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The UK has introduced legislation that requires net-zero greenhouse gas emissions to be achieved by 2050. Improving the energy efficiency of homes is a key objective to help reach this target, and the UK government’s Clean Growth Strategy aims to get many homes up to an Energy Performance Certificate (EPC) Band of C by 2035. The relationship between home energy-efficiency and occupant health and wellbeing remains an area of ongoing research. This paper explores the nexus between home energy efficiency, energy consumption and self-reported health—an indicator of the general health and wellbeing of the population. We focus on Greater London through secondary data analysis. Energy-efficiency ratings and air infiltration rates of dwellings, derived from EPCs, were aggregated and matched to local area self-reported health and energy consumption data obtained from the Greater London Authority’s (GLA) Lower Layer Super Output Area (LSOA) Atlas database. Our regression model indicates that improving the energy efficiency (SAP) rating by 10 points for a typical home may reduce household gas consumption by around 7% (95% CIs: 2%, 14%). Beta regression finds a positive, but not statistically significant association between median SAP rating and the proportion of the population reporting ‘good or very good’ health when considering all Greater London LSOAs (z score = 0.60, p value = 0.55). A statistically significant positive association is observed however when repeating the analysis for the lowest income quartile LSOAs (z score = 2.03, p value = 0.04). This indicates that the least well-off may benefit most from home energy efficiency programs. A statistically significant positive association is also observed for the relationship between self-reported health and air infiltration rates (z score = 2.62, p value = 0.01). The findings support existing evidence for the predominantly naturally ventilated UK housing stock, suggesting that home energy efficiency measures provide a co-benefit for occupant health provided that adequate air exchange is maintained.

Improved housing has the potential to advance health and contribute to the Sustainable Development Goals. Research examining housing, health and energy use in low-income countries is limited; understanding these connections is vital to inform interventions for healthy sustainable human settlements. This paper investigates the low-income setting of Delhi, where rapid urbanisation, a varied climate, high pollution levels, and a wide variation in housing quality could result in significant energy use and health risks. Drawing on approaches from health and the built environment and existing data and literature, a characterisation of energy use and health risks for Delhi’s housing stock is completed. Four broad settlement types were used to classify Delhi housing and energy use calculations and health risk assessment were performed for each variant. Energy use is estimated to be nearly two times higher per household among planned housing compared with other settlement types. Health risks, however, are found to be largest within informal slum settlements, with important contributions from heat and particulate matter across all settlements. This paper highlights intervention priorities and outlines the need for extensive further research, particularly through data gathering, to establish evidence to accelerate achieving healthy, sustainable and equitable housing in Delhi. publishedVersion

Recently, there has been a strong interest in the climate emergency and the human health impacts of climate change. Although estimates have been quoted, the modeling methods used have either been simplistic or opaque, making it difficult for policy makers to have confidence in these estimates. Providing central estimates of health impacts, without any quantification of their uncertainty, is deficient because such an approach does not acknowledge the inherent uncertainty in extreme environmental exposures associated with spiraling climate change and related health impacts. Furthermore, presenting only the uncertainty bounds around central estimates, without information on how the uncertainty in each of the model parameters and assumptions contribute to the total uncertainty, is insufficient because this approach hides those parameters and assumptions which contribute most to the total uncertainty. We propose a framework for calculating the catastrophic human health impacts of spiraling climate change and the associated uncertainties. Our framework comprises three building blocks: (A) a climate model to simulate the environmental exposure extremes of spiraling climate change; (B) a health impact model which estimates the health burdens of the extremes of environmental exposures; and (C) an analytical mathematical method which characterizes the uncertainty in (A) and (B), propagates the uncertainty in-between and through these models, and attributes the proportion of uncertainty in the health outcomes to model assumptions and parameter values. Once applied, our framework can be of significant value to policy makers because it handles uncertainty transparently while taking into account the complex interactions between climate and human health.

