School building energy efficiency and NO2 related risk of childhood asthma in England and Wales: Modelling study.

BACKGROUND: Climate change legislation will require dramatic increases in the energy efficiency of school buildings across the UK by 2050, which has the potential to affect air quality in schools. We assessed how different strategies for improving the energy efficiency of school buildings in England and Wales may affect asthma incidence and associated healthcare utilization costs in the future. METHODS: Indoor concentrations of traffic-related NO2 were modelled inside school buildings representing 13 climate regions in England and Wales using a building physics school stock model. We used a health impact assessment model to quantify the resulting burden of childhood asthma incidence by combining regional health and population data with exposure-response functions from a recent high-quality systematic review/meta-analysis. We compared the effects of four energy efficiency interventions consisting of combinations of retrofit and operational strategies aiming to improve indoor air quality and thermal comfort on asthma incidence and associated hospitalization costs. RESULTS: The highest childhood asthma incidence was found in the Thames Valley region (including London), in particular in older school buildings, while the lowest concentrations and health burdens were in the newest schools in Wales. Interventions consisting of only operational improvements or combinations of retrofit and operational strategies resulted in reductions in childhood asthma incidence (547 and 676 per annum regional average, respectively) and hospital utilization costs (£52,050 and £64,310 per annum regional average, respectively. Interventions that improved energy efficiency without operational measures resulted in higher childhood asthma incidence and hospital costs. CONCLUSION: The effect of school energy efficiency retrofit on NO2 exposure and asthma incidence in schoolchildren depends critically on the use of appropriate building operation strategies. The findings from this study make several contributions to fill the knowledge gap about the impact of retrofitting schools on exposure to air pollutants and their effects on children's health.

Viewing the global health system as a complex adaptive system - implications for research and practice.

The global health system (GHS) is ill-equipped to deal with the increasing number of transnational challenges. The GHS needs reform to enhance global resilience to future risks to health. In this article we argue that the starting point for any reform must be conceptualizing and studying the GHS as a complex adaptive system (CAS) with a large and escalating number of interconnected global health actors that learn and adapt their behaviours in response to each other and changes in their environment. The GHS can be viewed as a multi-scalar, nested health system comprising all national health systems together with the global health architecture, in which behaviours are influenced by cross-scale interactions. However, current methods cannot adequately capture the dynamism or complexity of the GHS or quantify the effects of challenges or potential reform options. We provide an overview of a selection of systems thinking and complexity science methods available to researchers and highlight the numerous policy insights their application could yield.   We also discuss the challenges for researchers of applying these methods and for policy makers of digesting and acting upon them. We encourage application of a CAS approach to GHS research and policy making to help bolster resilience to future risks that transcend national boundaries and system scales.

Projecting the impacts of housing on temperature-related mortality in London during typical future years.

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.

Understanding the maternal and child health system response to payment for performance in Tanzania using a causal loop diagram approach.

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.

Climate change, hunger and rural health through the lens of farming styles: An agent-based model to assess the potential role of peasant farming.

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 affect it. These models, however, capture just some of the processes that will shape future nutrition. We adopt an alternative standpoint, developing an agent-based model in which producer-consumer smallholders practice different 'styles of farming' in the global food system. The model represents a hypothetical rural community in which 'orphan' (subsistence) farmers may develop by adopting an 'entrepreneurial' style (highly market-dependent) or by maintaining a 'peasant' style (agroecology). We take a first look at the question: how might patterns of farming styles-under various style preference, climate, policy, and price transmission scenarios-impact on hunger and health-supporting conditions (incomes, work, inequality, 'real land productivity') in rural areas? imulations without climate change or agricultural policy found that style preference patterns influence production, food price, and incomes, and there were trade-offs between them. For instance, entrepreneurial-oriented futures had the highest production and lowest prices but were simultaneously those in which farms tended towards crisis. Simulations with climate change and agricultural policy found that peasant-orientated agroecology futures had the highest production, prices equal to or lower than those under entrepreneurial-oriented futures, and better supported rural health. There were, however, contradictory effects on nutrition, with benefits and harms for different groups. Collectively the findings suggest that when attempting to understand how climate change may impact on future nutrition and health, patterns of farming styles-along with the fates of the households that practice them-matter. 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: thus, they should be given more prominence in future work.

Catastrophic Health Impacts of Spiraling Climate Change: How Certain Can We Be About Their Magnitudes?

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

Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models.

