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The Global Burden of Disease study assumes that any missing data or data collected by a country outside of their domestic epidemiological parameters are missing at random (MAR). This implies that if one episode of a disorder is missing in a country, one episode of the same disorder in another country is also missing. Furthermore, if knowledge on a given parameter were present for any country, these too would be excluded. The GBD therefore uses Bayesian inference to make a best guess as to what the missing data value would likely have been had the knowledge of the data been more complete and representative.

Despite accounting for the fact that some data is missing, any assumptions it makes in making a best guess is a limitation. For example, the modelled prevalence estimates for some disorders may be up to 4.5 times the actual prevalence.

For the GBD project, the aim is that our global estimates of major depression by cause and geography, as well as other major causes of disease burden, should be accurate and as comprehensive as possible.

For each disorder, the estimate of the total number of years lost due to premature mortality is obtained by multiplying the total number of deaths and the mean, but truncated at the 95th percentile, of the overall disability-adjusted life years. For communicable diseases, for which it is not possible to capture or calculate DALYs for, the total number of life-years lost is multiplied by the estimated proportion of deaths due to the disorder. To obtain the total number of DALYs, the sum of these two is taken.

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