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A decision support system for evaluating UK air quality policies is presented. It combines the output from a chemistry transport model, a health impact model and other impact models within a multi-criteria decision analysis (MCDA) framework. As a proof-of-concept, the MCDA framework is used to evaluate and compare idealized emission reduction policies in four sectors (combustion in energy and transformation industries, non-industrial combustion plants, road transport and agriculture) and across six outcomes or criteria (mortality, health inequality, greenhouse gas emissions, biodiversity, crop yield and air quality legal compliance). To illustrate a realistic use of the MCDA framework, the relative importance of the criteria were elicited from a number of stakeholders acting as proxy policy makers. In the prototype decision problem, we show that reducing emissions from industrial combustion (followed very closely by road transport and agriculture) is more advantageous than equivalent reductions from the other sectors when all the criteria are taken into account. Extensions of the MCDA framework to support policy makers in practice are discussed.

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. …

Environmental changes are likely to affect agricultural production over the next 20–30 years. The interactions between environmental change, agricultural yields and crop quality, and the critical pathways to future diets and health outcomes remain largely undefined. There are currently no quantitative models to test the impact of multiple environmental changes on nutrition and health outcomes. Using an interdisciplinary approach, we developed a framework to link the multiple interactions between environmental change, agricultural productivity and crop quality, population-level food availability, dietary intake and health outcomes, with a specific focus on fruits and vegetables. The main components of the framework consist of: i) socio-economic and societal factors, ii) environmental change stressors, iii) interventions and policies, iv) food system activities, v) food and nutrition security, and vi) health and well-being outcomes. The framework, based on currently available evidence, provides an overview of the multidimensional and complex interactions between environmental change, diets and health, and forms the analytical baseline for future modelling and scenario testing. The framework identifies the inter-sectoral datasets and models that need to be defined and populated to assess the impacts of environmental change on agricultural production, food availability, nutrition and population health.

It is widely recognised that a major source of uncertainty in building performance simulation relates to occupancy and behavioural assumptions. This paper aims to assess the relative impact of lifestyle patterns, occupant-controlled window opening and shading use on indoor overheating risk levels in dwellings. The indoor thermal environment of a set of broadly representative archetypes of the London housing stock was simulated using dynamic thermal modelling. Two lifestyle patterns and four scenarios of window opening and shading use schedules were combined with multiple other varying parameters (building geometry and orientation, insulation levels, level of overshadowing by adjacent buildings), leading to a total of 27,648 modelled dwelling variants. It was found that the rankings obtained for dwellings occupied by a family with children at school and dwellings occupied by pensioners were broadly similar for all combinations of behaviour and the majority of overheating metrics. Lower ranking correlations were, however, observed between simple temperature-dependent window opening scenarios and a more sophisticated scenario of combined shading and night ventilation. This is an indication that shading and/or night cooling could modify indoor overheating risk significantly. The findings of the study add to a growing body of literature suggesting that the way inhabitants occupy and operate a building has a measurable impact on thermal discomfort and potentially the health risks associated with their exposure to high indoor temperatures. This should be taken into consideration in the design of retrofit interventions and public health strategies aiming to minimise such risks.

The risk of overheating in UK dwellings is predicted to increase due to anthropogenic climate change and local urban climate modification leading to an increased urban heat island effect. Dwelling geometry characteristics such as orientation, aspect, and glazing, and building fabric characteristics such as thermal mass and resistance can influence the risk of overheating. The majority of simulation-based studies have focused on identifying the importance of building characteristics on overheating risk using a small number of weather files, or focus solely on the impact of external temperatures rather than a full set of climatic variables. This study examines the overheating risk in London dwelling archetypes when simulated under different UK climates, both in the present and under ‘hot future’ conditions, with the objective of identifying whether the conclusions drawn from location-specific studies can be generically applied to different cities. Simulations were carried out using the dynamic thermal simulation tool EnergyPlus using 3456 dwelling variants and six different Design Summer Year (DSY) climate files from locations within the UK. In addition, a 2050 Medium Emissions scenario weather file was used to model a particularly hot summer in all locations. The results indicate that weather files can influence the ranking of relative overheating risk between dwelling types, with significant variations in the relative ranking between London, Scotland and the North of England, and the rest of England. These results show that studies examining the overheating risk across the UK need to consider the variability of building performance under regional weather conditions.

