University Hospital Marqués de Valdecilla, University of Cantabria, Santander, Spain
Department of Primary Care, University of Liverpool
STAKES Mental Health Research Group, Turku, Finland
Department of General Practice and Community Medicine, Oslo, Norway
Mater Hospital, University College Dublin, Ireland
Division of General Practice, University of Wales College of Medicine, Wrexham, Wales
Clinical and Social Psychiatry Research Unit, Santander, Spain
Department of Primary Care, University of Liverpool
School of Epidemiology and Health Sciences, University of Manchester
Department of Psychiatry, University of Liverpool
Correspondence: Professor Vázquez-Barquero, Clinical and Social Psychiatry Research Unit, Marqués de Valdecilla University Hospital, Avd. Valdecilla s/n Santander 39008, Spain
Declaration of interest Funding detailed in the Acknowledgements. G.W. is Editor of the British Journal of Psychiatry.
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ABSTRACT |
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Aims To assess the prevalence of depressive disorders in randomly selected samples of the general population in five European countries.
Method The study was designed as a cross-sectional two-phase community study using the Beck Depression inventory during Phase 1, and the Schedule for Clinical Assessment in Neuropsychiatry during Phase 2.
Results An analysis of the combined sample (n=8.764) gave an overall prevalence of depressive disorders of 8.56% (95% CI 7.05-10.37). The figures were 10.05% (95% CI 7.80-12.85) for women and 6.61% (95% CI 4.92-8.83) for men. The centres fall into three categories: high prevalence (urban Ireland and urban UK), low prevalence (urban Spain) and medium prevalence (the remaining sites).
Conclusions Depressive disorder is a highly prevalent condition in Europe. The major finding is the wide difference in the prevalence of depressive disorders found across the study sites.
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INTRODUCTION |
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METHOD |
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Sampling
Target populations and sampling frames
Adults aged 18-64 years were the primary survey population. Community study
samples were identified through census registers or lists of patients
registered with primary care physicians. These sampling frames may be
considered equally valid in terms of the reliability of the data sets
(Shanks et al, 1995).
Census registers may be highly reliable at the time of collection, but suffer
from attrition over time, and the quality of electoral registers in Britain
has been vitiated by their use for local taxation purposes. Primary care
registers, accessed conjointly across a locality, offer a slightly different
but equally accurate representation of the population. In the present study,
the different research teams involved made the choice to use one sampling
procedure or the other that is, either census records or lists of
patients registered with primary care physicians based on their
previous experience in community surveys. Three centres (Oslo, Turku and
Santander) had previously achieved high response rates through population
register surveys, and therefore used this method for Phase 1 screening in the
ODIN study. Patients in Britain and Ireland were identified through primary
health care registers, a selection process similar to the one used in the
EURODEP study that assessed the prevalence of depression among those aged
65 years at the Dublin and Liverpool centres
(Copeland et al,
1999). The Irish research team had to reduce the scope of its
intended sampling and interviewing procedures owing to operational problems
which arose during the study. At the rural site in Ireland, the registers of
five general practitioners were involved, out of a total of 27; in Dublin, the
registers of two general practitioners were involved, from a total of 390. At
the British rural site, seven of the nine practices that covered the
population area took part in the study; in Liverpool, 32 practices of the 106
that covered the population area participated.
In Oslo, Turku and Santander, the sample was randomly drawn from the population registers of the five sites (two in Norway and Finland, one in Spain) involved in the study. In Liverpool, a random set of patient names was obtained from health authorities, and interviewers contacted the practices with which the patients were listed. In northern Wales and Ireland, the procedure was to identify relevant practices, and obtain random sets of names from their patient lists. The entire sample was stratified by gender and age in all the centres.
Assessment methods
First phase
The first-phase assessment identified possible cases of depression using
the Beck Depression Inventory (BDI) (Beck
et al, 1961), with a threshold score above 12 (Nielsen
& Williams, 1980; Lasa et al,
2000). The BDI was combined with a questionnaire on social support
(details available from the author upon request), the List of Threatening
Experiences (Brugha et al,
1985) and socio-demographic details. In Santander, the first phase
was conducted by home-based personal interview. In all other centres, it was
conducted using an initial postal survey, with postal, then telephone, then
home-visit follow-ups. All refusals were accepted, and non-responders were
contacted up to three times.
