Skip to main content

Advertisement

SpringerLink
Log in

Differential item functioning of the CAHPS® In-Center Hemodialysis Survey

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

End-stage renal disease patients’ experience of care is an integral part of the assessment of the quality of the care provided at hemodialysis centers and is needed to promote patient choice, quality improvement, and accountability. The purpose of this study is to evaluate the In-Center Hemodialysis Consumer Assessment of Healthcare Providers and Systems (ICH-CAHPS®) survey and its equivalence in different age, gender, race, and education subgroups.

Methods

The ICH-CAHPS survey was administered to 1454 patients from 32 dialysis facilities. For the characteristics compared, the sample had 756 participants younger than 65 years old, 739 men, 516 Black, 567 White, and 970 with less than high school diploma. Three different patient experience constructs were studied including nephrologist’s communication and caring, quality of care and operations, and providing information to patients. We used item response theory analysis to examine the possibility of differential item functioning (DIF) by patient age, gender, race, and education separately after controlling for the other DIF characteristics and additional confounding variables including survey mode, mental, and general health status as well as duration on dialysis.

Results

The three constructs studied were unidimensional and no major DIF was observed on the composites. Some non-equivalences were observed when confounders were not controlled for, suggesting that such covariates can be important factors in understanding the possibility of disparity in patients’ experience.

Conclusions

The ICH-CAHPS is a promising survey to elicit hemodialysis patients’ experience that has good psychometric properties and provides a standardized tool for assessing age, gender, race, or education disparity.

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
Fig. 4

Similar content being viewed by others

References

  1. (USRDS), U. S. R. D. S. (2017). 2017 USRDS annual data report: Epidemiology of kidney disease in the United States. http://www.usrds.org. Accessed October 18, 2018.

  2. Saran, R., Robinson, B., Abbott, K. C., Agodoa, L. Y. C., Bragg-Gresham, J., Balkrishnan, R., et al. (2018). US Renal Data System 2017 annual data report epidemiology of kidney disease in the United States. American Journal of Kidney Diseases, 71(3), S1–S676. https://doi.org/10.1053/j.ajkd.2018.01.002.

    Article  Google Scholar 

  3. Couchoud, C., Kooman, J., Finne, P., Leivestad, T., Stojceva-Taneva, O., Ponikvar, J. B., et al. (2009). From registry data collection to international comparisons: Examples of haemodialysis duration and frequency. Nephrology, Dialysis, Transplantation, 24(1), 217–224. https://doi.org/10.1093/ndt/gfn442.

    Article  PubMed  Google Scholar 

  4. Kjellstrand, C. M. (1988). Age, sex, and race inequality in renal-transplantation. Archives of Internal Medicine, 148(6), 1305–1309.

    Article  CAS  Google Scholar 

  5. Rosansky, S. J. (2012). The sad truth about early initiation of dialysis in elderly patients. Jama-Journal of the American Medical Association, 307(18), 1919–1920. https://doi.org/10.1001/jama.2012.3522.

    Article  CAS  Google Scholar 

  6. Thorsteinsdottir, B., Swetz, K. M., Feely, M. A., Mueller, P. S., & Williams, A. W. (2012). Are there alternatives to hemodialysis for the elderly patient with end-stage renal failure? Mayo Clinic Proceedings, 87(6), 514–516. https://doi.org/10.1016/j.mayocp.2012.02.016.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Adler, N. E., Boyce, W. T., Chesney, M. A., Folkman, S., & Syme, S. L. (1993). Socioeconomic inequalities in health. No easy solution. Journal of the American Medical Association, 269, 3140–3145.

    Article  CAS  Google Scholar 

  8. Nelson, E. C., Hays, R. D., Arnold, S., Kwoh, K., & Sherbourne, C. (1989). Age and functional health status. Santa Monica, CA: The RAND Corporation.

    Google Scholar 

  9. Khattak, M., Sandhu, G. S., Desilva, R., & Goldfarb-Rumyantzev, A. S. (2012). Association of education level with dialysis outcome. Hemodialysis International, 16(1), 82–88. https://doi.org/10.1111/j.1542-4758.2011.00615.x.

