Socio-economic disparities in unplanned hospital admission and in- hospital outcomes among children with congenital heart disease

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We hypothesize that children with congenital heart disease (CHD) from lower socio-economic status backgrounds have higher rates of unplanned hospital admissions and increased hospital resource utilization. We used Kids’ Inpatient Database (2016 and 2019). We included children < 21 years of age with CHD, and excluded newborn hospitalization. We further categorized CHD into simple lesions, complex bi-ventricular lesions, and single ventricle lesions. Admission types were further divided into surgical and non-surgical admissions. We used a logistic regression model to calculate the risk of unplanned hospital admission, mortality, and hospital resource utilization in children with different socio-economic status backgrounds. Out of 4,722,684 admitted children, excluding those with newborn hospitalization, 199,757 had CHD and met the study criteria. 121,626 had mild CHD, 61,639 complex bi-ventricular lesions, and 16,462 single ventricle lesions. Surgical admission comprised 20%(n = 39,694). In the CHD cohort, 27% had elective admissions, while 73% had non-elective admissions. Mortality was higher in unplanned admissions vs elective admissions, 3.0% vs 0.93%, P < 0.001. Unplanned admissions were more common in lowest income neighborhoods vs highest income neighborhoods, aOR = 1.4(1.3–1.5), P < 0.001 and were consistent at different age groups. There were higher rates of unplanned admissions in lowest income neighborhoods for each category of CHD and for both medical and surgical admission groups. Lengths of hospitalization were longer in the poorest neighborhood compared to their wealthiest counterparts, median of 7 days (IQR 3–21) vs 6 (3–17), P < 0.001. In conclusion, children with CHD who live in lowest income neighborhoods have increased odds of unplanned hospitalization for both surgical and non-surgical admissions and have higher mortality and resource utilization. congenital heart disease unplanned admission elective admission socioeconomic disparity United States ICD-10 codes Introduction Congenital heart disease (CHD) represents the most prevalent category of congenital anomalies, impacting around 1% of the overall population and serving as a notable contributor to morbidity and mortality among children.[ 1 , 2 ] Despite the relatively uniform incidence of CHD across geographic, racial, and socio-economic conditions, disparities in access to care and outcomes have been observed among different population groups.[ 3 – 5 ] Socio-economic factors play a pivotal role in determining the healthcare access and outcomes for individuals with heart disease.[ 6 , 7 ] Previous studies have demonstrated that individuals with limited resources and limited access to healthcare experience worse outcomes.[ 8 – 12 ] Socio-economic disparities in healthcare utilization, such as delayed diagnosis, suboptimal management, and higher rates of complications, contribute to the unequal burden of disease in vulnerable populations. [ 9 , 10 , 13 ] In a recent meta-analysis of 28 studies, Best et al. showed that lower socio-economic status was associated with increased risk of short-term and long-term mortality at different ages (neonates, infants and children) of children with CHD.[ 14 ] There are national and regional data exploring disparities in outcomes based on race and socio-economic conditions in CHD populations[ 15 , 16 ] [ 17 – 23 ]. Several studies, which are predominantly single center based have reported that the low socio-economic status, Hispanic ethnicity, younger age and higher complexity of CHD lesions have shown increased readmissions and higher mortality after surgery for CHD.[ 24 – 27 ] However, to our knowledge, there were not reports on national or large scale regional data evaluating the role of socio-economic status in CHD admission types and the outcomes following admission. By utilizing the latest datasets of Kids' Inpatient Database (KID), this study attempts to bridge the existing knowledge gap and offer valuable insights regarding the influence of socio-economic factors on non-elective/emergent hospital admissions and subsequent clinical outcomes in children with CHD. Methods We utilized the Kids' Inpatient Database (KID) from 2016 to 2019.[ 28 , 29 ] The KID is a cross-sectional database created by the Agency for Healthcare Research and Quality (AHRQ) as part of the Healthcare Cost and Utilization Project (HCUP). It encompasses discharge data from a representative sample of hospitals across the United States. Each year, the KID database includes data from more than an estimated three million annual births and six million hospital discharges. KID is published every three years, with 2019 being the most recent year.[ 29 ] Our study population consisted of children up to 20 years of age, excluding newborn hospitalizations. The KID is a stratified, cross-sectional database that includes discharge data for approximately 10% of newborn discharges and 80% of other discharges in the United States. Sample weights were applied to patient-level discharge observations to generate a nationally representative estimate of US hospitalizations. Neighborhood ZIP Codes classify the estimated median household income of residents in a patient's ZIP Code into four quartiles. The quartiles are identified from lowest to highest, indicating the lowest-income neighborhoods to highest-income neighborhoods. These values are derived from ZIP Code demographic data obtained from Claritas.[ 30 ] To identify children with CHD, we used the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. We classified the CHD cases into three categories based on complexity: simple lesions, complex biventricular lesions, and single ventricle lesions. Simple/mild CHD included atrial septal defect (ASD), ventricular septal defect (VSD), patent ductus arteriosus (PDA) and other septal defects. Single ventricle lesions included hypoplastic left heart syndrome, tricuspid atresia/stenosis, aortic atresia/stenosis and common ventricle. The remaining lesions were classified as biventricular complex lesions as per the previous classification, which has been previously used in the pediatric cardiology literature.[ 31 ] To identify the children who underwent surgical repair during the hospitalization, we used ICD-10 procedure codes for surgical repair for congenital heart disease. We also divided cohorts into different age groups 0–2, 3–5, 6–10, 11–20 years. In the KID database, type of admission is classified as non-elective and elective. The primary aim was to study the rate or risk of unplanned hospital admissions in children with CHD from the lowest-income neighborhoods compared to those from the highest-income neighborhoods. Secondary outcomes included mortality rates in CHD admissions across different socio-economic quartiles, both in non-elective and elective hospital admissions and surgical and non-surgical admission categories. Additionally, we examined the length of hospital stay as another secondary outcome. Descriptive statistics were performed to summarize the characteristics of the study population. Categorical variables were presented using frequencies and percentages, while continuous variables were expressed as median and interquartile range (IQR). To compare categorical variables, we employed the chi-square test. To assess the association between neighborhood socio-economic status and the odds of unplanned admission, we conducted logistic regression analysis. Neighborhoods were classified into four groups based on their socio-economic conditions, ranging from the lowest-income to the highest-income. We adjusted for confounding variables including age, sex, year of admission, and the categorized complexity of CHD. We did not include race/ethnicity as around 10% of subjects have race missing and HCUP advises against using race/ethnicity in regular analysis. Weights provided by HCUP were used in all analyses to account for the complex sampling design and clustering for the analysis. All statistical analyses were conducted using Stata version 14 (StataCorp, College Station, TX, USA) and R-rstudio (R Foundation for Statistical Computing, Vienna, Austria). The complex survey design of the KID was accounted for using the survey package. [ 32 ] As this study utilized a publicly available de-identified database, it received an expedited institutional review board approval from the Community Regional Medical Center, University of California San Francisco, Fresno campus, CA, USA. Results A total of 4,722,684 admissions were included in the study, excluding newborn hospitalizations. Of these, 199,757 were diagnosed with CHD. Of the CHD cases, 121,626 had mild CHD, 61,639 had complex biventricular lesions, and 16,492 had single ventricle lesions. Surgical admissions comprised 20%(n = 39,694) of total admissions and the rest were non-surgical admissions. Unplanned admissions accounted for 79% of all non-newborn pediatric hospitalizations, and this percentage was 73% for the CHD-only cohort. Within the CHD-only cohort, the percentage of unplanned hospitalizations was 68% in the highest-income neighborhood, compared to a significantly higher percentage of 76% in the lowest-income neighborhood (p < 0.001). Table 1 . Logistic regression analysis, adjusting for covariables, revealed that the odds of unplanned hospitalization for CHD in children from the lowest-income neighborhoods were significantly higher than those from the highest-income neighborhoods, with an adjusted odds ratio (aOR) of 1.4 (95% confidence interval: 1.3–1.5; p < 0.001). Table 3 . Table 1 Unplanned and elective admission types in total non-newborn pediatric hospitalization and those with CHD cohort. Household zipcodes All children* (N = 4,722,684) CHD only children # (N = 199,757) Unplanned/non-elective Elective Unplanned/non-elective Elective 0-24th 1247954(79%) 327178(21%) 48564(76%) 15295(24%) 25-49th 924618(78%) 256205(22%) 36951(74%) 13288(26%) 50-74th 868009(79%) 227835(21%) 34443(72%) 13542 (28%) 75-100th 694240(80%) 176647(20%) 25724(68%) 11950(32%) Total 3734819(79%) 987865(21%) 145683(73%) 54074(27%) *P = 0.0116, # P < 0.001 When we performed the analysis categorizing surgical vs non-surgical groups, we found that the unplanned admissions were higher for both surgical and non-surgical groups in the lowest socio-economic neighborhood compared to the highest-income neighborhood.