Mary Lynch

Growing extreme weather risks increase the need for economically justified investment in climate adaptation and disaster risk reduction, yet evidence remains fragmented across strategies, hazards, and settings. This systematic review synthesised economic effectiveness and stated-preference evidence for proactive management of extreme weather risks. Six electronic search platforms covering eight database sources were searched for English-language peer-reviewed studies from January 2015 to May 2025. Heterogeneity required narrative synthesis rather than meta-analysis. Fifty-three unique studies were included: 32 contributed economic effectiveness evidence, 23 contributed stated-preference evidence, and two contributed to both evidence strands. Among the 32 economic effectiveness studies, 15 reported favourable conclusions, 15 mixed or conditional conclusions, one an unfavourable conclusion, and one an unclear conclusion. All 23 stated-preference studies reported positive willingness to pay, willingness to purchase or related stated demand, indicating expressed demand or perceived value rather than demonstrated economic effectiveness. Economic conclusions frequently depended on site, design, scale, time horizon, discounting, valuation boundaries, and modelling assumptions. Economic effectiveness evidence was absent for community-based and risk-financing strategies and concentrated on flood risk, Asia, and North America. Economic and financial factors were the most common barriers, whereas social and cultural factors were the most common facilitators. Investment decisions should consider evidence type, robustness, assumptions, and context.

As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health preparedness, and climate adaptation strategies, especially in parts of the world that are already subject to extreme heat, such as tropical regions. In recent years, machine learning (ML) has increasingly been applied to environmental and meteorological data to improve the prediction of heatwaves and extreme heat events. This scoping review examines global peer-reviewed literature on the application of ML techniques for extreme heat prediction using environmental variables. This includes heatwave prediction, environmental and meteorological predictors used in these models, and the geographical distribution of existing research. A total of 23 peer-reviewed studies meeting the inclusion criteria were included in the review, following the PRISMA-ScR guidelines. The findings indicate that artificial neural networks and random forest models were most frequently reported as high performing within individual studies. However, direct comparisons across studies are limited by heterogeneity in prediction targets, validation strategies, lead times, heatwave definitions, and performance metrics. Temperature-related variables, especially maximum temperature, were consistently identified as the most influential predictors across studies. Furthermore, the evidence base was heavily concentrated in Europe, Asia, and North America, with comparatively limited representation from low- and middle-income countries respective to population, despite these regions often experiencing disproportionate impacts of climate change and extreme heat exposure. By synthesising current evidence on ML-based heatwave prediction, associated environmental predictors, and geographical research trends, this review provides insights to support the development of more robust, context-aware, and globally representative heatwave forecasting frameworks.

As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health preparedness, and climate adaptation strategies, especially in the hottest parts of the world. In recent years, machine learning (ML) has increasingly been applied to environmental and meteorological data to improve the prediction of heatwaves and extreme heat events. This scoping review examines global peer-reviewed literature on the application of ML techniques for extreme heat prediction using environmental variables. This includes heatwave prediction, environmental and meteorological predictors used in these models, and the geographical distribution of existing research. A total of 23 peer-reviewed studies meeting the inclusion criteria were included in the review, following the PRISMA-ScR guidelines. The findings indicate that artificial neural networks and random forest models are among the most frequently reported high-performing approaches for heatwave prediction. Temperature-related variables, especially maximum temperatures, were consistently identified as the most influential predictors across studies. Furthermore, the evidence base was heavily concentrated in Europe and North America, with comparatively limited representation from low- and middle-income countries, despite these regions often experiencing disproportionate impacts of climate change and extreme heat exposure. By synthesising current evidence on ML-based heatwave prediction, associated environmental predictors, and geographical research trends, this review provides insights to support the development of more robust, context-aware, and globally representative heatwave forecasting frameworks.

