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      Using the GHQ-12 to screen for mental health problems among primary care patients: psychometrics and practical considerations

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          Abstract

          Background

          This study explores the factor structure of the Indonesian version of the GHQ-12 based on several theoretical perspectives and determines the threshold for optimum sensitivity and specificity. Through a focus group discussion, we evaluate the practicality of the GHQ-12 as a screening tool for mental health problems among adult primary care patients in Indonesia.

          Methods

          This is a prospective study exploring the construct validity, criterion validity and reliability of the GHQ-12, conducted with 676 primary care patients attending 28 primary care clinics randomised for participation in the study. Participants’ GHQ-12 scores were compared with their psychiatric diagnosis based on face-to-face clinical interviews with GPs using the CIS-R. Exploratory and Confirmatory Factor Analyses determined the construct validity of the GHQ-12 in this population. The appropriate threshold score of the GHQ-12 as a screening tool in primary care was determined using the receiver operating curve. Prior to data collection, a focus group discussion was held with research assistants who piloted the screening procedure, GPs, and a psychiatrist, to evaluate the practicality of embedding screening within the routine clinic procedures.

          Results

          Of all primary care patients attending the clinics during the recruitment period, 26.7% agreed to participate (676/2532 consecutive patients approached). Their median age was 46 (range 18–82 years); 67% were women. The median GHQ-12 score for our primary care sample was 2, with an interquartile range of 4. The internal consistency of the GHQ-12 was good (Cronbach’s α = 0.76). Four factor structures were fitted on the data. The GHQ-12 was found to best fit a one-dimensional model, when response bias is taken into consideration. Results from the ROC curve indicated that the GHQ-12 is ‘fairly accurate’ when discriminating primary care patients with indication of mental disorders from those without, with average AUC of 0.78. The optimal threshold of the GHQ-12 was either 1/2 or 2/3 point depending on the intended utility, with a Positive Predictive Value of 0.68 to 0.73 respectively. The screening procedure was successfully embedded into routine patient flow in the 28 clinics.

          Conclusions

          The Indonesian version of the GHQ-12 could be used to screen primary care patients at high risk of mental disorders although with significant false positives if reasonable sensitivity is to be achieved. While it involves additional administrative burden, screening may help identify future users of mental health services in primary care that the country is currently expanding.

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          Most cited references57

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            An introduction to ROC analysis

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              Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

              The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
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                Author and article information

                Contributors
                sabrina.anjara@gmail.com
                chiara.bonetto@univr.it
                tv250@medschl.cam.ac.uk
                cb105@medschl.cam.ac.uk
                Journal
                Int J Ment Health Syst
                Int J Ment Health Syst
                International Journal of Mental Health Systems
                BioMed Central (London )
                1752-4458
                10 August 2020
                10 August 2020
                2020
                : 14
                : 62
                Affiliations
                [1 ]GRID grid.5335.0, ISNI 0000000121885934, Cambridge Institute of Public Health, , University of Cambridge, School of Clinical Medicine, ; Cambridge Biomedical Campus, Forvie Site, Robinson Way, Box 113, Cambridge, CB2 0SR UK
                [2 ]GRID grid.5611.3, ISNI 0000 0004 1763 1124, Department of Neurosciences, Biomedicine and Movement Sciences, , University of Verona, ; Piazzale L.A. Scuro 10, 37134 Verona, Italy
                Author information
                http://orcid.org/0000-0002-1024-4899
                Article
                397
                10.1186/s13033-020-00397-0
                7418321
                32793301
                923ef2dc-f081-430e-9a44-8d2be9a511ca
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 20 April 2020
                : 4 August 2020
                Funding
                Funded by: FundRef http://data.crossref.org/fundingdata/funder/10.13039/100000865, Bill & Melinda Gates Foundation;
                Award ID: OPP1144
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Neurology
                mental health,primary care,screening,psychometrics,indonesia,low- and middle-income countries,receiver operating curve,confirmatory factor analysis

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