Background Undernutrition is a major contributor to the global burden of disease, and global-level health impact models suggest that climate change-mediated reductions in food quantity and quality will negatively impact undernutrition. These models, however, capture just some of the processes that will shape future nutrition. We aimed to assess how different farm development trajectories might impact on hunger and health-supporting conditions in rural areas under climate change. Methods We developed an agent-based model in which producer-consumer smallholders practise different styles of farming in the global food system. The model represents a hypothetical rural community in which subsistence farmers can develop their farm by adopting an entrepreneurial style (highly market dependent) or by maintaining a peasant style (agroecology). The model was parameterised using globally representative average estimates (eg, the rate of climate change, crop yields, and weather-related losses) from the existing literature. We used the model to explore how patterns of farming styles, under various style preference, climate, policy, and price transmission scenarios, impact on hunger and health-supporting conditions (ie, incomes, work, inequality, and real land productivity) in rural areas. Findings Simulations without climate change or agricultural policy found that style preference patterns influence production, food price, and incomes, and that there were trade-offs between them. For instance, entrepreneurial-oriented futures had the highest production and lowest prices but were simultaneously those in which farming livelihoods tended to be inviable. Simulations with climate change and agricultural policy found that peasant-orientated agroecology futures had the highest production and prices equal to or lower than those under entrepreneurial-oriented futures, and better supported health in rural areas. There were, however, contradictory effects on nutrition, with benefits and harms for different groups. Interpretation Collectively, the findings suggest that when attempting to understand how climate change might affect future nutrition and health, patterns of farming styles—along with the fates of the households that practise them—are of importance. These issues, including the potential role of peasant farming, have been neglected in previous global-level climate-nutrition modelling, but go to the heart of current debates on the future of farming, and thus should be given more prominence in future work. Funding None.

Background In 2016, 23% of children younger than 5 years were stunted. Global-level modelling has consistently found that 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 and food prices. Methods We developed a statistical model, driven by upstream poverty and food price models, to project moderate and severe stunting in children younger than 5 years in rural and urban areas in 2030 under low and high climate change scenarios combined with poverty and prosperity scenarios in 44 countries. Findings We estimated that, in the absence of climate change, 110 million children younger than 5 years 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 more than 1 million under the poverty/high climate change scenario. The projected impact of climate change on stunting was greater in rural versus urban areas under both socioeconomic scenarios. Patterns in the results showed that, in countries where incomes remain low and food prices are relatively high (compared with incomes), further price rises tend to increase stunting. In contrast, in countries where incomes rise and food prices are relatively low, slowly rising prices tend to decrease stunting. Interpretation Shifting the focus from food production to interactions between incomes and food price has provided new insights. The patterns in the results suggest that food prices that provide decent incomes to farmers alongside high employment with living wages will reduce undernutrition and vulnerability to climate change. Futures that protect health should consider not just availability, accessibility, and quality of food, but also the incomes generated by those producing the food. Funding The research was partially funded by the Office of the Chief Economist of the Climate Change Group and the Global Facility for Disaster Reduction and Recovery of the World Bank, and the NIHR Health Research Health Protection Research Unit in Environmental Change and Health at the London School of Hygiene & Tropical Medicine in partnership with Public Health England (PHE), and in collaboration with the University of Exeter, University College London, and the Met Office. The views expressed are those of the authors and not necessarily those of the World Bank, NHS, the NIHR, the Department of Health, or Public Health England.

OBJECTIVE: To determine the conditions under which the Cold Weather Plan (CWP) for England is likely to prove cost-effective in order to inform the development of the CWP in the short term before direct data on costs and benefits can be collected. STUDY DESIGN: Mathematical modelling study undertaken in the absence of direct epidemiological evidence on the effect of the CWP in reducing cold-related mortality and morbidity, and limited data or on its costs. METHODS: The model comprised a simulated temperature time series based on historical data; epidemiologically-derived relationships between temperature, and mortality and morbidity; and information on baseline unit costs of contacts with health care and community care services. Cost-effectiveness was assessed assuming varying levels of protection against cold-related burdens, coverage of the vulnerable population and willingness-to-pay criteria. RESULTS: Simulations showed that the CWP is likely to be cost effective under some scenarios at the high end of the willingness to pay threshold used by National Institute for Health and Care Excellence (NICE) in England, but these results are sensitive to assumptions about the extent of implementation of the CWP at local level, and its assumed effectiveness when implemented. The incremental cost-effectiveness ratio varied from £29,754 to £75,875 per Quality Adjusted Life Year (QALY) gained. Conventional cost-effectiveness (15%) in avoiding deaths and hospital admissions. CONCLUSIONS: Although the modelling relied on a large number of assumptions, this type of modelling is useful for understanding whether, and in what circumstances, untested plans are likely to be cost-effective before they are implemented and in the early period of implementation before direct data on cost-effectiveness have accrued. Steps can then be taken to optimize the relevant parameters as far as practicable during the early implementation period.

