Major mental disorders in Addis Ababa, Ethiopia. 11. Affective disorders

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1 A ~ ( P.yythiutr I Sand 1999: 100: Printed in UK AN rights reserved Copyriqht ( Munky,qurrd 1999 ~- ~ A( TA PSYCHIATRICA SCANDINAVICA ISSN OY02-444/ Major mental disorders in Addis Ababa, Ethiopia. 11. Affective disorders Kebede D, Alem A. Major mental disorders in Addis Ababa, Ethiopia. 11. Affective disorders. Acta Psychiatrica Scand 1999: 100: Munksgaard 1999 This report examines the prevalence and socio-demographic correlates of affective disorders based on a survey conducted in Addis Ababa between September and December of An Amharic version of the CIDI was used to collect data from a random community sample of 1420 individuals aged 15 and above. The lifetime prevalence for specific affective disorders was as follows: bipolar disorders 0.3%, depressive episodes 2.7%), recurrent depressive episodes 0.2% and persistent mood disorders 1.6%. The weighted lifetime prevalence of affective disorders was 5.0%) (women 7.7% and men 3.2%). One-month prevalence was 3.Wo (women 5.9% and men 2.30/0). After adjusting for several potential confounders, the risk of affective disorders was only 29% higher in women compared to men. This difference in risk was not statistically significant. Age was also not associated with risk of affective disorders. On the other hand, education was associated with the risk of disorder, the risk decreasing with increasing educational attainment. This inverse trend was statistically significant (P for trend=0.02). The risk was also 37%1 lower in the employed than the unemployed: Odds Ratio (OR), 95%) confidence interval (95% C1)=0.63 (0.39, 1.01). There were no statistically significant associations between affective disorders and marital status or ethnicity. D. Kebede', A. Alem2 'Department of Community Health, Faculty of Medicine, University of Addis Ababa and 'Amanuel Psychiatric Hospital, Addis Ababa, Ethiopia Key words: affective disorder; prevalence; sociodemographic correlates Derege Kebede, Department of Community Health, Faculty of Medicine, Addis Ababa University, P.O. Box 1176, Addis Ababa. Ethiopia Introduction Mood disorders are fairly common illnesses affecting about one in ten individuals during their lifetimes. Furthermore, suicide, which is an important cause of premature mortality, is strongly associated with mood disorders. In developed countries the cost of depression accounts for about a third of the cost for all mental disorders (1). Although the prevalence of mood disorders has been relatively well studied in developed countries, data from community surveys based on structured interviews have been reported from only a few developing countries. World Bank estimates rank depression as fifth in illness burden among women and seventh among men in developing countries. Depression has also been shown to account for a large proportion of the attendance at outpatient services in developing countries (2). Community-based surveys for mood disorders using structured interviews are rare in Africa and none has been reported from Ethiopia. A study from two rural Ugandan villages in the 1970s, for example, showed 14.3% of men and 22.6% of women as having depressive disorders, dispelling earlier notions that affective disorders are uncommon in Africa (3). Data from both communitybased surveys and health services in Ethiopia are usually presented as an aggregate of psychosis and neurosis and thus do not show the extent of affective disorders in the country (4, 5). In order to base health policy, particularly mental health policy, on scientific ground it is imperative to document the prevalence and correlates of mood disorders. We report here on a study which used the ia

2 Affective disorders Table 1 Lifetime and one month weighted prevalence per 100 population. of affective disorders (identified by the use of the Composite International Diagnostic Interview. CIDI) in Addis Ababa. Ethiopia (ICD-10 codes in parenthesis) Oisordei Lifetime prevalence One-month orevalence Total Male Female Total Male Female Affective disorders (F ) Bipolar disorder (F31) Depressive episode (F32) Recurrent depressive episode (F33) Persistent mood disorder (F34) Any CIDI/ICD-lO Diagnoses Any CIOl/lCO-lO Diagnoses (excluding substance dependence) Amharic translation of the Composite International Diagnostic Interview (CIDI) to estimate the prevalence and socio-demographic correlates of mood disorders in a representative sample of the adult population of Addis Ababa, Ethiopia. Material and methods The study was conducted in Addis Ababa in two stages. The first stage of screening using the Self Reporting Questionnaire (SRQ) was conducted between January and March of The secondstage survey employing the CIDI was conducted between September and December of the same year. We used as the source population an administrative division of the city proper which include 25 districts (woredm) and 284 sub-districts (keheles). Using estimates and projections based on an earlier census, the city had a population of (6). Sample selection The details of the methods followed in the first-stage SRQ survey have been reported elsewhere (7). Briefly, the study population of was selected from the entire city by a cluster sampling method proportionate to size (8). The