LOS. In addition to COI as the primary study exposure, each model included the following covariates: race and ethnicity (reference category: Non-Hispanic White/other), patient sex, preferred patient language (reference category: primary language other than English), insurance payer (reference category: commercial), presence of medical comorbidities (reference category: no comorbidities), STAT category (reference category: STAT 1\u0026ndash;3), and age. Models were fitted with a random patient intercept to account for patients with multiple surgeries. Collinearity was assessed with variance inflation factors (VIF), where an acceptable VIF was considered as \u0026lt;\u0026thinsp;5. Adjusted odds ratios (ORs), Beta coefficients (β), and corresponding 95% confidence intervals (CIs) were calculated. A complete case analysis was performed; therefore, no missing data were imputed.\u003c/p\u003e\u003cp\u003eAll analyses were performed with Stata/SE Version 17.1 (College Station, TX). A two-sided \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eDemographics\u003c/h2\u003e\n\u003cp\u003eThe study cohort included 1,568 patients who underwent congenital heart surgery. Over half of the cohort (57.4%) resided in very low or low COI areas, 22.6% lived in moderate opportunity areas, and the remaining\u0026thinsp;~\u0026thinsp;20% were from high or very high opportunity neighborhoods. The majority of patients self-identified as non-Hispanic White (51.2%), followed by 20.0% Hispanic, 13.3% non-Hispanic Black, 6.7% non-Hispanic of other races (excluding White or Black), and 8.8% with unknown/missing race and ethnicity. Most patients (64.1%) were insured through public programs. The primary caregiver language was English for the majority of families (~\u0026thinsp;88%), with Spanish (~\u0026thinsp;10%) being the most common non-English language [Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic and clinical characteristics by Childhood Opportunity Index (at first surgery)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eChildhood Opportunity Index (COI)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1568)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery Low (n\u0026thinsp;=\u0026thinsp;490)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow (n\u0026thinsp;=\u0026thinsp;410)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate (n\u0026thinsp;=\u0026thinsp;354)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh/Very High (n\u0026thinsp;=\u0026thinsp;314)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eRace, % (n)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e803 (51.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173 (35.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206 (50.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e203 (57.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e221 (70.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e208 (13.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e119 (24.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (11.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (8.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (4.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHispanic (of any race)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e314 (20.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122 (24.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92 (22.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66 (18.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (10.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic (other race\u003c/p\u003e\n\u003cp\u003ebesides White/Black)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e105 (6.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (5.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (6.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (6.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (8.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown/missing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138 (8.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (10.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (9.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (9.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (6.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e705 (44.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e239 (48.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184 (44.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156 (44.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126 (40.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e863 (55.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e251 (51.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e226 (55.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198 (55.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188 (59.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary Language, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnglish\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1387 (88.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e401 (81.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e364 (88.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e321 (90.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e301 (95.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpanish\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158 (10.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83 (16.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (9.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (7.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (3.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (1.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (2.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInsurance, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Medicaid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e563 (35.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73 (14.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127 (30.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158 (44.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e205 (65.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedicaid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1005 (64.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e417 (85.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e283 (69.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196 (55.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109 (34.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePreterm birth, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e289 (18.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78 (15.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83 (20.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74 (20.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55 (17.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1267 (80.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e409 (83.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e325 (79.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e279 (78.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e254 (80.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown/missing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (0.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAntenatal diagnosis of congenital heart defects, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e582 (37.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179 (36.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155 (37.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131 (37.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117 (37.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e957 (61.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e303 (61.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e246 (60.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e219 (61.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189 (60.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown/missing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (1.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (1.