Background: Social prescribing is an approach to improving health and wellbeing that links individuals to community-based supports, such as arts programs, physical activity initiatives, financial or housing advice, volunteering opportunities, and social groups. Although evidence supporting its effects on physical, functional, and mental health outcomes is expanding, no systematic review of the international evidence has yet assessed the health economics of social prescribing. Aim: To systematically review evidence on the health economic methods and tools that have been used to evaluate social prescribing initiatives internationally. Methods: A systematic search of seven electronic databases (PubMed, Embase, Cochrane Trials, APA PsycINFO, CINAHL, Web of Science, and Ovid Global Health) was conducted alongside gray literature and citation searching. Two reviewers independently screened titles, abstracts, and full texts using Covidence, with disagreements resolved with a third researcher. Data extraction followed a structured protocol, and thematic analysis informed a narrative synthesis of findings. Results: Eighteen studies met the inclusion criteria: 5 randomized controlled trials, one quasi-experimental study, and 12 mixed-methods studies. Interventions reflected four main categories: exercise-based or loneliness-prevention initiatives ( n = 10), coaching programs ( n = 3), nature-based interventions ( n = 3), and dance or movement-based programs ( n = 2). Use of standard economic methods and tools was limited. Social Return on Investment analyses reported positive returns for interventions targeting mental health and loneliness. Conclusion: Robust economic evidence on social prescribing remains limited. Despite the availability of established health economic methods and tools, these are rarely applied to social prescribing, limiting the usefulness of existing studies for healthcare planning and commissioning.

This study aims to develop, implement, and evaluate an Early Warning System (EWS) to alert communities and government agencies in KwaZulu-Natal, South Africa, about extreme weather events (EWEs) and related disease outbreaks. The project focuses on eThekwini and Ugu municipalities, using a participatory, co-creation approach with communities and health providers. A systematic review will be undertaken to understand the impact of climate change on disease outbreaks and design an EWS that integrates data from rural and urban healthcare and environmental contexts. It will assess disease burden at primary healthcare clinics, examine health needs and community experiences during EWEs, and evaluate health system resilience. The project will also evaluate the design, development, and performance of the EWS intervention, including its implementation costs. Ethical approval will be sought, and informed consent obtained from participants. Based on the findings, recommendations will be made to the Department of Health to enhance early warning systems and health system resilience in response to EWEs and disease outbreaks.

Demand for mental health support has exerted unprecedented pressure on statutory services. Innovative solutions such as Green or Nature-Based Social Prescribing (NBSP) programmes may help address unmet need, improve access to personalised treatment, and support the sustainable delivery of primary services within a prevention model of population health. We piloted an innovative health economics-informed Social Return on Investment (SROI) analysis and forecast of a ‘Making Well’ therapeutic craft and horticulture programme for mental health between October 2021 and March 2022. Quantitative and qualitative outcome data were collected from participants with mild-to-moderate mental health conditions at baseline and nine-weeks follow-up using a range of validated measures, including the Short Warwick–Edinburgh Mental Well-being Scale, ICEpop CAPability measure for Adults (ICECAP-A), General Self-Efficacy Scale (GSES), and a bespoke Client Service Receipt Inventory (CSRI). The acceptability and feasibility of these measures were explored. Results indicate that the Making Well programme generated well-being-related social value in the range of British Pound Sterling (GBP) GBP 3.30 to GBP 4.70 for every GBP 1 invested. Our initial pilot forecast suggests that the programme has the potential to generate GBP 5.40 to GBP 7.70 for every GBP 1 invested as the programme is developed and delivered over a 12-month period. Despite the small sample size and lack of a control group, our results contribute to the evidence-base for the effectiveness and social return on investment of NBSP as a therapeutic intervention for improving health and well-being and provides an example of the use of health economic well-being outcome measures such as ICECAP-A and CSRIs in social value analysis. Combining SROI evaluation and forecast methodologies with validated quantitative outcome measures used in the field of health economics can provide valuable social cost–benefit evidence to decision-makers.

Objectives: The aim of this scoping review is to explore the international evidence to identify the potential costs and gains of the development of a career pathway for Health Support Worker’s (HSW’s), the economic gains and benefits connected with continual professional development (CPD) and value for money.Methods: Scoping review following JBI methodology was conducted of peer-reviewed international literature using structured searches of electronic databases and grey literature (September 2013–November 2023) applying economic methodological terms to capture economic evidence and perspectives on the issues.Results: Seventeen papers were critically appraised and during the process of data extraction four key themes emerged: 1) Cost-benefits of employment and training 2) Organisational economic perspectives 3) Service economic perspectives and 4) Sector economic perspectives. This scoping review revealed a scarcity of economic evidence contributing to critical educational approaches, costs and benefits in development of career pathways for HSWs.Conclusion: Limited evidence was available on benefits of specific training programmes, and considerable gaps in the evidence to inform future investment. Recommendation is that future research should incorporate economic theory within evaluations to inform policy and practice.

