Skip to main content
SpringerLink
Log in

Brain tumor craniotomy outcomes for dual-eligible medicare and medicaid patients: a 10-year nationwide analysis

  • Clinical Study
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

Introduction

Dual-eligible (DE) patients, simultaneous Medicare and Medicaid beneficiaries, have been shown to have poorer clinical outcomes while incurring higher resource utilization. However, neurosurgical oncology outcomes for DE patients are poorly characterized. Accordingly, we examined the impact of DE status on perioperative outcomes following glioma, meningioma, or metastasis resection.

Methods

We identified all admissions undergoing a craniotomy for glioma, meningioma, or metastasis resection in the National Inpatient Sample from 2002 to 2011. Assessed outcomes included inpatient mortality, complications, discharge disposition, length of stay (LOS), and hospital costs. Multivariable regression adjusting for 13 patient, severity, and hospital characteristics assessed the association between DE status and outcomes, relative to four reference insurance groups (Medicare-only, Medicaid-only, private insurance, self-pay).

Results

Of 195,725 total admissions analyzed, 3.0% were dual-eligible beneficiaries (n = 5933). DEs were younger than Medicare admissions (P < 0.001) but older than Medicaid, private, and self-pay admissions (P < 0.001). Relative to other insurance groups, DEs also exhibited higher severity of illness, risk of mortality, and Charlson Comorbidity Index scores as well as treatment at low-volume hospitals (all P < 0.001). DEs had lower mortality than self-pay admissions (odds ratio [OR] 0.47, P = 0.017). Compared to Medicare, Medicaid, private, and self-pay admissions, DEs had lower rates of discharge disposition (OR 0.53, 0.50, 0.34, and 0.27, respectively, all P < 0.001). DEs also had higher complications (OR 1.23 and 1.20, respectively, both P < 0.05) and LOS (β = 1.06 and 1.13, respectively, both P < 0.01) than Medicare and private insurance beneficiaries. Differences in discharge disposition remained significant for all three tumor subtypes, but only glioma DE admissions continued to exhibit higher complications and LOS.

Conclusions

DEs undergoing definitive craniotomy for brain tumor had higher rates of unfavorable discharge disposition compared to all other insurance groups and, especially for glioma surgery, had higher inpatient complication rates and LOS. Practice and policy reforms to improve outcomes for this vulnerable clinical population are warranted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

The data for the study population in this project were not shared due to guidelines from the Healthcare Cost and Utilization Project restricting the sharing of National Inpatient Sample data on websites or publicly available data repositories (https://www.hcup-us.ahrq.gov/team/NationwideDUA.jsp). However, all scripts used for this project, including the code to identify and analyze the data population, are available upon request.

References

  1. Ostrom QT, Patil N, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS (2020) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2013–2017. Neuro-Oncology 22:iv1–iv96. https://doi.org/10.1093/neuonc/noaa200

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dewan MC, Rattani A, Baticulon RE, Faruque S, Johnson WD, Dempsey RJ, Haglund MM, Alkire BC, Park KB, Warf BC, Shrime MG (2018) Operative and consultative proportions of neurosurgical disease worldwide: estimation from the surgeon perspective. J Neurosurg. https://doi.org/10.3171/2017.10.JNS17347

    Article  PubMed  PubMed Central  Google Scholar 

  3. Sastry RA, Pertsch NJ, Tang O, Shao B, Toms SA, Weil RJ (2020) Frailty and outcomes after craniotomy for brain tumor. J Clin Neurosci 81:95–100. https://doi.org/10.1016/j.jocn.2020.09.002

    Article  PubMed  Google Scholar 

  4. Shahrestani S, Lehrich BM, Tafreshi AR, Brown NJ, Lien BV, Ransom S, Ransom RC, Ballatori AM, Ton A, Chen XT, Sahyouni R (2020) The role of frailty in geriatric cranial neurosurgery for primary central nervous system neoplasms. Neurosurg Focus 49:E15. https://doi.org/10.3171/2020.7.FOCUS20426

    Article  PubMed  Google Scholar 

  5. Youngerman BE, Neugut AI, Yang J, Hershman DL, Wright JD, Bruce JN (2018) The modified frailty index and 30-day adverse events in oncologic neurosurgery. J Neurooncol 136:197–206. https://doi.org/10.1007/s11060-017-2644-0