BACKGROUND: Mathematical modelling has been a vital research tool for exploring complex systems, most recently to aid understanding of health system functioning and optimisation. System dynamics models (SDM) and agent-based models (ABM) are two popular complementary methods, used to simulate macro- and micro-level health system behaviour. This systematic review aims to collate, compare and summarise the application of both methods in this field and to identify common healthcare settings and problems that have been modelled using SDM and ABM. METHODS: We searched MEDLINE, EMBASE, Cochrane Library, MathSciNet, ACM Digital Library, HMIC, Econlit and Global Health databases to identify literature for this review. We described papers meeting the inclusion criteria using descriptive statistics and narrative synthesis, and made comparisons between the identified SDM and ABM literature. RESULTS: We identified 28 papers using SDM methods and 11 papers using ABM methods, one of which used hybrid SDM-ABM to simulate health system behaviour. The majority of SDM, ABM and hybrid modelling papers simulated health systems based in high income countries. Emergency and acute care, and elderly care and long-term care services were the most frequently simulated health system settings, modelling the impact of health policies and interventions such as those targeting stretched and under resourced healthcare services, patient length of stay in healthcare facilities and undesirable patient outcomes. CONCLUSIONS: Future work should now turn to modelling health systems in low- and middle-income countries to aid our understanding of health system functioning in these settings and allow stakeholders and researchers to assess the impact of policies or interventions before implementation. Hybrid modelling of health systems is still relatively novel but with increasing software developments and a growing demand to account for both complex system feedback and heterogeneous behaviour exhibited by those who access or deliver healthcare, we expect a boost in their use to model health systems.

Materialising links between air pollution and health: How societal impact was achieved in an interdisciplinary project.

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.

MicroEnv: A microsimulation model for quantifying the impacts of environmental policies on population health and health inequalities.

The Sustainable Development Goals (SDGs) recognise the critical need to improve population health and environmental sustainability. This paper describes the development of a microsimulation model, MicroEnv, aimed at quantifying the impact of environmental exposures on health as an aid to selecting policies likely to have greatest benefit. Its methods allow the integration of morbidity and mortality outcomes and the generation of results at high spatial resolution. We illustrate its application to the assessment of the impact of air pollution on health in London. Simulations are performed at Lower Layer Super Output Area (LSOA), the smallest geographic unit (population of around 1500 inhabitants) for which detailed socio-demographic data are routinely available in the UK. The health of each individual in these LSOAs is simulated year-by-year using a health-state-transition model, where transition probabilities from one state to another are based on published statistics modified by relative risks that reflect the effect of environmental exposures. This is done through linkage of the simulated population in each LSOA with 1 × 1 km annual average PM2.5 concentrations and area-based deprivation indices. Air pollution is a leading cause of mortality and morbidity globally, and improving air quality is critical to the SDGs for Health (Goal 3) and Cities (Goal 11). The evidence of MicroEnv is aimed at providing better understanding of the benefits for population health and health inequalities of policy actions that affect exposure such as air quality, and thus to help shape policy decisions. Future work will extend the model to integrate other environmental determinants of health.

Developing agent-based models of complex health behaviour.

Managing non-communicable diseases requires policy makers to adopt a whole systems perspective that adequately represents the complex causal architecture of human behaviour. Agent-based modelling is a computational method to understand the behaviour of complex systems by simulating the actions of entities within the system, including the way these individuals influence and are influenced by their physical and social environment. The potential benefits of this method have led to several calls for greater use in public health research. We discuss three challenges facing potential modellers: model specification, obtaining required data, and developing good practices. We also present steps to assist researchers to meet these challenges and implement their agent-based model.

A Global-Level Model of the Potential Impacts of Climate Change on Child Stunting via Income and Food Price in 2030.

BACKGROUND: 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. OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Effect of environmental changes on vegetable and legume yields and nutritional quality.

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.

Comparison of indoor temperatures of homes with recommended temperatures and effects of disability and age: an observational, cross-sectional study.

OBJECTIVES: We examine if temperatures in winter in English homes meet the recommendation of being at least 18°C at all times. We analyse how many days meet this criterion and calculate the hours per day and night being at/above 18°C. These metrics are compared between households with occupants aged above 64 years or having a long-term disability (LTD) and those younger and without disability. DESIGN: Cross-sectional, observational. SETTING: England. PARTICIPANTS: 635 households. OUTCOMES MEASURES: (1) Mean temperatures, (2) proportion of days of the measurement period meeting the criterion, (3) average hours at/above 18°C, (4) average hours at night at/above 18°C. RESULTS: Mean winter temperatures in the bedroom were MBR=18.15°C (SD=2.51), the living room MLR=18.90°C (SD=2.46) and the hallway MHall=18.25°C (SD=2.57).The median number of days meeting the criterion was 19-31%. For the living room, more days meet the criterion in the group with a LTD (Mdisability=342 vs Mno_disability=301, 95% CI 8 to 74), and with someone over 64 years present (Mabove64=341, Mbelow65=301 95%, CI 8 to 74).The median number of hours/day meeting the criterion was 13-17. In the living room, households with a disability had more hours at 18°C (Mdisability=364, Mno_disability=297, 95% CI 17 to 83) as did the older age group (Mabove64=347, Mbelow65=296, 95% CI 18 to 84). In the hallway, more hours met the criterion in households with a disability (Mdisability=338, Mno_disability=302, 95% CI 3 to 70).247 homes had at least nine hours of at least 18°C at night; no effect of age or disability. CONCLUSIONS: Many households are at risk of negative health outcomes because of temperatures below recommendations.

Health protection planning for extreme weather events and natural disasters.

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.

Local health and social care responses to implementing the national cold weather plan.

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.

Effects of environmental change on population nutrition and health: A comprehensive framework with a focus on fruits and vegetables.

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.