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (2.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (1.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (2.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAny comorbidity, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e884 (56.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e272 (55.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e230 (56.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e201 (56.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181 (57.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e684 (43.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e218 (44.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e180 (43.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e153 (43.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e133 (42.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge in days at time of first surgery\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e227 (74 to 1555)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e200.5 (60 to 1299)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e252 (78 to 1671)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e235 (78 to 1564)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e294.5 (86 to 1938)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e314\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCOI in units\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (16 to 56.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (6 to 15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (26 to 34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (46 to 55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71 (65 to 78)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1568\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e410\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e314\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eUnadjusted Analyses\u003c/h3\u003e\n\u003cp\u003eIn Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the unadjusted analyses revealed that patient race and ethnicity, primary caregiver language, and insurance payer were associated with COI (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for all). Non-Hispanic Black patients and those with non-English-speaking caregivers were disproportionately represented in the lowest opportunity neighborhoods (very low COI/low COI: 79% and 75%), whereas non-Hispanic White patients were more likely to reside in higher opportunity areas (high COI/very high COI: 53%). Similarly, patients with public insurance were more likely to live in low-opportunity neighborhoods compared to those with private insurance (70% vs 36%).\u003c/p\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, a lower COI was associated with an increase in length of stay, while mortality and major complication rates did not differ significantly across COI categories [Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSurgical characteristics and outcomes by Childhood Opportunity Index (surgical encounter level)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eChildhood Opportunity Index\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1871)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVery Low (n\u0026thinsp;=\u0026thinsp;581)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLow (n\u0026thinsp;=\u0026thinsp;501)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModerate (n\u0026thinsp;=\u0026thinsp;430)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHigh/Very High (n\u0026thinsp;=\u0026thinsp;359)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eOutcomes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMortality at 30 days post-procedure, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1830 (97.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e563 (96.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e493 (98.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e422 (98.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e352 (98.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDead\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (2.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (1.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown/missing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (0.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMortality at hospital discharge, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1814 (96.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e558 (96.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e491 (98.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e418 (97.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e347 (96.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDead\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57 (3.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (3.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (2.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (3.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMajor complications, including death, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166 (8.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (10.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44 (8.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 (9.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20 (5.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e888 (47.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e288 (49.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e240 (47.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e190 (44.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170 (47.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown/missing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e817 (43.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e231 (39.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e217 (43.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e200 (46.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169 (47.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMajor complications, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107 (5.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (6.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (4.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (7.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (4.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1764 (94.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e544 (93.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e479 (95.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e399 (92.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e342 (95.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReoperation within admission, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlanned reoperation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (1.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnplanned reoperation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo reoperation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1846 (98.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e575 (98.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e491 (98.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e422 (98.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e358 (99.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLength of stay in days\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (5 to 28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (5 to 33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (5 to 29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (4 to 31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (4 to 23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e581\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e501\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e430\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e359\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSTAT Category, % (n)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 to 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1644 (87.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e508 (87.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e445 (88.