(Table 2 ). Table 2 Unplanned and elective admission types in total non-newborn CHD cohort, according to surgical and non-surgical admission Household zipcodes Surgical admission*(n = 39,694) Non-surgical admission # (n = 160,063) Unplanned/non-elective Elective Unplanned/non- elective Elective 0-24th 4157(37%) 7179(63%) 44407(85%) 8116(15%) 25-49th 3358(34%) 6405(66%) 33594(83%) 6883(17%) 50-74th 3206(32%) 6684(68%) 31237(82%) 6858 (18%) 75-100th 2681(31%) 16024(69%) 23043(80%) 5925(20%) Total 13402(34%) 26292(66%) 132281(83%) 27782(17%) *P < 0.001, # P < 0.001 We performed descriptive analyses and logistic regression models for each category of CHD. The percentage of unplanned hospitalizations was lower in single ventricle lesions (63%) compared to simple CHD (79%) and complex ventricular lesions (64%). Across all categories of CHD, the risk of unplanned hospitalization was consistently higher in the lowest-income neighborhoods compared to the highest-income neighborhoods. Table 3 . Table 3 Logistic regression of unplanned admissions in different zip codes (unadjusted and adjusted) in CHD subpopulation (N = 199,656). Adjusted for age, sex and year. Household zipcodes Emergent/Non-Elective Unadjusted OR P-value Adjusted OR P-value All CHD 0-24th 48564(76%) 1.5(1.4–1.6) < 0.001 1.4(1.3–1.5) < 0.001 25-49th 36951(74%) 1.3(1.2–1.4) < 0.001 1.2(1.2–1.3) < 0.001 50-74th 34443(72%) 1.2(1.1–1.2) < 0.001 1.1(1.1–1.2) < 0.001 75-100th 25724(68%) Reference - Reference - CHD category 1 0-24th 33100(82%) 1.5(1.4–1.7) < 0.001 1.4(1.3–1.6) < 0.001 25-49th 24363(79%) 1.3(1.2–1.4) < 0.001 1.2(1.1–1.3) < 0.001 50-74th 22361(78%) 1.2(1.1–1.3) < 0.001 1.2(1.1–1.3) < 0.001 75-100th 16191(75%) Reference - Reference - CHD category 2 0-24th 12279(67%) 1.4(1.3–1.5) < 0.001 1.4(1.2–1.5) < 0.001 25-49th 9891(65%) 1.3(1.2–1.4) < 0.001 1.3(1.2–1.4) < 0.001 50-74th 9597(63%) 1.2(1.1–1.2) < 0.001 1.1(1.1–1.2) < 0.001 75-100th 7566(59%) Reference - Reference - CHD category 3 0-24th 3186(64%) 1.1(1.1–1.3) 0.037 1.1(1.0-1.3) 0.037 25-49th 2697(63%) 1.1(1.0-1.2) 0.24 1.1(1.0-1.2) 0.25 50-74th 2485(61%) 1.0(0.9–1.1) 0.72 1.0(0.9–1.1) 0.70 75-100th 1968(62%) Reference - Reference - CHD = congenital heart disease As the age of children with CHD ranged from 0–20 years of age, we performed analysis on each category of age group and found that the risks of unplanned admissions are higher in each age group.Table 4 Table 4 Logistic regression of CHD non-elective/emergent admissions in different zip codes (according to age group) Adjusted for sex and year. Household zipcodes Emergent/Non-Elective Unadjusted OR P-value Adjusted OR P-value Age group: 0–2 years 0-24th 40081(80%) 1.4(1.3–1.5) < 0.001 1.4(1.3–1.6) < 0.001 25-49th 30099(79%) 1.3(1.1–1.3) < 0.001 1.2(1.2–1.3) < 0.001 50-74th 27401(77%) 1.1(1.1–1.2) < 0.001 1.1(1.1–1.2) < 0.001 75-100th 19963(75%) Reference - Reference - Age group: 3–5 years 0-24th 2497(59%) 1.4(1.2–1.6) < 0.001 1.4(1.2–1.5) < 0.001 25-49th 2099(56%) 1.2(1.1–1.4) 0.001 1.2(1.1–1.4) 0.001 50-74th 2061(54%) 1.1(1.0-1.3) 0.021 1.1(1.1–1.3) 0.02 75-100th 1515(51%) Reference - Reference - Age group: 6–10 years 0-24th 2251(58%) 1.4(1.2–1.6) < 0.001 1.4(1.2–1.5) < 0.001 25-49th 1650(54%) 1.2(1.0-1.3) 0.007 1.2(1.0-1.4) 0.007 50-74th 1849(56%) 1.3(1.1–1.4) < 0.001 1.3(1.1–1.4) 0.001 75-100th 1384(50%) Reference - Reference - Age group: 11–20 years 0-24th 3735(63%) 1.4(1.2–1.5) < 0.001 1.4(1.2–1.6) < 0.001 25-49th 3104(61%) 1.2(1.1–1.4) 0.001 1.2(1.1–1.3) 0.001 50-74th 3133(59%) 1.1(1.0-1.3) 0.026 1.1(1.0-1.2) 0.03 75-100th 2862(55%) Reference - Reference - CHD = congenital heart disease Next, we assessed mortality rates in CHD cases based on neighborhood zip codes. We observed a higher risk of mortality in the lowest-income neighborhoods compared to the highest-income neighborhoods, with mortality rates of 2.6% (n = 1673) versus 2.1% (n = 798), p = 0.0012. Table 5 . We also found the mortality rates were higher in the surgical group compared to the non-surgical cohorts in the non-elective cohort. Table 6 . Although we found higher rates of mortality in lower income neighborhoods compared to highest-income neighborhoods in both surgical and non-surgical non-elective admission, the differences were not significant statistically. Table 6 . Furthermore, the mortality rates were three times higher in non-elective admissions compared to elective admissions within the CHD cohort, with rates of 3.0% (n = 4380) vs 0.93% (n = 513), p < 0.001. Upon further analysis, we found that in elective admissions for CHD, the mortality rate was almost two fold higher in children from the lowest-income neighborhoods compared to the highest-income neighborhoods, with rates of 1.2% (n = 181) vs 0.65% (n = 78), and an aOR of 1.7 (95% CI: 1.2–2.4; p = 0.005). Table 7 . In the unplanned admission group, although the difference was not statistically significant, the mortality rate was 11% higher in the lowest-income neighborhoods compared to the highest-income neighborhoods (3.1% vs. 2.8%). Table 5 Mortality according to zip code for all non-newborn pediatric and CHD admissions. In-hospital mortality Household income zip code quartile 0-24th 25-49th 50-74th 75-100th All pediatric hospitalization(Population size = 4,719,922) No 1566795 1174353 1089907 866338 P = 0.0018 Yes 7785(0.49%) 5698(0.48%) 5237(0.48%) 3809(0.44%) CHD only cohort (Population size = 199,656) No 62164 48988 46847 36847 Yes 1673(2.6%) 1230(2.4%) 1109(2.3%) 798(2.1%) P = 0.0012 Table 6 Mortality according to ZIP code for all non-newborn CHD non-elective admissions(surgical and non-surgical). In-hospital mortality Household income zip code quartile 0-24th 25-49th 50-74th 75-100th Surgical CHD non-elective admission (Population size = 13391) No 3953(95%) 3197(95%) 3061(95%) 2551(95%) P = 0.93 Yes 201(4.8%) 161(4.8%) 145(4.5%) 122(4.6%) Nonsurgical CHD non-elective admission (Population size = 132,204) No 43099 32633 30357 22430 Yes 1291(2.9%) 944(2.8%) 851(2.7%) 598(2.6%) P = 0.28 Table 7 Mortality by ZIP code in CHD children in unplanned and elective admissions. In-hospital mortality Household income zip code quartile 0-24th 25-49th 50-74th 75-100th P-value Non-elective admission No 47051(97%) 35830(97%) 33419(97%) 24981(97%) 0.37 Yes 1493(3.1%) 1105(3.0%) 996(2.9%) 720(2.8%) Elective admission No 15113(99%) 13158(99%) 13428(99%) 11866(99%) P = 0.0061 Yes 181(1.2%) 125(0.94%) 112(0.83%) 78(0.65%) Furthermore, we assessed the length of stay (LOS) in the CHD cohort with unplanned hospital admissions based on neighborhood quartiles. Our findings revealed that children from the lowest-income neighborhoods had a longer median LOS (7 days, interquartile range [IQR]: 3–21) compared to those from the highest-income neighborhoods (6 days, IQR: 3–17), p < 0.001. Discussion This study aimed to explore the association between socio-economic conditions, as measured by neighborhood zip codes, and types of hospital admissions in children with CHD, along with their outcomes. This study demonstrates considerable discrepancies in hospital admission rates, mortality rates, and hospital length of stay associated with the socio-economic status of patients’ neighborhood of residence. One of the major findings of this study was that children with CHD from the lowest-income neighborhoods experienced a higher rate of unplanned hospital admissions across all categories of CHD and age groups. This result indicates that socio-economic conditions play a crucial role in access to timely and appropriate healthcare. Best et al.'s meta-analysis, comprising five studies investigating socio-economic disparities in CHD outcomes, revealed a clear association between lower socio-economic status and poorer outcomes across different CHD severities and age groups. These negative effects were observed in both short-term and long-term outcomes.[ 17 , 23 , 33 – 35 ] Families residing in economically disadvantaged neighborhoods may face challenges in accessing regular medical care, leading to delayed diagnosis and treatment of CHD. These delays likely contribute to the higher rates of unplanned hospitalizations as patients present with more advanced disease stages. This study could lay the groundwork for future strategies to reduce CHD-related mortality in infants and children. By informing decision makers in public health strategic planning and tackling healthcare disparities, we hope to pave the way for improved healthcare access to improve outcomes for children with CHD. Furthermore, we observed higher mortality rates in children with CHD from the lower-income neighborhoods compared to their counterparts from higher-income neighborhoods. This aligns with previous studies reporting an increased risk of mortality in children from lower socio-economic backgrounds. Anderson et al showed similar findings from PHIS (Pediatric Health Information System database), 2011–2015 database, that children admitted for cardiac surgery from lowest-income neighborhoods had 1.2 (95% CI, 1.03–1.35) times higher mortality than those from higher-income neighborhoods.