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.

Summary box To function effectively, health systems require adequate financing; an effective workforce; reliable information for decision making; good governance; and available medicines and health technologies to deliver quality services to their populations.1 In low-income and middle-income countries (LMICs), health systems are often limited in this respect and fail to provide comprehensive population coverage of quality healthcare interventions. Health system strengthening comprises strategies to improve one or more of the functions of the health system in order to improve access, coverage, quality or efficiency,1 and is recognised to be an essential step towards achieving universal coverage goals.2 Only when health systems function efficiently and effectively can they deliver services to meet population needs. …

Background: Policy decisions regarding climate change mitigation are increasingly incorporating the beneficial and adverse health impacts of greenhouse gas emission reduction strategies. Studies of such co-benefits and co-harms involve modeling approaches requiring a range of analytic decisions that affect the model output. Objective: Our objective was to assess analytic decisions regarding model framework, structure, choice of parameters, and handling of uncertainty when modeling health co-benefits, and to make recommendations for improvements that could increase policy uptake. Methods: We describe the assumptions and analytic decisions underlying models of mitigation co-benefits, examining their effects on modeling outputs, and consider tools for quantifying uncertainty. Discussion: There is considerable variation in approaches to valuation metrics, discounting methods, uncertainty characterization and propagation, and assessment of low-probability/high-impact events. There is also variable inclusion of adverse impacts of mitigation policies, and limited extension of modeling domains to include implementation considerations. Going forward, co-benefits modeling efforts should be carried out in collaboration with policy makers; these efforts should include the full range of positive and negative impacts and critical uncertainties, as well as a range of discount rates, and should explicitly characterize uncertainty. We make recommendations to improve the rigor and consistency of modeling of health co-benefits. Conclusion: Modeling health co-benefits requires systematic consideration of the suitability of model assumptions, of what should be included and excluded from the model framework, and how uncertainty should be treated. Increased attention to these and other analytic decisions has the potential to increase the policy relevance and application of co-benefits modeling studies, potentially helping policy makers to maximize mitigation potential while simultaneously improving health. Citation: Remais JV, Hess JJ, Ebi KL, Markandya A, Balbus JM, Wilkinson P, Haines A, Chalabi Z. 2014. Estimating the health effects of greenhouse gas mitigation strategies: addressing parametric, model, and valuation challenges. Environ Health Perspect 122:447–455; http://dx.doi.org/10.1289/ehp.1306744

Environmental health impact assessment models are subjected to great uncertainty due to the complex associations between environmental exposures and health. Quantifying the impact of uncertainty is important if the models are used to support health policy decisions. We conducted a systematic review to identify and appraise current methods used to quantify the uncertainty in environmental health impact assessment. In the 19 studies meeting the inclusion criteria, several methods were identified. These were grouped into random sampling methods, second-order probability methods, Bayesian methods, fuzzy sets, and deterministic sensitivity analysis methods. All 19 studies addressed the uncertainty in the parameter values but only 5 of the studies also addressed the uncertainty in the structure of the models. None of the articles reviewed considered conceptual sources of uncertainty associated with the framing assumptions or the conceptualisation of the model. Future research should attempt to broaden the way uncertainty is taken into account in environmental health impact assessments.

