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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 PM 2.5 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. Unlabelled Image • We describe a microsimulation model for quantifying environmental risks to health. • The model outputs temporal health impacts at a high spatial resolution. • Our approach allows the integration of estimated morbidity and mortality impacts. • The model may be used to assess health impacts of air pollution policy. • Multiple environmental exposures may be overlaid with SDGs in mind. [ABSTRACT FROM AUTHOR]

Identifying the overall environmental, and socioeconomic impacts of different estate regeneration scenarios can contribute to the overall sustainability of such schemes. Life Cycle Sustainability Assessment (LCSA) is an appropriate tool for assessing holistic sustainability. To achieve resilient societies, the interests of communities should be considered in decision making. This paper proposes a method for incorporating community needs in identifying sustainability metrics for the sustainability assessment of estate regeneration schemes. A literature review in the field of sustainability assessment of buildings is conducted followed by a mixed methods empirical research. Collection of data has been through surveys, an interview, and an evaluation questionnaire. Data has been analysed through statistical and thematic analysis and triangulation of the results. The findings have consistently yielded the limitations of the scope of the current sustainability assessment methodologies, especially for lack of attention to societal impacts of regeneration. The results have justified the need for this research to employ participatory approaches for identifying a relevant set of sustainability indicators and criteria for assessing the lifetime impacts of estate regeneration schemes. Issues related to community involvement in decision making, maintenance and management, community facilities, refurbishment, and disruption have been identified as the stakeholders' top priorities. Mental Health and Socioeconomic Values have been introduced as new criteria. The findings confirm the need for an in-depth approach towards identifying the regeneration priorities of the communities for the scope of LCSA studies. The identified list of criteria can apply to other studies of this context for an equitable approach for selecting the indicators across different criteria and for communicating the LCSA results with different stakeholders., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sahar Nava reports financial support was provided by 10.13039/501100000266Engineering and Physical Sciences Research Council. Pablo Sendra reports financial support was provided by Knowledge Exchange Innovation fund, Higher Education Innovation Fund (10.13039/501100013589Research England). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

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., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Borghi J et al.)

Health systems worldwide face major challenges in anticipating, planning for and responding to shocks from infectious disease epidemics, armed conflict, climatic and other crises. Although the literature on health system resilience has grown substantially in recent years, major uncertainties remain concerning approaches to resilience conceptualisation and measurement. This narrative review revisits literatures from a range of fields outside health to identify lessons relevant to health systems. Four key insights emerge. Firstly, shocks can only be understood by clarifying how, where and over what timescale they interact with a system of interest, and the dynamic effects they produce within it. Shock effects are contingent on historical path-dependencies, and on the presence of factors or system pathways (e.g. financing models, health workforce capabilities or supply chain designs) that may amplify or dampen impact in unexpected ways. Secondly, shocks often produce cascading effects across multiple scales, whereas the focus of much of the health resilience literature has been on macro-level, national systems. In reality, health systems bring together interconnected sub-systems across sectors and geographies, with different components, behaviours and sometimes even objectives - all influencing how a system responds to a shock. Thirdly, transformability is an integral feature of resilient social systems: cross-scale interactions help explain how systems can show both resilience and transformational capability at the same time. We illustrate these first three findings by extending the socioecological concept of adaptive cycles in social systems to health, using the example of maternal and child health service delivery. Finally, we argue that dynamic modelling approaches, under-utilised in research on health system resilience to date, have significant promise for identification of shock-moderating or shock-amplifying pathways, for understanding effects at multiple levels and ultimately for building resilience., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Ismail SA et al.)

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., Competing Interests: The authors have declared no competing interests exist.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Chalabi and Foss.)

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.

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.

Background: More than one-fifth of the United Kingdom population has poor vitamin D status (serum 25-hydroxyvitamin D [25(OH)D] concentration <25 nmol/L), particularly individuals with low sun exposure or poor dietary intake., Objective: We identified the fortification vehicle and concentration most likely to safely increase population vitamin D intakes and vitamin D status., Design: 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., Results: 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., Conclusion: 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., (© 2015 American Society for Nutrition.)

