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Jennifer L. Dotson, MD, MPH Assistant Professor of Pediatrics

Healthcare Disparities in Children and Adolescents with Crohn’s Disease: Is Race Associated with the Need for Readmissions?. Jennifer L. Dotson, MD, MPH, Michael D. Kappelman , MD, MPH, Deena Chisolm, PhD, and Wallace V. Crandall, MD. Jennifer L. Dotson, MD, MPH

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Jennifer L. Dotson, MD, MPH Assistant Professor of Pediatrics

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  1. Healthcare Disparities in Children and Adolescents with Crohn’s Disease: Is Race Associated with the Need for Readmissions? Jennifer L. Dotson, MD, MPH, Michael D. Kappelman, MD, MPH, Deena Chisolm, PhD, and Wallace V. Crandall, MD Jennifer L. Dotson, MD, MPH Assistant Professor of Pediatrics Division of Gastroenterology, Hepatology and Nutrition The Ohio State University College of Medicine Principal Investigator, Center for Innovation in Pediatric Practice The Research Institute at Nationwide Children's Hospital December 14, 2013

  2. I have no financial disclosures or conflicts of interest

  3. Background: • Healthcare disparities account for a large portion of morbidity and mortality in children, yet remain understudied1-5 • Race may influence the distribution, phenotype and treatment of Crohn’s disease (CD)6-9 1. NembhardWN, et al. Pediatrics. May 2011 2. Berry JG, et al. Pediatrics. Dec 2010 3. Howell E, et al. Am J Public Health. Dec 2010 4. Hakmeh W, et al. AcadEmerg Med. Aug 2010 5. Singh GK, et al. Am J Public Health. Sep 2007 6. BasuD, et al. Am J Gastroenterol. Oct 2005 7. Nguyen GC, et al. Am J Gastroenterol. May 2006 8. Benchimol EI, et al. J Pediatr. Jun 2011 9. Nguyen GC, et al. Inflamm Bowel Dis. Nov 2007

  4. Background: • Healthcare disparity research in adult IBD and other diseases suggest that race/ethnicity, gender, insurance status and socioeconomic status associated with suboptimal care and outcomes6,7,9-12 6. BasuD, et al. Am J Gastroenterol. Oct 2005 7. Nguyen GC, et al. Am J Gastroenterol. May 2006 9. Nguyen GC, et al. Inflamm Bowel Dis. Nov 2007 10. FlasarMH, et al. Inflamm Bowel Dis. Jan 2008 11. MangatBK, et al. Can J Gastroenterol. Feb 2011 12. Straus WL, et al. Am J Gastroenterol. Feb 2000

  5. Background: • Extent of racial differences is uncertain • Differences may be due to intrinsic biologic differences between races, differences in access and treatment, or both • Effects of race on hospital admissions in children with CD are unknown

  6. Objectives • Determine if Black children hospitalized for CD are more likely to be readmitted and have longer LOS compared to White children • Determine if steroid, biologic, and TPN usage differs between Black and White children

  7. Hypothesis • Black children with Crohn’s disease will have worse outcomes than White children • Decreased time to readmission • Increased length of LOS • Higher number of readmissions

  8. Methods: Data Source • The Pediatric Health Information System (PHIS) is an administrative database containing data from 44 not-for-profit children’s hospitals in the US • Est. 2002 by Children’s Hospital Association • Data abstracted and coded using PHIS data quality guidelines • MRNs allow longitudinal tracking of individual patients

  9. Represents ~ 25% of pediatric centers in the U.S. and majority of the tertiary care centers

  10. Methods: Study Cohort • Patients 21 years of age admitted between January 1, 2004 and June 30, 2012 • White cohort was matched 2:1 based on hospital to a Black cohort

  11. Methods: Study Cohort

  12. Methods: Outcomes • Primary outcome: time from index hospital discharge to readmission • Readmission categories: • Early readmission (<30 days) • Late readmission (30 days to 1 year)