Background The UK government has an ambitious goal to reduce carbon emissions from the housing stock through energy efficiency improvements. This single policy goal is a strong driver for change in the housing system, but comes with positive and negative “unintended consequences” across a broad range of outcomes for health, equity and environmental sustainability. The resulting policies are also already experiencing under-performance through a failure to consider housing as a complex system. This research aimed to move from considering disparate objectives of housing policies in isolation to mapping the links between environmental, economic, social and health outcomes as a complex system. We aimed to support a broad range of housing policy stakeholders to improve their understanding of housing as a complex system through a collaborative learning process. Methods We used participatory system dynamics modelling to develop a qualitative causal theory linking housing, energy and wellbeing. Qualitative interviews were followed by two interactive workshops to develop the model, involving representatives from national and local government, housing industries, non-government organisations, communities and academia. Results More than 50 stakeholders from 37 organisations participated. The process resulted in a shared understanding of wellbeing as it relates to housing; an agreed set of criteria against which to assess to future policy options; and a comprehensive set of causal loop diagrams describing the housing, energy and wellbeing system. The causal loop diagrams cover seven interconnected themes: community connection and quality of neighbourhoods; energy efficiency and climate change; fuel poverty and indoor temperature; household crowding; housing affordability; land ownership, value and development patterns; and ventilation and indoor air pollution. Conclusions The collaborative learning process and the model have been useful for shifting the thinking of a wide range of housing stakeholders towards a more integrated approach to housing. The qualitative model has begun to improve the assessment of future policy options across a broad range of outcomes. Future work is needed to validate the model and increase its utility through computer simulation incorporating best quality data and evidence. Combining system dynamics modelling with other methods for weighing up policy options, as well as methods to support shifts in the conceptual frameworks underpinning policy, will be necessary to achieve shared housing goals across physical, mental, environmental, economic and social wellbeing. Electronic supplementary material The online version of this article (doi:10.1186/s12940-016-0098-z) contains supplementary material, which is available to authorized users.

Background The Cold Weather Plan (CWP) for England was launched by the Department of Health in 2011 to prevent avoidable harm to health by cold weather by enabling individuals to prepare and respond appropriately. This study sought the views of local decision makers involved in the implementation of the CWP in the winter of 2012/13 to establish the effects of the CWP on local planning. It was part of a multi-component independent evaluation of the CWP. Methods Ten LA areas were purposively sampled which varied in level of deprivation and urbanism. Fifty-two semi-structured interviews were held with health and social care managers involved in local planning between November 2012 and May 2013. Results Thematic analysis revealed that the CWP was considered a useful framework to formalize working arrangements between agencies though local leadership varied across localities. There were difficulties in engaging general practitioners, differences in defining vulnerable individuals and a lack of performance monitoring mechanisms. Conclusions The CWP was welcomed by local health and social care managers, and improved proactive winter preparedness. Areas for improvement include better integration with general practice, and targeting resources at socially isolated individuals in cold homes with specific interventions aimed at reducing social isolation and building community resilience.

OBJECTIVES: To inform development of Public Health England's Cold Weather Plan (CWP) by characterizing pre-existing relationships between wintertime weather and mortality and morbidity outcomes, and identification of groups most at risk. STUDY DESIGN: Time-series regression analysis and episode analysis of daily mortality, emergency hospital admissions, and accident and emergency visits for each region of England. METHODS: Seasonally-adjusted Poisson regression models estimating the percent change in daily health events per 1 °C fall in temperature or during individual episodes of extreme weather. RESULTS: Adverse cold effects were observed in all regions, with the North East, North West and London having the greatest risk of cold-related mortality. Nationally, there was a 3.44% (95% CI: 3.01, 3.87) increase in all-cause deaths and 0.78% (95% CI: 0.53, 1.04) increase in all-cause emergency admissions for every 1 °C drop in temperature below identified thresholds. The very elderly and people with COPD were most at risk from low temperatures. A&E visits for fractures were elevated during heavy snowfall periods, with adults (16-64 years) being the most sensitive age-group. Since even moderately cold days are associated with adverse health effects, by far the greatest health burdens of cold weather fell outside of the alert periods currently used in the CWP. CONCLUSIONS: Our findings indicate that levels 0 ('year round planning') and 1 ('winter preparedness and action') are crucial components of the CWP in comparison to the alerts. Those most vulnerable during winter may vary depending on the type of weather conditions being experienced. Recommendations are made for the CWP.