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

Food-based dietary recommendations (FBR) play an essential role in promoting a healthy diet. To support the process of formulating a set of population-specific FBR, a probabilistic model was developed specifically to predict the changes in the percentage of a population at risk of inadequate nutrient intakes after the adoption of alternative sets of FBR. The model simulates the distribution of the number of servings per week from food groups or food items at baseline and after the hypothetical successful adoption of alternative sets of FBR, while ensuring that the population's energy intake distribution remains similar. The simulated changes from baseline in median nutrient intakes and the percentage of the population at risk of inadequate nutrient intakes are calculated and compared across the alternative sets of FBR. The model was illustrated using a hypothetical population of 12- to 18-month-old breast-feeding children consuming a cereal-based diet low in animal source foods.

This study investigated the determinants of the proportion of hours a dwelling meets the indoor temperature threshold for healthy living of at least 18 °C in winter, as recommended in a recent review. Secondary data analysis of a nationally representative survey of English homes (N = 635) showed that older dwellings, detached homes, single occupancy and living in the North of England are associated with the lowest share of hours at the recommended temperature threshold in bedroom, living room, and hallway. These identified determinants could be used to target households at greatest risk of ‘unhealthy’ temperatures. Being aged above 64 and having a disability is linked to more hours at the criterion; an encouraging finding that the most vulnerable experience low temperatures to a lesser extent. This study also highlights the need for further research particularly in relation to those variables where results were equivocal and / or there was weak supporting evidence, such as tenure, wall type, and EPC rating.

Anthropogenic climate change is likely to add significant pressure to the determinants of public health, and to current health and social protection measures in high, middle and low income countries. Adaptation strategies within the health sector are being developed to address the multi-dimensional nature of the costs and impacts. We further develop and apply a new generic conceptual framework for development-compatible climate policy planning to evaluate policy options for middle and low income countries that reduce the adverse health effects of climate change. The criteria used for comparative evaluation included economic, environment, social and institutional factors. The proposed framework, incorporating system dynamics, provides a foundation for a decision-analytical approach to support the formulation of robust climate change adaptation policies to protect human health.

There are growing calls for identifying climate mitigation and adaptation policy packages that would also support human development objectives at the national and regional levels. The literature on climate policy analysis and impact assessment continues to be driven by standard economics with its body of competitive general equilibrium optimization models and cost-benefit analysis techniques of aggregation and monetization. However, its recommendations for climate action are often based on highly restrictive underlying assumptions, which have been increasingly criticized for being too prescriptive, not adequately capturing salient observed socioeconomic realities, and not acknowledging pluralism in values. The main aim of this paper is to put forward a new methodological approach that seeks to address these deficiencies. A generic but comprehensive framework eliciting mitigation-adaptation-development interactions, accounting for institutional barriers, and drawing on a combination of an emerging body of new climate economics and multi-criteria decision analysis is suggested. We purport that, by using this framework, multi-dimensional impacts and multi-stakeholder interests could be better represented when planning climate policy actions. We also argue that analytical tools drawing on economic thinking which embraces interdisciplinary analysis and deep uncertainty and avoids the fallacy of unique optimal solutions, may deliver more effective strategies for pushing economies onto the transformational pathways required.

