Diagnosing mental disorders in epidemiological studies:

The German Health Interview and Examination Survey and its Mental

Health Supplement (GHS-MHS) as an example of

standardized data collection with clinical features

Along with a report on:

Epidemiology of depressive disorders in a nationally representative

population sample in Germany: Prevalence, comorbidity,

sociodemographic correlates, impairment, and treatment rates

Dissertation (International Master in Affective Neuroscience)

Dr. rer. nat. Frank Jacobi, Dipl.-Psych.

Dr. Frank Jacobi

Institute of Clinical Psychology and Psychotherapy

Epidemiology and Service Research Unit

Technical University of Dresden

Chemnitzer Straße 46

01187 Dresden

Number of tables: 6

Number of figures: 1

Word count (without table of contents, abstract, references, tables, figures): 8880

Table of contents Page

Abstract

1

I. The German Health Interview and Examination Survey and its Mental Health Supplement (GHS-MHS) as an example of standardized data collection with clinical features

1. Diagnosing mental disorders in large scale epidemiological studies

2

2. Two assessment perspectives: “Fully standardized interviews carried out by lay interviewers” vs. “Semi-structured interviews including experienced clinical judgement”

4

3. Diagnostic procedure of the GHS-MHS

8

II. Prevalence, comorbidity and correlates of depressive disorders in a nationally representative population sample in Germany

4. Background

12

5. Methods

14

6. Results

16

7. Discussion

20

8. Acknowledgements

23

9. References

24

10. Tables 1-6, Figure 1 32

1

Abstract

Background: First, the ongoing debate on assessment strategies in epidemiological studies investigating mental

disorders (“fully standardized interviews carried out by lay interviewers” vs. “structured interviews including

experienced clinical judgement”) is outlined. Against this background, methods of the German National Health

Interview and Examination Survey, Mental Health Supplement (GHS-MHS), are discussed. Second, this thesis

provides for the first time nationally representative prevalence estimates for mood disorders along with selected

sociodemographic correlates and data on comorbidity, impairment and treatment in the general German adult

population.

Methods: Results are based on the GHS-MHS that was carried out in 1998/99. A standardized clinical interview

(M-CIDI), administered by trained clinical interviewers, was used to assess mood disorders according to DSM-

IV in a sample of 4181 respondents (multistage stratified random sample drawn from population registries). The

conditional response rate was 89%; data were weighted for nonresponse and design factors. Thus, results can be

regarded as representative for the German population aged 18-65. Other data linked to the diagnoses stem from

self report questionnaires.

Results: 25% of the population reported at least some clinically significant lifetime depressive symptoms of at

least 2 weeks duration without necessarily fulfilling criteria for a DSM-IV diagnosis (women: 32%, men: 18%;

12-month: 15%; w:19%, m:11%). The total lifetime prevalence of any mood disorder was 19% (women: 25%,

men: 12%; 12-month: 12%; w:16%, m:9%). The most frequent type of mood disorders were episodes of major

depression, followed by dysthymic disorder. Bipolar disorders were rare (lifetime: 1%). Women were more

frequently affected from all types of depressive, but not from bipolar disorders; they also revealed a higher

recurrence risk, a higher prevalence of episodes rated as severe, and had higher rates of 12-month comorbid

disorders. Other correlates associated with a higher risk for a depressive disorder are: being separated, divorced

or widowed, poor physical health status, and low social class. All depressive disorders – in particular dysthymic

disorder – were associated with markedly reduced health related quality of life, elevated clinical complaints, and

elevated disability days (overall about two fold compared to the rest of the population). Treatment rates were 30-

40% in pure depressive disorders and 60-70% in comorbid diagnoses, disregarding if adequate treatment was

provided.

Conclusion: The study confirmed in accordance with studies from other countries that 1/5 of the adult

population will suffer from depressive disorders at some point in their life course, and more than 1/10 had a

depressive disorder in the past year or currently. The study findings go beyond previous investigations in

showing a remarkably high proportion of recurrent depressive disorders and of very severe depressive disorders.

Also remarkable are prevalence, comorbidity and impairment of dysthymia – potentially a result of the

hierarchy-free diagnostic procedure, since only one out of five dysthymic cases had the single diagnosis.

Substantial impairment and reduction of quality of life indicate the lower bound of the remarkably high

individual and societal costs of depressive disorders. In this light treatment rates have to be considered as low.

2

I. The German Health Interview and Examination Survey and its Mental Health

Supplement (GHS-MHS) as an example of standardized data collection with clinical

features

1. Diagnosing mental disorders in large scale epidemiological studies

Population-based nationally representative data about prevalence and distribution of somatic diseases

and mental disorders, along with associated impairments, disabilities and handicaps are of core

importance for health care policy-makers and providers. They are instrumental for determining met

and unmet needs and for the development of programs to improve the structure and the quality of care

(as well as access to appropriate health care). Further, such data are relevant for studying health

economic issues and provide some guidance in developing more appropriate and cost-efficient

allocation and financing models. More generally speaking, representative community surveys that

describe patterns of health and morbidity are helpful to educate the public and politicians about the

scope and the consequences of somatic as well as mental disorders.

Key requirements of such epidemiological studies are (a) the definition of the target population under

study, that can be either the total population of a region or a country, or representative samples, (b)

explicit, reliable and valid criteria for diseases or, more generally, what constitutes a case (key

symptoms or syndromes), (c) explicit, reliable and valid criteria for variables and factors that might be

associated with a disease, and (d) use of epidemiological methods for measuring outcome occurrence

as prevalence rates (in specified time frames) as well as for measuring associations (risk and protective

factors) and impact (i.e. course of illness, associated impairments/disability, help-seeking and

treatment). The present thesis is based on the first nationwide study investigating somatic and mental

disorders in Germany. The target population are German adults aged 18-65 – presumably the findings

can be generalized to many other adult populations in Europe or other “Western” societies. The

disorders under study are depressive disorders (as described in the latest international classification

systems), which are worldwide considered to be among the most frequent and disabeling conditions.

With regard to methodological aspects, issues as reliability and validity of the assessment of mental

disorders (e.g., “standardization” vs. “clinical judgement”, hierarchy and exclusion rules within the

diagnostic classification systems, categorial vs. dimensional assessment) are addressed. Correlates and

impact of depressive disorders are presented in terms of sociodemographic variables, quality of life

measures, productivity loss (disability days), and treatment rates.

Historically, there have been only few nationally representative community surveys on the prevalence

of mental disorders before the 1980´s and those available revealed tremendous variation in findings

(Weissman et al., 1993). This has been explained by various factors, including: the misconception that

mental disorders are fairly infrequent phenomena, the lack of reliable diagnostic criteria and diagnostic

3

instruments, the almost exclusive focus on broad diagnostic categories of severe psychotic and

neurotic disorders, the lack of efficient treatments and the lack of a broader spectrum of mental health

services. Parallel to the introduction of explicit diagnostic criteria for specific forms of mental

disorders in the DSM-III (APA, 1980), and subsequent to the landmark Epidemiological Catchment

Area study (ECA; Regier et al., 1984; Robins & Regier, 1991) in 1980, this situation changed

considerably. The ECA demonstrated not only that mental disorders can be assessed with a similar

level of reliability, validity and accuracy as the assessment in surveys of somatic disorders, but also

suggested that the risk of developing mental disorders in adolescence, over the course of life (lifetime),

as well as the current prevalence and comorbidity (e.g. 4-week, 12-month) had been heavily

underestimated.

Continuing interest in mental disorders has prompted the conduct of numerous nationwide mental

health surveys, which have shown fairly convergently and with increasing sophistication that mental

disorders affect at least one third of the population over their lifetime. Examples include the Munich

Follow-up study in former West Germany (Wittchen et al., 1992), subsequent reanalyses of the Cross

National Collaborative Group (e.g. Weissman et al., 1996), the National Comorbidity Surveys in the

US (NCS; Kessler et al., 1994, and NCS-R, Kessler et al., 2003), the Australian National Mental

Health Survey (ANMHS; Andrews et al., 2001), the National Psychiatric Morbidity surveys of Great

Britain (NPMS; Jenkins et al., 1997), the Netherlands Mental Health Survey and Incidence Study

(NEMESIS; Bijl et al., 1998), the Sesto Fiorentino Study in Italy (Faravelli et al., in press a,b), and

several other countries around the world that have been involved in the cross national comparative

studies of the WHO International Consortium in Psychiatric Epidemiology (Andrade et al., 2000,

2003).

Despite these developments, numerous of critical and unresolved issues make it still difficult to draw

firm conclusions about the size and scope of mental disorders, their associated correlates, and

consequences which could potentially provide guidance for health care planning. These include: (1)

Even though the majority of epidemiological studies made use of the same diagnostic instrument to

obtain diagnoses according to the criteria of DSM-III-R and IV (American Psychiatric Association,

1985, 1994), the Diagnostic Interview Schedule (DIS; Robins et al., 1981) and its successor, the

Composite International Diagnostic Interview (CIDI; Robins et al., 1988; Wittchen et al., 1991), there

is still substantial and sometimes confusing variation in findings (even more when different diagnostic

instruments are used). (2) Some variations can be explained by design issues: the prevalence of mental

disorders has been shown to differ by age groups and age cohort, thus the year the study was

conducted and the age range of the sample is of relevance. (3) Almost all studies made at least some

modification in their assessment instruments, by either adding diagnoses, omitting diagnoses,

changing diagnostic thresholds or algorithms, changing the order of sections, adding questions on

impairment and help-seeking or dimensional measures; other important sources of variance might

result from the use of lay or alternatively clinical interviewers. All of these modifications have been

4

shown to have potentially significant effects on prevalence estimates as well as comorbidity figures

(Brugha et al., 1999; Regier et al., 1998; Wittchen et al., 1999) which will be dealt with in a special

section below. (4) A critical issue for health care policy decisions and implications derived from such

studies is either lack or inconsistency in which disability and severity as well as help-seeking

behaviour associated with mental disorders is evaluated. Such considerations are of major importance

for health care planners to determine met and unmet needs appropriately for different target groups.