  13. Methods: Outcomes • Secondary outcomes: • LOS • Number of readmissions • Steroid, biologic, and TPN usage • Secondary predictors: • Payor status • Median neighborhood income associated with zip code

  14. Methods: Analyses • Due to multiple comparisons, a p-value of <0.005 was considered statistically significant

  15. Excluded = 3893 • Outside date range=27 • ICD-9 46.5x=144 • Same admit/discharge date=1228 • Not Black or White=1408 • Hispanic=610 • Missing race=416 • Multiracial=54 • Gender mismatch=6

  16. Excluded = 3893 • Outside date range=27 • ICD-9 46.5x=144 • Same admit/discharge date=1228 • Not Black or White=1408 • Hispanic=610 • Missing race=416 • Multiracial=54 • Gender mismatch=6

  17. Excluded = 3893 • Outside date range=27 • ICD-9 46.5x=144 • Same admit/discharge date=1228 • Not Black or White=1408 • Hispanic=610 • Missing race=416 • Multiracial=54 • Gender mismatch=6 Total Cohort 4377

  18. Results: Demographics 1Commercial=Blue Cross, HMO, TRICARE, Commercial HMO, Commercial PPO, Commercial Other; Medicaid=Medicaid, In-state Medicaid (managed care), In-state Medicaid (other), Out-of-state Medicaid (all); Other=Medicare, Title V, Other government, Workers Compensation, other insurance company, self-pay, no charge, other payor, charity, hospital chose not to bill; Missing=not recorded, invalid code, unknown 2Based on 2010 US Census Data compared to reported zip code

  19. Results: Demographics 1Commercial=Blue Cross, HMO, TRICARE, Commercial HMO, Commercial PPO, Commercial Other; Medicaid=Medicaid, In-state Medicaid (managed care), In-state Medicaid (other), Out-of-state Medicaid (all); Other=Medicare, Title V, Other government, Workers Compensation, other insurance company, self-pay, no charge, other payor, charity, hospital chose not to bill; Missing=not recorded, invalid code, unknown 2Based on 2010 US Census Data compared to reported zip code

  20. Results: Time to Readmission • Kaplan-Meier analysis for time to the first readmission (p=0.009)

  21. Results: LOS (index hospitalization) • Black children had a longer LOS (6.8±7.1 days, median=5) than White children (6.3±8.9 days, median=4) (p<0.0001)

  22. Results: Readmissions

  23. Results: Readmissions

  24. Results: Medications

  25. Results: Medications

  26. Results: Medications

  27. Results: Predictive Factors of Readmission* *Multivariate logistic regression: age, gender, race, biologics, steroids, TPN, region, income, insurance status

  28. Results: Predictive Factors of Readmission* *Multivariate logistic regression: age, gender, race, biologics, steroids, TPN, region, income, insurance status

  29. Results: Predictive Factors of Readmission* *Multivariate logistic regression: age, gender, race, biologics, steroids, TPN, region, income, insurance status

  30. Key Limitations • Generalizability • Not weighted for extrapolation to national estimates • Misclassification errors (administrative database)

  31. Key Strengths • Large sample size • Regionally diverse • Minimizing confounding by severity by focusing on a hospitalized cohort (restricted study to the more severe patients)

  32. Conclusions • Blacks had lower median neighborhood income and were more likely to have Medicaid • Blacks were more likely to be treated with biologic agents (overall) and receive steroids (on late readmissions) • Blacks had an increased number of readmissions and increased LOS

  33. Summary • This study supports that there are differences in hospital readmissions related to race • Unclear whether this is due to disparities in care or phenotypic variation in disease between racial groups • Difference in readmissions could suggest worse intrinsic disease, adherence, access or treatment disparities

  34. Mentorship and Funding • Wallace V. Crandall, MD • Michael D. Kappelman, MD, MPH • Deena Chisolm, PhD • Ben Nwomeh, MD, MPH • Kelly Kelleher, MD, MPH • This study was supported by the NASPGHAN Foundation/CCFA Young Investigator Development Award • MDK was supported by a grant from the NIH/NIDDK (K08 DK088957)

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