Background Investment in pandemic preparedness is a long-term gamble, with the return on investment coming at an unknown point in the future. Many countries have chosen to stockpile key resources, and the number of pandemic economic evaluations has risen sharply since 2009. We assess the importance of uncertainty in time-to-pandemic (and associated discounting) in pandemic economic evaluation, a factor frequently neglected in the literature to-date. Methods We use a probability tree model and Monte Carlo parameter sampling to consider the cost effectiveness of antiviral stockpiling in Cambodia under parameter uncertainty. Mean elasticity and mutual information (MI) are used to assess the importance of time-to-pandemic compared with other parameters. We also consider the sensitivity to choice of sampling distribution used to model time-to-pandemic uncertainty. Results Time-to-pandemic and discount rate are the primary drivers of sensitivity and uncertainty in pandemic cost effectiveness models. Base case cost effectiveness of antiviral stockpiling ranged between is US$112 and US$3599 per DALY averted using historical pandemic intervals for time-to-pandemic. The mean elasticities for time-to-pandemic and discount rate were greater than all other parameters. Similarly, the MI scores for time to pandemic and discount rate were greater than other parameters. Time-to-pandemic and discount rate were key drivers of uncertainty in cost-effectiveness results regardless of time-to-pandemic sampling distribution choice. Conclusions Time-to-pandemic assumptions can “substantially” affect cost-effectiveness results and, in our model, is a greater contributor to uncertainty in cost-effectiveness results than any other parameter. We strongly recommend that cost-effectiveness models include probabilistic analysis of time-to-pandemic uncertainty.

We employ a single-country dynamically-recursive Computable General Equilibrium model to make health-focussed macroeconomic assessments of three contingent UK Greenhouse Gas (GHG) mitigation strategies, designed to achieve 2030 emission targets as suggested by the UK Committee on Climate Change. In contrast to previous assessment studies, our main focus is on health co-benefits additional to those from reduced local air pollution. We employ a conservative cost-effectiveness methodology with a zero net cost threshold. Our urban transport strategy (with cleaner vehicles and increased active travel) brings important health co-benefits and is likely to be strongly cost-effective; our food and agriculture strategy (based on abatement technologies and reduction in livestock production) brings worthwhile health co-benefits, but is unlikely to eliminate net costs unless new technological measures are included; our household energy efficiency strategy is likely to breakeven only over the long term after the investment programme has ceased (beyond our 20 year time horizon). We conclude that UK policy makers will, most likely, have to adopt elements which involve initial net societal costs in order to achieve future emission targets and longer-term benefits from GHG reduction. Cost-effectiveness of GHG strategies is likely to require technological mitigation interventions and/or demand-constraining interventions with important health co-benefits and other efficiency-enhancing policies that promote internalization of externalities. Health co-benefits can play a crucial role in bringing down net costs, but our results also suggest the need for adopting holistic assessment methodologies which give proper consideration to welfare-improving health co-benefits with potentially negative economic repercussions (such as increased longevity). We employ a single-country dynamically-recursive Computable General Equilibrium model to make health-focussed macroeconomic assessments of three contingent UK Greenhouse Gas (GHG) mitigation strategies, designed to achieve 2030 emission targets as suggested by the UK Committee on Climate Change. In contrast to previous assessment studies, our main focus is on health co-benefits additional to those from reduced local air pollution. We employ a conservative cost-effectiveness methodology with a zero net cost threshold. Our urban transport strategy (with cleaner vehicles and increased active travel) brings important health co-benefits and is likely to be strongly cost-effective; our food and agriculture strategy (based on abatement technologies and reduction in livestock production) brings worthwhile health co-benefits, but is unlikely to eliminate net costs unless new technological measures are included; our household energy efficiency strategy is likely to breakeven only over the long term after the investment programme has ceased (beyond our 20 year time horizon). We conclude that UK policy makers will, most likely, have to adopt elements which involve initial net societal costs in order to achieve future emission targets and longer-term benefits from GHG reduction. Cost-effectiveness of GHG strategies is likely to require technological mitigation interventions and/or demand-constraining interventions with important health co-benefits and other efficiency-enhancing policies that promote internalization of externalities. Health co-benefits can play a crucial role in bringing down net costs, but our results also suggest the need for adopting holistic assessment methodologies which give proper consideration to welfare-improving health co-benefits with potentially negative economic repercussions (such as increased longevity).