[ 18 ] Similarly, other population based reports have highlighted the increased risk of mortality in children from lower socioeconomic status and social minority groups.[ 36 , 37 ]. A systematic review conducted by Tregay et al. regarding unexpected deaths and unplanned readmissions in infants discharged home after cardiac surgery identified several factors significantly associated with increased mortality or risk of unplanned readmission. These factors include non-Caucasian ethnicity, lower socio-economic status, presence of comorbid conditions, age at surgery, and operative complexity.[ 24 ] In a similar investigation by Benavidez et al., the risk of readmission following cardiac surgery was assessed. Their study revealed that younger children, individuals of Hispanic ethnicity, recipients of government insurance, and those undergoing complex heart surgeries were at heightened risk of readmission.[ 25 ] Both studies by Tregay et al. and Benavidez et al. were based on single-center studies.[ 24 , 25 ] In contrast, our investigation, utilizing a national database, corroborated these findings by demonstrating that emergent admissions were prevalent across all age groups, various complexities of heart disease, and different socio-economic statuses. Lengths of hospital stay also demonstrated significant variation based on the neighborhood's socio-economic conditions. Children from the lower-income neighborhoods experienced longer hospital length of stay compared to those from higher-income neighborhoods. The findings are similar to what was reported by Anderson et al, where they found that the children from the lowest-income neighborhoods stayed 7% longer length of stay when compared to children from highest-income neighborhoods.[ 18 ] Studies by Spigel et al[ 16 ] and Vashist et al[ 38 ] in their single center studies also report longer hospital in lower socio-economic families in children with congenital heart disease including those with single ventricle lesions. This may be attributed to several factors, including delayed presentation to healthcare facilities, complications arising from inadequate disease management, and limited access to appropriate post-hospitalization care and resources. Research indicates that individuals with lower educational attainment [ 39 ] experience poorer health outcomes compared to other populations.[ 40 ] This trend is linked to significant health disparities that stem from differences in educational opportunities.[ 41 , 42 ] Hence, gaining a comprehensive health advantage associated with education could play a pivotal role in mitigating health disparities and enhancing the overall well-being of children and families with CHD. Promoting awareness and educating families residing in economically disadvantaged neighborhoods on early signs and symptoms of congenital heart disease (CHD), alongside promoting regular check-ups with primary care physicians and cardiologists, could serve as a strategy to prevent unplanned hospitalizations. Empowering these families to identify potential complications early may result in more effective disease management, ultimately leading to decreased hospitalization rates and improved overall outcomes. This study provides valuable insights into the healthcare disparities based on socio-economic conditions of children with CHD. The disparities in resource utilization among children from economically disadvantaged neighborhoods, driven by higher rates of unplanned hospitalizations and longer lengths of stay, necessitate targeted effective interventions. Healthcare disparities and worse clinical outcomes in children with some complex CHD have been recently reported by Lopez et al[ 43 ] and Krishnan et al[ 44 ], and our study confirms those findings on a large sample of CHD. This could allow policymakers and healthcare providers to collaborate in developing programs, such as educational and community outreach programs, that could improve healthcare access in economically disadvantaged areas. In this study, we used the International Classification of Diseases, Tenth Revision (ICD-10) codes to extract data of congenital heart disease (CHD). It's important to note that the reliance on administrative databases in this study introduces certain inherent limitations. These limitations encompass the potential for coding errors, as well as the possibility of both undercounting and overcounting cases of CHD. However, AHRQ (Agency for Healthcare Research and Quality) and HCUP (Healthcare Cost and Utilization Project) implement rigorous protocols and methodologies to ensure that the data provided is accurate prior to the release of the database. We utilized neighborhood ZIP Codes to categorize the estimated median household income of residents in a patient's ZIP Code into four quartiles, serving as a proxy for their socio-economic status (SES). While household income is a vital aspect of SES, we were unable to incorporate other significant factors such as education and occupation. These factors are not interchangeable, however, we are limited as those factors are not included in the KID database. Additionally, we lacked individual household-level data and confined our analysis to the neighborhood level by ZIP code. Despite the limitations of using Zip Code data for SES, household income has been more commonly employed as an SES indicator in studies conducted in the United States compared to studies conducted elsewhere. In conclusion, this study highlights the critical impact of socio-economic conditions on unplanned hospital admissions, outcomes, and utilization of resources among children with CHD using a nationwide database. Addressing these disparities is paramount in ensuring equitable healthcare access and improving overall outcomes for children with CHD, regardless of their socio-economic backgrounds. Declarations Conflict of interest: There is no conflict of interest for any author. No author has any financial or personal relationship with other people or organizations that could inappropriately influence his/her work. Funding: None Author Contribution L.V.G conceptualized the study, performed data analysis and wrote manuscript. S.K, Z.T, H.E, C.C wrote manuscript and prepared tables. F.S.C, O.A wrote manuscript and critically provided feedback to the manuscript. A.J.M conceptualized the study and supervised the study. All authors reviewed the manuscript. Acknowledgement None References Hoffman JIE, Kaplan S (2002) The incidence of congenital heart disease. J Am Coll Cardiol 39:1890–1900 van der Linde D, Konings EEM, Slager MA et al (2011) Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. J Am Coll Cardiol 58:2241–2247 Agha MM, Glazier RH, Moineddin R et al (2011) Socioeconomic status and prevalence of congenital heart defects: does universal access to health care system eliminate the gap? Birth Defects Res A. 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Accessed 20 Jun 2023 Edelson JB, Rossano JW, Griffis H et al (2018) Emergency Department Visits by Children With Congenital Heart Disease. J Am Coll Cardiol 72:1817–1825 Lumley T (2019) Survey: analysis of complex survey samples. R package version 3.35-1 Kucik JE, Cassell CH, Alverson CJ et al (2014) Role of health insurance on the survival of infants with congenital heart defects. Am J Public Health 104:e62–70 Kempny A, Diller G-P, Dimopoulos K et al (2016) Determinants of outpatient clinic attendance amongst adults with congenital heart disease and outcome. Int J Cardiol 203:245–250 Crowe S, Ridout DA, Knowles R et al (2016) Death and Emergency Readmission of Infants Discharged After Interventions for Congenital Heart Disease: A National Study of 7643 Infants to Inform Service Improvement. J Am Heart Assoc 5. https://doi.org/10.1161/JAHA.116.003369 Wang Y, Liu G, Canfield MA et al (2015) Racial/ethnic differences in survival of United States children with birth defects: a population-based study. J Pediatr 166:819–826 e1–2 Nembhard WN, Salemi JL, Ethen MK et al (2011) Racial/Ethnic disparities in risk of early childhood mortality among children with congenital heart defects. Pediatrics 127:e1128–e1138 Vashist S, Dudeck BS, Sherfy B et al (2023) Neighborhood socioeconomic status and length of stay after congenital heart disease surgery. Front Pediatr 11:1167064 Peyvandi S, Baer RJ, Moon-Grady AJ et al (2018) Socioeconomic Mediators of Racial and Ethnic Disparities in Congenital Heart Disease Outcomes: A Population-Based Study in California. J Am Heart Assoc 7:e010342 Zajacova A, Lawrence EM (2018) The Relationship Between Education and Health: Reducing Disparities Through a Contextual Approach. Annu Rev Public Health 39:273–289 Raghupathi V, Raghupathi W (2020) The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Arch Public Health 78:20 The Lancet Public Health (2020) Education: a neglected social determinant of health. Lancet Public Health 5:e361 