The Frequency Of Depressive Disorder

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Frequency can be measured in a variety of ways: incidence; point, period, and lifetime prevalence; and morbid risk. Table 1.3 defines commonly used rates in epidemiology. General population surveys usually report period or lifetime prevalence rates, while investigations of clinical series often use first contact or admission as a proxy for incidence. In this chapter, I shall rely largely on studies of prevalence.

While community psychiatric surveys date back nearly a century, it is only in the past 25 years that they have used standardized methods of assessment that allow the comparison of research from different locations. I have reviewed studies based on the superseded

Table 1.3 Epidemiological rates

Incidence rate: the number of new cases in a given period as a proportion of a population at risk

Point prevalence rate: the number of cases identified at a point in time as a proportion of a total population

Period prevalence rate: the number of cases identified as in existence during a specified period as a proportion of a total population

Lifetime prevalence rate: a variant of period prevalence where the period for case identification comprises the entire lifetime of each subject at the point of ascertainment

PSE-ID-CATEGO system (Wing et al., 1978) elsewhere (Bebbington, 1998). Those using the Diagnostic Interview Schedule (DIS) (Robins et al., 1985) developed for the US Epi-demiological Catchment Area (ECA) surveys have been summarized by Weissman et al. (1996). Table 1.4 provides 1-year and lifetime prevalence rates according to this system from around the world.

Although Weissman and her colleagues (1996) argue that variation between locations is not great, the annual prevalences rates, in fact, range from 0.8% to 5.8%, and the lifetime prevalence from 1.5% to 16.4%.

The range of prevalences given in Table 1.4 is difficult to explain, as it does not correspond to the obvious cultural differences between the locations of the surveys. So, for instance, there are high rates in Europe compared with the USA, but the studies from Canada and New Zealand are also high. High rates in Beirut are perhaps understandable. The difficulties in the overall interpretation of these results suggest differences in the application of the interview.

Since these surveys, data have been published from a number of large-scale investigations based on national probability samples. These include the US National Comorbidity Survey (Kessler et al., 1993, 1994); two British National Surveys of Psychiatry Morbidity (Jenkins et al., 1997; Singleton et al., 2001), the Australian National Survey of Mental Health and Well-Being (Henderson et al., 2000), and the Finnish National Survey (Lindeman et al., 2000). They each involved interviews with several thousand subjects (see Table 1.5). All but the British surveys used variants of CIDI (the Composite International Diagnostic Interview)

Table 1.4 Annual and lifetime prevalence of major depressive episode from studies using DIS

Table 1.4 Annual and lifetime prevalence of major depressive episode from studies using DIS

Site

N

Annual

Lifetime

United States (ECA) (Robins & Regier, 1991)

18 571

3.0

5.2

Edmonton, Alberta (Bland et al., 1988)

3258

5.2

9.6

Puerto Rico (Canino et al., 1987)

1513

3.0

4.3

Paris (Lepine et al., 1989)

1746

4.5

16.4

West Germany1 (Wittchen et al., 1992)

481

5.0

9.2

Florence (Faravelli et al., 1990)

1000

12.4

Beirut (Karam, 1992)

528

-

19.0

Taiwan (Hwu et al., 1989)

11 004

0.8

1.5

Korea (Lee et al., 1990)

5100

2.3

2.9

Christchurch, New Zealand (Oakley-Browne et al., 1989)

1498

5.8

Data from Weissman et al. (1996). References in table are to base papers from original studies.

Data from Weissman et al. (1996). References in table are to base papers from original studies.

Table 1.5 Prevalence rates for depressive disorders: recent large-scale surveys

Survey

N

Prevalence

Notes

National Comorbidity Survey (Kessler et al., 1993)

8098

Age 15-54; 1-year prevalence University of Michigan version of CIDI DSM-IV major depressive disorder

Australian National Survey (Henderson et al., 2000)

10 600

5.1%

1-year prevalence ICD-10 depressive episode Automated presentation of CIDI

Finnish National Survey (Lindeman et al., 2000)

5993

9.3%

1-year prevalence Automated presentation of short form of UM—CIDI

First British National Survey (Jenkins et al., 1997)

10108

2.3%

1-week prevalence—CIS-R ICD-10 depressive disorder

Second British National Survey (Singleton et al., 2001)

8580

2.6%

1-week prevalence—CIS-R ICD-10 depressive disorder

(Robins et al., 1988). The British Surveys also used lay interviewers, but was based on the revised version of the Clinical Interview Schedule (CIS-R) (Lewis et al., 1992), an interview that provides ICD-10 diagnoses (WHO, 1992). It can be seen from Table 1.5 that despite the similar procedure of the CIDI and the DIS, the prevalences in the ECA studies, which used the latter, are closer to those in the British National Survey of Psychiatric Morbidity than to the CIDI-based surveys. The rate of affective disorders with CIDI in Ethiopia was low, and it is not clear whether this represents a methodological problem or a real difference (Kebede & Alem, 1999).

Some community psychiatric studies using SCAN have now been published (WHO, 1992). Two are British and are located in Camberwell (inner south London) and Derry (Londonderry), Northern Ireland, respectively (Bebbington et al., 1997; McConnell et al., 2002). The 1-year prevalence rates of ICD-10 depressive episode were 6% and 7%, respectively. The rate in Derry would be expected to be higher than in towns of comparable size, given its considerable poverty and legacy of sectarian violence. The multinational ODIN project also used SCAN, but the results so far published are difficult to interpret, as the authors provide prevalences for depressive disorder of all types, whether identified according to ICD-10 or DSM-IV (Ayuso-Mateos et a!., 2001). Not surprisingly, in view of this confused decision, the prevalences are among some of the highest quoted, and cannot be compared with any other studies.

What, then, can be concluded from this exercise in counting? First, if we are going to establish a category of depressive disorder, we should be consistent. There is no real way of choosing between ICD-10 and DSM-IV criteria, although they differ somewhat in the cases they identify, but one or other system should be adhered to, as they are the most commonly used. The criteria for depressive disorder (DSM-IV) are more restrictive than those of ICD-10 major depressive episode, and, in principle, should result in lower prevalences. However, epidemiological studies presenting DSM-IV major depressive disorder (MDD) often use CIDI, and this probably results in quoted prevalences around 50% above what they would be if a semi-structured clinical interview were used. The short form of the CIDI, as used in the Finnish study (Lindeman et al., 2000), may result in particularly high prevalences (Patten, 1997; 2000).

The consequence of these two countervailing influences is that we cannot use the epi-demiological surveys to make very sensible statements about whether the different locations in which they were carried out are characterized by true differences in prevalence. For that, we have to rely on studies that cover more than one area, in a way that permits inferences about consistent influences; for instance, the relatively low rates of depression in rural areas (Paykel et al., 2000).

My best guess is that annual prevalence rates of ICD depressive episode may be around 4%, and that of DSM depressive disorder around 5%. Prevalence related to shorter periods (1-week, 1-month) will be around one-half to two-thirds these rates.

Nevertheless, one can use these essentially questionable procedures to come to certain limited but interesting conclusions concerning the factors that influence prevalence.

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