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Assessing distress in the community: psychometric properties and crosswalk comparison of eight measures of psychological distress

Published online by Cambridge University Press:  02 October 2017

P. J. Batterham*
Affiliation:
Centre for Mental Health Research, The Australian National University, Canberra, Australia
M. Sunderland
Affiliation:
NHMRC Centre of Research Excellence in Mental Health and Substance Use, University of New South Wales, Sydney, Australia
T. Slade
Affiliation:
NHMRC Centre of Research Excellence in Mental Health and Substance Use, University of New South Wales, Sydney, Australia
A. L. Calear
Affiliation:
Centre for Mental Health Research, The Australian National University, Canberra, Australia
N. Carragher
Affiliation:
Office of Medical Education, University of New South Wales, Sydney, NSW, Australia
*
Author for correspondence: P. J. Batterham, E-mail: philip.batterham@anu.edu.au

Abstract

Background

Many measures are available for measuring psychological distress in the community. Limited research has compared these scales to identify the best performing tools. A common metric for distress measures would enable researchers and clinicians to equate scores across different measures. The current study evaluated eight psychological distress scales and developed crosswalks (tables/figures presenting multiple scales on a common metric) to enable scores on these scales to be equated.

Methods

An Australian online adult sample (N = 3620, 80% female) was administered eight psychological distress measures: Patient Health Questionnaire-4, Kessler-10/Kessler-6, Distress Questionnaire-5 (DQ5), Mental Health Inventory-5, Hopkins Symptom Checklist-25 (HSCL-25), Self-Report Questionnaire-20 (SRQ-20) and Distress Thermometer. The performance of each measure in identifying DSM-5 criteria for a range of mental disorders was tested. Scale fit to a unidimensional latent construct was assessed using Confirmatory Factor Analysis (CFA). Finally, crosswalks were developed using Item Response Theory.

Results

The DQ5 had optimal performance in identifying individuals meeting DSM-5 criteria, with adequate fit to a unidimensional construct. The HSCL-25 and SRQ-20 also had adequate fit but poorer specificity and/or sensitivity than the DQ5 in identifying caseness. The unidimensional CFA of the combined item bank for the eight scales showed acceptable fit, enabling the creation of crosswalk tables.

Conclusions

The DQ5 had optimal performance in identifying risk of mental health problems. The crosswalk tables developed in this study will enable rapid conversion between distress measures, providing more efficient means of data aggregation and a resource to facilitate interpretation of scores from multiple distress scales.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2017 

