Minority Serving Hospitals and Cancer Surgery Readmissions: A Reason for Concern
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1 Minority Serving Hospitals and Cancer Surgery : A Reason for Concern Young Hong, Chaoyi Zheng, Russell C. Langan, Elizabeth Hechenbleikner, Erin C. Hall, Nawar M. Shara, Lynt B. Johnson, Waddah B. Al-Refaie Department of Surgery at Georgetown University Hospital MedStar-Georgetown Surgical Outcomes Research Center Medstar Health Research Institute Lombardi Comprehensive Cancer Center
2 No Financial Disclosures
3 Penalties for After Surgery have recently been evaluated as a metric of healthcare quality. Affordable Care Act 2010 Hospital Readmission Reduction Program (HRRP) Penalized hospitals up to 3% Medicare repayment for higher than average readmission rates for medical conditions. Financial penalties have since been expanded for orthopedic procedures. These penalties will likely expand to other surgical procedures in the near future. Fontanarosa PB. JAMA Weber SM. Surgery. 2014
4 Minority Serving Hospitals are Vulnerable Minority Serving Hospitals (MSH) Provide care to large proportion of Blacks and Hispanics Compared to Non-MSH: 2x as likely to be penalized for higher readmissions (61% vs. 32%) Penalties are projected to be $112M vs. $41M 2x higher operative mortality rates after major surgery However, little is known about readmission rates after major cancer surgery at Minority Serving Hospitals Al-Refaie WB et al. Adv Surg 2012 Dudeja et al. Ann Surg Onc 2011 Al-Refaie WB et al. JACS 2012 Shih T. Ann Surg. 2015
5 Objectives Hypothesis Minority Serving Hospitals have higher readmission rates after major cancer surgery than Non-Minority Serving Hospitals. Primary Aim Quantify the impact of Minority Serving Hospitals on readmission rates after Major Cancer Surgery Secondary Aim Identify patient- and hospital- level contributors of readmissions
6 Data Source and Cohort Use of 2 complimentary Data sources: State Inpatient Database of California: Large and racially diverse population Linked to Annual Survey Database of American Hospital Association: Rich in hospital factors Patient selection: 110,857 patients in 491 hospitals in California Operative procedures: Resections of lung, esophageal, gastric, pancreatic, hepatobiliary, rectal, and kidney cancers.
7 Vulnerable Hospitals in California Performing Major Cancer Surgery (n=355) Non-Vulnerable Hospitals (n=189) Minority Serving Hospital (Top 25%) Minority Serving Hospital (MSH) (n=111) High Medicaid Hospitals (HMH) (n=36) Safety Net Hospital (SNH) (n=19) High Medicaid Hospital (Top 10%) Safety Net Hospital (California Association of Public Hospital and Health System)
8 Statistical Methods Minority Serving Hospital Top quartile (25%) in proportions of Blacks and Hispanics served Asians were excluded due to improved SES/lower readmissions Covariates: Patients Age, Race/Ethnicity, Insurance, Multi-morbidity Procedure status Emergent Hospitals Bed Size, Teaching Status, Case Volume, Residency program Readmission Diagnosis
9 Statistical Methods Outcome (Dependent) Variables 30-day readmissions (Affordable Care Act priority) 90-day and repeated readmissions (clinical relevance) Multivariable Analyses: MSH and readmission patterns (Hierarchical model with adjustment for case mix) Block-wise regression analyses by sequentially adding patient, procedure then hospital factors Repeated sensitivity analyses using different MSH proportional (top quartile or decile)
10 Results
11 Results 111 Minority Serving Hospitals (MSH) Performed 18% of all major cancer surgery
12 MSH patients are Younger, Multi-morbid and Undergo Emergency Surgery Non-MSH (%) MSH (%) P-Value Age < Charlson Comorbidity Index < Primary Insurance Medicare Medicaid Private Other < Emergency Status No Yes <0.0001
13 MSH are Teaching Hospitals, Non-Designated Cancer Program, and Low Procedure Volume Non-MSH (%) MSH (%) P-Value Teaching Status Designated Cancer Program Teaching Yes Procedure Volume (Tertile/Year) Low Medium High < High
