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 The descriptive epidemiology of major depression is important for 2 reasons: for the generation of etiologic hypotheses and for administrative and planning purposes. In Canada, past descriptive data about major depression have been available from several sources. In the 1980s and 1990s, prevalence estimates were available from epidemiologic studies conducted in regional (1) and provincial (2) populations. Since the mid-1990s, the NPHS has been a source of national cross-sectional (3) and longitudinal (4,5) data. The NPHS, however, used a brief predictive version of the CIDI (6) rather than a full version of the interview. The version of the CIDI used in the CCHS 1.2 was the WMH-CIDI, developed for the World Mental Health 2000 Project. Although detailed validation data have not been reported, this instrument has been greatly refined compared with previous versions of the CIDI (7). The high standard of epidemiologic measurement and the large and nationally representative nature of the CCHS 1.2 sample provide an unprecedented opportunity to describe the epidemiology of major depression in the Canadian population. MethodsThe CCHS 1.2 was a nationally representative community mental health survey conducted by Statistics Canada (the national statistical agency) between May 2002 and December 2002. The target population included persons aged 15 years or over and living in private occupied dwellings (98% of the population). Excluded were individuals living in health care institutions, on Indian Reserves, on government-owned land, in 1 of the 3 northern territories, or in remote regions. Full-time members of the armed forces were sampled separately and are not included in these analyses. One person aged 15 years or over was randomly selected from sampled households. A significant effort was made to interview respondents in person at their place of residence (86% of cases). Interviews were conducted in English, French, Chinese, or Punjabi (as required or requested by the interviewee). From the initially selected 48 047 households, there was an 86.5% household-level response rate, and among responding households, there was an 89.0% person-level response rate. The overall response rate was thus 77.0%, resulting in a total sample size of 36 984 respondents. The CCHS 1.2 interview is based on the WMH-CIDI. Well-trained lay interviewers using computer-assisted interviewing procedures administered the survey. Five disorders were evaluated: major depression, BD (as indicated by the presence of one or more manic episodes), social phobia, agoraphobia, and panic disorder. Diagnostic algorithms followed DSM-IV criteria, with the exception of the duration requirement for a manic episode. The CCHS asked only whether manic symptoms had lasted "several days or longer," whereas a duration of 7 days is required by the DSM-IV unless hospitalization is necessary. For every respondent, each disorder was assessed for both lifetime and past-12-month history. The CCHS 1.2 used a multistage stratified cluster design to select eligible households. To correct the potential bias resulting from this complex survey design, Statistics Canada recommends bootstrapping of all tests according to a set of replicate weights that they supply. All results presented here were produced with this approach and are therefore representative of the targeted population. Similarly, statistics dependent on standard errors (including P values and CIs) are adjusted for survey design effects. All analyses were conducted at the Prairie Regional Data Centre on the University of Calgary campus, using SAS software (8). Conducting a detailed analysis of comorbidity data was beyond the scope of this analysis. However, the epidemiologic distinction between MDE and MDD, defined by the presence of one or more MDEs in the absence of manic, hypomanic, or mixed episodes (9), is an important one. In this analysis we defined probable BD by identifying subjects with one or more lifetime manic episodes according to the WMH-CIDI. We evaluated the epidemiology of MDD by identifying subjects with MDEs who did not meet this definition. The capacity of the WMH-CIDI to diagnose manic episodes is somewhat uncertain, as prevalence estimates from the CCHS 1.2 (10) have been higher than expected according to published structured reviews (11). Despite these uncertainties, conducting analyses with both disorder-based and episode-based definitions was considered useful because this approach could offer a more refined sense of the impact of this distinction on the prevalence estimates. ResultsThe overall annual prevalence of MDE was 4.8% (95%CI, 4.5% to 5.1%). The lifetime prevalence was 12.2% (95%CI, 11.7% to 12.7%), and the 30-day prevalence, an approximation of point prevalence, was 1.8% (95%CI, 1.6% to 1.9%). After the subjects with BD were removed, the annual prevalence of MDD was found to be 4.0% (95%CI, 3.7% to 4.2%), the lifetime prevalence was 10.8% (95%CI, 10.3% to 11.3%), and the point prevalence was 1.3% (95%CI, 1.1% to 1.4%). Some of the associations between major depression and demographic factors in the CCHS 1.2 were anticipated. Table 1 presents annual prevalence data for MDD by sex, age group, marital status, income category, level of education, urban or rural status, the presence (or absence) of one or more chronic medical conditions, and past-year employment status. MDD was more common in women, in younger age categories, in single (never-married) or previously married subjects (divorced, widowed, or separated), in those who reported one or more chronic medical conditions, and in those who were not employed within the past year. There was no evidence of difference across educational categories. The 12-month prevalence estimates for urban, compared with rural, subjects suggested a higher prevalence in urban areas, but the estimates were not statistically definitive.