Extreme weather events (EWEs) are increasing in frequency and intensity due to climate change, exacerbating health risks, particularly for vulnerable populations. Infectious diseases are climate- sensitive, yet microbial dynamics during and after EWEs remain poorly understood. This systematic review examines emerging trends in infectious disease outbreaks following EWEs in South Africa. A comprehensive search across fifteen electronic databases was conducted using Cochrane systematic review principles. Studies focusing on infectious diseases related to EWEs in South Africa were included, considering all study designs. A PRISMA diagram details the screening process, and quality appraisal was conducted using JBI checklist tools or a mixed-methods assessment tool. No primary studies specifically linking recent infectious disease outbreaks to EWEs in South Africa were identified. Existing literature focuses on symptom presentation rather than microbial identification. However, findings indicate an increase in infectious diseases post-EWEs, with three key themes emerging: (1) climatic impacts on infectious diseases, (2) population vulnerabilities, and (3) the role of infrastructure and policy. The review highlights the need for longitudinal microbiological data to improve outbreak prediction and preparedness. This review underscores a significant research gap and calls for an integrated approach combining environmental monitoring with pathogen diagnostics. Large-scale longitudinal studies and enhanced collaboration between education and healthcare sectors are needed. Early Warning Systems should incorporate climate variables to predict disease outbreaks, and a One Health approach is essential to address climate-driven infectious disease risks effectively.

Background Extreme weather events (EWEs) in KwaZulu-Natal province, South Africa are increasingly common causing disease outbreaks, loss of homes, damage to infrastructure, increased mortality and life as well as increasing the pressure on the health system. An Early Warning System (EWS) intervention is needed to alert communities and government increasing preparedness for floods, improving resilience of health services to predict, detect and respond to emergencies and disease outbreaks. The aim of this mixed methods study is to develop, implement and evaluate an EWS as an alert system for communities and government agencies to detect, identify and prevent disease outbreaks strengthening the health system response. Methods The study sites are eThekwini and Ugu, municipalities in KwaZulu-Natal province, South Africa and will comprise the selected community and health facilities in selected sites in these two health districts. This transdisciplinary project will build on prior engagement with communities/health providers (e.g., communities, professionals, policy makers) integrating a participatory, co-creation approach. To build the foundation for this study and understand the impact of climate change on disease outbreaks, a systematic review will be conducted. We will also design, develop and evaluate the performance of an EWS and will link data on the two (rural and urban) healthcare and environmental contexts and integrate EWS for change, clarify how these will be delivered for vulnerable communities, and test and adapt the EWS. Following, an assessment of the disease burden at primary health care clinics in Ugu and eThekwini will be undertaken. Subsequently, an examination of the health needs along with community experiences, during, an EWE, as well as assessment of the health systems resilience in responding to an EWE. Finally, an evaluation will be conducted on the design, development and evaluation of the performance of an EWS intervention and an evaluation and costing of the implementation of the EWS. Ethics for the project will be sought from the relevant ethical committees and informed consent taken by voluntary participants in the study. Discussion Based on the study findings, recommendations will be made to the Department of Health with respect to an early warning system and health systems resilience. Trial registration Not applicable Funding This study is funded by funded by a Research and Innovation for Global Health Transformation National Institute for Health and Care Research (NIHR) award (NIHR204825).

Objectives: Extreme Weather Events (EWEs) are increasingly frequent in the UK and can lead to adverse physical and mental health outcomes which may result in additional pressure on the NHS. The objective of this study is therefore to investigate the health impacts of EWEs on the population in the UK. Method: A systematic review of the evidence was conducted. Seven databases were searched for studies related to the public health outcomes of EWEs. Results: 48 papers met the criteria for inclusion (22 flood, 25 extreme temperature, 1 wind). Flooding is most associated with mental health concerns and associated social effects, whilst temperature extremes impact more on physical health. Conclusions: Whilst there is a great deal of research on the physical and mental health impacts of EWEs in the UK, there is limited evidence on social health impacts, and little consideration of the economic costs of holistic health outcomes. Building resilience against the health impacts of EWEs is essential. Future studies should consider incorporating cost-benefit analyses (CBA) to investigate the economic costs of EWEs on populations and health systems in the UK.