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 DOI: 10.1177/1403494813500591

published online 27 August 2013Scand J Public HealthPer Bech

Linda L. Magnusson Hanson, Hugo Westerlund, Constanze Leineweber, Reiner Rugulies, Walter Osika, Töres Theorell andscale for the assessment of depression

) scale: Psychometric properties of a brief six item6The Symptom Checklist-core depression (SCL-CD  

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© 2013 the Nordic Societies of Public HealthDOI: 10.1177/1403494813500591

Scandinavian Journal of Public Health, 2013; 0: 1–7

Introduction

Major depressive disorders are common, with sub-stantial impact on individuals and society. The clas-sification of depression in the international standard classification systems, the International Classification of Diseases (ICD) 10 and Diagnostic and Statistical Manual of Mental Disorders (DSM) IV, are based on a list of symptoms all of which are not necessary for a diagnosis [1]. This means that persons with the diagnosis share a large proportion of symptoms but

the exact combination of symptoms can vary from person to person [2]. While this makes sense from a clinical perspective, it creates measurement problems when the goal is to measure differences or changes in severity. A better dimensional measure may instead be based on a smaller number of characteristics, all of which are necessary for diagnosis [1] so that the scores of the individual items can summed up to a meaningful severity measure. Brief scales for

The Symptom Checklist-core depression (SCL-CD6) scale: Psychometric properties of a brief six item scale for the assessment of depression

LINDA L. MAGNUSSON HANSON1, HUGO WESTERLUND1, CONSTANZE LEINEWEBER1, REINER RUGULIES2,3, WALTER OSIKA1, TÖRES THEORELL1 & PER BECH4

1Stress Research Institute, Stockholm University, Sweden, 2National Research Centre for the Working Environment, Denmark, 3Department of Public Health, University of Copenhagen, Denmark, and 4Psychiatric Research Unit, Mental Health Centre North Zealand, University of Copenhagen, Denmark

AbstractAims: Major depressive disorders are common, with substantial impact on individuals/society. Brief scales for depression severity, based on a small number of characteristics all of which are necessary for diagnosis, have been recommended in self-reported versions for clinical work or research when aiming to quickly and accurately measure depression. We have examined psychometric properties of a brief 6-item version of the Symptom Checklist (SCL), the Symptom Checklist core depression scale (SCL-CD6) and aimed to identify a cut-point for epidemiological research. Methods: The psychometric evaluation of the SCL-CD6 was mainly performed by a Mokken analysis of unidimensionality in a random sample of 1476 residents in the Stockholm County, aged 18–64 years. The standardization of SCL-CD6 was based on ROC analysis, using the Major Depression Inventory as index of validity. Predictive validity was subsequently assessed using register data on hospital admissions and purchases of prescribed medications linked to a sample of 5985 participants in the Swedish Longitudinal Occupational Survey of Health (SLOSH). Results: The SCL-CD6 obtained a coefficient of homogeneity of 0.70 by Mokken analysis, which indicates high unidimensionality and a meaningful dimensional measure of depression severity. By ROC we identified a score of 17 or higher (total range 0–24) as the best cut-point for major depression (sensitivity 0.68, specificity 0.98) which predicted subsequent purchases of antidepressants as well as hospitalisations with a depressive episode. Conclusions: The SCL-CD6 was found a valid depression scale with higher unidimensionality than longer epidemiological instruments and thus particularly suitable for assessment in larger population surveys.

Key Words: Depressive disorder, epidemiology, major depressive disorder, psychometrics, questionnaires, validation study

Correspondence: Linda L. Magnusson Hanson, Research Division of Epidemiology, Stress Research Institute, Stockholm University, SE-106 91 Stockholm, Sweden. E-mail: linda.hanson@stressforskning.su.se

(Accepted 12 July 2013)

500591SJP0010.1177/1403494813500591L. L. Magnusson Hanson et al.Psychometric properties of the SCL-CD₆2013

ORIGINAL ARTICLE

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2 L. L. Magnusson Hanson et al.

