jennifer l. dotson, md, mph assistant professor of pediatrics

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Jennifer L. Dotson, MD, MPHAssistant 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

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

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I have no financial disclosures or conflicts of interest

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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. Nembhard WN, et al. Pediatrics. May 20112. Berry JG, et al. Pediatrics. Dec 20103. Howell E, et al. Am J Public Health. Dec 2010

7. Nguyen GC, et al. Am J Gastroenterol. May 20068. Benchimol EI, et al. J Pediatr. Jun 20119. Nguyen GC, et al. Inflamm Bowel Dis. Nov 2007

4. Hakmeh W, et al. Acad Emerg Med. Aug 20105. Singh GK, et al. Am J Public Health. Sep 20076. Basu D, et al. Am J Gastroenterol. Oct 2005

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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

10. Flasar MH, et al. Inflamm Bowel Dis. Jan 200811. Mangat BK, et al. Can J Gastroenterol. Feb 201112. Straus WL, et al. Am J Gastroenterol. Feb 2000

6. Basu D, et al. Am J Gastroenterol. Oct 20057. Nguyen GC, et al. Am J Gastroenterol. May 20069. Nguyen GC, et al. Inflamm Bowel Dis. Nov 2007

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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

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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

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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

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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

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

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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

Methods: Study CohortInclusion Criteria Exclusion Criteria

• Crohn’s disease (555.x): Primary or secondary diagnosis

• Patient admitted for an ostomy take - down (CPT 44625, ICD-9 procedure code 46.5x)

• Single race reported• Missing race, multiple

races reported; Hispanic ethnicity

• Gender reported • Missing gender, gender mismatch

• Same day admit/discharge

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Methods: Outcomes• Primary outcome: time from index hospital

discharge to readmission• Readmission categories:

• Early readmission (<30 days)• Late readmission (30 days to 1 year)

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Methods: Outcomes• Secondary outcomes:

• LOS • Number of readmissions• Steroid, biologic, and TPN usage

• Secondary predictors: • Payor status • Median neighborhood income associated

with zip code

Methods: Analyses

• Due to multiple comparisons, a p-value of <0.005 was considered statistically significant

Comparisons Statistical TestBaseline and demographic characteristics Standard descriptive statistics

Categorical variables Chi-Square test

Time from index hospital discharge to readmission

Kaplan-Meier analysis with Log-rank test

LOS and number of readmissions Wilcoxon two-sample test

Race difference on outcomes at several time intervals

Multivariate logistic regression (controlled for age, gender, region, medications, payor status, and income)

26,381 Total Encounters


11,190 Distinct MRNs



Blacks = 1701Whites = 7083

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




Matched 1:2Blacks = 1456Whites = 2921






Matched 1:2Blacks = 1456Whites = 2921

TotalCohort 4377



Results: DemographicsVariable Black (n, %) White (n, %) P valueTotal 1456 (33) 2921 (67)Male 781 (54) 1545 (53) 0.632

Age at index admission 14.6±3.4 (median: 15.1)

14.1±3.7 (median: 14.7) <0.0001

Region Midwest 381 (26) 765 (26) NA Northeast 267 (18) 536 (18) South 731 (50) 1465 (50) West 77 (5) 155 (5) Payor1 Commercial 378 (27) 1305 (46) <0.0001 Medicaid 626 (44) 462 (16) Other 406 (29) 1056 (37) Median neighborhood income (at 1st admission)2 $36,423 $49,763 <0.0001

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, unknown2Based on 2010 US Census Data compared to reported zip code

Results: DemographicsVariable Black (n, %) White (n, %) P valueTotal 1456 (33) 2921 (67)Male 781 (54) 1545 (53) 0.632

Age at index admission 14.6±3.4 (median: 15.1)

14.1±3.7 (median: 14.7) <0.0001

Region Midwest 381 (26) 765 (26) NA Northeast 267 (18) 536 (18) South 731 (50) 1465 (50) West 77 (5) 155 (5) Payor1 Commercial 378 (27) 1305 (47) <0.0001 Medicaid 626 (44) 462 (16) Other 406 (29) 1056 (37) Median neighborhood income (at 1st admission)2 $36,423 $49,763 <0.0001

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, unknown2Based on 2010 US Census Data compared to reported zip code

• Kaplan-Meier analysis for time to the first readmission (p=0.009)

Results: Time to Readmission

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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)

Variable Black White P value

Total number of readmissions 1.4±2.6 (median: 0)

0.9±1.9 (median: 0) <0.0001

Early Readmission (<30 days) 154 (11%) 329 (11%) 0.537

Late readmission (30 days-1 year) 410 (28%) 677 (23%) <0.001

Results: Readmissions

Results: MedicationsVariable Black

n (%)Whiten (%)

P value

Any Steroid 1025 (70) 1977 (68) 0.067 Biologic agent 473 (33) 642 (22) <0.0001 TPN 399 (27) 719 (25) 0.047Index Steroid 943 (65) 1786 (61) 0.020 Biologic agent 278 (19) 405 (14) <0.0001 TPN 262 (18) 508 (17) 0.622 Early (<30 days) Steroid 103 (7) 234 (8) 0.270 Biologic agent 34 (2) 74 (3) 0.689 TPN 36 (3) 95 (3) 0.147 Late (30 days-1yr) Steroid 287 (20) 416 (14) <0.0001 Biologic agent 130 (9) 169 (6) 0.0001 TPN 89 (6) 149 (5) 0.168

Greater Likelihood of Overall Readmission

Race: Black patients (OR=1.28, p=0.003)

TPN given during index hospitalization

(OR=1.32, p=0.003)

Steroids given during index hospitalization (OR=1.53, p<0.0001)

Results: Predictive Factors of Readmission*

*Multivariate logistic regression: age, gender, race, biologics, steroids, TPN, region, income, insurance status


Greater Likelihood of Early Readmission


Age: Older patients (OR=1.05, p=0.004)


(OR=1.32, p=0.003)


(OR=1.54, p=0.001)





Greater Likelihood of Early Readmission

Greater Likelihood of Late Readmission


Age: Older patients (OR=1.05, p=0.004)


(OR=1.32, p=0.003)


(OR=1.54, p=0.001)





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Key Limitations

• Generalizability • Not weighted for extrapolation to national

estimates• Misclassification errors (administrative

database)

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Key Strengths

• Large sample size• Regionally diverse • Minimizing confounding by severity by focusing

on a hospitalized cohort (restricted study to the more severe patients)

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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

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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

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Mentorship and Funding

Wallace V. Crandall, MDMichael D. Kappelman, MD, MPHDeena 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)

jennifer l. dotson, md, mph assistant professor of pediatrics

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