The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises.

TRANSLATIONS: For the Chinese, French, German, and Spanish translations of the abstract see Supplementary Materials section.

Intergovernmental engagement on health impacts of climate change.

OBJECTIVE: To examine countries' engagement with the health impacts of climate change in their formal statements to intergovernmental organizations, and the factors driving engagement. METHODS: We obtained the texts of countries' annual statements in United Nations (UN) general debates from 2000 to 2019 and their nationally determined contributions at the Paris Agreement in 2016. To measure countries' engagement, we used a keyword-in-context text search with relevant search terms to count the total number of references to the relationship of health to climate change. We used a machine learning model (random forest predictions) to identify the most important country-level predictors of engagement. The predictors included political and economic factors, health outcomes, climate change-related variables and membership of political negotiating groups in the UN. FINDINGS: For both UN general debate statements and nationally determined contributions, low- and middle-income countries discussed the health impacts of climate change much more than did high-income countries. The most important predictors of engagement were health outcomes (infant mortality, maternal deaths, life expectancy), countries' income levels (gross domestic product per capita), and fossil fuel consumption. Membership of political negotiating groups (such as the Group of 77 and Small Island Developing States) was a less important predictor. CONCLUSION: Our analysis indicated a higher engagement in countries that carry the heaviest climate-related health burdens, but lack necessary resources to address the impacts of climate change. These countries are shouldering responsibility for reminding the global community of the implications of climate change for people's health.

Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration.

Public sector organizations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high-uncertainty environments. The long-term success of data science and artificial intelligence (AI) in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and the public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities for and challenges of AI for the public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.

Born to Lead? A Twin Design and Genetic Association Study of Leadership Role Occupancy.

We address leadership emergence and the possibility that there is a partially innate predisposition to occupy a leadership role. Employing twin design methods on data from the National Longitudinal Study of Adolescent Health, we estimate the heritability of leadership role occupancy at 24%. Twin studies do not point to specific genes or neurological processes that might be involved. We therefore also conduct association analysis on the available genetic markers. The results show that leadership role occupancy is associated with rs4950, a single nucleotide polymorphism (SNP) residing on a neuronal acetylcholine receptor gene (CHRNB3). We replicate this family-based genetic association result on an independent sample in the Framingham Heart Study. This is the first study to identify a specific genotype associated with the tendency to occupy a leadership position. The results suggest that what determines whether an individual occupies a leadership position is the complex product of genetic and environmental influences; with a particular role for rs4950.

Engagement with health in national climate change commitments under the Paris Agreement: a global mixed-methods analysis of the nationally determined contributions.

BACKGROUND: Instituted under the Paris Agreement, nationally determined contributions (NDCs) outline countries' plans for mitigating and adapting to climate change. They are the primary policy instrument for protecting people's health in the face of rising global temperatures. However, evidence on engagement with health in the NDCs is scarce. In this study, we aimed to examine how public health is incorporated in the NDCs, and how different patterns of engagement might be related to broader inequalities and tensions in global climate politics. METHODS: We analysed the NDCs in the UN Framework Convention on Climate Change registry submitted by 185 countries. Using content analysis and natural language processing (NLP) methods, we developed measures of health engagement. Multivariate regression analyses examined whether country-level factors (eg, population size, gross domestic product [GDP], and climate-related exposures) were associated with greater health engagement. Using NLP methods, we compared health engagement with other climate-related challenges (ie, economy, energy, and agriculture) and examined broader differences in the keyword terms used in countries with high and low health engagement in their NDCs. FINDINGS: Countries that did not mention health in their NDCs were clustered in high-income countries, whereas greater health engagement was concentrated in low-income and middle-income countries. Having a low GDP per capita and being a small island developing state were associated with higher levels of health engagement. In addition, higher levels of population exposure to temperature change and ambient air pollution were associated with more health coverage included in a country's NDC. Variation in health engagement was greater than for other climate-related issues and reflected wider differences in countries' approaches to the NDCs. INTERPRETATION: A focus on health in the NDCs follows broader patterns of global inequalities. Poorer and climate-vulnerable countries that contribute least to climate change are more likely to engage with health in their NDCs, while richer countries focus on non-health sectors in their NDCs, such as energy and the economy. FUNDING: This work was in part funded through an unrestricted grant from the Wellcome Trust and supported by The Economic and Social Research Council.

Multiplex communities and the emergence of international conflict.

Advances in community detection reveal new insights into multiplex and multilayer networks. Less work, however, investigates the relationship between these communities and outcomes in social systems. We leverage these advances to shed light on the relationship between the cooperative mesostructure of the international system and the onset of interstate conflict. We detect communities based upon weaker signals of affinity expressed in United Nations votes and speeches, as well as stronger signals observed across multiple layers of bilateral cooperation. Communities of diplomatic affinity display an expected negative relationship with conflict onset. Ties in communities based upon observed cooperation, however, display no effect under a standard model specification and a positive relationship with conflict under an alternative specification. These results align with some extant hypotheses but also point to a paucity in our understanding of the relationship between community structure and behavioral outcomes in networks.