Estimates of health care costs associated with excess weight are needed to inform the development of cost-effective obesity prevention efforts. However, commonly used cost estimates are not sensitive to changes in weight across the entire body mass index (BMI) distribution as they are often based on discrete BMI categories.

For most pathogens, transmission is driven by interactions between the behaviours of infectious individuals, the behaviours of the wider population, the local environment, and immunity. Phylogeographic approaches are currently unable to disentangle the relative effects of these competing factors. We develop a spatiotemporally structured phylogenetic framework that addresses these limitations by considering individual transmission events, reconstructed across spatial scales.

Artificial intelligence (AI) offers great potential for transforming healthcare delivery leading to better patient-outcomes and more efficient care delivery. However, despite these advantages, integration of AI in healthcare has not kept pace with technological advancements. Previous research indicates the importance of understanding various organisational factors that shape integration of new technologies in healthcare. Therefore, the aim of this study is to provide an overview of the existing organisational factors influencing adoption of AI in healthcare from the perspectives of different relevant stakeholders. By conducting this review, the various organisational factors that facilitate or hinder AI implementation in healthcare could be identified.

Leprosy is known to be unevenly distributed between and within countries. High risk areas or ‘hotspots’ are potential targets for preventive interventions, but the underlying epidemiologic mechanisms that enable hotspots to emerge, are not yet fully understood. In this study, we identified and characterized leprosy hotspots in Bangladesh, a country with one of the highest leprosy endemicity levels globally.

Current hypertension guidelines vary substantially in their definition of who should be offered blood pressure-lowering medications. Understanding the effect of guideline choice on the proportion of adults who require treatment is crucial for planning and scaling up hypertension care in low- and middle-income countries.

There is an interest to understand how social impact bonds (SIBs), a type of innovative financing instrument used in impact investment, can be used to finance the prevention of non-communicable diseases (NCDs). This is the first scoping review that explores the evidence of SIBs for NCDs and their key characteristics and performance. The review used both published and grey literature from eight databases (MEDLINE, NCBI, Elsevier, Cochrane Library, Google, Google Scholar, WHO publications and OECD iLibrary). A total of 83 studies and articles were eligible for inclusion, identifying 11 SIBs implemented in eight countries. The shared characteristics of the SIBs used for NCDs were impact investment companies as investors, local governments as outcome payers, not-for-profit service providers and an average US$2 015 456 private initial investment. The review revealed a lack of empirical evidence on SIBs for NCDs. Conflict of interest and lack of public disclosure were common issues in both the published and grey literature on SIBs. Furthermore, only three SIBs implemented for financing NCDs were meeting all their target outcomes. The common characteristics of the SIBs meeting their target outcomes were evidence-based interventions, multiple service providers and an intermediated structure. Overall, there is a need for more high-quality studies, particularly economic evaluations and qualitative studies on the benefits to target populations, and greater transparency from the private sector, in order to ensure improved SIBs for preventing NCDs.

Active case-finding (ACF) is an important component of the End TB Strategy. However, ACF is resource-intensive, and the economics of ACF are not well-understood. Data on the costs of ACF are limited, with little consistency in the units and methods used to estimate and report costs. Mathematical models to forecast the long-term effects of ACF require empirical measurements of the yield, timing and costs of case detection. Pragmatic trials offer an opportunity to assess the cost-effectiveness of ACF interventions within a ‘real-world´ context. However, such analyses generally require early introduction of economic evaluations to enable prospective data collection on resource requirements. Closing the global case-detection gap will require substantial additional resources, including continued investment in innovative technologies. Research is essential to the optimal implementation, cost-effectiveness, and affordability of ACF in high-burden settings. To assess the value of ACF, we must prioritize the collection of high-quality data regarding costs and effectiveness, and link those data to analytical models that are adapted to local settings.

We adapted a mathematical modeling approach to estimate tuberculosis (TB) incidence and fraction treated for 101 municipalities of Brazil during 2008-2017. We found the average TB incidence rate decreased annually (0.95%), and fraction treated increased (0.30%). We estimated that 9% of persons with TB did not receive treatment in 2017.