The estimates in Table 1 are for the annual prevalence of MDD. In other words, subjects categorized as having had a manic episode during their lifetime were excluded. The pattern for MDEs was similar to that of MDD, except that the prevalence was slightly higher. A similar pattern was also observed for lifetime prevalence, with the exception of 2 variables. The first variable was the lifetime prevalence of MDD, which increased from the group aged 15 to 25 years (8.8%; 95%CI, 7.9% to 9.7%) to the group aged 26 to 45 years (12.2%; 95%CI, 11.3% to 13.0%) . It remained approximately the same in the group aged 46 to 64 years (12.4%; 95%CI, 11.5% to 13.3%); it then declined in the group aged 65 years and over (6.4%; 95%CI, 5.7% to 7.2%). Urban or rural place of residence was the other variable that differed in the examination of lifetime prevalence. According to the associated CIs in the lifetime prevalence data, the slightly higher prevalence of major depression in urban areas was less likely to have emerged by chance: the lifetime prevalence of MDD was 11.1% in urban residents (95%CI, 10.5% to 11.6%) and 9.5% in the rural residents (95%CI, 8.6% to 10.4%). The pattern for point prevalence resembled that of annual prevalence (see Table 1), except for the age distribution. Unlike annual prevalence, the point prevalence did not decline with age until age 65 years. To explore the epidemiologic pattern further, a logistic regression analysis was conducted. As the objective of the modelling was descriptive (as opposed to the more analytic goal of elucidating etiologic effects), the modelling focused on demographic predictors rather than on potential risk factors. The logistic models predicted 12-month prevalence of MDD. Neither education nor urban residence was significantly associated with major depression in the logistic regression models. No interactions were observed between marital status and sex. A reduced model is presented in Table 2. There were 3 statistically significant interactions related to age. A sex-by-age interaction (Wald statistic 14.169, P = 0.0002) indicated that the effect of sex on major depression prevalence decreased with age. Both never-married (Wald statistic 12.030, P = 0.00052) and previously married (divorced, widowed, or separated) (Wald Statistic 8.503, P = 0.00355) status interacted with age. The interaction terms are presented in Table 2 and suggest that the effect of single marital status increases with age, whereas the effect of previously married status diminishes with age.
These results are complex and challenging to interpret on an intuitive level. The lack of higher-order interaction terms (age by sex by marital status) can be interpreted to mean that the interactions between marital status and age are consistent in men and women (at least that there is no statistical evidence to the contrary). The sex-by-age interaction, however, also means that the model=s predicted prevalence in relation to marital status and age unfold differently in men and women. The more rapid decrease in prevalence with age in women results in the model-based prediction that prevalence declines in single women with age (although at a slower rate than in married subjects) but that the prevalence may actually increase slightly in single men with age. This was an unexpected result. To further assess the predictions, annual prevalence was calculated in single men aged 45 years or under and in those aged 46 years and over. In men, the prevalence was 3.7% in the younger group and 4.1% in the older group. In women, the prevalence in the younger group was 7.3% and 6.9% in the older age category. Fitted proportions from the model depicting major depression prevalence as a function of age in single men and women are presented in Figure 1.
We had some concern that the grouping of divorced, widowed, and separated subjects was too broad, so we made a post hoc tabulation of individual marital status categories in relation to annual MDD prevalence. It was also possible to stratify these by age (using age 65 years as a cut point), except that it was not possible to estimate the prevalence in the subset aged 65 years and over who were single. This tabulation is presented in Table 3. Each unmarried group had a prevalence greater than the respective married group. The divorced and separated subjects had a prevalence approximately 2 to 3 times that of the married subjects, irrespective of age.