Second phase
All of those scoring at or above the BDI threshold and a random 5% of
responders were offered detailed interviews with research workers trained in
mental health, conducted in the participant's language. To date, most of the
epidemiological studies on depression in the general population have used
strict definitions of depression, according to DSM-III/IV (American
Psychiatric Association, 1980,
1994) and ICD-10
(World Health Organization,
1992) criteria, focusing on the prevalence of depressive episodes
or major depression. This may lead to a tendency to consider severe/major
depression as the only affective disorder worthy of intervention. In order to
overcome such a prejudice, in our study we extended the definition of
depressive disorders to include dysthymia and adjustment disorders with
depressive mood. The Schedule for Clinical Assessment in Neuropsychiatry
(SCAN) Version 2.0 (World Health
Organization, 1994) was used to generate diagnoses of depressive
disorders on the basis of ICD-10 and DSM-IV categories. For ICD-10, these
include single and recurrent depressive episodes (F32, F33), bipolar and
persistent affective disorders (F31, F34) and adjustment disorders with a
depressive component (F43.2). For DSM-IV, these include depressive, bipolar
and adjustment disorders with a depressive component (codes 293.83, 296.xx,
300.4, 309.xx, 311, V62.82). Other instruments administered at this phase have
been described elsewhere (Dowrick et
al, 1998).
Training and quality control
The diagnostic interviewers were psychiatrists, general practitioners or
psychologists. All interviewers received an initial weeklong training course
at an approved SCAN training centre, and subsequently practised the full
diagnostic interview schedule on at least 10 volunteers. Interrater
reliability over time was monitored by means of assessment and feedback using
a standardised videotaped consultation. A videotape including a full SCAN
interview was used for this exercise, supplied by the WHO-approved SCAN
training centre that trained ODIN's first-phase diagnostic interviewers. Each
diagnostic interviewer was asked to rate and score the interview, then send
their score sheets to a central analysis centre (Liverpool). Scores were
compared with the official set of ratings which accompanied the
video. The videotaped interview contained 113 questions that could be rated,
and all 13 of the interviewers were included in this exercise. A 100%
agreement was reached for overall diagnosis (moderate depressive episode) and
for diagnostic category (F32.1). There was 70% interrater agreement on scores
for individual questions.
Statistical analysis
Routine data management and description of the results were carried out
using SPSS 7.5 for Windows (SPSS
Corporation, 1997). Prevalence estimates were carried out using
STATA Release 6.0 (Stata Corporation,
1999) after allowing for both the two-phase sampling procedure and
different response rates across sites through the use of weights
(Pickles et al, 1995;
Dunn et al, 1999).
Information arising from the Phase 1 screening results and the Phase 2
sampling mechanism was processed by assigning a sampling weight
to each individual participant, given by the inverse of the Phase 2 sampling
fraction. The sampling weight is an indicator of how many participants in
Phase 1 are represented by each of the Phase 2 records. We used
adjustment weights in order to standardise the overall prevalence estimates at
each centre for the age and gender distribution of the population of
Santander, which served as our reference. Prevalence estimates were obtained
via a logistic model, producing a symmetrical confidence interval for the
regression coefficient, and then reversing the logistic transformation to
produce the corresponding interval for the prevalence itself. Prevalence
estimates were calculated separately for each of the study sites. We also
carried out a meta-analysis of the pooled data from the nine centres to obtain
an overall weighted prevalence.
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RESULTS |
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Response rates
Table 2 describes the
response rate and exclusions owing to errors in census registers or lists of
patients registered with primary care physicians. The overall response rate
for the first phase of the survey was 65%, which included variations by study
site, ranging from 54% (rural Ireland) to 90% (urban Spain). The overall
response rate for the Phase 2 survey was 73%, with a variation from 55% (rural
Ireland) to 92% (rural Finland). Non-responders were more likely to be male,
young and socio-economically disadvantaged. For the screening phase of the
survey, gender differences in non-response rate were significant at P
< 0.05 in rural Britain, Ireland and Norway. Response rates increased with
age.
