Skip to main content

Advertisement

Log in

Cross-sectional validation of the PROMIS-Preference scoring system by its association with social determinants of health

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

Abstract

Purpose

PROMIS-Preference (PROPr) is a generic, societal, preference-based summary score that uses seven domains from the Patient-Reported Outcomes Measurement Information System (PROMIS). This report evaluates construct validity of PROPr by its association with social determinants of health (SDoH).

Methods

An online panel survey of the US adult population included PROPr, SDoH, demographics, chronic conditions, and four other scores: the EuroQol-5D-5L (EQ-5D-5L), Health Utilities Index (HUI) Mark 2 and Mark 3, and the Short Form-6D (SF-6D). Each score was regressed on age, gender, health conditions, and a single SDoH. The SDoH coefficient represents the strength of its association to PROPr and was used to assess known-groups validity. Convergent validity was evaluated using Pearson correlations between different summary scores and Spearman correlations between SDoH coefficients from different summary scores.

Results

From 4142 participants, all summary scores had statistically significant differences for variables related to education, income, food and financial insecurity, and social interactions. Of the 42 SDoH variables tested, the number of statistically significant variables was 27 for EQ-5D-5L, 17 for HUI Mark 2, 23 for HUI Mark 3, 27 for PROPr, and 27 for SF-6D. The average SDoH coefficients were − 0.086 for EQ-5D-5L, − 0.039 for HUI Mark 2, − 0.063 for HUI Mark 3, − 0.064 for PROPr, and − 0.037 for SF-6D. Despite the difference in magnitude across the measures, Pearson correlations were 0.60 to 0.76 and Spearman correlations were 0.74 to 0.87.

Conclusions

These results provide evidence of construct validity supporting the use of PROPr monitor population health in the general US population.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

https://osf.io/63548/

References

  1. Ashraf, K., Ng, C. J., Teo, C. H., & Goh, K. L. (2019). Population indices measuring health outcomes: A scoping review. Journal of Global Health. https://doi.org/10.7189/jogh.09.010405

    Article  PubMed  PubMed Central  Google Scholar 

  2. Kindig, D. A. (2000). Purchasing population health: Paying for results. Ann Arbor: University of Michigan Press.

    Google Scholar 

  3. Breslow, L. (2006). Health measurement in the third era of health. American Journal of Public Health, 96(1), 17–19. https://doi.org/10.2105/ajph.2004.055970

    Article  PubMed  PubMed Central  Google Scholar 

  4. Jones, N., Jones, S. L., & Miller, N. A. (2004). The Medicare Health Outcomes Survey program: Overview, context, and near-term prospects. Health and Quality of Life Outcomes. https://doi.org/10.1186/1477-7525-2-33

    Article  PubMed  PubMed Central  Google Scholar 

  5. Medicare Health Outcomes Survey. Retrieved April 10, 2020, from https://www.hosonline.org/

  6. Cohen, S. B. (2003). Design strategies and innovations in the medical expenditure panel survey. Medical Care. https://doi.org/10.1097/01.mlr.0000076048.11549.71

    Article  PubMed  Google Scholar 

  7. McDowell, I. (2006). Measuring health: A guide to rating scales and questionnaires. New York: Oxford University Press.

    Book  Google Scholar 

  8. Torrance, G. W. (1986). Measurement of health state utilities for economic appraisal. Journal of Health Economics, 5(1), 1–30. https://doi.org/10.1016/0167-6296(86)90020-2

    Article  CAS  PubMed  Google Scholar 

  9. Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value trade-offs. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  10. Cherepanov, D., Palta, M., Fryback, D. G., & Robert, S. A. (2010). Gender differences in health-related quality-of-life are partly explained by sociodemographic and socioeconomic variation between adult men and women in the US: Evidence from four US nationally representative data sets. Quality of Life Research, 19(8), 1115–1124. https://doi.org/10.1007/s11136-010-9673-x

    Article  PubMed  PubMed Central  Google Scholar 

  11. Coley, S. L., Leon, C. F. M. D., Ward, E. C., Barnes, L. L., Skarupski, K. A., & Jacobs, E. A. (2017). Perceived discrimination and health-related quality-of-life: gender differences among older African Americans. Quality of Life Research, 26(12), 3449–3458. https://doi.org/10.1007/s11136-017-1663-9

    Article  PubMed  PubMed Central  Google Scholar 

  12. Robert, S. A., Cherepanov, D., Palta, M., Dunham, N. C., Feeny, D., & Fryback, D. G. (2009). Socioeconomic status and age variations in health-related quality of life: Results from the national health measurement study. The Journals of Gerontology Series B, 64B(3), 378–389. https://doi.org/10.1093/geronb/gbp012

    Article  Google Scholar 

  13. Mills, S. D., Fox, R. S., Bohan, S., Roesch, S. C., Sadler, G. R., & Malcarne, V. L. (2020). Psychosocial and neighborhood correlates of health-related quality of life: A multi-level study among Hispanic adults. Cultural Diversity and Ethnic Minority Psychology, 26(1), 1–10. https://doi.org/10.1037/cdp0000274