depression severity such as the six-item Hamilton Depression Scale (HAM-D6) have been recom-mended in a self-reported version for measurement-based care [3] to quickly and accurately assess depression in the daily routine, for genetic studies [4] and for psychopharmacological trials [5]. HAM-D6 has been validated by experienced psychiatrists [6] as well as by item response theory models [7]. The total score is a sufficient statistic, indicating that a decrease of for instance two HAM-D6 points represents a uni-form improvement. This is because the six items (sadness, feeling no interests in things, lack of energy, worrying, guilt feelings and psychomotor retarda-tion) have their rank ordering on the underlying dimension of depression severity. In epidemiological research alike we often need to rely on brief question-naires in which the scores are widely distributed in the population and where the overt, clinical depres-sion is a matter of reaching the standardized cut-off score on the scale. The most frequently used scales are, however, relatively comprehensive self-reported depression scales and few, if any, based on a small number of characteristics which are necessary for diagnosis and for which the total score have been shown to be a sufficient statistic. A brief six-item sub-scale (SCL-CD6) covering the six core items for depression corresponding to HAM-D6 has, however, been suggested and previously found psychometri-cally valid from an item response theory point of view in a population sample [7–8].

Here, we further examine the psychometric prop-erties of the SCL-CD6 from an epidemiological point of view, and, with reference to the DSM-IV diagnosis of depression, we aimed to identify a cut-point for major depression.

Methods

Samples

The study was based on two samples, both approved by the regional ethics board. All individuals were informed of the purpose of the respective studies and gave informed consent by responding to questionnaires.

Sample one was based on 3988 randomly selected residents in the County of Stockholm aged 18–64 years (excluding people from SWES 2003 or 2005 see below). Data were collected trough a self-admin-istered questionnaire by Statistics Sweden between 16 April and 11 June 2009. Out of the 3924 indi-viduals with known addresses, 1476 (38%) com-pleted the survey that contained questions mainly about stress, working conditions and depressive symptoms. Depressive symptoms were measured by the Major Depression Inventory (MDI), the Centre

for Epidemiological Studies-Depression Scale (CES-D), and the conventional 13-item depression subscale of the SCL-90 [9]. Year of birth, sex, marital status, birth country, citizenship, income and educational level (2008) were additionally retrieved from popula-tion registers. A higher degree of women, older, mar-ried/registered partners, gainfully employed and persons with university education responded to the survey.

Sample two consisted of respondents to the Swedish Longitudinal Occupational Survey of Health (SLOSH) 2006 [8]. These respondents origi-nally participated in the Swedish Work Environment Survey (SWES) in 2003, first sampled into the Labor Force Survey through stratification by county, sex, citizenship and inferred employment status from which a subsample inferred as employed was invited to participate in SWES. Out of 9154 eligible partici-pants in SWES 2003, 5985 (65%) responded in 2006. The participants could choose between two self-administered questionnaires, one for “gainfully employed” who worked 30% or more during the past 3 months (n = 5141) and one for those working less or had left the labour market (n = 844). Depressive symptoms were measured with the SCL-CD6, described more in detail below. This data has been supplemented with information from official regis-ters, e.g., from the Swedish Prescribed Drug Register and the National Patient Register (NPR). The Swedish Prescribed Drug Register contains informa-tion about prescription drugs according to the Anatomical Therapeutic Classification (ATC) sys-tem dispensed at Swedish pharmacies. We extracted all purchases of antidepressants (ATC group N06A) during 2005–2007 which gives information on all dispensed antidepressants at Swedish pharmacies. Based on this information, we classified people into two groups depending on whether they had made any purchases of antidepressants during the 12 months after the date of receipt of the SLOSH ques-tionnaire or not. In addition, we extracted informa-tion from NPR about depressive episodes (F32-F33 according to the International Classification of Diseases – ICD 10) during 12 months after the date of receipt of the SLOSH questionnaire. Women, older (above 50 years of age) subjects, and married/registered partners were more inclined to respond to the SLOSH survey.

