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The expanding research of complexity economics has been signalling its preference for a formal quantitative investigation of diverse interactions between heterogeneous agents at the lower, micro-level resulting in emergent, realistic socioeconomic dynamics at the higher, macro-level. However, there is scarcity in research that explicitly links complexity perspectives in economics with the systems thinking literature, despite these being highly compatible, with strong connections and common historical traces. We aim to address this gap by exploring commonalities and differences between the two bodies of knowledge, seen particularly through an economics lens. We argue for a hybrid approach, in that agent-based complexity perspectives in economics could more closely connect to two main systems thinking attributes: a macroscopic approach to analytically capturing the complex dynamics of systems, and an inter-subjective interpretivist dimension, when investigating complex social-economic order. Illustrative discussions of city sustainability are provided, with an emphasis on decarbonisation and residential energy demand aspects.

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.

We interrogate mathematical modelling as an instrument of knowledge co-production by concentrating on the classical probabilistic operationalization of decision-making under uncertainty used for in...

Graphical abstract, Climate change means the UK will experience warmer winters and hotter summers in the future. Concurrent energy efficiency improvements to housing may modify indoor exposures to heat or cold, while population aging may increase susceptibility to temperature-related mortality. We estimate heat and cold mortality and energy consumption in London for typical (non-extreme) future climates, given projected changes in population and housing. Building physics models are used to simulate summertime and wintertime indoor temperatures and space heating energy consumption of London dwellings for ‘baseline’ (2005–2014) and future (2030s, 2050s) periods using data from the English Housing Survey, historical weather data, and projected future weather data with temperatures representative of ‘typical’ years. Linking to population projections, we calculate future heat and cold attributable mortality and energy consumption with demolition, construction, and alternative scenarios of energy efficiency retrofit. At current retrofit rates, around 168–174 annual cold-related deaths per million population would typically be avoided by the 2050s, or 261–269 deaths per million under ambitious retrofit rates. Annual heat deaths would typically increase by 1 per million per year under the current retrofit rate, and 12–13 per million under ambitious rates without population adaptation to heat. During typical future summers, an estimated 38–73% of heat-related deaths can be avoided using external shutters on windows, with their effectiveness lower during hotter weather. Despite warmer winters, ambitious retrofit rates are necessary to reduce typical annual energy consumption for heating below baseline levels, assuming no improvement in heating system efficiencies. Concerns over future overheating in energy efficient housing are valid but increases in heat attributable mortality during typical and hot (but not extreme) summers are more than offset by significant reductions in cold mortality and easily mitigated using passive measures. More ambitious retrofit rates are critical to reduce energy consumption and offer co-benefits for reducing cold-related mortality.

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

Societal impact is an increasingly important imperative of academic funding. However, there is little research to date documenting how impact is accomplished in practice. Drawing on insights from Actor–Network Theory, we explore the research–policy interface within an interdisciplinary research project on the relationships between air pollution and human health. Health policy impact was important to the researchers for moral as well as pragmatic reasons but it was a goal that was seen as potentially in tension with that of doing science. In fields such as air pollution and health, networks of policymakers and researchers are inevitably entangled, and we found that processes of engagement operated to delineate science from policy. Health was initially black-boxed and under-explicated, used as a signifier in itself for societal impact. By mobilising networks of policy actors, brought together in workshops to rank the importance of policy scenarios for the research team, the connections between air pollution and health were materialised and made actionable. This was achieved by framing existing data sets, emission technologies, policy expertise, pollutant species and human health in particular ways and, in doing so, excluding others. The process of linking air pollution and health research to achieve societal impact not only influenced how these phenomena were known but, critically, enabled and constrained potential policy responses. Tracing these research arrangements made the material discursive processes of ‘impact’ visible and analysable as objects of social science scholarship, and therefore generated a productive site for critically engaging with processes of environment and health science and policy.