    Article  PubMed  Google Scholar 

  6. Chandra A, Rick JW, Dalle Ore C, Lau D, Nguyen AT, Carrera D, Bonte A, Molinaro AM, Theodosopoulos PV, McDermott MW, Berger MS, Aghi MK (2018) Disparities in health care determine prognosis in newly diagnosed glioblastoma. Neurosurg Focus 44:E16. https://doi.org/10.3171/2018.3.FOCUS1852

    Article  PubMed  PubMed Central  Google Scholar 

  7. Cote DJ, Ostrom QT, Gittleman H, Duncan KR, CreveCoeur TS, Kruchko C, Smith TR, Stampfer MJ, Barnholtz-Sloan JS (2019) Glioma incidence and survival variations by county-level socioeconomic measures. Cancer 125:3390–3400. https://doi.org/10.1002/cncr.32328

    Article  PubMed  Google Scholar 

  8. Momin EN, Adams H, Shinohara RT, Frangakis C, Brem H, Quinones-Hinojosa A (2012) Postoperative mortality after surgery for brain tumors by patient insurance status in the United States. Arch Surg 147:1017–1024. https://doi.org/10.1001/archsurg.2012.1459

    Article  PubMed  PubMed Central  Google Scholar 

  9. Muhlestein WE, Akagi DS, Chotai S, Chambless LB (2017) The impact of race on discharge disposition and length of hospitalization after craniotomy for brain tumor. World Neurosurg 104:24–38. https://doi.org/10.1016/j.wneu.2017.04.061

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jiang HJ, Wier LM, Potter DEB, Burgess J (2006) Potentially Preventable Hospitalizations among Medicare-Medicaid Dual Eligibles, 2008: Statistical Brief #96. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville (MD)

  11. Office CM-MC (2016) Data Analysis Brief: Medicare-Medicaid Dual Enrollment from 2006 through 2015. Center for Medicare & Medicaid Services. https://www.cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid-Coordination/Medicare-Medicaid-Coordination-Office/Downloads/DualEnrollment_2006-2015.pdf. 2020

  12. Lyons B, O’’Malley Watts M (2009) Health Reform Opportunities: Improving Policy for Dual Eligibles. Kaiser Family Foundation. https://www.kff.org/health-reform/issue-brief/health-reform-opportunities-improving-policy-for-dual/. 2020

  13. Leifheit EC, Wang Y, Howard G, Howard VJ, Goldstein LB, Brott TG, Lichtman JH (2018) Outcomes after carotid endarterectomy among elderly dual Medicare-Medicaid-eligible patients. Neurology 91:e1553–e1558. https://doi.org/10.1212/WNL.0000000000006380

    Article  PubMed  PubMed Central  Google Scholar 

  14. Corcoran Ruiz KM, Rivera Perla KM, Tang OY, Toms SA, Weil RJ (2021) Outcomes after clipping and endovascular coiling for aneurysmal subarachnoid hemorrhage among dual-eligible beneficiaries. J Clin Neurosci 90:48–55. https://doi.org/10.1016/j.jocn.2021.05.008

    Article  PubMed  Google Scholar 

  15. Wang MC, Shivakoti M, Sparapani RA, Guo C, Laud PW, Nattinger AB (2012) Thirty-day readmissions after elective spine surgery for degenerative conditions among US Medicare beneficiaries. Spine J 12:902–911. https://doi.org/10.1016/j.spinee.2012.09.051

    Article  PubMed  Google Scholar 

  16. Austin AM, Chakraborti G, Columbo J, Ramkumar N, Moore K, Scheurich M, Goodney P (2019) Outcomes after peripheral artery disease intervention among Medicare-Medicaid dual-eligible patients compared with the general medicare population in the Vascular Quality Initiative registry. BMJ Surg Interv Health Technol 1:e000018. https://doi.org/10.1136/bmjsit-2019-000018

    Article  PubMed  PubMed Central  Google Scholar 

  17. Li Y, Ying M, Cai X, Kim Y, Thirukumaran CP (2020) Trends in postacute care use and outcomes after hip and knee replacements in dual-eligible Medicare and Medicaid Beneficiaries, 2013–2016. JAMA Netw Open 3:e200368. https://doi.org/10.1001/jamanetworkopen.2020.0368

    Article  PubMed  PubMed Central  Google Scholar 

  18. (2021) Overview of the National (Nationwide) Inpatient Sample (NIS). Healthcare Cost and Utilization Project. https://www.hcup-us.ahrq.gov/nisoverview.jsp. Accessed 31 Aug 2021