Lopez KN, Morris SA, Krishnan A et al (2023) Associations Between Maternal Sociodemographics and Hospital Mortality in Newborns With Prenatally Diagnosed Hypoplastic Left Heart Syndrome. Circulation 148:283–285 Krishnan A, Jacobs MB, Morris SA et al (2021) Impact of Socioeconomic Status, Race and Ethnicity, and Geography on Prenatal Detection of Hypoplastic Left Heart Syndrome and Transposition of the Great Arteries. Circulation 143:2049–2060 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2024 Read the published version in Pediatric Cardiology → Version 1 posted Editorial decision: Revision requested 24 Oct, 2024 Reviews received at journal 24 Sep, 2024 Reviewers agreed at journal 09 Sep, 2024 Reviewers agreed at journal 05 Sep, 2024 Reviewers agreed at journal 27 May, 2024 Reviewers invited by journal 21 May, 2024 Submission checks completed at journal 21 May, 2024 Editor assigned by journal 21 May, 2024 First submitted to journal 20 May, 2024 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-4446999","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307368308,"identity":"d7b59674-49f9-4a43-af82-e665b9c77a63","order_by":0,"name":"Laxmi V Ghimire","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACAxCRwGCXwMbA2MDAUAHkMR9gYOAhrCUZquUMkMeWQIQWBoYDCWCKsY0YLexnTDc8YDiQxyd2uO3Dx3mH5eXbGBgfvG3Do4UnLe1GAsOBYjbpxOaZM7cdNtxwjIHZcC4+LQzJx4Bajie2AbUw8247nGAg38AmzYtPC//DNqCWwxAtf+ccTgA6jP03Xi0SYFugWhgbDicwHGNgY8av5RnQLwbJYC2MPcfSgX5hbJaccw63Fvv+HLObPyrsEufPTn/M8KPGGhhizAc/vCnDrQUWCMgAFKejYBSMglEwCigCAHkQUYSvI43/AAAAAElFTkSuQmCC","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":true,"prefix":"","firstName":"Laxmi","middleName":"V","lastName":"Ghimire","suffix":""},{"id":307368310,"identity":"dd7d54f5-2751-4fdb-ac46-2a1230a34555","order_by":1,"name":"Sagya Khanal","email":"","orcid":"","institution":"Nepal Medical College","correspondingAuthor":false,"prefix":"","firstName":"Sagya","middleName":"","lastName":"Khanal","suffix":""},{"id":307368311,"identity":"88c3a509-3020-40e1-a0a9-faaa2bfc8642","order_by":2,"name":"Zareh Torabyan","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Zareh","middleName":"","lastName":"Torabyan","suffix":""},{"id":307368312,"identity":"71c71f9a-9a11-49b8-8ef1-3348ab9d142b","order_by":3,"name":"Hiba El-Rahi","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Hiba","middleName":"","lastName":"El-Rahi","suffix":""},{"id":307368313,"identity":"326e1118-eb17-4ea5-9081-c646c0603b32","order_by":4,"name":"Catherine Cong","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Cong","suffix":""},{"id":307368314,"identity":"8233919b-e2ad-4988-9454-a7c76c90a112","order_by":5,"name":"Fu-Sheng Chou","email":"","orcid":"","institution":"Kaiser Permanente Riverside Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Fu-Sheng","middleName":"","lastName":"Chou","suffix":""},{"id":307368315,"identity":"dd72c5ba-a519-4a47-a9dc-fe926891fc3b","order_by":6,"name":"Othman A. Aljohani","email":"","orcid":"","institution":"Benioff Children’s Hospital, University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Othman","middleName":"A.","lastName":"Aljohani","suffix":""},{"id":307368319,"identity":"953d5a16-57c9-4408-9f7b-ffd915565165","order_by":7,"name":"Anita J. Moon-Grady","email":"","orcid":"","institution":"Benioff Children’s Hospital, University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Anita","middleName":"J.","lastName":"Moon-Grady","suffix":""}],"badges":[],"createdAt":"2024-05-20 06:24:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4446999/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4446999/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00246-024-03755-8","type":"published","date":"2024-12-26T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72641411,"identity":"452a2256-c2ba-4aef-9608-bbf7de8f0502","added_by":"auto","created_at":"2024-12-30 16:11:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":674474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4446999/v1/7e609c6e-1b6a-4afd-83b5-713fb0d67ba4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socio-economic disparities in unplanned hospital admission and in- hospital outcomes among children with congenital heart disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCongenital heart disease (CHD) represents the most prevalent category of congenital anomalies, impacting around 1% of the overall population and serving as a notable contributor to morbidity and mortality among children.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Despite the relatively uniform incidence of CHD across geographic, racial, and socio-economic conditions, disparities in access to care and outcomes have been observed among different population groups.[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Socio-economic factors play a pivotal role in determining the healthcare access and outcomes for individuals with heart disease.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Previous studies have demonstrated that individuals with limited resources and limited access to healthcare experience worse outcomes.[\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Socio-economic disparities in healthcare utilization, such as delayed diagnosis, suboptimal management, and higher rates of complications, contribute to the unequal burden of disease in vulnerable populations. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] In a recent meta-analysis of 28 studies, Best et al. showed that lower socio-economic status was associated with increased risk of short-term and long-term mortality at different ages (neonates, infants and children) of children with CHD.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] There are national and regional data exploring disparities in outcomes based on race and socio-economic conditions in CHD populations[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Several studies, which are predominantly single center based have reported that the low socio-economic status, Hispanic ethnicity, younger age and higher complexity of CHD lesions have shown increased readmissions and higher mortality after surgery for CHD.[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] However, to our knowledge, there were not reports on national or large scale regional data evaluating the role of socio-economic status in CHD admission types and the outcomes following admission. By utilizing the latest datasets of Kids' Inpatient Database (KID), this study attempts to bridge the existing knowledge gap and offer valuable insights regarding the influence of socio-economic factors on non-elective/emergent hospital admissions and subsequent clinical outcomes in children with CHD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe utilized the Kids' Inpatient Database (KID) from 2016 to 2019.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] The KID is a cross-sectional database created by the Agency for Healthcare Research and Quality (AHRQ) as part of the Healthcare Cost and Utilization Project (HCUP). It encompasses discharge data from a representative sample of hospitals across the United States. Each year, the KID database includes data from more than an estimated three million annual births and six million hospital discharges. KID is published every three years, with 2019 being the most recent year.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] Our study population consisted of children up to 20 years of age, excluding newborn hospitalizations. The KID is a stratified, cross-sectional database that includes discharge data for approximately 10% of newborn discharges and 80% of other discharges in the United States. Sample weights were applied to patient-level discharge observations to generate a nationally representative estimate of US hospitalizations.\u003c/p\u003e \u003cp\u003eNeighborhood ZIP Codes classify the estimated median household income of residents in a patient's ZIP Code into four quartiles. The quartiles are identified from lowest to highest, indicating the lowest-income neighborhoods to highest-income neighborhoods. These values are derived from ZIP Code demographic data obtained from Claritas.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTo identify children with CHD, we used the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. We classified the CHD cases into three categories based on complexity: simple lesions, complex biventricular lesions, and single ventricle lesions. Simple/mild CHD included atrial septal defect (ASD), ventricular septal defect (VSD), patent ductus arteriosus (PDA) and other septal defects. Single ventricle lesions included hypoplastic left heart syndrome, tricuspid atresia/stenosis, aortic atresia/stenosis and common ventricle. The remaining lesions were classified as biventricular complex lesions as per the previous classification, which has been previously used in the pediatric cardiology literature.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] To identify the children who underwent surgical repair during the hospitalization, we used ICD-10 procedure codes for surgical repair for congenital heart disease. We also divided cohorts into different age groups 0\u0026ndash;2, 3\u0026ndash;5, 6\u0026ndash;10, 11\u0026ndash;20 years. In the KID database, type of admission is classified as non-elective and elective.