Assessing framing assumptions in quantitative health impact assessments: a housing intervention example.

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

Budgetary policies and available actions: a generalisation of decision rules for allocation and research decisions

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

Four issues in undernutrition-related health impact modeling.

Undernutrition modeling makes it possible to evaluate the potential impact of such events as a food-price shock or harvest failure on the prevalence and severity of undernutrition. There are, however, uncertainties in such modeling. In this paper we discuss four methodological issues pertinent to impact estimation: (1) the conventional emphasis on energy intake rather than dietary quality; (2) the importance of the distribution of nutrient intakes; (3) the timing of both the 'food shock' and when the response is assessed; and (4) catch-up growth and risk accumulation

Modelling inhalation exposure to combustion-related air pollutants in residential buildings: Application to health impact assessment.

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

A probabilistic model to evaluate population dietary recommendations.

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

Climate Change, Crop Yields, and Undernutrition: Development of a Model to Quantify the Impact of Climate Scenarios on Child Undernutrition

Background: Global climate change is anticipated to reduce future cereal yields and threaten food security, thus potentially increasing the risk of undernutrition. The causation of undernutrition is complex, and there is a need to develop models that better quantify the potential impacts of climate change on population health

A Community-based Classification of Impact Criteria for Life Cycle Sustainability Assessment in the Context of Estate Regeneration

Life Cycle Sustainability Assessment (LCSA) has been introduced in 2008 by Klopffer (Visentin et al., 2020) as a methodology for assessing the overall sustainability of products and systems (Klopffer and Grahl, 2014). LCSA has been used to support decision-making for the appraisal of building projects by assessing the environmental, social, and economic impacts of those schemes (Klopffer and Grahl, 2014; Sadhukhan, Sen and Gadkari, 2021). Other sustainability certification and assessment schemes can be used to assist the decision-makers in the options appraisal of estate regeneration scenarios. However, there is inconsistency in classifying the impact categories of those assessment methodologies. In addition, the impact categories of these schemes mostly do not reflect the priorities of communities in the context of estate regeneration. Sala et al. (2012), Zamangi et al. (2013), and Souza et al. (2015) have raised the importance of stakeholder involvement in the selection of sustainability indicators as one of the main gaps in conducting sustainability assessments. The aim of this paper is to identify a community-focused list of impact categories for LCSA to be used in the context of estate regeneration in the UK

Use of an agent-based model to explore urban transitions in commuter cycling

Encouraging a cycling culture while reducing car usage can lead to substantial health and environmental benefits. In this exploratory work, we use an agent-based model of commuter cycling focusing on the emergence of social norms due to interactions between agents and their environment (including their social networks). The overall goal is to develop an understanding of change and continuity in cycle commuting, and how this is shaped by dynamic relationships between social expectations and individual attributes. Initial characteristics of agents and the distribution of cycling in the population come from the Census and secondary quantitative data. The theoretical basis of our work comes from qualitative studies on cycling and from 'Theories of Practice', which see practices as enrolling individuals, depending on whether they have the material and cultural resources required to participate. Thus, rather than treating humans as rational individuals who, for example, follow the precepts of utility maximization, we explore how changes in norms surrounding cycling practices (such as social expectations around clothing and accessories) shape uptake, and how uptake then affects social norms relating to cycling., We test policies for increasing cycling usage based on provision of cycling stuff and improvements to the environment

On the robustness of thermal comfort against uncertain future climate: A Bayesian bootstrap method

Climate change mitigation and adaptation warrants their synergetic consideration in the building design process, yet past decades have witnessed an unbalanced focus on the mitigation of energy and carbon. In redressing the imbalance, the major challenge lies in the accurate prediction of future building performance via building energy modelling, which is considerably hindered by uncertainties in future climate data. Robustness analysis is a promising technique to inform uncertainty-based decision-making, but its application to future thermal comfort has yet to be sufficiently explored in the built environment. From the perspective of domestic overheating, this paper represents an initial investigation into the implementation of the Bayesian bootstrap method, to quantitatively evaluate the robustness of thermal comfort against uncertain future climate. This is demonstrated using a case study of two typical post-retrofit dwellings in England, where the Bayesian bootstrap also enables the statistical comparison of their expected future overheating risk with climate uncertainty considered. The main findings reveal the magnitude of both overheating risk and its variability experienced during nocturnal occupancy in regulation-compliant dwellings, respectively comprising nearly 15 and 12 times greater than during daytime in extreme cases. Results also imply that adaptive ventilation is potentially the key measure to enhance the robustness of thermal comfort against climate uncertainty. Overall, the Bayesian bootstrap is shown to provide a systematically consistent approach to the robustness assessment of future thermal comfort, which can facilitate the comparability of design alternatives that is vital to the building design decision-making process integrating both mitigation and adaptation strategies