Accuracy of the Arabic HCL - 32 and MDQ in detecting patients with bipolar disorder

Accuracy of the Arabic HCL - 32 and MDQ in detecting patients with bipolar disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Accuracy of the Arabic HCL - 32 and MDQ in detecting patients with bipolar disorder Uta Ouali, Yosra Zgueb, Lamia Jouini, Amina Aissa, Rabaa Jomli, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1821130/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jan, 2023 Read the published version in BMC Psychiatry → Version 1 posted 9 You are reading this latest preprint version Abstract Background: Studies about two most used and validated instruments for the early detection of BD, the 32 - item Hypomania Checklist (HCL - 32) and the Mood Disorder Questionnaire (MDQ)screeners, in non-Western countries are scarce. This study aimed to explore the reliability, factor structure, and criterion validity of the Arabic versions in a sample of Tunisian patients diagnosed with mood disorder. Methods: The sample included 59 patients with BD, 86 with unipolar MDD and 281 controls. Confirmatory factor analysis was applied to show that a single global score was an appropriate summary measure of the screeners in the sample. Receiver Operating Characteristic analysis was used to assess the capacity of the translated screeners to distinguish patients with BD from those with MDD and controls. Results: Reliability was good for both tools in all samples. The bifactor implementation of the most reported two-factor model had the best fit for both screeners. Both were able to distinguish patients diagnosed with BD from putatively healthy controls, and equally able to distinguish patients diagnosed with BD from patients with MDD. Conclusion: Both screeners work best in excluding the presence of BD in patients with MDD, which is an advantage in deciding whether or not to prescribe an antidepressant. Bipolar Disorder Depression Screening Hypomania Check List (HCL - 32) Mood Disorder Questionnaire (MDQ) Hypomania Figures Figure 1 Figure 2 Figure 3 1. Background Bipolar disorder (BD) is a severe mental disorder with a chronic-recurring course. Since the first episode of a BD is often of a depressive kind [ 1 ], BD is often misdiagnosed as major depressive disorder (MDD) [ 2 , 3 ] and treated as such. This may lead to adverse consequences, such as increased suicide risk [ 4 , 5 ], greater probability of hospitalization [ 5 ], poorer response to antidepressants, and antidepressant-induced switch to mania [ 6 ]. Because of its course characterized by recurring episodes separated by periods of euthymia with no or scant symptoms of hypomania, BD may persist undiagnosed for a long time unless a frank episode of mania erupts [ 7 , 8 ]. Minor hypomanic episodes are often overlooked, and, indeed, the differentiation of clinically elated and irritable mood or increased activity from "normal" variation in the population is often challenging. On average, the duration of undiagnosed, hence untreated, BD may last up to 10 years, and there is some evidence that up to one-third of patients with BD are misdiagnosed at least once during their lifetime [ 9 , 10 ]. Early identification of BD is essential for appropriate treatment [ 11 ]. Several self-report tools have been developed to identify people with possible or probable BD [ 12 ]. Self-report screening tools are brief and cost-effective and can be preferred in the busy clinical setting to standardized interviews, which are more accurate but are time-consuming and require appropriate training for the administration and scoring. Nevertheless, caution should be applied in deriving epidemiologic estimates from case-finding based on screening tools [ 13 ]. Two of the most used and validated instruments for the early detection of BD are the 32-item Hypomania Checklist (HCL − 32) [ 11 ], and the Mood Disorder Questionnaire (MDQ) [ 14 ]. These two instruments have been validated in many countries [ 11 , 15 – 20 , 20 – 24 ]. There is evidence that both the HCL − 32 and the MDQ have acceptable psychometric properties and appear to be useful screening tools for BD [ 25 ]. Most studies on the HCL − 32 and the MDQ have been carried out in Western countries. The epidemiology of BD shows minor variations by country and ethnicity [ 26 , 27 ], and the disorder has a likely genetic basis rooted in evolutionary mechanisms [ 28 , 29 ]. However, cultural factors may influence how symptoms leading to the diagnosis of bipolar disorder are evaluated [ 26 , 30 ]. For example, geographical variations in the prevalence of bipolar disorder might be in part a reflection of the relevance given to the occurrence of psychotic features in bipolar disorder. The diagnosis of schizophrenia is given priority when the possibility that psychotic features may also occur in the course of bipolar disorder is overlooked [ 31 ]. More subtle influences are related to cultural variations in the patients' attitudes towards their symptoms. For example, there is evidence from factor analysis that greater involvement in sexual activity, an oft-observed correlate of hypomania, is perceived as a favorable trait by Latin-Mediterranean patients while Asian patients attribute a negative value to hypersexuality, which they tend to associate with other risky behaviors, such as excessive spending or getting in troubles [ 32 , 33 ]. Studies about the MDQ and the HCL − 32 in non-Western countries are scarce, and most of them are from Asian countries. So far, two studies had explored the reliability and the factor structure of the HCL – 32 [ 34 ] and the MDQ [ 35 ], respectively, in Arabic-speaking countries. This study aimed at further exploring the reliability, factor structure, and criterion validity of the Arabic version of the HCL − 32 and MDQ in a sample of Tunisian patients diagnosed with a mood disorder (either MDD or BD) by comparison with a sample of putatively healthy people drawn from the general population of Tunisia. 2. Material And Methods The study complies with the guidelines of the 1995 Declaration of Helsinki and its revisions [ 36 ]. Approval to the study protocol has been granted by the Institutional Review Board (IRB) of Razi Hospital, Tunis, with the authorization signed on 8 Oct 2014. 2.1. Participants The study was conducted between February 2015 and September 2019 and included a patient group and a control group. 2.1.1. Patient group: All consecutive individuals who consulted for the first time at the Department of Psychiatry A of Razi Hospital La Manouba, Tunisia, for signs and symptoms of depression were invited to take part in the study. Individuals were included when the clinician formulated a diagnosis of a current major depressive episode. Thereafter, the Mood Disorder Section of the Tunisian Arabic adapted version of the Structured Clinical Interview for DSM-IV-TR (SCID) was administered by one single researcher (UO) to confirm the diagnosis of Major Depressive Episode and to ascribe the episode to a unipolar or bipolar mood disorder. Resulting diagnosis was Bipolar Disorder (BD) I or II in case a past manic or hypomanic episode were identified, and Major Depressive Disorder (MDD) in case no past manic or hypomanic episode were identified. Additional inclusion criteria were: aged between 18 and 65 years old; having the capacity of providing informed consent. Exclusion criteria were: illiteracy or other cause of inability to read; documented history of mental retardation; and cognitive decline. 2.1.2 Healthy control group: Healthy control subjects were included from the general population upon completion of patient recruitment. Control subjects were gender- and age-matched. Inclusion criteria were the absence of a personal history of any psychiatric disorder or consultation in psychiatry, and the absence of a family history of psychiatric disorder in a first-degree relative. In addition, subjects had to answer “no” to both “A” criteria questions for a lifetime major depressive episode of the SCID. After inclusion and the administration of the SCID in the patient group, subjects of both groups filled out the MDQ and the HCL − 32. All included subjects provided written informed consent. 2.2. Measures The Arabic version of the MDQ has been used [ 35 ]. The MDQ is a self-report tool aimed at screening for potential lifetime indicators of a manic or hypomanic syndrome. It consists of 13 yes/no items evaluating manic symptoms according to DSM-IV criteria [ 37 ]. A cut-off of 7 out of 13 items is optimal, in terms of sensitivity and specificity, for identifying bipolar disorder against healthy people or patients diagnosed with MDD [ 25 ]. A Tunisian Arabic version of the HCL − 32 has been used, which was prepared according to standard procedures [ 38 ]. At the time of the planning of the study, there was no Arabic version of the HCL − 32. An Arabic version of the HCL − 32 has been published thereafter only [ 34 ]. Moreover, each Arabic country has its own dialect, and although there is a standard Arabic language, many people grasp the concepts better in their local Arabic language. As the HCL − 32 has quite a few items which are culturally sensitive (and could therefore be interpreted differently if not understood at 100% - and for detecting hypomania, nuances can sometimes be very important), we preferred to develop a Tunisian Arabic version. Thus, the HCL − 32 was translated into the Tunisian Arabic language by a bilingual native editor, then back-translated into English by another bilingual native editor. A third, independent researcher, with a deep knowledge of the tool, contributed to harmonize the translation and back-translation of the HCL − 32. Potential issues in reading or unclear items were addressed in a pilot study with eight patients, whose help served to complete the translation of the HCL − 32 in its final form. The HCL − 32 is a self-report questionnaire comprising a list of 32 possible hypomanic symptoms, to be rated as present or absent in a yes/no format. Additional questions concern the duration of the hypomanic experience and the impact on the family, social, and work life. A total score is yielded by the sum of all "yes" replies. A cut-off of 14 out of 32 items is optimal, in terms of sensitivity and specificity, for identifying bipolar disorder [ 25 ]. 