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. 2013 Jul 29;8(7):e69637.
doi: 10.1371/journal.pone.0069637. Print 2013.

The epidemiological modelling of major depressive disorder: application for the Global Burden of Disease Study 2010

Alize J Ferrari  1 , Fiona J Charlson, Rosana E Norman, Abraham D Flaxman, Scott B Patten, Theo Vos, Harvey A Whiteford
Affiliations

The epidemiological modelling of major depressive disorder: application for the Global Burden of Disease Study 2010

Alize J Ferrari et al. PLoS One. .

Abstract

Background: Although the detrimental impact of major depressive disorder (MDD) at the individual level has been described, its global epidemiology remains unclear given limitations in the data. Here we present the modelled epidemiological profile of MDD dealing with heterogeneity in the data, enforcing internal consistency between epidemiological parameters and making estimates for world regions with no empirical data. These estimates were used to quantify the burden of MDD for the Global Burden of Disease Study 2010 (GBD 2010).

Method: Analyses drew on data from our existing literature review of the epidemiology of MDD. DisMod-MR, the latest version of the generic disease modelling system redesigned as a Bayesian meta-regression tool, derived prevalence by age, year and sex for 21 regions. Prior epidemiological knowledge, study- and country-level covariates adjusted sub-optimal raw data.

Results: There were over 298 million cases of MDD globally at any point in time in 2010, with the highest proportion of cases occurring between 25 and 34 years. Global point prevalence was very similar across time (4.4% (95% uncertainty: 4.2-4.7%) in 1990, 4.4% (4.1-4.7%) in 2005 and 2010), but higher in females (5.5% (5.0-6.0%) compared to males (3.2% (3.0-3.6%) in 2010. Regions in conflict had higher prevalence than those with no conflict. The annual incidence of an episode of MDD followed a similar age and regional pattern to prevalence but was about one and a half times higher, consistent with an average duration of 37.7 weeks.

Conclusion: We were able to integrate available data, including those from high quality surveys and sub-optimal studies, into a model adjusting for known methodological sources of heterogeneity. We were also able to estimate the epidemiology of MDD in regions with no available data. This informed GBD 2010 and the public health field, with a clearer understanding of the global distribution of MDD.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Incidence-prevalence-mortality model.
Illustrates the generic relationship between epidemiological parameters used by DisMod-MR to model epidemiological data.
Figure 2
Figure 2. Summary of the raw data on prevalence (P), incidence (I), duration (D) and excess mortality (EM) of MDD.
Summarises the available epidemiological data used as inputs in the DisMod-MR modelling of MDD.
Figure 3
Figure 3. Country- and study-level covariate adjustments for MDD.
Illustrates the effect of the covariates used in the DisMod-MR modelling of prevalence data.
Figure 4
Figure 4. Prevalence of MDD before and after covariate adjustments for females from North Africa/Middle East, 2010.
Compares the raw prevalence estimates to the final pooled prevalence output generated by DisMod-MR for females from North Africa/Middle East, for 2010.
Figure 5
Figure 5. Prevalence, incidence, duration and excess mortality of MDD in females from North America, High income, 2010.
Compares the raw prevalence, incidence, duration and excess mortality estimates to the final pooled prevalence, incidence, duration and excess mortality output generated by DisMod-MR for females from North America, High income, for 2010.
Figure 6
Figure 6. Regional point prevalence of MDD by age and sex, 2010.
Presents the prevalence of MDD (as derived by DisMod-MR) by region, age and sex for 2010.
Figure 7
Figure 7. The overall point prevalence of MDD and 95% uncertainty by region and age, 2010.
Presents the prevalence of MDD (as derived by DisMod-MR) by region and age for 2010.
Figure 8
Figure 8. The number of point prevalent cases (in millions) of MDD by region, age and sex, 2010.
Presents the prevalent cases of MDD (as derived by DisMod-MR) by region, age and sex for 2010.
See this image and copyright information in PMC

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