The diagnostic category of mood disorders is among the most prevalent of all mental health diagnoses (1–3). (For a definition of prevalence and incidence, refer to Goldner and colleagues; 4). Conducting a review of this category of mental disorders is challenging for several reasons. One challenge is that the various conditions that comprise this grouping of disorders can be quite disparate: some are transient, others are chronic or recurrent. Therefore, it is difficult to speak in generalities about the characteristics of mood disorders. Although most mood disorders result in some degree of temporary cognitive difficulty and mild deficits in attention or concentration, some forms of mood disorders are associated with profound cognitive and perceptual disturbance with delusional and hallucinatory features. Another set of challenges to reviewing the epidemiology of mood disorders involves the nosological frameworks and systems of psychiatric nomenclature currently in use (5,6). Mood disorders are constructs (that is, entities that cannot be directly measured or observed) developed through inference, hypothesis, deduction, and conjecture (1). Although mood disorders are described as taxonic (that is, defined in categorical terms) in current diagnostic nosological systems, some of their features may be more usefully conceptualized in dimensional terms (7). Given the uncertainties of our diagnostic constructs for mood disorders, previous reviews unsurprisingly report highly variable rates for all mood disorders (3,8–12) (Table 1). In addition, individuals may alternate between various mood disorders during their lifetime (13), making it difficult to determine the onset and cessation of disease episodes.
This review addresses some of the challenges described above by presenting the results of a systematic review of literature published between January 1, 1980, and December 31, 2000, that reports findings of the prevalence and incidence of mood disorders in both general population and primary care settings. We examine the variability of the rates of disorders across studies and attempt to determine how much of this variance can be accounted for by various factors, using hetero- geneity analysis. This technique should decrease the variance associated with heterogeneous study methods and diagnostic constructs and subsequently provide a more robust estimate of the variation in rates of disorders.
We focus our discussion on the mood disorders that have been most extensively examined in epidemiologic studies: major depressive disorder (MDD), dysthymia, and bipolar I disorder (BD I). MDD has been found to be one of the most prevalent mood disorders in previous reviews (Table 1). It is important not only because of its prevalence but also because of its associated disability. The Global Burden of Disease study found that the disability associated with MDD is surpassed only by that associated with ischemic heart disease in industrialized countries (14). Previous reports have also highlighted recent trends toward an increasing prevalence of depression. This may mean that depression will be even more of a burden on society in future years (14,15). We selected the epidemiology of MDD in primary care as an additional topic of interest for this review. Several jurisdictions are currently attempting to address care gaps in the management of depression in primary care, such as the low detection rate by primary care physicians (16). Establishing robust estimates of the prevalence of depression in primary care will inform these policy initiatives.
Dysthymia is characterized by chronic low-grade depressive symptoms and can be particularly disabling when it is comorbid with other mood disorders, such as MDD. About 70% of those with dysthymia may eventually go on to develop MDD (13). Relative to other mood disorders, research on the epidemiology of dysthymia has been scarce (3); however, several recent studies have examined this disorder in more detail (17–19). BD I, also known as manic-depressive disorder, may be the most disabling of mood disorders (14). About 10% of people with MDD eventually develop BD, which makes studying the epidemiology of this disorder difficult (13).
The methods employed in this review have been presented in more detail elsewhere (4). We searched the Medline and HealthSTAR databases for relevant studies, using the key indexing terms epidemiology, prevalence, and incidence, combined with the search terms mental disorders, mood disorders, depressive disorders, major depression, dysthymia, and bipolar disorders. We limited the search to English-language studies published between January 1, 1980, and December 31, 2000. Reference lists of relevant primary and review articles identified were also searched.
General population prevalence and incidence studies were eligible for inclusion if they were community surveys using probability sampling techniques. For primary care studies, those that randomly or consecutively sampled a population of primary care attendees for a given period were included. Eligible publications were restricted to studies having sample sizes of 450 or over. We chose this number as the lower limit of sample size based on preliminary calculations (using the formula in Kelsey and colleagues; 20, p 282) that demonstrated adequate error rates for a range of expected prevalence rates. Studies were also eligible for inclusion if they examined age ranges covering the adult population. Only studies using operationalized diagnostic criteria and case identification based on either standardized instruments or clinician diagnosis were included. Prevalence data, including overall, sex-specific, and age-specific rates, were extracted from eligible studies.