The importance of reducing adventitious infiltration in order to save energy is highlighted by the relevant building standards of many countries. This operational infiltration is often inferred via the measurement of the air leakage rate at a pressure differential of 50 Pascals. Some building codes, such as the UK's Standard Assessment Procedure, assume a simple relationship between the air leakage rate and mean infiltration rate during the heating season, the so-called leakage-infiltration ratio, which is scaled to account for the physical and environmental properties of a dwelling. The scaling does not take account of the permeability of party walls in conjoined dwellings and so cannot be used to differentiate between the infiltration of unconditioned ambient air that requires heating, and conditioned air from an adjacent dwelling that does not. This article evaluates the leakage infiltration ratio afresh using a theoretical model of adventitious infiltration for a conjoined dwelling. The model is used to predict the mean infiltration rate during the heating season for an apartment and a terraced house located in 14 different UK cities for two extreme assumptions of party wall permeability. The first assumption is that party walls are permeable - this results in a predicted leakageinfiltration ratio that is significantly greater than that used by building codes to evaluate the energy and environmental performance of dwellings. The second assumption is that party walls are impermeable - this results in a predicted leakage-infiltration ratio close to that used by building codes. Knowledge of party wall permeability is not provided by a standard measurement of air leakage but is shown to be vital for making informed decisions on the implementation of energy efficiency measures. These findings have significant energy and health implications and should be of great interest to the policy makers of any country with a large number of conjoined dwellings.

A large proportion of particulate air pollution exposure in urban areas occurs due to the penetration of outdoor pollution into the residential indoor environment. Theoretical considerations suggest that quantifying health effects due to changes to indoor particulate concentrations derived from outdoor sources requires the adjustment of exposure-response coefficients based on epidemiological studies of outdoor air. Using the PM2.5-mortality coefficient from the American Cancer Society (ACS) cohort study as an example, we developed a theoretical model to quantify the relationship between the published coefficient and one based on personal exposure, and explored how this adjusted coefficient might be applied to changes in indoor PM2.5 from outdoor sources. Using a probabilistic approach, our estimated average mortality coefficient for personal PM2.5 exposure is 30–50% greater than the ACS coefficient. However, since the indoor PM2.5 of outdoor origin accounts for only a proportion of the overall exposure, the average net adjustment required for indoor exposure is very modest. The results suggest that it is generally appropriate to apply unadjusted exposure-response functions derived from cohort studies to assess the health impact of changes in indoor particle concentrations from outdoor sources. However, it may be important to re-scale the coefficients for assessing exposures of population groups who spend a greater proportion of their time at home.

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.

The optimal ventilation rate in a dwelling is a trade-off between the requirement to minimize ventilation heat losses to help meet national greenhouse gas emission targets and the need to minimize adverse health impacts arising from exposure to cold temperatures and pollutants from indoor and outdoor origin. This paper presents approaches for exploring these trade-offs based on two implementations of multi-objective optimization that consider both energy efficiency and health impacts. Both methods aggregate the various performance criteria into a single criterion, but the first method monetizes the performance criteria, while the second method weights them in a more general way. Unlike in the monetization approach, the generalized multi-objective optimization approach is found to be robust against scaling of the health impacts and energy savings that is independent of the ventilation rate. As a result it is less sensitive to assumptions made in the models regarding heating system efficiency, absolute health burden level, and dwelling occupancy. It is however sensitive to assumptions regarding pollutant production rates and balance-point temperatures, which affect health impacts and energy savings in a way correlated with ventilation rate. A preliminary application of the methods to a typical UK flat and detached house finds that the optimal ventilation rate may vary with built form. Application of the generalized multi-objective optimization approach in which health impacts and energy savings are equally weighted, suggests an optimal annual average air change rate of 0.4/h for the detached house, and 0.7/h for the flat.

We used Comparative Risk Assessment methods to estimate the health effects of alternative urban land transport scenarios for two settings—London, UK, and Delhi, India. For each setting, we compared a business-as-usual 2030 projection (without policies for reduction of greenhouse gases) with alternative scenarios—lower-carbon-emission motor vehicles, increased active travel, and a combination of the two. We developed separate models that linked transport scenarios with physical activity, air pollution, and risk of road traffic injury. In both cities, we noted that reduction in carbon dioxide emissions through an increase in active travel and less use of motor vehicles had larger health benefits per million population (7332 disability-adjusted life-years [DALYs] in London, and 12 516 in Delhi in 1 year) than from the increased use of lower-emission motor vehicles (160 DALYs in London, and 1696 in Delhi). However, combination of active travel and lower-emission motor vehicles would give the largest benefits (7439 DALYs in London, 12 995 in Delhi), notably from a reduction in the number of years of life lost from ischaemic heart disease (10–19% in London, 11–25% in Delhi). Although uncertainties remain, climate change mitigation in transport should benefit public health substantially. Policies to increase the acceptability, appeal, and safety of active urban travel, and discourage travel in private motor vehicles would provide larger health benefits than would policies that focus solely on lower-emission motor vehicles.