\u003c/p\u003e \u003cp\u003eThe primary aim was to study the rate or risk of unplanned hospital admissions in children with CHD from the lowest-income neighborhoods compared to those from the highest-income neighborhoods. Secondary outcomes included mortality rates in CHD admissions across different socio-economic quartiles, both in non-elective and elective hospital admissions and surgical and non-surgical admission categories. Additionally, we examined the length of hospital stay as another secondary outcome.\u003c/p\u003e \u003cp\u003eDescriptive statistics were performed to summarize the characteristics of the study population. Categorical variables were presented using frequencies and percentages, while continuous variables were expressed as median and interquartile range (IQR). To compare categorical variables, we employed the chi-square test.\u003c/p\u003e \u003cp\u003eTo assess the association between neighborhood socio-economic status and the odds of unplanned admission, we conducted logistic regression analysis. Neighborhoods were classified into four groups based on their socio-economic conditions, ranging from the lowest-income to the highest-income. We adjusted for confounding variables including age, sex, year of admission, and the categorized complexity of CHD. We did not include race/ethnicity as around 10% of subjects have race missing and HCUP advises against using race/ethnicity in regular analysis.\u003c/p\u003e \u003cp\u003eWeights provided by HCUP were used in all analyses to account for the complex sampling design and clustering for the analysis. All statistical analyses were conducted using Stata version 14 (StataCorp, College Station, TX, USA) and R-rstudio (R Foundation for Statistical Computing, Vienna, Austria). The complex survey design of the KID was accounted for using the \u003cem\u003esurvey\u003c/em\u003e package. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e As this study utilized a publicly available de-identified database, it received an expedited institutional review board approval from the Community Regional Medical Center, University of California San Francisco, Fresno campus, CA, USA.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 4,722,684 admissions were included in the study, excluding newborn hospitalizations. Of these, 199,757 were diagnosed with CHD. Of the CHD cases, 121,626 had mild CHD, 61,639 had complex biventricular lesions, and 16,492 had single ventricle lesions. Surgical admissions comprised 20%(n\u0026thinsp;=\u0026thinsp;39,694) of total admissions and the rest were non-surgical admissions.\u003c/p\u003e\n\u003cp\u003eUnplanned admissions accounted for 79% of all non-newborn pediatric hospitalizations, and this percentage was 73% for the CHD-only cohort. Within the CHD-only cohort, the percentage of unplanned hospitalizations was 68% in the highest-income neighborhood, compared to a significantly higher percentage of 76% in the lowest-income neighborhood (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Logistic regression analysis, adjusting for covariables, revealed that the odds of unplanned hospitalization for CHD in children from the lowest-income neighborhoods were significantly higher than those from the highest-income neighborhoods, with an adjusted odds ratio (aOR) of 1.4 (95% confidence interval: 1.3\u0026ndash;1.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnplanned and elective admission types in total non-newborn pediatric hospitalization and those with CHD cohort.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHousehold zipcodes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAll children* (N\u0026thinsp;=\u0026thinsp;4,722,684)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCHD only children\u003csup\u003e#\u003c/sup\u003e(N\u0026thinsp;=\u0026thinsp;199,757)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnplanned/non-elective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnplanned/non-elective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1247954(79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327178(21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48564(76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15295(24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e924618(78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256205(22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36951(74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13288(26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868009(79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227835(21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34443(72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13542 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e694240(80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176647(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25724(68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11950(32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3734819(79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e987865(21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145683(73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54074(27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e*P\u0026thinsp;=\u0026thinsp;0.0116, \u003csup\u003e#\u003c/sup\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhen we performed the analysis categorizing surgical vs non-surgical groups, we found that the unplanned admissions were higher for both surgical and non-surgical groups in the lowest socio-economic neighborhood compared to the highest-income neighborhood.(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnplanned and elective admission types in total non-newborn CHD cohort, according to surgical and non-surgical admission\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHousehold zipcodes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSurgical admission*(n\u0026thinsp;=\u0026thinsp;39,694)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon-surgical admission\u003csup\u003e#\u003c/sup\u003e(n\u0026thinsp;=\u0026thinsp;160,063)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnplanned/non-elective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnplanned/non-\u003c/p\u003e\n \u003cp\u003eelective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4157(37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7179(63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44407(85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8116(15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3358(34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6405(66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33594(83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6883(17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3206(32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6684(68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31237(82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6858 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2681(31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16024(69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23043(80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5925(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13402(34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26292(66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132281(83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27782(17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003csup\u003e#\u003c/sup\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWe performed descriptive analyses and logistic regression models for each category of CHD. The percentage of unplanned hospitalizations was lower in single ventricle lesions (63%) compared to simple CHD (79%) and complex ventricular lesions (64%). Across all categories of CHD, the risk of unplanned hospitalization was consistently higher in the lowest-income neighborhoods compared to the highest-income neighborhoods. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLogistic regression of unplanned admissions in different zip codes (unadjusted and adjusted) in CHD subpopulation (N\u0026thinsp;=\u0026thinsp;199,656). Adjusted for age, sex and year.