Policymakers need decision tools to determine when to use physical distancing interventions to maximize the control of COVID-19 while minimizing the economic and social costs of these interventions. We develop a pragmatic decision tool to characterize adaptive policies that combine real-time surveillance data with clear decision rules to guide when to trigger, continue, or stop physical distancing interventions during the current pandemic. In model-based experiments, we find that adaptive policies characterized by our proposed approach prevent more deaths and require a shorter overall duration of physical distancing than alternative physical distancing policies. Our proposed approach can readily be extended to more complex models and interventions.

The Coronavirus SARS-CoV-2 has spread rapidly since the first cases hit Wuhan, China at the end of 2019, and has now landed in almost every part of the world. By mid-February 2020, China, South Korea, Singapore, Taiwan, and – to some extent – Japan began to contain and control the spread of the virus, while conversely, cases increased rapidly in Europe and the United States. In response to the pandemic, many countries have had to introduce drastic legally mandated lockdowns to enforce physical separation, which are ravaging economies worldwide. Although it will be many months or even years before the final verdict can be reached, we believe that it is already possible to identify 12 key lessons that we can learn from to reduce the tremendous economic and social costs of this pandemic and which can inform responses to future crises. These include lessons around the importance of transparency, solidarity, coordination, decisiveness, clarity, accountability and more.

Value of information (VOI) analyses can help policy makers make informed decisions about whether to conduct and how to design future studies. Historically a computationally expensive method to compute the expected value of sample information (EVSI) restricted the use of VOI to simple decision models and study designs. Recently, 4 EVSI approximation methods have made such analyses more feasible and accessible. Members of the Collaborative Network for Value of Information (ConVOI) compared the inputs, the analyst’s expertise and skills, and the software required for the 4 recently developed EVSI approximation methods. Our report provides practical guidance and recommendations to help inform the choice between the 4 efficient EVSI estimation methods. More specifically, this report provides: (1) a step-by-step guide to the methods’ use, (2) the expertise and skills required to implement the methods, and (3) method recommendations based on the features of decision-analytic problems.

There is great interest for integrating care for non-communicable diseases (NCDs) into routine HIV services in sub-Saharan Africa (SSA) due to the steady rise of the number of people who are ageing with HIV. Suggested health system approaches for intervening on these comorbidities have mostly been normative, with little actionable guidance on implementation, and on the practical, economic and ethical considerations of favouring people living with HIV (PLHIV) versus targeting the general population. We summarize opportunities and challenges related to leveraging HIV treatment platforms to address NCDs among PLHIV. We emphasize key considerations that can guide integrated care in SSA and point to possible interventions for implementation.

Multimorbidity, the presence of two or more mental or physical chronic non-communicable diseases, is a major challenge for the health system in China, which faces unprecedented ageing of its population. Here we examined the distribution of physical multimorbidity in relation to socioeconomic status; the association between physical multimorbidity, health-care service use, and catastrophic health expenditures; and whether these associations varied by socioeconomic group and social health insurance schemes.

Strong surgical systems are necessary to prevent premature death and avoidable disability from surgical conditions. The epidemiological transition, which has led to a rising burden of non-communicable diseases and injuries worldwide, will increase the demand for surgical assessment and care as a definitive healthcare intervention. Yet, 5 billion people lack access to timely, affordable and safe surgical and anaesthesia care, with the unmet demand affecting predominantly low-income and middle-income countries (LMICs). Rapid surgical care scale-up is required in LMICs to strengthen health system capabilities, but adequate financing for this expansion is lacking. This article explores the critical role of innovative financing in scaling up surgical care in LMICs. We locate surgical system financing by using a modified fiscal space analysis. Through an analysis of published studies and case studies on recent trends in the financing of global health systems, we provide a conceptual framework that could assist policy-makers in health systems to develop innovative financing strategies to mobilise additional investments for scale-up of surgical care in LMICs. This is the first time such an analysis has been applied to the funding of surgical care. Innovative financing in global surgery is an untapped potential funding source for expanding fiscal space for health systems and financing scale-up of surgical care in LMICs.

Despite growing enthusiasm for integrating treatment of non-communicable diseases (NCDs) into human immunodeficiency virus (HIV) care and treatment services in sub-Saharan Africa, there is little evidence on the potential health and financial consequences of such integration. We aim to study the cost-effectiveness of basic NCD-HIV integration in a Ugandan setting.

Machine learning (ML) has been used in bio-medical research, and recently in clinical and public health research. However, much of the available evidence comes from high-income countries, where different health profiles challenge the application of this research to low/middle-income countries (LMICs). It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health. We aim to address this gap by conducting a scoping review of health-related ML applications in LMICs.