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.

This paper presents a new decision-support framework and software platform for an integrated assessment of options for sustainable management of urban pollution. The framework involves three steps: (1) mapping the flow of pollutants associated with human activities in the urban environment; (2) modelling the fate and transport of pollutants; and (3) quantifying the environmental, health and socio-economic impacts of urban pollution. It comprises a suite of different models and tools to support sustainability appraisals including life cycle assessment, substance flow analysis, source and pollutants characterisation, pollutant fate and transport modelling, health impact analysis, ecological impact assessment, and multi-criteria decision analysis. The framework can be used at different levels, from simple screening studies to more detailed assessments. The paper describes the decision-support framework and outlines several case studies to demonstrate its application. The software tool is available free of charge at www.pureframework.org. Practical applications: The PUrE framework and software platform can be applied to assess and compare the sustainability of different technologies, products, human activities or policies. Example applications of the framework have so far included sustainability comparisons of technologies for thermal treatment of municipal solid waste; generation of electricity from coal and biomass; environmental and health impacts of a mixture of pollutants in Sheffield; the role of urban green space in reducing the levels of particulate matter in London and the impacts of environmental policy on legacy pollution in Avenmouth. © The Chartered Institution of Building Services Engineers 2011.

Buildings in developed countries are becoming increasingly airtight as a response to stricter energy efficiency requirements. At the same time, changes are occurring to the ways in which household energy is supplied, distributed and used. These changes are having important impacts on exposure to indoor air pollutants in residential buildings and present new challenges for professionals interested in assessing the effects of housing on public health. In many circumstances, models are the most appropriate way with which to examine the potential outcomes of future environmental and/or building interventions and policies. As such, there is a need to consider the current state of indoor air pollution exposure modelling.Various indoor exposure modelling techniques are available, ranging from simple statistical regression and mass-balance approaches, to more complex multizone and computational fluid dynamics tools that have correspondingly large input data requirements. This review demonstrates that there remain challenges which limit the applicability of current models to health impact assessment. However, these issues also present opportunities for better integration of indoor exposure modelling and epidemiology in the future. The final part of the review describes the application of indoor exposure models to health impact assessments, given current knowledge and data, and makes recommendations aimed at improving model predictions in the future. Keywords: Indoor air quality, Exposure, Exposure modelling, Microenvironment, Computational fluid dynamics, Health impact assessment

The association between episodes of extreme temperature and ambulance 999 calls has not yet been properly quantified. In this study we propose a statistical physics-based method to estimate the true mean number of ambulance 999 calls during episodes of extreme temperatures. Simple arithmetic mean overestimates the true number of calls during such episodes. Specifically, we apply the physics-based framework of nonextensive statistical mechanics (NESM) for estimating the probability distribution of extreme events to model the positive daily variation of ambulance calls. In addition, we combine NESM with the partitioned multiobjective method (PMRM) to determine the true mean of the positive daily difference of calls during periods of extreme temperature. We show that the use of the standard mean overestimates the true mean number of ambulance calls during episodes of extreme temperature. It is important to correctly estimate the mean value of ambulance 999 calls during such episodes in order for the ambulance service to efficiently manage their resources.

