Skip to main content

Advertisement

SpringerLink
Log in

Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children

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

Abstract

Purpose

Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm.

Methods

We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.

Results

SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.

Conclusions

The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.

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

Similar content being viewed by others

Notes

  1. Utilities are measured on a cardinal scale, anchored at 0 (death) and 1 (full health) [1, 5, 9]. Utilities less than zero are possible if a health state is considered to be worse than death [1].

  2. MAUIs are a health-related quality of life questionnaires which are associated with an algorithm to convert responses to the included questions into utilities.

  3. Mapping is the process of establishing a statistical relationship between a non-PBM and a PBM using regression techniques.

  4. It can certainly be updated using the new version of the CHU9D instrument but to our best knowledge such an updated algorithm is not publicly available. In this study we obtained a subsample of the original data used in Furber et al. [24] from the lead author and updated their algorithm to be used as a comparator of our new algorithm in the external validation.

  5. For GLM and EEE the disutility was used as the outcome variable to avoid non-negative values.

  6. Brant test was used to check proportional odds assumption to determine the ordered nature of CHU9D domains. Parallel assumption or proportional odds assumption is an assumption of an ordered logit model. This assumption assumes that coefficients between different categories of the dependent variable are equal.

  7. The MAE was calculated as mean of the absolute values of the difference between the observed and predicted CHU9D utilities.

  8. The MSE were computed as the mean squared differences between the predicted and observed CHU9D utilities.

  9. Formula: \(\% \; {\text{Error}}\; {\text{for}}\;{\text{GMAE}} = 100 \times \frac{\text{MAE}}{{\left( {{\text{Max}}\left( {{\text{CHU}}9{\text{D}}\; {\text{utilities}}} \right) - {\text{Min}}\left( {{\text{CHU}}9{\text{D}}\; {\text{utilities}}} \right)} \right)}}.\)

  10. Formula: \(\% \;{\text{Error}}\; {\text{for}}\; {\text{GMSE}} = 100 \times \frac{\text{MSE}}{{\left( {{\text{Max}}\left( {{\text{CHU}}9{\text{D}}\;{\text{utilities}}} \right) - {\text{Min}}\left( {{\text{CHU}}9{\text{D }}\;{\text{utilities}}} \right)} \right)}}.\)

    Note: \(\left( {{\text{Max}}\left( {{\text{CHU}}9{\text{D}}\;{\text{utilities}}} \right)} \right) = 1\;\;{\text{and}}\;\;\left( {{\text{Min}}\left( {{\text{CHU}}9{\text{D}}\;{\text{utilities}}} \right)} \right) = - \,0.1059\)

  11. We used new CHU9D tariff to obtain observed CHU9D variable in the CAMHS dataset, ran an OLS regression with SDQ subscales as predictors and obtained coefficients to be used as new or modified mapping algorithm for Furber et al.

References

  1. Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press.

    Google Scholar 

  2. Canadian Agency for Drugs Technologies in Health. (2006). Guidelines for economic evaluation of pharmaceuticals: Canada. Ottawa: Canadian Agency for Drugs and Technologies in Health.

    Google Scholar 

  3. National Institute for Health and Clinical Excellence. (2013). Guide to the methods of technology appraisal 2013.

  4. Pharmaceutical Benefits Advisory Committee. (2016). Guidelines for preparing a submission to the Pharmaceutical Benefits Advisory Committee (version 5.0). Australian Government Department of Health.

  5. Whitehead, S. J., & Ali, S. (2010). Health outcomes in economic evaluation: The QALY and utilities. British Medical Bulletin, 96(1), 5–21. https://doi.org/10.1093/bmb/ldq033.

    Article  PubMed  Google Scholar 

  6. Brazier, J. E., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. The European Journal of Health Economics, 11(2), 215–225.

    Article  PubMed  Google Scholar 

  7. Harris, A. H., Hill, S. R., Chin, G., Li, J. J., & Walkom, E. (2008). The role of value for money in public insurance coverage decisions for drugs in Australia: A retrospective analysis 1994–2004. Medical Decision Making, 28(5), 713–722.