    Article  PubMed  Google Scholar 

  14. Sellers, S. L., Cherepanov, D., Hanmer, J., Fryback, D. G., & Palta, M. (2013). Interpersonal discrimination and health-related quality of life among black and white men and women in the United States. Quality of Life Research, 22(6), 1313–1318. https://doi.org/10.1007/s11136-012-0278-4

    Article  PubMed  PubMed Central  Google Scholar 

  15. Twardzik, E., Clarke, P., Elliott, M. R., Haley, W. E., Judd, S., & Colabianchi, N. (2019). Neighborhood socioeconomic status and trajectories of physical health-related quality of life among stroke survivors. Stroke, 50(11), 3191–3197. https://doi.org/10.1161/strokeaha.119.025874

    Article  PubMed  PubMed Central  Google Scholar 

  16. Committee on Educating Health Professionals to Address the Social Determinants of Health; Board on Global Health; Institute of Medicine; National Academies of Sciences, Engineering, and Medicine. A framework for educating health professionals to address the social determinants of health. Washington (DC): National Academies Press (US); 2016 Oct 14. 3, Frameworks for addressing the social determinants of health. Available from: https://www.ncbi.nlm.nih.gov/books/NBK395979/

  17. World Health Organization. (2010). A conceptual framework for action on the social determinants of health: debates, policy & practice, case studies. In A conceptual framework for action on the social determinants of health: debates, policy & practice, case studies. Geneva.

  18. Brooks, R. G., Rabin, R., & Charro, F. D. (2010). The measurement and valuation of health status using Eq-5D: a European perspective: Evidence from the EuroQol Biomed Research Programme. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  19. Pickard, A., Law, E., Jiang, R., Oppe, M., Shaw, J., Xie, F., … Balch, A. (2019). United States valuation of EQ-5D-5L health states: an initial model using a standardized protocol. Value in Health, 22(8), 931–941. doi:10.1016/j.jval.2019.02.009

  20. Feeny, D., Torrance, G. W., & Furlong, W. (1996). Health Utilities Index. In B. Spilker (Ed.), Quality of life and pharmacoeconomics in clinical trials (2nd ed., pp. 239–252). Philadelphia: Lippincott-Raven Press.

    Google Scholar 

  21. Feeny, D., Furlong, W., Torrance, G.W., Goldsmith, C.H., Zhu, Z., Depauw, S., … Boyle, M. (2002). Multiattribute and single-attribute utility functions for the Health Utilities Index Mark 3 System. Medical Care, 40(2), 113–128. doi:10.1097/00005650-200202000-00006

  22. Brazier, J. E., & Roberts, J. (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care, 42(9), 851–859. https://doi.org/10.1097/01.mlr.0000135827.18610.0d

    Article  PubMed  Google Scholar 

  23. Hanmer, J., Feeny, D., Fischhoff, B., Hays, R.D., Hess, R., Pilkonis, P.A., … Yu, L. (2015). The PROMIS of QALYs. Health and Quality of Life Outcomes, 13(1). doi:https://doi.org/10.1186/s12955-015-0321-6

  24. Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K., Reeve, B., … Rose, M. (2007). The Patient-Reported Outcomes Measurement Information System (PROMIS). Medical Care, 45(Suppl 1). doi:https://doi.org/10.1097/01.mlr.0000258615.42478.55

  25. Embretson, S. E., & Reise, S. P. (2013). Item response theory. New York: Psychology Press. https://doi.org/10.4324/9781410605269

    Book  Google Scholar 

  26. Dewitt, B., Feeny, D., Fischhoff, B., Cella, D., Hays, R.D., Hess, R., … Hanmer, J. (2018). Estimation of a preference-based summary score for the Patient-Reported Outcomes Measurement Information System: The PROMIS®-Preference (PROPr) scoring system. Medical Decision Making, 38(6), 683–698. doi:10.1177/0272989x18776637

  27. Hanmer, J., Dewitt, B., Yu, L., Tsevat, J., Roberts, M., Revicki, D., … Cella, D. (2018). Cross-sectional validation of the PROMIS-Preference scoring system. Plos One, 13(7). doi:https://doi.org/10.1371/journal.pone.0201093

  28. NORC . N.d. “AmeriSpeak: NORC’s Breakthrough Panel-Based Research Platform”. Retrieved April 13, 2020. https://amerispeak.norc.org/Pages/default.aspx

  29. Hanmer, J. Developing the PROMIS-preference score for monitoring population health outcomes. https://osf.io/63548/.

  30. Hanmer, J., Cella, D., Feeny, D., Fischhoff, B., Hays, R.D., Hess, R., … Yu, L. (2017). Selection of key health domains from PROMIS® for a generic preference-based scoring system. Quality of Life Research, 26(12), 3377–3385. doi: 10.1007/s11136-017-1686-2

  31. Hanmer, J., Cella, D., Feeny, D., Fischhoff, B., Hays, R. D., Hess, R., … Yu, L. (2018). Evaluation of options for presenting health-states from PROMIS® item banks for valuation exercises. Quality of Life Research, 27(7), 1835–1843. doi:10.1007/s11136-018-1852-1

