Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond.

Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities. Our approach involves multi-level engagement, innovative methodologies, and novel data streams. We take advantage of intelligence generated locally and empirically to quantify effects in areas experiencing rapid urban transformation and heterogeneous climate-induced disease threats. Our goal is to reduce the knowledge-to-action gap by developing an integrated One Health-Climate Risk framework.

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework.

OBJECTIVES: To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). METHODS: A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. RESULTS: A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. CONCLUSIONS: We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

Tracking the impacts of climate change on human health via indicators: lessons from the Lancet Countdown.

BACKGROUND: In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the "Lancet Countdown: Tracking Progress on Health and Climate Change", an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics. DISCUSSION: This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change's impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators' relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs. CONCLUSIONS: Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive.

Attributing changes in food insecurity to a changing climate.

It is generally accepted that climate change is having a negative impact on food security. However, most of the literature variously focuses on the complex and many mechanisms linking climate stressors; the links with food production or productivity rather than food security; and future rather than current effects. In contrast, we investigate the extent to which current changes in food insecurity can be plausibly attributed to climate change. We combine food insecurity data for 83 countries from the FAO food insecurity experience scale (FIES) with reanalysed climate data from ERA5-Land, and use a panel data regression with time-varying coefficients. This framework allows us to estimate whether the relationship between food insecurity and temperature anomaly is changing over time. We also control for Human Development Index, and drought measured by six-month Standardized Precipitation Index. Our empirical findings suggest that for every 1 [Formula: see text] of temperature anomaly, severe global food insecurity has increased by 1.4% (95% CI 1.3-1.47) in 2014 but by 1.64% (95% CI 1.6-1.65) in 2019. This impact is higher in the case of moderate to severe food insecurity, with a 1 [Formula: see text] increase in temperature anomaly resulting in a 1.58% (95% CI 1.48-1.68) increase in 2014 but a 2.14% (95% CI 2.08-2.20) increase in 2019. Thus, the results show that the temperature anomaly has not only increased the probability of food insecurity, but the magnitude of this impact has increased over time. Our counterfactual analysis suggests that climate change has been responsible for reversing some of the improvements in food security that would otherwise have been realised, with the highest impact in Africa. Our analysis both provides more evidence of the costs of climate change, and as such the benefits of mitigation, and also highlights the importance of targeted and efficient policies to reduce food insecurity. These policies are likely to need to take into account local contexts, and might include efforts to increase crop yields, targeted safety nets, and behavioural programs to promote household resilience.

Impact of COVID-19 on food insecurity using multiple waves of high frequency household surveys.

In response to the rapid spread of COVID-19, governments across the globe have implemented local lockdowns that have led to increased unemployment and have disrupted local and international transport routes and supply chains. Whilst such efforts aim to slow or stop the spread of the SARS-CoV-2 virus, they have also resulted in increased food insecurity, whether due to reduced incomes or increased food prices. This is the first paper to track food insecurity and its determinants during the pandemic using multi-country and multi-wave evidence. Using data from 11 countries and up to 6 waves of High-Frequency Phone Survey data (household-level surveys) on COVID-19 and its impacts, we use a fixed-effects linear probability model to investigate the socioeconomic determinants of food insecurity during the pandemic for each country using household-level data over multiple waves. We control for socioeconomic characteristics including gender and education of the household head; income and poverty status of the households during the pandemic; safety nets in the form of cash and food assistance; coping strategies adopted by households; and price effects of major food items. Our findings suggest that cash safety nets appear to have been more effective than food in terms of reducing food insecurity during the pandemic; and that those particularly hard hit are female headed-households (highest in Malawi: 0.541, 95% CI 0.516, 0.569; lowest in Cambodia: 0.023, 95% CI 0.022, 0.024), the less educated (highest in Djibouti: - 0.232, 95% CI - 0.221, - 0.244; lowest in Nigeria: 0.006, 95% CI - 0.005, - 0.007), and poorer households (highest in Mali: 0.382, 95% CI 0.364, 0.402; lowest in Chad: 0.135, 95% CI 0.129, 0.142). In line with the existing literature, our results show that, even controlling for income loss and poverty status, those households who had to borrow rather than rely on savings had a higher probability of suffering from food insecurity. Distinct differences in the efficacy of safety nets across the 11 countries, and the differential impact of the pandemic on different groups within societies, suggest in-depth country-specific studies are needed to understand why some countries have coped better than others. Our paper highlights the importance of improving household resilience to future systemic crises, and using evidence-based best practice in the design of relevant policy instruments.

Food Insecurity, Safety Nets, and Coping Strategies during the COVID-19 Pandemic: Multi-Country Evidence from Sub-Saharan Africa.

The COVID-19 pandemic has affected food security across the world. As governments respond in different ways both with regards to containing the pandemic and addressing food insecurity, in parallel detailed datasets are being collected and analysed. To date, literature addressing food insecurity during the pandemic, using these datasets, has tended to focus on individual countries. By contrast, this paper provides the first detailed multi-country cross-sectional snapshot of the social dimensions of food insecurity during the COVID-19 pandemic across nine African countries (Chad, Djibouti, Ethiopia, Kenya, Malawi, Mali, Nigeria, South Africa, and Uganda). Econometric analysis reveals that female-headed households, the poor, and the less-formally educated, appear to suffer more in terms of food insecurity during this global pandemic. Importantly, our findings show that the negative consequences of the pandemic are disproportionately higher for lower-income households and those who had to borrow to make ends meet rather than relying on savings; impacts are country-specific; and there is considerable spatial heterogeneity within country food insecurity, suggesting that tailored policies will be required. These nine countries employ both food and cash safety nets, with the evidence suggesting that, at least when these data were collected, cash safety nets have been slightly more effective at reducing food insecurity. Our results provide a baseline that can be used by governments to help design and implement tailored policies to address food insecurity. Our findings can also be used as lessons to reshape policies to tackle the heterogeneous impacts of climate change.