We conducted qualitative analyses of variables related to methodology to summarize and elucidate any observed differences between rates. As well, we pooled each set of rates according to a Bayesian approach to metaanalysis, using the Fastpro software program. Readers interested in a more detailed discussion of this approach should refer to Eddy and colleagues (21). We analyzed each of the pooled rates for heterogeneity, using chi-square tests according to Fleiss’ method (22).
Description of Studies
Of the 77 prevalence studies for which full-text articles were reviewed, 42 studies were excluded: 30 studies did not meet eligibility criteria, and 12 presented duplicate data. Thus, 35 prevalence papers of mood disorders met eligibility criteria for the review (17–19,23–54); 18 unique primary investigations of prevalence were included. The reasons for excluding prevalence studies fulfilling all but 1 of the inclusion criteria are documented in Table 2 (55–66). Of the 27 incidence studies identified, 22 were excluded: 16 did not meet inclusion criteria, and 6 were based on duplicate data. Therefore, 5 general population incidence studies of mood disorders were included (17,44,46,67,68). Five of the excluded studies almost met inclusion criteria, and reasons for their exclusion are presented in Table 2 (61,69–72).
Primary Care Studies. For MDD in primary care, 9 prevalence and 2 incidence studies as well as 4 review papers were identified and retrieved. An additional 34 prevalence studies and 1 incidence study resulted from the reference lists of identified articles. Of the 43 prevalence studies identified, 33 were excluded as they did not fulfill inclusion criteria. Thus, 10 primary care prevalence studies met eligibility criteria for the review (73–82). Excluded studies almost meeting eligibility criteria are presented in Table 2 (83–89). Of the 3 incidence studies identified, 2 met eligibility criteria (90,91).
Prevalence Studies. We present findings for the 24 papers reporting overall or sex-specific 1-year and lifetime prevalence rates and age-specific lifetime prevalence rates for mood disorders in general, for MDD, for dysthymic disorder, and for BD I (Table 3 to 5, Figure 1). The results of 2 studies reporting only 1-year age-specific rates are not reported here (35,44). The results of studies reporting only data for point prevalence or 6-month prevalence (23,30,38–40,45,46) or for mood disorder categories other than those stated above (23,42,92) are also not presented.
Because the diagnostic nomenclature for mood disorders has undergone revisions from DSM-III to DSM-IV criteria, it was necessary to make assumptions with respect to the classification of various terms in these studies to be in accordance with DSM-IV criteria. These terms, along with the DSM-IV categories to which they are assumed to be equivalent, are in Table 6.
All the studies presented are community surveys using samples ranging in size from approximately 500 (49) to 20 000 (26) subjects. For each study, the percentage confidence interval (CI) width or error rate for estimated prevalence at a 95% confidence level may be calculated using the formula provided by Kelsey and colleagues (20, p 282). Most studies used either the Diagnostic Interview Schedule (DIS) or the Composite International Diagnostic Interview (CIDI) administered by trained lay interviewers and applied algorithms to derive diagnoses.
For dysthymic disorder, 1-year prevalence ranged from 0.8 per 100 in Ontario (19) to 3.3 per 100 in the US Epidemiologic Catchment Area (ECA) study (26), a variation of slightly more than 4-fold. For BD I, 1-year prevalence rates ranged from 0.2 per 100 in Udmurtia (17), New Zealand (43), and Edmonton (24) to 1.5 per 100 in Florence, Italy (54), for a difference of 7.5-fold. (42).