2.3. Statistics Data were imputed in Excel, then they were coded and analyzed using the Statistical Package for Social Sciences (SPSS) version 27. Specific analyses were done with dedicated packages running [ 39 ] in R. All tests were two-tailed, with alpha set at p < 0.05. Descriptive statistics were reported as means with standard deviation, or as counts and percentages. Non-parametric tests were used to assess differences between groups or correlations among variables, except for age. To assess the usability of the scale in the target population, we calculated floor and ceiling effects [ 40 ]. They occur when more than 15% of respondents score at the minimum (in this case, zero) or the maximum scores (either 13 for the MDQ or 32 for the HCL-32). The occurrence of floor or ceiling effects indicates that extreme items are missing in the lower or upper end of the scale, indicating limited content validity. Reliability was measured as internal coherence using Cronbach’s alpha. The Bayesian reliability analysis, as implemented in JASP 0.14.1 version [ 41 ], has been used to calculate the Cronbach’s alpha. According to a shared rule-of-thumb, Cronbach's alpha is considered “moderate” when it is > 0.6 and “good” when it is > 0.7 [ 42 ]. Before testing the criterion validity of the MDQ and the HCL-32, confirmatory factor analysis (CFA) was applied to the items of both questionnaires to make sure that a single global score was an appropriate summary measure of the screeners in the total sample. Preliminary analysis with the Mardia’s test [ 43 ] revealed a violation of multivariate normality in the data for both the MDQ and the HCL-32 (skew's p < 0.0001 in both analyses). Therefore, the Diagonally Weighted Least Squares (DWLS) estimator was used in CFA. To assess goodness of fit estimation, we used the following parameters: the chi-square, the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). In the presence of a chi-square with p 300), RMSEA values of 0.08 or lower, SRMR values of 0.09 or lower, and CFI values of 0.90 or higher were considered an indication of acceptable fit according to conventional rules of thumb [ 44 ]. The following model were tested: an unidimensional model, which assumes all core items of the MDQ or the HCL-32 tap into a single dimension of propensity to the manic/hypomanic syndrome; a two-factor model of elated and irritable dimensions, as in Ouali et al., 2020 for the MDQ [ 35 ] and in Meyer et al., 2007 for the HCL-32 [ 15 ]; and these two-factor models' bifactor implementation [ 45 ], which assumes that most variance in the scores is attributable to a general factor resulting from the loading of all items on a single dimension of propensity to the manic/hypomanic syndrome, with an additional but residual variance purportedly explained by the loading of the items on the "elated" and the "irritable" dimensions, as defined above. To check for reasonable unidimensionality of the general factor extracted from the bifactor model, the explained common variance (ECV), the percentage of uncontaminated correlations (PUC), and the Omega Hierarchical (⎤H) were calculated Rodriguez et al., 2016). We also calculated the construct replicability H index of Hancock and Mueller (2001). H values of .80 or higher indicate a well-defined latent variable, which is more likely to be stable across studies. The presence of multidimensionality might be discarded when ECV is higher than .60 and ⎤H > .70 or PUC > .70 [ 46 ]. CFA models were tested with the “lavaan” package running in R [ 47 ].The calculation of the bifactor indices was done with the "Bifactor Indices Calculator" package running in R [ 48 ]. The receiver operating characteristics (ROC) curve was used to test for the criterion validity of the tools. Criterion validity was intended the degree to which the scores of the instrument were an adequate reflection of a ‘‘gold standard’’[ 49 ]. For the purposes of this study, we used the diagnosis assigned after the SCID interview as a "gold standard" for reference. Thus, the ROC curve analysis was used to distinguish between diagnostic groups for both the MDQ and the HCL-32. Sensitivity was defined as the probability of a true positive case, i.e. the probability of identifying a patient with BD. Specificity was the probability of a true negative case, i.e. the probability of identifying a patient without BD. We also derived the positive predictive value (PPV), i.e., the probability that a person is a case of BD when a positive test result is observed; the negative predictive value (NPV), i.e., the probability that a person is not a case of BD when a negative test result is observed; and the positive diagnostic likelihood ratio, which is the odds ratio that a positive test will be observed in a population of people with BD compared to the odds that the same result will be observed among a population of people without BD. The accuracy in the prediction was estimated from the area under the curve (AUC; with 95% confidence interval). Agreed threshold for the AUC were: ≤ .70, poor; between .70 and .80, fair; between .80 and .90, good; above .90, excellent [ 50 ]. We used the "pROC" package running in R to perform the ROC analysis [ 51 ], while the best cut-off point for the MDQ and the HCL-32 was established according to the Youden (1950) method with the "Optimal Cut points" package [ 52 ]. The comparison of the two paired ROC curves for MDQ and HCL-32 in the same sample was done with a bootstrap test according to Hanley and McNeil (1983). The test was performed with the "pROC" package. 3. Results The sample included 86 patients diagnosed with MDD, 22 patients diagnosed with BP-I and 37 patients diagnosed with BP-II. There were also 281 putatively healthy controls (Table 1). There were no differences by gender or maximum education level among participants; controls were marginally younger than the patients (partial eta-squared=0.020). Clinical data were available for patients only. There was no relevant difference in the age of onset of the psychopathology among groups. A family history of depression was observed more often in patients diagnosed with BD-II, while a family history of bipolar disorder was observed in just 5% of patients diagnosed with MDD and in about 25% of those diagnosed with BD (Table 1 for details). Patients diagnosed with BD-I were more likely to have attempted suicide and have been more often admitted to a psychiatric service than patients with MDD or BD-II. A prescription of an antidepressant was received by most patients, with no differences by diagnosis. A second-generation antipsychotic was prescribed in about 10% of cases, again with no difference by diagnosis. Lithium was rarely prescribed and only in patients diagnosed with BD-I. Overall, 86 patients with MDD, 58 patients with BD (either BD-I or BD-II), and 265 controls completed the MDQ; while the HCL-32 was completed by 64 patients with MDD, 32 with BD, and 225 controls. 3.1. Floor or ceiling effects There were no floor effects for the MDQ: 25 controls (8.9%) and just 1 with MDD (1%) scored zero on the MDQ (÷ 2 =11.85; df=2; p=0.003). However, a modest ceiling effect was observed for the MDQ: 4 controls (1.4%) and 11 patients with BD (17.7%) scored 13 on the MDQ (÷ 2 =44.38; df=2; p<0.0001). There were no floor and ceiling effects for the HCL-32. Overall, in the sample 7 patients scored zero on the HCL-32: 5 controls, 2 with MDD, none with BD (÷ 2 =1.28; df=2; p=0.52). No participants scored 32 on the HCL-32. 3.2. Reliability of the questionnaires Cronbach’s alpha for MDQ was 0.79 (95%CI: 0.76 – 0.83) in controls; 0.78 (0.75 – 0.82) in patients with MDD; and 0.71 (0.60 – 0.81) in patients diagnosed with BD. Cronbach’s alpha for HCL-32 was, respectively, 0.85 (0.82 – 0.87) in controls, 0.80 (0.74 – 0.85) in MDD, and 0.76 (0.68 – 0.85) in BD. 3.3. Confirmatory factor analysis of the factor structure of the MDQ and the HCL-32 For both the MDQ and the HCL-32, the bifactor implementation of the two-factor model had the best fit according to the predefined parameters (Table 2). For the bifactor model of the MDQ, H=0.79, ECV=0.54, PUC=0.60, and ùH=0.64. For the bifactor model of the HCL-32, H=0.80, ECV=0.33, PUC=0.48, and ùH=0.37. Thus, for both the MDQ and the HCL-32 there is some indication in favor of a single, reproducible latent component. However, the multidimensionality in the data might influence the results that can be derived from a global summary score. 3.4. Discriminant capacity of the MDQ and the HCL-32 Patients diagnosed with BD scored higher than patients diagnosed with MDD and controls on both the MDQ and the HCL-32 (Table 3). According to the epsilon-squared effect size (Tomczak and Tomczak, 2014), about 20% of the variance in the sample was attributable to the differences in MDQ by groups, and 10% was attributable to the differences in HCL-32 by groups. 