Depression questionnaires

The SCL-90 or SCL-92 or brief versions are availa-ble in many languages. Table I shows the SCL-D13 subscale with the original 13 item numbers from the SCL-90 in brackets [9]. The six top listed SCL-90

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The Symptom Checklist-core depression (SCL-CD6) scale 3

items in Table I are included in the SCL-CD6 corre-sponding to the core symptoms in HAM-D6 [7]. Both HAM-D6 and SCL-CD6 are measuring the intensity of the items (SCL by asking how much peo-ple have been bothered: 1 = Not at all, to 5 = Extremely). In contrast, the CES-D consisting of 20 items is measuring the frequency of symptoms (1 = Rarely or none of the time, to 4 = Most or all of the time) as the MDI (12 items rated according to: 1 = At no time, to 6 = All the time). Moreover, the time frame is the past week for CES-D whereas it is the past 2 weeks for MDI with reference to ICD-10 and DSM-IV [7, 9,10]. Within the CES-D we have listed at the top the CES-D5 which corresponds to the SCL-D6/HAM-D6. However, only five items have been included, since the items “low energy” or “fati-gability” are not included in the universe of symp-toms in CES-D.

Other health indicators

Other health-related measures among sample one included frequency or intensity ratings of cognitive problems based on 4 items [11], a one-item measure of general health status [12] and sleep quality and sleep problems measured by means of the Karolinska

Sleep Questionnaire [13]. Insomnia-related symp-toms was measured with four questions forming an index on disturbed sleep (reflecting lack of sleep con-tinuity) and three questions forming in index of awakening problems (feelings of being insufficiently restored) [13,14].

Work characteristics

Among sample one, we also calculated summary indices for working conditions including demands, decision authority and support measured by the Swedish version of the Demand Control Questionnaire [15] among the subpopulation report-ing being gainfully employed. Demands at work were assessed by frequency ratings using five ques-tions, amount of decision authority with two ques-tions and social support was measured with six questions [14].

Analyses

In clinical psychometrics [7] the clinical validity is considered prior to the pure psychometric analysis. The Mokken analysis is a non-parametric item response theory model by which to test to what

Table I. The Symptom Checklist depression subscale (SCL-D13) with the original item numbers from the SCL-90 in brackets and the Center for Epidemiological Studies Depression scale (CES-D) subdivided by items corresponding to the Hamilton Depression Scale subscale with six core items for depression (HAM-D6) and/or SCL-D items that make up the Symptom Checklist core depression scale (SCL-CD6) - the “CES-D5”.

SCL-D13a,c CES-D20

a,d HAM-D6c

SCL-CD6ab

Feeling blue/sad (30)No interest in things (32)Low in energy (14)Everything an effort (71)Worrying too much (31)Blaming yourself (26)

CES-“D5”Feeling sad (18)Could not get going (20)Everything an effort (7)Felt fearful (10)Life been a failure (9)

Depressed mood (1)Work and interests (7)Fatigability (13)Retardation (8)Psychic anxiety (10)Guilt feelings (2)

Remaining items Remaining items Hopeless about future (54)Worthlessness (79)Being trapped (22)Feeling lonely (29)Crying easily (20)Loss of sexual interests (5)Thoughts of ending life (15)

Hopeful about future (8)As good as other people (4)People dislike me (19)Felt lonely (14)Crying spells (17)Bothered by things (1)Felt depressed (6)Appetite poor (2)Could not shake off the blues (3)Trouble keeping my mind (5)Sleep very restless (11)Was happy (12)Talked less than usual (13)People were unfriendly (15)Enjoyed life (16)

aMeasured in sample onebMeasured in sample twocMeasuring intensity dMeasuring frequency

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4 L. L. Magnusson Hanson et al.