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References

Askew, RL, Kim, J, Chung, H, Cook, KF, Johnson, KL, Amtmann, D (2013) Development of a crosswalk for pain interference measured by the BPI and PROMIS pain interference short form. Quality of Life Research 22, 27692776.Google Scholar
Australian Bureau of Statistics (2014) 3101.0 Australian Demographic Statistics. Commonwealth of Australia: Canberra.Google Scholar
Baksheev, GN, Robinson, J, Cosgrave, EM, Baker, K, Yung, AR (2011) Validity of the 12-item general health questionnaire (GHQ-12) in detecting depressive and anxiety disorders among high school students. Psychiatry Research 187, 291296.Google Scholar
Batterham, PJ, Calear, AL, Christensen, H, Carragher, N, Sunderland, M (2017 a). Independent effects of mental disorders on suicidal behaviour in the community. Suicide and Life-Threatening Behavior in press. doi: 10.1111/sltb.12379.Google Scholar
Batterham, PJ, Sunderland, M, Carragher, N, Calear, AL (2017 b). Psychometric properties of 7- and 30-day versions of the PROMIS emotional distress item banks in an Australian adult sample. Assessment 1073191116685809.Google Scholar
Batterham, PJ, Sunderland, M, Carragher, N, Calear, AL, Mackinnon, AJ, Slade, T (2016) The distress questionnaire-5: population screener for psychological distress was more accurate than the K6/K10. Journal of Clinical Epidemiology 71, 3542.Google Scholar
Bauer, DJ, Hussong, AM (2009) Psychometric approaches for developing commensurate measures across independent studies: traditional and new models. Psychological Methods 14, 101125.CrossRefGoogle ScholarPubMed
Berwick, DM, Murphy, JM, Goldman, PA, Ware, JE Jr., Barsky, AJ, Weinstein, MC (1991) Performance of a five-item mental health screening test. Medical Care 29, 169176.Google Scholar
Beusenberg, M, Orley, JH, World Health Organization (1994) A User's Guide to the Self Reporting Questionnaire (SRQ). World Health Organization: Geneva, Switzerland.Google Scholar
Bock, RD, Aitkin, M (1981) Marginal maximum-likelihood estimation of item parameters – application of an Em algorithm. Psychometrika 46, 443459.Google Scholar
Bower, P, Kontopantelis, E, Sutton, A, Kendrick, T, Richards, DA, Gilbody, S, Knowles, S, Cuijpers, P, Andersson, G, Christensen, H, Meyer, B, Huibers, M, Smit, F, van Straten, A, Warmerdam, L, Barkham, M, Bilich, L, Lovell, K, Liu, ET (2013) Influence of initial severity of depression on effectiveness of low intensity interventions: meta-analysis of individual patient data. BMJ 346, f540.Google Scholar
Brown, TA (2015) Confirmatory Factor Analysis for Applied Research. Guilford Publications: New York, NY.Google Scholar
Brown, TA, Barlow, DH (2005) Dimensional versus categorical classification of mental disorders in the fifth edition of the diagnostic and statistical manual of mental disorders and beyond: comment on the special section. Journal of Abnormal Psychology 114, 551556.Google Scholar
Carlson, LE, Groff, SL, Maciejewski, O, Bultz, BD (2010) Screening for distress in lung and breast cancer outpatients: a randomized controlled trial. Journal of Clinical Oncology 28, 48844891.Google Scholar
Chalmers, RP (2012) Mirt: a multidimensional item response theory package for the R environment. Journal of Statistical Software 48, 129.Google Scholar
Choi, SW, Schalet, B, Cook, KF, Cella, D (2014) Establishing a common metric for depressive symptoms: linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychological Assessment 26, 513527.CrossRefGoogle ScholarPubMed
Christensen, H, Griffiths, KM, Jorm, AF (2004) Delivering interventions for depression by using the internet: randomised controlled trial. BMJ 328, 265.Google Scholar
Culverhouse, RC, Bowes, L, Breslau, N, Nurnberger, JI Jr., Burmeister, M, Fergusson, DM, Munafo, M, Saccone, NL, Bierut, LJ, Httlpr, S, Depression, C (2013) Protocol for a collaborative meta-analysis of 5-HTTLPR, stress, and depression. BMC Psychiatry 13, 304.Google Scholar
Cuthbert, BN (2014) The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry 13, 2835.Google Scholar
Donovan, KA, Grassi, L, McGinty, HL, Jacobsen, PB (2014) Validation of the distress thermometer worldwide: state of the science. Psycho-Oncology 23, 241250.CrossRefGoogle ScholarPubMed
Duijts, SF, Faber, MM, Oldenburg, HS, van Beurden, M, Aaronson, NK (2011) Effectiveness of behavioral techniques and physical exercise on psychosocial functioning and health-related quality of life in breast cancer patients and survivors – a meta-analysis. Psycho-Oncology 20, 115126.Google Scholar
Furukawa, TA, Kessler, RC, Slade, T, Andrews, G (2003) The performance of the K6 and K10 screening scales for psychological distress in the Australian national survey of mental health and well-being. Psychological Medicine 33, 357362.Google Scholar
Goldberg, D (1992) General Health Questionnaire (GHQ-12). NFER-Nelson: Windsor, UK.Google Scholar
Hickman, NJ III, Delucchi, KL, Prochaska, JJ (2014) Menthol use among smokers with psychological distress: findings from the 2008 and 2009 national survey on drug Use and health. Tobacco Control 23, 713.Google Scholar
Hu, LT, Bentler, PM (1998) Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychological Methods 3, 424453.CrossRefGoogle Scholar
Hu, LT, Bentler, PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6, 155.CrossRefGoogle Scholar
Hussong, AM, Curran, PJ, Bauer, DJ (2013) Integrative data analysis in clinical psychology research. Annual Review of Clinical Psychology 9, 6189.CrossRefGoogle ScholarPubMed
