Assessing cost-effectiveness - Mental health: introduction to the study and methods

Objective: The Assessing Cost-Effectiveness - Mental Health (ACE-MH) study aims to assess from a health sector perspective, whether there are options for change that could improve the effectiveness and efficiency of Australia's current mental health services by directing available resources toward 'best practice' cost-effective services. Method: The use of standardized evaluation methods addresses the reservations expressed by many economists about the simplistic use of League Tables based on economic studies confounded by differences in methods, context and setting. The cost-effectiveness ratio for each intervention is calculated using economic and epidemiological data. This includes systematic reviews and randomised controlled trials for efficacy, the Australian Surveys of Mental Health and Wellbeing for current practice and a combination of trials and longitudinal studies for adherence. The cost-effectiveness ratios are presented as cost (A$) per disability-adjusted life year (DALY) saved with a 95% uncertainty interval based on Monte Carlo simulation modelling. An assessment of interventions on 'second filter' criteria ('equity', 'strength of evidence', 'feasibility' and 'acceptability to stakeholders') allows broader concepts of 'benefit' to be taken into account, as well as factors that might influence policy judgements in addition to cost-effectiveness ratios. Conclusions: The main limitation of the study is in the translation of the effect size from trials into a change in the DALY disability weight, which required the use of newly developed methods. While comparisons within disorders are valid, comparisons across disorders should be made with caution. A series of articles is planned to present the results.

Body size and ovarian cancer: case-control study and systematic review (Australia)

Objective: Although increased body mass is an established risk factor for a variety of cancers, its relation with cancer of the ovary is unclear. We therefore investigated the association between measures of body mass index (BMI) and ovarian cancer risk. Methods: Data from an Australian case-control study of 775 ovarian cancer cases and 846 controls were used to examine the association with BMI. We have also summarized the results from a number of other studies that have examined this association. Results: There was a significant increased risk of ovarian cancer with increasing BMI, with women in the top 15% of the BMI range having an odds ratio (OR) of 1.9 (95% confidence interval (CI), 1.3-2.6) compared with those in the middle 30%. Stratifying by physical activity showed a stronger effect among inactive women (OR = 3.0, 95% CI 1.3-6.9). The overall effect was consistent with the findings of most prior population-based case-control studies, while cohort studies reported positive effects closer to the null. Hospital-based studies gave variable results. Conclusions: Taken together, the evidence is in favor of a small to moderate positive relation between high BMI and occurrence of ovarian cancer.