Cold temperature extremes can have a detrimental effect on human health, public services and the economy of a country. From a public health services perspective, it is important to quantify the frequency of occurrence of extreme cold events and how this frequency changes over time in order to develop cost-effective anticipatory plans to reduce the potential impact of cold extremes on the exposed vulnerable population. Using non-stationary extreme-value analysis, the geographical and temporal distribution of cold temperature extremes over the last 160 years in several locations in England and Scotland was investigated. The temperature data were obtained from weather stations. It is then shown that the 5, 10, 50 and 100 year return levels of minimum winter temperature have increased throughout the 20th century. It was also shown that the probability of experiencing extreme cold temperatures has become very low in most locations particularly in years with a positive phase of the North Atlantic Oscillation (NAO) index. Finally, an estimate of the approximate financial risk to the UK economy of consecutive days of extreme cold temperatures is presented.

Energy used in dwellings is an important target for actions to avert climate change. Properly designed and implemented, such actions could have major co-benefits for public health. To investigate, we examined the effect of hypothetical strategies to improve energy efficiency in UK housing stock and to introduce 150 million low-emission household cookstoves in India. Methods similar to those of WHO's Comparative Risk Assessment exercise were applied to assess the effect on health that changes in the indoor environment could have. For UK housing, the magnitude and even direction of the changes in health depended on details of the intervention, but interventions were generally beneficial for health. For a strategy of combined fabric, ventilation, fuel switching, and behavioural changes, we estimated 850 fewer disability-adjusted life-years (DALYs), and a saving of 0.6 megatonnes of carbon dioxide (CO(2)), per million population in 1 year (on the basis of calculations comparing the health of the 2010 population with and without the specified outcome measures). The cookstove programme in India showed substantial benefits for acute lower respiratory infection in children, chronic obstructive pulmonary disease, and ischaemic heart disease. Calculated on a similar basis to the UK case study, the avoided burden of these outcomes was estimated to be 12 500 fewer DALYs and a saving of 0.1-0.2 megatonnes CO(2)-equivalent per million population in 1 year, mostly in short-lived greenhouse pollutants. Household energy interventions have potential for important co-benefits in pursuit of health and climate goals.

A conceptual framework and methodological approach are developed to understand the potential linkage between urban domestic heat demand and the heatwave vulnerability index, using the London building stock as a case study. A geographic information system (GIS)-based systematic approach towards exploring the impact of the urban built form and the heat island phenomenon on domestic space heating needs and heat-related mortality is demonstrated. The physical properties of individual dwellings were inferred from existing GIS databases as a function of given attributes (such as the form and age of the property). Localized annual heating degree-days (HDD) across London were predicted using a site-specific model. Each dwelling was modelled using a modified version of the steady-state annual domestic energy model. The energy consumption profiles generated were subsequently compared with existing top-down regional energy statistics. In addition, local environmental factors such as built density, green coverage rat...

The role of vegetation in mitigating the effects of PM 10 pollution has been highlighted as one potential benefit of urban greenspace. An integrated modelling approach is presented which utilises air dispersion (ADMS-Urban) and particulate interception (UFORE) to predict the PM 10 concentrations both before and after greenspace establishment, using a 10 × 10 km area of East London Green Grid (ELGG) as a case study. The corresponding health benefits, in terms of premature mortality and respiratory hospital admissions, as a result of the reduced exposure of the local population are also modelled. PM 10 capture from the scenario comprising 75% grassland, 20% sycamore maple (Acer pseudoplatanus L.) and 5% Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) was estimated to be 90.41 t yr -1, equating to 0.009 t ha -1 yr -1 over the whole study area. The human health modelling estimated that 2 deaths and 2 hospital admissions would be averted per year. Crown Copyright © 2009.

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.

Monitoring and modelling studies of the indoor environment indicate that there are often discrepancies between simulation results and measurements. The availability of large monitoring datasets of domestic buildings allows for more rigorous validation of the performance of building simulation models derived from limited building information, backed by statistical significance tests and goodness-of-fit metrics. These datasets also offer the opportunity to test modelling assumptions. This paper investigates the performance of domestic housing models using EnergyPlus software to predict maximum daily indoor temperatures over the summer of 2011. Monitored maximum daily indoor temperatures from the English Housing Survey's (EHS) Energy Follow-Up Survey (EFUS) for 823 nationally representative dwellings are compared against predictions made by EnergyPlus simulations. Due to lack of information on the characteristics of individual dwellings, the models struggle to predict maximum temperatures in individual dwellings and performance was worse on days when the outdoor maximum temperatures were high. This research indicates that unknown factors such as building characteristics, occupant behaviour and local environment makes the validation of models for individual dwellings a challenging task. The models did, however, provide an improved estimate of temperature exposure when aggregated over dwellings within a particular region.