Background: Investment in pandemic preparedness is a long-term gamble, with the return on investment coming at an unknown point in the future. Many countries have chosen to stockpile key resources, and the number of pandemic economic evaluations has risen sharply since 2009. We assess the importance of uncertainty in time-to-pandemic (and associated discounting) in pandemic economic evaluation, a factor frequently neglected in the literature to-date., Methods: We use a probability tree model and Monte Carlo parameter sampling to consider the cost effectiveness of antiviral stockpiling in Cambodia under parameter uncertainty. Mean elasticity and mutual information (MI) are used to assess the importance of time-to-pandemic compared with other parameters. We also consider the sensitivity to choice of sampling distribution used to model time-to-pandemic uncertainty., Results: Time-to-pandemic and discount rate are the primary drivers of sensitivity and uncertainty in pandemic cost effectiveness models. Base case cost effectiveness of antiviral stockpiling ranged between is US$112 and US$3599 per DALY averted using historical pandemic intervals for time-to-pandemic. The mean elasticities for time-to-pandemic and discount rate were greater than all other parameters. Similarly, the MI scores for time to pandemic and discount rate were greater than other parameters. Time-to-pandemic and discount rate were key drivers of uncertainty in cost-effectiveness results regardless of time-to-pandemic sampling distribution choice., Conclusions: Time-to-pandemic assumptions can "substantially" affect cost-effectiveness results and, in our model, is a greater contributor to uncertainty in cost-effectiveness results than any other parameter. We strongly recommend that cost-effectiveness models include probabilistic analysis of time-to-pandemic uncertainty., (Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2013; all rights reserved.)

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.

Objective: To investigate the effect of reducing home ventilation as part of household energy efficiency measures on deaths from radon related lung cancer., Design: Modelling study., Setting: England., Intervention: Home energy efficiency interventions, motivated in part by targets for reducing greenhouse gases, which entail reduction in uncontrolled ventilation in keeping with good practice guidance., Main Outcome Measures: Modelled current and future distributions of indoor radon levels for the English housing stock and associated changes in life years due to lung cancer mortality, estimated using life tables., Results: Increasing the air tightness of dwellings (without compensatory purpose-provided ventilation) increased mean indoor radon concentrations by an estimated 56.6%, from 21.2 becquerels per cubic metre (Bq/m(3)) to 33.2 Bq/m(3). After the lag in lung cancer onset, this would result in an additional annual burden of 4700 life years lost and (at peak) 278 deaths. The increases in radon levels for the millions of homes that would contribute most of the additional burden are below the threshold at which radon remediation measures are cost effective. Fitting extraction fans and trickle ventilators to restore ventilation will help offset the additional burden but only if the ventilation related energy efficiency gains are lost. Mechanical ventilation systems with heat recovery may lower radon levels and the risk of cancer while maintaining the advantage of energy efficiency for the most airtight dwellings but there is potential for a major adverse impact on health if such systems fail., Conclusion: Unless specific remediation is used, reducing the ventilation of dwellings will improve energy efficiency only at the expense of population wide adverse impact on indoor exposure to radon and risk of lung cancer. The implications of this and other consequences of changes to ventilation need to be carefully evaluated to ensure that the desirable health and environmental benefits of home energy efficiency are not compromised by avoidable negative impacts on indoor air quality.

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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

Background: Amid the calls for 'more public health evidence', we also need simple understandable methods of determining when more research really is needed. This paper describes a simple decision aid to help policymakers, researchers and other decision makers assess the potential 'information value' of a new public health randomised controlled trial., Methods: The authors developed a flow chart to help make explicit (1) the user's information needs, (2) the intended use of the new information that the study will produce, (3) the added value of the evidence to be derived from the new study and (4) the levels of precision, bias and generalisability required by the user., Results: The flow chart is briefly illustrated, first in generic form and then in a worked example, showing how it may be used in deciding whether a new study should be commissioned to evaluate the health impact of allowing motorcycles to use bus lanes in London., Conclusions: In this paper, the authors have presented a flow chart for enacting an informal 'Value-of-Information'-like approach to deciding when a new public health evaluation is needed. The authors do not suggest that the flow chart approach is technically the equivalent of Value-of-Information methods. Nonetheless, it represents a valuable perspective and process to adopt, and this structured approach will be more revealing than an unstructured thought experiment as the basis for decisions about a new study. To aid in its development as an effective tool, we invite users from a variety of perspectives and contexts to review it, to use it in practice and to send us their comments.

Transition to diets that are high in saturated fat and sugar has caused a global public health concern, as the pattern of food consumption is a major modifiable risk factor for chronic non-communicable diseases. Although agri-food systems are intimately associated with this transition, agriculture and health sectors are largely disconnected in their priorities, policy, and analysis, with neither side considering the complex inter-relation between agri-trade, patterns of food consumption, health, and development. We show the importance of connection of these perspectives through estimation of the eff ect of adopting a healthy diet on population health, agricultural production, trade, the economy, and livelihoods,with a computable general equilibrium approach. On the basis of case-studies from the UK and Brazil, we suggest that benefits of a healthy diet policy will vary substantially between different populations, not only because of population dietary intake but also because of agricultural production, trade, and other economic factors.