Objective: There is a need to develop cost-effective methods to support public health policy makers plan ahead and make robust decisions on protective measures to safeguard against severe impacts of extreme weather events and natural disasters in the future, given competing demands on the social and healthcare resources, large uncertainty associated with extreme events and their impacts, and the opportunity costs associated with making ineffective decisions. Design: The authors combine a physics-based method known as nonextensive statistical mechanics for modeling the probability distribution of systems or processes exhibiting extreme behavior, with a decision-analytical method known as partitioned multiobjective risk method to determine the optimal decision option when planning for potential extreme events. Results: The method is illustrated using a simple hypothetical example. It is shown that partitioning the exceedance probability distribution of health impact into three ranges (low severity / high exceedance probability, moderate severity / medium exceedance probability, and high severity / low exceedance probability) leads to the correct estimation of the conditional expected impact in each range. Multiobjective optimization is used to determine the optimal decision option based on the perspective of the policy maker. Conclusion: This method constitutes a robust generic framework for the quantification of impacts and supporting decision-making under scenarios of extreme and catastrophic health risks.

As operations of windows by occupants can greatly affect building energy consumption and indoor air quality, understanding the driving factors of this adaptive control behaviour is of great importance. The present paper reports on an investigation into the influencing factors for window operation behaviour in eighteen newly-built, low-energy apartments in London, UK. A range of indoor and outdoor environmental variables (including temperature, relative humidity, CO2 and particulate matter) as well as the window status (open/closed) were monitored for 6–11 months. About half of the apartments included monitoring for nearly three months during a national pandemic lockdown. Additionally, each apartment was provided with a portable home air purifier (HAP) to use during most of the study period. The effects of environmental variables and the use of HAPs on occupants' operations of windows in the main bedroom were analysed according to different periods (free-running, heating and lockdown period) and occupancy stages (arrival, departure and intermediate occupied times). Results indicated that analysing the heating period alone could lead to explanations of window operation behaviour that were contradictory to those from analysing other periods, and separating the dataset based on different occupancy stages to develop behaviour models was of little value. The results of statistical significance tests showed that indoor temperature was the leading driving factor for occupants’ window opening and closing behaviour, whereas neither air quality-related variables nor the use of air purifiers had a statistically significant impact on window operation behaviour.

Payment for performance (P4P) has been employed in low and middle-income (LMIC) countries to improve quality and coverage of maternal and child health (MCH) services. However, there is a lack of consensus on how P4P affects health systems. There is a need to evaluate P4P effects on health systems using methods suitable for evaluating complex systems. We developed a causal loop diagram (CLD) to further understand the pathways to impact of P4P on delivery and uptake of MCH services in Tanzania. The CLD was developed and validated using qualitative data from a process evaluation of a P4P scheme in Tanzania, with additional stakeholder dialogue sought to strengthen confidence in the diagram. The CLD maps the interacting mechanisms involved in provider achievement of targets, reporting of health information, and population care seeking, and identifies those mechanisms affected by P4P. For example, the availability of drugs and medical commodities impacts not only provider achievement of P4P targets but also demand of services and is impacted by P4P through the availability of additional facility resources and the incentivisation of district managers to reduce drug stock outs. The CLD also identifies mechanisms key to facility achievement of targets but are not within the scope of the programme; the activities of health facility governing committees and community health workers, for example, are key to demand stimulation and effective resource use at the facility level but both groups were omitted from the incentive system. P4P design considerations generated from this work include appropriately incentivising the availability of drugs and staffing in facilities and those responsible for demand creation in communities. Further research using CLDs to study heath systems in LMIC is urgently needed to further our understanding of how systems respond to interventions and how to strengthen systems to deliver better coverage and quality of care., Highlights • Holistic analysis key to avoiding suboptimal P4P performance. • Availability of drugs is influenced by P4P and affects success of P4P. • Need to incentivise groups outside facility that support service coverage.

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.