This problem is particularly evident in prevalence estimates of studies based on the lifetime version of

the DIS or CIDI. The lifetime version of the CIDI ascertains primarily whether the diagnostic criteria

are met at some point in the respondents life and then asks for the first (onset) and last occurrence

(recency) of at least some of the core features of the respective diagnosis. In these studies, current

prevalence estimates merely indicate that the person had the diagnosis in the past and still has some

symptoms without necessarily determining that the person meets the full set of diagnostic criteria. For

health care purposes and need estimations, however, this type of diagnostic cross-sectional

information is crucial, especially in conjunction with information on associated impairment, disability

and help-seeking behavior in order to make an appropriate evaluation of need.

2. Two assessment perspectives: “Fully standardized interviews carried out by lay

interviewers” vs. “Structured interviews including experienced clinical judgement”

2.1. Critizising the “CIDI-approach”

Diagnostic measures adopted in most of the above mentioned community studies are considerably

different from those commonly employed in clinical practice. In particular, most of the

epidemiological surveys have relied upon lay interviewers with no clinical experience, using

specifically-designed, fully-structured diagnostic interviews that eliminated the need for clinical

judgement (standardized “close ended” questions without the possibility to question the subject’s

answers, no availability of additional information that is usually present in clinical practice).

In the debate about standardized vs. clinical assessment – what is in part a debate about categorial

classification systems vs. dimensional approaches to psychopathology – several statements against

standardization were made (e.g. Brugha et al., 1999; Brugha, 2002; Faravelli et al., in press a):

• Different surveys even in the same population yield different results, e.g. 12-months prevalence

rates of major depression of 4.2% (ECA, DIS) and 10.1% (NCS, UM-CIDI) in the U.S., calling

into question the validity of these assessments.

5

• Founded on the historical legacy of psychopathology, clinical knowledge and experience of

abnormal states of mind, the skills to elicit them and to make judgements as to their presence and

significance should be the crucial elements of gold-standard assessment instruments and shall

therefore not be ommitted. Idiosyncrasies of individual clinicians as a possible source of poor

reliability can be eliminated in semi-structured interviews as the Schedules for Clinical

Assessment in Neuropsychiatry (SCAN; Wing et al., 1990), a successor of the Present State

Examination (PSE; Wing et al., 1974).

• For the first generation of the standardized interviews, poor concordance between at least some

diagnoses obtained by these methods and by clinicians using less structured assessment methods

were reported (Anthony et al, 1985; Brugha et al., 1999b; Brugha et al., 2001; Erdman et al., 1987;

Helzer et al., 1985; Wittchen, 1994). Given that clinical semi-structured assessment is the gold

standard, poor concordance has to be interpreted as poor validity of the fully standardized

methods.

• Standardized assessment and categorical coding neglect the dimensional nature of

psychopathological syndromes (in particular in terms of severity) and are therefore inappropriate

to assess disability and need for treatment. Moreover, categorizing (or even worse: dichotomising)

mental health in principle contradicts reality and the nature of the phenomena under study.

• In standardized interviews, a “psychiatric symptom” is the answer to a standardized question.

Answers given by the respondent are taken a priori as valid (coded without questioning possible

misunderstandings, inconsestencies, biases due to subjective filtering, social acceptability etc.).

This contradicts knowledge about self report data (in general and in particular in populations with

psychiatric problems) and can only be overcome by non-standardized, clinically experienced cross

questioning.

• The fully atheoretical and therefore “objective” assessment perspective underlying the philosophy

of categorisation and standardization reflects a naïve sort of Empiricism which implies a merely

sensorial theory of knowledge in which the observer is conceived as a tabula rasa registering

external inputs. This is in open contrast with the views of modern epistemology as well as with

studies of psychology. It is now clear that what a scientist does is not “seeing” but rather “seeing

that” (Popper, 1934), suggesting that perception is invariably influenced by the observer’s

experiences and expectations (Kent & Dalgleish, 1996). In other words, one cannot see if one does

not know what to see.

• It is difficult or impossible for lay interviewers to decide on some specific points required by

diagnostic systems, e.g. “not due to another medical condition” or “not better accounted for by…”.

As a result, the exclusion criteria are usually not applied in modern epidemiology which is distant

from use in clinical practice.

6

In fact, the only aspects in favour of standardized interviews applied by lay interviewers in

epidemiological studies (labelled here as the “CIDI-approach”) mentioned by the critics are cost-

effectiveness/feasability in large scale studies and high reproducability of the results – both at the

expense of validity and therefore a bad tradeoff.

2.1. ... is easy when one´s neglecting the potentials of the “CIDI-approach”

All of the above mentioned critiques of the “CIDI-approach” certainly do not lack clinical expertise,

some empirical basis from validity studies, and the striving for the best methods to solve the present

problems of reliability and validity in the epidemiological assessment of mental health in the

community. However, most of these critiques are somewhat misleading if taken as absolute, i.e. when

creating an atmosphere of “We have to overcome the dead end strategy of standardized objectivism

and should return to the real core of honest psychopathology”.

When commenting these critiques – exemplarily objected by Brugha et al. (1999) and Faravelli et al.

(in press a) – in the following section, I will focus on the notions a) that many of the methodological

problems inherent to standardized interviewing are resolvable (or in fact have been resolved in recent

studies), and b) that a greater synthesis of clinical and structured methods is clearly needed in the

future (or in fact has been applied in recent studies).

The at first sight astonishing differences of findings on the prevalence of mental disorders even when

standardized methods were applied (ECS/DIS vs. NCS/UM-CIDI) can be explained by investigating

methodological sources of variance in re-analyses of the data in detail (Regier et al., 1998),

considering sampling issues (age frame, sites, and other sociodemographic and design factors), as well

as instrument construction issues (e.g. question wordings), or changes in included disorders or applied

criteria (DSM-III vs. DSM-III-R).

Considering the outstanding changes in the operationalization of psychopathology over the last

decades, even skeptics of classification systems and structured assessment acknowledge the vast

reliability shift as a necessary condition for an improvement of validity of diagnoses or syndromes.

However, despite the substantial scientific exploration and examination that went into instruments like

the CIDI, SCID, SCAN, and CIS, basic problems of reliability and validity inherent in mental disorder

assessment are yet unresolved. These critical issues are now themselves serious subjects of scientific

research that encourage close collaborations between the designers of diagnostic manuals, clinical

researchers and epidemiologists.

Researchers are utilizing the rich data base of psychometric evaluations that have resulted from

diagnostic interview research, cognitive psychology, and survey methodologists (Kessler et al., 2000).

In the center of discussion is no longer the old question of whether to go categorical or dimensional

(there seems to be agreement that diagnostic interviews should offer both), but rather to what degree

7

and for which psychological conditions “clinical judgement and probing” should be regarded as a

mandatory core element (Wittchen, in press). Future methodological studies will hopefully resolve this

question. Empirical evidence must also be gathered to determine in which diagnostic domains semi-

structured clinical instruments are really superior to fully standardized instruments like the CIDI,

which try to explicitly identify the latent variables behind the vagueness of clinical judgment.

Progress in the resolution of this issue will lead to more appropriate strategies in resolving the "gold

standard" question of the optimal strategy for validating epidemiological instruments. Wittchen et al.

(1999) are highlighting doubts about the ultimate superiority of methods that resemble clinical

practice: practical issues of need assesment and severity ratings should not rule out a scientifically

driven evaluation of symptom patterns and categorization of threshold and sub-threshold conditions as

a basis for studies about pathways and “explanations” of psychopathology. This refers to a major

concern proposed by representatives of the “clinical approach”: Do the standardized methods produce

a substantial amount of “false positives” (Spitzer & Wakefield, 1999), i.e. create a useless category of

artefactual, non-significant “cases”? The counter question here is: How far do current practically

orientated “clinical” methods produce “false negatives”?

The theoretical advantage of a clinical approach diminishes when there is no empirical evidence that

this approach reveals more promising psychometric properties than standardized interviews do – in

particular in non-clinical settings or community/population based samples. The use of semi-structured

clinical interviews as the central approach to carrying out such surveys on a larger scale might be

likely to create more problems than it solves.

Anyway, the results of CIDI- and SCID diagnoses seem to converge, presumably due to the

development of improving accuracy within the latest CIDI versions (Kessler et al., 2003).

According to representatives of the “CIDI-approach” (Kessler et al., 1998; Kessler et al., 2000;

Wittchen et al., 1999), there is substantial progress in overcoming validity problems in standardized

interviews, grounded on fruitful collaborations between survey methodologists and cognitive scientists

using insights from cognitive research on basic processes of understanding and motivation. There is

some evidence that standardized approaches minimize validity problems in the areas of

misunderstanding or biases due to resistance against self-disclosure even better than clinical strategies

resembling therapeutic relationships, or another intense interaction between expert and patient (e.g.

Turner et al., 1998).

Following Wittchen et al.´s (1999) arguments, a synthesis or complementary use of standardized and

clinical methods is more likely to be reliable and valid when supplementing mandatory standardized

assessment by dimensional and open-ended probing procedures, preferably administered by clinically

trained interviewers, than if this synthesis is done the other way round (e.g. adjusting standardized data

according to clinical semi-structured gold standards).