  19. Tang OY, Rivera Perla KM, Lim RK, Weil RJ, Toms SA (2021) The impact of hospital safety-net status on inpatient outcomes for brain tumor craniotomy: a 10-year nationwide analysis. Neurooncol Adv 3:vdaa167. https://doi.org/10.1093/noajnl/vdaa167

    Article  PubMed  Google Scholar 

  20. Trinh VT, Davies JM, Berger MS (2015) Surgery for primary supratentorial brain tumors in the United States, 2000–2009: effect of provider and hospital caseload on complication rates. J Neurosurg 122:280–296. https://doi.org/10.3171/2014.9.JNS131648

    Article  PubMed  Google Scholar 

  21. Zygourakis CC, Liu CY, Yoon S, Moriates C, Boscardin C, Dudley RA, Lawton MT, Theodosopoulos P, Berger MS, Gonzales R (2017) Analysis of cost variation in craniotomy for tumor using 2 National Databases. Neurosurgery 81:972–979. https://doi.org/10.1093/neuros/nyx133

    Article  PubMed  Google Scholar 

  22. Curry WT Jr, Carter BS, Barker FG 2nd (2010) Racial, ethnic, and socioeconomic disparities in patient outcomes after craniotomy for tumor in adult patients in the United States, 1988–2004. Neurosurgery 66:427–437. https://doi.org/10.1227/01.NEU.0000365265.10141.8E (discussion 437–428)

    Article  PubMed  Google Scholar 

  23. Houchens R (2015) Missing Data Methods for the NIS and the SID. HCUP Methods Series. U.S. Agency for Healthcare Research and Quality

  24. Ma Y, Zhang W, Lyman S, Huang Y (2018) The HCUP SID imputation project: improving statistical inferences for health disparities research by imputing missing race data. Health Serv Res 53:1870–1889. https://doi.org/10.1111/1475-6773.12704

    Article  PubMed  Google Scholar 

  25. Tang OY, Rivera Perla KM, Lim RK, Yoon JS, Weil RJ, Toms SA (2020) Interhospital competition and hospital charges and costs for patients undergoing cranial neurosurgery. J Neurosurg. https://doi.org/10.3171/2020.6.JNS20732

    Article  PubMed  Google Scholar 

  26. Barker FG 2nd, Curry WT Jr, Carter BS (2005) Surgery for primary supratentorial brain tumors in the United States, 1988 to 2000: the effect of provider caseload and centralization of care. Neuro-Oncology 7:49–63. https://doi.org/10.1215/S1152851704000146

    Article  PubMed  PubMed Central  Google Scholar 

  27. Brandel MG, Rennert RC, Lopez Ramos C, Santiago-Dieppa DR, Steinberg JA, Sarkar RR, Wali AR, Pannell JS, Murphy JD, Khalessi AA (2018) Management of glioblastoma at safety-net hospitals. J Neurooncol 139:389–397. https://doi.org/10.1007/s11060-018-2875-8

    Article  PubMed  Google Scholar 

  28. Tang OY, Yoon JS, Kimata AR, Lawton MT (2019) Volume-outcome relationship in pediatric neurotrauma care: analysis of two national databases. Neurosurg Focus 47:E9. https://doi.org/10.3171/2019.8.FOCUS19486

    Article  PubMed  Google Scholar 

  29. Durand WM, Johnson JR, Li NY, Yang J, Eltorai AEM, DePasse JM, Daniels AH (2018) Hospital competitive intensity and perioperative outcomes following lumbar spinal fusion. Spine J 18:626–631. https://doi.org/10.1016/j.spinee.2017.08.256

    Article  PubMed  Google Scholar 

  30. Cost and Utilization Project H (2019) Cost-to-Charge Ratio Files. Healthcare Cost and Utilization Project. https://www.hcup-us.ahrq.gov/db/state/costtocharge.jsp. Accessed 31 Aug 2018

  31. Leuven E, Sianesi B (2018) PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing.

  32. Abdullah A, Eigbire G, Salama A, Wahab A, Awadalla M, Hoefen R, Alweis R (2018) Impact of delirium on patients hospitalized for myocardial infarction: a propensity score analysis of the National Inpatient Sample. Clin Cardiol 41:910–915. https://doi.org/10.1002/clc.22972