    Article  PubMed  Google Scholar 

  10. Peipert, J. D., Bentler, P., Klicko, K., & Hays, R. D. (2018). Negligible impact of differential item functioning between Black and White dialysis patients on the Kidney Disease Quality of Life 36-item short form survey (KDQOL((TM))-36). Quality of Life Research, 27(10), 2699–2707. https://doi.org/10.1007/s11136-018-1879-3.

    Article  PubMed  Google Scholar 

  11. Salisbury, C. (2009). Using patient experience within pay for performance programmes. Bmj-British Medical Journal. https://doi.org/10.1136/bmj.b4224.

    Article  Google Scholar 

  12. Al-Abri, R., & Al-Balushi, A. (2014). Patient satisfaction survey as a tool towards quality improvement. Oman Medical Journal, 29(1), 3–7. https://doi.org/10.5001/omj.2014.02.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gleeson, H., Calderon, A., Swami, V., Deighton, J., Wolpert, M., & Edbrooke-Childs, J. (2016). Systematic review of approaches to using patient experience data for quality improvement in healthcare settings. BMJ Open. https://doi.org/10.1136/bmjopen-2016-011907.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Weidmer, B. A., Cleary, P. D., Keller, S., Evensen, C., Hurtado, M. P., Kosiak, B., et al. (2014). Development and evaluation of the CAHPS (Consumer Assessment of Healthcare Providers and Systems) survey for in-center hemodialysis patients. American Journal of Kidney Diseases, 64(5), 753–760. https://doi.org/10.1053/j.ajkd.2014.04.021.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Setodji, C. M., Reise, S. P., Morales, L. S., Fongwa, M. N., & Hays, R. D. (2011). Differential item functioning by survey language among older hispanics enrolled in medicare managed care a new method for anchor item selection. Medical Care, 49(5), 461–468. https://doi.org/10.1097/MLR.0b013e318207edb5.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41.

    Article  Google Scholar 

  17. Wang, Y., Cai, H., Li, C., Jiang, Z., Wang, L., Song, J., et al. (2013). Optimal caliper width for propensity score matching of three treatment groups: A Monte Carlo study. PLoS ONE, 8(12), e81045. https://doi.org/10.1371/journal.pone.0081045.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6(4), 330–351. https://doi.org/10.1037/1082-989X.6.4.330.

    Article  CAS  PubMed  Google Scholar 

  19. Reise, S. P., Cook, K. F., & Moore, T. M. (2015). Evaluating the impact of multidimensionality on unidimensional item response theory model parameters. In S. P. Reise & D. A. Revicki (Eds.), Multivariate applications series. Handbook of item response theory modeling: Applications to typical performance assessment. Taylor & Francis Group: New York, NY.

    Google Scholar 

  20. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling-A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.

    Article  Google Scholar 

  21. Samejima, F. (1996). Evaluation of mathematical models for ordered polychrotomous responses. Behaviormetrika, 23, 17–35. https://doi.org/10.2333/bhmk.23.17.

    Article  Google Scholar 

  22. Wang, M., & Woods, C. M. (2017). Anchor selection using the wald test anchor-all-test-all procedure. Applied Psychological Measurement, 41(1), 17–29. https://doi.org/10.1177/0146621616668014.

    Article  PubMed  Google Scholar 

  23. Woods, C. M., Cai, L., & Wang, M. A. (2013). The langer-improved wald test for DIF testing with multiple groups: Evaluation and comparison to two-group IRT. Educational and Psychological Measurement, 73(3), 532–547. https://doi.org/10.1177/0013164412464875.

    Article  Google Scholar 

  24. Kim, J., & Oshima, T. C. (2013). Effect of multiple testing adjustment in differential item functioning detection. Educational and Psychological Measurement, 73(3), 458–470. https://doi.org/10.1177/0013164412467033.

    Article  Google Scholar 

  25. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate—A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B-Methodological, 57(1), 289–300.

    Google Scholar 

  26. Teresi, J. A., Kleinman, M., & Ocepek-Welikson, K. (2000). Modern psychometric methods for detection of differential item functioning: Application to cognitive assessment measures. Statistics in Medicine, 19(11–12), 1651–1683. https://doi.org/10.1002/(SICI)1097-0258(20000615/30)19:11/12%3c1651:AID-SIM453%3e3.0.CO;2-H.

    Article  CAS  PubMed  Google Scholar 

  27. Raju, N. S., van der Linden, W. J., & Fleer, P. F. (1995). IRT-based internal measures of differential functioning of items and tests. Applied Psychological Measurement, 19, 353–368.