8

From this perspective, the validity trade-off is worse when turning back towards the “classic” way of

fully dimensional and hierarchical understanding of the assessment of mental disorders focused on the

management of “cases”.

2.3 Two examples of complementary approaches

Obviously a perfect way of assessing mental disorders in large scale non-clinical samples is not yet

established. But it has to be mentioned that the problems outlined in the previous sections have already

been tackled in quite promising ways. The Sesto Fiorentino Study (Faravelli et al., in press a,b) offers

unique and complex sampling and assessment procedures and is relying on an exceptionally broad and

naturalistic/clinically relevant data base allowing for estimating representative prevalences as well as

investigating issues of severity, help-seeking behavior, needs assessment, and subthreshold/residual

syndromes (Wittchen, in press). Interestingly, the prevalence rates found by Faravelli et al. are quite

comparable to prevalence rates found in studies applying standardized methods with lay interviewers

that are more distant from clinical practice.

Another example is the the Mental Health Supplement of the German Health Interview and

Examination Survey (GHS-MHS; Jacobi et al., 2002b; Jacobi et al., in press; Wittchen et al., 1998),

using the latest “CIDI-approach” and including some core features promoted by the advocates of the

“clinical” perspective. Since results of this study on depressive disorders will be presented in the

empirical section of this thesis, the assessment methods will be described in some more detail below.

3. Sampling and diagnostic procedure of the German Health Interview and Examination

Survey (Mental Health Supplement; GHS-MHS)

3.1. The quest for representative data

With regard to representativeness of epidemiological data about mental disorders there have been

some problems in the past, since mental disorders where mainly counted and studied in patients beeing

in psychiatric treatment or primary care attenders (i.e. clinical populations). In Germany, for example,

until now there existed only administrative data on the national level, with very restricted figures

focusing only a relatively small range of mental disorders still based on ICD-9 diagnoses (mainly

schizophrenia, depression, alcohol dependence, and suicide). Apart from the overall scientifically low

quality of datasets of this sort, there is much reason to believe that the picture would be incomplete

even if diagnostic assessment and data handling were valid because it is known that subjects with

mental disorders often do not seek psychiatric consultation (Goldberg & Huxley, 1980). Therefore

9

cases that come under the observation of specialists cannot be considered fully representative of the

characteristics of psychiatric disorders in the general population. Epidemiological community surveys

have confirmed that the number of cases referred to mental health specialists is relatively small and

unlikely to be representative of psychiatric disorders as they occur in the general population.

Psychiatric samples, therefore, could be biased not only quantitatively but also qualitatively (Cohen &

Cohen, 1984; Regier et al., 1990; Galbaud du Fort et al., 1993; Newman et al., 1998). Thus, studies

conducted on non-clinical samples are necessary in order to complete our knowledge of psychiatric

pathology (Faravelli et al., in press a).

In the present study, mental disorders were assessed in the Mental Health Supplement of the German

National Health Interview and Examination Survey (GHS-MHS) in a subsample of its core survey

(GHS-CS, Bellach et al., 1998). The core survey covered a range of medical and social assessments

and was administered between June 1998 and October 1999. Its sample was a stratified random

sample from 113 communities throughout Germany with 130 sampling units (sampling steps: 1.

selection of communities, 2. selection of sampling units, and 3. selection of inhabitants). The sample

was drawn from the population registries of subjects aged 18-79 living in Germany in the year 1997.

As a result a gross sample of 13222 people were eligible, representative according to the age, sex, and

community type criteria. The response rate (completing the total assessment in the GHS-CS) was

61.4% (N=7124). The response rate including subjects completing parts of the assessment was 77.8%.

Reasons for non-participation, analyses of nonresponse, and further information on sample and

weighting in the GHS-CS are provided elsewhere (Thefeld, Stolzenberg, & Bellach, 1999).

For financial and logistical reasons the data for mental disorders were gathered by use of a two-stage

design. The first stage entailed the administration of a 12-item screening questionnaire for mental

disorders at the end of the medical examination of the core survey (CID-S; sensitivity for any 12-

month diagnosis (“caseness”): 85.3%, specificity: 55.3%, positive predictive value: 38.3%, negative

predictive value: 92.0%; Wittchen et al., 1999a).

The second stage involved the separate administration of a complete, structured, clinical

psychopathological interview to all core survey respondents who had been screened positive for a

mental disorder and to a random sample of 50% who had been screened negative. Due to the resulting

oversampling of screen positives, data were weighted in the later analyses. In order to adjust the net

sample to German age, sex and community distribution, data were also weighted for age, sex, and

region corresponding to the national administrative statistics of December 1997.

The presented weighted results can be regarded as representative for the German noninstutionalized

adult population from 18 to 65 years of age with sufficient language skills to follow the interviews.

Hospitalised patients who were hospitalised throughout the recruitment phase (0.5-0.8% of the target

population in the sample points) were only omitted if they were in institutions during the entire

recruitment period. Therefore it is unlikely that this exclusion criterion might have an effect on the

reported prevalences.

10

Respondents of the German Health Survey older than 65 years were excluded because the

psychometric properties of the CIDI, the interview used in the study, have not yet been satisfactorily

established for use in older populations (Knäuper & Wittchen, 1994).

After exclusion of the subjects older than 65 and 50% of the screen negatives, the eligible sample size

for the GHS-MHS was N=4773. The conditional response rate of the GHS-MHS was 87.6%, resulting

in a total of 4181 respondents who completed the mental health assessment.

3.2. Diagnostic instrument and fieldwork

Particular requirements for the German mental health survey were: (a) a broad coverage of specific

mental disorders, including all substance use and anxiety disorders (except PTSD) and previously

neglected disorders like somatoform and eating disorders, as well as disorders due to substance and

general medical factors; (b) the provision of predominantly strict 12-month estimates for symptoms

and diagnoses of mental disorders in order to overcome restricted precision regarding time frames

(residual symptoms etc.) especially in former CIDI lifetime versions, allowing for the examination of

associations with physical morbidities assessed for the same time frame in the core survey (GHS-CS)

and to make comparative analyses between disorders with regard to disabilities, quality of life and

health service utilization; c) use of the latest computer assisted CIDI version (M-CIDI; Wittchen &

Pfister, 1997) that has shown better psychometric properties than some of its predecessors (Wittchen

et al., 1998) and that was d) administrated not by lay but by clinically trained interviewers.

In particular the latter point is important in the light of some of the above discussed weaknesses of the

“CIDI-approach” because some parts of the interview required graded probing procedures (instead of

taking every reported symptom as dichotomous and at face value).

Almost all interviews of the second stage of the mental health supplement were conducted in the

homes of the respondents between November 1997 and April 1999. Only in exceptional cases was the

interview conducted by telephone. The computer assisted interviews were conducted by 24 trained

interviewers, most of whom had already worked in other M-CIDI studies during the previous five

years. The average interviewer worked in eight sampling units and conducted 174 interviews.

Interviewers had the following professional backgrounds: 18 with graduate degrees in psychology,

two with graduate degrees in sociology, two graduate students in psychology, one medical doctor, and

one other health professional. Thus, with regard to the difficulties in the assessment of exclusion

criteria or psychotic symptoms, these interviewers were clinically less qualified and experienced than

the interviewers in the Sesto Fiorentino Study (Faravelli et al., in press) but significantly more

qualified than the average lay interviewer of the world wide conducted WHO-CIDI studies.

Interviewers completed a three-day training session for the GHS-MHS. As the computerized version

of the interview is more flexible, easier to use and freer from errors than the paper and pencil version

11

of the M-CIDI, this training focused on the administration of the interview by application of the

portable computers. Additionally, interviewers were required to attend M-CIDI refresher courses every

three months throughout the field period. Interviewers were closely monitored throughout the field

period by trained M-CIDI clinical editors, who regularly checked all interviews according to a

standard procedure. Feedback was given to every interviewer to avoid errors in later interviews, and

interviewers promptly re-contacted subjects by telephone whenever missing data, unclear responses, or

errors were found. A final quality control eliminated eight interviews from the sample due to missing

or inconsistent datasets.

3.3 Comment

Representative data from the general population are clearly needed to investigate the scope, the

distribution and the impact of mental disorders in the society. In this first part I intended to give an

overview of the ongoing discussion about diagnosing mental disorders in such large scale studies.

Methods and design of the also described GHS-MHS can be regarded as a state-of-the-art “CIDI-

approach” that tried to tackle some of the critiques made from the “clinical/semi-structured approach”.

Parts of this dissertation have been already published (Jacobi et al., in press). The second part of this

thesis will focus on depressive disorders as assessed in the GHS-MHS. Prevalence, comorbidity,

sociodemographic correlates, impairment and help-seeking will be reported there based on an

extended and revised version of a not yet submitted manuscript. Co-authors of the final publication(s)

will be: Dipl.-Psych. Susanne Winter (Munich), PD Dr. Roselind Lieb (Munich), and Prof. Dr. Hans-

Ulrich Wittchen (Dresden/Munich) who was also the principle investigator of the GHS-MHS.