We are grateful to the commentators for their stimulating and constructive comments, and for the opportunity to respond. The commentators query the use of the flowchart in particular. Our intention was not to reduce all such decisions to a flowchart but to suggest one way of making explicit when a particular type of evidence may really be needed or not, in order to move the field beyond calls for more evidence, which may or may not contribute usefully to future decisions. Our intention was therefore to put the emphasis firmly back on the decision to be taken, rather than on the collection of evidence for unclear purposes. …

Background Amid the calls for ‘more public health evidence’, we also need simple understandable methods of determining when more research really is needed. This paper describes a simple decision aid to help policymakers, researchers and other decision makers assess the potential ‘information value’ of a new public health randomised controlled trial. Methods The authors developed a flow chart to help make explicit (1) the user’s information needs, (2) the intended use of the new information that the study will produce, (3) the added value of the evidence to be derived from the new study and (4) the levels of precision, bias and generalisability required by the user. Results The flow chart is briefly illustrated, first in generic form and then in a worked example, showing how it may be used in deciding whether a new study should be commissioned to evaluate the health impact of allowing motorcycles to use bus lanes in London. Conclusions In this paper, the authors have presented a flow chart for enacting an informal ‘Value-ofInformation’-like approach to deciding when a new public health evaluation is needed. The authors do not suggest that the flow chart approach is technically the equivalent of Value-of-Information methods. Nonetheless, it represents a valuable perspective and process to adopt, and this structured approach will be more revealing than an unstructured thought experiment as the basis for decisions about a new study. To aid in its development as an effective tool, we invite users from a variety of perspectives and contexts to review it, to use it in practice and to send us their comments.

There is an ongoing interest in studying the effect of common recurrent infections and conditions, such as diarrhoea, respiratory infections, and fever, on the nutritional status of children at risk of malnutrition. Epidemiological studies exploring this association need to measure infections with sufficient accuracy to minimize bias in the effect estimates. A versatile model of common recurrent infections was used for exploring how many repeated measurements of disease are required to maximize the power and logistical efficiency of studies investigating the effect of infectious diseases on malnutrition without compromising the validity of the estimates. Depending on the prevalence and distribution of disease within a population, 15-30 repeat measurements per child over one year should be sufficient to provide unbiased estimates of the association between infections and nutritional status. Less-frequent measurements lead to a bias in the effect size towards zero, especially if disease is rare. In contrast, recall error can lead to exaggerated effect sizes. Recall periods of three days or shorter may be preferable compared to longer recall periods. The results showed that accurate estimation of the association between recurrent infections and nutritional status required closer follow-up of study participants than studies using recurrent infections as an outcome measure. The findings of the study provide guidance for choosing an appropriate sampling strategy to explore this association.

Acute associations between mortality and ozone are largely accepted, though recent evidence is less conclusive. Evidence on ozone-heat interaction is sparse. We assess effects of ozone, heat, and their interaction, on mortality in Britain.Acute effects of summer ozone on mortality were estimated using data from 15 conurbations in England and Wales (May-September, 1993-2003). 2-day means of daily maximum 8-h ozone were entered into case series analyses, controlling for particulate matter with aerodynamic diameter of10 μm, natural cubic splines of temperature, and other factors. Heat effects were estimated, comparing adjusted mortality rates at 97.5th and 75th percentiles of 2-day mean temperature. A separate model employed interaction terms to assess whether ozone effects increased on 'hot days' (where 2-day mean temperature exceeded the whole-year 95th percentile). Other heat metrics, and non-linear ozone effects, were also examined.Adverse ozone and heat effects occurred in nearly all conurbations. The mean mortality rate ratio for heat effect across conurbations was 1.071 (1.050-1.093). The mean ozone rate ratio was 1.003 per 10 μg/m(3) ozone increase (95% CI 1.001 to 1.005). On 'hot days' the mean ozone effect reached 1.006 (1.002-1.009) per 10 μg/m(3), though ozone-heat interaction was significant in London only. On substituting maximum for mean temperature, the overall ozone effect reduced to null, though evidence remained of effects on hot days, particularly in London. An estimated ozone effect threshold was below current guidelines in 'mean temperature' models.While heat showed robust effects on summer mortality, estimates for ozone depended upon the modelling of temperature. However, there was some evidence that ozone effects were worse on hot days, whichever temperature measure was used.

