• Energy Research

Climate change and population ageing are converging challenges that are expected to significantly worsen the health impacts of high temperatures. We aimed to remeasure the implications of ageing for heat-related mortality by comparing time trends based on chronological age (number of years already lived) with those derived from the application of state-of-the-art demographic methodology which better captures the dynamics of evolving longevity: prospective age (number of years still to be lived). We conducted a nationwide time-series analysis of 13 regions in Spain over 1980-2018 using all-cause mortality microdata for people aged 65+ and annual life tables from the Spanish National Institute of Statistics, and daily mean temperatures from E-OBS. Based on confounder-adjusted quasi-Poisson regression with distributed lag non-linear models and multivariate meta-analysis in moving 15-year timeslices, we assessed sex-specific changes in absolute risk and impacts for heat-related mortality at extreme and moderate temperatures, for chronological and prospective age groups. In the conventional chronological age analysis, absolute risk fell over the study period (e.g. females, extreme heat: -54%; moderate heat: -23%); after accounting for rising longevity, the prospective age analysis, however, found a smaller decline in risk for extreme heat (-15%) and a rise for moderate heat (+46%). Additionally, while the chronological age analysis suggested a shift in mortality towards higher ages, the prospective age analysis showed that over the study period, people of largely the same (prospective) age were impacted. Further, the prospective age analysis revealed excess risk in females (compared to males) rose from 20% to 27% for extreme heat, and from 40% to 70% for moderate heat. Assessing the implications of ageing using a prospective age perspective showed the urgency of re-doubling risk reduction efforts, including accelerating healthy ageing programs that incorporate climate considerations. The age patterns of impacts suggested that such actions have the potential to mitigate ageing-related heat-health threats to generate climate change-ready, healthy societies.

Background: Environmental improvement is a priority for urban sustainability and health and achieving it requires transformative change in cities. An approach to achieving such change is to bring together researchers, decision-makers, and public groups in the creation of research and use of scientific evidence. Methods: This article describes the development of a programme theory for Complex Urban Systems for Sustainability and Health (CUSSH), a four-year Wellcome-funded research collaboration which aims to improve capacity to guide transformational health and environmental changes in cities. Results: Drawing on ideas about complex systems, programme evaluation, and transdisciplinary learning, we describe how the programme is understood to “work” in terms of its anticipated processes and resulting changes. The programme theory describes a chain of outputs that ultimately leads to improvement in city sustainability and health (described in an ‘action model’), and the kinds of changes that we expect CUSSH should lead to in people, processes, policies, practices, and research (described in a ‘change model’). Conclusions: Our paper adds to a growing body of research on the process of developing a comprehensive understanding of a transdisciplinary, multiagency, multi-context programme. The programme theory was developed collaboratively over two years. It involved a participatory process to ensure that a broad range of perspectives were included, to contribute to shared understanding across a multidisciplinary team. Examining our approach allowed an appreciation of the benefits and challenges of developing a programme theory for a complex, transdisciplinary research collaboration. Benefits included the development of teamworking and shared understanding and the use of programme theory in guiding evaluation. Challenges included changing membership within a large group, reaching agreement on what the theory would be ‘about’, and the inherent unpredictability of complex initiatives.

In 2016, 23% of children (155 million) aged [Formula: see text] were stunted. Global-level modeling has consistently found climate change impacts on food production are likely to impair progress on reducing undernutrition.We adopt a new perspective, assessing how climate change may affect child stunting via its impacts on two interacting socioeconomic drivers: incomes of the poorest 20% of populations (due to climate impacts on crop production, health, labor productivity, and disasters) and food prices.We developed a statistical model to project moderate and severe stunting in children aged [Formula: see text] at the national level in 2030 under low and high climate change scenarios combined with poverty and prosperity scenarios in 44 countries.We estimated that in the absence of climate change, 110 million children aged [Formula: see text] would be stunted in 2030 under the poverty scenario in comparison with 83 million under the prosperity scenario. Estimates of climate change-attributable stunting ranged from 570,000 under the prosperity/low climate change scenario to [Formula: see text] under the poverty/high climate change scenario. The projected impact of climate change on stunting was greater in rural vs. urban areas under both socioeconomic scenarios. In countries with lower incomes and relatively high food prices, we projected that rising prices would tend to increase stunting, whereas in countries with higher incomes and relatively low food prices, rising prices would tend to decrease stunting. These findings suggest that food prices that provide decent incomes to farmers alongside high employment with living wages will reduce undernutrition and vulnerability to climate change.Shifting the focus from food production to interactions between incomes and food price provides new insights. Futures that protect health should consider not just availability, accessibility, and quality of food, but also the incomes generated by those producing the food. https://doi.org/10.1289/EHP2916.

The work of MSo and RH has been performed within the scope of H2020 project EXHAUSTION (grant number 820655) and Academy of Finland HEATCOST (grant number 334798). JMA acknowledges support from the Spanish Ministry of Science and Innovation and State Research Agency through the Centro de Excelencia Severo Ochoa 2019–23 programme (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA programme. JB acknowledges funding from the Ministry of Science and Innovation (MCIU) under grant agreement number RYC2018-025446-I (programme Ramón y Cajal). The computations of the mechanistic dengue-models (MOS and HS) were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N. JR has been awarded a Chair in Artificial Intelligence in the Research of Infectious Diseases Impacted by Climate Change provided by the Alexander von Humboldt Foundation in the framework of the Alexander von Humboldt Professorship endowed by the Federal Ministry of Education and Research. HA, MQ-Z, and SJL were supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 865564 (European Research Council Consolidator Grant EARLY-ADAPT). JP was supported by Academy of Finland projects PS4A and ALL-Impress. The Lancet Countdown in Europe received invaluable support from Shuzhou Yuan, Ran Zhang, Krishnamoorthy Manohara, and Reed Garvin (Data Science Lab, Hertie School, Germany), Tom de Groeve and Peter Salamon (European Commission), and Raúl Fernando Méndez Turrubiates (ISGlobal, Barcelona, Spain). We also thank Wenjia Cai, Shihui Zhang, and Jiyao Zhao (Department of Earth System Science, Tsinghua University, Beijing, China) for their technical advice.

Significance This study is the first multimalaria model intercomparison exercise. This is carried out to estimate the impact of future climate change and population scenarios on malaria transmission at global scale and to provide recommendations for the future. Our results indicate that future climate might become more suitable for malaria transmission in the tropical highland regions. However, other important socioeconomic factors such as land use change, population growth and urbanization, migration changes, and economic development will have to be accounted for in further details for future risk assessments.