12

Epidemiology of depressive disorders in a nationally representative

population sample in Germany: Prevalence, comorbidity, sociodemographic correlates,

impairment, and treatment rates

Background

Depressive disorders are considered a major public health problem for various reasons. (i) They are

highly prevalent – previous point prevalence estimates of affective disorders in Germany range from

4.8% in men to 7.8% in women (Wittchen et al., 1999c). (ii) Depressive disorders are usually

associated with major impairment and disability (Herrman et al., 2002; Ormel et al., 1999; Von Korff

et al., 1992; Weissmann et al. 1996; Wells et al., 1989), leading to direct or indirect loss of financial

values, prolonged role dysfunctions and a reduced quality of life for the individuals affected

(Broadhead, et al., 1990; Herrman et al., 2002). (iii) depressive disorders are strongly linked to

premature death (Cuijpers & Smit, in press), particularly because of a substantial risk of completed

suicide or consequences of repeated suicide attempts (Penninx et al., 1999; Wunderlich et al,. 1998).

(iv) Depressive disorders are also highly comorbid with other mental disorders (e.g. Kessler et al.,

1996; Wittchen et al., 1999c) adding considerably to the individual and societal burden by increased

disability, lower remission rates and an unfavourable natural course and prognosis. The overall

substantial burden of depression in terms of direct costs (for treatment, medication) as well as indirect

costs (through work loss, decreased productivity) has been examined and underscored recently

(Andrews et al., 2000; Judd et al., 1996; Kessler et al., 1999; Kessler & Frank, 1997; Simon et al.,

1995; Simon et al., 1997). The “Global Burden of Disease Study”, taking into account some of these

factors, estimated that major depression will be one of the top three most burdensome disorders

(physical and mental) worldwide by the year 2020 in terms of disability adjusted life years (DALYs)

(Murray et al., 1996).

Previous prevalence studies on the national level

However, the accuracy of such estimations is hampered by the fact that for many countries – and

Germany in particular – no reliable prevalence data were available for the population as a whole. This

paper tries to correct partially for this deficit on the national level by reporting data on prevalence,

correlates and comorbidity of mood disorders in the German general adult population. Data are

derived from the Mental Health Supplement of the German National Health Interview and

Examination Survey (GHS-MHS) (Jacobi et al., 2002b; Wittchen et al., 1998a). The GHS- MHS,

commissioned in 1997 by the German Ministry of Health, sponsored jointly by the Ministry of

Education and Research, is in fact the first nationally representative survey of mental disorders in

13

Germany that assessed the prevalence of mental disorders with a fully structured, standardised

instrument.

Until recently prevalence estimates of mental disorders could only be extrapolated from various

smaller and regional limited studies. Examples are the EDSP-Study (Early Developmental Stages of

Psychopathology (Wittchen et al., 1998b), which examines psychiatric morbidity in adolescents and

young adults in the Munich region, and studies in Upper Bavaria (Fichter, 1990) or West Germany

(Wittchen & v. Zerrsen , 1987). These studies have suggested that 8-10% of the males and 20-26% of

the females in Germany’s adult population have been affected by depressive disorders during their

lives (Fichter, 1990). Given their young age, unexpectedly similar lifetime estimates of mood

disorders (17.1%) were found for adolescents and young adults aged 14-24 in a sample of 3021

community respondents in the greater Munich area (Wittchen et al., 1998b).

International epidemiological studies

During the two last decades, the development of standardised instruments for the assessment of mental

disorders made it possible to obtain reliable estimates of the frequency of various mental disorders.

The Epidemiologic Catchment Area study (ECA, USA (Robins & Regier, 1991) was the first large

survey and landmark study to use such a research instrument (the Diagnostic Interview Schedule

(Robins, 1981). In the beginning of the 1990s the WHO developed the Composite International

Diagnostic Interview (CIDI) (World Health Organization, 1997). The CIDI is a fully structured

interview designed to be administered by trained interviewers to generate diagnoses according to the

diagnostic criteria of DSM-III-R/IV (American Psychiatric Association, 1994) as well as ICD-10

(World Health Organization, 1993). The CIDI and its modified versions have since been used in a

variety of national epidemiological surveys worldwide as for example the U. S. National Comorbidity

Survey (NCS, USA (Kessler et al., 1994), the Netherlands Mental Health Survey and Incidence Study

(NEMESIS; Bijl et al., 1998), and the Australian National Mental Health Survey (Andrews et al.,

2001). Despite using almost identical instruments, however, these studies reveal some remarkable

variations in prevalence estimates. The NCS for example found a 1-month prevalence of 6.1% (Blazer

et al., 1994). In Australia’s National Survey only 3.2% were diagnosed as having a 1-month major

depressive disorder (DSM-IV criteria) (Andrews et al., 2001). In order to coordinate comparative

analyses of data obtained by studies all using the CIDI, and to control for artificial sources of variance

created by different sample composition, the International Consortium in Psychiatric Epidemiology

(ICPE) reanalysed these data. Among 10 countries considered, Japan reported the lowest lifetime

prevalence (3.0%), whereas the highest was found for the USA (16.9%). Prevalence rates in other

countries ranged from 6.3% (Turkey), 7.8% (Czech Republic), 8.1% (Mexico), 8.3% (Canada), 9.0%

(Chile), 11.5% (EDSP, Germany), 12.6% (Brazil) to 15.7% (the Netherlands) (Andrade et al., in

press). Caution, however, is warranted to interpret these findings as indication for true differences in

prevalence. It should be noted that comparability of these prevalence estimates might be limited

14

through various other factors that could not be controlled for in this analysis, such as the use of

different underlying diagnostic criteria (DSM-III-R vs. DSM-IV) or differing cultural backgrounds,

which might lead to differences in the respondent’s willingness to reveal personal information in

CIDI-interviews (Andrade et al., in press).

Prevalence of depressive disorders in primary health care

In addition to these community studies it should be noted that the prevalence of depressive disorders

was also examined in primary care (Üstün et al., 1995). Prevalence of current major depression varied

widely across 15 centres too (Simon et al., 2002); from a low of 1.6% in Japan to a high of 26.3% in

Chile. In Germany recent studies in primary care report point prevalence estimates of 11.3% for

depressive disorder (ICD-10 or DSM-IV) (Jacobi et al., 2002a) respectively 10.9% prevalence of

current episode of depression (according to ICD-10) (Wittchen & Pittrow, 2002; Wittchen, 2000).

Methods

Methods and design of the study have been presented in greater detail elsewhere (Jacobi et al., 2002b).

Therefore only a few background data will be provided.

Design and sample

The core survey of the German National Health Interview and Examination Survey (GHS-CS) is based

on a stratified, nationally representative sample of 130 sites all over Germany. It was administered to a

total of 7124 persons of the resident population aged 18-79 years (response rate 61.4% (Thefeld et al.,

1999). The GHS-CS assessed physical health, sociodemographic variables, health services utilisation,

health related behaviour and living conditions by using self-report measures, laboratory tests as well as

data from a comprehensive health interview administered by trained medical doctors.

The Mental Health Supplement (GHS-MHS) was carried out as an extension in the same sample by

the Max-Planck-Institute of Psychiatry, Munich, to examine (1) the prevalence and symptom severity

of mental disorders within the general population, and the type of impairments and disabilities

associated with mental disorders, (2) the comorbidity of mental disorders among one another

respectively with somatic conditions, and (3) to gather crude estimates of met and unmet needs of

treatment and service utilisation patterns. Due to practicability and financial issues the GHS-MHS is

based on a two-stage design. As a first stage, the Munich Composite International Diagnostic Screener

(CID-S; Wittchen et al., 1999a), a 12-item self-report questionnaire, was applied to each of the 7124

participants at the end of the core survey medical interview in order to screen for history or presence

of mental disorders. In the second stage, all participants who screened positive, and a random sample

of 50% of the subjects who screened negative were then invited to participate in a computer assisted

15

personal interview, the DIA-X/M-CIDI (Wittchen & Pfister, 1997). Core survey participants of 66-79

years of age were excluded because the psychometric properties of the CIDI have not been yet

satisfactorily established for use in older populations (Knäuper & Wittchen, 1994).

The interview was administered by trained clinical interviewers to 4181 participants (conditional

response rate: 87.6%). Sociodemographic variables of this sample are listed in the first column of

Table 3. Psychiatric diagnoses were assigned by means of the computerised M-CIDI diagnostic

program using the diagnostic criteria of DSM-IV (American Psychiatric Association, 1994). Selected

4-week prevalence findings for mood disorders have already been reported by Wittchen et al. (1999c)

in a preliminary report. In the present paper, 12-month and lifetime prevalence estimates of mood

disorders (major depression – single and recurrent episodes, dysthymic disorder as well as bipolar I

and II disorders) will be reported. In addition some information will be provided on the prevalence of

clinically significant depressive symptoms (present over 2 weeks nearly every day) as well as severely

disabling depressive episodes.

Assessment of somatic health correlates and psychosocial variables

Somatic illness was assessed in the GHS-CS by study doctors who used a computer assisted medical

interview and laboratory tests (Jacobi et al., 2002b). In the present analysis a crude self-report

estimator of “good”, “fair” and “poor” somatic health was used (depending on the number of

diagnosed somatic conditions).

In this paper we will examine associations of depressive disorders with social class as well, based on

the “Social Strata Index” (Winkler & Stolzenberg, 1999) used in the GHS-CS. It combines

information on education, net income and current professional position to a multidimensional additive

index.

Assessment of impairment and treatment

Health related quality of life was assessed with the German version of the Medical Outcomes Study

Short Form-36 Health Survey (SF-36; Brazier et al. 1992; McHorney et al., 1993; Bullinger, 1995;

Bullinger & Kirchberger, 1998), a quality of life questionnaire that measures health functioning and

well-being. This instrument measures a broad range of health concepts (in eight domains) that are

neither disease- nor treatment-specific and meets the psychometric standards of validity and reliability

(Ware & Sherbourne, 1992). Two summary scores represent quality of life related to mental health

(psk) and physical heahlth (ksk).