    Article  Google Scholar 

  28. Flowers, C. P., Oshima, T. C., & Raju, N. S. (1999). A description and demonstration of the polytomous-DFIT framework. Applied Psychological Measurement, 23(4), 309–326. https://doi.org/10.1177/01466219922031437.

    Article  Google Scholar 

  29. Raju, N. S. (1999). DFITP5: A Fortran program for calculating dichotomous DIF/DTF. Chicago: Illinois Institute of Technology.

    Google Scholar 

  30. Muthén, L., & Muthén, B. (2001). Mplus User’s Guide. CA: Los Angeles.

    Google Scholar 

  31. Cai, L. (2012). Vector Psychometric Group. WA: Seattle.

    Google Scholar 

  32. Wood, R., Paoli, C. J., Hays, R. D., Taylor-Stokes, G., Piercy, J., & Gitlin, M. (2014). Evaluation of the consumer assessment of healthcare providers and systems in-center hemodialysis survey. Clinical Journal of the American Society of Nephrology, 9(6), 1099–1108. https://doi.org/10.2215/Cjn.10121013.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Kjellstrand, C. (1997). All elderly patients should be offered dialysis. Geriatric Nephrology and Urology, 6, 129–136. https://doi.org/10.1007/BF00249628.

    Article  Google Scholar 

  34. Avorn, J. (1984). Benefit and cost analysis in geriatric care. Turning age discrimination into health policy. New England Journal of Medicine, 310(20), 1294–1301. https://doi.org/10.1056/nejm198405173102005.

    Article  CAS  PubMed  Google Scholar 

  35. Veerappan, I., Arvind, R. M., & Ilayabharthi, V. (2012). Predictors of quality of life of hemodialysis patients in India. Indian Journal of Nephrology, 22(1), 18–25. https://doi.org/10.4103/0971-4065.91185.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Peipert, J. D., Bentler, P. M., Klicko, K., & Hays, R. D. (2018). Psychometric properties of the kidney disease quality of life 36-item short-form survey (KDQOL-36) in the United States. American Journal of Kidney Diseases, 71(4), 461–468. https://doi.org/10.1053/j.ajkd.2017.07.020.

    Article  PubMed  Google Scholar 

  37. Groves, R., Floyd, J., & Fowler, J. M. (2013). Survey methodology. Hoboken: Wiley.

    Google Scholar 

  38. Halbesleben, J. R. B., & Whitman, M. V. (2013). Evaluating survey quality in health services research: A decision framework for assessing nonresponse bias. Health Services Research, 48(3), 913–930. https://doi.org/10.1111/1475-6773.12002.

    Article  PubMed  Google Scholar 

  39. Richardson, M. M., & Grobert, M. E. (2014). ICH-CAHPS: What signal on the chadburn? American Journal of Kidney Diseases, 64(5), 670–672. https://doi.org/10.1053/j.ajkd.2014.09.002.

    Article  PubMed  Google Scholar 

  40. Peipert, J. D., & Hays, R. D. (2017). Methodological considerations in using patient reported measures in dialysis clinics. Journal of Patient-Reported Outcomes, 1(1), 11. https://doi.org/10.1186/s41687-017-0010-9.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This research was supported through cooperative agreements from the Agency for Healthcare Research and Quality: U18 HS016980 and U18 HS016978.

Author information

Authors and Affiliations

  1. RAND Corporation, 4570, 5th Avenue, Suite 600, Pittsburgh, PA, 15213-2665, USA

    Claude M. Setodji & Ron D. Hays

  2. Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA

    John D. Peipert

  3. Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Ron D. Hays

Authors
  1. Claude M. Setodji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John D. Peipert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ron D. Hays
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claude M. Setodji.

Ethics declarations

Conflict of interest

None of the authors have conflicts of interest or any financial relationships relevant to this article to disclose.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Ethical approval

All data collection were approved by the RAND institutional review boards (FWA00003425, effective until June 22, 2023). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 89 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Setodji, C.M., Peipert, J.D. & Hays, R.D. Differential item functioning of the CAHPS® In-Center Hemodialysis Survey. Qual Life Res 28, 3117–3135 (2019). https://doi.org/10.1007/s11136-019-02250-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-019-02250-5

Keywords

Search

Navigation