For dysthymic disorder, 1-year prevalence ranged from 0.8 per 100 in Ontario (19) to 3.3 per 100 in the US Epidemiologic Catchment Area (ECA) study (26), a variation of slightly more than 4-fold. For BD I, 1-year prevalence rates ranged from 0.2 per 100 in Udmurtia (17), New Zealand (43), and Edmonton (24) to 1.5 per 100 in Florence, Italy (54), for a difference of 7.5-fold. (42).
Lifetime Prevalence. The lifetime prevalence rates for mood disorders in general ranged from 5.4 per 100 in Korea (37) to 31.4 per 100 in Montreal (31), for a 5.8-fold difference. Studies with high rates were based on DSM-III-R or ICD-10 criteria, and lower rates were associated with studies using DSM-III criteria. For MDD, lifetime prevalence varied from 0.88 per 100 in Taipei (34) to 29.6 per 100 in Montreal (31). This is a difference of more than 33-fold. Studies reporting the highest rates employed DSM-III-R criteria, while studies reporting lower rates used DSM-III criteria, with the lowest of rates being reported in Asian countries. With respect to dysthymic disorder, lifetime prevalence ranged from 0.92 per 100 in Taipei (34) to 14.0 per 100 in Montreal (31), a variation of approximately 15-fold. Again, Asian studies reported the lowest rates. Lifetime prevalence rates for BD I ranged from 0.15 per 100 in Hong Kong (29) to 1.8 per 100 in the Netherlands (18), which is a variation of 12-fold. Examination of the studies does not reveal any regional patterns or relevant methodological differences that may help to explain the variation in rates of BD I.
Sex-Specific Prevalence. Table 5 presents findings from studies reporting sex-specific 1-year and lifetime prevalence rates for mood disorders. For mood disorders (in general), 1-year and lifetime prevalence rates were consistently found to be between 1.5 and 2 times higher for women than for men. Studies reporting 1-year and lifetime sex-specific rates for MDD also consistently demonstrated rates for women that were approximately 1.5- to 2.5-fold higher than for men. For dysthymic disorder, 1-year and lifetime prevalence rates were again generally between 1.5 and 2.5 times higher for women than men, with the exception of 1 study that reported a lifetime prevalence for women to be almost 5 times that for men (27). There was less consistency observed for sex-specific rates of BD I. While most 1-year and lifetime rates for BD I were found to be very similar for men and women, some studies demonstrated higher rates for women (18,52,54) and some for men (27,34,93).
Age-Specific Lifetime Prevalence. Results from each study reporting age-specific lifetime prevalence rates for mood disorders are presented in Figure 1. As shown, lifetime prevalence rates for MDD and BD I disorder seem fairly stable through ages 18 to 64 years. Dysthymic disorder slightly increases in prevalence with increasing age.
Estimation and Heterogeneity Analysis of Pooled Best-Estimate Rates.
Major Depressive Disorder. The best-estimate rates for 1-year and lifetime prevalence were 4.1 per 100 (95%CI, 2.4 to 6.2 per 100) and 6.7 per 100 (95%CI, 4.2 to 10.1 per 100), respectively (Tables 3 and 4). The variations in the 1-year and lifetime prevalence rates, as shown by the CIs, are 2.6-fold and 2.4-fold, respectively, which are much lower than the respective 24-fold and over 30-fold differences observed across individual rates. Heterogeneity analysis demonstrated significant differences across 1-year and lifetime prevalence rates of MDD. For studies conducted in Europe, the pooled 1-year rates were found to be approximately 3 times higher than those of studies carried out in other parts of the world (Table 7). Conversely, for studies conducted in Asia, pooled 1-year and lifetime rates were approximately 7 times lower, compared with studies carried out in non-Asian countries. Studies using DSM-III-R criteria had pooled 1-year and lifetime rates that were around 3 to 4 times higher than those for studies using other diagnostic criteria. Finally, studies having response rates of under 75% had a pooled lifetime rate approximately 3 times greater than that of studies having response rates of 75% or over.