3.5. ROC analysis The MDQ and the HCL-32 were able to distinguish patients diagnosed with BD from putatively healthy controls, with better AUC in MDQ (82.7; 95%CI: 75.3 – 90.2) than in HCL-32 (73.4; 63.9 – 83.0) (Figure 1). The MDQ (AUC: 88.9; 81.4 – 96.3) and the HCL-32 (AUC: 83.3; 74.5 – 92.1) were equally able to distinguish patients diagnosed with BD from patients with MDD (Figure 2). When compared with the Hanley and McNeil’s test, the MDQ was confirmed better than the HCL-32 in distinguishing patients with BD from putatively healthy controls, while no difference was found between the two screeners in the differentiation of patients with BD from those with MDD (Figure 3). The best threshold for the differentiation of patients with BD from patients with MDD was 7 for the MDQ (Figure A1) and 15 for the HCL-32 (Figure A2). Sensitivity and specificity at the best threshold were 87% and 77%, respectively, for the MDQ, and 87% and 69% for the HCL-32. Both screeners had a better NPV (92.3% and 91.4%, respectively) than PPV (65.8% and 58.7%). The positive diagnostic likelihood ratio was modestly higher for the MDQ (3.86) than for the HCL-32 (2.84). In the investigated samples, 109 controls (41.1%), 21 patients with MDD (24.4%), and 52 patients with BD (89.7%) scored at or above the cut-off on the MDQ (÷ 2 =63.14; df=2; p<0.0001). The corresponding figures for the HCL-32 were 108 (48%) among controls, 21 (32.8%) among patients with MDD, and 28 (87.5%) among patients with BD (÷ 2 =25.78; df=2; p<0.0001). 4. Discussion In this study, both the MDQ and the HCL-32 were able to distinguish patients diagnosed with BD from patients diagnosed with MDD, with a good accuracy (when measured with AUC) and an informative positive diagnostic likelihood ratio (above 2). Both screeners were more able to exclude the presence of a BD than to confirm it, on the basis of their PPV and NPV. Reliability was good for both the MDQ and the HCL-32, and apparently better in the putatively health controls than in the patients. The controls were probably more straightforward than the patients in admitting hypomanic behaviors or thoughts. This may explain the higher fraction of controls than of MDD patients scoring at or above the cut-off for screening a BD. However, the higher reporting of hypomanic symptoms by controls was not corroborated by an independent source. Thus, we are unable to determine whether the reporting of hypomanic-like symptoms by controls corresponded to real, true episodes of hypomania or was an admission of socially acceptable hyperthymic traits, such as being more sociable than the peers or being exuberant in social circumstances. This is the first study to have tested a bifactor structure of the MDQ and the HCL-32. In past investigations, a two-factor structure was repeatedly reported to explain the distribution of the scores of the two screeners, with some items reflecting a propensity to elated behaviors, and another set of items being a reflection of an impulsive/irritable mood [ 24 , 32 , 53 , 54 ]. In this study, this two-factor solution did not show a good fit according to the predefined parameters. The bifactor implementation of this two-factor model, instead, showed a good fit to the data. The excessive reliance on the exploratory factor analysis over the confirmatory factor analysis of past studies might in part explain the difference between this and previous investigations of the topic. It should be noted that both the MDQ and the HCL-32 are usually applied as a single factor screener, thus a bifactor model of a multidimensional structure of the screeners is the best approximation to the expected factor structure of the tools. It should be noted that in this study, the indicators of the appropriateness of the general factor of the bifactor model were below the accepted threshold for full acceptance of the general factor as a single summary score of the tools. This may depend on the application of the model to a sample that included both patients and putatively healthy controls. This might have inflated the impact of the multidimensionality of both tools, since the elated and impulsive/irritable experience of the patients might be qualitatively different from the corresponding experience in people without a mood disorder. In this sample, the best cut-off for the HCL-32 was close to the one reported in past studies that were carried out in the Western samples, usually about 14 or 15. However, in some non-Western samples, such as in the Arabian study of Fornaro et al (2015) [ 34 ] or the Brazilian sample of patients of Soares et al. (2010), higher cut-offs were reported, around 17/18. Fornaro et al. (2015) included inpatients, while Soares et al. (2010) [ 18 ] enrolled outpatients. Probably both severity and cultural differences in admitting some hypomanic symptoms might have had a role in explaining the higher cut-offs in those studies. 4.1. Strengths and limitations The major strength of the study is its design, which was as close as possible to clinical reality, as we included patients only complaining of depressive signs and symptoms, but did not have any precompiled diagnosis of unipolar or bipolar depression when they first presented. This is a major difference from most of the other studies about MDQ and HCL-32, which often included patients that had already received a diagnosis of bipolar disorder[ 1 , 34 ], and might have received some clue about the symptoms they are expected to admit [ 55 ]. Several limitations have to be taken into account. Some of the questionnaires, either MDQ or HCL-32, were incomplete, especially among patients with BD. This depended mainly on patients leaving blank some items, such as item 6 (about wanting to travel) or 7 (about risky driving) of the HCL-32 because they do not habitually do the enquired action (they do not travel or drive a car), thus they didn't know how to reply to the question. As a consequence, we had to discard some of the cases and this resulted in a loss of power for the analysis. In particular, we had not enough cases with BD-II to test the discriminant capacity of the tools with respect to MDD, the main usage of a screening tool to identify BD. Indeed, while manic episodes are more likely to be recognized by clinicians and to be remembered by the patients, the hypomanic episodes are precisely those that complicate the diagnosis of BD in the clinical setting. 5. Conclusion Despite its limitations, this study showed the good capacity of both the MDQ and the HCL-32 as screening tools to be used to differentiate patients with BD from patients with MDD. Both screeners work best in excluding the presence of BD in patients with MDD, which is an advantage in deciding whether or not to prescribe an antidepressant, which can have known negative effects in patients with BD [ 56 ]. When the screener is positive for the presence of BD, it may prompt a deeper investigation of past manic/hypomanic episodes that might have been overlooked at the first assessment. Declarations Ethics approval and consent to participate The study was approved by the Institutional Review Board of Razi Hospital, with the authorization signed on 8 Oct 2014. All participants provided informed consent to participate in the study. Consent for publication The authors provide consent for publication. Availability of data and material The dataset of this study is available from the corresponding author on reasonable request. Disclosure statement The authors declare no competing interests. Funding details No funding was provided for this study. Authors' contributions UO, AOm and FN designed the study. Data collection was conducted by UO, YZ, LJ,AA, RJ, AOu. Data were analyzed by AP and MC. Data was interpreted by AP, MC, UO, AOm, and FN. AP and UO have drafted the work, which was reviewed by all authors. All authors read and approved the final manuscript. Acknowledgements None. References An D, Hong KS, Kim J-H. Exploratory Factor Analysis and Confirmatory Factor Analysis of the Korean Version of Hypomania Checklist-32. Psychiatry Investig 2011; 8:334. https://doi.org/10.4306/pi.2011.8.4.334. Phillips ML, Kupfer DJ. Bipolar disorder diagnosis: challenges and future directions. The Lancet 2013; 381:1663–71. https://doi.org/10.1016/S0140-6736(13)60989-7. Culpepper L. Misdiagnosis of Bipolar Depression in Primary Care Practices. J Clin Psychiatry 2014;75:e05. https://doi.org/10.4088/JCP.13019tx1c. McCombs JS, Ahn J, Tencer T, Shi L. 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Factor structure and reliability of the Arabic adaptation of the Hypomania Check List-32, second revision (HCL-32-R2). Compr Psychiatry 2015; 59:141–50. https://doi.org/10.1016/j.comppsych.2015.02.015. Ouali U, Jouini L, Zgueb Y, Jomli R, Omrani A, Nacef F, et al. The Factor Structure of the Mood Disorder Questionnaire in Tunisian Patients. Clin Pract Epidemiol Ment Health 2020; 16:82–92. https://doi.org/10.2174/1745017902016010082. World Medical Association. Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA 2000; 284:3043. https://doi.org/10.1001/jama.284.23.3043. Hirschfeld RMA, Williams JBW, Spitzer RL, Calabrese JR, Flynn L, Keck PE, et al. Development and Validation of a Screening Instrument for Bipolar Spectrum Disorder: The Mood Disorder Questionnaire. Am J Psychiatry 2000; 157:1873–5. https://doi.org/10.1176/appi.ajp.157.11.1873. Guillemin F, Bombardier C, Beaton D. 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Biometrika 1970; 57:519–30. https://doi.org/10.1093/biomet/57.3.519. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model Multidiscip J 1999; 6:1–55. https://doi.org/10.1080/10705519909540118. Reise SP. The Rediscovery of Bifactor Measurement Models. Multivar Behav Res 2012; 47:667–96. https://doi.org/10.1080/00273171.2012.715555. Rodriguez A, Reise SP, Haviland MG. Evaluating bifactor models: Calculating and interpreting statistical indices. Psychol Methods 2016; 21:137–50. https://doi.org/10.1037/met0000045. Rosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Softw 2012;48. https://doi.org/10.18637/jss.v048.i02. Dueber D. Bifactor Indices Calculator: Bifactor Indices Calculator. (2021) R package version 0.2.2. https://github.com/ddueber/BifactorIndicesCalculator Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol 2010; 63:737–45. https://doi.org/10.1016/j.jclinepi.2010.02.006. Statistics in Medicine. Wiley Online Libr n.d. https://doi.org/10.1002/(ISSN)1097-0258. