Research reportPrevalence and correlates of DSM-IV major depression in an Australian national survey
Introduction
Over the past two decades, there has been growing interest in the estimation of prevalence of depressive disorders in the general community. The first estimates of major depression and dysthymia were reported for the Epidemiologic Catchment Area (ECA) study (Regier et al., 1984), which used the Diagnostic Interview Schedule (DIS) based on DSM-III diagnoses (Robins et al., 1981). The ECA prevalence estimates of major depression were questioned because the estimates were lower than previous studies (Parker, 1987). The USA National Comorbidity Survey (NCS) (Blazer et al., 1994) represented the first epidemiological data from a national sample of community residents from which estimates of prevalence distribution and risk factors for selected DSM-III-R psychiatric disorders could be derived. It was designed to improve on methodology used in the ECA study and used the Composite International Diagnostic Interview (CIDI) Version 1.1. The NCS attempted to improve lifetime case detection rates with the use of ‘commitment’ and ‘motivation’ probes and by weighting for undetected cases among non-respondents. The British Psychiatric Morbidity Survey (BPMS), also a national community study, utilised the Revised Clinical Interview Schedule (CIS-R), an alternative lay-administered diagnostic interview to measure rates for ICD-10 categories (Brugha et al., 1999).
To determine the prevalence of mental disorders in the Australian population, the Australian Government financed a national survey (Australian Bureau of Statistics, 1998). The household survey of a population sample of adults aged 18 years and over, using the CIDI Version 2.1 as the case-finding instrument, was closely modeled on the USA NCS cross-sectional study.
We have examined these data to address the following questions:
- 1.
What are the current (30-day) prevalence rates for major depression? Do the Australian rates compare with other comparable national studies?
- 2.
Are there gender differences in current prevalence rates for depression?
- 3.
What is the relationship between current prevalence rates of major depression and disability, demographic, behavioural and physical illness variables?
Section snippets
Description of sample
The National Survey of Mental Health and Well-being (NSMHWB) sampled adults in urban and rural areas of Australia. It was conducted by the Australian Bureau of Statistics (1998) using the computerised version of the Composite International Diagnostic Interview (CIDI) Version 2.1 (WHO, 1996). More details on the design of the NSMHWB are presented elsewhere (Andrews et al., 2001). Interviewers, trained by the WHO Training and Reference Centre for CIDI in Sydney, were employed by the ABS to
Prevalence of major depression
The total prevalence rate of DSM-IV MD was 3.2% (males 2.4%, females 3.9%) with the highest rate for females in mid life (Table 1). The ICD-10 rate was very similar at 3.3% (males 2.4%, females 4.2%).
Disability
The disability associated with MD, as measured from the SF-12 scale, showed greater mental disability for both males and females in mid life, and greater physical disability for both males and females in later life. The highest mean days spent ‘out of role’ for males was 7.1 days in later life, and
Relationship of major depression to age, gender and socioeconomic status
The current (30-day) weighted prevalence rate for MD in Australia, as assessed by the NSMHWB, was 3.2%. The prevalence of DSM-IIIR depression in the NCS was 4.9% (Blazer et al., 1994) and 2.2% for DSM-III cases in the ECA study (Regier et al., 1993). Women had higher rates of depression in all three studies. However, we found that disability scores showed a different picture. While scores varied with age, the experience for both sexes on mental and physical scales was very similar, with no
Acknowledgements
This paper was supported by a contract from the Australian Department of Health and Aged Services to the WHO Collaborating Centre for Mental Health, Sydney, to support a survey data analysis consortium (G. Andrews, V. Carr, G. Carter, R. Crino, W. Hall, A. Henderson, I. Hickie, C. Hunt, L. Lampe, J. McGrath, A. McFarlane, P. Mitchell, L. Peters, M. Teesson, K. Wilhelm) and also by NH&MRC Program Grant 993208. We thank those members of the Australian population who willingly participated in the
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