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHousehold zipcodes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmergent/Non-Elective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnadjusted OR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted OR\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 \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAll CHD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48564(76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5(1.4\u0026ndash;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.3\u0026ndash;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36951(74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3(1.2\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.2\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34443(72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.1\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.1\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25724(68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eCHD category 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33100(82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5(1.4\u0026ndash;1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.3\u0026ndash;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24363(79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3(1.2\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.1\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22361(78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.1\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.1\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16191(75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eCHD category 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12279(67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.3\u0026ndash;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.2\u0026ndash;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9891(65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3(1.2\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3(1.2\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9597(63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.1\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.1\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7566(59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eCHD category 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3186(64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.1\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.0-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2697(63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.0-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.0-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\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\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2485(61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(0.9\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(0.9\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1968(62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eCHD\u0026thinsp;=\u0026thinsp;congenital heart disease\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAs the age of children with CHD ranged from 0\u0026ndash;20 years of age, we performed analysis on each category of age group and found that the risks of unplanned admissions are higher in each age group.Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic regression of CHD non-elective/emergent admissions in different zip codes (according to age group) Adjusted for sex and year.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHousehold zipcodes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmergent/Non-Elective\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnadjusted OR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted OR\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 \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAge group: 0\u0026ndash;2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40081(80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.3\u0026ndash;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.3\u0026ndash;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30099(79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3(1.1\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.2\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27401(77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.1\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.1\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19963(75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAge group: 3\u0026ndash;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2497(59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.2\u0026ndash;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.2\u0026ndash;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2099(56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.1\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.1\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2061(54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.0-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.1\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1515(51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAge group: 6\u0026ndash;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2251(58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.2\u0026ndash;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.2\u0026ndash;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1650(54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.0-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.0-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1849(56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3(1.1\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3(1.1\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1384(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAge group: 11\u0026ndash;20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3735(63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.2\u0026ndash;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4(1.2\u0026ndash;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3104(61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.1\u0026ndash;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2(1.1\u0026ndash;1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3133(59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.0-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1(1.0-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2862(55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eCHD\u0026thinsp;=\u0026thinsp;congenital heart disease\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eNext, we assessed mortality rates in CHD cases based on neighborhood zip codes. We observed a higher risk of mortality in the lowest-income neighborhoods compared to the highest-income neighborhoods, with mortality rates of 2.6% (n\u0026thinsp;=\u0026thinsp;1673) versus 2.1% (n\u0026thinsp;=\u0026thinsp;798), p\u0026thinsp;=\u0026thinsp;0.0012. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. We also found the mortality rates were higher in the surgical group compared to the non-surgical cohorts in the non-elective cohort. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Although we found higher rates of mortality in lower income neighborhoods compared to highest-income neighborhoods in both surgical and non-surgical non-elective admission, the differences were not significant statistically. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Furthermore, the mortality rates were three times higher in non-elective admissions compared to elective admissions within the CHD cohort, with rates of 3.0% (n\u0026thinsp;=\u0026thinsp;4380) vs 0.93% (n\u0026thinsp;=\u0026thinsp;513), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Upon further analysis, we found that in elective admissions for CHD, the mortality rate was almost two fold higher in children from the lowest-income neighborhoods compared to the highest-income neighborhoods, with rates of 1.2% (n\u0026thinsp;=\u0026thinsp;181) vs 0.65% (n\u0026thinsp;=\u0026thinsp;78), and an aOR of 1.7 (95% CI: 1.2\u0026ndash;2.4; p\u0026thinsp;=\u0026thinsp;0.005). Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. In the unplanned admission group, although the difference was not statistically significant, the mortality rate was 11% higher in the lowest-income neighborhoods compared to the highest-income neighborhoods (3.1% vs. 2.8%).\u003c/p\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMortality according to zip code for all non-newborn pediatric and CHD admissions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIn-hospital mortality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eHousehold income zip code quartile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAll pediatric hospitalization(Population size\u0026thinsp;=\u0026thinsp;4,719,922)\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\u003e1566795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1174353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1089907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e866338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0018\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\u003e7785(0.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5698(0.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5237(0.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3809(0.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eCHD only cohort (Population size\u0026thinsp;=\u0026thinsp;199,656)\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\u003e62164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36847\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\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1673(2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1230(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1109(2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e798(2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMortality according to ZIP code for all non-newborn CHD non-elective admissions(surgical and non-surgical).