details of the methods followed for the second-stage CIDI study have also been reported elsewhere (9). Briefly, we selected individuals based on the responses to the SRQ questions. SRQscreened individuals were included in this sample if they fulfilled at least one of the following criteria: Positive response to at least 10 out of 20 SRQ items for neuroses (n=480); 2 out of 4 SRQ items for psychoses (n= 5 10); 7 out of 9 questions on somatic symptoms (n=356); 2 out of 4 CAGE (screening instrument for alcohol) items (n=408); 2 out of 5 questions on eating disorders (n=415); and those who reported as currently smoking cigarettes (n=503), chewing khat (n=763) and having attempted suicide in the past (n=89). Because of the overlap in 'the above eight categories, the total number of individuals sampled (2042) is less than that expected from a simple summation of individuals in each category. We used the CIDI for interviewing respondents. The feasibility, reliability, and acceptance of the Amharic translation of the CIDI (version 1.0) has been evaluated and reported earlier (10). We were able to obtain complete interviews for 1420 (70%) individuals. Reasons for non-response were change of address (15.9%), unavailable for interview (8.8'%), refusals (1.40/0), death (1.2%) and other reasons (2.9%). ICD-10 and DSM-III-R diagnoses were generated by the CIDI computer algorithms, and the ICD-I0 diagnoses were used for analysis. Data analysis We used the following ICD-I 0 categories and codes to evaluate for associations with socio-demographic and other characteristics: bipolar disorder (F3 1.O, F31.6, F31.8, F31.9), depressive episode (F32.0, F32.1, F32.1, F32.2, F32.3), recurrent depressive episode (F33.0, F33. I, F33. 1, F33.2), and persistent mood disorder (F34. 1). To evaluate for associations in a multivariate model, we categorized as affective disorders 10 bipolar, 77 depressive episode, 6 recurrent depressive episode and 52 persistent mood disorder cases. We used the EPI-INFO version 5 program for data entry and preliminary analysis. The Statistical Analysis System program, version 6.04, was used for both bivariate and multivariate analysis. As stated earlier, the sample selection for the CIDI interviews weighed heavily on the high scorers of screening questions for probable psychoses. This 19

3 Kebede and Alem Sex Male Female Age ~ t Educational level No schooling Elementary Secondary I Unemployed d-1 Employed I Work status Percent Bipolar Depressive episodes! Persistent mood disorder Fig. 1. Lifetime prevalence of affective disorder in different socio-demographic strata. means that high scorers will have a higher (but known) probability of being selected into the sample than low scorers. To adjust for this, we computed weighted prevalence by weighing respondents in inverse proportion to their probability of selection (1 1). The logistic regression method was employed to adjust relative risks for potential confounding variables. Thus, affective disorders were included in the logistic model as a dependent variable. As independent variables, the following were included in the model: sex, age (4 levels), marital status (5 levels), ethnicity (5 levels), education (4 levels), religion (4 levels), and employment (2 levels). Test for trend was obtained from a logistic regression model with the adjusted relative risk estimates included as continuous variables. Results There was a total of 106 cases who were diagnosed as currently (one month) having affective disorder. Of these, 5 were bipolar, 56 had depressive episodes, 6 had recurrent depressive episodes, and 39 had persistent mood disorders. One hundred and fortyfive had a lifetime diagnosis of affective disorders: I0 had bipolar conditions, 77 had depressive episodes, 6 had recurrent depressive episodes, and 52 had persistent mood disorders. The weighted Table 2. Distribution of lifetime affective disorders (defined by use of Composite International Diagnostic Interview, CIDi) according to individual characteristics, Addis Ababa, 1994 Number of Adjusted OR Variable Population cases (YO) (95% CI) P for trendb Sex Male Female AY e Education No schooling Elementary Secondary Above Work status Unemployed Employed Marital Status Married Single Divorced Separated Widowed Ethnicity Amhara Oromo Gurage Tigrai Other Totai (7.6) 87 (13 3) 34 (7 8) 62 (10.2) 27 (1 1 4) ) 43 (17 5) 47 (12 1) 47 (6.7) 8 (96) 119 (12.3) 26 ( (8 7) ) ) 3 (6 4) 21 (18.3) 84 (1 1.8) 26 (10 6) 19 (7 0) 4 ( (9.9) (0 87, 1 91) ( ) , 0 74) 1 02 (0 46, 2 28) (047, 1 23) 0 42 ( ) 0 61 ( ) 1 no 0 63 (0 39, 1 01) (0 68, 1 92) 0 77 (0 26, 2 26) 0 49 ( ) 1 06 (0 59, 1 92) 1 no 0 80 (0 50, 1 29) 069 ( (0 18, 1 45) 0 72 ( ) a Terms included in the logistic model sex age (4 levels). marital status (5 levels). ethnicity (5 levels), family size (4 levels), education (4 ievels). and employment (2 levels) Test for trend was obtained from a regression model with the adjusted relative risk estimates included as a continuaus variable Reference category prevalence for affective disorders is shown in Table 1. Lifetime prevalence for all affective disorders was 5%, higher in women than men. One-month point prevalence was also higher among women. The lifetime prevalence of bipolar disorders in women was five times that of men. However, onemonth prevalence rates between men and women were close. Lifetime prevalence of depressive episodes, recurrent depressive episodes, and persistent mood disorders were also higher in women. The relative distribution of sub-groups of affective disorders according to socio-demographic characteristics is given in Fig. 1. This crude analysis shows that the frequency of affective disorders increased with age and decreased with educational attainment. They occurred more frequently in women than men; in the unemployed than the employed; and in those divorced, separated, or widowed than those single or married. This pattern also occurs for bipolar disorders, depressive episodes, and persistent mood disorders. 20