To provide another dimensional measure, results of the Beschwerdenliste (von Zerssen & Köller,

1976) are presented, a rating scale for the assessment of clinical complaints, consisting of 24 items that

represent general complaints, bodily complaints and mental complaints. A summary score can be

calculated.

16

One module on previous treatments embedded in the diagnostic interview assessed contact with

institutions associated with mental health care (psychiatrist, several types of psychotherapists, several

types of spezialized inpatient settings, counseling and information centers etc.). The results shown

here are only crude measures of lifetime contact with these instututions disregarding which treatment

was actually provided; also treatment adequacy cannot be determined here, as well as the contact

reason (e.g. a specific diagnosis).

Statistical Analysis

Diagnoses of mental disorders reported below are based on DSM-IV (American Psychiatric

Association, 1994). Prevalence estimates (N, %, 95% confidence intervals) were calculated weighted

for age, gender, region, and screening status in order to address different sampling probabilities and

systematic non-response. Multiple logistic regression (odds ratios, mean ratios, 95% confidence

intervals) was used to quantify the associations between mental disorders (comorbidity) and their

correlates. Age and sex were adjusted for in each analysis to control for different base distributions of

prevalence and comorbidity within the sample. To account for the weighting scheme as well as the

stratified sampling design by screening status, statistical inference (standard errors, confidence

intervals and p-values) was based on the Huber-White sandwich estimator of variance (Binder, 1993;

Royall, 19986; Woodruff, 1971). This was done with the Stata software package, release 7.0

(StataCorp., 2001).

Results

Prevalence

The frequency of 12-month and lifetime mood disorders (according to DSM-IV, weighted numbers,

with 95% confidence intervals) along with information about the total prevalence of depressive

symptoms are shown in table 1. “Depressive symptoms” are defined as follows: at least two of three

core symptoms (sad/depressed mood, tired/continuing fatigue, loss of interest) have nearly

permanently been present for as long as two weeks.

Table 1

17

A total of 18.6% of respondents were assigned a lifetime diagnosis of any mood disorder, women

more frequently than men (25% vs. 12.3%). 12% of the sample were diagnosed as having any mood

disorder during the last 12 months, women met criteria more frequently than men (15.4% vs. 8.5%).

Depressive disorders (defined as any major depressive disorder and/or dysthymic disorder) account for

almost all mood disorders: 17.3% of the population met criteria for any depressive disorder (women:

23.5%, men: 11.1%) at least once during their life course. 10.9% were diagnosed as having any

depressive disorder (women: 14.2%, men: 7.6%) in the past 12 months. Bipolar disorders were rather

rare disorders; only one percent of the total population had a lifetime bipolar disorder (women: 1.2%,

men: 0.8%; 12-month: women: 1.1%, men: 0.6%), despite the fact that we ascertained both the

presence of hypomania =bipolar II) and manic episodes (bipolar I).

Clinically significant depressive symptoms were even more widespread. 25% of the population (lower

portion of Table 1) reported having experienced lifetime depressive symptoms, nearly one third

(31.7%) of the female and 18.4% of the male population. 12-month depressive symptoms are

distributed likewise: more women (18.5%) are affected than men (11%); a total of 14.8% of the

general population was suffering from depressive symptoms in the past 12 months.

Table 2

From the much wider range of existing subtypes of depressive and bipolar disorders, Table 2 reports

the rates separately for bipolar I and II disorders, single and recurrent episodes of major depression,

dysthymic disorder, and also the rates of major depressive episodes, rated as very severe, due to the

number of symptoms, the need for hospitalisation, and disability. It is noteworthy that except for

dysthymic disorder the prevalence estimates by age group are fairly similar for both 12-month as well

as lifetime frames. However, for dysthymic disorder – a mild variant of depression of chronic duration

(at least 2 years) – a fairly steady increase by age group is visible, especially for females. In all

subtypes of depressive disorders females have almost twice the rates of males. Fewer respondents

were diagnosed as having a lifetime bipolar disorder I (0.6%) and bipolar disorder II (0.4%), and

differently from the findings for depressive disorders, there was no gender difference.

It should be noted, that the rates for depressive disorders add up to over 100%, because 2% of the

sample met criteria for both major depression and dysthymic disorder (“double depression”).

Correlates

Table 3 reports the proportions of selected sociodemographic and health related variables in the total

sample (first column) and the proportions among respondents with 12-month major depression and

18

dysthymic disorder. For each group of correlates odds ratios are presented with their 95% confidence

intervals.

Table 3

The comparisons reveal significant associations for being female, as compared male, for all correlates;

the remarkably higher odds ratio for recurrent depression is particular noteworthy. Respondents over

35 years as well as over 50 years of age have a higher risk of dysthymic disorder than those in the

youngest age group, but there are no significant differences for the other depressive disorders.

Regarding marital status, single persons show a higher risk than married ones for having a 12-month

depressive episode. There is also an association between being separated (i.e. married but separated,

divorced or widowed) and MDD (single and recurrent) as well as dysthymic disorder.

Regarding social class an only slightly higher proportion of subjects from the lower social class was

found for major depression (both single and recurrent major depression), whereas risk for dysthymic

disorder are significantly lower for the middle and upper classes as compared to lower social class.

All types of depressive disorders are associated with increased odds of reporting only “fair” or even

“poor” physical health state. Again odds ratios are more pronounced in the dysthymic disorder-group.

Table 4

Figure 1

Comorbidity

Table 4 examines whether depressive disorders reveal higher proportions of comorbidity than found in

the group of respondents with at least one diagnosis of any mental disorder (N=1301). The table

reveals that major depression – whether single or recurrent – has a lower proportion of pure cases and

higher proportions of subjects with two or even three and more additional diagnoses. Indeed only a

minority of cases with a 12-month depression occur in pure forms, not aggravated by other mental

disorders.

Dysthymic disorder differs from major depression because of exceedingly high comorbidity figures.

Only 18% occur in pure form, whereas 42% are highly comorbid (3 or more additional diagnoses).

Figure 1 describes a similar rank of types of comorbid disorders that emerges for each group. Anxiety

disorders are the group of disorders most frequently associated with all types of depressive disorders,

19

followed by somatoform and substance use disorders. It is remarkable, that no significant association

was found for single episodes of major depression with substance use disorders, whereas the

associations with both recurrent major depression and dysthymic disorders are significant.

Table 5

Impairment measures

Health related quality of life (QoL), clinical complaints and impairment/disability (self reported

disability days within last 12 months) in depressive disorders are reported in Table 5. The sum score of

the mental health component of the SF-36 showed a 20% QoL reduction in major depression (single

and recurrent) compared to the rest of the population and nearly 30% QoL reduction in dysthymia.

QoL reductions in the sum score of the physical component were lower but still highly significant. In

Table 5 also the respective results in the eight subscales of the SF-36 are listed.

Depressed subjects reported 40% (major depression single and recurrent) to 80% (dysthymia) more

clinical complaints than the rest of the population (measured by the sum score of the Zerssen-

Beschwerdeliste; von Zerssen & Köller, 1976).

Overall, depressive disorders showed about twice as much disability days compared to the rest of the

population. Major depression (single) was associated with a 75% increase of disability days (self

reported days completely disabled to carry out usual activities). Surprisingly, disability days in

recurrent major depression were not significantly elevated (15%), whereas dysthymia showed a more

than threefold elevation.

Table 6

Health care utilization

Treatment rates of 12-month depressive disorders are presented in Table 6 without further statistical

comparisons. As expected, comorbidity plays a major role wether treatment is seeked (overall 62-68%

compared to 35-47% in pure disorders). Specialist treatment by psychiatrists is reported from 3.5% in

pure dysthymia up to 29% in comorbid single major depression. Psychotherapist or primary care

treatment (the latter only counted if consulted due to mental health problems) show comparable

figures. Overall, 11.8% of the subjects with a depressive diagnosis reported having had an inpatient

treatment (psychiatry, psychosomatic clinic or other mental health inpatient setting) and 14.2% had

contact with a non-medical mental health care setting.

20

It has to be noted that it cannot be determined in the present analysis whether treatment contacts were

due to the depressive diagnoses or to comorbid conditions, or both. This applies in particular to the

very high comorbidity rates found in dysthymia (e.g. one out of five comorbid subjects reported an

inpatient treatment). Moreover, it cannot be determined here if patients were really treated or whether

the treatment provided was adequate (e.g. according to evidence based medicine standards).

Discussion

The GHS-MHS provides for the first time reliable lifetime and 12-month prevalence estimates, along

with some crude characterisation by age and gender, of mood disorders in Germany. Because of the

use of the M-CIDI as the diagnostic benchmark, data can be directly compared to other international

studies. Due to its cross-sectional design the study does not allow for detailed analysis of the course of

mood and depressive disorders and their longitudinal associations with other disorders. It should also

be noted that the results might slightly underestimate the prevalence because patients in clinics were

not included. Despite these limitations the paper provides some basic, nationally representative

prevalence data, which could be regarded as a prerequisite for comprehensive estimation of the scope

of depressive disorders and for effective health-care planning.

In accordance with other epidemiological studies the findings show that mood disorders rank among

the prevalent disorders in the German population with a lifetime estimate of 18.6% and a 12-month

prevalence of 11.9%. The prevalence estimates are similar to data reported from the Netherlands (Bijl

et al., 1998) and the USA (both NCS, Kessler et al., 1994, and the currently published replication

study NCS-R; Kessler et al., 2003), i.e. from studies using comparable designs and instruments.

