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Life satisfaction and happiness were modestly associated with other indicators of population mental health. Since all such markers have their limitations, surveys of well-being may be useful indicators of population mental health.

BackgroundFew studies have examined suicide risk in individuals once they have left the military. We aimed to investigate the rate, timing, and risk factors for suicide in all those who had left the UK Armed Forces (1996–2005).Methods and FindingsWe carried out a cohort study of ex-Armed Forces personnel by linking national databases of discharged personnel and suicide deaths (which included deaths receiving either a suicide or undetermined verdict). Comparisons were made with both general and serving populations. During the study period 233,803 individuals left the Armed Forces and 224 died by suicide. Although the overall rate of suicide was not greater than that in the general population, the risk of suicide in men aged 24 y and younger who had left the Armed Forces was approximately two to three times higher than the risk for the same age groups in the general and serving populations (age-specific rate ratios ranging from 170 to 290). The risk of suicide for men aged 30–49 y was lower than that in the general population. The risk was persistent but may have been at its highest in the first 2 y following discharge. The risk of suicide was greatest in males, those who had served in the Army, those with a short length of service, and those of lower rank. The rate of contact with specialist mental health was lowest in the age groups at greatest risk of suicide (14% for those aged under 20 y, 20% for those aged 20–24 y).ConclusionsYoung men who leave the UK Armed Forces were at increased risk of suicide. This may reflect preservice vulnerabilities rather than factors related to service experiences or discharge. Preventive strategies might include practical and psychological preparation for discharge and encouraging appropriate help-seeking behaviour once individuals have left the services.

Projections based on incidence and mortality data collected by cancer registries are important for estimating current rates in the short term, and public health planning in the longer term. Classical approaches are dependent on questionable parametric assumptions. We implement a Bayesian age-period-cohort model, allowing the inclusion of prior belief concerning the smoothness of the parameters. The model is described by a directed acyclic graph. Computations are carried out by using Markov chain Monte Carlo methods (implemented in BUGS) in which the degree of smoothing is learnt from the data. Results and convergence diagnostics are discussed for an exemplary data set. We then compare the Bayesian projections with other methods in a range of situations to demonstrate its flexibility and robustness. Copyright 2002 The Royal Statistical Society.