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011; 12:77. https://doi.org/10.1186/1471-2105-12-77. López-Ratón M, Rodríguez-Álvarez MX, Suárez CC, Sampedro FG. Optimal Cut points: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests. J Stat Softw 2014;61. https://doi.org/10.18637/jss.v061.i08. Benazzi F, Akiskal HS. The dual factor structure of self-rated MDQ hypomania: energized-activity versus irritable-thought racing. J Affect Disord 2003;73:59–64. https://doi.org/10.1016/S0165-0327(02)00333-6. Angst J, Meyer TD, Adolfsson R, Skeppar P, Carta M, Benazzi F, et al. Hypomania: a transcultural perspective. World Psychiatry 2010;9:41–9. https://doi.org/10.1002/j.2051-5545.2010.tb00268.x. Carta MG, Hardoy MC, Fryers T. Are structured interviews truly able to detect and diagnose Bipolar II disorders in epidemiological studies? The king is still nude! Clin Pract Epidemiol Ment Health CP EMH 2008;4:28. https://doi.org/10.1186/1745-0179-4-28. Young A, Seim D. Review: long term use of antidepressants for bipolar disorder reduces depressive episodes but increases risk of mania. Evid Based Ment Health 2009;12:49–49. https://doi.org/10.1136/ebmh.12.2.49. Tables Table 1 General characteristics of the participants included in the study. Data were expressed as counts and percentage or as mean and standard deviation. MDD BD-I BD-II Healthy Controls N = 86 N = 22 N = 37 N = 281 Gender Men Women 30 (35%) 56 (65%) 13 (59%) 9 (41%) 16 (43%) 21 (57%) 110 (39%) 171 (61%) |2 =4.52; df=3; p=0.21 Age 42 (11) 42 (9) 41 (9) 38 (13) F[3;421]=2.85; p=0.037 Education College or University 39 (45%) 8 (36%) 17 (46%) 102 (55%) |2 =12.03; df=9; p=0.21 Age of onset of psychopathology 35 (12) 35 (10) 31 (10) -- F[2;142]=1.34; p=0.26 Family history of depression 26 (31%) 6 (27%) 21 (57%) -- |2 =8.40; df=2; p=0.015 Family history of bipolar disorder 4 (5%) 6 (27%) 9 (24%) -- |2 =12.9; df=2; p=0.002 Suicide attempt 14 (16%) 12 (54%) 11 (30%) -- |2 =14.03; df=2; p=0.001 Admission to psychiatric services 16 (18%) 11 (50%) 10 (27%) -- |2 =9.14; df=2; p=0.010 Received a prescription of antidepressants 70 (84%) 19 (86%) 33 (89%) -- |2 =0.73; df=2; p=0.69 Received a prescription of a second-generation antipsychotic 9 (10%) 3 (13%) 4 (11%) Freeman-Halton extension of Fisher’s exact test: p=0.93 Received a prescription of lithium 0 (0%) 2 (9%) 0 (0%) -- Freeman-Halton extension of Fisher’s exact test: p=0.026 Table 2 Confirmatory factor analysis of the MDQ and the HCL-32. Goodness-of-fit indices of the tested models. Model |2 df p CFI RMSEA (90%CI) SRMR MDQ Unidimensional 329.2 65 <0.0001 0.894 0.100 (0.089 – 0.111) 0.089 Two-factor 213.8 53 <0.0001 0.924 0.086 (0.074 – 0.098) 0.045 Bifactor 77.3 53 0.016 0.990 0.034 (0.015 – 0.049) 0.045 HCL-32 Unidimensional 1864.6 464 <0.0001 0.798 0.097 (0.093 – 0.102) 0.109 Two-factor 1562.0 463 <0.0001 0.842 0.086 (0.081 – 0.091) 0.098 Bifactor 715.8 432 0.05 0.90 ≥0.08 ≥0.09 Table 3 Scores of the HCL-32 and the MDQ by subgroup of participants Data were expressed as mean and standard deviation. MDD BD Healthy Controls Kruskal-Wallis N = 86 N = 58 N = 265 MDQ 5.3 (2.3) 10.0 (2.5) 5.7 (3.4) H=77.9; df=2; p<0.0001 Σ 2 = 0.191 N = 64 n = 32 N = 225 HCL-32 11.9 (5.8) 19.5 (5.3) 14.5 (6.1) H=31.4; df=2; p<0.0001 Σ 2 = 0.098 Additional Declarations No competing interests reported. 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Background","content":"\u003cp\u003eBipolar disorder (BD) is a severe mental disorder with a chronic-recurring course. Since the first episode of a BD is often of a depressive kind [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], BD is often misdiagnosed as major depressive disorder (MDD) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and treated as such. This may lead to adverse consequences, such as increased suicide risk [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], greater probability of hospitalization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], poorer response to antidepressants, and antidepressant-induced switch to mania [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Because of its course characterized by recurring episodes separated by periods of euthymia with no or scant symptoms of hypomania, BD may persist undiagnosed for a long time unless a frank episode of mania erupts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Minor hypomanic episodes are often overlooked, and, indeed, the differentiation of clinically elated and irritable mood or increased activity from \"normal\" variation in the population is often challenging. On average, the duration of undiagnosed, hence untreated, BD may last up to 10 years, and there is some evidence that up to one-third of patients with BD are misdiagnosed at least once during their lifetime [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly identification of BD is essential for appropriate treatment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Several self-report tools have been developed to identify people with possible or probable BD [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Self-report screening tools are brief and cost-effective and can be preferred in the busy clinical setting to standardized interviews, which are more accurate but are time-consuming and require appropriate training for the administration and scoring. Nevertheless, caution should be applied in deriving epidemiologic estimates from case-finding based on screening tools [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Two of the most used and validated instruments for the early detection of BD are the 32-item Hypomania Checklist (HCL \u0026minus;\u0026thinsp;32) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and the Mood Disorder Questionnaire (MDQ) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These two instruments have been validated in many countries [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. There is evidence that both the HCL \u0026minus;\u0026thinsp;32 and the MDQ have acceptable psychometric properties and appear to be useful screening tools for BD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost studies on the HCL \u0026minus;\u0026thinsp;32 and the MDQ have been carried out in Western countries. The epidemiology of BD shows minor variations by country and ethnicity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and the disorder has a likely genetic basis rooted in evolutionary mechanisms [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, cultural factors may influence how symptoms leading to the diagnosis of bipolar disorder are evaluated [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For example, geographical variations in the prevalence of bipolar disorder might be in part a reflection of the relevance given to the occurrence of psychotic features in bipolar disorder. The diagnosis of schizophrenia is given priority when the possibility that psychotic features may also occur in the course of bipolar disorder is overlooked [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. More subtle influences are related to cultural variations in the patients' attitudes towards their symptoms. For example, there is evidence from factor analysis that greater involvement in sexual activity, an oft-observed correlate of hypomania, is perceived as a favorable trait by Latin-Mediterranean patients while Asian patients attribute a negative value to hypersexuality, which they tend to associate with other risky behaviors, such as excessive spending or getting in troubles [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Studies about the MDQ and the HCL \u0026minus;\u0026thinsp;32 in non-Western countries are scarce, and most of them are from Asian countries. So far, two studies had explored the reliability and the factor structure of the HCL \u0026ndash; 32 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and the MDQ [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], respectively, in Arabic-speaking countries. This study aimed at further exploring the reliability, factor structure, and criterion validity of the Arabic version of the HCL \u0026minus;\u0026thinsp;32 and MDQ in a sample of Tunisian patients diagnosed with a mood disorder (either MDD or BD) by comparison with a sample of putatively healthy people drawn from the general population of Tunisia.\u003c/p\u003e"},{"header":"2. Material And Methods","content":"\u003cp\u003eThe study complies with the guidelines of the 1995 Declaration of Helsinki and its revisions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Approval to the study protocol has been granted by the Institutional Review Board (IRB) of Razi Hospital, Tunis, with the authorization signed on 8 Oct 2014.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eThe study was conducted between February 2015 and September 2019 and included a patient group and a control group.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Patient group:\u003c/h2\u003e \u003cp\u003eAll consecutive individuals who consulted for the first time at the Department of Psychiatry A of Razi Hospital La Manouba, Tunisia, for signs and symptoms of depression were invited to take part in the study. Individuals were included when the clinician formulated a diagnosis of a current major depressive episode. Thereafter, the Mood Disorder Section of the Tunisian Arabic adapted version of the Structured Clinical Interview for DSM-IV-TR (SCID) was administered by one single researcher (UO) to confirm the diagnosis of Major Depressive Episode and to ascribe the episode to a unipolar or bipolar mood disorder. Resulting diagnosis was Bipolar Disorder (BD) I or II in case a past manic or hypomanic episode were identified, and Major Depressive Disorder (MDD) in case no past manic or hypomanic episode were identified. Additional inclusion criteria were: aged between 18 and 65 years old; having the capacity of providing informed consent. Exclusion criteria were: illiteracy or other cause of inability to read; documented history of mental retardation; and cognitive decline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Healthy control group:\u003c/h2\u003e \u003cp\u003eHealthy control subjects were included from the general population upon completion of patient recruitment. Control subjects were gender- and age-matched. Inclusion criteria were the absence of a personal history of any psychiatric disorder or consultation in psychiatry, and the absence of a family history of psychiatric disorder in a first-degree relative. In addition, subjects had to answer \u0026ldquo;no\u0026rdquo; to both \u0026ldquo;A\u0026rdquo; criteria questions for a lifetime major depressive episode of the SCID.