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIn-hospital mortality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eHousehold income zip code quartile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e75-100th\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eSurgical CHD non-elective admission (Population size\u0026thinsp;=\u0026thinsp;13391)\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\u003e3953(95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3197(95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3061(95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2551(95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.93\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\u003e201(4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161(4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145(4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122(4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eNonsurgical CHD non-elective admission (Population size\u0026thinsp;=\u0026thinsp;132,204)\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\u003e43099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22430\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\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1291(2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e944(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e851(2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e598(2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMortality by ZIP code in CHD children in unplanned and elective admissions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIn-hospital mortality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eHousehold income zip code quartile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0-24th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e25-49th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e50-74th\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e75-100th\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 \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eNon-elective admission\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\u003e47051(97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35830(97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33419(97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24981(97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.37\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\u003e1493(3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1105(3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e996(2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e720(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eElective admission\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\u003e15113(99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13158(99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13428(99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11866(99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0061\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\u003e181(1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125(0.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112(0.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78(0.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, we assessed the length of stay (LOS) in the CHD cohort with unplanned hospital admissions based on neighborhood quartiles. Our findings revealed that children from the lowest-income neighborhoods had a longer median LOS (7 days, interquartile range [IQR]: 3\u0026ndash;21) compared to those from the highest-income neighborhoods (6 days, IQR: 3\u0026ndash;17), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to explore the association between socio-economic conditions, as measured by neighborhood zip codes, and types of hospital admissions in children with CHD, along with their outcomes. This study demonstrates considerable discrepancies in hospital admission rates, mortality rates, and hospital length of stay associated with the socio-economic status of patients\u0026rsquo; neighborhood of residence.\u003c/p\u003e \u003cp\u003eOne of the major findings of this study was that children with CHD from the lowest-income neighborhoods experienced a higher rate of unplanned hospital admissions across all categories of CHD and age groups. This result indicates that socio-economic conditions play a crucial role in access to timely and appropriate healthcare. Best et al.'s meta-analysis, comprising five studies investigating socio-economic disparities in CHD outcomes, revealed a clear association between lower socio-economic status and poorer outcomes across different CHD severities and age groups. These negative effects were observed in both short-term and long-term outcomes.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Families residing in economically disadvantaged neighborhoods may face challenges in accessing regular medical care, leading to delayed diagnosis and treatment of CHD. These delays likely contribute to the higher rates of unplanned hospitalizations as patients present with more advanced disease stages. This study could lay the groundwork for future strategies to reduce CHD-related mortality in infants and children. By informing decision makers in public health strategic planning and tackling healthcare disparities, we hope to pave the way for improved healthcare access to improve outcomes for children with CHD.\u003c/p\u003e \u003cp\u003eFurthermore, we observed higher mortality rates in children with CHD from the lower-income neighborhoods compared to their counterparts from higher-income neighborhoods. This aligns with previous studies reporting an increased risk of mortality in children from lower socio-economic backgrounds. Anderson et al showed similar findings from PHIS (Pediatric Health Information System database), 2011\u0026ndash;2015 database, that children admitted for cardiac surgery from lowest-income neighborhoods had 1.2 (95% CI, 1.03\u0026ndash;1.35) times higher mortality than those from higher-income neighborhoods.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Similarly, other population based reports have highlighted the increased risk of mortality in children from lower socioeconomic status and social minority groups.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA systematic review conducted by Tregay et al. regarding unexpected deaths and unplanned readmissions in infants discharged home after cardiac surgery identified several factors significantly associated with increased mortality or risk of unplanned readmission. These factors include non-Caucasian ethnicity, lower socio-economic status, presence of comorbid conditions, age at surgery, and operative complexity.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] In a similar investigation by Benavidez et al., the risk of readmission following cardiac surgery was assessed. Their study revealed that younger children, individuals of Hispanic ethnicity, recipients of government insurance, and those undergoing complex heart surgeries were at heightened risk of readmission.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Both studies by Tregay et al. and Benavidez et al. were based on single-center studies.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] In contrast, our investigation, utilizing a national database, corroborated these findings by demonstrating that emergent admissions were prevalent across all age groups, various complexities of heart disease, and different socio-economic statuses.\u003c/p\u003e \u003cp\u003eLengths of hospital stay also demonstrated significant variation based on the neighborhood's socio-economic conditions. Children from the lower-income neighborhoods experienced longer hospital length of stay compared to those from higher-income neighborhoods. The findings are similar to what was reported by Anderson et al, where they found that the children from the lowest-income neighborhoods stayed 7% longer length of stay when compared to children from highest-income neighborhoods.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Studies by Spigel et al[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and Vashist et al[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] in their single center studies also report longer hospital in lower socio-economic families in children with congenital heart disease including those with single ventricle lesions. This may be attributed to several factors, including delayed presentation to healthcare facilities, complications arising from inadequate disease management, and limited access to appropriate post-hospitalization care and resources.\u003c/p\u003e \u003cp\u003eResearch indicates that individuals with lower educational attainment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] experience poorer health outcomes compared to other populations.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] This trend is linked to significant health disparities that stem from differences in educational opportunities.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] Hence, gaining a comprehensive health advantage associated with education could play a pivotal role in mitigating health disparities and enhancing the overall well-being of children and families with CHD. Promoting awareness and educating families residing in economically disadvantaged neighborhoods on early signs and symptoms of congenital heart disease (CHD), alongside promoting regular check-ups with primary care physicians and cardiologists, could serve as a strategy to prevent unplanned hospitalizations. Empowering these families to identify potential complications early may result in more effective disease management, ultimately leading to decreased hospitalization rates and improved overall outcomes.