  32. Stolk, E., Ludwig, K., Rand, K., Hout, B. V., & Ramos-Goñi, J. M. (2019). Overview, update, and lessons learned from the International EQ-5D-5L valuation work: Version 2 of the EQ-5D-5L valuation protocol. Value in Health, 22(1), 23–30. https://doi.org/10.1016/j.jval.2018.05.010

    Article  PubMed  Google Scholar 

  33. -Item Short Form Survey from the RAND Medical Outcomes Study. (n.d.). Retrieved April 13, 2020. https://www.rand.org/health-care/surveys_tools/mos/12-item-short-form.html

  34. NHIS - Questionnaires, Datasets, and Related Documentation. (2019). Retrieved April 13, 2020, from https://www.cdc.gov/nchs/nhis/nhis_questionnaires.htm

  35. NHANES - National Health and Nutrition Examination Survey Homepage. (2020). Retrieved April 13, 2020, from https://www.cdc.gov/nchs/nhanes/index.htm

  36. US Census Bureau. (2020). American Community Survey (ACS). Retrieved April 13, 2020, from https://www.census.gov/programs-surveys/acs/

  37. Air Quality System (AQS). (2020). Retrieved April 13, 2020, from https://www.epa.gov/aqs

  38. Uniform Crime Reporting (UCR) Program. (2018). Retrieved April 13, 2020, from https://www.fbi.gov/services/cjis/ucr

  39. Evans, J. D. (1996). Straightforward statistics for the behavioral sciences. Pacific Grove, CA: Brooks/Cole Publishing Company.

    Google Scholar 

  40. Feeny, D. H., & Torrance, G. W. (1989). Incorporating utility-based quality-of-life assessment measures in clinical trials: Two examples. Medical Care, 27(S3), 190-S204. https://doi.org/10.1097/00005650-198903001-00016

    Article  Google Scholar 

  41. Wolowacz, S. E., Briggs, A., Belozeroff, V., Clarke, P., Doward, L., Goeree, R., et al. (2016). Estimating health-state utility for economic models in clinical studies: An ISPOR good research practices task force report. Value in Health, 19(6), 704–719. https://doi.org/10.1016/j.jval.2016.06.001

    Article  PubMed  Google Scholar 

  42. Neumann, P.J., Sanders, G.D., Russell, L.B., Siegel, J.E. and Ganiats, T.G. eds. (2016). Cost-effectiveness in health and medicine. Oxford University Press. doi: 10.1093/acprof:oso/9780190492939.003

  43. Fryback, D. G., Palta, M., Cherepanov, D., Bolt, D., & Kim, J. S. (2010). Comparison of 5 health-related quality-of-life indexes using item response theory analysis. Medical Decision Making, 30(1), 5–15. https://doi.org/10.1177/0272989X09347016

    Article  PubMed  Google Scholar 

  44. Liu, H., Cella, D., Gershon, R., Shen, J., Morales, L. S., Riley, W., & Hays, R. D. (2010). Representativeness of the patient-reported outcomes measurement information system internet panel. Journal of Clinical Epidemiology, 63(11), 1169–1178. https://doi.org/10.1016/j.jclinepi.2009.11.021

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hanmer, J., Cherepanov, D., Palta, M., Kaplan, R. M., Feeny, D., & Fryback, D. G. (2016). Health condition impacts in a nationally representative cross-sectional survey vary substantially by preference-based health index. Medical Decision Making, 36(2), 264–274. https://doi.org/10.1177/0272989x15599546

    Article  PubMed  Google Scholar 

  46. Tengs, T. O., & Wallace, A. (2000). One thousand health-related quality-of-life estimates. Medical Care, 38(6), 583–637. https://doi.org/10.1097/00005650-200006000-00004

    Article  CAS  PubMed  Google Scholar 

  47. Mcdonough, C. M., & Tosteson, A. N. A. (2007). Measuring preferences for cost-utility analysis. PharmacoEconomics, 25(2), 93–106. https://doi.org/10.2165/00019053-200725020-00003

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

I would like to extend my thanks to the participants in the survey without whom this work would not be possible. This work was supported by the Robert Wood Johnson Foundation (ID 74695).

Funding

This work was supported by the Robert Wood Johnson Foundation (ID 74695).

Author information

Authors and Affiliations

Authors

Contributions

JH was solely responsible for the study concept and design, acquisition of data, analysis and interpretation of data, drafting and revising, and final approval of the article to be published.

Corresponding author

Correspondence to Janel Hanmer.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Consent to participate

Voluntary consent was obtained before participants could proceed to the survey, and participants were free to withdraw their participation at any time.

Consent for publication

The author hereby gives consent for publication this article in Quality of Life Research.

Ethical approval

University of Pittsburgh IRB PRO17080294.

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 23 kb)

Supplementary material 2 (DOCX 28 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hanmer, J. Cross-sectional validation of the PROMIS-Preference scoring system by its association with social determinants of health. Qual Life Res 30, 881–889 (2021). https://doi.org/10.1007/s11136-020-02691-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-020-02691-3

Keywords

Navigation