Dysthymic Disorder. The best-estimate rates for 1-year and lifetime prevalence were 2.0 per 100 (95%CI, 1.3 to 2.8 per 100) and 3.6 per 100 (95%CI, 2.5 to 5.0), respectively (Tables 3 and 4). The variations in the CIs for these 1-year and lifetime prevalence rates are 2.2-fold and 2.0-fold, respectively, which is much smaller than the respective variations of 4.1-fold and 15.2-fold observed across individual rates. Heterogeneity was demonstrated for 1-year and lifetime prevalence rates of dysthymic disorder. Studies conducted in Asian countries produced a pooled lifetime rate that was almost 4 times lower than that for studies carried out in non-Asian countries (Table 7). For studies using the CIDI (DSM-III-R), pooled lifetime rates were approximately twice as high, compared with studies using other instruments or criteria.
Bipolar I Disorder. The best-estimate rates for 1-year and lifetime prevalence were found to be 0.72 per 100 (95%CI, 0.5 to 1.0) and 0.82 per 100 (95%CI, 0.56 to 1.1), respectively (Tables 3 and 4). The CI variations for the 1-year and lifetime rates are 2.0-fold and 2.0-fold, respectively, which are much lower than the respective 7.5-fold and 12-fold differences observed across individual rates. Significant differences were found among 1-year and lifetime prevalence rates of BD I. For studies using the CIDI, the pooled lifetime prevalence was twice that of studies using other diagnostic instruments (Table 7). Similarly, for studies using DSM-III-R criteria, the pooled lifetime prevalence was 2.7 times that for studies using other diagnostic criteria.
The incidence studies conducted in Edmonton, Alberta, (67) and in the US (68) were prospective follow-up studies of community-based samples with total populations of 1964 and 10 285, respectively. The studies conducted in Norway (46) and Udmurtia (17) were also based on community samples but involved retrospective assessments of incidence. The 2 prospective studies used the DIS and an algorithm to extract diagnoses, while the retrospective studies employed the CIDI and a clinician diagnosis.
Qualitative Analysis. The 1-year incidence for MDD ranged from 1.6 per 100 in the US ECA study (68) to 7.5 per 100 in Udmurtia (17). This represents a 4.7-fold variation. Excluding the outlying rate from the study conducted in Udmurtia, the rates vary up to 2.8 per 100, a difference of only 1.8-fold. With regard to sex-specific 1-year incidence rates, 3 of the studies reported rates for women that were approximately twice those for men, while the Norwegian study (46) found the rate for women to be more than 6 times that for men.
Estimation and Heterogeneity Analysis of Pooled Best-Estimate Rates. The best-estimate rate for 1-year incidence of MDD was 2.9 per 100 (Table 8). Heterogeneity analysis of the 1-year incidence rates for MDD revealed significant differences across rates. Pooled sex-specific rates were not calculated, as 1 study did not provide the required demographic data. Further analysis to determine the variables contributing to heterogeneity was not carried out because of the small number of rates.
Primary Care Studies
Qualitative Analysis. Prevalence rates of MDD in primary care ranged from 2.2 per 100 in New Hampshire (74) to 36.1 per 100 in female primary care attendees in Wisconsin (77) (Table 9). Although many studies attempted to determine point prevalence rates of MDD, studies used widely varying methods. For instance, the denominator value in this calculation at times used unique patients, and at other times no definition was provided at all. This is problematic as rates generated using unique visits in the denominator would likely be different from those using unique patients. Even assuming all studies used either unique patients or unique visits, there is variation in the period of subject recruitment or an absence of study-period description. Owing to this wide variation in even the basic definitions of prevalence rates in primary care, we did not attempt to perform any data analysis on the rates and only described them qualitatively.
One intriguing finding of this review is the relatively low lifetime prevalence rate of MDD in similarly conducted, high-quality studies, compared with the typically reported rates of 5% to 12% for men and 10% to 25% for women (6). The finding of selective reporting of high rates for MDD warrants further examination in other domains of health care to determine whether this is specific to depression or whether it is a more generalized phenomenon.