Effects of climate change on combined labour productivity and supply: an empirical, multi-model study.

BACKGROUND: Although effects on labour is one of the most tangible and attributable climate impact, our quantification of these effects is insufficient and based on weak methodologies. Partly, this gap is due to the inability to resolve different impact channels, such as changes in time allocation (labour supply) and slowdown of work (labour productivity). Explicitly resolving those in a multi-model inter-comparison framework can help to improve estimates of the effects of climate change on labour effectiveness. METHODS: In this empirical, multi-model study, we used a large collection of micro-survey data aggregated to subnational regions across the world to estimate new, robust global and regional temperature and wet-bulb globe temperature exposure-response functions (ERFs) for labour supply. We then assessed the uncertainty in existing labour productivity response functions and derived an augmented mean function. Finally, we combined these two dimensions of labour into a single compound metric (effective labour effects). This combined measure allowed us to estimate the effect of future climate change on both the number of hours worked and on the productivity of workers during their working hours under 1·5°C, 2·0°C, and 3·0°C of global warming. We separately analysed low-exposure (indoors or outdoors in the shade) and high-exposure (outdoor in the sun) sectors. FINDINGS: We found differentiated empirical regional and sectoral ERF's for labour supply. Current climate conditions already negatively affect labour effectiveness, particularly in tropical countries. Future climate change will reduce global total labour in the low-exposure sectors by 18 percentage points (range -48·8 to 5·3) under a scenario of 3·0°C warming (24·8 percentage points in the high-exposure sectors). The reductions will be 25·9 percentage points (-48·8 to 2·7) in Africa, 18·6 percentage points (-33·6 to 5·3) in Asia, and 10·4 percentage points (-35·0 to 2·6) in the Americas in the low-exposure sectors. These regional effects are projected to be substantially higher for labour outdoors in full sunlight compared with indoors (or outdoors in the shade) with the average reductions in total labour projected to be 32·8 percentage points (-66·3 to 1·6) in Africa, 25·0 percentage points (-66·3 to 7·0) in Asia, and 16·7 percentage points (-45·5 to 4·4) in the Americas. INTERPRETATION: Both labour supply and productivity are projected to decrease under future climate change in most parts of the world, and particularly in tropical regions. Parts of sub-Saharan Africa, south Asia, and southeast Asia are at highest risk under future warming scenarios. The heterogeneous regional response functions suggest that it is necessary to move away from one-size-fits-all response functions to investigate the climate effect on labour. Our findings imply income and distributional consequences in terms of increased inequality and poverty, especially in low-income countries, where the labour effects are projected to be high. FUNDING: COST (European Cooperation in Science and Technology).

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.

A review of estimating population exposure to sea-level rise and the relevance for migration.

This review analyses global or near-global estimates of population exposure to sea-level rise (SLR) and related hazards, followed by critically examining subsequent estimates of population migration due to this exposure. Our review identified 33 publications that provide global or near-global estimates of population exposure to SLR and associated hazards. They fall into three main categories of exposure, based on definitions in the publications: (i) the population impacted by specified levels of SLR; (ii) the number of people living in floodplains that are subject to coastal flood events with a specific return period; and (iii) the population living in low-elevation coastal zones. Twenty of these 33 publications discuss connections between population migration and SLR. In our analysis of the exposure and migration data, we consider datasets, analytical methods, and the challenges of estimating exposure to SLR followed by potential human migration. We underscore the complex connections among SLR, exposure to its impacts, and migration. Human mobility to and from coastal areas is shaped by diverse socioeconomic, demographic, institutional, and political factors; there may be 'trapped' populations as well as those who prefer not to move for social, cultural, and political reasons; and migration can be delayed or forestalled through other adaptive measures. While global estimates of exposed and potentially migrating populations highlight the significant threats of SLR for populations living in low-lying areas at or near coastlines, further research is needed to understand the interactions among localised SLR and related hazards, social and political contexts, adaptation possibilities, and potential migration and (im)mobility decision-making.

Burden of climate change on malaria mortality.

In 2016, an estimated 445,000 deaths and 216 million cases of malaria occurred worldwide, while 70% of the deaths occurred in children under five years old. Changes in climatic exposures such as temperature and precipitation make malaria one of the most climate sensitive outcomes. Using a global malaria mortality dataset for 105 countries between 1980 and 2010, we find a non-linear relationship between temperature and malaria mortality and estimate that the global optimal temperature threshold beyond which all-age malaria mortality increases is 20.8 °C, while in the case of child mortality; a significantly lower optimum temperature of 19.3° is estimated. Our results also suggest that this optimal temperature is 28.4 °C and 26.3 °C in Africa and Asia, respectively - the continents where malaria is most prevalent. Furthermore, we estimate that child mortality (ages 0-4) is likely to increase by up to 20% in some areas due to climate change by the end of the 21st century.