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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.

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.

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.

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.

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.

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.

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.

Quantitative health impact assessment (HIA) is increasingly being used to assess the health impacts attributable to an environmental policy or intervention. As a consequence, there is a need to assess uncertainties in the assessments because of the uncertainty in the HIA models. In this paper, a framework is developed to quantify the uncertainty in the health impacts of environmental interventions and is applied to evaluate the impacts of poor housing ventilation. The paper describes the development of the framework through three steps: (i) selecting the relevant exposure metric and quantifying the evidence of potential health effects of the exposure; (ii) estimating the size of the population affected by the exposure and selecting the associated outcome measure; (iii) quantifying the health impact and its uncertainty. The framework introduces a novel application for the propagation of uncertainty in HIA, based on fuzzy set theory. Fuzzy sets are used to propagate parametric uncertainty in a non-probabilistic space and are applied to calculate the uncertainty in the morbidity burdens associated with three indoor ventilation exposure scenarios: poor, fair and adequate. The case-study example demonstrates how the framework can be used in practice, to quantify the uncertainty in health impact assessment where there is insufficient information to carry out a probabilistic uncertainty analysis. Keywords: Environmental health, Uncertainty, Modelling, Health impact assessment, Housing

Health impact assessment (HIA) is often used to determine ex ante the health impact of an environmental policy or an environmental intervention. Underpinning any HIA is the framing assumption, which defines the causal pathways mapping environmental exposures to health outcomes. The sensitivity of the HIA to the framing assumptions is often ignored. A novel method based on fuzzy cognitive map (FCM) is developed to quantify the framing assumptions in the assessment stage of a HIA, and is then applied to a housing intervention (tightening insulation) as a case-study. Framing assumptions of the case-study were identified through a literature search of Ovid Medline (1948–2011). The FCM approach was used to identify the key variables that have the most influence in a HIA. Changes in air-tightness, ventilation, indoor air quality and mould/humidity have been identified as having the most influence on health. The FCM approach is widely applicable and can be used to inform the formulation of the framing assumptions in any quantitative HIA of environmental interventions. We argue that it is necessary to explore and quantify framing assumptions prior to conducting a detailed quantitative HIA during the assessment stage. Keywords: Environmental health, Risk assessment, Modelling, Health impact assessment, Housing

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

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.

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.

A two-stage stochastic mathematical programming formulation has been developed to optimally allocate resources within and between healthcare programmes when there is an exogenous budget and the parameters of the healthcare models are variable and uncertain. This formulation solves the optimal resource allocation problem and calculates the expected value of acquiring additional information to resolve the uncertainties within the allocation. It is shown that the proposed formulation has several advantages over the chance constrained and robust mathematical programming methods.

Background To address the large wintertime health burden in England, the Department of Health introduced a Cold Weather Plan (CWP) in 2011, which provides advice to relevant organisations and the general public on actions that should be taken to protect vulnerable individuals during cold weather. The Department of Health commissioned the Policy Innovation Research Unit at the London School of Hygiene and Tropical Medicine to undertake an assessment of the implementation of the CWP over winter 2012–13 and to begin to assess its potential health benefits and costs. Methods The research consisted of time-series regression and episode analysis of historic records of daily mortality, 1993–2006 (from the Office for National Statistics) and emergency hospital admissions and accident and emergency department visits, 1997–2010 (both from the Health and Social Care Information Centre), linked to weather data for each region of England. Models were adjusted for seasonal factors unrelated to temperature and possible day-of-week effects. Semistructured telephone interviews (n=52) with staff involved in the implementation of the CWP were done in ten local authorities selected from within regions of the country with different winter weather patterns to provide a spread of urban versus rural areas and levels of socioeconomic deprivation. Interviews focused on the operational policy changes and health-care or social-care services' response to the introduction of the CWP for comparison with the expectations contained in the plan. 25 local authority managers, 13 National Health Service (NHS) commissioners, four NHS acute or community trust managers, two NHS ambulance trust managers, six care home managers, one family doctor, and one voluntary sector manager were interviewed, dependent on the local allocation of responsibility for CWP implementation between agencies. Analysis of CWP-related plans and related documents in the ten local authority areas was done. A national survey was undertaken of 8500 community nurse members of the Royal College of Nursing, of whom 437 (5%) responded. Semi-structured telephone interviews (n=35) with individuals at high risk from cold weather exposure living in their own homes in two local authorities were also done. People were interviewed shortly after a level 3 cold weather alert had been issued, which is issued when severe winter weather is occurring. Findings Cold-related effects were noted for both mortality and hospital use but the biggest cold burdens occurred at temperatures outside the alert thresholds used in the CWP. Effects could be delayed by up to 4 weeks after initial exposure. The CWP and the cold weather alerts were viewed as a helpful exposition of good practice by local health-care and social-care managers. The CWP formalised existing activity and helped to coordinate the actions of a wide range of staff in different agencies. There were examples of innovations that local staff attributed directly to the CWP (eg, a push to raise influenza immunisation levels of front-line staff). Analysis of the interviews with people living in the community suggested that interventions aimed at people who are vulnerable to cold should focus on people who live in cold homes and are socially isolated. Interpretation The CWP is broadly welcomed and has the potential to reduce cold-related public health effects and health services usage, but the alerts may need revision. The present study is limited by the fact that the most robust quantitative evidence on the effectiveness of the CWP will only come once there are sufficient years after intervention to assess any changes in weather–health relations. Funding Department of Health Policy Research Programme.