    Article  PubMed  PubMed Central  Google Scholar 

  33. McCutcheon BA, Chang DC, Marcus L, Gonda DD, Noorbakhsh A, Chen CC, Talamini MA, Carter BS (2015) Treatment biases in traumatic neurosurgical care: a retrospective study of the Nationwide Inpatient Sample from 1998 to 2009. J Neurosurg 123:406–414. https://doi.org/10.3171/2015.3.JNS131356

    Article  PubMed  Google Scholar 

  34. Tanenbaum JE, Lubelski D, Rosenbaum BP, Benzel EC, Mroz TE (2017) Propensity-matched analysis of outcomes and hospital charges for anterior versus posterior cervical fusion for cervical spondylotic myelopathy. Clin Spine Surg 30:E1262–E1268. https://doi.org/10.1097/BSD.0000000000000402

    Article  PubMed  PubMed Central  Google Scholar 

  35. Jordan JT, Gerstner ER, Batchelor TT, Cahill DP, Plotkin SR (2016) Glioblastoma care in the elderly. Cancer 122:189–197. https://doi.org/10.1002/cncr.29742

    Article  PubMed  Google Scholar 

  36. Zreik J, Moinuddin FM, Yolcu YU, Alvi MA, Chaichana KL, Quinones-Hinojosa A, Bydon M (2020) Improved 3-year survival rates for glioblastoma multiforme are associated with trends in treatment: analysis of the national cancer database from 2004 to 2013. J Neurooncol 148:69–79. https://doi.org/10.1007/s11060-020-03469-w

    Article  PubMed  Google Scholar 

  37. Bradley CJ, Dahman B, Shickle LM, Lee W (2012) Surgery wait times and specialty services for insured and uninsured breast cancer patients: does hospital safety net status matter? Health Serv Res 47:677–697. https://doi.org/10.1111/j.1475-6773.2011.01328.x

    Article  PubMed  Google Scholar 

  38. Liu JH, Zingmond DS, McGory ML, SooHoo NF, Ettner SL, Brook RH, Ko CY (2006) Disparities in the utilization of high-volume hospitals for complex surgery. JAMA 296:1973–1980. https://doi.org/10.1001/jama.296.16.1973

    Article  CAS  PubMed  Google Scholar 

  39. Mukherjee D, Zaidi HA, Kosztowski T, Chaichana KL, Brem H, Chang DC, Quinones-Hinojosa A (2010) Disparities in access to neuro-oncologic care in the United States. Arch Surg 145:247–253. https://doi.org/10.1001/archsurg.2009.288

    Article  PubMed  Google Scholar 

  40. Rahman M, Grabowski DC, Gozalo PL, Thomas KS, Mor V (2014) Are dual eligibles admitted to poorer quality skilled nursing facilities? Health Serv Res 49:798–817. https://doi.org/10.1111/1475-6773.12142

    Article  CAS  PubMed  Google Scholar 

  41. Stupp R, Taillibert S, Kanner A, Read W, Steinberg D, Lhermitte B, Toms S, Idbaih A, Ahluwalia MS, Fink K, Di Meco F, Lieberman F, Zhu JJ, Stragliotto G, Tran D, Brem S, Hottinger A, Kirson ED, Lavy-Shahaf G, Weinberg U, Kim CY, Paek SH, Nicholas G, Bruna J, Hirte H, Weller M, Palti Y, Hegi ME, Ram Z (2017) Effect of tumor-treating fields plus maintenance temozolomide vs maintenance temozolomide alone on survival in patients with glioblastoma: a randomized clinical trial. JAMA 318:2306–2316. https://doi.org/10.1001/jama.2017.18718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Parikh-Patel A, Morris CR, Kizer KW (2017) Disparities in quality of cancer care: the role of health insurance and population demographics. Medicine (Baltimore) 96:e9125. https://doi.org/10.1097/MD.0000000000009125

    Article  Google Scholar 

  43. Chen BK, Yang YT, Gajadhar R (2018) Early evidence from South Carolina’s Medicare-Medicaid dual-eligible financial alignment initiative: an observational study to understand who enrolled, and whether the program improved health? BMC Health Serv Res 18:913. https://doi.org/10.1186/s12913-018-3721-6

    Article  PubMed  PubMed Central  Google Scholar 

  44. Glauser G, Piazza M, Berger I, Osiemo B, McClintock SD, Winter E, Chen HI, Ali ZS, Malhotra NR (2020) The Risk Assessment and Prediction Tool (RAPT) for discharge planning in a posterior lumbar fusion population. Neurosurgery 86:E140–E146. https://doi.org/10.1093/neuros/nyz419

