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.

The world is not on track to achieve the goals for immunization coverage and equity described by the World Health Organization in the Global Vaccine Action Plan. In India, only 62 percent of children had received a full course of basic vaccines in 2016.

Despite improvement in health outcomes over the past few decades, China still experiences striking rural-urban health inequalities. There is limited research on the rural-urban differences in health system performance in China.

Epidemics pose a growing threat. Our cities are increasingly densely populated, we are more connected than ever before, and in recent years we have witnessed successive waves of new (severe acute respiratory syndrome [SARS], Zika virus, Ebola virus, and now coronavirus disease 2019 [COVID-19]) and old (influenza) infectious disease threats causing global pandemics. Urgent investment in surveillance systems and global partnerships are needed to prepare for the pandemics that will continue to emerge in the coming decades. There has been discussion of the promise of integrating sophisticated epidemiological models and new big data streams—for example, from mobile phones, satellites, or social media—at various stages of the public health response, particularly in the context of epidemic forecasting and decision making. These new data streams provide important, real-time information about travel patterns that spread disease and spatial shifts in populations at risk, which until recently have been very difficult to quantify on timescales relevant to a fast-moving epidemic. With growing mobility and increasing global connectivity, this information will be key to planning surveillance and containment strategies.