Background It is well known that high ambient temperatures are associated with increased mortality, even in temperate climates, but some important details are unclear. In particular, how heat–mortality associations (for example, slopes and thresholds) vary by climate has previously been considered only qualitatively. Methods An ecological time-series regression analysis of daily counts of all-cause mortality and ambient temperature in summers between 1993 and 2006 in the 10 government regions was carried out, focusing on all-cause mortality and 2-day mean temperature (lags 0 and 1). Results All regions showed evidence of increased risk on the hottest days, but the specifics, in particular the threshold temperature at which adverse effects started, varied. Thresholds were at about the same centile temperatures (the 93rd, year-round) in all regions—hotter climates had higher threshold temperatures. Mean supra-threshold slope was 2.1%/°C (95% CI 1.6 to 2.6), but regions with higher summer temperatures showed greater slopes, a pattern well characterised by a linear model with mean summer temperature. These climate-based linear-threshold models capture most, but not all, the association; there was evidence for some non-linearity above thresholds, with slope increasing at highest temperatures. Conclusion Effects of high daily summer temperatures on mortality in English regions are quite well approximated by threshold-linear models that can be predicted from the region9s climate (93rd centile and mean summer temperature). It remains to be seen whether similar relationships fit other countries and climates or change over time, such as with climate change.

Background. We describe a project to quantify the burden of heat and ozone on mortality in the UK, both for the present-day and under future emission scenarios. Methods. Mortality burdens attributable to heat and ozone exposure are estimated by combination of climate-chemistry modelling and epidemiological risk assessment. Weather forecasting models (WRF) are used to simulate the driving meteorology for the EMEP4UK chemistry transport model at 5 km by 5 km horizontal resolution across the UK; the coupled WRF-EMEP4UK model is used to simulate daily surface temperature and ozone concentrations for the years 2003, 2005 and 2006, and for future emission scenarios. The outputs of these models are combined with evidence on the ozone-mortality and heat-mortality relationships derived from epidemiological analyses (time series regressions) of daily mortality in 15 UK conurbations, 1993-2003, to quantify present-day health burdens. Results. During the August 2003 heatwave period, elevated ozone concentrations > 200 μg m-3 were measured at sites in London and elsewhere. This and other ozone photochemical episodes cause breaches of the UK air quality objective for ozone. Simulations performed with WRF-EMEP4UK reproduce the August 2003 heatwave temperatures and ozone concentrations. There remains day-to-day variability in the high ozone concentrations during the heatwave period, which on some days may be explained by ozone import from the European continent. Preliminary calculations using extended time series of spatially-resolved WRF-EMEP4UK model output suggest that in the summers (May to September) of 2003, 2005 & 2006 over 6000 deaths were attributable to ozone and around 5000 to heat in England and Wales. The regional variation in these deaths appears greater for heat-related than for ozone-related burdens. Changes in UK health burdens due to a range of future emission scenarios will be quantified. These future emissions scenarios span a range of possible futures from assuming current air quality legislation is fully implemented, to a more optimistic case with maximum feasible reductions, through to a more pessimistic case with continued strong economic growth and minimal implementation of air quality legislation. Conclusion. Elevated surface ozone concentrations during the 2003 heatwave period led to exceedences of the current UK air quality objective standards. A coupled climate-chemistry model is able to reproduce these temperature and ozone extremes. By combining model simulations of surface temperature and ozone with ozone-heat-mortality relationships derived from an epidemiological regression model, we estimate present-day and future health burdens across the UK. Future air quality legislation may need to consider the risk of increases in future heatwaves.

The allocation problem in health care can be characterised as a mathematical programming problem but attempts to incorporate uncertainty in costs and effect have suffered from important limitations. A two-stage stochastic mathematical programming formulation is developed and applied to a numerical example to explore and demonstrate the implications of this more general and comprehensive approach. The solution to the allocation problem for different budgets, budgetary policies, and available actions are then demonstrated. This analysis is used to evaluate different budgetary policies and examine the adequacy of standard decision rules in cost-effectiveness analysis. The research decision is then considered alongside the allocation problem. This more general formulation demonstrates that the value of further research depends on: (i) the budgetary policy in place; (ii) the realisations revealed during the budget period; (iii) remedial actions that may be available; and (iv) variability in parameters values.

