Development assistance for health (DAH), the value of which peaked in 2013 and fell in 2015, is unlikely to rise substantially in the near future, increasing reliance on domestic and innovative financing sources to sustain health programmes in low-income and middle-income countries. We examined innovative financing instruments (IFIs)-financing schemes that generate and mobilise funds-to estimate the quantum of financing mobilised from 2002 to 2015. We identified ten IFIs, which mobilised US$8·9 billion (2·3% of overall DAH) in 2002-15. The funds generated by IFIs were channelled mostly through GAVI and the Global Fund, and used for programmes for new and underused vaccines, HIV/AIDS, malaria, tuberculosis, and maternal and child health. Vaccination programmes received the largest amount of funding ($2·6 billion), followed by HIV/AIDS ($1080·7 million) and malaria ($1028·9 million), with no discernible funding targeted to non-communicable diseases.

On 11 March 2011, the Great East Japan Earthquake, followed by a tsunami and nuclear-reactor meltdowns, produced one of the most severe disasters in the history of Japan. The adverse impact of this ‘triple disaster’ on the health of local populations and the health system was substantial. In this study we examine population-level health indicator changes that accompanied the disaster, and discuss options for re-designing Fukushima’s health system, and by extension that of Japan, to enhance its responsiveness and resilience to current and future shocks.

Mathematical simulation models are commonly used to inform health policy decisions. These health policy models represent the social and biological mechanisms that determine health and economic outcomes, combine multiple sources of evidence about how policy alternatives will impact those outcomes, and synthesize outcomes into summary measures salient for the policy decision. Calibrating these health policy models to fit empirical data can provide face validity and improve the quality of model predictions. Bayesian methods provide powerful tools for model calibration. These methods summarize information relevant to a particular policy decision into (1) prior distributions for model parameters, (2) structural assumptions of the model, and (3) a likelihood function created from the calibration data, combining these different sources of evidence via Bayes’ theorem. This article provides a tutorial on Bayesian approaches for model calibration, describing the theoretical basis for Bayesian calibration approaches as well as pragmatic considerations that arise in the tasks of creating calibration targets, estimating the posterior distribution, and obtaining results to inform the policy decision. These considerations, as well as the specific steps for implementing the calibration, are described in the context of an extended worked example about the policy choice to provide (or not provide) treatment for a hypothetical infectious disease. Given the many simplifications and subjective decisions required to create prior distributions, model structure, and likelihood, calibration should be considered an exercise in creating a reasonable model that produces valid evidence for policy, rather than as a technique for identifying a unique theoretically optimal summary of the evidence.

Differences in methods and data used in past studies have limited comparisons of the cost of illness of diabetes across countries. We estimate the full global economic burden of diabetes in adults aged 20-79 years in 2015, using a unified framework across all countries. Our objective was to highlight patterns of diabetes-associated costs as well as to identify the need for further research in low-income regions.

The impact of user-fee policies on the equity of health care utilization and households’ financial burdens has remained largely unexplored in Latin American and the Caribbean, as well as in upper-middle-income countries. This paper assesses the short- and long-term impacts of Jamaica’s user-fee-removal for children in 2007.

Cancer care is liable to medication errors due to the complex nature of cancer treatment, the common presence of comorbidities and the involvement of a number of clinicians in cancer care. While the frequency of medication errors in cancer care has been reported, little is known about their causal factors and effective prevention strategies. With a unique insight into the main safety issues in cancer treatment, frontline staff can help close this gap. In this study, we aimed to identify medication safety priorities in cancer patient care according to clinicians in North West London using PRIORITIZE, a novel priority-setting approach.

Mathematical models of chlamydia transmission can help inform disease control policy decisions when direct empirical evaluation of alternatives is impractical. We reviewed published chlamydia models to understand the range of approaches used for policy analyses and how the studies have responded to developments in the field.

To systematically categorise cancer research investment awarded to United Kingdom (UK) institutions in the period 2000-2013 and to estimate research investment relative to disease burden as measured by mortality, disability-adjusted life years (DALYs) and years lived with disability (YLDs).