Building a decision-support tool to inform sustainability approaches under complexity: Case study on managing wild ruminants

In wildlife management, differing perspectives among stakeholders generate conflicts about how to achieve disparate sustainability goals that include ecological, economic, and sociocultural dimensions. To mitigate such conflicts, decisions regarding wildlife management must be taken thoughtfully. To our knowledge, there exists no integrative modeling framework to inform these decisions, considering all dimensions of sustainability. We constructed a decision-support tool based on stakeholder workshops and a Bayesian decision network to inform management of wild ruminants in the federal state of Lower Austria. We use collaborative decision analysis to compare resource allocations while accounting for trade-offs among dimensions of sustainability. The tool is designed for application by non-technical users across diverse decision-making contexts with particular sets of wildlife management actions, objectives, and uncertainties. Our tool represents an important step toward developing and evaluating a transparent and replicable approach for mitigating wildlife-based conflicts in Europe and beyond.

A web-based pesticide risk assessment tool for drinking water protection zones in Sweden

To protect human health, wildlife and the aquatic environment, "safe uses" of pesticides are determined at the EU level while product authorization and terms of use are established at the national level. In Sweden, extra precaution is taken to protect drinking water, and permits are therefore required for pesticide use within abstraction zones. This paper presents MACRO-DB, a tool for assessing pesticide contamination risks of groundwater and surface water, used by authorities to support their decision-making for issuing such permits. MACRO-DB is a meta-model based on 583,200 simulations of the physically-based MACRO model used for assessing pesticide leaching risks at EU and national level. MACRO-DB is simple to use and runs on widely available input data. In a qualitative comparative assessment for two counties in Sweden, MACRO-DB outputs were in general agreement with groundwater monitoring data and matched or were more protective than the national risk assessment procedure for groundwater.

Using simulation modelling to transform hospital planning and management to address health inequalities

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models.

Combating antibiotic resistance using wastewater surveillance: Significance, applications, challenges, and future directions

The global increase of antibiotic resistance (AR) and resistant infections call for effective surveillance methods for understanding and mitigating (re)-emerging public health risks. Wastewater surveillance (WS) of antibiotic resistance is an emerging, but currently under-utilized decision-support tool in public health systems. Recent years have witnessed an increase in evidence linking antibiotic resistance in wastewaters to that of the community. To date, very few comprehensive reviews exist on the application of WS to understand AR and resistant infections in population. Current and emerging AR detection methods, and their merits and limitations are discussed. Wastewater surveillance has several merits relative to individual testing, including; (1) low per capita testing cost, (2) high spatial coverage, (3) low requirement for diagnostic equipment, and (4) detection of health threats ahead of real outbreaks. The applications of WS as an early warning system and decision support tool to understand and mitigate AR are discussed. Wastewater surveillance could be a tool of choice in low-income settings lacking resources and diagnostic facilities for individual testing. To demonstrate the utility of WS, empirical evidence from field case studies is presented. However, constraints still exist, including; (1) lack of standardized protocols, (2) the clinical utility and sensitivity of WS-based data, (3) uncertainties in relating WS data to pathogenic and virulent bacteria, and (4) whether or not AR in stools and ultimately wastewater represent the complete human resistome. Finally, further prospects are presented, include knowledge gaps on; (1) development of low-cost biosensors for AR, (2) development of WS protocols (sampling, processing, interpretation), (3) further pilot scale studies to understand the opportunities and limits of WS, and (4) development of computer-based analytical tools to facilitate rapid data collection, visualization and interpretation. Therefore, the present paper discusses the principles, opportunities, and constraints of wastewater surveillance applications to understand AR and safeguard public health.

Spatial priorities for invasive alien species control in protected areas

Given the limited funds available for the management of invasive alien species (IASs), there is a need to design cost-effective strategies to prioritize their control. In this paper, we propose a cost-benefit optimization framework that incorporates the spatially explicit costs and benefits of invasion control, as well as the spatial invasion dynamics. Our framework offers a simple yet operational priority-setting criterion for the spatially explicit management of IASs under budget constraints. We applied this criterion to the control of the invasion of primrose willow (genus Ludwigia) in a protected area in France. Using a unique geographic information system panel dataset on control costs and invasion levels through space for a 20-year period, we estimated the costs of invasion control and a spatial econometric model of primrose willow invasion dynamics. Next, we used a field choice experiment to estimate the spatially explicit benefits of invasion control. Applying our priority criterion, we show that, unlike the current management strategy that controls the invasion in a spatially homogeneous manner, the criterion recommends targeted control on heavily invaded areas that are highly valued by users. We also show that the returns on investment are high, justifying the need to increase the allocated budgets and to treat the invasion more drastically. We conclude with policy recommendations and possible extensions, including the development of operational cost-benefit decision-support tools to assist local decision-makers in setting management priorities.

Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs

OBJECTIVE

Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the concordance of expert-determined standards with a clinical gold standard (herein, pathological confirmation) and the effects of different expert-determined reference standards on the estimates of radiologists' diagnostic performance to detect malignant pulmonary nodules on chest radiographs with and without the assistance of a DLAD model.

MATERIALS AND METHODS

This study included chest radiographs from 50 patients with pathologically proven lung cancer and 50 controls. Five expert-determined standards were constructed using the interpretations of 10 experts: individual judgment by the most experienced expert, majority vote, consensus judgments of two and three experts, and a latent class analysis (LCA) model. In separate reader tests, additional 10 radiologists independently interpreted the radiographs and then assisted with the DLAD model. Their diagnostic performance was estimated using the clinical gold standard and various expert-determined standards as the reference standard, and the results were compared using the t test with Bonferroni correction.

RESULTS

The LCA model (sensitivity, 72.6%; specificity, 100%) was most similar to the clinical gold standard. When expert-determined standards were used, the sensitivities of radiologists and DLAD model alone were overestimated, and their specificities were underestimated (all p-values < 0.05). DLAD assistance diminished the overestimation of sensitivity but exaggerated the underestimation of specificity (all p-values < 0.001). The DLAD model improved sensitivity and specificity to a greater extent when using the clinical gold standard than when using the expert-determined standards (all p-values < 0.001), except for sensitivity with the LCA model (p = 0.094).

CONCLUSION

The LCA model was most similar to the clinical gold standard for malignant pulmonary nodule detection on chest radiographs. Expert-determined standards caused bias in measuring the diagnostic performance of the artificial intelligence model.

Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review

The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.

Targeting Sagebrush (Artemisia Spp.) Restoration Following Wildfire with Greater Sage-Grouse (Centrocercus Urophasianus) Nest Selection and Survival Models

Unprecedented conservation efforts for sagebrush (Artemisia spp.) ecosystems across the western United States have been catalyzed by risks from escalated wildfire activity that reduces habitat for sagebrush-obligate species such as Greater Sage-Grouse (Centrocercus urophasianus). However, post-fire restoration is challenged by spatial variation in ecosystem processes influencing resilience to disturbance and resistance to non-native invasive species, and spatial and temporal lags between slower sagebrush recovery processes and faster demographic responses of sage-grouse to loss of important habitat. Decision-support frameworks that account for these factors can help users strategically apply restoration efforts by predicting short and long-term ecological benefits of actions. Here, we developed a framework that strategically targets burned areas for restoration actions (e.g., seeding or planting sagebrush) that have the greatest potential to positively benefit sage-grouse populations through time. Specifically, we estimated sagebrush recovery following wildfire and risk of non-native annual grass invasion under four scenarios: passive recovery, grazing exclusion, active restoration with seeding, and active restoration with seedling transplants. We then applied spatial predictions of integrated nest site selection and survival models before wildfire, immediately following wildfire, and at 30 and 50 years post-wildfire based on each restoration scenario and measured changes in habitat. Application of this framework coupled with strategic planting designs aimed at developing patches of nesting habitat may help increase operational resilience for fire-impacted sagebrush ecosystems.

AIS explorer: Prioritization for watercraft inspections-A decision-support tool for aquatic invasive species management

Invasions of aquatic invasive species have caused significant economic and ecological damage to global aquatic ecosystems. Once an invasive population has established in a new habitat, eradication can be financially and logistically impossible, motivating management strategies to rely heavily upon prevention measures to reduce the introduction and spread. To be productive, on-the-ground management of aquatic invasive species requires effective decision-making surrounding the allocation of limited resources. Watercraft inspections play an important role in managing aquatic invasive species by preventing the overland transport of invasive species between waterbodies and providing education to boaters. In this study, we developed and tested an interactive web-based decision-support tool, AIS Explorer: Prioritization for Watercraft Inspections, to guide AIS managers in developing efficient watercraft inspection plans. The decision-support tool is informed by a network-based algorithm that maximized the number of inspected watercraft that move from AIS infested to uninfested lakes within and between counties in Minnesota, USA. It was iteratively built with stakeholder feedback, including consultations with county managers, beta-testing of the web-based application, and workshops to educate and train end-users. The co-development and implementation of data-driven decision support tools demonstrate how interdisciplinary methods can be used to connect science and management to support decision-making. The AIS Explorer: Prioritization for Watercraft Inspections application makes optimized research outputs accessible in multiple dynamic forms that maintain pace with discovery of new infestations and local needs. In addition, the decision support tool has supported improved and closer communication between AIS managers and researchers on this topic.