Environmental health models comprise a series of linked connected models including models of the production of pollutants, dispersal of pollutants in the environment, exposure of the affected populations to the pollutants, and exposure–health outcome relationships. Quantifying uncertainty in environmental health models is important particularly if the models are to be used to evaluate the cost–benefits of environmental health interventions. There are two main types of uncertainty in environmental health models: parametric and structural. It is imperative that both types of uncertainty are addressed to quantify the overall uncertainty in a model. Different representations of parametric uncertainty can be used which span deterministic, probabilistic, and fuzzy methods; the specifics of a representation would depend on the nature of the parameter. In relation to dealing with structural uncertainty, deterministic and probabilistic approaches can be used depending on availability of data observations. In cost–benefits analysis the benefits are converted to monetary costs and so dealing with uncertainty in cost–benefits is essentially one-dimensional. Ensuring the robustness of model-based decisions in the face of model uncertainty is important from the perspective of policy makers.

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.

In public health, model predictions are used by decision-makers to minimise health burdens and monetary costs of non-communicable diseases. Fully understanding the uncertainty underlying those predictions is thus crucial. One-parameter-at-a-time methods are typically used for model uncertainty analysis but are often impractical for large-scale nonlinear models containing a very large number of parameters and cannot be used to examine the uncertainty associated with parameter interaction. An individual-based chronic disease model was developed to model the impact of Body Mass Index (BMI) trends on the rates of non-communicable diseases. The model was simulated for overweight and obese male case studies and used to predict their life expectancy, disease-free life expectancy and quality-adjusted life years. Uncertainty was estimated by carrying out a global sensitivity analysis by assessing the contribution to the overall uncertainty from a selection of parameters individually and from their interactions. Results show that the uncertainty of the BMI input parameter had the greatest impact on the disease-free life expectancy and quality adjusted life year uncertainty compared to the relative risks of colorectal cancer and stroke. Life expectancy uncertainty was influenced by BMI and colorectal cancer relative risks. Global sensitivity analysis enables the assessment of the parametric uncertainty for individual parameters and their interaction. This allows the communication of the uncertainty of different policy options. A strategy for scaling-up uncertainty analysis from an individual to a population level is discussed.

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.

Environmental changes threaten agricultural production, food security, and health. Previous reviews suggest that environmental changes will substantially affect future yields of starchy dietary staples. To date, no comprehensive global analysis of the impacts of environmental change on (nonstaple) vegetables and legumes-important constituents of healthy diets-has been reported. We systematically searched for articles published between 1975 and 2016 on the effects of ambient temperature, tropospheric carbon dioxide (CO2), and ozone (O3) concentrations, water availability, and salinization on yields and nutritional quality of vegetables and legumes. We estimated mean effects of standardized environmental changes using observed exposure-response relationships and conducted meta-analyses where possible. We identified 174 relevant papers reporting 1,540 experiments. The mean (95% CI) reported yield changes for all vegetables and legumes combined were +22.0% (+11.6% to +32.5%) for a 250-ppm increase in CO2 concentration, -8.9% (-15.6% to -2.2%) for a 25% increase in O3 concentration,-34.7% (-44.6% to -24.9%) for a 50% reduction in water availability, and -2.3% (-3.7% to -0.9%) for a 25% increase in salinity. In papers with baseline temperatures >20 °C, a 4 °C increase in temperature reduced mean yields by -31.5% (-41.4% to -21.5%). Impacts of environmental changes on nutritional quality were mixed. In a business-as-usual scenario, predicted changes in environmental exposures would lead to reductions in yields of nonstaple vegetables and legumes. Where adaptation possibilities are limited, this may substantially change their global availability, affordability, and consumption in the mid to long term. Our results stress the importance of prioritizing agricultural developments, to minimize potential reductions in vegetable and legume yields and associated negative health effects.