Figures of the Sesto Fiorentino Study (Faravelli et al., in press b) with an overall 12 month prevalence

of mood disorders of 7.2% are remarkably lower. It would be an interesting challenge to determine if

the different methology of this study just reduced “positives” (if “correct” or “false” positives cannot

be dicussed here), or if the different numbers reflect true prevalence differences (as this might be the

case in alcohol disorders; Faravelli et al., in press a,b).

In our analyses we made the attempt to provide at least a crude grading of severity (“depressive

symptoms”: depressive core symptoms present at least two weeks (14.8%), “threshold” : non-

hierarchical DSM-IV criteria (10.9%), “severe”: hospitalization, maximum severity rating, or at least

eight depressive symptoms (3.1%) ). Further, the GHS-MHS findings have pinpointed some

remarkable differences between types of disorders, especially with regard to differences between

dysthymic disorder as a highly comorbid, chronic depression and major depression with a more

episodic pattern, as manifested by the large difference between lifetime- and 12-month rates. Beyond

previous studies, we can estimate more precisely the proportions of single episodes of major

21

depression (9.3%) that may occur at any point of life with a similar frequency and the recurrent type of

major depression with full remission in between episodes. The latter group constitutes with a lifetime

prevalence of 5.5% more than one third of all depressive disorders and even 50% with regard to the

past 12-month. Since treatment and relapse prevention in this large group of recurrent depression is of

major public concern, the size of this group should be highlighted. This is also true for the finding, that

2% of the sample suffer from double depression, that is both major depressive episodes superimposed

on a chronic milder depression (dysthymic disorder). The natural course and disability profile of this

group has been described as particular malignant.

Although by and large the sociodemographic correlates in this study do not differ from the profile

found in other epidemiological studies (higher rates for women in all forms of mood disorders, women

are at higher risk of having severe forms of major depression, recurrent major depressive disorder and

dysthymic disorder, separated and single respondents have a higher risk than those married) there are

two noteworthy observations rarely discussed. First, there is no remarkable social class effect

compared to US studies. This might reflect important differences in the social integration of the less

advantaged, as well as the lower drop out rate of school that has been confirmed for Germany in

comparison to the US. The second difference relates to the fact that higher age was found to be a risk

factor only for dysthymic disorder. This finding is inconsistent with the frequent speculation, that

dysthymic disorder is merely a personality disorder with onset in early adulthood. In conjunction with

high comorbidity rates the findings for dysthymic disorder are more consistent with the fact that

dysthymic disorder is frequently a severe demoralisation syndrome as consequence of multiple

stressors and various chronic difficulties. This view is supported by the finding that reduced quality of

life, elevated clinical complaints, and in particular the strongly elevated disability days indicate a

significantly higher impairment in this diagnosis than in major depressions – even though dysthymia

was originally conceptualized as a “milder” form of depression.

The comorbidity findings from our study are in accordance with al large number of other studies

(Andrews et al., 2001; Broadhead et al., 1990; Kessler et al., 1996; Robins et al., 1991; Wittchen et al.,

1999b) that have pointed to the fact that the already substantial suffering from and burden of

depression alone is aggravated by additional burden of anxiety, somatoform and substance disorders

for the majority of all depressive disorders. Again, presumably in major part due to the more

hierarchical diagnostic procedure, Faravelli et al. (in press b) find considerably lower comorbidity.

Relatively low reported treatment rates are comparable to other studies with the same methodology

(Bijl et al., 2003).

The results of GHS-MHS underline the substantial burden of depressive disorders on the individual,

their families and the society, which is comparable or even larger than other major public health

problems like hypertension or type II diabetes. Depressive disorders are a major public health problem

not only because of their high prevalence, but also because of their chronic, episodic course,

comorbidity, and associated impairment. The elevated disability days (about twice as much as the

22

average rest of the population) indicate only the lower bound of the burden from indirect costs of

depression, since productivity loss due to reduced performance even exceeds the cost of total disability

days (Steward et al., 2003).

Especially in light of the current discussion about Germany’s health care system it is essential to

establish a balance between need for treatment and the available resources for that treatment. Findings

of the GHS show that only 50-60% of participants with 12-month major depression had any contact

with an institution of the German health services system due to psychological distress (which is only a

crude estimation of treatment because it does not differentiate between for example one session of

counselling or admission to a clinic; Wittchen & Jacobi, 2001). It is also evident from studies

examining health utilisation in primary care that patients with major depression are frequently not

recognised; and even if correctly diagnosed, they are not properly treated (Wittchen & Pittrow, 2002).

There still seems to be a gap between one the one hand awareness of the disorder, for established

treatment in the public and among health care providers (e.g. general practitioners), and the high

relevance of depressive disorders for society in terms of frequency and severity on the other hand.

23

Acknowledgements

This study was supported by grant 01EH970/8 (German Federal Ministry of Research, Education and

Science; BMBF). Reported data on mental disorders were assessed in the Mental Health Supplement

of the German Health Survey (GHS-MHS), conducted by the Max-Planck-Institute of Psychiatry,

Munich. Principal investigator was Prof. Dr. Hans-Ulrich Wittchen. Reported sociodemographic and

somatic health status variables come from the Core Survey (GHS-CS), conducted by the Robert Koch-

Institute, Berlin. Principal investigators of the GHS-CS were Dr. Bärbel-Maria Kurth and Dr.

Wolfgang Thefeld.

Note

Data of this study are available as Public Use File from Dr. Frank Jacobi, Institute of Clinical

Psychology and Psychotherapy, Chemnitzer Str. 46, D-01187 Dresden, Germany; e-mail:

jacobi@psychologie.tu-dresden.de

For further information about the Core Survey (GHS-CS) and its Public Use File contact the Robert

Koch-Institute, Dr. Heribert Stolzenberg, Nordufer 20, D-13353 Berlin, Germany; e-mail:

stolzenbergh@rki.de

24

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Age Nw %w Nw %w Nw %w Nw %w Nw %w Nw %w

total 617 14.77 (13.67 - 15.94) 1.046 25.03 (23.67 - 26.44) 384 18.51 (16.89 - 20.24) 659 31.71 (29.73 - 33.75) 233 11.07 (9.64 - 12.70) 387 18.42 (16.60 - 20.39)

18-34 211 14.63 (12.78 - 16.69) 350 24.26 (21.98 - 26.70) 135 19.12 (16.30 - 22.31) 228 32.39 (28.91 - 36.07) 76 10.32 (8.09 - 13.09) 121 16.47 (13.68 - 19.71)35-49 234 16.47 (14.53 - 18.61) 390 27.44 (25.09 - 29.92) 141 20.09 (17.39 - 23.10) 237 33.83 (30.57 - 37.24) 93 12.95 (10.30 - 16.15) 153 21.23 (17.96 - 24.91)50-65 173 13.10 (11.32 - 15.10) 307 23.26 (20.92 - 25.78) 109 16.22 (13.58 - 19.26) 194 28.79 (25.40 - 32.43) 63 9.84 (7.63 - 12.59) 113 17.51 (14.42 - 21.10)

total 499 11.93 (10.95 - 12.99) 779 18.63 (17.44 - 19.88) 320 15.37 (13.68 - 17.01) 520 25.00 (23.16 - 26.93) 179 8.52 (7.28 - 9.95) 259 12.33 (10.84 - 14.01)

18-34 163 11.32 (9.70 - 13.19) 247 17.17 (15.21 - 19.32) 94 13.38 (11.03 - 16.15) 154 21.83 (18.85 - 25.13) 69 9.35 (7.17 - 12.10) 93 12.70 (10.19 - 15.72)35-49 179 12.62 (10.99 - 14.46) 292 20.53 (18.48 - 22.75) 118 16.84 (14.29 - 19.73) 199 28.45 (25.25 - 31.88) 61 8.53 (6.56 - 11.02) 93 12.85 (10.36 - 15.82)50-65 156 11.84 (10.12 - 13.80) 240 18.17 (16.12 - 20.42) 107 15.93 (13.23 - 19.07) 166 24.73 (21.54 - 28.21) 49 7.57 (5.63 - 10.11) 73 11.34 (8.87 - 14.40)

total 454 10.85 (9.9 - 11.87) 722 17.26 (16.11 - 18.48) 295 14.17 (12.71 - 15.77) 488 23.49 (21.69 - 25.39) 159 7.56 (6.39 - 8.93) 234 11.11 (9.68 - 12.72)

18-34 136 9.44 (7.94 - 11.19) 215 14.99 (13.13 - 17.05) 81 11.52 (9.31 - 14.16) 139 19.65 (16.79 - 22.88) 55 7.44 (5.50 - 10.0) 77 10.51 (8.22 - 13.34)35-49 165 11.58 (10.01 - 13.36) 271 19.10 (17.11 - 21.26) 108 15.46 (13.02 - 18.27) 187 26.64 (23.53 - 30.01) 56 7.80 (5.92 - 10.23) 85 11.77 (9.39 - 14.65)50-65 153 11.60 (9.90 - 13.55) 234 17.78 (15.73 - 20.02) 105 15.61 (12.29 - 18.73) 163 24.22 (21.05 - 27.69) 48 7.43 (5.51 - 9.96) 71 11.06 (8.61 - 14.10)

total 34 0.82 (0.59 - 1.15) 42 1.00 (0.74 - 1.35) 22 1.06 (0.71 - 1.58) 24 1.17 (0.8 - 1.71) 12 0.59 (0.33 - 1.05) 17 0.83 (0.51 - 1.36)

18-34 16 1.10 (6.70 - 1.79) 18 1.28 (0.82 - 2.01) 8 1.16 (6.1 - 2.18) 9 1.31 (0.71 - 2.38) 8 1.04 (0.49 - 2.20) 9 1.26 (0.65 - 2.44)35-49 15 1.08 (0.65 - 1.78) 18 1.29 (0.80 - 2.07) 11 1.50 (0.81 - 2.76) 11 1.57 (0.87 - 2.83) 5 0.66 (0.27 - 1.61) 7 1.01 (0.46 - 2.23)50-65 3 0.25 (0.00 - 0.65) 5 0.38 (0.17 - 0.82) 3 0.50 (0.19 - 1.27) 4 0.61 (0.26 - 1.42) 0 - - - 1 0.14 (0.00 - 0.97)

3: Bipolar I, bipolar II2: Major depressive disorder, dysthymic disorder, bipolar I disorder, bipolar II disorder, single hypomanic episode. Unipolar and bipolar mood disorders don´t add up to 100% because single hypomanic episode was not included in either subcategory.1: At least two of the following three core symptoms were nearly always present at least for a two week period (without necessarily meeting all other criteria for a DSM-IV disorder): depressed mood, fatigue without physical exertion, loss of interest.