\u003c/p\u003e \u003cp\u003eAfter inclusion and the administration of the SCID in the patient group, subjects of both groups filled out the MDQ and the HCL \u0026minus;\u0026thinsp;32. All included subjects provided written informed consent.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measures\u003c/h2\u003e \u003cp\u003eThe Arabic version of the MDQ has been used [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The MDQ is a self-report tool aimed at screening for potential lifetime indicators of a manic or hypomanic syndrome. It consists of 13 yes/no items evaluating manic symptoms according to DSM-IV criteria [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A cut-off of 7 out of 13 items is optimal, in terms of sensitivity and specificity, for identifying bipolar disorder against healthy people or patients diagnosed with MDD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA Tunisian Arabic version of the HCL \u0026minus;\u0026thinsp;32 has been used, which was prepared according to standard procedures [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. At the time of the planning of the study, there was no Arabic version of the HCL \u0026minus;\u0026thinsp;32. An Arabic version of the HCL \u0026minus;\u0026thinsp;32 has been published thereafter only [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, each Arabic country has its own dialect, and although there is a standard Arabic language, many people grasp the concepts better in their local Arabic language. As the HCL \u0026minus;\u0026thinsp;32 has quite a few items which are culturally sensitive (and could therefore be interpreted differently if not understood at 100% - and for detecting hypomania, nuances can sometimes be very important), we preferred to develop a Tunisian Arabic version. Thus, the HCL \u0026minus;\u0026thinsp;32 was translated into the Tunisian Arabic language by a bilingual native editor, then back-translated into English by another bilingual native editor. A third, independent researcher, with a deep knowledge of the tool, contributed to harmonize the translation and back-translation of the HCL \u0026minus;\u0026thinsp;32. Potential issues in reading or unclear items were addressed in a pilot study with eight patients, whose help served to complete the translation of the HCL \u0026minus;\u0026thinsp;32 in its final form.\u003c/p\u003e \u003cp\u003eThe HCL \u0026minus;\u0026thinsp;32 is a self-report questionnaire comprising a list of 32 possible hypomanic symptoms, to be rated as present or absent in a yes/no format. Additional questions concern the duration of the hypomanic experience and the impact on the family, social, and work life. A total score is yielded by the sum of all \"yes\" replies. A cut-off of 14 out of 32 items is optimal, in terms of sensitivity and specificity, for identifying bipolar disorder [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistics\u003c/h2\u003e \u003cp\u003eData were imputed in Excel, then they were coded and analyzed using the Statistical Package for Social Sciences (SPSS) version 27. Specific analyses were done with dedicated packages running [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] in R. All tests were two-tailed, with alpha set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eDescriptive statistics were reported as means with standard deviation, or as counts and percentages. Non-parametric tests were used to assess differences between groups or correlations among variables, except for age.\u003c/p\u003e \u003cp\u003eTo assess the usability of the scale in the target population, we calculated floor and ceiling effects [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. They occur when more than 15% of respondents score at the minimum (in this case, zero) or the maximum scores (either 13 for the MDQ or 32 for the HCL-32). The occurrence of floor or ceiling effects indicates that extreme items are missing in the lower or upper end of the scale, indicating limited content validity.\u003c/p\u003e \u003cp\u003eReliability was measured as internal coherence using Cronbach\u0026rsquo;s alpha. The Bayesian reliability analysis, as implemented in JASP 0.14.1 version [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], has been used to calculate the Cronbach\u0026rsquo;s alpha. According to a shared rule-of-thumb, Cronbach's alpha is considered \u0026ldquo;moderate\u0026rdquo; when it is \u0026gt;\u0026thinsp;0.6 and \u0026ldquo;good\u0026rdquo; when it is \u0026gt;\u0026thinsp;0.7 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBefore testing the criterion validity of the MDQ and the HCL-32, confirmatory factor analysis (CFA) was applied to the items of both questionnaires to make sure that a single global score was an appropriate summary measure of the screeners in the total sample. Preliminary analysis with the Mardia\u0026rsquo;s test [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] revealed a violation of multivariate normality in the data for both the MDQ and the HCL-32 (skew's p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 in both analyses). Therefore, the Diagonally Weighted Least Squares (DWLS) estimator was used in CFA. To assess goodness of fit estimation, we used the following parameters: the chi-square, the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). In the presence of a chi-square with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, as expected with large samples (n\u0026thinsp;\u0026gt;\u0026thinsp;300), RMSEA values of 0.08 or lower, SRMR values of 0.09 or lower, and CFI values of 0.90 or higher were considered an indication of acceptable fit according to conventional rules of thumb [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The following model were tested: an unidimensional model, which assumes all core items of the MDQ or the HCL-32 tap into a single dimension of propensity to the manic/hypomanic syndrome; a two-factor model of elated and irritable dimensions, as in Ouali et al., 2020 for the MDQ [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and in Meyer et al., 2007 for the HCL-32 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; and these two-factor models' bifactor implementation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], which assumes that most variance in the scores is attributable to a general factor resulting from the loading of all items on a single dimension of propensity to the manic/hypomanic syndrome, with an additional but residual variance purportedly explained by the loading of the items on the \"elated\" and the \"irritable\" dimensions, as defined above. To check for reasonable unidimensionality of the general factor extracted from the bifactor model, the explained common variance (ECV), the percentage of uncontaminated correlations (PUC), and the Omega Hierarchical (⎤H) were calculated Rodriguez et al., 2016). We also calculated the construct replicability H index of Hancock and Mueller (2001). H values of .80 or higher indicate a well-defined latent variable, which is more likely to be stable across studies. The presence of multidimensionality might be discarded when ECV is higher than .60 and ⎤H\u0026thinsp;\u0026gt;\u0026thinsp;.70 or PUC\u0026thinsp;\u0026gt;\u0026thinsp;.70 [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. CFA models were tested with the \u0026ldquo;lavaan\u0026rdquo; package running in R [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].The calculation of the bifactor indices was done with the \"Bifactor Indices Calculator\" package running in R [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe receiver operating characteristics (ROC) curve was used to test for the criterion validity of the tools. Criterion validity was intended the degree to which the scores of the instrument were an adequate reflection of a \u0026lsquo;\u0026lsquo;gold standard\u0026rsquo;\u0026rsquo;[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. For the purposes of this study, we used the diagnosis assigned after the SCID interview as a \"gold standard\" for reference. Thus, the ROC curve analysis was used to distinguish between diagnostic groups for both the MDQ and the HCL-32. Sensitivity was defined as the probability of a true positive case, i.e. the probability of identifying a patient with BD. Specificity was the probability of a true negative case, i.e. the probability of identifying a patient without BD. We also derived the positive predictive value (PPV), i.e., the probability that a person is a case of BD when a positive test result is observed; the negative predictive value (NPV), i.e., the probability that a person is not a case of BD when a negative test result is observed; and the positive diagnostic likelihood ratio, which is the odds ratio that a positive test will be observed in a population of people with BD compared to the odds that the same result will be observed among a population of people without BD. The accuracy in the prediction was estimated from the area under the curve (AUC; with 95% confidence interval). Agreed threshold for the AUC were: \u0026le; .70, poor; between .70 and .80, fair; between .80 and .90, good; above .90, excellent [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe used the \"pROC\" package running in R to perform the ROC analysis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], while the best cut-off point for the MDQ and the HCL-32 was established according to the Youden (1950) method with the \"Optimal Cut points\" package [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The comparison of the two paired ROC curves for MDQ and HCL-32 in the same sample was done with a bootstrap test according to Hanley and McNeil (1983). The test was performed with the \"pROC\" package.