4 Affective disorders Results of multivariate analysis of the data using multiple logistic regression are shown in Table 2. After adjusting for several potential confounders, the risk of affective disorders was only 29% higher in women compared to men. This difference in risk was not statistically significant. Age was also not associated with risk of hffective disorders. A more detailed examination of the model using stepwise regression showed that education was an important confounder for the positive association between affective disorder and the female sex seen in the crude analysis. The odds ratio decreased substantially when education was adjusted for in the multivariate analysis. Likewise, the association between the disorder and increasing age seen in the crude analysis also decreased when educational attainment was adjusted for in the model. Education was inversely associated with the risk of disorder, the risk decreasing with increasing educational attainment. This inverse trend was statistically significant. The risk was also 37% lower in the employed than the unemployed. This association is nearly statistically significant. Associations of family size, marital status, or ethnicity with affective disorders did not achieve statistical significance. Discussion We have reported lifetime and point prevalence of bipolar disorders of 0.3%) and 0.1%, respectively. The lifetime prevalence of bipolar I and 11 disorders in the Epidemiologic Catchment Area (ECA) study in the USA were 0.8% and 0.5%, respectively. Onemonth prevalence ranged from 0.1% to 0.60/1 (12). Tohen and Goodwin (1 3) have recently reviewed prevalence studies on bipolar disorders. They reported lifetime prevalence for mania of 0.8% and for hypomania of 0.8% in a survey in New Haven in 1987, and 0.46% for mania in two settlements of the Amish sect in the USA. In the National Comorbidity Survey (NCS), the lifetime prevalence for manic episodes was 1.6% (14). Outside of the USA, prevalence reports ranged between 0.2% and 1.86% in Florence in 1990, 0.7% and 1.6% in Taiwan in 1989, 0.4% and 0.7% in Puerto Kico and Alberta, Canada in 1987 (13). A survey of adults in an inner-city district in Addis Ababa showed a lifetime prevalence of O.8%, but a rural survey in south central Ethiopia had no cases of bipolar disorders in 600 randomly sampled adults surveyed (1 5). Our prevalence findings for bipolar disorders are, therefore, within ranges reported in the literature. The finding in rural Ethiopia may be due to the small sample (about 500), or it may indicate the rare occurrence of bipolar disorder in that area. Elsewhere, studies have noted a lower prevalence of bipolar disorders in rural areas compared to urban areas (12). Because our sample of bipolar cases was small it was not possible to evaluate for associations with the various socio-demographic factors. Our finding of lifetime and point prevalence of 2.9% and 2.3% for depressive episodes (including recurrent episodes) is lower than DSM-I11 based reports from the USA ( %) and Canada (8.6%). It is also lower than reports from European studies utilizing ICD-9 and CATEGO classifications, which reported lifetime prevalence rates in the range of 4.6% to 7.4%. This could be due to real population differences in occurrence, or due to differences in the expression of the disorder. The European studies may have included neurotic depression in their reports and this may have also increased the prevalence reports (I 6). The prevalence of dysthymia was 3.0% in the ECA study, 3.7% in Edmonton, Canada, 4.7% in Puerto Rico, but lower in Florence (2.3'1/0), Seoul, Korea (2.2%)) and Taiwan (0.9%1.5%) (16). Our finding of lifetime prevalence rates for persistent mood disorders of 1.6% is thus closer to these latter studies. Our combined affective disorders show a lifetime prevalence for affective disorders of 5.0% Onemonth point prevalence was 3.8% in total, 2.3% for men, and 5.9% for women. Our figures are lower than those reported from the Epidemiologic Catchment Area (ECA) study and the National Comorbidity Study (NCS). Lifetime prevalence ranging from 6.1% to 9.5% were reported from the ECA sites of Baltimore, New Haven and St. Louis (1 7). One-month point prevalence estimates were 5.1'%1 for the total population of the ECA study, with 3.5% for men and 6.6% for women (1 2). Much higher lifetime prevalence (1 7.1%) was reported from the NCS (18). In the absence of reports of similar studies from Africa, it is difficult to interpret the lower rates found in our study. It is possible that the prevalence of affective disorders is lower in less developed countries, including Ethiopia. A survey (using the CIDI) conducted in a primary care setting in Nigeria, for example, showed a prevalence of 8.8% for all depressive disorders, indicating that the community prevalence would be on the lower side (19). A study conducted in inner-city Addis Ababa using the CIDI also reported a lifetime prevalence of 5.6% for affective disorders (9). Therefore, our finding may be a true reflection of the prevalence of affective disorders in Addis Ababa. In multivariate analysis, female sex was not associated with affective disorder when age, education, marital status, and employment status were 21