Any unipolar depressive disorder

Any bipolar disorder3

12-month lifetime

95% CI

Table 1: Prevalence of depressive symptoms (>=two weeks duration)1 and aggregated mood disorders (12-month and lifetime, M-CIDI/DSM-IV) in the general population (GHS-MHS; N=4.181) by age and gender

totallifetime

female12-month

Any mood disorder2

95% CI

male

95% CI

lifetime

95% CI95% CI95% CI

Depressive symptoms1

12-month

Age Nw %w Nw %w Nw %w Nw %w Nw %w Nw %w

total 179 4.28 (3.66 - 4.99) 388 9.27 (8.37 - 10.26) 107 5.13 (4.25 - 6.17) 256 12.30 (10.93 - 13.81) 72 3.43 (2.62 - 4.48) 132 6.29 (5.18 - 7.61)

18-34 57 3.98 (2.97 - 5.31) 123 8.56 (7.08 - 10.31) 27 3.79 (2.61 - 5.47) 74 10.43 (8.30 - 13.03) 31 4.15 (2.67 - 6.41) 50 6.76 (4.86 - 9.31)35-49 66 4.68 (3.70 - 5.90) 144 10.15 (8.66 - 11.85) 44 6.22 (4.72 - 8.16) 101 14.37 (11.99 - 17.13) 23 3.18 (2.06 - 4.87) 44 6.05 (4.44 - 8.20)50-65 55 4.17 (3.12 - 5.55) 120 9.12 (7.55 - 10.97) 36 5.39 (3.83 - 7.55) 81 12.10 (9.77 - 14.89) 19 2.90 (1.69 - 4.91) 39 6.01 (4.12 - 8.70)

total 168 4.03 (3.45 - 4.69) 231 5.53 (4.87 - 6.28) 126 6.05 (5.09 - 7.18) 175 8.44 (7.34 - 9.69) 43 2.03 (1.46 - 2.80) 56 2.65 (1.98 - 3.54)

18-34 54 3.75 (2.84 - 4.93) 68 4.75 (3.75 - 6.01) 40 5.73 (4.16 - 7.86) 52 7.38 (5.61 - 9.65) 14 1.84 (1.08 - 3.13) 16 2.23 (1.39 - 3.57)35-49 62 4.40 (3.42 - 5.64) 91 6.38 (5.18 - 7.84) 46 6.51 (4.93 - 8.54) 67 9.54 (7.65 - 11.85) 17 2.35 (1.35 - 4.04) 24 3.31 (2.02 - 5.37)50-65 52 3.94 (2.99 - 5.17) 72 5.46 (4.37 - 6.80) 40 5.92 (4.36 - 7.99) 57 8.41 (6.58 - 10.67) 12 1.88 (1.02 - 3.45) 15 2.40 (1.43 - 3.99)

total 129 3.09 (2.60 - 3.67) 234 5.60 (4.92 - 6.36) 94 4.54 (3.74 - 5.51) 174 8.39 (7.30 - 9.62) 35 1.66 (1.15 - 2.37) 60 2.84 (2.11 - 3.81)

18-34 37 2.54 (1.84 - 3.49) 62 4.32 (3.39 - 5.49) 30 4.21 (2.94 - 6.00) 50 7.06 (5.40 - 9.18) 7 0.93 (0.46 - 1.86) 12 1.70 (0.99 - 2.90)35-49 49 3.43 (2.64 - 4.45) 90 6.33 (5.20 - 7.68) 35 5.05 (3.74 - 6.79) 65 9.31 (7.48 - 11.55) 13 1.85 (1.09 - 3.14) 25 3.42 (2.26 - 5.16)50-65 44 3.33 (2.42 - 4.57) 82 6.20 (4.89 - 7.84) 29 4.36 (3.04 - 6.24) 59 8.81 (6.90 - 11.20) 15 2.26 (1.20 - 4.22) 23 3.48 (1.96 - 6.11)

total 188 4.49 (3.90 - 5.16) 189 4.53 (3.39 - 5.2) 120 5.57 (4.82 - 6.85) 120 5.76 (4.83 - 6.86) 68 3.24 (2.55 - 4.09) 69 3.31 (2.62 - 4.17)

18-34 45 3.15 (2.36 - 4.18) 46 3.18 (2.39 - 4.22) 26 3.69 (2.52 - 5.35) 26 3.69 (2.53 - 5.35) 19 2.63 (1.67 - 4.11) 20 2.70 (1.73 - 4.18)35-49 67 4.69 (4.57 - 7.97) 68 4.76 (3.78 - 5.98) 42 6.05 (4.57 - 7.97) 42 6.06 (4.58 - 7.98) 24 3.37 (2.22 - 5.08) 25 3.50 (2.33 - 5.24)50-65 76 5.74 (4.58 - 7.15) 76 5.74 (4.59 - 7.16) 51 7.61 (5.74 - 10.03) 51 7.61 (5.74 - 10.03) 24 3.78 (2.61 - 5.44) 24 3.79 (2.62 5.45)

total 21 0.50 (0.33 - 0.76) 24 0.57 (0.39 - 0.84) 12 0.56 (0.33 - 0.96) 13 0.62 (0.38 - 1.03) 9 0.44 (0.23 - 0.85) 11 0.52 (0.29 - 0.94)

18-34 10 0.68 (0.36 - 1.26) 11 0.73 (0.41 - 1.33) 3 0.49 (0.19 - 1.26) 3 0.49 (0.19 - 1.26) 6 0.86 (0.38 - 1.92) 7 0.97 (0.46 - 2.05)35-49 9 0.63 (0.33 - 1.20) 9 0.66 (0.35 - 1.24) 6 0.85 (0.38 - 1.90) 6 0.92 (0.43 - 1.97) 3 0.41 (0.14 - 1.22) 3 0.41 (0.14 - 1.22)50-65 2 0.17 (0.01 - 0.48) 4 0.30 (0.13 - 0.68) 2 0.34 (0.12 - 0.93) 3 0.45 (0.18 - 1.11) 0 0.00 (0.00 - 0.00) 1 0.14 (0.00 - 0.97)

total 14 0.33 (0.19 - 0.56) 18 0.43 (0.26 - 0.70) 10 0.50 (0.27 - 0.91) 11 0.55 (0.31 - 0.98) 3 0.15 (0.01 - 0.51) 6 0.31 (0.13 - 0.74)

18-34 6 0.43 (0.19 - 0.94) 8 0.55 (0.28 - 1.09) 5 0.67 (0.29 - 1.57) 6 0.82 (0.38 - 1.79) 1 0.19 (0.00 - 1.32) 2 0.29 (0.01 - 1.22)35-49 6 0.45 (0.20 - 1.00) 9 0.63 (0.30 - 1.29) 5 0.65 (0.25 - 1.66) 5 0.65 (0.25 - 1.66) 2 0.25 (0.01 - 1.16) 4 0.61 (0.02 - 1.81)50-65 1 0.08 (0.00 - 0.58) 1 0.08 (0.00 - 0.58) 1 0.16 (0.00 - 1.14) 1 0.16 (0.00 - 1.14) 0 0.00 (0.00 - 0.00) 0 0.00 (0.00 - 0.00)

Major depressive disorder (recurrent)

1: Severe (without or with psychotic features): in hospital due to depressive disorder, heavy impairment due to depressive disorder (maximum rating in respective CIDI-item), or at least eight depressive symptoms.