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe sample included 86 patients diagnosed with MDD, 22 patients diagnosed with BP-I and 37 patients diagnosed with BP-II. There were also 281 putatively healthy controls (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were no differences by gender or maximum education level among participants; controls were marginally younger than the patients (partial eta-squared=0.020).\u003c/p\u003e\n\u003cp\u003eClinical data were available for patients only. There was no relevant difference in the age of onset of the psychopathology among groups. A family history of depression was observed more often in patients diagnosed with BD-II, while a family history of bipolar disorder was observed in just 5% of patients diagnosed with MDD and in about 25% of those diagnosed with BD (Table 1 for details).\u003c/p\u003e\n\u003cp\u003ePatients diagnosed with BD-I were more likely to have attempted suicide and have been more often admitted to a psychiatric service than patients with MDD or BD-II. A prescription of an antidepressant was received by most patients, with no differences by diagnosis. A second-generation antipsychotic was prescribed in about 10% of cases, again with no difference by diagnosis. Lithium was rarely prescribed and only in patients diagnosed with BD-I.\u003c/p\u003e\n\u003cp\u003eOverall, 86 patients with MDD, 58 patients with BD (either BD-I or BD-II), and 265 controls completed the MDQ; while the HCL-32 was completed by 64 patients with MDD, 32 with BD, and 225 controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1. Floor or ceiling effects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no floor effects for the MDQ: 25 controls (8.9%) and just 1 with MDD (1%) scored zero on the MDQ (\u0026divide;\u003csup\u003e2\u003c/sup\u003e=11.85; df=2; p=0.003). However, a modest ceiling effect was observed for the MDQ: 4 controls (1.4%) and 11 patients with BD (17.7%) scored 13 on the MDQ (\u0026divide;\u003csup\u003e2\u003c/sup\u003e=44.38; df=2; p\u0026lt;0.0001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were no floor and ceiling effects for the HCL-32. Overall, in the sample 7 patients scored zero on the HCL-32: 5 controls, 2 with MDD, none with BD (\u0026divide;\u003csup\u003e2\u003c/sup\u003e=1.28; df=2; p=0.52). No participants scored 32 on the HCL-32.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Reliability of the questionnaires\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCronbach\u0026rsquo;s alpha for MDQ was 0.79 (95%CI: 0.76 \u0026ndash; 0.83) in controls; 0.78 (0.75 \u0026ndash; 0.82) in patients with MDD; and 0.71 (0.60 \u0026ndash; 0.81) in patients diagnosed with BD. Cronbach\u0026rsquo;s alpha for HCL-32 was, respectively, 0.85 (0.82 \u0026ndash; 0.87) in controls, 0.80 (0.74 \u0026ndash; 0.85) in MDD, and 0.76 (0.68 \u0026ndash; 0.85) in BD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Confirmatory factor analysis of the factor structure of the MDQ and the HCL-32\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor both the MDQ and the HCL-32, the bifactor implementation of the two-factor model had the best fit according to the predefined parameters (Table 2).\u003c/p\u003e\n\u003cp\u003eFor the bifactor model of the MDQ, H=0.79, ECV=0.54, PUC=0.60, and\u0026nbsp;\u0026ugrave;H=0.64.\u003c/p\u003e\n\u003cp\u003eFor the bifactor model of the HCL-32, H=0.80, ECV=0.33, PUC=0.48, and\u0026nbsp;\u0026ugrave;H=0.37.\u003c/p\u003e\n\u003cp\u003eThus, for both the MDQ and the HCL-32 there is some indication in favor of a single, reproducible latent component. However, the multidimensionality in the data might influence the results that can be derived from a global summary score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Discriminant capacity of the MDQ and the HCL-32\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients diagnosed with BD scored higher than patients diagnosed with MDD and controls on both the MDQ and the HCL-32 (Table 3).\u003c/p\u003e\n\u003cp\u003eAccording to the epsilon-squared effect size (Tomczak and Tomczak, 2014), about 20% of the variance in the sample was attributable to the differences in MDQ by groups, and 10% was attributable to the differences in HCL-32 by groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. ROC analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MDQ and the HCL-32 were able to distinguish patients diagnosed with BD from putatively healthy controls, with better AUC in MDQ (82.7; 95%CI: 75.3 \u0026ndash; 90.2) than in HCL-32 (73.4; 63.9 \u0026ndash; 83.0) (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe MDQ (AUC: 88.9; 81.4 \u0026ndash; 96.3) and the HCL-32 (AUC: 83.3; 74.5 \u0026ndash; 92.1) were equally able to distinguish patients diagnosed with BD from patients with MDD (Figure 2).\u003c/p\u003e\n\u003cp\u003eWhen compared with the Hanley and McNeil\u0026rsquo;s test, the MDQ was confirmed better than the HCL-32 in distinguishing patients with BD from putatively healthy controls, while no difference was found between the two screeners in the differentiation of patients with BD from those with MDD (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe best threshold for the differentiation of patients with BD from patients with MDD was 7 for the MDQ (Figure A1) and 15 for the HCL-32 (Figure A2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSensitivity and specificity at the best threshold were 87% and 77%, respectively, for the MDQ, and 87% and 69% for the HCL-32. Both screeners had a better NPV (92.3% and 91.4%, respectively) than PPV (65.8% and 58.7%). The positive diagnostic likelihood ratio was modestly higher for the MDQ (3.86) than for the HCL-32 (2.84).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the investigated samples, 109 controls (41.1%), 21 patients with MDD (24.4%), and 52 patients with BD (89.7%) scored at or above the cut-off on the MDQ (\u0026divide;\u003csup\u003e2\u003c/sup\u003e=63.14; df=2; p\u0026lt;0.0001). The corresponding figures for the HCL-32 were 108 (48%) among controls, 21 (32.8%) among patients with MDD, and 28 (87.5%) among patients with BD (\u0026divide;\u003csup\u003e2\u003c/sup\u003e=25.78; df=2; p\u0026lt;0.0001).\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, both the MDQ and the HCL-32 were able to distinguish patients diagnosed with BD from patients diagnosed with MDD, with a good accuracy (when measured with AUC) and an informative positive diagnostic likelihood ratio (above 2). Both screeners were more able to exclude the presence of a BD than to confirm it, on the basis of their PPV and NPV. Reliability was good for both the MDQ and the HCL-32, and apparently better in the putatively health controls than in the patients. The controls were probably more straightforward than the patients in admitting hypomanic behaviors or thoughts. This may explain the higher fraction of controls than of MDD patients scoring at or above the cut-off for screening a BD. However, the higher reporting of hypomanic symptoms by controls was not corroborated by an independent source. Thus, we are unable to determine whether the reporting of hypomanic-like symptoms by controls corresponded to real, true episodes of hypomania or was an admission of socially acceptable hyperthymic traits, such as being more sociable than the peers or being exuberant in social circumstances.\u003c/p\u003e \u003cp\u003eThis is the first study to have tested a bifactor structure of the MDQ and the HCL-32. In past investigations, a two-factor structure was repeatedly reported to explain the distribution of the scores of the two screeners, with some items reflecting a propensity to elated behaviors, and another set of items being a reflection of an impulsive/irritable mood [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In this study, this two-factor solution did not show a good fit according to the predefined parameters. The bifactor implementation of this two-factor model, instead, showed a good fit to the data. The excessive reliance on the exploratory factor analysis over the confirmatory factor analysis of past studies might in part explain the difference between this and previous investigations of the topic. It should be noted that both the MDQ and the HCL-32 are usually applied as a single factor screener, thus a bifactor model of a multidimensional structure of the screeners is the best approximation to the expected factor structure of the tools. It should be noted that in this study, the indicators of the appropriateness of the general factor of the bifactor model were below the accepted threshold for full acceptance of the general factor as a single summary score of the tools. This may depend on the application of the model to a sample that included both patients and putatively healthy controls. This might have inflated the impact of the multidimensionality of both tools, since the elated and impulsive/irritable experience of the patients might be qualitatively different from the corresponding experience in people without a mood disorder.