\u003c/p\u003e \u003cp\u003eThis study provides valuable insights into the healthcare disparities based on socio-economic conditions of children with CHD. The disparities in resource utilization among children from economically disadvantaged neighborhoods, driven by higher rates of unplanned hospitalizations and longer lengths of stay, necessitate targeted effective interventions. Healthcare disparities and worse clinical outcomes in children with some complex CHD have been recently reported by Lopez et al[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and Krishnan et al[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and our study confirms those findings on a large sample of CHD. This could allow policymakers and healthcare providers to collaborate in developing programs, such as educational and community outreach programs, that could improve healthcare access in economically disadvantaged areas.\u003c/p\u003e \u003cp\u003eIn this study, we used the International Classification of Diseases, Tenth Revision (ICD-10) codes to extract data of congenital heart disease (CHD). It's important to note that the reliance on administrative databases in this study introduces certain inherent limitations. These limitations encompass the potential for coding errors, as well as the possibility of both undercounting and overcounting cases of CHD. However, AHRQ (Agency for Healthcare Research and Quality) and HCUP (Healthcare Cost and Utilization Project) implement rigorous protocols and methodologies to ensure that the data provided is accurate prior to the release of the database. We utilized neighborhood ZIP Codes to categorize the estimated median household income of residents in a patient's ZIP Code into four quartiles, serving as a proxy for their socio-economic status (SES). While household income is a vital aspect of SES, we were unable to incorporate other significant factors such as education and occupation. These factors are not interchangeable, however, we are limited as those factors are not included in the KID database. Additionally, we lacked individual household-level data and confined our analysis to the neighborhood level by ZIP code. Despite the limitations of using Zip Code data for SES, household income has been more commonly employed as an SES indicator in studies conducted in the United States compared to studies conducted elsewhere.\u003c/p\u003e \u003cp\u003eIn conclusion, this study highlights the critical impact of socio-economic conditions on unplanned hospital admissions, outcomes, and utilization of resources among children with CHD using a nationwide database. Addressing these disparities is paramount in ensuring equitable healthcare access and improving overall outcomes for children with CHD, regardless of their socio-economic backgrounds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest:\u003c/h2\u003e\n\u003cp\u003eThere is no conflict of interest for any author. No author has any financial or personal relationship with other people or organizations that could inappropriately influence his/her work.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eL.V.G conceptualized the study, performed data analysis and wrote manuscript. S.K, Z.T, H.E, C.C wrote manuscript and prepared tables. F.S.C, O.A wrote manuscript and critically provided feedback to the manuscript. A.J.M conceptualized the study and supervised the study. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoffman JIE, Kaplan S (2002) The incidence of congenital heart disease. J Am Coll Cardiol 39:1890\u0026ndash;1900\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Linde D, Konings EEM, Slager MA et al (2011) Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. J Am Coll Cardiol 58:2241\u0026ndash;2247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgha MM, Glazier RH, Moineddin R et al (2011) Socioeconomic status and prevalence of congenital heart defects: does universal access to health care system eliminate the gap? Birth Defects Res A. Clin Mol Teratol 91:1011\u0026ndash;1018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnowles RL, Ridout D, Crowe S et al (2019) Ethnic-specific mortality of infants undergoing congenital heart surgery in England and Wales. Arch Dis Child 104:844\u0026ndash;850\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnowles RL, Ridout D, Crowe S et al (2017) Ethnic and socioeconomic variation in incidence of congenital heart defects. Arch Dis Child 102:496\u0026ndash;502\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiley WJ (2012) Health disparities: gaps in access, quality and affordability of medical care. Trans Am Clin Climatol Assoc 123:167\u0026ndash;172 discussion 172\u0026ndash;4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Rathore SS, Radford MJ et al (2001) Racial differences in the use of cardiac catheterization after acute myocardial infarction. N Engl J Med 344:1443\u0026ndash;1449\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy S, Showstark M, Tolchin B et al (2021) The potential impact of triage protocols on racial disparities in clinical outcomes among COVID-positive patients in a large academic healthcare system. 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Circulation 143:2049\u0026ndash;2060\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, unplanned admission, elective admission, socioeconomic disparity, United States, ICD-10 codes","lastPublishedDoi":"10.21203/rs.3.rs-4446999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4446999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnplanned/non-elective admissions have been associated with worse clinical outcomes and increased use of hospital resources. We hypothesize that children with congenital heart disease (CHD) from lower socio-economic status backgrounds have higher rates of unplanned hospital admissions and increased hospital resource utilization. We used Kids\u0026rsquo; Inpatient Database (2016 and 2019). We included children\u0026thinsp;\u0026lt;\u0026thinsp;21 years of age with CHD, and excluded newborn hospitalization. We further categorized CHD into simple lesions, complex bi-ventricular lesions, and single ventricle lesions. Admission types were further divided into surgical and non-surgical admissions. We used a logistic regression model to calculate the risk of unplanned hospital admission, mortality, and hospital resource utilization in children with different socio-economic status backgrounds. Out of 4,722,684 admitted children, excluding those with newborn hospitalization, 199,757 had CHD and met the study criteria. 121,626 had mild CHD, 61,639 complex bi-ventricular lesions, and 16,462 single ventricle lesions. Surgical admission comprised 20%(n\u0026thinsp;=\u0026thinsp;39,694). In the CHD cohort, 27% had elective admissions, while 73% had non-elective admissions. Mortality was higher in unplanned admissions vs elective admissions, 3.0% vs 0.93%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Unplanned admissions were more common in lowest income neighborhoods vs highest income neighborhoods, aOR\u0026thinsp;=\u0026thinsp;1.4(1.3\u0026ndash;1.5), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and were consistent at different age groups. There were higher rates of unplanned admissions in lowest income neighborhoods for each category of CHD and for both medical and surgical admission groups. Lengths of hospitalization were longer in the poorest neighborhood compared to their wealthiest counterparts, median of 7 days (IQR 3\u0026ndash;21) vs 6 (3\u0026ndash;17), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. In conclusion, children with CHD who live in lowest income neighborhoods have increased odds of unplanned hospitalization for both surgical and non-surgical admissions and have higher mortality and resource utilization.\u003c/p\u003e","manuscriptTitle":"Socio-economic disparities in unplanned hospital admission and in- hospital outcomes among children with congenital heart disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 05:50:52","doi":"10.21203/rs.3.rs-4446999/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-24T16:12:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-24T14:05:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270421108047212626112115843706217855323","date":"2024-09-09T18:24:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115046236426354919140833964974614400834","date":"2024-09-05T15:58:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244872391442737955304564995938603691413","date":"2024-05-27T17:04:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-21T18:59:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-21T08:03:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-21T08:03:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pediatric Cardiology","date":"2024-05-20T06:23:17+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":"42c94014-acb8-4bb3-9366-c9159a2f86f7","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:08:29+00:00","versionOfRecord":{"articleIdentity":"rs-4446999","link":"https://doi.org/10.1007/s00246-024-03755-8","journal":{"identity":"pediatric-cardiology","isVorOnly":false,"title":"Pediatric Cardiology"},"publishedOn":"2024-12-26 15:57:44","publishedOnDateReadable":"December 26th, 2024"},"versionCreatedAt":"2024-06-04 05:50:52","video":"","vorDoi":"10.1007/s00246-024-03755-8","vorDoiUrl":"https://doi.org/10.1007/s00246-024-03755-8","workflowStages":[]},"version":"v1","identity":"rs-4446999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4446999","identity":"rs-4446999","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}