    Article  PubMed  Google Scholar 

  8. Neumann, P. J., Cohen, J. T., & Weinstein, M. C. (2014). Updating cost-effectiveness—The curious resilience of the $50,000-per-QALY threshold. New England Journal of Medicine, 371(9), 796–797.

    Article  CAS  PubMed  Google Scholar 

  9. Shiell, A., Donaldson, C., Mitton, C., & Currie, G. (2002). Health economic evaluation. Journal of Epidemiology and Community Health, 56(2), 85–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tolley, K. (2009). What are health utilities. London: Hayward Medical Communications.

    Google Scholar 

  11. Torrance, G. W. (1987). Utility approach to measuring health-related quality of life. Journal of Chronic Diseases, 40(6), 593–600.

    Article  CAS  PubMed  Google Scholar 

  12. Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35, 1095–1108.

    Article  CAS  PubMed  Google Scholar 

  13. Shaw, J. W., Johnson, J. A., & Coons, S. J. (2005). US valuation of the EQ-5D health states: Development and testing of the D1 valuation model. Medical Care, 43, 203–220.

    Article  PubMed  Google Scholar 

  14. Brazier, J., Roberts, J., & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21(2), 271–292.

    Article  PubMed  Google Scholar 

  15. 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.

    Article  PubMed  Google Scholar 

  16. Torrance, G. W. (1976). Social preferences for health states: An empirical evaluation of three measurement techniques. Socio-economic Planning Sciences, 10(3), 129–136.

    Article  Google Scholar 

  17. Farquhar, P. H. (1984). State of the art—Utility assessment methods. Management Science, 30(11), 1283–1300.

    Article  Google Scholar 

  18. Kontodimopoulos, N., Argiriou, M., Theakos, N., & Niakas, D. (2011). The impact of disease severity on EQ-5D and SF-6D utility discrepancies in chronic heart failure. The European Journal of Health Economics, 12(4), 383–391.

    Article  PubMed  Google Scholar 

  19. Kularatna, S., Byrnes, J., Chan, Y. K., Carrington, M. J., Stewart, S., & Scuffham, P. A. (2017). Comparison of contemporaneous responses for EQ-5D-3L and Minnesota living with heart failure: A case for disease specific multiattribute utility instrument in cardiovascular conditions. International Journal of Cardiology, 227, 172–176.

    Article  PubMed  Google Scholar 

  20. Wailoo, A. J., Hernandez-Alava, M., Manca, A., Mejia, A., Ray, J., Crawford, B., et al. (2017). Mapping to estimate health-state utility from non-preference-based outcome measures: An ISPOR good practices for outcomes research task force report. Value in Health, 20(1), 18–27.

    Article  PubMed  Google Scholar 

  21. Calxton, K., Martin, S., Soares, M., Rice, N., Spackman, E., Hinde, S., et al. (2013). Methods for the estimation of the NICE cost effectiveness threshold. New York: Centre for Health Economics, University of York.

    Google Scholar 

  22. Committee, Pharmaceutical Benefits Advisory. (2016). Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee (PBAC). Version 5.0. Canberra: Department of Health.

    Google Scholar 

  23. Kearns, B., Ara, R., Wailoo, A., Manca, A., Alava, M. H., Abrams, K., et al. (2013). Good practice guidelines for the use of statistical regression models in economic evaluations. Pharmacoeconomics, 31(8), 643–652.

    Article  PubMed  Google Scholar 

  24. Furber, G., Segal, L., Leach, M., & Cocks, J. (2014). Mapping scores from the strengths and difficulties questionnaire (SDQ) to preference-based utility values. Quality of Life Research, 23(2), 403–411.

    Article  PubMed  Google Scholar 

  25. Ratcliffe, J., Flynn, T., Terlich, F., Stevens, K., Brazier, J., & Sawyer, M. (2012). Developing adolescent-specific health state values for economic evaluation. Pharmacoeconomics, 30(8), 713–727.