extent the total score of a scale is a sufficient statistic [7,16].The Loevinger coefficient of homogeneity is calculated as the weighted average of the individual items [7]. A coefficient of homogeneity of 0.40 or higher should be interpreted as a demonstration of unidimensionality, i.e., that such items are additive. Based on sample one, we assessed the Loevinger coefficient with reference both to the conventional SCL-D13 and to the CES-D which was originally designed for surveys. We also report results from fac-tor analysis in terms of principal component analysis since that is a method to collect all the useful com-mon variance in a scale [17]. Because the items in the depression questionnaires obviously have been selected for their positive inter-correlation it was expected that the eigenvalue of the first principal component should be high and explain more than 50% of the variance. We further report Cronbach’s coefficient alphas as the traditional test for evaluating the positive inter-correlations between items [7]. Because this test is rather sensitive for scales with overlapping items and for scales with a higher num-ber of items [7] we expected to obtain higher alpha coefficients for CES-D20 and CSL-D13 than for SCL-CD6. Construct validity was also assessed through Spearman correlations with work character-istics and other health indicators.

By use of a Receiver-Operating-Characteristic (ROC) analysis using the MDI cut-off score of ≥ 26 as index of clinical depression we further identified a cut-point for the SCL-CD6 suitable for epidemio-logical research when a categorical approach is preferred. MDI has been developed as an instru-ment both for the diagnosis of depression accord-ing to ICD-10 and/or DSM-IV and for a severity

assessment [7]. Although based on all symptoms of the DSM-IV and ICD systems, item-response theory models have shown that the total score of MDI is a sufficient statistic [18]. MDI has been found a useful tool in diagnosing and monitoring depressive patients [19] and high sensitivity and specificity has been observed when used as a measuring instrument in a population study [20]. We chose weighted kappa as the method for determining a cut-point with the best possible trade-off between cost of false positives and negatives. We looked for the cut-point with the high-est weighted kappa value κ(r), and used an r of 0.5. κ(0.5) gives equal concern to false positives and neg-atives and is used to indicate a good differential diag-nostic or prognostic test [21,22]. This method was chosen since it favors specificity to a higher degree than other methods. In epidemiological research a high specificity rather than sensitivity has been dem-onstrated preferable in order to obtain precise esti-mates for relatively rare outcomes [23]. Finally, we quantified predictive validity trough odds ratios (ORs) representing the increased odds of using anti-depressant medication or being hospitalized with depressive episodes during the following 12 months among those classified with probable major depres-sion according the proposed cut-point for SCL-CD6 based on sample two. Most analyses were conducted in STATA 12.0 while SAS 9.2 was primarily used for analysing data on antidepressants.

Results

Table II presents characteristics of the two study samples. Most respondents reported that they were working. Participants in sample two, approximately

Table II. Characteristics of the two study samples.

Sample one (n = 1476) Sample two (n = 5985)

n/range %/mean (SD) n/range %/mean (SD)

Age (years) 18–65 42.9 (12.7) 19–68 47.6 (11.6)Sex Men 598 40.5 2720 45.4 Women 878 59.5 3265 54.6Marital status Married 631 42.8 3264 54.5 Unmarried/previously

married845 57.2 2721 45.5

Educational level Compulsory school 149 10.2 1202 20.3 Upper secondary

school539 36.7 2447 41.4

University or equivalent

779 53.1 2268 38.3

Occupancya Employed/working 1104 76.7 5141 85.9 “Non working” 306 21.3 844 14.1 Uncertain/other 30 2.1 – –

aOccupancy was inferred from a question with the three response alternatives in sample one and from choice of questionnaire for sample two, i.e., “non working” includes those who worked less than 30% during the past 3 months.

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The Symptom Checklist-core depression (SCL-CD6) scale 5

representative of the Swedish workforce in 2003, were generally of higher age and included more mar-ried people and mainly people still active on the labour market.

The Loevinger coefficient of homogeneity was 0.70 for the SCL-CD6, while it was 0.59 for the SCL-D13, 0.45 for the CES-D20 and 0.63 for CES-D5. This indicates that SCL-CD6 has strong unidi-mensional properties.

The principal component analysis showed that the eigenvalue of the first principal component of SCL-D6 was 4.3, explaining 72% of the variance, whereas the eigenvalue of the SCL-D13 was 7.7, explaining 59% of the variance. The eigenvalue of the SCL-D20 was 9.3, explaining 46% of the variance, whereas 3.3 for CES-D5 explained 65% of the variance.