With the UK climate projected to warm in future decades, there is an increased research focus on the risks of indoor overheating. Energy-efficient building adaptations may modify a buildings risk of overheating and the infiltration of air pollution from outdoor sources. This paper presents the development of a national model of indoor overheating and air pollution, capable of modelling the existing and future building stocks, along with changes to the climate, outdoor air pollution levels, and occupant behaviour. The model presented is based on a large number of EnergyPlus simulations run in parallel. A metamodelling approach is used to create a model that estimates the indoor overheating and air pollution risks for the English housing stock. The performance of neural networks (NNs) is compared to a support vector regression (SVR) algorithm when forming the metamodel. NNs are shown to give almost a 50% better overall performance than SVR.

The standard decision rules of cost-effectiveness analysis either require the decision maker to set a threshold willingness to pay for additional health care or to set an overall fixed budget. In practice, neither are generally taken, but instead an arbitrary decision rule is followed that may not be consistent with the overall budget, lead to an allocation of resources that is less than optimal, and is unable to identify the program that should be displaced at the margin. Recent work has shown how mathematical programming can be used as a generalization of the standard decision rules. The authors extend the use of mathematical programming, first to incorporate more complex budgetary rules about when expenditure can be incurred, and show the opportunity loss, in terms of health benefit forgone, of each budgetary policy. Second, the authors demonstrate that indivisibility in a patient population can be regarded as essentially a concern for horizontal equity and represent this and other equity concerns as constraints in the program. Third, the authors estimate the different opportunity costs of a range of equity concerns applied to particular patient populations, and when imposed on all patient populations. They apply this framework of analysis to a realistic and policy-relevant problem. Key words: cost-effectiveness analysis; cost-benefit analysis; mathematical programming; resource allocation. (Med Decis Making 2007;27:128—137)

INTRODUCTION We compare winter fuel payments (WFPs) with subsidized home energy efficiency (HEE) investments in terms of health, carbon dioxide (CO2) emissions, energy costs and equity in the conte...

Introduction: The UK is committed to ambitious targets for improving the energy performance of the housing stock to reduce greenhouse gas emissions. This work aimed to examine the health impacts an...

Introduction Mortality during hot weather is predicted to become more significant in the UK due to changes in the climate. We investigated the spatial variation in heat-related mortality risks acro...

Climate change is expected to affect health through changes in exposure to weather disasters. Vulnerability to coastal flooding has decreased in recent decades but remains disproportionately high in low-income countries. We developed a new statistical model for estimating future storm surge-attributable mortality. The model accounts for sea-level rise and socioeconomic change, and allows for an initial increase in risk as low-income countries develop. We used observed disaster mortality data to fit the model, splitting the dataset to allow the use of a longer time-series of high intensity, high mortality but infrequent events. The model could not be validated due to a lack of data. However, model fit suggests it may make reasonable estimates of log mortality risk but that mortality estimates are unreliable. We made future projections with and without climate change (A1B) and sea-based adaptation, but given the lack of model validation we interpret the results qualitatively. In low-income countries, risk initially increases with development up to mid-century before decreasing. If implemented, sea-based adaptation reduces climate-associated mortality in some regions, but in others mortality remains high. These patterns reinforce the importance of implementing disaster risk reduction strategies now. Further, while average mortality changes discontinuously over time, vulnerability and risk are evolving conditions of everyday life shaped by socioeconomic processes. Given this, and the apparent importance of socioeconomic factors that condition risk in our projections, we suggest future models should focus on estimating risk rather than mortality. This would strengthen the knowledge base for averting future storm surge-attributable health impacts.