    Article  PubMed  Google Scholar 

  26. Gray, L. A., Alava, M. H., & Wailoo, A. J. (2017). Development of methods for the mapping of utilities using mixture models: Mapping the AQLQ-S to the EQ-5D-5L and the HUI3 in patients with asthma. Value in Health, 21(6), 748–757.

    Article  PubMed  Google Scholar 

  27. Varni, J. W., Burwinkle, T. M., & Lane, M. M. (2005). Health-related quality of life measurement in pediatric clinical practice: An appraisal and precept for future research and application. Health and Quality of Life Outcomes, 3(1), 34.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Edwards, B. (2014). Growing up in Australia: The longitudinal study of Australian children: Entering adolescence and becoming a young adult. Family Matters, 95, 5.

    Google Scholar 

  29. Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25(5), 464–469.

    Article  CAS  PubMed  Google Scholar 

  30. Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8(1), 3–15.

    Article  CAS  PubMed  Google Scholar 

  31. Refaeilzadeh, P., Tang, L., & Liu, H. (2016). Cross-validation. Encyclopedia of database systems (pp. 1–7). Boston: Springer.

    Google Scholar 

  32. Goodman, R. (1997). The strengths and difficulties questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38(5), 581–586.

    Article  CAS  PubMed  Google Scholar 

  33. Stevens, K. (2011). Assessing the performance of a new generic measure of health-related quality of life for children and refining it for use in health state valuation. Applied Health Economics and Health Policy, 9(3), 157–169.

    Article  PubMed  Google Scholar 

  34. Stevens, K. (2009). Developing a descriptive system for a new preference-based measure of health-related quality of life for children. Quality of Life Research, 18(8), 1105–1113.

    Article  PubMed  Google Scholar 

  35. StataCorp. (2017). Stata statistical software: Release 15. College Station, TX: StataCorp LLC.

    Google Scholar 

  36. Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., et al. (2015). The MAPS reporting statement for studies mapping onto generic preference-based outcome measures: Explanation and elaboration. Pharmacoeconomics, 33(10), 993–1011.

    Article  PubMed  Google Scholar 

  37. Tosh, J. C., Longworth, L. J., & George, E. (2011). Utility values in National Institute for Health and Clinical Excellence (NICE) technology appraisals. Value in Health, 14(1), 102–109.

    Article  PubMed  Google Scholar 

  38. Round, J., & Hawton, A. (2017). Statistical alchemy: Conceptual validity and mapping to generate health state utility values. PharmacoEconomics-Open, 1(4), 233–239.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Schroeder, M. A., Lander, J., & Levine-Silverman, S. (1990). Diagnosing and dealing with multicollinearity. Western Journal of Nursing Research, 12(2), 175–187.

    Article  PubMed  Google Scholar 

  40. Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Some decision rules. Psychological Bulletin, 81(6), 358.

    Article  Google Scholar 

  41. Tobias, S., & Carlson, J. E. (1969). Brief report: Bartlett’s test of sphericity and chance findings in factor analysis. Multivariate Behavioral Research, 4(3), 375–377.

    Article  CAS  PubMed  Google Scholar 

  42. Brown, J. (2001). What is an eigenvalue? JALT Testing & Evaluation SIG Newsletter, 5(1), 15–19.

    Google Scholar 

  43. Izquierdo, I., Olea, J., & Abad, F. J. (2014). Exploratory factor analysis in validation studies: Uses and recommendations. Psicothema, 26(3), 395–400.

    PubMed  Google Scholar 

  44. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. London: MIT Press.

    Google Scholar 

  45. Nelder, J. A., & Baker, R. J. (2004). Generalized linear models. Encyclopedia of Statistical Sciences. New York: Wiley.

    Google Scholar 

  46. Masyn, K., Nathan, P., & Little, T. (2013). The Oxford handbook of quantitative methods. Statistical analysis (Vol. 2). Oxford: Oxford University Press.

    Google Scholar 

  47. Manning, W. G., & Mullahy, J. (2001). Estimating log models: To transform or not to transform? Journal of Health Economics, 20(4), 461–494.