The alpha coefficient for the SCL-CD6 was 0.92, for the SCL-D13 0.94, 0.93 for the CES-D20 and 0.86 for CES-D5.

The SCL-CD6 correlated significantly with all health indicators and work characteristics in sample one (Table III). SCL-CD6 correlated relatively strongly with sleep and self-rated health. Furthermore, moderate correlations between SCL-CD6 and sup-port as well as demands at work were also found based on sample one. Correlation coefficients were generally of equal size for the different depression scales.

Results from the ROC analysis of SCL-CD6 are presented in Figure 1.

The scores with the best weighted kappa value were 16/17, with a sensitivity of 0.68 and specificity of 0.98, suggesting an optimal cut-point of ≥ 17. An almost identical weighted kappa value was observed for scores 15/16 with a corresponding sensitivity of 0.76 and specificity of 0.96. A substantial agreement was indicated (kappa 0.69).

According to the derived cut-point of 17 on the SCL-CD6 scale, 8.5% of all individuals in sample one were classified with probable major depression,

compared to 10.1% if the MDI severity score of 26 or higher was used. The corresponding proportion was 5.2% in the SLOSH sample. Analyses showed increased odds of using antidepressant drugs during the following 12 months among those with scores of 17 or more on the SCL-CD6, as compared to those with scores below 17 (Table IV). The odds ratio was 4.8, with 95 % confidence interval (CI) 3.7–6.4. This group also showed higher odds of being hospital-ized with a depressive episode during the following 12 months (OR 12.2 CI 3.4–43.4).

Discussion

The SCL-CD6 scale was in this study based on a Swedish population data set from the Stockholm County found to be a psychometrically valid brief measurement of depression severity. The psychometric properties of the SCL-CD6 were compared with the longer conventional SCL depression scale SCL-D13

Figure 1. Results of the ROC analysis for Symptom Checklist Core Depression scale (SCL-CD6), using Major Depression Inventory (MDI) severity score with a cut-off of ≥ 26 as the index of validity.

Table III. Correlations between depression scales and measures of work characteristics, sleep, and self-rated health.

SCL-CD6a SCL-D13

b CES-D20c MDIsd

Demands 0.34 0.33 0.30 0.34Decision authority −0.29 −0.29 −0.30 −0.26Support −0.43 −0.45 −0.45 −0.43Sleep quality (global) 0.51 0.51 0.54 0.56Disturbed sleep 0.52 0.52 0.54 0.58Awakening problems 0.62 0.60 0.57 0.63Self-rated health 0.54 0.54 0.55 0.54

aSCL-CD6 – Symptom Checklist Core Depression scalebSCL-D13 – Symptom Checklist Depression scalecCES-D20 – Center for Epidemiological Studies Depression scaledMDIs – Major Depression Inventory severity score

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6 L. L. Magnusson Hanson et al.

and to the much longer CES-D20 which is the most frequently-used depression scale in epidemiological research of depression. We observed correlations with other measures in line with findings on e.g., associa-tions between depression and insomnia, cognitive dys-function, various chronic diseases, as well as work characteristics such as job demands and social support at work [24–28]. The construct validity of the SCL-CD6 thus seems adequate and congruent with the other scales. Although the SCL-D and SCL-CD6, in contrast to the MDI and CES-D, have no items covering sleep problems, the correlation coefficients with instruments that specifically measure sleep were of similar magni-tude. In contrast to CES-D and MDI the SCL-CD6 also measures intensity, not frequency, of symptoms – as does the HAM-D6. The depression subscale of SCL-90 has been in use since 1954 [9] and the layout and the formulation of the items have had high applicability all over the world. The homogeneity coefficient for SCL-CD6, however, exceeded that of the conventional SCL depression scale and the CES-D20, and may also exceed that of other comprehensive self-reported scales often applied in epidemiological research and the nine-item Patient Health Questionnaire (PHQ-9) which has been suggested to guide clinicians with diagnosis according to DSM-V [29]. Even the short five-item CES-D subscale (CES-D5) was found to be psycho-metrically less precise, possibly explained by a lack of questions about symptoms of fatigability or low energy. Fatigability is a core item in the IDC-10 algorithm of clinical depression and has been found clinically valid by experienced psychiatrists when developing the HAM-D6 [6]. A CES-D10 version has also been sug-gested, but this subscale covers only two of the clinically valid items in CES-D5 and has accordingly not been considered on our analysis. From our findings of a high coefficient of homogeneity we are looking forward to much more evaluations of this scale especially its stand-ardization which is essential also when used in meas-urement-based depression care.