The prevalence of (lifetime) BD was 2.4% (95% CI, 2.1% to 2.6%), which is higher than expected. This may reflect measurement issues related to the CIDI interview. Most past authors have either not reported BD prevalence (12), have qualified their CIDI-derived estimates of BD by excluding some CIDI positive subjects (13), or have presented estimates comparable to the CCHS 1.2 result (14). DiscussionThe NCS-R in the US reported a slightly higher annual prevalence of MDD (6.6%) (15). The European ESEMeD survey (12) reported a 3.9% annual prevalence, which is comparable to that of the CCHS 1.2. The NCS-R report excluded subjects with manic episodes in its definition of MDD; however, it is not clear whether the ESEMeD study did so. The tendency of the sex difference in major depression prevalence to diminish with age has been previously reported. One British study even reported a reversal of the prevalence difference (16). In the Canadian literature, interactions between age and sex in predicting major depression prevalence have not been previously identified (3,17). The occurrence of this interaction in the CCHS 1.2 may relate to the survey=s large sample size (and therefore diminished probability of Type II error). The CCHS 1.2 sample was more than twice as large as that of the NPHS. The WMH-CIDI is also a more sophisticated instrument than the CIDI Short Form (6) used in previous national surveys in Canada. Since improved measurement may reduce the extent to which associations are diluted by nondifferential misclassification bias (18), this could also explain why previously undetected interactions were identified in this study. Some of the associations observed in the CCHS 1.2 may reflect etiologic factors, but the cross-sectional nature of the study in most cases precludes reaching this conclusion. Low income, for example, may be a cause or a result of major depression. The association with chronic conditions is important for population health because increased mortality has been associated with depression in various clinical contexts (19–22), most notably in association with cardiovascular disease (23–26). Depression can also have a negative impact on treatment adherence (27–30), impairment (31), and symptom expression (32). Perhaps most important, depression is a key determinant of quality of life in various illness contexts (33–40). Data from the CCHS 1.2 help to clarify an important question about the urban–rural distribution of major depression. The CCHS 1.2 data indicate that there is an urban–rural prevalence difference in Canada, as has been previously suggested (41), but the data also demonstrate that the difference is small. The result is comparable to what has been reported by other WMH surveys (12,15). The lack of association of major depression prevalence with education level in the CCHS is consistent with the results of methodologically similar studies in Europe (12) but differs from a recent US finding (7). The CCHS 1.2 results generate the hypothesis that international differences may exist in the association of education level with major depression prevalence. Descriptive epidemiologic data can be used for hypothesis generation and for planning purposes. These results point toward several research questions that should be further evaluated by analytical studies. Several key questions are highlighted here: Is the diminishing prevalence of major depression with age related to psychological (for example, maturational) factors, to age-related endocrine or other biological changes, or to social roles? Why are sex differences seen in these associations? Again, potential explanations may relate to biological, psychological, or social determinants or to a combination of them. Why does being single present different patterns of association with major depression in men and in women? Is the higher prevalence in urban areas related to etiologic differences (for example, those related to the psychosocial environment) or to the migration of those with illnesses into cities and towns? How well does the pattern of prevalence reflect a need for treatment, and to what extent are health care needs being met? This question seems particularly important as the annual prevalence peaks in the youngest age group, where the risk–benefit ratio for antidepressant treatments may differ (42). Finally, in planning for the future, it will be necessary to know whether the decline in prevalence in older age groups represents a measurement artifact (that is, the possibility that the CIDI may be insensitive in this age group) or recall failure (43,44) or whether a genuine cohort effect may cause an increased future demand for services. Funding and SupportThis project was supported by an operating grant from the Canadian Institutes of Health Research (CIHR 119681). Dr Patten is a Health Scholar with the Alberta Heritage Foundation for Medical Research (AHFMR). Dr Beck is an AHFMR Clinical Fellow, and holds a CIHR Fellowship. Dr Maxwell is a CIHR New Investigator and an AHFMR Population Health Investigator. AcknowledgementsThe data upon which these analyses are based is derived from a survey conducted by Statistics Canada. The opinions expressed in this paper do not represent the opinions of Statistics Canada. References1. Spaner D, Bland RC, Newman SC. Major depressive disorder. Acta Psychiatr Scand 1994;Suppl 376:7–15. 