Kaat, AJ, Newcomb, ME, Ryan, DT, Mustanski, B (2017) Expanding a common metric for depression reporting: linking two scales to PROMIS(R) depression. Quality of Life Research 26, 11191128.Google Scholar
Kang, T, Chen, TT (2011) Performance of the generalized S-X-2 item fit index for the graded response model. Asia Pacific Education Review 12, 8996.Google Scholar
Kaul, S, Avila, JC, Mutambudzi, M, Russell, H, Kirchhoff, AC, Schwartz, CL (2017) Mental distress and health care use among survivors of adolescent and young adult cancer: a cross-sectional analysis of the national health interview survey. Cancer 123, 869878.Google Scholar
Kessler, RC, Andrews, G, Colpe, LJ, Hiripi, E, Mroczek, DK, Normand, SL, Walters, EE, Zaslavsky, AM (2002) Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine 32, 959976.Google Scholar
Kroenke, K, Spitzer, RL, Williams, JB, Lowe, B (2009) An ultra-brief screening scale for anxiety and depression: the PHQ-4. Psychosomatics 50, 613621.Google Scholar
Linton, SJ (2000) A review of psychological risk factors in back and neck pain. Spine 25, 11481156.Google Scholar
McCabe, CJ, Thomas, KJ, Brazier, JE, Coleman, P (1996) Measuring the mental health status of a population: a comparison of the GHQ-12 and the SF-36 (MHI-5). British Journal of Psychiatry 169, 516521.CrossRefGoogle ScholarPubMed
Mitchell, AJ (2007) Pooled results from 38 analyses of the accuracy of distress thermometer and other ultra-short methods of detecting cancer-related mood disorders. Journal of Clinical Oncology 25, 46704681.Google Scholar
Orlando, M, Thissen, D (2000) Likelihood-based item-fit indices for dichotomous item response theory models. Applied Psychological Measurement 24, 4862.Google Scholar
Overholser, JC, Freiheit, SR, DiFilippo, JM (1997) Emotional distress and substance abuse as risk factors for suicide attempts. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie 42, 402408.Google Scholar
Payton, AR (2009) Mental health, mental illness, and psychological distress: same continuum or distinct phenomena? Journal of Health and Social Behavior 50, 213227.Google Scholar
Reeve, BB, Hays, RD, Bjorner, JB, Cook, KF, Crane, PK, Teresi, JA, Thissen, D, Revicki, DA, Weiss, DJ, Hambleton, RK, Liu, H, Gershon, R, Reise, SP, Lai, JS, Cella, D, Group, PC (2007) Psychometric evaluation and calibration of health-related quality of life item banks: plans for the patient-reported outcomes measurement information system (PROMIS). Medical Care 45, S22S31.Google Scholar
Sandanger, I, Moum, T, Ingebrigtsen, G, Dalgard, OS, Sorensen, T, Bruusgaard, D (1998) Concordance between symptom screening and diagnostic procedure: the hopkins symptom checklist-25 and the composite international diagnostic interview I. Social Psychiatry and Psychiatric Epidemiology 33, 345354.Google Scholar
Schalet, BD, Cook, KF, Choi, SW, Cella, D (2014) Establishing a common metric for self-reported anxiety: linking the MASQ, PANAS, and GAD-7 to PROMIS anxiety. Journal of Anxiety Disorders 28, 8896.Google Scholar
Slade, T, Johnston, A, Oakley Browne, MA, Andrews, G, Whiteford, H (2009) 2007 national survey of mental health and wellbeing: methods and key findings. Australian and New Zealand Journal of Psychiatry 43, 594605.Google Scholar
Stansfeld, SA, Fuhrer, R, Shipley, MJ, Marmot, MG (2002) Psychological distress as a risk factor for coronary heart disease in the whitehall II study. International Journal of Epidemiology 31, 248255.Google Scholar
Sunderland, M, Carragher, N, Buchan, H, Batterham, PJ, Slade, T (2014) Comparing profiles of mental disorder across birth cohorts: results from the 2007 Australian national survey of mental health and wellbeing. Australian and New Zealand Journal of Psychiatry 48, 452463.Google Scholar
Sunderland, M, Slade, T, Stewart, G, Andrews, G (2011) Estimating the prevalence of DSM-IV mental illness in the Australian general population using the Kessler psychological distress scale. Australian and New Zealand Journal of Psychiatry 45, 880889.Google Scholar
Thissen, D, Pommerich, M, Billeaud, K, Williams, VSL (1995) Item response theory for scores on tests including polytomous items with ordered responses. Applied Psychological Measurement 19, 3949.Google Scholar
Umegaki, Y, Todo, N (2017) Psychometric properties of the Japanese CES-D, SDS, and PHQ-9 depression scales in university students. Psychological Assessment 29, 354359.Google Scholar
Ventevogel, P, De Vries, G, Scholte, WF, Shinwari, NR, Faiz, H, Nassery, R, van den Brink, W, Olff, M (2007) Properties of the hopkins symptom checklist-25 (HSCL-25) and the self-reporting questionnaire (SRQ-20) as screening instruments used in primary care in Afghanistan. Social Psychiatry and Psychiatric Epidemiology 42, 328335.Google Scholar
Wahl, I, Lowe, B, Bjorner, JB, Fischer, F, Langs, G, Voderholzer, U, Aita, SA, Bergemann, N, Brahler, E, Rose, M (2014) Standardization of depression measurement: a common metric was developed for 11 self-report depression measures. Journal of Clinical Epidemiology 67, 7386.Google Scholar
Zbozinek, TD, Rose, RD, Wolitzky-Taylor, KB, Sherbourne, C, Sullivan, G, Stein, MB, Roy-Byrne, PP, Craske, MG (2012) Diagnostic overlap of generalized anxiety disorder and major depressive disorder in a primary care sample. Depression and Anxiety 29, 10651071.CrossRefGoogle Scholar
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