Background The UK Government has set specific targets for greenhouse gas emissions to lower the risk of dangerous climate change. Previous research has shown that important health co-benefits could result from strategies targeting the domains of transport, built environment, and agriculture. This study assesses the full general equilibrium economy-wide macroeconomic effects of health co-benefits from three similar UK strategies to meet locally specific 2030 greenhouse gas emission targets. Methods Economy-wide effects of health co-benefits were modelled with a dynamic extension of the widely used International Food Policy Research Institute standard computable general equilibrium model for 2011–30. Four forms of economic agents are modelled: firms (who combine resource inputs to maximise profits), consumers (who consume and save to maximise their welfare), government, and foreign agents. The method consists of simulation of three greenhouse gas policy scenarios and a counterfactual do-nothing scenario. Basic health co-benefits (years lived with disability [YLD] and years of life lost [YLL]) were measured for a range of illnesses, on the basis of the comparative risk assessment approach. Combined with incidence numbers and prevalence trends, these basic YLD (morbidity) and YLL (mortality) co-benefits were used to calculate dynamic sequences of demographic and labour market effects on population and productive labour supply, and public budget implications for averted health-care costs and increased social security transfers (including benefit payments for working-age individuals and pension payments for old-age individuals). These economic shocks were subsequently imposed on the computable general equilibrium model and used to measure the combined macroeconomic effect of health co-benefits. The method for measuring averted health-care costs was published in The Lancet in 2012. Three scenarios were modelled: active travel (transport sector; health co-benefits of an assumed transformation of urban transport behaviour to reduce motorised transportation and increase walking [2·5-times] and cycling [8·0-times] in urban England and Wales); healthy diet (food and agriculture sector; health co-benefits of an assumed UK-wide 30% reduction in consumption of dietary saturated fat); and household energy (household energy sector; health co-benefits of an assumed UK-wide improvement in home insulation and ventilation, including reduced household energy use, improved indoor temperature, and associated changes in indoor pollutants). Findings For all scenarios, the macroeconomic effects of health co-benefits are positive. Overall, substantial savings on health-care costs represent the main contributing factor. Increased labour supplies also contribute positively, whereas increased social security transfers (due to larger working-age and old-age population segments) detract. The largest potential cumulative gross domestic product gains from health co-benefits are associated with the active travel scenario (around £19 billion), in which increased physical activity averts large-scale and long-term chronic disease burdens and health-system costs. The healthy diet scenario also leads to important potential gains (around £5 billion), whereas the full potential health co-benefits from the household energy scenario will not be realised until beyond 2030. Three economic sensitivity analyses were undertaken to test the sensitivity of results to variations in assumptions concerning: the substitutability of labour for other factors of production; the effectiveness of the interventions; and changes in the discount rate (the present value of the economic effects). Overall, the core results can be considered relatively robust to changes in these three factors. Interpretation Strategies to reduce greenhouse gas emissions and improve health are likely to result in substantial and increasing positive contributions to the economy. This effect might offset some economic costs and thereby allow such strategies to be seen more favourably, especially in times of economic austerity. Funding Department of Health Policy Research Programme.