With respect to the incidence findings, the rates seem implausibly large. If these incidence rates were applied to the calculation of morbidity risk (that is, cumulative annual incidence rates), over 100% of the population would suffer from depression over some period of time. Although 3 of the 4 incidence studies explicitly state that only first-onset cases of depression were sought, it is possible that, owing to recall bias, some recurrent or chronic cases were also included in the incidence figures, leading to overestimated rates.
Another important finding related to MDD is that rates in primary care were not comparable because of differing study methodologies. This is unfortunate, as robust estimates of point prevalence rates would allow for the estimate of pretest probabilities for screening tools used by primary care physicians (94), and 1-year prevalence rates in primary care would allow for comparison with rates in community studies.
The major flaw in primary care studies was the lack of standardized definitions for point and period prevalence. Many studies defined cases and the population at risk differently and measured these factors over differing time periods. In future studies, case definitions should be explicit and multiple rates should be provided, distinguishing cases and the population at risk over standard time periods, such as 6 months or 1 year.
Consistent with other studies (1–3), we found that women had a higher rate of MDD and dysthymia but that men and women were equally affected by BD I. This review, unlike earlier reviews, was able to incorporate the findings of several recent studies that examined the diagnosis of dysthymia. The inclusion of such studies confirmed that dysthymia is approximately one-half as common as MDD.
Similar to other mental disorders examined in this series (4), despite controlling for several methodological factors, there was persistent variation in the prevalence and incidence rates of mood disorders across studies. This was particularly noticeable when the range of rates was used as a measure of variation. When the CI was used as a measure of variation (a technique that reduces the effect of outliers and smaller studies), there was a lower, but still notable, 2-fold variation in rates. This may imply a degree of “real” variation in mood disorders, potentially related to variation in the distribution of risk factors for these conditions. Because of this persistence of variation across studies, the best-estimate rates in this review should not be reported without their CIs. This issue of unexplained variation warrants further study.
There are some important clinical implications of the findings from this review. When clinicians discuss the prevalence of mood disorders with consumers, they may need to revise the estimates provided in standard medical texts downward. The absence of consistently reported prevalence figures in primary care settings for MDD makes it difficult for clinicians to determine the pretest probability of this disorder in these settings. This may add uncertainty to accurately diagnosing MDD with brief diagnostic tools.
This review quantified several explanatory variables for the remaining variation in prevalence rates. The effects of diagnostic criteria were specifically examined, with studies using CIDI having approximately 2 times the rate of dysthymic disorder and BD I, compared with studies using DIS (DSM-III) criteria. Although interviewer effects are a significant matter (95), the type of person administering the interview (that is, layperson vs clinician) was not an important source of variation in the studies reviewed. Studies of lifetime prevalence of mood disorders reported significantly higher rates when published after 1992. Generally, there were higher rates in European studies and lower rates in Asian studies. All these findings regarding heterogeneity should be tempered by the fact that these are ecological comparisons rather than comparisons at the level of individuals; thus, the findings may be confounded by multiple variables. Although this review attempted to limit variation as a result of study methodology, further unexamined sources of potential variation could be important, such as differences in the measured concept of depression across cultures, differences in respondent reporting rates, differing age of onset, marital status and employment status, duration of illness, recovery and mortality rates between jurisdictions, and the effects of migration (3,15,96).
Funding and Support
All authors received salary support from the British Columbia Ministry of Health.
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Manuscript received August 2002, revised, and accepted May 2003.
1. Bioethicist, Centre for Addiction and Mental Health, Joint Centre for Bioethics and Faculty of Law, Department of Psychiatry, University of Toronto, Toronto, Ontario.
2. Associate Professor, Department of Psychiatry, Head, Mental Health Evaluation and Community Consultation Unit (MHECCU), University of British Columbia, Vancouver, British Columbia
3.Research Assistant, Mental Health Evaluation and Community Consultation Unit (MHECCU), University of British Columbia, Vancouver, British Columbia
Address for correspondence: Dr P Waraich, Mental Health Evaluation and Community Consultation Unit, St Paul’s Hospital, 306 A-1081 Burrard Street, Vancouver, BC V6Z 1Y6
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