    Article  CAS  PubMed  Google Scholar 

  48. Pregibon, D. (1980). Goodness of link tests for generalized linear models. Applied Statistics, 29, 14–15.

    Article  Google Scholar 

  49. Pearson, E., & Please, N. (1975). Relation between the shape of population distribution and the robustness of four simple test statistics. Biometrika, 62(2), 223–241.

    Article  Google Scholar 

  50. Hosmer, D. W., Jr., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). New York: Wiley.

    Book  Google Scholar 

  51. Glick, H. A., Doshi, J. A., Sonnad, S. S., & Polsky, D. (2014). Economic evaluation in clinical trials. Oxford: Oxford University Press.

    Book  Google Scholar 

  52. Basu, A. (2005). Extended generalized linear models: Simultaneous estimation of flexible link and variance functions. Stata Journal, 5(4), 501.

    Article  Google Scholar 

  53. Swearingen, C. J., Castro, M. M., & Bursac, Z. (2012). Inflated beta regression: Zero, one and everything in between. In: SAS global forum, 2012 (pp. 1–11)

  54. McDonald, J. F., & Moffitt, R. A. (1980). The uses of Tobit analysis. The Review of Economics and Statistics, 62, 318–321.

    Article  Google Scholar 

  55. Longworth, L., Yang, Y., Young, T., Mulhern, B., Hernandez Alava, M., Mukuria, C., et al. (2014). Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: A systematic review, statistical modelling and survey. Health Technology Assessment, 18, 1–224.

    Article  PubMed  Google Scholar 

  56. Brennan, D. S., & Spencer, A. J. (2006). Mapping oral health related quality of life to generic health state values. BMC Health Services Research, 6(1), 96.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Powell, J. L. (1984). Least absolute deviations estimation for the censored regression model. Journal of Econometrics, 25(3), 303–325.

    Article  Google Scholar 

  58. Sullivan, P. W., & Ghushchyan, V. (2006). Mapping the EQ-5D index from the SF-12: US general population preferences in a nationally representative sample. Medical Decision Making, 26(4), 401–409.

    Article  PubMed  PubMed Central  Google Scholar 

  59. McLachlan, G., & Peel, D. (2004). Finite mixture models. New York: Wiley.

    Google Scholar 

  60. Alava, M. H., Wailoo, A. J., & Ara, R. (2012). Tails from the peak district: Adjusted limited dependent variable mixture models of EQ-5D questionnaire health state utility values. Value in Health, 15(3), 550–561.

    Article  Google Scholar 

  61. Hernandez Alava, M., & Wailoo, A. (2015). Fitting adjusted limited dependent variable mixture models to EQ-5D. Stata Journal, 15(3), 737–750.

    Article  Google Scholar 

  62. Grun, B., & Leisch, F. (2008). FlexMix version 2: Finite mixtures with concomitant variables and varying and constant parameters. Journal of Statistical Software, 28(4), 1–35.

    Article  Google Scholar 

  63. Ratcliffe, J., Huynh, E., Chen, G., Stevens, K., Swait, J., Brazier, J., et al. (2016). Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Social Science and Medicine, 157, 48–59.

    Article  PubMed  Google Scholar 

  64. Le, Q. A., & Doctor, J. N. (2011). Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: An empirical analysis converting SF-12 into EQ-5D utility index in a national US sample. Medical Care, 49, 451–460.

    Article  PubMed  Google Scholar 

  65. Gray, A. M., Rivero-Arias, O., & Clarke, P. M. (2006). Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Medical Decision Making, 26(1), 18–29.