Prevalence estimates
Figure 1 gives the weighted
prevalence of depressive disorders (ICD-10 and/or DSM-IV criteria) for survey
responders on each site, together with the 95% CIs. An analysis of the
combined sample (n=8.764) gave an overall prevalence of 8.56% (95% CI
7.05-10.37). The figures were 10.05% (95% CI 7.80-12.85) for women and 6.61%
(95% CI 4.92-8.83) for men. Rates in Liverpool were more than six times
higher, and in Oslo over three times higher, than those in Santander. There
was relatively little variation among the four rural areas, with weighted
prevalence ranging from 6.1% in Wales to 9.3% in rural Norway. In Britain and
Ireland urban rates were two to three times higher than in their rural
communities, but in Norway and Finland there was little difference between the
urban and rural figures. Figures
2 and
3 show that, at seven study
sites, women present higher proportions of depressive disorders than men,
although 95% CIs overlap at all the centres. A higher prevalence of depressive
disorders in the female population was found in the five urban settings.
Gender differences in prevalence were not so evident in the rural
settings.
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Subtypes of depressive disorders
Table 3 shows the weighted
prevalence of the different diagnostic categories according to the ICD-10
classification included under the depressive disorder umbrella diagnosis used
in the ODIN study. In the global sample, the weighted prevalence of depressive
episodes (F31-F33.3) was more than six times higher than the weighted
prevalence of dysthymia and adjustment disorder. This pattern was also found
at each of the nine study sites, and in both genders.
Table 4 shows the weighted
prevalence of the different categories according to the DSM-IV classification.
When DSM-IV and ICD-10 criteria were compared, there were notable similarities
in the overall prevalence figures for all diagnostic categories. However, in
Liverpool and Dublin and at the rural UK site, to a lesser extent, there is a
discordance between ICD-10 and DSM-IV diagnoses.
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DISCUSSION |
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Critical analysis of the data from our study, in which very significant morbidity differences can be seen between some centres, as well as excessively wide confidence intervals, suggests that in some of the participating centres, bias could have been introduced in some of these elements of the sample designing process. Thus, with regard to the element of guaranteeing the design of an initially representative sample, the differing age profiles of the surveyed samples and census populations in Britain and Dublin may reflect a discrepancy between samples drawn from census and primary care sources. Younger people may be less likely to be registered with a general practitioner, may be less motivated to take part in a study and may be more mobile and have a less structured lifestyle both obstacles to participation in a community study like the one presented here. There is evidence of systematic differences between survey responders and the populations from which they were drawn. Responders were more likely to be female, and older, than non-responders. Both of these factors may have introduced a bias towards higher prevalence rates among responders than among the survey populations. This trend may have been compounded at the British sites and in Dublin, where the primary care groups were older than their corresponding census populations. In addition, one of the nine study sites (Dublin) used a sample covering only part of the city, so it is possible that subgroups of the urban population characterised by the presence of negative psychosocial characteristics were over-represented. Some of the differences between centres may relate more to the socio-economic indices of the area sampled than to the city as a whole.
Regarding possible bias derived from non-responders, in our study there were highly significant differences in the response rates. The response rates for Phase 1 of the community survey were more than 20% higher in Santander than elsewhere. It is probable that this difference reflected the decision of the Spanish team to use an initial home interview, rather than the initial postal approach employed at the other centres. This hypothesis is supported by the fact that there was no such discrepancy in response rates between Santander and the other centres for the later phases of the survey, when similar methods were used across all centres. During the later phases, the Finnish sites tended to have the highest response rates. This may have been due to the efficiency and persistence of the research team, or to the relatively higher acceptability of such studies in general within the survey sample. This problem is more evident in urban areas than in rural ones, and within the former, precisely in large cities with areas suffering from social disintegration (Dublin and Liverpool) in which the sample selection process is more complicated and difficult than in middle-sized cities.
