Analysis of Mammography Screening Schedules under Varying Resource Constraints for Planning Breast Cancer Control Programs in Low- and Middle-Income Countries: A Mathematical Study.

Background. Low-and-middle-income countries (LMICs) have higher mortality-to-incidence ratio for breast cancer compared to high-income countries (HICs) because of late-stage diagnosis. Mammography screening is recommended for early diagnosis, however, the infrastructure capacity in LMICs are far below that needed for adopting current screening guidelines. Current guidelines are extrapolations from HICs, as limited data had restricted model development specific to LMICs, and thus, economic analysis of screening schedules specific to infrastructure capacities are unavailable. Methods. We applied a new Markov process method for developing cancer progression models and a Markov decision process model to identify optimal screening schedules under a varying number of lifetime screenings per person, a proxy for infrastructure capacity. We modeled Peru, a middle-income country, as a case study and the United States, an HIC, for validation. Results. Implementing 2, 5, 10, and 15 lifetime screens would require about 55, 135, 280, and 405 mammography machines, respectively, and would save 31, 62, 95, and 112 life-years per 1000 women, respectively. Current guidelines recommend 15 lifetime screens, but Peru has only 55 mammography machines nationally. With this capacity, the best strategy is 2 lifetime screenings at age 50 and 56 years. As infrastructure is scaled up to accommodate 5 and 10 lifetime screens, screening between the ages of 44-61 and 41-64 years, respectively, would have the best impact. Our results for the United States are consistent with other models and current guidelines. Limitations. The scope of our model is limited to analysis of national-level guidelines. We did not model heterogeneity across the country. Conclusions. Country-specific optimal screening schedules under varying infrastructure capacities can systematically guide development of cancer control programs and planning of health investments.

Productivity losses due to premature mortality from cancer in Brazil, Russia, India, China, and South Africa (BRICS): A population-based comparison.

BACKGROUND: Over two-thirds of the world's cancer deaths occur in economically developing countries; however, the societal costs of cancer have rarely been assessed in these settings. Our aim was to estimate the value of productivity lost in 2012 due to cancer-related premature mortality in the major developing economies of Brazil, the Russian Federation, India, China and South Africa (BRICS). METHODS: We applied an incidence-based method using the human capital approach. We used annual adult cancer deaths from GLOBOCAN2012 to estimate the years of productive life lost between cancer death and pensionable age in each country, valued using national and international data for wages, and workforce statistics. Sensitivity analyses examined various methodological assumptions. RESULTS: The total cost of lost productivity due to premature cancer mortality in the BRICS countries in 2012 was $46·3 billion, representing 0·33% of their combined gross domestic product. The largest total productivity loss was in China ($28 billion), while South Africa had the highest cost per cancer death ($101,000). Total productivity losses were greatest for lung cancer in Brazil, the Russian Federation and South Africa; liver cancer in China; and lip and oral cavity cancers in India. CONCLUSION: Locally-tailored strategies are required to reduce the economic burden of cancer in developing economies. Focussing on tobacco control, vaccination programs and cancer screening, combined with access to adequate treatment, could yield significant gains for both public health and economic performance of the BRICS countries.

Combination therapy for visceral leishmaniasis.

Combination therapy for the treatment of visceral leishmaniasis has increasingly been advocated as a way to increase treatment efficacy and tolerance, reduce treatment duration and cost, and limit the emergence of drug resistance. We reviewed the evidence and potential for combination therapy, and the criteria for the choice of drugs in such regimens. The first phase 2 results of combination regimens are promising, and have identified effective and safe regimens as short as 8 days. Several phase 3 trials are underway or planned in the Indian subcontinent and east Africa. The limited data available suggest that combination therapy is more cost-effective and reduces indirect costs for patients. Additional advantages are reduced treatment duration (8-17 days), with potentially better patient compliance and lesser burden on the health system. Only limited data are available on how best to prevent acquired resistance. Patients who are coinfected with visceral leishmaniasis and HIV could be a reservoir for development and spread of drug-resistant strains, calling for special precautions. The identification of a short, cheap, well-tolerated combination regimen that can be given in ambulatory care and needs minimal clinical monitoring will most likely have important public health implications. Effective monitoring systems and close regulations and policy will be needed to ensure effective implementation. Whether combination therapy could indeed help delay resistance, and how this is best achieved, will only be known in the long term.