Modular programming for tuberculosis control, the "AuTuMN" platform.

BACKGROUND: Tuberculosis (TB) is now the world's leading infectious killer and major programmatic advances will be needed if we are to meet the ambitious new End TB Targets. Although mathematical models are powerful tools for TB control, such models must be flexible enough to capture the complexity and heterogeneity of the global TB epidemic. This includes simulating a disease that affects age groups and other risk groups differently, has varying levels of infectiousness depending upon the organ involved and varying outcomes from treatment depending on the drug resistance pattern of the infecting strain. RESULTS: We adopted sound basic principles of software engineering to develop a modular software platform for simulation of TB control interventions ("AuTuMN"). These included object-oriented programming, logical linkage between modules and consistency of code syntax and variable naming. The underlying transmission dynamic model incorporates optional stratification by age, risk group, strain and organ involvement, while our approach to simulating time-variant programmatic parameters better captures the historical progression of the epidemic. An economic model is overlaid upon this epidemiological model which facilitates comparison between new and existing technologies. A "Model runner" module allows for predictions of future disease burden trajectories under alternative scenario situations, as well as uncertainty, automatic calibration, cost-effectiveness and optimisation. The model has now been used to guide TB control strategies across a range of settings and countries, with our modular approach enabling repeated application of the tool without the need for extensive modification for each application. CONCLUSIONS: The modular construction of the platform minimises errors, enhances readability and collaboration between multiple programmers and enables rapid adaptation to answer questions in a broad range of contexts without the need for extensive re-programming. Such features are particularly important in simulating an epidemic as complex and diverse as TB.

Adjuvant trastuzumab chemotherapy in early breast cancer: meta-analysis of randomised trials and cost-effectiveness analysis.

BACKGROUND: Trastuzumab has a large financial impact on the average cost of breast cancer treatment. This study reassessed the cost-effectiveness of listing the drug on the subsidised Australian Pharmaceutical Benefits Scheme. METHODS: Using a continuous-time, discrete-event microsimulation model, we examined the effect of 1-year trastuzumab on the total number of disability-adjusted life-years (DALYs) averted among Australian women with human epidermal growth factor receptor-2 (HER2) positive early breast cancer. Target population was women aged 30–100 years and newly diagnosed with the disease in 2003. The model adjusted for tumour size and followed the women up until death or age 100 years. Uncertainty was examined in univariate and probabilistic multivariate sensitivity analyses. RESULTS: The incremental cost-effectiveness ratio (ICER) was A$132,537 (95% confidence interval 91,172–200,485) per DALY averted. Results were sensitive to restriction of trastuzumab to high-risk (large tumour) and/or high-benefit (young) patients. Suitable combinations of tumour size and age restrictions would improve the cost-effectiveness of trastuzumab. Specifically, restricting trastuzumab to women aged 40 years or younger with tumour sizes 40+ mm reduced the ICER to A$35,290 per DALY averted. CONCLUSION: Trastuzumab for HER2-positive early breast cancer had a high ICER. It is unclear why the Pharmaceutical Benefits Scheme listing does not use restrictions to improve the cost-effectiveness of the drug.