With the predicted increase in heatwave frequency in the UK due to climate change, there has been an increasing research focus on mortality during hot weather. This paper examines the risk of mortality in London during hot weather by combining data on population age and distribution, Urban Heat Island (UHI), and dwelling propensity to overheat due to geometry and fabric characteristics derived using building physics in order to calculate the spatial variation in heat-related mortality risk across London. Spatial variation of heat-related mortality was found to reflect background mortality rates due to population age, while dwelling characteristics were found to cause a larger variation in temperature exposure (and therefore risk) than UHI. The highest levels of excess mortality were found in areas with larger elderly populations, towards the outskirts of the Greater London Authority (GLA).

Mathematical models can be used to evaluate the health impacts of housing energy efficiency interventions. However by their nature, models are subject to uncertainty and variability, which are important to quantify if used to support policy decisions. Models that are used to assess the impacts on health of housing energy efficiency interventions are likely to be based on a pair of linked component models: a building physics model which calculates changes in exposures and whose outputs then feed into a health impact model. Current methods to propagate uncertainty in a series of models, where the outputs of one model are inputs to another, invariably use Monte Carlo (MC) numerical simulation. In this paper, two methods are used to quantify the uncertainty in the impact of draught proofing on childhood asthma: the MC simulation method and a semi-analytical method based on integral transforms. Both methods give close results but it is argued that the semi-analytical method has some advantages over the MC method, particularly in quantifying the uncertainties in the main outputs of the building physics model before propagating them to the health model.

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues.We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion.The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models.Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context., Environmental Research, 142, pp.319-327; 2015

Strategies to reduce domestic heating loads by minimizing the infiltration of cold air through adventitious openings located in the thermal envelopes of houses are highlighted by the building codes of many countries. Consequent reductions of energy demand and CO2e emission are often unquantified by empirical evidence. Instead, a mean heating season infiltration rate is commonly inferred from an air leakage rate using a simple ratio 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 differentiate between the infiltration of unconditioned ambient air that requires heating, and conditioned air from adjacent dwellings that does not. A stochastic method is presented that applies a theoretical model of adventitious infiltration to predict distributions of mean infiltration rates and the associated total heat loss in any stock of dwellings during heating hours. The method is applied to the English and UK housing stocks and provides probability distribution functions of stock infiltration rates and total heat loss during the heating season for two extremes of party wall permeability. The distributions predict that up to 79% of the current English stock could require additional purpose-provided ventilation to limit negative health consequences. National models predict that fewer dwellings are under-ventilated. The distributions are also used to predict that infiltration is responsible for 3–5% of total UK energy demand, 11–15% of UK housing stock energy demand, and 10–14% of UK housing stock carbon emissions.

More than one-fifth of the United Kingdom population has poor vitamin D status (serum 25-hydroxyvitamin D [25(OH)D] concentration25 nmol/L), particularly individuals with low sun exposure or poor dietary intake.We identified the fortification vehicle and concentration most likely to safely increase population vitamin D intakes and vitamin D status.Wheat flour and milk were identified as primary fortification vehicles for their universal consumption in population groups most at risk of vitamin D deficiency including children aged 18-36 mo, females aged 15-49 y, and adults aged ≥65 y. With the use of data from the first 2 y (2008-2010) of the National Diet and Nutrition Survey Rolling Program, we simulated the effect of fortifying wheat flour and milk with vitamin D on United Kingdom food consumption. Empirically derived equations for the relation between vitamin D intake and the serum 25(OH)D concentration were used to estimate the population serum 25(OH)D concentration for each fortification scenario.At a simulated fortification of 10 μg vitamin D/100 g wheat flour, the proportion of at-risk groups estimated to have vitamin D intakes below United Kingdom Reference Nutrient Intakes was reduced from 93% to 50%, with no individual exceeding the United Kingdom Tolerable Upper Intake Level; the 2.5th percentile of the population winter serum 25(OH)D concentration rose from 20 to 27 nmol/L after fortification. The simulation of the fortification of wheat flour at this concentration was more effective than that of the fortification of milk (at concentrations between 0.25 and 7 mg vitamin D/100 L milk) or of the fortification of milk and flour combined.To our knowledge, this study provides new evidence that vitamin D fortification of wheat flour could be a viable option for safely improving vitamin D intakes and the status of United Kingdom population groups at risk of deficiency without increasing risk of exceeding current reference thresholds.