5 Kebede and Alem adjusted for. There could be several possible explanations for this finding. The aggregating of three sub-categories of affective disorders into one category may have led to a random misclassification. This may have biased the risk estimate to the null value. Except for bipolar disorders, higher risk in women has been noted in both major depression and dysthymia in the literature (20). This is also the case in our study. Because the number of cases of bipolar disorders in this study was small to begin with, the degree of misclassification introduced by aggregating them with others is likely to be low. Variations in the reporting of depressive symptoms between the sexes in our setting may also have biased the results, particularly since we have used lifetime reports as outcome measures in our analyses. Differences in the duration of illness in women in our setting compared to other studies may also account for our findings. This is because of our use of prevalence instead of incidence in evaluating for association. Educational level may account for the variations seen in the crude analysis. As stated earlier, the magnitude of the relative risk estimates decreased substantially when education was included in the multivariate model. Although in the crude analysis the prevalence of mood disorders increased with increasing age, this association disappeared when sex, education, work status, family size and marital status were included in the logistic model. Studies, mainly from the developed world, have given conflicting results. Although earlier studies have shown increasing risk of affective disorders with increasing age, later studies, including the ECA study, have shown higher rates for younger age groups (18). We also found a decreased risk of affective disorders with increasing educational attainment and employment. This finding is congruent with several studies, including the ECA study and the NCS (19), that have shown an increased risk in the unemployed and in members from lower socioeconomic groups (20, 21). Although other studies have shown associations between mood disorders and poor marital relationships, divorce and widowhood (20, 21), our study did not show any association between these factors when other potential confounders were adjusted for in a multivariate model. This may be a reflection of the fact that in Addis Ababa many couples remain married in spite of poor marital relationships because of societal or peer pressure. It is also possible that marital status or marital relationships, per se, may have no independent association with affective disorders. One limitation of this study is our use of lifetime prevalence estimates. Several investigators have questioned whether lifetime prevalence of mental disorders can be meaningfully measured using interviews (22, 23). Others have demonstrated, however, that it is possible (24). If, indeed, there is some loss of data due to recall problems, our findings may be underestimates of the true population prevalence affective disorders. The nature of the resultant misclassifications are probably random, and would therefore tend to bias our relative risk estimates to the null value. Thus, our relative risk estimates may also be underestimates of the true population value. In conclusion, we have shown that affective disorders are fairly common in Addis Ababa, occurring in the lifetime of one in twenty adults. The disorders are associated with low educational status and unemployment. We recommend that plans be worked out to increase awareness of the prevalence of mood disorders among physicians and other health care providers. Further studies to investigate risk factors in more detail and prevalence in rural areas and other urban centres in the country are needed. Our findings also indicate the importance of enhancing current mental health interventions to address these public health problems. Acknowledgements Financial assistance for the study was obtained from IDRC through the McGill Ethiopia Community Health Project. Additional financial assistance was also obtained from the Swedish Medical Research Council through the Department of Psychiatry, University of UmeB. Material assistance from the Department of Community Health, Addis Ababa University is also acknowledged. We would like to thank Professors R. Giel, L. Jacobsson and G. Kullgren for their advice, and Drs. Barbara Singleton and Robert Kohn for their comments on an earlier draft of this manuscript. We also thank all study workers and all those study participants who kindly agreed to be interviewed. References 1. AKlSKAL HS. Mood disorders: Introduction and overview. In: KAPLAN HI, SADOCK BJ, eds. Comprehensive textbook of psychiatry/vi. 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