There is growing evidence that projected climate change has the potential to significantly affect public health. In the UK, much of this impact is likely to arise by amplifying existing risks related to heat exposure, flooding, and chemical and biological contamination in buildings. Identifying the health effects of climate change on the indoor environment, and risks and opportunities related to climate change adaptation and mitigation, can help protect public health.We explored a range of health risks in the domestic indoor environment related to climate change, as well as the potential health benefits and unintended harmful effects of climate change mitigation and adaptation policies in the UK housing sector. We reviewed relevant scientific literature, focusing on housing-related health effects in the UK likely to arise through either direct or indirect mechanisms of climate change or mitigation and adaptation measures in the built environment. We considered the following categories of effect: (i) indoor temperatures, (ii) indoor air quality, (iii) indoor allergens and infections, and (iv) flood damage and water contamination.Climate change may exacerbate health risks and inequalities across these categories and in a variety of ways, if adequate adaptation measures are not taken. Certain changes to the indoor environment can affect indoor air quality or promote the growth and propagation of pathogenic organisms. Measures aimed at reducing greenhouse gas emissions have the potential for ancillary public health benefits including reductions in health burdens related heat and cold, indoor exposure to air pollution derived from outdoor sources, and mould growth. However, increasing airtightness of dwellings in pursuit of energy efficiency could also have negative effects by increasing concentrations of pollutants (such as PM2.5, CO and radon) derived from indoor or ground sources, and biological contamination. These effects can largely be ameliorated by mechanical ventilation with heat recovery (MVHR) and air filtration, where such solution is feasible and when the system is properly installed, operated and maintained. Groups at high risk of these adverse health effects include the elderly (especially those living on their own), individuals with pre-existing illnesses, people living in overcrowded accommodation, and the socioeconomically deprived.A better understanding of how current and emerging building infrastructure design, construction, and materials may affect health in the context of climate change and mitigation and adaptation measures is needed in the UK and other high income countries. Long-term, energy efficient building design interventions, ensuring adequate ventilation, need to be promoted. Keywords: Climate change, Overheating, Air quality, Mould, Adaptation, Public health

There are a few sampling methods available to survey households in situations where sample frames are either unavailable or are unreliable. The most popular of these methods is the expanded programme of immunization (EPI) sampling method, which has been used in low-income countries. The purpose of this paper is to explain how mathematical programming can be used to optimize EPI and other household survey sampling methods in these situations.

It is well known that vasopressin cells fire action potentials in bursts, but also have the ability to continuously discharge or have long periods of silence. Experimentally, various external stimuli can be applied to the vasopressin cell in order to manipulate the patterns of discharge observed. In this paper, the Rose-Hindmarsh model is used to describe the discharging of a single vasopressin cell. The range of parameter values which makes the model display the various patterns observed in vivo are described. It is shown that the model can be controlled to follow desired patterns of discharge and thus mimic external stimulation.

A stability analysis is carried out for a mathematical model which describes the electrical firing of a single vasopressin neuron. The model used in a FitzHugh-Nagumo-type system which is driven by impulses. The analysis is based on recent developments in the stability theory of impulsive differential equations. Conditions are derived under which the system of differential equations is stable at two of its equilibrium points. Biologically this bistability represents the cell alternating between periods of electrical activity and silence. The conditions for stability are specified in terms of the amplitude and frequency of the impulses perturbing the system. Both stochastic and deterministic impulses are considered.

This paper presents a new decision-support methodology and software tool for sustainable management of urban pollution. A number of different methods and tools are integrated within the same platform, including GIS, LCA, fate and transport modelling, health impact assessment and multi-criteria decision analysis. The application of the framework is illustrated on a case study which investigates the environmental and health impacts of pollution arising from different industrial, domestic and transport sources in a city. The example city chosen for the study is Sheffield, UK, and the main pollutants considered are NOx, SO2 and PM10. The results suggest that the absence of the current large industrial sources in the city would lead to a 90% reduction of the SO2 and 70% of the NO2 ground concentrations, consequently preventing 27 deaths and 18 respiratory hospital admissions per annum for a population of 500,000. Based on the total annual mortality and hospital admissions in Sheffield for the year of the assessment, this means that 0.53% of premature deaths and 0.49% of respiratory hospital admissions would be prevented by the estimated reduction in air pollution. © 2012 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.