2. Offord DR, Boyle MH, Campbell D, Goering P, Lin E, Wong M, and others. One year prevalence of psychiatric disorder in Ontarians 15 to 64 years of age. Can J Psychiatry 1996;41:559–63. 3. Beaudet MP. Depression. Health Reports 1996;7(4):11–24. 4. Beaudet MP. Psychological health–depression. 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Bush DE, Ziegelstein RC, Tayback M, Richter D, Stevens S, Zahalsky H, and others. Even minimal symptoms of depression increase mortality risk after acute myocardial infarction. Am J Cardiol 2001;88:341. 27. Carney RM, Freedland KE, Eisen SA, Rich MW, Jaffe AS. Major depression and medication adherence in elderly patients with coronary artery disease. Health Psychol 1995;14(1):88–90. 28. Mohr DC, Goodkin DE, Likosky W, Gatto N, Baumann KA, Rudick RA. Treatment of depression improves adherence to interferon b-1b therapy for multiple sclerosis. Arch Neurol 1997;54:531–3. 29. Ciechanowski PS, Katon WJ, Russo JE. Depression and diabetes. Impact of depressive symptoms on adherence, function, and costs. Arch Intern Med 2000;160:3278–85. 30. Singh N, Squier C. Determinants of compliance with antiretroviral therapy in patients with human immunodeficiency virus: prospective assessment with implications for enhancing compliance. Aids Care 1996;8:261–9. 31. Ramasubbu R, Robinson RG, Flint AJ, Kosier JT, Price TR. Functional impairment associated with acute poststroke depression: the Stroke Data Bank Study. J Neuropsychiatry Clin Neurosci 1998;10:26–33. 32. Hotopf M, Mayou R, Wadsworth M, Wessely S. Temporal relationships between physical symptoms and psychiatric disorder. Results from a National Birth Cohort. Br J Psychiatry 1998;173:255–61. 33. Beghi E, Roncolato M, VinonB G. Depression and altered quality of life in women with epilepsy of childbearing age. Epilepsia 2004;45:64–70. 34. Boylan LS, Flint LA, Labovitz DL, Jackson SC, Starner K, Devinsky O. Depression but not seizure frequency predicts quality of life in treatment-resistant epilepsy. Neurology 2004;62:258–61. 35. Smith EM, Gomm SA, Dickens CM. Assessing the independent contribution to quality of life from anxiety and depression in patients with advanced cancer. Palliative Medicine 2003;17:509–13. 36. Janssens ACJW, van Doorn PA, de Boer JB, Kalkers NF, van der Meché FGA, Passchier J, and others. Anxiety and depression influence the relation between disability status and quality of life in multiple sclerosis. Mult Scler 2003;9:397–403. 37. Ruo B, Rumsfeld JS, Hlatky MA, Liu H, Browner WS, Whooley MA. Depressive symptoms and health-related quality of life. The Heart and Soul Study. JAMA 2003;290:215–21. 38. Benito-León J, Morales JM, Rivera-Navarro J. Health-related quality of life and its relationship to cogntive and emotional functioning in multiple sclerosis patients. Eur J Pharmacol 2002;9:497–502. 39. Fruehwald S, Loeffler-Stastka H, Eher R, Saletu B, Baumhackl U. Depression and quality of life in multiple sclerosis. Acta Neurol Scand 2001;104:257–61. 40. Wang JL, Reimer MA, Metz LM, Patten SB. Major depression and quality of life in individuals with multiple sclerosis. Int J Psychiatr Med 2000;30:309–17. 41. Patten SB, Stuart HL, Russell ML, Maxwell CJ, Arboleda-Florez J. Epidemiology of depression in a predominantly rural health region. Soc Psychiatry Psychiatr Epidemiol 2003;38:360–5. 42. Lam RW, Kennedy SH. Prescribing antidepressants for depression in 2005: Recent concerns and recommendations. An addendum to the Canadian Psychiatric Association clinical practice guidelines for treatment of depressive disorders. Can J Psychiatry 2004;49(12) (addendum to Can J Psychiatry 2001;46[suppl 1]):Insert Page1–Insert Page 6. 43. Andrews G, Anstey K, Brodaty H, Issakidis C, Luscombe G. Recall of depressive episode 25 years previously. Psychol Med 1999;29:787–91. 44. Patten SB. Recall bias and major depression lifetime prevalence. Soc Psychiatry Psychiatr Epidemiol 2003;38:290–6. Author(s)Manuscript received May 2005, revised, and accepted August 2005. Previously presented at the Canadian Academy of Psychiatry Epidemiology (CAPE) Scientific Symposium, 2004 October; Montreal (QC). 1. Associate Professor, Departments of Community Health Sciences and Psychiatry, University of Calgary, Calgary, Alberta. 2. Associate Professor, Department of Psychiatry, University of Calgary, Calgary, Alberta. 3. PhD Candidate, Department of Community Health Sciences, University of Calgary, Calgary, Alberta. Address for correspondence: Dr Scott B Patten, 3330 Hospital Drive NW, Calgary, AB,T2N 4N1 e-mail: patten@ucalgary.ca
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