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e368 (85.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e323 (89.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 to 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e227 (12.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73 (12.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56 (11.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62 (14.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (10.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eMultivariable Analysis\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eCOI Disparities and Study Outcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter adjustment for patient demographics, clinical risk factors, insurance type, race and ethnicity, and caregiver primary language, there was no association observed between COI and in-hospital mortality, major complications, or LOS [Figures 1-3].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLegend:\u003c/em\u003e\u003c/strong\u003e In figures 1-3, variables included in the model included Childhood Opportunity Index or COI (ref: very low), race/ethnicity (ref: Non-Hispanic White), preferred patient language (ref: primary language English), presence of medical comorbidities (ref: no comorbidities), Congenital Heart Surgery Mortality Category or STAT category (ref: STAT 1-3), insurance payer (ref: commercial), patient sex, and age.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRacial and Ethnic Disparities and Study Outcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatient race and ethnicity were strongly associated with outcomes even after controlling for COI and other covariates. Non-Hispanic black patients had an increased odds of mortality compared to non-Hispanic white patients with an adjusted odds ratio of 2.2 (95% CI 1.07-5.3) [Figure 1]. Non-Hispanic Black patients also experienced longer hospitalizations (25% increase in mean LOS, 95% CI 0.09\u0026ndash;0.42; Figure 3). No statistically significant differences in mortality or LOS were observed for other racial/ethnic groups (e.g., Hispanic vs. non-Hispanic White) in the adjusted analysis. Adjusted major complication rates were similar across racial/ethnic categories [Figure 2].\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLanguage Disparities in Outcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong patients whose caregivers primarily spoke a non-English language, the mean LOS was significantly prolonged (30% increase in mean LOS; 95% CI: 0.098\u0026ndash;0.51) [Figure 3]. There were no differences in mortality or major complications based on language.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this single-center study of children undergoing congenital heart surgery, neighborhood opportunity, as measured by COI, was not independently associated with in-hospital mortality, major postoperative complications, or LOS after adjustment for race and ethnicity, caregiver language, insurance status, surgical complexity, and other clinical covariates. Non-Hispanic Black patients in our cohort experienced significantly higher in-hospital mortality and longer hospital stays compared to non-Hispanic White peers, even after adjusting for COI, insurance, and clinical risk. Patients with caregivers who spoke a non-English primary language also had significantly longer LOS.\u003c/p\u003e\n\u003cp\u003eThe lack of association between COI and outcomes diverges from prior multi-institutional studies, which reported a graded relationship between lower COI and adverse outcomes following congenital heart operations [3,4,7,8,18]. However, our results align with more recent analyses, which also found no significant association between COI and short-term morbidity in infants with single-ventricle physiology [9]. A possible explanation for this discrepancy lies in model specification. Many previous studies did not include race and ethnicity as variables on the grounds that observed disparities by race and ethnicity were mediated by COI, due to the strong association between race and ethnicity and COI quintiles, rather than an independent sociopolitical exposure. Additionally, few studies incorporated caregiver language as a proxy for institutional or interpersonal bias [11].\u003c/p\u003e\n\u003cp\u003eNot including race and ethnicity as explanatory predictors can mask the impact of systemic racism on outcomes. While COI captures spatially anchored disadvantage, it may obscure within-tract or zip code heterogeneity, fail to reflect real-time socioeconomic shifts, and overlook institutional bias at the point of care [13,19]. When these additional axes of inequity are omitted, variance attributable to structural racism may be misattributed to neighborhood-level metrics such as COI. In contrast, our modeling strategy included race and language as covariates, thereby allowing us to isolate their independent effects. Other studies also cautioned against overreliance on geographic proxies for individual or institutional exposure.\u003c/p\u003e\n\u003cp\u003eNotably, race and ethnicity were independently associated with outcomes, with non-Hispanic Black patients in our cohort experiencing significantly higher in-hospital mortality and longer hospital stays compared to non-Hispanic White peers, independent of clinical risk as measured by comorbidities and STAT scores. This persistent disparity aligns with other studies and underscores that race continues to act as a proxy for exposure to systemic inequities, not as a biologic risk factor [20-23]. When using race as an independent variable, it is vital to remember that race is a social construct and that disparities seen based on race are due to societal factors and do not represent immutable biological factors.\u003c/p\u003e\n\u003cp\u003ePatients with caregivers who spoke a non-English primary language had significantly longer LOS, consistent with prior research linking LEP to communication breakdowns, delayed discharge planning, and reduced engagement with ancillary services [12-14]. This association persisted even after adjusting for socioeconomic and clinical covariates, suggesting that language discordance represents a distinct barrier to timely recovery. Although few studies have directly examined the role of LEP in congenital heart surgery, broader pediatric literature supports the association of LEP with adverse outcomes, including hospital readmission and missed follow-up [12,14,24].\u003c/p\u003e\n\u003cp\u003eThe implications of these findings are twofold. First, the lack of independent association between COI and outcomes in either univariate or fully adjusted models highlights that neighborhood-level indices alone, in this cohort, may be insufficient to account for structural vulnerability. Second, the observed disparities by race and ethnicity and caregiver language underscore the need to retain these variables when evaluating health disparities. Importantly, this is not to reinforce biological essentialism, but to quantify the effects of structural racism and communication barriers when direct measures of discrimination are unavailable [10,11].\u003c/p\u003e\n\u003cp\u003eOur findings reinforce growing calls to move beyond composite indices in health equity research and adopt more granular, theory-informed approaches that directly measure institutional discrimination, communication barriers, and differential treatment patterns [11,25]. While neighborhood opportunity remains a meaningful contextual factor, it does not fully capture the lived experience of structural racism or language discordance within the healthcare system, or in fact, the lived experience of direct racism. Interventions focused on COI factors remain important; however, disparities within each COI quintile are likely to continue. Our research supports the need to broaden efforts to address how race and language directly impact outcomes. To address these inequities, future work should evaluate system-level interventions such as structured bias mitigation training, language-concordant care teams, and culturally tailored navigation programs. In addition, hospital-level characteristics (e.g., interpreter staffing, diversity of care teams, and equity-focused protocols) should be incorporated into future predictive models to better understand how institutional factors mediate disparate outcomes [26,27]. By systematically addressing these factors, it may be possible to significantly reduce inequities in post-operative outcomes for CHD patients and move closer toward achieving health equity in congenital cardiac care, resulting in improved outcomes for all.