Background: Blood transfusions carry the risk of transmitting infections. This risk has been studied in detail in high-income countries but not in sub-Saharan Africa. This study estimates the risks of acquiring human immunodeficiency virus (HIV), hepatitis B virus (HBV), or hepatitis C virus (HCV) from a single unit of blood in sub-Saharan Africa., Study Design and Methods: A mathematical model was constructed to quantify transfusion risks across 45 sub-Saharan African countries using three components: the risk of a contaminated unit entering the blood supply, the risk that the unit will be given to a susceptible patient, and the risk that receipt of the unit will lead to infection in the recipient. Variables included prevalence of infection in donors, extent of blood testing, test sensitivity, and susceptibility of recipients. Data from the World Health Organization (WHO) African Region and a systematic review of the literature were used to parameterize the model. Uncertainty in the risk estimates was quantified using probabilistic sensitivity analysis., Results: The median overall risks of becoming infected with HIV, HBV, and HCV from a blood transfusion in sub-Saharan Africa were 1, 4.3, and 2.5 infections per 1000 units, respectively. If annual transfusion requirements projected by the WHO were met, transfusions alone would be responsible for 28,595 HBV infections, 16,625 HCV infections, and 6650 HIV infections every year. Sensitivity analysis suggests that the true risks may be even higher., Conclusions: This study is the first to systematically quantify the risks of transfusion-transmitted infections across sub-Saharan Africa. Although the results are limited by the quality and quantity of available data, these may be the most reliable estimates at this time.

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., (Copyright 2009 Elsevier B.V. All rights reserved.)

Agricultural food production and agriculturally-related change in land use substantially contribute to greenhouse-gas emissions worldwide. Four-fifths of agricultural emissions arise from the livestock sector. Although livestock products are a source of some essential nutrients, they provide large amounts of saturated fat, which is a known risk factor for cardiovascular disease. We considered potential strategies for the agricultural sector to meet the target recommended by the UK Committee on Climate Change to reduce UK emissions from the concentrations recorded in 1990 by 80% by 2050, which would require a 50% reduction by 2030. With use of the UK as a case study, we identified that a combination of agricultural technological improvements and a 30% reduction in livestock production would be needed to meet this target; in the absence of good emissions data from Brazil, we assumed for illustrative purposes that the required reductions would be the same for our second case study in São Paulo city. We then used these data to model the potential benefits of reduced consumption of livestock products on the burden of ischaemic heart disease: disease burden would decrease by about 15% in the UK (equivalent to 2850 disability-adjusted life-years [DALYs] per million population in 1 year) and 16% in São Paulo city (equivalent to 2180 DALYs per million population in 1 year). Although likely to yield benefits to health, such a strategy will probably encounter cultural, political, and commercial resistance, and face technical challenges. Coordinated intersectoral action is needed across agricultural, nutritional, public health, and climate change communities worldwide to provide affordable, healthy, low-emission diets for all societies.

The sexually transmitted infection (STI) Herpes simplex virus type-2 (HSV-2) is of public health concern because it is a very common frequently unrecognized lifelong infection, which may facilitate HIV transmission. Within HIV/STI modeling, structural uncertainty has received less attention than parametric uncertainty. By merging the compartments of a "complex" model, a "simple" HSV-2 model is developed. Sexual interactions between female sex workers (FSWs) and clients are modeled using data from India. Latin Hypercube Sampling selects from parameter distributions and both models are run for each of the 10,000 parameter sets generated. Outputs are compared (except for 2,450 unrealistic simulations). The simple model is a good approximation to the complex model once the HSV-2 epidemic has reached 60% of the equilibrium prevalence (95% of the 7,550 runs produced <10% relative error). The simple model is a reduced version of the complex model that retains details implicitly. For late-stage epidemics, the simple model gives similar prevalence trends to the complex model. As HSV-2 epidemics in many populations are advanced, the simple model is accurate in most instances, although the complex model may be preferable for early epidemics. The analysis highlights the issue of structural uncertainty and the value of reducing complexity.

Few cost-effectiveness analysis (CEA) models have accounted for geographic variation in input parameters. This paper describes a deterministic discrete-time multi-state model to estimate the cost-effectiveness of vector control policies for Chagas disease, where implementation varies according to village characteristics. The model outputs include the total number of new infections, disability adjusted life years (DALYs) incurred, costs of associated healthcare, and total costs of the Ministry of Health's control policy for house surveillance and spraying. Incremental net benefits were estimated to determine Colombian villages in which it is cost-effective to implement the control policy. The robustness of these conclusions was evaluated by deterministic sensitivity analyses. The model should help provide a decision-support system to compare control policies and to allocate resources geographically.

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