Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Conducting Research with Vulnerable Populations: Cautions and Considerations in Interpreting Outliers in Disparities Research

1 Department of Biobehavioral Health Sciences, NewCourtland Center for Transitions and Health, University of Pennsylvania School of Nursing, 418 Curie Boulevard, Philadelphia, PA 19104, USA;
2 Department of Family Health Care Nursing, University of California San Francisco School of Nursing, 2 Koret Way, San Francisco, CA 94143, USA

Addressing the needs of understudied and vulnerable populations first and foremost necessitate correct application and interpretation of research that is designed to understand sources of disparities in healthcare or health systems outcomes. In this brief research report, we discuss some important concerns and considerations in handling “outliers” when conducting disparities-related research. To illustrate these concerns, we use data from our recently completed study that investigated sources of disparities in cancer pain outcomes between African Americans and Whites with cancer-related pain. A choice-based conjoint (CBC) study was conducted to compare preferences for analgesic treatment for cancer pain between African Americans and Whites. Compared to Whites, African Americans were both disproportionately more likely to make pain treatment decisions based on analgesic side-effects and were more likely to have extreme values for the CBC-elicited utilities for analgesic “side-effects.” Our findings raise conceptual and methodological consideration in handling extreme values when conducting disparities-related research. Extreme values or outliers can be caused by random variations, measurement errors, or true heterogeneity in a clinical phenomenon. The researchers should consider: 1) whether systematic patterns of extreme values exist and 2) if systematic patterns of extreme values are consistent with a clinical pattern (e.g., poor management of cancer pain and side-effects in racial/ethnic subgroups as documented by many previous studies). As may be evident, these considerations are particularly important in health disparities research where extreme values may actually represent a clinical reality, such as unequal treatment or disproportionate burden of symptoms in certain subgroups. Approaches to handling outliers, such as non-parametric analyses, log transforming clinically important extreme values, or removing outliers may represent a missed opportunity in understanding a potentially targetable area of intervention.
  Figure/Table
  Supplementary
  Article Metrics

References

1. Institute of Medicine (2011) Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research. Washington, DC: The National Academies Press.

2. National Research Council (2013) Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis. Washington, DC: The National Academies Press.

3. American Cancer Society (2011) Cancer Facts & Figures for African Americans 2011-2012. Atlanta: American Cancer Society Inc.

4. Smedley BD, Stith AY, Nelson AR (2002) Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Washington, DC: The National Academies Press.

5. Meghani SH, Byun E, Gallagher RM (2012) Time to take stock: a meta-analysis and systematic review of analgesic treatment disparities for pain in the United States. Pain Med 13: 150-174.    

6. Anderson KO, Green CR, Payne R (2009) Racial and ethnic disparities in pain: causes and consequences of unequal care. J Pain 10: 1187-1204.    

7. Cintron A, Morrison RS (2006) Pain and ethnicity in the United States: A systematic review. J Palliat Med 9:1454-1473.    

8. Meghani SH, Polomano RC, Tait RC, et al. (2012) Advancing a national agenda to eliminate disparities in pain care: directions for health policy, education, practice, and research. Pain Med13: 5-28.

9. Meghani SH, Hanlon A, Bubanj J, et al. (2013) Do self-reported analgesic barriers translate into objective analgesic adherence for cancer pain? J Pain 14: S38.

10. Rhee YO, Kim E, Kim B (2012) Assessment of pain and analgesic use in African American cancer patients: factors related to adherence to analgesics. J Immigr Minor Health 14:1045-1051.    

11. Green P, Rao V (1971) Conjoint measurement for quantifying judgmental data. J Mark Res 8:355-363.    

12. Orme BK (2006) Getting started with conjoint analysis: Strategies for product design and pricing research. Madison: Research Publishers LLC.

13. Meghani SH, Chittams J, Hanlon A, et al. (2013) Measuring preferences for analgesic treatment for cancer pain: How do African Americans and Whites perform on choice-based conjoint analysis experiments. BMC Med Inform Decis Mak 12: 118

14. Barnett V, Lewis T (1994) Outliers in Statistical Data, 3 Eds. , Chichester: John Wiley & Sons Ltd.

15. Sawtooth Software, Inc (2009) The CBC/HB System for Hierarchical Bayes Estimation Version 5. 0 Technical Paper. Sequim: Sawtooth Software, Inc.

16. Rosner B (2006) Fundamentals of Biostatistics, 6 Eds. , Belmont: Thompson Brooks/Cole, 325.

17. Anderson KO, Green CR, Payne R (2009) Racial and ethnic disparities in pain: causes and consequences of unequal care. J Pain 10: 1187-1204.    

18. Cintron A, Morrison RS (2006) Pain and ethnicity in the United States: A systematic review. J Palliat Med 9: 1454-1473.    

19. Krishnan, V. (2006) Probability and Random Processes. Hoboken: John Wiley & Sons, Inc.

20. Aguinis H, Gottfredson RK, Joo H (2013) Best-Practice Recommendations for Defining, Identifying, and Handling Outliers. Organ Res Meth 16: 270-301.    

Copyright Info: © 2014, Eeeseung Byun, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved