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When less is more: reducing redundancy in mental health and psychosocial instruments using Item Response Theory.

ABSTRACT: Background:There is a need for accurate and efficient assessment tools that cover a range of mental health and psychosocial problems. Existing, lengthy self-report assessments may reduce accuracy due to respondent fatigue. Using data from a sample of adults enrolled in a psychotherapy randomized trial in Thailand and a cross-sectional sample of adolescents in Zambia, we leveraged Item Response Theory (IRT) methods to create brief, psychometrically sound, mental health measures. Methods:We used graded-response models to refine scales by identifying and removing poor performing items that were not well correlated with the underlying trait, and by identifying well-performing items at varying levels of a latent trait to assist in screening or monitoring purposes. Results:In Thailand, the original 17-item depression scale was shortened to seven items and the 30-item Posttraumatic Stress Scale (PTS) was shortened to 10. In Zambia, the Child Posttraumatic Stress Scale (CPSS) was shortened from 17 items to six. Shortened scales in both settings retained the strength of their psychometric properties. When examining longitudinal intervention effects in Thailand, effect sizes were comparable in magnitude for the shortened and standard versions. Conclusions:Using Item Response Theory (IRT) we created shortened valid measures that can be used to help guide clinical decisions and function as longitudinal research tools. The results of this analysis demonstrate the reliability and validity of shortened scales in each of the two settings and an approach that can be generalized more broadly to help improve screening, monitoring, and evaluation of mental health and psychosocial programs globally.

SUBMITTER: Haroz EE

PROVIDER: S-EPMC7003529 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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When less is more: reducing redundancy in mental health and psychosocial instruments using Item Response Theory.

Haroz Emily E EE Kane Jeremy C JC Nguyen Amanda J AJ Bass Judith K JK Murray Laura K LK Bolton Paul P

Global mental health (Cambridge, England) 20200109

<h4>Background</h4>There is a need for accurate and efficient assessment tools that cover a range of mental health and psychosocial problems. Existing, lengthy self-report assessments may reduce accuracy due to respondent fatigue. Using data from a sample of adults enrolled in a psychotherapy randomized trial in Thailand and a cross-sectional sample of adolescents in Zambia, we leveraged Item Response Theory (IRT) methods to create brief, psychometrically sound, mental health measures.<h4>Method ...[more]

PMID: 32076573

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Project description:BackgroundThe investigation of patient-reported outcomes and psycho-oncological interventions mainly focuses on psychological distress or psychopathology. However, the recognition of the equal importance of positive mental health (PMH) has increased lately. The PMH-scale is a brief questionnaire allowing to assess well-being in individuals in the general population and in patients. Previous studies evaluated the psychometric properties of the PMH-scale using classical test theory (CTT). This study is the first to investigate the PMH-scale in patients with cancer using item analysis according to the Rasch model.MethodsIn total, N = 357 cancer patients participated in the study. A Rasch analysis of the PMH-scale was conducted including testing of unidimensionality, local independence, homogeneity and differential item functioning (DIF) with regard to age, gender, type of cancer, the presence of metastases, psycho-oncological support, and duration of disease. Additionally, the ordering of the item thresholds as well as the targeting of the scale were investigated.ResultsAfter excluding one misfitting item and accounting for local dependence by forming superitems, a satisfactory overall fit to the Rasch model was established (χ2 = 30.34, p = 0.21). The new PMH-8 scale proved to be unidimensional, and homogeneity of the scale could be inferred. All items showed ordered thresholds, there was no further item misfit. DIF was found for age, but as the impact of DIF was not substantial, no adjustment related to the age-DIF had to be made. The Person Separation Index (PSI = 0.89) was excellent, indicating excellent discriminatory power between different levels of positive mental health. Overall, the targeting of the PMH-8 was good for the majority of the present sample. However, at both ends of the scale item thresholds are missing as indicated by a slight floor effect (1.4%) and a considerable ceiling effect (9.8%).ConclusionsOverall, the results of the analysis according to the Rasch model support the use of the revised PMH-scale in a psycho-oncological context.

Project description:Allostatic load is commonly operationalized using a sum-score of high-risk biomarkers. However, this method implies that biomarkers contribute equally to allostatic load, as each is given equal weight. Our goal in this methodological paper is to evaluate this, and complementarily, to identify biomarkers that are most informative and least informative for developing an allostatic load index. Item response theory models provide an alternate approach to calculating the allostatic load score, by treating individual biomarkers (e.g. “items”) as indicators of a latent allostatic load construct. Item response theory scores account for the data-driven discriminating power of each biomarker, and an individual’s pattern of biomarker responses. To demonstrate feasibility of this approach, we used data from the 2015–2016 National Health Examination and Nutrition Survey (NHANES; N = 3751), with twelve allostatic load biomarkers representing immune response, metabolic function and cardiovascular health. Item response theory models revealed that body-mass-index and C-reactive protein were the most informative biomarkers for allostatic load. Both higher allostatic load sum-score and allostatic load item response theory score were associated with lower socio-economic status (p = 0.008; p<0.001, respectively). Further, both formulations of allostatic load were positively associated with a nine-item depression screener (p<0.001 for both), but only the item response theory score was also positively associated with the impact of depressive symptoms on daily life (p = 0.045). Item response theory scores may be more finely tuned to tease out effects, compared to sum-scores, and also provide more flexibility when there are missing biomarker measurements. Supplemental R code for our approach are included. Highlights • Methodological paper to introduce item response theory for calculating allostatic load.• Biomarker data from NHANES 2015–2016 representative of United States adults.• Body-mass-index and C-reactive protein most informative for allostatic load.• Item response theory captures more variability in allostatic load compared to sum-scores.• Future work - item response theory can standardize allostatic load across datasets.

Dataset Information

When less is more: reducing redundancy in mental health and psychosocial instruments using Item Response Theory.

Publications

When less is more: reducing redundancy in mental health and psychosocial instruments using Item Response Theory.

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