Dysthymic disorder

Bipolar I disorder

Bipolar II disorder

Major depressive disorder (severe)1

Major depressive disorder (single)

95% CI

male

95% CI

lifetime

95% CI95% CI95% CI 95% CI

Table 2: Prevalence of specific affective (sub-) diagnoses (12-month and lifetime M-CIDI/DSM-IV) in the general population (GHS-MHS; N=4.181) by age and gender

total

lifetime

female

12-month12-month 12-month lifetime

Total sample

%w %w OR4 %w OR4 %w OR4

Gendermale 50.28 40.37 25.30 36.25female 49.72 59.63 1.52* (1.08 - 2.14) 74.70 3.12** (2.13 - 4.57) 63.75 1.82** (1.33 - 2.47)

Age (years)18-34 34.46 32.04 32.05 24.1535-49 33.99 37.19 1.18 (0.80 - 1.75) 37.08 1.18 (0.80 - 1.75) 35.52 1.51* (1.03 - 2.23)50-65 31.55 30.77 1.04 (0.68 - 1.60) 30.87 1.03 (0.70 - 1.56) 40.33 1.86** (1.26 - 2.72)

Marital statusMarried 64.06 51.40 54.28 54.95Single 24.90 27.71 1.69* (1.05 - 2.72) 23.18 1.26 (0.77 - 2.05) 18.32 1.15 (0.74 - 1.80)Separated1 11.04 20.81 2.32** (1.48 - 3.64) 22.54 2.22** (1.44 - 3.44) 26.74 2.75** (1.88 - 4.02)

Social class2

lower 19.11 22.43 24.00 34.98middle 57.63 59.01 0.87 (0.57 - 1.33) 55.73 0.78 (0.53 - 1.16) 55.04 0.52** (0.37 - 0.73)upper 23.26 18.56 0.67 (0.40 - 1.11) 20.28 0.71 (0.43 - 1.16) 9.97 0.21** (0.13 - 0.36)

good 29.93 21.70 16.44 11.73fair 45.35 47.95 1.44 (0.92 - 2.25) 48.61 1.82* (1.14 - 2.90) 37.79 2.04** (1.24 - 3.35)poor 24.72 30.35 1.70* (1.01 - 2.88) 34.95 2.29** (1.36 - 3.84) 50.48 4.99** (3.01 - 8.29)

1 Married but separated, divorced, widowed

2 Social Strata Index [36] derived from information on education, household income and current (job) position

3 crude somatic health indicator: number of physical disorders in the last 12 months:

good: no disorder (corresponding to lower tercile of whole sample),

fair: 1-2 disorders (middle tercile)

poor: 3 or more disorders

4 Odds ratio (OR) (logistic regression); reference group are subjects without the respective diagnosis;

*: p <0.05, **:p<0.01

95% CI 95% CI 95% CI

Physical health3

Major depressive disorder (single episode)

Major depressive disorder (recurrent)

Dysthymic disorder

Table 3 : Correlates of depressive disorders: distribution of sociodemographic variables and somatic health status in the total sample (GHS-MHS; N=4181), MDD single episode (12 month; N=179), MDD recurrent (12 month; N=168) and dysthymic disorder (12 month, N=188)

any disorder1

%w %w OR3 %w OR3 %w OR3

Number of comorbid disorders2

0 (pure) 60.46 42.67 0.43** (0.30 - 0.61) 40.15 0.41** (0.29 - 0.58) 18.37 0.11** (0.07 - 0.16)1 20.30 17.49 0.81 (0.54 - 1.22) 19.70 0.93 (0.61 - 1.41) 22.25 1.14 (0.78 - 1.67)2 8.98 15.43 2.10** (1.32 - 3.34) 15.94 2.14** (1.30 - 3.53) 17.45 2.52** (1.60 - 3.99)3+ 10.26 24.41 3.73** (2.47 - 5.62) 24.21 3.29** (2.18 - 4.97) 41.93 14.28** (9.55 - 21.34)

2:

3: Odds ratio (logistic regression), controlled for age and gender; reference group are subjects without the respective diagnosis; *: p <0.05, **:p<0.01

Table 4: Comorbidity of 12-month depressive disorders: proportions of pure and comorbid disorders among subjects with MDD single episode (N=179), MDD recurrent (N=168), dysthymic disorder (N=188), and among subjects fulfilling at least one of the assessed DSM-IV 12-month diagnoses (N=1.301)

1: total of subjects with at least one disorder

Major depressive disorder (single)

Major depressive disorder (recurrent)

95% CI 95% CI 95% CI

abuse or dependence, possible psychotic disorder, panic disorder (with or without agoraphobia), any phobia (any simple phobia, agoraphobia withoutpanic, social phobia, anxiety disorder nos), generalized anxiety disorder, obsessive compulsive disorder, SSI4/6, pain disorder, any eating disorder.

included comorbid diagnostic categories are: any mental disorder due to general medical condition, alcohol abuse or dependence, any iIlicit substance

Dysthymic disorder

health related quality of life M SD M SD MR4 M SD M SD MR5 M SD M SD MR5

SF-36 PSK 41.2 12.0 50.9 8.5 0,81** (0,77 - 0.85) 40.6 10.5 50.9 8.6 0,80** (0,77 - 0.84) 36.7 11.6 51.1 8.2 0,72** (0,68 - 0.75)SF-36 KSK 47.6 8.9 49.3 8.8 0,96** (0,94 - 0.99) 47.0 9.9 49.3 8.7 0,96* (0,93 - 0.99) 44.6 10.6 49.4 8.7 0,92** (0,89 - 0.95)

physical functioning 83.4 18.7 88.0 18.4 0,95* (0,92 - 0.98) 81.2 21.5 88.1 18.2 0,93* (0,90 - 0.97) 74.0 25.8 88.4 17.7 0,85** (0,81 - 0.90)role physical 75.2 35.6 84.6 30.4 0,89* (0,82 - 0.96) 66.6 40.5 84.9 30.0 0,79** (0,72 - 0.87) 62.0 41.6 85.2 29.7 0,74** (0,67 - 0.82)

bodily pain 55.6 25.1 68.4 25.3 0,82** (0,76 - 0.87) 55.7 25.8 68.4 25.3 0,84** (0,78 - 0.90) 46.5 23.5 68.8 25.1 0,70** (0,65 - 0.75)general health perceptions 56.8 19.4 67.9 17.6 0,83** (0,79 - 0.87) 59.1 17.5 67.7 17.8 0,87** (0,83 - 0.92) 50.1 18.4 68.2 17.4 0,74** (0,71 - 0.78)

vitality 45.7 18.4 60.5 17.4 0,76** (0,71 - 0.81) 46.0 17.4 60.4 17.5 0,78** (0,73 - 0.82) 39.8 17.9 60.8 17.1 0,66** (0,62 - 0.71)social functioning 69.9 26.0 87.2 18.8 0,80** (0,76 - 0.85) 67.6 24.2 87.3 18.8 0,78** (0,74 - 0.83) 60.6 28.0 87.6 18.1 0,70** (0,65 - 0.75)

role emotional 69.9 38.5 90.0 25.2 0,78** (0,71 - 0.85) 63.2 40.5 90.2 24.9 0,71** (0,64 - 0.78) 54.7 43.3 90.8 24.0 0,61** (0,54 - 0.68)mental health 55.1 19.9 73.0 15.9 0,76** (0,72 - 0.80) 55.3 16.9 73.0 16.1 0,77** (0,74 - 0.81) 45.9 17.0 73.5 15.4 0,63** (0,60 - 0.67)

clinical complaintsZerssen-Score 24.0 12.7 16.5 10.8 1,41** (1,29 - 1.53) 25.0 11.8 16.4 10.8 1,45** (1,34 - 1.56) 30.7 12.4 16.1 10.5 1,8** (1,69 - 1.92)

disability days 24.4 54.4 13.4 39.2 1,75** (1,22 - 2,51) 16.9 34.7 13.8 40.3 1.15 (0,88 - 1,49) 39.5 80.9 12.5 35.9 3,14** (2,32 - 4,26)

2: Beschwerdenliste (von Zerssen & Köller, 1976): rating scale for the assessment of clinical complaints (24 items that represent general, bodily and mental complaints)

3: self report: days within past 12 months completely disabled to carry out usual activities

4: Mean ratio (negative binomial regression); controlled for age and gender; reference group are subjects without the respective diagnosis; *: p <0.05, **:p<0.01

1: German version of the Medical Outcomes Study Short Form 36 Health Survey (SF-36; Bullinger, 1995; Ware and Sherbourne, 1992);two sum scores can be calculated: psk (mental health), ksk (physical health)

95% ciMD single

no MD single

dystymic disorder

no dystymic disorder

Table 5: Health related quality of life (SF-361), clinical complaints (Zerssen-score2), and disability days within last year3 in depressive disorders (GHS-MHS; N=4181)

95% ci 95% ci

MD recurrent

no MD recurrent

pure%

comorbid%

pure%

comorbid%

pure%

comorbid%

any treatment1 39.5 62.0 46.5 66.9 34.4 68.0

psychiatrist 10.9 29.4 7.7 24.8 3.5 24.5

psychotherapist 16.2 24.3 9.9 30.1 17.7 31.3

primary care doctor2 10.6 34.2 17.5 36.0 12.3 33.9

inpatient 7.8 14.8 3.6 14.7 8.2 20.9

non-medical setting 8.7 15.1 14.3 19.7 9.7 19.1

2: only if due to mental health problems

Table 6: Treatment rates of 12-month depressive disorders: proportions of contact with health care system among subjects with (pure and comorbid) MDD single episode (N=179), MDD recurrent (N=168), dysthymic disorder (N=188) (GHS-MHS)

1: self reported contact with a psychosocial institution, disregarding if (adequate) treatment was provided

Major depressive disorder (single)

Major depressive disorder

(recurrent)Dysthymic disorder

46 44

56

25 27

40

3 6 714 11 4

0

20

40

60

80

total sample MDD single MDD recurrent Dysthymic disorder

any anxiety disorderany somatoform disorderany substance use disorder%w

OR2

2.6*

OR3

0.9

OR1

4.3*

OR2

2.7*

OR3

2.3*

OR1

5.5*

OR1

8.5*

OR2

5.7*

OR3

2.5*

Figure 1: Comorbidity of 12-month depressive disorders: proportions and associations with anxiety, somatoform and substance use disorders in respondents with MDD single, MDD recurrent and dysthymic disorder (GHS-MHS; N=4181)

Odds ratio 1: association with any anxiety disorderOdds ratio 2: association with any somatoform disorderOdds ratio 3: association with any substance use disorder*: p<0.05