    Article  PubMed  Google Scholar 

  45. Asher AL, Khalafallah AM, Mukherjee D, Alvi MA, Yolcu YU, Khan I, Pennings JS, Davidson CA, Archer KR, Moshel YA, Knightly J, Roguski M, Zacharia BE, Harbaugh RE, Kalkanis SN, Bydon M (2021) Launching the Quality Outcomes Database Tumor Registry: rationale, development, and pilot data. J Neurosurg. https://doi.org/10.3171/2021.1.JNS201115

    Article  PubMed  Google Scholar 

  46. Miller KD, Ostrom QT, Kruchko C, Patil N, Tihan T, Cioffi G, Fuchs HE, Waite KA, Jemal A, Siegel RL, Barnholtz-Sloan JS (2021) Brain and other central nervous system tumor statistics, 2021. CA Cancer J Clin 71:381–406. https://doi.org/10.3322/caac.21693

    Article  PubMed  Google Scholar 

  47. Kind AJ, Jencks S, Brock J, Yu M, Bartels C, Ehlenbach W, Greenberg C, Smith M (2014) Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Ann Intern Med 161:765–774. https://doi.org/10.7326/M13-2946

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The authors report no funding concerning the materials or methods used in this study or the findings specified in this paper.

Author information

Authors and Affiliations

  1. The Warren Alpert Medical School of Brown University, Providence, RI, USA

    Oliver Y. Tang, Ross A. Clarke, Krissia M. Rivera Perla, Kiara M. Corcoran Ruiz & Steven A. Toms

  2. Harvard T.H Chan School of Public Health, Boston, MA, USA

    Krissia M. Rivera Perla

  3. Department of Neurosurgery, Rhode Island Hospital, Providence, RI, USA

    Steven A. Toms

  4. Southcoast Brain & Spine, Southcoast Health, Dartmouth, MA, USA

    Robert J. Weil

Authors
  1. Oliver Y. Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ross A. Clarke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krissia M. Rivera Perla
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kiara M. Corcoran Ruiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Steven A. Toms
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robert J. Weil
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceived and designed the study: OYT, KMPR, KMCR, SAT, RJW. Analyzed the data: all authors. Wrote the paper: all authors. Critically revised the manuscript: all authors. Reviewed and approved the final manuscript: all authors.

Corresponding author

Correspondence to Oliver Y. Tang.

Ethics declarations

Conflict of interest

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Ethical approval

While this study involved human subjects, this study was exempt from Institutional Review Board approval, due to the data source (National Inpatient Sample) being anonymized and publicly available.

Informed consent

This study was exempt from informed consent and Institutional Review Board approval, due to the data source (National Inpatient Sample) being anonymized and publicly available.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Figure 1:

States Reporting Data on Dual-Eligible Admissions States. States reporting data on dual-eligible admissions in the NIS are shaded blue. (PNG 166 KB)

Supplementary Figure 2:

Forest Plot of Adjusted Outcomes Following Propensity Matching. A: Binary outcomes for dual-eligible patients relative to Medicare and Medicaid admissions. B: Continuous outcomes for dual-eligible patients relative to Medicare and Medicaid admissions. Propensity matching was used to match admissions based on all 13 confounding variables used in multivariate regression. Logistic regression was conducted for binary outcomes and odds ratios reported. Gamma log-link regression was conducted for continuous variables and β-coefficients reflecting percent changes in outcome were reported. (*) denotes P<0.05, (**) denotes P<0.01, and (***) denotes P<0.001. (JPG 1,086 KB)

Supplementary file3 (DOCX 26 KB)

Supplementary Text 1:

Supplementary Methods. Supplementary text discussing the NIS’ sampling design as well as how the present study addressed missing data in the study population, particularly patient race. (DOCX 15 KB)

Supplementary Text 2:

Practice and Policy Implications. Supplementary text discussing additional practice and policy implications related to the management of brain tumors for dual-eligible beneficiaries. (DOCX 16 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, O.Y., Clarke, R.A., Rivera Perla, K.M. et al. Brain tumor craniotomy outcomes for dual-eligible medicare and medicaid patients: a 10-year nationwide analysis. J Neurooncol 156, 387–398 (2022). https://doi.org/10.1007/s11060-021-03922-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11060-021-03922-4

Keywords

Search

Navigation