Multinational, multi-centric epidemiological studies often run into problems due to differing sampling frames and refusal rate levels (Copeland et al, 1999). In such studies, each centre must, necessarily, adapt the common research methodology in order to meet local administrative and ethical norms. Among other factors involved in such adaptation are different social attitudes towards collaborating in epidemiological studies, the quality of population registers, budgetary constraints, and the experience and ability of the local research team. The impact of these problems can partially be minimised in the morbidity analysis by applying appropriate statistical strategies. We have weighted our results to take different response rates into account; in addition, we used adjustment weights in order to standardise the overall prevalence estimates at each centre for the age and gender distribution of Santander's population, which served as our standard of reference. Nevertheless, these features of the survey impose some limitation on the generalisability of the findings at some of the study sites.
Other methodological considerations should also be considered in trying to explain the differences that we encountered, and the wide confidence intervals at some centres. The methodological decision to offer diagnostic interviews to only a 5% sample of participants below BDI cut-off led to considerably higher standard errors (and hence wider confidence intervals) than would have been the case if a larger proportion of BDI-negative participants had been included in the second phase of the community survey.
Prevalence estimates
The methodology used in this project, two-phase sampling, is a type of
stratified design that has been proposed in psychiatric research as an
efficient way of estimating prevalence of psychopathology in large
epidemiological surveys. The diagnostic instrument used in the second phase of
the study is the latest version of the SCAN, which has been presented by some
authors (Brugha et al,
1999) as closely approximating a clinical gold
standard.
The major finding of the present study is the wide difference in the prevalence of depressive disorders found across the study sites and between urban and rural centres. Taking the genders together, the centres fall into three categories: high prevalence (urban Ireland and urban UK: 12.8-17.1% respectively), low prevalence (urban Spain: 2.6%) and medium prevalence (the rest of the sites: 6-9.3%). The study found high proportions of depression among survey responders in some centres, particularly among the female population in urban areas. At seven of the nine study sites, the prevalence of depressive disorders was higher among women than among men, confirming the results of several previous studies (Bebbington et al, 1984; Weissman et al, 1996). Over the past few decades, there has been growing evidence of significant intergender differences in the rates of specific mental disorders (Lehtinen et al, 1990). A variety of social and medical factors have been considered in an attempt to explain the higher rate of depressive disorders in women (Bebbington et al, 1984; Vázquez-Barquero, 1987). Further analysis of the data collected in the epidemiological arm of the ODIN study will enable us to test whether some of the gender differences in the depressive disorders prevalence estimates across sites could be explained by different levels of exposure to life events and other social factors.
When DSM-IV and ICD-10 criteria were compared, there were notable similarities in the global prevalence figures for all diagnostic categories; however, at some centres a discordance was found between ICD-10 and DSM-IV diagnoses. Previous studies have also documented discordance between these two major psychiatric classification systems, for the diagnosis of cases in community surveys (Andrews et al, 1999). This could be due to the ICD-10's lower threshold in the number of symptoms required for the diagnosis of a depressive episode.
Urban-rural differences
Most research on the epidemiology of depression in Europe has been
conducted in urban settings. The few available studies assessing differences
in prevalence of depression between urban and rural areas vary widely in their
findings (Brown & Prudo,
1981; Vázquez-Barquero
et al, 1987;
Sievewright et al,
1991). This variety of outcomes could be attributable to
differences in the measures used, and in the selection and sampling of rural
and urban areas. In the ODIN study we tried to overcome these problems by
using the same methodology in the urban and rural sites at each centre. We
found that the weighted prevalence of depressive disorders among responders in
the four rural communities was relatively uniform, ranging between 6.5% and
9.3%. However, the weighted prevalence in the five urban communities varied
markedly, from 2.6% in Santander to 17.1% in Liverpool and 12.8% in Dublin.