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations. As a single-center retrospective cohort study, our findings may not be generalizable to other institutions, especially those with different patient demographics or resource profiles [28]. Although we adjusted for several clinical and sociodemographic factors, unmeasured confounders may persist, including detailed care process variables or clinician-level biases. Language proficiency was inferred from caregiver-reported preferred language rather than validated assessments of fluency, and our dataset lacked granular information on interpreter use or care navigation services. Finally, while our modeling approach aimed to disentangle the effects of race, language, and COI, these exposures remain intertwined through historical and structural processes that may be incompletely captured by available variables [29].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this single-center cohort, neighborhood opportunity, as measured by COI, was not independently associated with postoperative mortality, complications, or length of stay following congenital heart surgery. Non-Hispanic Black race and caregiver non-English language were each independently associated with poorer outcomes, including increased in-hospital mortality and prolonged hospitalizations, even after adjusting for clinical and socioeconomic risk factors. These findings suggest that while SDOH metrics such as COI provide important context, they may not fully capture the impact of structural racism and communication barriers within healthcare delivery systems. A multifaceted approach that includes institutional accountability, culturally responsive care models, and bias-mitigation strategies is essential to closing persistent equity gaps in congenital cardiac surgery outcomes [30]. Future work should prioritize prospective, multi-center evaluations of targeted interventions aimed at improving equity in both perioperative care and long-term outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no specific grant from any funding agency, commercial entity, or not-for-profit organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u003c/strong\u003e Approved by the Johns Hopkins All Children\u0026rsquo;s Hospital Institutional Review Board with waiver of informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e All authors consent to publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u003c/strong\u003e Data available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e Drs. Coca and Ng conceptualized and designed the study, collected and managed data, drafted the manuscript, and critically revised it. Dr. Fierstein and Mr. Manipadam conducted analyses and revised the manuscript. Drs. Morrison and Ignjatovic contributed to study design and review and revised the manuscript. Drs. Johnson and Puchalski provided critical revisions. All authors approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePeterson JK, Love K, Koehl D et al (2017) Current trends in racial, ethnic, and healthcare disparities associated with pediatric cardiac surgery outcomes. 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Pediatrics 150(4):e2022057191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1542/peds.2022-057191\u003c/span\u003e\u003cspan address=\"10.1542/peds.2022-057191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pediatric-cardiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pedc","sideBox":"Learn more about [Pediatric Cardiology](http://link.springer.com/journal/246)","snPcode":"246","submissionUrl":"https://submission.nature.com/new-submission/246/3","title":"Pediatric Cardiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"congenital heart disease, child opportunity index, race and ethnicity, language barriers, health disparities","lastPublishedDoi":"10.21203/rs.3.rs-8022074/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8022074/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRacial and ethnic disparities and language barriers coexist with inequities in a child\u0026rsquo;s educational, environmental, and economic opportunity. We evaluated the association between surgical outcomes and a composite child neighborhood opportunity index (COI), race and ethnicity, and language barriers among children undergoing congenital heart surgery.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUtilizing the Society of Thoracic Surgeons database, we conducted a single-center retrospective cohort study of patients who underwent congenital cardiac surgery from 2010 to 2023. Patients were classified by quintile COI scores based on their reported address. Outcomes included in-hospital mortality, length of stay (LOS), and major complications. Adjusted analyses were performed using generalized linear mixed models.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e\u003cp\u003eAmong 1,568 patients, 51.2% were non-Hispanic White and 64.1% had public insurance. 57.4% lived in very low or low COI neighborhoods. Race, ethnicity, primary language, and insurance payer were significantly associated with COI (p\u0026thinsp;\u0026lt;\u0026thinsp;.0001). After adjustment, COI was not associated with mortality, LOS, or complications. Compared with non-Hispanic White patients, non-Hispanic Black patients had higher odds of in-hospital mortality (OR 2.19, 95% CI 1.07\u0026ndash;5.30) and longer LOS (β\u0026thinsp;=\u0026thinsp;0.25, 95% CI 0.09\u0026ndash;0.42). Patients with non-English-speaking caregivers had longer LOS (β\u0026thinsp;=\u0026thinsp;0.30, 95% CI 0.10\u0026ndash;0.51).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eNeighborhood opportunity was not independently associated with surgical outcomes. However, disparities by race and ethnicity and language persisted, suggesting that COI may not fully capture structural racism or communication barriers in pediatric cardiac care.\u003c/p\u003e","manuscriptTitle":"Child Opportunity, Race and Ethnicity, and Language in Congenital Cardiac Surgery Outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 08:41:48","doi":"10.21203/rs.3.rs-8022074/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T17:08:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T02:09:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T10:55:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125674849079546098775534239606744340679","date":"2025-11-10T08:53:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317632360233275927138710327486397437638","date":"2025-11-07T15:33:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T09:34:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-04T02:56:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-04T02:54:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pediatric Cardiology","date":"2025-11-03T19:04:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pediatric-cardiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pedc","sideBox":"Learn more about [Pediatric Cardiology](http://link.springer.com/journal/246)","snPcode":"246","submissionUrl":"https://submission.nature.com/new-submission/246/3","title":"Pediatric Cardiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4820015e-c8e7-4ac8-bafd-3ebfc88907e4","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:07:26+00:00","versionOfRecord":{"articleIdentity":"rs-8022074","link":"https://doi.org/10.1007/s00246-026-04159-6","journal":{"identity":"pediatric-cardiology","isVorOnly":false,"title":"Pediatric Cardiology"},"publishedOn":"2026-01-22 15:58:31","publishedOnDateReadable":"January 22nd, 2026"},"versionCreatedAt":"2025-11-18 08:41:48","video":"","vorDoi":"10.1007/s00246-026-04159-6","vorDoiUrl":"https://doi.org/10.1007/s00246-026-04159-6","workflowStages":[]},"version":"v1","identity":"rs-8022074","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8022074","identity":"rs-8022074","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}