Overweight and obesity are associated with increased morbidity and mortality, although the range of body weights that is optimal for health is controversial. It is less clear whether weight loss benefits longevity and hence whether weight reduction is justified as a prime goal for all individuals who are overweight (normally defined as BMI . 25 kg/m 2 ). The purpose of the present review was to examine the evidence base for recommending weight loss by diet and lifestyle change as a means of prolonging life. An electronic search identified twenty-six eligible prospective studies that monitored subsequent mortality risk following weight loss by lifestyle change, published up to 2008. Data were extracted and further analysed by meta-analysis, giving particular attention to the influence of confounders. Moderator variables such as reason for weight loss (intentional, unintentional), baseline health status (healthy, unhealthy), baseline BMI (normal, overweight, obese), method used to estimate weight loss (measured weight loss, reported weight loss) and whether models adjusted for physical activity (adjusted data, unadjusted data) were used to classify subgroups for separate analysis. Intentional weight loss per se had a neutral effect on all-cause mortality (relative risk (RR) 1·01; P ¼ 0·89), while weight loss which was unintentional or ill-defined was associated with excess risk of 22 to 39 %. Intentional weight loss had a small benefit for individuals classified as unhealthy (with obesity-related risk factors) (RR 0·87 (95 % CI 0·77, 0·99); P ¼ 0·028), especially unhealthy obese (RR 0·84 (95 % CI 0·73, 0·97); P ¼ 0·018), but appeared to be associated with slightly increased mortality for healthy individuals (RR 1·11 (95 % CI 1·00, 1·22); P ¼ 0·05), and for those who were overweight but not obese (RR 1·09 (95 % CI 1·02, 1·17); P ¼ 0·008). There was no evidence for weight loss conferring either benefit or risk among healthy obese. In conclusion, the available evidence does not support solely advising overweight or obese individuals who are otherwise healthy to lose weight as a means of prolonging life. Other aspects of a healthy lifestyle, especially exercise and dietary quality, should be considered. However, well-designed intervention studies are needed clearly to disentangle the influence of physical activity, diet strategy and body composition, in order to define appropriate advice to those populations that might be expected to benefit.

The incidence of unsatisfactory death certificates within a hospital setting is high. Increased education and better documentation leads to improvements in accuracy and legitimacy.

BackgroundThe current lifetable approach to survival estimation is favoured by CF registries. Recognising the limitation of this approach, we examined the utility of a parametric survival model to project birth cohort survival estimates beyond the follow-up period, where short duration of follow-up meant median survival estimates were indeterminable.MethodsParametric models were fitted to observed survivorship data from the US CF Foundation (CFF) Patient Registry 1980–1994 birth cohort. Model-predicted median survival was estimated. The best fitting model was applied to a Cystic Fibrosis Registry of Ireland dataset to allow an evaluation of the model's ability to estimate predicted median survival. This involved a comparison of birth cohort lifetable predicted and observed (Kaplan–Meier) median survival estimates.ResultsA Weibull model with main effects of gender and birth cohort was developed using a US CFF dataset (n=13 115) for which median survival was not directly estimable. Birth cohort lifetable predicted median survival for male and female patients born between 1985 and 1994 and surviving their first birthday was 50.9 and 42.4 years respectively. To evaluate the accuracy of a Weibull model in predicting median survival, a model was developed for the 1980–1984 Cystic Fibrosis Registry of Ireland birth cohort (n=243), which had an observed (Kaplan–Meier) median survival of 27.7 years. Model-predicted median survival estimates were calculated using data censored at different follow-up periods. The estimates converged to the true value as length of follow-up increased.ConclusionsAccurate prognostic information that is clinically critical for care of patients affected by rare, life-limiting disorders can be provided by parametric survival models. Problems associated with short duration of follow-up for recent birth cohorts can be overcome using this approach, providing better opportunities to monitor survival and plan services locally.

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