These differences in the prevalence figures suggest that there are cultural
differences or different risk-factor profiles across countries and sites which
may affect the expression of the disorder. The role of psychosocial factors
(mainly life events and social support) in explaining these wide differences
in the prevalence of depressive disorder across study sites will also be
examined in later publications. Our finding of lower prevalence of depressive
disorders in rural areas than in urban ones, in three of the four countries
where urban and rural samples were studied, agrees with the results of other
epidemiological studies (Crowell et
al, 1986). In Britain, the National Psychiatric Morbidity
Survey reported higher rates of depression in urban areas than in rural ones,
but relied on the interviewers' opinion of whether subjects lived in an urban,
semi-rural or rural areas (Jenkins et
al, 1997). The Netherlands Mental Health Survey and Incidence
Study also found that people living in rural regions showed lower prevalences
of mood disorders (Bijl et al,
1998).
In the ODIN study, not only did the rural communities show a lower prevalence of depressive disorders than the urban ones, but the prevalence figures in rural areas remained strikingly similar across the different sites, whereas the urban figures varied markedly from one country to another. A methodological factor in our design could have influenced this uniform distribution of depressive disorders among rural communities in Europe found in the ODIN study. In our methodology, rural was defined according to a similar socio-economic definition: rural areas were those having no centre of population greater than 15 000 people, with at least 20% of economically active citizens engaged in occupations directly related to agriculture, fishing or forestry. On the contrary, urban was defined solely in terms of population density criteria. Thus, the uniform distribution of depressive disorders across the rural sites could be merely a reflection of a similar distribution of socio-economic factors. On the contrary, the heterogeneity of the prevalence figures in the urban sites could be related in part to the heterogeneous socio-economic circumstances across the study sites. Further analysis of the ODIN sample will enable us to study to what extent these urban-rural differences in the prevalence of depressive disorders may be related to differential exposure to life events or differential levels of social support networks, as has been recently proposed by Paykel et al (2000).
Implications
Although the representation of European countries is incomplete, the
centres in this study are spread between northern and southern Europe,
representing different religious denominations and covering mainland areas and
islands. The information gathered in the ODIN study could be used to evaluate
needs for treatment and the allocation of health resources. However, some
authors have raised concerns about the health policy implications of high
prevalence rates of psychiatric disorders for determining treatment needs
(Regier et al, 1998;
Spitzer, 1998). In particular,
they question whether making a psychiatric disorder diagnosis can be equated
with demonstrating a treatment need. It is still unclear to what extent the
psychiatric disorders, as defined by rigorous criteria such as the ICD-10 or
DSM-IV used in our project, identified in community populations are equivalent
to those identified with the same criteria in clinical settings, or whether
they have the same clinical significance and response to treatment. The design
of the ODIN enables us to introduce new elements of discussion into this
debate, and adds fresh evidence that validates the ability of epidemiological
studies to identify subjects with depressive disorders who might benefit from
therapeutic interventions. As we have described elsewhere
(Dowrick et al,
1998), this is the first population-based study that incorporated
into its design a randomised controlled trial of individual problem-solving
treatment and a group psychoeducation programme. Participants identified as
having depression in the epidemiological phase were offered the chance to take
part in the controlled trial, the results of which
(Dowrick et al, 2000)
suggest that psychological interventions are effective in reducing depressive
caseness, symptoms and personal disability in the short term. Thus, our
prevalence estimates identified a segment of the population with a depressive
disorder that could benefit from an intervention.
We believe that the data in the present study confirm that depressive disorder is a highly prevalent condition among working-age adults in Europe, particularly in urban centres, and that this epidemiological information should be used to inform and implement equitable and effective health policies across the continent in order to address this public health challenge.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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The ODIN Group is composed of the academic colleagues and research and administrative staff who have worked on this part of the Outcome of Depression International Network (ODIN) Project. They include: Javier Ballesteros, Gail Birkbeck, Trygve Børve, Maura Costello, Pim Cuijpers, loana Davies, Juan Francisco Diez-Manrique, Nicholas Fenlon, Mette Finne, Fiona Ford, Luis Gaite, Andres Gomez del Barrio, Claire Hayes, Andrés Herrán, Ann Horgan, Tarja Koffert, Nicola Jones, Marja Lehtilä, Catherine McDonough, Erin Michalak, Christine Murphy, Anna Nevra, Teija Nummelin and Britta Sohlman.
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Received for publication July 3, 2000. Revision received April 5, 2001. Accepted for publication April 6, 2001.
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