Housing interventions for energy efficiency and greenhouse gas emission reduction have the potential to reduce exposure to indoor air pollution if they are implemented correctly. This work assessed the health impacts of home energy efficiency measures in England and Wales resulting in a reduction in average indoor PM2.5 exposures of 3 μg m−3. The assessment was performed using a new multistate life table model which allows transition into and between multiple morbid states, including recovery to disease-free status and relapse, with transition rates informed by age- and cause-specific disease prevalence, incidence and mortality data. Such models have not previously included disease recovery. The results demonstrate that incorporation of recovery in the model is necessary for conditions such as asthma which have high incidence in early life but likelihood of recovery in adulthood. The impact assessment of the home energy efficiency intervention showed that the reduction in PM2.5 exposure would be associated with substantial benefits for mortality and morbidity from asthma, coronary heart disease and lung cancer. The overall impact would be an increase in life expectancy of two to three months and approximately 13 million QALYs gained over the 90 year follow-up period. Substantial quality-of-life benefits were also observed, with a decrease in asthma over all age groups and larger benefits due to reduced coronary heart disease and lung cancer, particularly in older age groups. The multistate model with recovery provides important additional information for assessing the impact on health of environmental policies and interventions compared with mortality-only life tables, allowing more realistic representation of diseases with substantial non-mortality burdens. Keywords: Indoor air pollution, Particulate matter, Health impact assessment, Quality of life, Disease recovery

Major energy efficiency refurbishment of the UK housing stock is needed to help attain emission reduction targets of greenhouse gases. Such measures typically entail some planned or incidental reduction of uncontrolled ventilation in dwellings. This paper examines the trade-offs for health and sustainability objectives of typical retrofit refurbishments in UK homes. While reducing ventilation can help protect against the ingress of harmful pollutants from the outdoor air, our results demonstrate that reducing permeability to low levels, without additional purpose-provided ventilation, is likely to lead to substantial increases in pollutants derived from indoor sources, including indoor-generated particles, radon and environmental tobacco smoke. The monetised equivalent cost of the health dis-benefits associated with these exposures may exceed the potential benefits of reducing energy costs and greenhouse gas emissions. Practical application: Reducing uncontrolled ventilation of dwellings helps to improve energy efficiency and can protect against the ingress of pollutants from the outdoor environment. However, simulation studies suggest that at high degrees of airtightness (very low permeability) there is a potentially steep rise in pollutants of indoor origin, whose adverse effects on health may outweigh the benefits of reduced energy use, lower CO2 emissions and protection against outdoor pollution. Though the optimal permeability level for a given dwelling will vary with local circumstances, considerations of health protection suggest the need to avoid reducing permeability to low levels.

The UK has committed to reduce greenhouse gas (GHG) emissions by 80 % relative to 1990 levels by 2050, and it has been suggested that this should include a 70 % reduction in emissions from food. Meeting this target is likely to require significant changes to diets, but the likely effect of these changes on population nutritional intakes is currently unknown. However, the current average UK diets for men and women do not conform to WHO dietary recommendations, and this presents an opportunity to improve the nutritional content of diets while also reducing the associated GHG emissions. The results of this study show that if, in the first instance, average diets among UK adults conformed to WHO recommendations, their associated GHG emissions would be reduced by 17 %. Further GHG emission reductions of around 40 % could be achieved by making realistic modifications to diets so that they contain fewer animal products and processed snacks and more fruit, vegetables and cereals. However, our models show that reducing emissions beyond 40 % through dietary changes alone will be unlikely without radically changing current consumption patterns and potentially reducing the nutritional quality of diets.