The importance of expert selection when identifying threatened ecosystems.

Identifying threatened ecosystem types is fundamental to conservation and management decision-making. When identification relies on expert judgment, decisions are vulnerable to inconsistent outcomes and can lack transparency. We elicited judgements of the occurrence of a widespread, critically endangered Australian ecosystem from a diverse pool of 83 experts. We asked 4 questions. First, how many experts are required to reliably conclude that the ecosystem is present? Second, how many experts are required to build a reliable model for predicting ecosystem presence? Third, given expert selection can narrow the range opinions, if enough experts are selected, do selection strategies affect model predictions? Finally, does a diverse selection of experts provide better model predictions? We used power and sample size calculations with a finite population of 200 experts to calculate the number of experts required to reliably assess ecosystem presence in a theoretical scenario. We then used boosted regression trees to model expert elicitation of 122 plots based on real-world data. For a reliable consensus (90% probability of correctly identifying presence and absence) in a relatively certain scenario (85% probability of occurrence), at least 17 experts were required. More experts were required when occurrence was less certain, and fewer were needed if permissible error rates were relaxed. In comparison, only ∼20 experts were required for a reliable model that could predict for a range of scenarios. Expert selection strategies changed modeled outcomes, often overpredicting presence and underestimating uncertainty. However, smaller but diverse pools of experts produced outcomes similar to a model built from all contributing experts. Combining elicited judgements from a diverse pool of experts in a model-based decision support tool provided an efficient aggregation of a broad range of expertise. Such models can improve the transparency and consistency of conservation and management decision-making, especially when ecosystems are defined based on complex criteria.

La importancia de seleccionar expertos para identificar ecosistemas amenazados Resumen La identificación de los tipos de ecosistemas amenazados es fundamental para decidir sobre su conservación y gestión. Cuando la identificación se basa en la opinión de expertos, las decisiones son vulnerables a resultados incoherentes y pueden carecer de transparencia. Recabamos la opinión de 83 expertos sobre la presencia de un ecosistema australiano extendido y en peligro crítico. Se plantearon cuatro preguntas: ¿Cuántos expertos son necesarios para concluir con fiabilidad que el ecosistema está presente?; ¿Cuántos expertos son necesarios para construir un modelo fiable de predicción de la presencia del ecosistema?; ya que la selección de expertos puede reducir el rango de opiniones, si se seleccionan suficientes expertos, ¿afectan las estrategias de selección a las predicciones del modelo; y ¿Una selección diversa de expertos proporciona mejores predicciones del modelo? Utilizamos cálculos de potencia y tamaño de muestra con una población finita de 200 expertos para obtener el número de expertos necesarios para evaluar de forma fiable la presencia de ecosistemas en un escenario teórico. Después usamos árboles de regresión reforzada para modelar la consulta de expertos de 122 parcelas basadas en datos del mundo real. Para obtener un consenso fiable (90% de probabilidad de identificar correctamente la presencia y la ausencia) en un escenario relativamente seguro (85% de probabilidad de ocurrencia), se necesitaban al menos 17 expertos. Se necesitaban más expertos cuando la ocurrencia era menos segura, y menos si se relajaban los porcentajes de error permitidos. En comparación, sólo se necesitaron unos 20 expertos para obtener un modelo fiable que pudiera predecir una serie de escenarios. Las estrategias de selección de expertos modificaron los resultados modelados, a menudo con sobre predicción de la presencia y subestimación de la incertidumbre. Sin embargo, los grupos de expertos más pequeños pero diversos produjeron resultados similares a los de un modelo construido a partir de todos los expertos participantes. La combinación de las opiniones obtenidas de un grupo diverso de expertos en una herramienta de apoyo a la toma de decisiones basada en un modelo proporcionó una agregación eficiente de una amplia gama de conocimientos. Estos modelos pueden mejorar la transparencia y coherencia de la toma de decisiones en materia de conservación y gestión, especialmente cuando los ecosistemas se definen en función de criterios complejos.

Palliative Non-Operative Management in Geriatric Hip Fracture Patients: When Would Surgeons Abstain from Surgery?

