Advertisement

Quality of Life Research

, Volume 25, Issue 3, pp 739–751 | Cite as

Concurrent validity of the PROMIS® pediatric global health measure

  • Christopher B. Forrest
  • Carole A. Tucker
  • Ulrike Ravens-Sieberer
  • Ramya Pratiwadi
  • JeanHee Moon
  • Rachel E. Teneralli
  • Brandon Becker
  • Katherine B. Bevans
Article

Abstract

Purpose

To evaluate the concurrent validity of the PROMIS Pediatric Global Health measure (PGH-7), child-report and parent-proxy versions.

Methods

Surveys were administered via home computer on two separate occasions (December, 2011 and August/September, 2012) to a convenience sample of 4636 children 8–17 years old and 2609 parents who participated in a national Internet panel. Data analysis included: (1) evaluations of differences in PGH-7 scores between groups defined by sociodemographics, clinical characteristics, and access to health care; (2) associations with 15 PROMIS pediatric measures; and (3) correlations with two health-related quality-of-life instruments, the KIDSCREEN-10 and PedsQL-15.

Results

PGH-7 scores were lower for children with chronic conditions, Hispanic ethnicity, low socioeconomic status, and barriers to accessing health care. The PGH-7 showed excellent convergent and discriminant validity with PROMIS pediatric measures of physical, mental, and social health. The PGH-7 was strongly correlated with the KIDSCREEN-10, which assesses positive health, and moderately correlated with the PedsQL-15, which assesses problems with a child’s health.

Conclusions

The PGH-7 measures global health, summarizing a child’s physical, mental, and social health into a single score. These properties make it a useful clinical, population health, and research tool for applications that require an efficient, precise, and valid summary measure of a children’s self-reported health status. Future research should prospectively evaluate the PGH-7’s capacity to detect change that results from alterations in clinical status, transformations of the healthcare delivery system, and children’s health development.