14 Comparable Readmission Diagnosis Non-MSH (%) MSH (%) Septicemia Intestinal Obstruction without hernia Pneumonia Complication of device; implant or graft Hypovolemia Acute and unspecified renal failure
15 Minority Serving Hospitals Had Higher Adjust Readmission Patterns Percent of Minority Served at Hospital 30-Day OR (95% CI) 90-Day OR (95% CI) Repeated OR (95% CI) 2nd Quartile (vs. Q1) 1.05 ( ) ( ) ( ) 3rd Quartile (vs. Q1) 1.13 ( )** 1.14 ( )*** 1.20 ( )** 4th Quartile (MSH) vs. (Q1) 1.16 ( )** 1.18 (1.08,1.29)** 1.28 (1.10,1.50)** Multivariable regression adjusted for age, sex, comorbidity, type of procedure, race, and year of admission. ** p < 0.01; *** p <
16 Predominately Driven by Patient Factors Q4 vs. Q1-3 Unadjusted 30 Day OR (95% CI) 1.15 (,1.24) 90 Day Repeated % Change OR (95% CI) % Change OR (95% CI) % Change 1.16 (1.09,1.25) 1.21 (1.09,1.34)
17 Predominately Driven by Patient Factors Q4 vs. Q1-3 Unadjusted +HRRP 30 Day OR (95% CI) 1.15 (,1.24) 1.13 (1.04,1.23) 90 Day Repeated % Change OR (95% CI) % Change OR (95% CI) % Change 11.8% 1.16 (1.09,1.25) 1.15 (1.07,1.24) 9.0% 1.21 (1.09,1.34) 1.16 (1.05,1.29) 21.9%
18 Predominately Driven by Patient Factors Q4 vs. Q1-3 Unadjusted +HRRP +Patient Factors 30 Day OR (95% CI) 1.15 (,1.24) 1.13 (1.04,1.23) (0.96,1.16) 90 Day Repeated % Change OR (95% CI) % Change OR (95% CI) % Change 11.8% 50.9% 1.16 (1.09,1.25) 1.15 (1.07,1.24) 1.05 (0.96,1.15) 9.0% 59.0% 1.21 (1.09,1.34) 1.16 (1.05,1.29) (0.93,1.22) 21.9% 47.8%
19 Predominately Driven by Patient Factors Q4 vs. Q1-3 Unadjusted +HRRP +Patient Factors 30 Day OR (95% CI) 1.15 (,1.24) 1.13 (1.04,1.23) (0.96,1.16) 90 Day Repeated % Change OR (95% CI) % Change OR (95% CI) % Change 11.8% 50.9% 1.16 (1.09,1.25) 1.15 (1.07,1.24) 1.05 (0.96,1.15) 9.0% 59.0% 1.21 (1.09,1.34) 1.16 (1.05,1.29) (0.93,1.22) 21.9% 47.8% +Hospital Factors (0.96,1.15) 0.0% (0.97,1.15) -4.5% 1.08 (0.96,1.22) -9.4%
20 Predominately Driven by Patient Factors Q4 vs. Q1-3 Unadjusted +HRRP +Patient Factors 30 Day OR (95% CI) 1.15 (,1.24) 1.13 (1.04,1.23) (0.96,1.16) 90 Day Repeated % Change OR (95% CI) % Change OR (95% CI) % Change 11.8% 50.9% 1.16 (1.09,1.25) 1.15 (1.07,1.24) 1.05 (0.96,1.15) 9.0% 59.0% 1.21 (1.09,1.34) 1.16 (1.05,1.29) (0.93,1.22) 21.9% 47.8% +Hospital Factors (0.96,1.15) 0.0% (0.97,1.15) -4.5% 1.08 (0.96,1.22) -9.4% Sensitivity Analysis using top decile/top quartile
21 Predominately Driven by Patient Factors Q4 vs. Q1-3 Unadjusted +HRRP +Hospital Factors 30 Day OR (95% CI) 1.15 (,1.24) 1.13 (1.04,1.23) 1.12 (1.04,1.21) 90 Day Repeated Readmission % Change OR (95% CI) % Change OR (95% CI) % Change 11.8% 6.0% 1.16 (1.09,1.25) 1.15 (1.07,1.24) 1.15 (1.07,1.24) 9.0% -0.4% 1.21 (1.09,1.34) 1.16 (1.05,1.29) 1.19 (1.08,1.32) 21.9% -11.8% +Patient Factors (0.96,1.15) 44.8% (0.97,1.15) 55.0% 1.08 (0.96,1.22) 50.2% Alternative order of block regression demonstrated similar outcomes.
22 Limitations and Strengths Limitations Administrative data are prone to variations in coding diagnosis (ICD) Lack of patient staging/treatments Advanced stage may have higher readmissions Strengths Large and racially diverse cohort Results generalizable to many US states
23 Implications and Significance HRRP program should account for social determinants Policy implications for adding race and socioeconomic factors into risk adjustment model of HRRP penalty system. Explore readmission patterns after major cancer surgery at other vulnerable hospitals. High Medicaid Hospitals (HMH) Safety Net Hospitals (SNH) MedStar Surgical Readmission Risk Score (SR2) with link to Electronic Medical Record (EMR) decision support tool.
24 Conclusions Minority Serving Hospitals had higher readmission rates than Non-Minority Serving Hospitals. The increase in readmissions were driven more by patient rather than hospital factors. Unintended consequences of HRRP penalties place additional financial strain on MSH and may crowd out minorities.
25 Acknowledgements Dr. Waddah B. Al-Refaie Mina Zheng Dr. Lynt B. Johnson Dr. Lizzy Hechenbleikner Dr. Erin Hall Dr. Russell Langan Dr. Nawar Shara Michelle Lee-Clements
26 Thank You!
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