\u003c/p\u003e \u003cp\u003eIn this sample, the best cut-off for the HCL-32 was close to the one reported in past studies that were carried out in the Western samples, usually about 14 or 15. However, in some non-Western samples, such as in the Arabian study of Fornaro et al (2015) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] or the Brazilian sample of patients of Soares et al. (2010), higher cut-offs were reported, around 17/18. Fornaro et al. (2015) included inpatients, while Soares et al. (2010) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] enrolled outpatients. Probably both severity and cultural differences in admitting some hypomanic symptoms might have had a role in explaining the higher cut-offs in those studies.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Strengths and limitations\u003c/h2\u003e \u003cp\u003eThe major strength of the study is its design, which was as close as possible to clinical reality, as we included patients only complaining of depressive signs and symptoms, but did not have any precompiled diagnosis of unipolar or bipolar depression when they first presented. This is a major difference from most of the other studies about MDQ and HCL-32, which often included patients that had already received a diagnosis of bipolar disorder[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and might have received some clue about the symptoms they are expected to admit [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Several limitations have to be taken into account. Some of the questionnaires, either MDQ or HCL-32, were incomplete, especially among patients with BD. This depended mainly on patients leaving blank some items, such as item 6 (about wanting to travel) or 7 (about risky driving) of the HCL-32 because they do not habitually do the enquired action (they do not travel or drive a car), thus they didn't know how to reply to the question. As a consequence, we had to discard some of the cases and this resulted in a loss of power for the analysis. In particular, we had not enough cases with BD-II to test the discriminant capacity of the tools with respect to MDD, the main usage of a screening tool to identify BD. Indeed, while manic episodes are more likely to be recognized by clinicians and to be remembered by the patients, the hypomanic episodes are precisely those that complicate the diagnosis of BD in the clinical setting.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eDespite its limitations, this study showed the good capacity of both the MDQ and the HCL-32 as screening tools to be used to differentiate patients with BD from patients with MDD. Both screeners work best in excluding the presence of BD in patients with MDD, which is an advantage in deciding whether or not to prescribe an antidepressant, which can have known negative effects in patients with BD [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. When the screener is positive for the presence of BD, it may prompt a deeper investigation of past manic/hypomanic episodes that might have been overlooked at the first assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Review Board of Razi Hospital, with the authorization signed on 8 Oct 2014. All participants provided informed consent to participate in the study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors provide consent for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset of this study is available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was provided for this study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUO, AOm and FN designed the study. Data collection was conducted by UO, YZ, LJ,AA, RJ, AOu. Data were analyzed by AP and MC. Data was interpreted by AP, MC, UO, AOm, and FN.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAP and UO have drafted the work, which was reviewed by all authors. All authors read and approved the final manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAn D, Hong KS, Kim J-H. Exploratory Factor Analysis and Confirmatory Factor Analysis of the Korean Version of Hypomania Checklist-32. Psychiatry Investig 2011; 8:334. https://doi.org/10.4306/pi.2011.8.4.334.\u003c/li\u003e\n\u003cli\u003ePhillips ML, Kupfer DJ. Bipolar disorder diagnosis: challenges and future directions. The Lancet 2013; 381:1663\u0026ndash;71. https://doi.org/10.1016/S0140-6736(13)60989-7.\u003c/li\u003e\n\u003cli\u003eCulpepper L. 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Wiley Online Libr n.d. https://doi.org/10.1002/(ISSN)1097-0258.\u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011; 12:77. https://doi.org/10.1186/1471-2105-12-77.\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Rat\u0026oacute;n M, Rodr\u0026iacute;guez-\u0026Aacute;lvarez MX, Su\u0026aacute;rez CC, Sampedro FG. Optimal Cut points: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests. J Stat Softw 2014;61. https://doi.org/10.18637/jss.v061.i08.\u003c/li\u003e\n\u003cli\u003eBenazzi F, Akiskal HS. The dual factor structure of self-rated MDQ hypomania: energized-activity versus irritable-thought racing. J Affect Disord 2003;73:59\u0026ndash;64. https://doi.org/10.1016/S0165-0327(02)00333-6.\u003c/li\u003e\n\u003cli\u003eAngst J, Meyer TD, Adolfsson R, Skeppar P, Carta M, Benazzi F, et al. Hypomania: a transcultural perspective. World Psychiatry 2010;9:41\u0026ndash;9. https://doi.org/10.1002/j.2051-5545.2010.tb00268.x.\u003c/li\u003e\n\u003cli\u003eCarta MG, Hardoy MC, Fryers T. Are structured interviews truly able to detect and diagnose Bipolar II disorders in epidemiological studies? The king is still nude! Clin Pract Epidemiol Ment Health CP EMH 2008;4:28. https://doi.org/10.1186/1745-0179-4-28.\u003c/li\u003e\n\u003cli\u003eYoung A, Seim D. Review: long term use of antidepressants for bipolar disorder reduces depressive episodes but increases risk of mania. Evid Based Ment Health 2009;12:49\u0026ndash;49. https://doi.org/10.1136/ebmh.12.2.49.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style=\"text-align: center;\"\u003eTable 1\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u0026nbsp;General characteristics of the participants included in the study.\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eData were expressed as counts and percentage or as mean and standard deviation.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003eBD-I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003eBD-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003eHealthy Controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003eN = 86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003eN = 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003eN = 37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003eN = 281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003cp\u003eWomen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30 (35%)\u003c/p\u003e\n \u003cp\u003e56 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13 (59%)\u003c/p\u003e\n \u003cp\u003e9 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (43%)\u003c/p\u003e\n \u003cp\u003e21 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e110 (39%)\u003c/p\u003e\n \u003cp\u003e171 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003e\u003csup\u003e|2\u003c/sup\u003e=4.52; df=3; p=0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e42 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e42 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e41 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e38 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003eF[3;421]=2.85; p=0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eEducation\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCollege or University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e102 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003e\u003csup\u003e|2\u003c/sup\u003e=12.03; df=9; p=0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eAge of onset of psychopathology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e35 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e35 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e31 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003eF[2;142]=1.34; p=0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eFamily history of depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e26 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e6 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e21 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003e\u003csup\u003e|2\u003c/sup\u003e=8.40; df=2; p=0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eFamily history of bipolar disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e4 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e6 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e9 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003e\u003csup\u003e|2\u003c/sup\u003e=12.9; df=2; p=0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eSuicide attempt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e14 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e12 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e11 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003e\u003csup\u003e|2\u003c/sup\u003e=14.03; df=2; p=0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eAdmission to psychiatric services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e16 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e11 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e10 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003e\u003csup\u003e|2\u003c/sup\u003e=9.14; df=2; p=0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eReceived a prescription of antidepressants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e70 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e19 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e33 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003e\u003csup\u003e|2\u003c/sup\u003e=0.73; df=2; p=0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eReceived a prescription of a second-generation antipsychotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e9 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e3 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e4 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003eFreeman-Halton extension of Fisher\u0026rsquo;s exact test: p=0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.338257016248154%\"\u003e\n \u003cp\u003eReceived a prescription of lithium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e2 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.112259970457902%\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.47562776957164%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.849335302806498%\"\u003e\n \u003cp\u003eFreeman-Halton extension of Fisher\u0026rsquo;s exact test: p=0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 2\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u0026nbsp;Confirmatory factor analysis of the MDQ and the HCL-32. Goodness-of-fit indices of the tested models.