Keywords

Person-reported outcome Global health Child Quality of life Health status 

Abbreviations

PRO

Person-reported outcome

PROMIS

Patient-reported outcomes measurement information system

PGH

Pediatric global health

IEP

Individualized educational program

EAP

Expected A posteriori

References

  1. 1.
    Rebok, G., Riley, A., Forrest, C., et al. (2001). Elementary school-aged children’s reports of their health: A cognitive interviewing study. Quality of Life Research, 10(1), 59–70.CrossRefPubMedGoogle Scholar
  2. 2.
    Bevans, K. B., Riley, A. W., Moon, J., & Forrest, C. B. (2010). Conceptual and methodological advances in child-reported outcomes measurement. Expert Review Pharmacoeconomic Outcomes Research, 10(4), 385–396.CrossRefGoogle Scholar
  3. 3.
    Hays, R. D., Bjorner, J. B., Revicki, D. A., Spritzer, K. L., & Cella, D. (2009). Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items. Quality of Life Research, 18(7), 873–880.PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    DeSalvo, K. B., Fan, V. S., McDonell, M. B., & Fihn, S. D. (2005). Predicting mortality and healthcare utilization with a single question. Health Services Research, 40(4), 1234–1246.PubMedCentralCrossRefPubMedGoogle Scholar
  5. 5.
    DeSalvo, K. B., Bloser, N., Reynolds, K., He, J., & Muntner, P. (2006). Mortality prediction with a single general self-rated health question. A meta-analysis. Journal of General Internal Medicine, 21(3), 267–275.PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Bloom, B., Cohen, R. A., & Freeman, G. (2011). Summary health statistics for U.S. children: National Health Interview Survey, 2010. Vital and Health Statistics. Series 10, Data from the National Health Survey, 250, 1–80.Google Scholar
  7. 7.
    Forrest, C. B., Bevans, K. B., Pratiwadi, R., et al. (2014). Development of the PROMIS (R) pediatric global health (PGH-7) measure. Quality of Life Research, 23(4), 1221–1231.PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Riley, W. T., Rothrock, N., Bruce, B., et al. (2010). Patient-reported outcomes measurement information system (PROMIS) domain names and definitions revisions: Further evaluation of content validity in IRT-derived item banks. Quality of Life Research, 19(9), 1311–1321.PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Bock, R. D., & Mislevy, R. J. (1982). Adaptive EAP estimation of ability in a microcomputer environment. Applied Psychological Measurement, 6(4), 431–444.CrossRefGoogle Scholar
  10. 10.
    Bevans, K. B., Gardner, W., Pajer, K., Riley, A. W., & Forrest, C. B. (2013). Qualitative development of the PROMIS® pediatric stress response item banks. Journal of Pediatric Psychology, 38(2), 173–191.PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    Ravens-Sieberer, U., Devine, J., Bevans, K., et al. (2014). Subjective well-being measures for children were developed within the PROMIS project: Presentation of first results. Journal of Clinical Epidemiology, 67(2), 207–218.PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    DeWitt, E. M., Stucky, B. D., Thissen, D., et al. (2011). Construction of the eight-item patient-reported outcomes measurement information system pediatric physical function scales: Built using item response theory. Journal of Clinical Epidemiology, 64(7), 794–804.PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Lai, J. S., Stucky, B. D., Thissen, D., et al. (2013). Development and psychometric properties of the PROMIS((R)) pediatric fatigue item banks. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 22(9), 2417–2427.CrossRefGoogle Scholar
  14. 14.
    Varni, J. W., Stucky, B. D., Thissen, D., et al. (2010). PROMIS Pediatric Pain Interference Scale: An item response theory analysis of the pediatric pain item bank. The Journal of Pain: Official Journal of the American Pain Society, 11(11), 1109–1119.CrossRefGoogle Scholar
  15. 15.
    Irwin, D. E., Stucky, B., Langer, M. M., et al. (2010). An item response analysis of the pediatric PROMIS anxiety and depressive symptoms scales. Quality of Life Research, 19(4), 595–607.PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Irwin, D. E., Stucky, B. D., Langer, M. M., et al. (2012). PROMIS Pediatric Anger Scale: An item response theory analysis. Quality of Life Research, 21(4), 697–706.PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Dewalt, D. A., Thissen, D., Stucky, B. D., et al. (2013). PROMIS Pediatric Peer Relationships Scale: Development of a peer relationships item bank as part of social health measurement. Health Psychology, 32(10), 1093–1103.CrossRefPubMedGoogle Scholar
  18. 18.
    Ravens-Sieberer, U., Erhart, M., Rajmil, L., et al. (2010). Reliability, construct and criterion validity of the KIDSCREEN-10 score: A short measure for children and adolescents’ well-being and health-related quality of life. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 19(10), 1487–1500.CrossRefGoogle Scholar
  19. 19.
    Ravens-Sieberer, U., Gosch, A., Rajmil, L., et al. (2005). KIDSCREEN-52 quality-of-life measure for children and adolescents. Expert Review Pharmacoeconomics Outcomes Research, 5(3), 353–364.CrossRefGoogle Scholar
  20. 20.
    Ravens-Sieberer, U., Gosch, A., Rajmil, L., et al. (2008). The KIDSCREEN-52 quality of life measure for children and adolescents: Psychometric results from a cross-cultural survey in 13 European countries. Value Health, 11(4), 645–658.CrossRefPubMedGoogle Scholar
  21. 21.
    KIDSCREEN Group. (2006). The KIDSCREEN Questionnairesquality of life questionnaires for children and adolescentshandbook. Lengerich: Papst Science Publisher.Google Scholar
  22. 22.
    Varni, J. W., Burwinkle, T. M., & Seid, M. (2006). The PedsQL 4.0 as a school population health measure: Feasibility, reliability, and validity. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 15(2), 203–215.CrossRefGoogle Scholar
  23. 23.
    Bethell, C. D., Read, D., Neff, J., et al. (2002). Comparison of the children with special health care needs screener to the questionnaire for identifying children with chronic conditions—revised. Ambulatory Pediatrics, 2(1), 49–57.CrossRefPubMedGoogle Scholar
  24. 24.
    Bethell, C. D., Read, D., Stein, R. E., Blumberg, S. J., Wells, N., & Newacheck, P. W. (2002). Identifying children with special health care needs: Development and evaluation of a short screening instrument. Ambulatory Pediatrics, 2(1), 38–48.CrossRefPubMedGoogle Scholar
  25. 25.
    Agency for Healthcare Research and Quality. (2012). Medical Expenditure Panel Survey (MEPS). http://www.ahrq.gov/research/data/meps/index.html. Accessed December 13, 2014.
  26. 26.
    Choi, S. W., Gibbons, L. E., & Crane, P. K. (2011). lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations. Journal of Statistical Software, 39(8), 1–30.PubMedCentralCrossRefPubMedGoogle Scholar
  27. 27.
    Simon, A. E., Chan, K. S., & Forrest, C. B. (2008). Assessment of children’s health-related quality of life in the United States with a multidimensional index. Pediatrics, 121(1), e118–e126.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christopher B. Forrest
    • 1
    • 2
  • Carole A. Tucker
    • 3
  • Ulrike Ravens-Sieberer
    • 4
  • Ramya Pratiwadi
    • 1
  • JeanHee Moon
    • 1
  • Rachel E. Teneralli
    • 1
  • Brandon Becker
    • 1
  • Katherine B. Bevans
    • 1
  1. 1.Department of PediatricsChildren’s Hospital of PhiladelphiaPhiladelphiaUSA
  2. 2.Leonard Davis Institute of Health EconomicsUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.College of Public HealthTemple UniversityPhiladelphiaUSA
  4. 4.Department of Child and Adolescent Psychiatry, Psychotherapy and PsychosomaticsUniversity Medical Center Hamburg-EppendorfHamburgGermany

Personalised recommendations