Wastewater, waste, and water-based epidemiology (WWW-BE): A novel hypothesis and decision-support tool to unravel COVID-19 in low-income settings?

Traditional wastewater-based epidemiology (W-BE) relying on SARS-CoV-2 RNA detection in wastewater is attractive for understanding COVID-19. Yet traditional W-BE based on centralized wastewaters excludes putative SARS-CoV-2 reservoirs such as: (i) wastewaters from shared on-site sanitation facilities, (ii) solid waste including faecal sludge from non-flushing on-site sanitation systems, and COVID-19 personal protective equipment (PPE), (iii) raw/untreated water, and (iv) drinking water supply systems in low-income countries (LICs). A novel hypothesis and decision-support tool based on Wastewater (on-site sanitation, municipal sewer systems), solid Waste, and raw/untreated and drinking Water-based epidemiology (WWW-BE) is proposed for understanding COVID-19 in LICs. The WWW-BE conceptual framework, including components and principles is presented. Evidence on the presence of SARS-CoV-2 and its proxies in wastewaters, solid materials/waste (papers, metals, fabric, plastics), and raw/untreated surface water, groundwater and drinking water is discussed. Taken together, wastewaters from municipal sewer and on-site sanitation systems, solid waste such as faecal sludge and COVID-19 PPE, raw/untreated surface water and groundwater, and drinking water systems in LICs act as potential reservoirs that receive and harbour SARS-CoV-2, and then transmit it to humans. Hence, WWW-BE could serve a dual function in estimating the prevalence and potential transmission of COVID-19. Several applications of WWW-BE as a hypothesis and decision support tool in LICs are discussed. WWW-BE aggregates data from various infected persons in a spatial unit, hence, putatively requires less resources (analytical kits, personnel) than individual diagnostic testing, making it an ideal decision-support tool for LICs. The novelty, and a critique of WWW-BE versus traditional W-BE are presented. Potential challenges of WWW-BE include: (i) biohazards and biosafety risks, (ii) lack of expertise, analytical equipment, and accredited laboratories, and (iii) high uncertainties in estimates of COVID-19 cases. Future perspectives and research directions including key knowledge gaps and the application of novel and emerging technologies in WWW-BE are discussed.

Variation in estimates of heat-related mortality reduction due to tree cover in U.S. cities

Heat-related mortality is one of the leading causes of weather-related deaths in the United States. With changing climates and an aging population, effective adaptive strategies to address public health and environmental justice issues associated with extreme heat will be increasingly important. One effective adaptive strategy for reducing heat-related mortality is increasing tree cover. Designing such a strategy requires decision-support tools that provide spatial and temporal information about impacts. We apply such a tool to estimate spatially and temporally explicit reductions in temperature and mortality associated with a 10% increase in tree cover in 10 U.S. cities with varying climatic, demographic, and land cover conditions. Two heat metrics were applied to represent tree impacts on moderately and extremely hot days (relative to historical conditions). Increasing tree cover by 10% reduced estimated heat-related mortality in cities significantly, with total impacts generally greatest in the most populated cities. Mortality reductions vary widely across cities, ranging from approximately 50 fewer deaths in Salt Lake City to about 3800 fewer deaths in New York City. This variation is due to differences in demographics, land cover, and local climatic conditions. In terms of per capita estimated impacts, hotter and drier cities experience higher percentage reductions in mortality due to increased tree cover across the season. Phoenix potentially benefits the most from increased tree cover, with an estimated 22% reduction in mortality from baseline levels. In cooler cities such as Minneapolis, trees can reduce mortality significantly on days that are extremely hot relative to historical conditions and therefore help mitigate impacts during heat wave conditions. Recent studies project highest increases in heat-related mortality in the cooler cities, so our findings have important implications for adaptation planning. Our estimated spatial and temporal distributions of mortality reductions for each city provide crucial information needed for promoting environmental justice and equity. More broadly, the methods and model can be applied by both urban planners and the public health community for designing targeted, effective policies to reduce heat-related mortality. Additionally, land use managers can use this information to optimize tree plantings. Public stakeholders can also use these impact estimates for advocacy.

Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department

BACKGROUND

Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, frequently surgical. However, repeated APCT's includes increased ionizing radiation exposure. Guidance regarding APCT performance is mostly clinical and empiric.

AIMS

We used a machine learning (ML) approach for predicting CAF on APCT in the ED.

METHODS

We performed a retrospective cohort study of patients with CD who presented to the ED and underwent APCT. CAF were defined as bowel obstruction, perforation, intra-abdominal abscess or complicated fistula. ML was used to predict the probability of having CAF on APCT, using routine clinical variables.

RESULTS

Of 101 admissions included, 44 (43.5%) had CAF on APCT. ML successfully identified patients at low (NPV 91.6%, CI-95% 90.6-92.5) and high (PPV 92.8%, CI-95%, 92.3-93.2) risk for CAF (AUROC = 0.774), using beats-per-minute, mean arterial pressure, neutrophil-to-lymphocyte ratio and sex. This allowed the construction of a risk stratification scheme according to patients' probability for CAF on APCT.

CONCLUSION

We present a novel artificial intelligence-based approach, utilizing readily available clinical variables to better select patients with CD in the ED for APCT. This might reduce the number of APCTs performed, avoiding related hazards while ensuring high-risk patients undergo APCT.

A decision-aid tool for ICU admission triage is associated with a reduction in potentially inappropriate intensive care unit admissions

PURPOSE

Intensive care unit (ICU) admission triage occurs frequently and often involves highly subjective decisions that may lead to potentially inappropriate ICU admissions. In this study, we evaluated the effect of implementing a decision-aid tool for ICU triage on ICU admission decisions.

METHODS

This was a prospective, before-after study. Urgent ICU referrals to ten ICUs in a tertiary hospital in Brazil were assessed before and after the implementation of the decision-aid tool. Our primary outcome was the proportion of potentially inappropriate ICU referrals (defined as priority 4B or 5 referrals, accordingly to the Society of Critical Care Medicine guidelines of 1999 and 2016, respectively) admitted to the ICU within 48 h. We conducted multivariate analyses to adjust for potential confounders and evaluated the interaction between phase and triage priority.

RESULTS

Of the 2201 patients analyzed, 1184 (53.8%) patients were admitted to the ICU. After adjustment for confounders, implementation of the decision-aid tool was associated with a reduction in potentially inappropriate ICU admissions using either the 1999 [adjOR (95% CI) = 0.36 (0.13-0.97)] or 2016 [adjOR (95%CI) = 0.35 (0.13-0.96)] definitions.

CONCLUSION

Implementation of a decision-aid tool for ICU triage was associated with a reduction in potentially inappropriate ICU admissions.

OPTIMIZING USABILITY OF AN ECONOMIC DECISION SUPPORT TOOL: PROTOTYPE OF THE EQUIPT TOOL

OBJECTIVES

Economic decision-support tools can provide valuable information for tobacco control stakeholders, but their usability may impact the adoption of such tools. This study aims to illustrate a mixed-method usability evaluation of an economic decision-support tool for tobacco control, using the EQUIPT ROI tool prototype as a case study.

METHODS

A cross-sectional mixed methods design was used, including a heuristic evaluation, a thinking aloud approach, and a questionnaire testing and exploring the usability of the Return of Investment tool.

RESULTS

A total of sixty-six users evaluated the tool (thinking aloud) and completed the questionnaire. For the heuristic evaluation, four experts evaluated the interface. In total twenty-one percent of the respondents perceived good usability. A total of 118 usability problems were identified, from which twenty-six problems were categorized as most severe, indicating high priority to fix them before implementation.

CONCLUSIONS

Combining user-based and expert-based evaluation methods is recommended as these were shown to identify unique usability problems. The evaluation provides input to optimize usability of a decision-support tool, and may serve as a vantage point for other developers to conduct usability evaluations to refine similar tools before wide-scale implementation. Such studies could reduce implementation gaps by optimizing usability, enhancing in turn the research impact of such interventions.

Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN): evolution of a content management system for point-of-care clinical decision support

Background

The Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN) is an international collaborative project with the overall objective of standardizing the approach to the evaluation and treatment of critically ill patients world-wide, in accordance with best-practice principles. One of CERTAIN’s key features is clinical decision support providing point-of-care information about common acute illness syndromes, procedures, and medications in an index card format.

Methods

This paper describes 1) the process of developing and validating the content for point-of-care decision support, and 2) the content management system that facilitates frequent peer-review and allows rapid updates of content across different platforms (CERTAIN software, mobile apps, pdf-booklet) and different languages.

Results

Content was created based on survey results of acute care providers and validated using an open peer-review process. Over a 3 year period, CERTAIN content expanded to include 67 syndrome cards, 30 procedure cards, and 117 medication cards. 127 (59 %) cards have been peer-reviewed so far. Initially MS Word® and Dropbox® were used to create, store, and share content for peer-review. Recently Google Docs® was used to make the peer-review process more efficient. However, neither of these approaches met our security requirements nor has the capacity to instantly update the different CERTAIN platforms.

Conclusion

Although we were able to successfully develop and validate a large inventory of clinical decision support cards in a short period of time, commercially available software solutions for content management are suboptimal. Novel custom solutions are necessary for efficient global point of care content system management.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-016-0367-3) contains supplementary material, which is available to authorized users.

Understanding the stakeholders' intention to use economic decision-support tools: A cross-sectional study with the tobacco return on investment tool

BACKGROUND

Despite an increased number of economic evaluations of tobacco control interventions, the uptake by stakeholders continues to be limited. Understanding the underlying mechanism in adopting such economic decision-support tools by stakeholders is therefore important. By applying the I-Change Model, this study aims to identify which factors determine potential uptake of an economic decision-support tool, i.e., the Return on Investment tool.

METHODS

Stakeholders (decision-makers, purchasers of services/pharma products, professionals/service providers, evidence generators and advocates of health promotion) were interviewed in five countries, using an I-Change based questionnaire. MANOVA's were conducted to assess differences between intenders and non-intenders regarding beliefs. A multiple regression analysis was conducted to identify the main explanatory variables of intention to use an economic decision-support tool.

FINDINGS

Ninety-three stakeholders participated. Significant differences in beliefs were found between non-intenders and intenders: risk perception, attitude, social support, and self-efficacy towards using the tool. Regression showed that demographics, pre-motivational, and motivational factors explained 69% of the variation in intention.

DISCUSSION

This study is the first to provide a theoretical framework to understand differences in beliefs between stakeholders who do or do not intend to use economic decision-support tools, and empirically corroborating the framework. This contributes to our understanding of the facilitators and barriers to the uptake of these studies.

A multi-analysis approach for space-time and economic evaluation of risks related with livestock diseases: the example of FMD in Peru

This study presents a multi-disciplinary decision-support tool, which integrates geo-statistics, social network analysis (SNA), spatial-stochastic spread model, economic analysis and mapping/visualization capabilities for the evaluation of the sanitary and socio-economic impact of livestock diseases under diverse epidemiologic scenarios. We illustrate the applicability of this tool using foot-and-mouth disease (FMD) in Peru as an example. The approach consisted on a flexible, multistep process that may be easily adapted based on data availability. The first module (mI) uses a geo-statistical approach for the estimation (if needed) of the distribution and abundance of susceptible population (in the example here, cattle, swine, sheep, goats, and camelids) at farm-level in the region or country of interest (Peru). The second module (mII) applies SNA for evaluating the farm-to-farm contact patterns and for exploring the structure and frequency of between-farm animal movements as a proxy for potential disease introduction or spread. The third module (mIII) integrates mI-II outputs into a spatial-stochastic model that simulates within- and between-farm FMD-transmission. The economic module (mIV) connects outputs from mI-III to provide an estimate of associated direct and indirect costs. A visualization module (mV) is also implemented to graph and map the outputs of module I-IV. After 1000 simulated epidemics, the mean (95% probability interval) number of outbreaks, infected animals, epidemic duration, and direct costs were 37 (1, 1164), 2152 (1, 13, 250), 63 days (0, 442), and US$ 1.2 million (1072, 9.5 million), respectively. Spread of disease was primarily local (<4.5km), but geolocation and type of index farm strongly influenced the extent and spatial patterns of an epidemic. The approach is intended to support decisions in the last phase of the FMD eradication program in Peru, in particular to inform and support the implementation of risk-based surveillance and livestock insurance systems that may help to prevent and control potential FMD virus incursions into Peru.