In this study, we made a standardization of the scale for application to epidemiological research for which we tested predictive validity. The results showed that major depression, assessed with SCL-CD6 and the suggested cut-point, was predictive of both

subsequent use of antidepressants and hospitalization with a depressive episode. This implicates an associa-tion with clinical depression, in the majority of cases with a diagnosis of depression validated by a physi-cian. However, not all people with a high degree of symptoms of depression seek treatment, and those who do seek treatment are not always recommended antidepressants. It is also possible that people scoring below the cut-point for major depression, are pre-scribed antidepressants for depressive symptoms for other reasons than depression, e.g., neuropathic pain [30]. Thus the predictive validity cannot be expected to be very high. In this study, however, the predictive validity may be somewhat underestimated since some people already under treatment at the time of respond-ing to the survey can experience a low level of symp-toms but still have continued their treatment subsequent to survey response. A higher-predictive validity was indicated for hospitalizations with depres-sive episodes which is interesting since only very severe cases are presumably hospitalized. However, only a small proportion of the patients with major depression need hospitalization, and in this case it is also possible that during the relatively long period considered, some people scoring low in the survey later experience high level of symptoms requiring hospitalizations.

A limitation of the study is that self-reported scale scores on the MDI are used as index of validity rather than diagnoses by psychiatrists. The sensitivity and specificity and accuracy in comparison to clinical rat-ings or diagnosis may be lower. The proposed cut-point should ideally be validated in other settings and against psychiatric diagnosis. A high specificity, and consequently a high cut-point, has been suggested appropriate for epidemiological research [23] but results in lower sensitivity and thus more false nega-tives. This may be undesirable for other applications, for which other cut-points should be identified. Given that the six items were selected based on rat-ings of experienced psychiatrists, acceptable clinical validity with regard to severity can, however, be assumed. When interpreting the findings of this study, it should also be kept in mind that the study was conducted mainly in an urban sample, and there was a relatively low response rate resulting in a

Table IV. Odds ratios and 95% confidence intervals quantifying predictive validity according to Symptom Checklist Core Depression scale (SCL-CD6) and the cut-point determined in previous analysis.

Use of antidepressants according to the Prescribed drug register

Hospitalization with a depressive episode

OR 95% CI OR 95% CI

Major depression according to the SCL-CD6 ≥ 17

4.8 3.7–6.4 12.2 3.4–43.4

CI = Confidence interval

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The Symptom Checklist-core depression (SCL-CD6) scale 7

somewhat selected sample. People with mental health problems may also have been unable or unwilling to take part though some may actually have been more inclined to participate as the focus of the study was mainly on mental health problems. The generaliza-bility of the results may therefore be questioned and the proportion of major depression cases should not be regarded to represent population prevalence. Still, in comparison to some other self-report scales, this brief SCL depression scale appears to be a valid depression scale with a higher unidimensionality which may be particularly suitable for assessment in larger population surveys. With a cut-point adapted for epidemiological research, its use might also con-tribute to advanced knowledge about aetiology and prevention of depressive disorder. However, it may also be a candidate for measurement-based care of depression.

Acknowledgements

The authors would like to thank all participants in the two studies and Statistics Sweden for assisting with data collection.

Conflicts of interest

None declared.

Funding

This research has been partly supported by the Henrik Granholm foundation and the Swedish Council for Working Life and Social Research (FAS) [grant number 2008-1103], who also supports research based at the Stress Research Institute through the Stockholm Stress centre [grant number 2009-1758].

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