    Article  PubMed  Google Scholar 

  66. Steyerberg, E. (2009). Validation of prediction models. Clinical prediction models (pp. 299–311). Berlin: Springer.

    Chapter  Google Scholar 

  67. Kaiser, H. F., & Rice, J. (1974). Little jiffy, mark IV. Educational and Psychological Measurement, 34(1), 111–117.

    Article  Google Scholar 

  68. Collado-Mateo, D., Chen, G., Garcia-Gordillo, M. A., Iezzi, A., Adsuar, J. C., Olivares, P. R., et al. (2017). Fibromyalgia and quality of life: Mapping the revised fibromyalgia impact questionnaire to the preference-based instruments. Health and Quality of Life Outcomes, 15(1), 114.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Teckle, P., McTaggart-Cowan, H., Van der Hoek, K., Chia, S., Melosky, B., Gelmon, K., et al. (2013). Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D. Health and Quality of Life Outcomes, 11(1), 203.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kay, S., Tolley, K., Colayco, D., Khalaf, K., Anderson, P., & Globe, D. (2013). Mapping EQ-5D utility scores from the Incontinence Quality of Life Questionnaire among patients with neurogenic and idiopathic overactive bladder. Value in Health, 16(2), 394–402.

    Article  PubMed  Google Scholar 

  71. Jones, A. M., Lomas, J., Moore, P., & Rice, N. (2013). A quasi-Monte Carlo comparison of developments in parametric and semi-parametric regression methods for heavy tailed and non-normal data: With an application to healthcare costs. Health Econometrics and Data Group Working Paper, 13, 30.

    Google Scholar 

  72. Lamu, A. N., & Olsen, J. A. (2018). Testing alternative regression models to predict utilities: Mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D. Quality of Life Research, 27(11), 2823–2839.

    Article  PubMed  Google Scholar 

  73. Rowen, D., Brazier, J., & Roberts, J. (2009). Mapping SF-36 onto the EQ-5D index: How reliable is the relationship? Health and Quality of Life Outcomes, 7(1), 27.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Goldsmith, K. A., Dyer, M. T., Buxton, M. J., & Sharples, L. D. (2010). Mapping of the EQ-5D index from clinical outcome measures and demographic variables in patients with coronary heart disease. Health and Quality of Life Outcomes, 8(1), 54.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Some of the results in this manuscript were presented at the 40th Australian Health Economics Society Conference in Hobart, Australia, in September 2018. The authors are grateful to the audience for helpful comments. The authors would like to acknowledge the Department of Social Services and National Centre for Longitudinal Data for reviewing and approving the study protocol and for providing assistance in using the datasets. The authors would like to thank Dr. Gareth Furber for making the CAMHS dataset available for external validation. The authors would also like to acknowledge the feedback from Anirban Basu, Professor of Health Economics at the University of Washington.

Funding

This paper is a part of a Ph.D. project funded by International Macquarie University Research Excellence Scholarship (iMQRES). Yuanyuan Gu’s research is supported by a Marie Sklodowska-Curie Individual Fellowship (No. 740654).

Author information

Authors and Affiliations

  1. Faculty of Business and Economics, Macquarie University Centre for the Health Economy, Macquarie University, 3 Innovation Road, Sydney, NSW, 2109, Australia

    Rajan Sharma, Yuanyuan Gu, Mona Aghdaee & Bonny Parkinson

  2. Department of Economics and Related Studies, University of York, Heslington Road, York, YO10 5DD, UK

    Yuanyuan Gu

  3. Department of Economics, Macquarie University, Sydney, NSW, 2109, Australia

    Kompal Sinha

Authors
  1. Rajan Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanyuan Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kompal Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mona Aghdaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bonny Parkinson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RS, YG, KS, MA and BP contributed to the conception and design of this mapping exercise. RS conducted the statistical analysis. All the contributors contributed to the interpretation of data; drafting the article, revising it critically for the intellectual content and final approval version to be published.

Corresponding author

Correspondence to Rajan Sharma.

Ethics declarations

Conflict of interest

RS, YG, KS, MA and BP declare that there is no conflict of interest regarding the publication of this article.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors. Department of Social Services and National Centre for Longitudinal Data reviewed and approved the study protocol and made the LSAC datasets available.

Additional information

Publisher's Note

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

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, R., Gu, Y., Sinha, K. et al. Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children. Qual Life Res 28, 2429–2441 (2019). https://doi.org/10.1007/s11136-019-02220-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-019-02220-x

Keywords

Search

Navigation