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" style=\"border-collapse: collapse; margin: 0px auto;\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003eModel\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e\u003csup\u003e|2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003eRMSEA (90%CI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003e\u003cem\u003eMDQ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003eUnidimensional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e329.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e0.100 (0.089 \u0026ndash; 0.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003eTwo-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e213.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e0.086 (0.074 \u0026ndash; 0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003eBifactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e77.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e0.034 (0.015 \u0026ndash; 0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003e\u003cem\u003eHCL-32\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003eUnidimensional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1864.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e0.097 (0.093 \u0026ndash; 0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003eTwo-factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e1562.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e0.086 (0.081 \u0026ndash; 0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003eBifactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e715.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e0.045 (0.039 \u0026ndash; 0.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.160409556313994%\"\u003e\n \u003cp\u003e\u003cem\u003eThreshold for fit\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.433447098976108%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.020477815699659%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.604095563139932%\"\u003e\n \u003cp\u003ep\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.703071672354948%\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.81569965870307%\"\u003e\n \u003cp\u003e\u0026ge;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.262798634812286%\"\u003e\n \u003cp\u003e\u0026ge;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 3\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u0026nbsp;Scores of the HCL-32 and the MDQ by subgroup of participants\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eData were expressed as mean and standard deviation.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" style=\"border-collapse: collapse; margin: 0px auto;\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.424100156494523%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003eBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.50078247261346%\"\u003e\n \u003cp\u003eHealthy Controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.699530516431924%\"\u003e\n \u003cp\u003eKruskal-Wallis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.77151799687011%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.424100156494523%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003eN = 86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003eN = 58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.50078247261346%\"\u003e\n \u003cp\u003eN = 265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.699530516431924%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.77151799687011%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.424100156494523%\"\u003e\n \u003cp\u003eMDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003e5.3 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003e10.0 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.50078247261346%\"\u003e\n \u003cp\u003e5.7 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.699530516431924%\"\u003e\n \u003cp\u003eH=77.9; df=2; p\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.77151799687011%\"\u003e\n \u003cp\u003e\u003cspan style='color: rgb(0, 0, 0); font-family: \"Times New Roman\"; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;'\u003e\u0026Sigma;\u003c/span\u003e \u003csup\u003e2\u003c/sup\u003e = 0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.424100156494523%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.50078247261346%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.699530516431924%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.77151799687011%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.424100156494523%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003eN = 64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003en = 32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.50078247261346%\"\u003e\n \u003cp\u003eN = 225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.699530516431924%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.77151799687011%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.424100156494523%\"\u003e\n \u003cp\u003eHCL-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003e11.9 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.302034428794991%\"\u003e\n \u003cp\u003e19.5 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.50078247261346%\"\u003e\n \u003cp\u003e14.5 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.699530516431924%\"\u003e\n \u003cp\u003eH=31.4; df=2; p\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.77151799687011%\"\u003e\n \u003cp\u003e\u0026Sigma;\u003csup\u003e2\u003c/sup\u003e = 0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bipolar Disorder, Depression, Screening, Hypomania Check List (HCL - 32), Mood Disorder Questionnaire (MDQ), Hypomania","lastPublishedDoi":"10.21203/rs.3.rs-1821130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1821130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Studies about two most used and validated instruments for the early detection of BD, the 32 - item Hypomania Checklist (HCL - 32) and the Mood Disorder Questionnaire (MDQ)screeners, in non-Western countries are scarce. This study aimed to explore the reliability, factor structure, and criterion validity of the Arabic versions in a sample of Tunisian patients diagnosed with mood disorder. \u003c/p\u003e\u003cp\u003eMethods: The sample included 59 patients with BD, 86 with unipolar MDD and 281 controls. Confirmatory factor analysis was applied to show that a single global score was an appropriate summary measure of the screeners in the sample. Receiver Operating Characteristic analysis was used to assess the capacity of the translated screeners to distinguish patients with BD from those with MDD and controls. \u003c/p\u003e\u003cp\u003eResults: Reliability was good for both tools in all samples. The bifactor implementation of the most reported two-factor model had the best fit for both screeners. Both were able to distinguish patients diagnosed with BD from putatively healthy controls, and equally able to distinguish patients diagnosed with BD from patients with MDD. \u003c/p\u003e\u003cp\u003eConclusion: Both screeners work best in excluding the presence of BD in patients with MDD, which is an advantage in deciding whether or not to prescribe an antidepressant.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Accuracy of the Arabic HCL - 32 and MDQ in detecting patients with bipolar disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-12 15:09:14","doi":"10.21203/rs.3.rs-1821130/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-10-31T06:16:24+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"d7d22077-3f19-42ab-8d5a-1888066d7baa","date":"2022-09-15T03:34:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-07-27T19:42:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1c1f51a4-88f3-4638-9c12-a6c439c994ab","date":"2022-07-12T07:42:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-07-06T04:46:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-07-06T04:19:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-07-04T09:14:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-07-04T09:10:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2022-07-03T16:05:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ceefd5c-0cef-40ea-b6f3-1fb51ac6456d","owner":[],"postedDate":"July 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:34:26+00:00","versionOfRecord":{"articleIdentity":"rs-1821130","link":"https://doi.org/10.1186/s12888-023-04529-x","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2023-01-26 18:28:52","publishedOnDateReadable":"January 26th, 2023"},"versionCreatedAt":"2022-07-12 15:09:14","video":"","vorDoi":"10.1186/s12888-023-04529-x","vorDoiUrl":"https://doi.org/10.1186/s12888-023-04529-x","workflowStages":[]},"version":"v1","identity":"rs-1821130","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1821130","identity":"rs-1821130","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}