Background: For hip fracture patients with a limited life expectancy, operative and palliative non-operative management (P-NOM) can yield similar quality of life outcomes. However, evidence on when to abstain from surgery is lacking. The aim of this study was to quantify the influence of patient characteristics on surgeons' decisions to recommend P-NOM. Methods: Dutch surgical residents and orthopaedic trauma surgeons were enrolled in a conjoint analysis and structured expert judgement (SEJ). The participants assessed 16 patient cases comprising 10 clinically relevant characteristics. For each case, they recommended either surgery or P-NOM and estimated the 30-day postoperative mortality risk. Treatment recommendations were analysed using Bayesian logistic regression, and perceived risks were pooled with equal and performance-based weights using Cooke's Classical Model. Results: The conjoint analysis and SEJ were completed by 14 and 9 participants, respectively. Participants were more likely to recommend P-NOM to patients with metastatic carcinomas (OR: 4.42, CrI: 2.14-8.95), severe heart failure (OR: 4.05, CrI: 1.89-8.29), end-stage renal failure (OR: 3.54, CrI: 1.76-7.35) and dementia (OR: 3.35, CrI: 1.70-7.06). The patient receiving the most P-NOM recommendations (12/14) had a pooled perceived risk of 30-day mortality between 50.8 and 62.7%. Conclusions: Overall, comorbidities had the strongest influence on participants' decisions to recommend P-NOM. Nevertheless, practice variation and heterogeneity in risk perceptions were substantial. Hence, more decision support for considering P-NOM is needed.

Alternative COVID-19 mitigation measures in school classrooms: analysis using an agent-based model of SARS-CoV-2 transmission.

The SARS-CoV-2 epidemic has impacted children's education, with schools required to implement infection control measures that have led to periods of absence and classroom closures. We developed an agent-based epidemiological model of SARS-CoV-2 transmission in a school classroom that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties. Our approach is based on a contact model constructed using random networks, informed by structured expert judgement. The effectiveness of mitigation strategies in suppressing infection outbreaks and limiting pupil absence are considered. COVID-19 infections in primary schools in England in autumn 2020 were re-examined and the model was then used to estimate infection levels in autumn 2021, as the Delta variant was emerging and it was thought likely that school transmission would play a major role in an incipient new wave of the epidemic. Our results were in good agreement with available data. These findings indicate that testing-based surveillance is more effective than bubble quarantine, both for reducing transmission and avoiding pupil absence, even accounting for insensitivity of self-administered tests. Bubble quarantine entails large numbers of absences, with only modest impact on classroom infections. However, maintaining reduced contact rates within the classroom can have a major benefit for managing COVID-19 in school settings.

Anticipated impacts of Brexit scenarios on UK food prices and implications for policies on poverty and health: a structured expert judgement approach.

INTRODUCTION: Food insecurity is associated with increased risk for several health conditions and with poor chronic disease management. Key determinants for household food insecurity are income and food costs. Whereas short-term household incomes are likely to remain static, increased food prices would be a significant driver of food insecurity. OBJECTIVES: To investigate food price drivers for household food security and its health consequences in the UK under scenarios of Deal and No-deal for Britain's exit from the European Union. To estimate the 5% and 95% quantiles of the projected price distributions. DESIGN: Structured expert judgement elicitation, a well-established method for quantifying uncertainty, using experts. In July 2018, each expert estimated the median, 5% and 95% quantiles of changes in price for 10 food categories under Brexit Deal and No-deal to June 2020 assuming Brexit had taken place on 29 March 2019. These were aggregated based on the accuracy and informativeness of the experts on calibration questions. PARTICIPANTS: Ten specialists with expertise in food procurement, retail, agriculture, economics, statistics and household food security. RESULTS: When combined in proportions used to calculate Consumer Price Index food basket costs, median food price change for Brexit with a Deal is expected to be +6.1% (90% credible interval -3% to +17%) and with No-deal +22.5% (90% credible interval +1% to +52%). CONCLUSIONS: The number of households experiencing food insecurity and its severity is likely to increase because of expected sizeable increases in median food prices after Brexit. Higher increases are more likely than lower rises and towards the upper limits, these would entail severe impacts. Research showing a low food budget leads to increasingly poor diet suggests that demand for health services in both the short and longer terms is likely to increase due to the effects of food insecurity on the incidence and management of diet-sensitive conditions.