Background: Pedestrian injuries are a leading cause of death and disability. Transport policy decisions have a major impact on the risk of pedestrian injury, but the effects cannot usually be quantified in controlled studies. However, mathematical modeling can help to establish the injury consequences of transport policy decisions. Methods: A stochastic mathematical model was developed to estimate the effect of alternative transport scenarios on pedestrian injury risk. The model is based on a mechanistic description of pedestrian injury causation and comprises four sub-models: vehicle dynamics, pedestrian dynamics, collision incidence, and injury severity. Results: The model was used to estimate the yearly pedestrian injury rate for a baseline scenario, corresponding to current traffic conditions in London, UK, and three alternative scenarios, comprising reductions in vehicle speed, traffic volume, and vehicle mass. The model simulated a baseline injury rate of 88 per 100 000. Compared with baseline, a 15% reduction in mean speed resulted in a 21% reduction in injury rate and a 75% reduction in fatality rate. A 15% reduction in traffic volume resulted in a 14% reduction in injury rate and a 25% reduction in fatality rate. Reducing vehicle mass by 15% did not reduce the number of injuries, but a 25% reduction resulted in less severe injuries. Conclusions: The model simulated well the rates and severity of pedestrian injury corresponding to the baseline scenario and made predictions for different transport policy scenarios. However, it is offered primarily as a generic decision support tool for the assessment of alternative policies by transport authorities.

Background: Global climate change is anticipated to reduce future cereal yields and threaten food security, thus potentially increasing the risk of undernutrition. The causation of undernutrition is complex, and there is a need to develop models that better quantify the potential impacts of climate change on population health. Objectives: We developed a model for estimating future undernutrition that accounts for food and nonfood (socioeconomic) causes and can be linked to available regional scenario data. We estimated child stunting attributable to climate change in five regions in South Asia and sub-Saharan Africa (SSA) in 2050. Methods: We used current national food availability and undernutrition data to parameterize and validate a global model, using a process-driven approach based on estimations of the physiological relationship between a lack of food and stunting. We estimated stunting in 2050 using published modeled national calorie availability under two climate scenarios and a reference scenario (no climate change). Results: We estimated that climate change will lead to a relative increase in moderate stunting of 1–29% in 2050 compared with a future without climate change. Climate change will have a greater impact on rates of severe stunting, which we estimated will increase by 23% (central SSA) to 62% (South Asia). Conclusions: Climate change is likely to impair future efforts to reduce child malnutrition in South Asia and SSA, even when economic growth is taken into account. Our model suggests that to reduce and prevent future undernutrition, it is necessary to both increase food access and improve socioeconomic conditions, as well as reduce greenhouse gas emissions.

Few cost-effectiveness analysis (CEA) models have accounted for geographic variation in input parameters. This paper describes a deterministic discrete-time multi-state model to estimate the cost-effectiveness of vector control policies for Chagas disease, where implementation varies according to village characteristics. The model outputs include the total number of new infections, disability adjusted life years (DALYs) incurred, costs of associated healthcare, and total costs of the Ministry of Health's control policy for house surveillance and spraying. Incremental net benefits were estimated to determine Colombian villages in which it is cost-effective to implement the control policy. The robustness of these conclusions was evaluated by deterministic sensitivity analyses. The model should help provide a decision-support system to compare control policies and to allocate resources geographically.

gies. 3 Although the first of these was published in 1999, the majority (75%) were published from 2009 onwards. Perez Velasco et al. evaluated these 44 studies against a checklist of standard practices in the field of economic evaluation, but their stated aim was to summarize the literature and offer policy recommendations rather than to examine methodological approaches. In 2009, Lugner & Postma conducted a review of 12 economic evaluations and recommended using dynamic rather than static transmission modelling in economic evaluations of pandemic influenza preparedness strategies.