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Association of Primary Language with Outcomes After Operations Typically Performed to Treat Cancer: Analysis of a Statewide Database

  • Timothy FeeneyEmail author
  • Michael Cassidy
  • Yorghos Tripodis
  • David McAneny
  • Maureen Kavanah
  • Teviah Sachs
  • Jennifer F. Tseng
  • Frederick Thurston Drake
Health Services Research and Global Oncology
  • 21 Downloads

Abstract

Background

Few studies have evaluated the effect of primary language on surgical outcomes, and no studies have addressed operations typically performed for cancer diagnoses. This study aimed to determine the effect of primary languages other than English on outcomes after surgical oncology operations.

Methods

This study retrospectively analyzed adults undergoing operations typically performed to treat cancer using the NJ Healthcare Cost and Utilization Project State Inpatient Database during the interval of 2009–2014. Language was grouped according to English-, Spanish-, and non-English/non-Spanish (NENS)-speaking groups. The study evaluated in-hospital mortality, 7-day readmission, and hospital length of stay (LOS). Logistic and negative binomial regression methods were applied, and generalized linear mixed models were used to account for nesting within a hospital.

Results

This study analyzed 37,531 cases. Non-English speakers were of lower economic status, more likely to be admitted on the weekend, and more likely to undergo higher-risk operations. The likelihood of death in the risk-adjusted multi-level models did not differ between Spanish speakers (odds ratio [OR], 0.67; 95% confidence interval [CI], 0.41–1.10) and NENS speakers (OR, 1.16; 95% CI, 0.77–1.75). Readmission rates exhibited high inter-hospital variability (intra-class correlation, 53%). The odds of readmission among Spanish speakers in the non-hierarchical model was increased (OR, 1.50; 95% CI, 1.11–2.02), but this was ameliorated in the multilevel modeling that accounted for variability between hospitals (OR, 1.29; 95% CI, 0.93–1.80). No changes in LOS were observed.

Conclusions

No independent association was observed between primary language and outcomes after operations typically performed to treat cancer in the study population. The higher proportion of weekend admissions may suggest more acute or advanced presentations for non-English speakers. Long-term outcomes may be necessary to discern an impact.

Notes

Acknowledgement

Dr. Feeney had full access to all the data in the study and takes responsibility for the integrity and accuracy of the analysis.

Disclosures

Timothy Feeney, Michael Cassidy, Yorghos Tripodis, David McAneny, Maureen Kavanah, Teviah Sachs, Jennifer F. Tseng, Frederick Thurston Drake have no conflicts of interest to declare.

Supplementary material

10434_2019_7484_MOESM1_ESM.eps (9 kb)
Fig. S1 Proportion of languages as a function of readmission proportion quarter. (EPS 8 kb)
10434_2019_7484_MOESM2_ESM.eps (9 kb)
Fig. S2 The readmission rate for each language group as a function of the overall readmission rate. The readmission rate is calculated as readmissions per language per total patients in the language group. (EPS 8 kb)

References

  1. 1.
    United States Census Bureau-Language Use. Retrieved 5 June 2018 at https://www.census.gov/topics/population/language-use.html.
  2. 2.
    Tuot DS, Lopez M, Miller C, Karliner LS. Impact of an easy-access telephonic interpreter program in the acute care setting: an evaluation of a quality improvement intervention. Jt Comm J Qual Patient Saf. 2012;38:81–8.Google Scholar
  3. 3.
    Sillo T, Joshi M. Surgeons’ perceptions on the impact of language barriers in the delivery of healthcare. Int J Surg. 2015;23:S42.Google Scholar
  4. 4.
    Karliner LS, Kim SE, Meltzer DO, Auerbach AD. Influence of language barriers on outcomes of hospital care for general medicine inpatients. J Hosp Med. 2010;5:276–82.CrossRefGoogle Scholar
  5. 5.
    John-Baptiste A, Naglie G, Tomlinson G, et al. The effect of English language proficiency on length of stay and in-hospital mortality. J Gen Intern Med. 2004;19:221–8.CrossRefGoogle Scholar
  6. 6.
    Tang EW, Go J, Kwok A, et al. The relationship between language proficiency and surgical length of stay following cardiac bypass surgery. Eur J Cardiovasc Nurs. 2016;15:438–46.CrossRefGoogle Scholar
  7. 7.
    Inagaki E, Farber A, Kalish J, et al. Role of language discordance in complication and readmission rate after infrainguinal bypass. J Vasc Surg. 2017;66:1473–8.CrossRefGoogle Scholar
  8. 8.
    Project HCaU. Overview of the State Inpatient Databases (SID). Retrieved 13 June 2018 at https://www.hcup-us.ahrq.gov/sidoverview.jsp.
  9. 9.
  10. 10.
    Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217:833–42 e1–3.Google Scholar
  11. 11.
    ACS-NSQIP. Surgical Risk Calculator. Retrieved 26 April 2018 at https://riskcalculator.facs.org/RiskCalculator/index.jsp.
  12. 12.
    Statacorp. Stata Statistical Software: Release 15.: Statacorp LP, College Station, TX, 2017.Google Scholar
  13. 13.
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2017.Google Scholar
  14. 14.
    Wickham H. tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. 2017. https://CRAN.R-project.org/package=tidyverse.
  15. 15.
    Wickham H, Miiller E. haven: Import and Export “SPSS,” “Stata,” and “SAS” Files. R package version 1.1.1. 2018. Retrieved at https://CRAN.R-project.org/package=haven.
  16. 16.
    Harrell FEJ. hmisc: Harrell Miscellaneous. R package version 4.1-1. 2018. https://CRAN.R-project.org/package=Hmisc.
  17. 17.
    Yoshida K, Bohn. J. tableone: Create “Table 1” to Describe Baseline Characteristics. R package version 0.8.1. 2017. https://CRAN.R-project.org/package=tableone.
  18. 18.
    Kassambara A. ggpubr: “ggplot2”-Based Publication Ready Plots. R package version 0.1.6. 2017. Retrieved at https://CRAN.R-project.org/package=ggpubr.
  19. 19.
    Wickham H. Reshaping data with the {reshape} package. J Stat Softw. 2007;21:1–20.CrossRefGoogle Scholar
  20. 20.
    Tierney N, Cook D, McBain M, Fay C. naniar: Data Structures, Summaries, and Visualisations for Missing Data. R package version 0.3.1. 2018. Retrieved at https://CRAN.R-project.org/package=naniar.
  21. 21.
    Ludecke D. sjstats: Statistical Functions for Regression Models. R package version 0.15.0. 2018. https://CRAN.R-project.org/package=sjstats.
  22. 22.
    Aly SS, Zhao J, Li B, Jiang J. Reliability of environmental sampling culture results using the negative binomial intraclass correlation coefficient. Springerplus. 2014;3:40.CrossRefGoogle Scholar

Copyright information

© Society of Surgical Oncology 2019

Authors and Affiliations

  1. 1.Section of Surgical EndocrinologyBoston Medical CenterBostonUSA
  2. 2.Department of SurgeryBoston University School of MedicineBostonUSA
  3. 3.Section of Surgical OncologyBoston Medical CenterBostonUSA
  4. 4.Department of BiostatisticsBoston University School of Public HealthBostonUSA

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