Underestimation Of Risk Research Articles

Optimistic bias: Concept analysis

Purpose: The purpose of this study was to analyze and clarify the concept of ‘optimistic bias.’ Methods: A review of the literature was conducted using several databases. The databases were searched using the following keywords: optimistic bias, optimism bias, and concept analysis. The literature on optimistic bias was reviewed using the framework of Walker and Avant’s conceptual analysis process. Results: Optimistic bias can be defined by the following attributes: 1) underestimation of personal risk, 2) vague confidence, 3) positive future prediction and 4) lack of risk recognition. The antecedents of optimistic bias are as follows: 1) risk perception, 2) self-efficacy, and 3) risk controllability. The consequences of optimistic bias are as follows: 1) reduced anxiety, 2) providing emotional stability, 3) negative consequences for preventive health behavior, 4) lack of attention to risk-related information, and 5) negative consequences for self-protection behavior. Conclusion: The definition and attributes of optimistic bias identified by this study can provide a common understanding of this concept and help to develop a nursing intervention program effective in preventing, protecting, and improving health of subjects in the field of nursing practice.

Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo

AbstractFires occurring over the peatlands in Indonesian Borneo accompanied by droughts have posed devastating impacts on human health, livelihoods, economy and the natural environment, and their prevention requires comprehensive understanding of climate‐associated risk. Although it is widely known that the droughts are associated with El Niño events, the onset process of El Niño and thus the drought precursors and their possible changes under the future climate are not clearly understood. Here, we use a causal network approach to quantify the strength of teleconnections to droughts at a seasonal timescale shown in observations and climate models. We portray two drivers of June‐July‐August (JJA) droughts identified through literature review and causal analysis, namely Niño 3.4 sea surface temperature (SST) in JJA (El Niño Southern Oscillation [abbreviated as ENSO]) and SST anomaly over the eastern North Pacific to the east of the Hawaiian Islands (abbreviated as Pacific SST) in March‐April‐May (MAM) period. We argue that the droughts are strongly linked to ENSO variability, with drier years corresponding to El Niño conditions. The droughts can be predicted with a lead time of 3 months based on their associations with Pacific SST, with higher SST preceding drier conditions. We find that under the SSP585 scenario, the Coupled Model Intercomparison Phase 6 (CMIP6) multi‐model ensembles show significant increase in both the maximum number of consecutive dry days in the Indonesian Borneo region in JJA (p = 0.006) and its linear association with Pacific SST in MAM (p = 0.001) from year 2061 to 2100 compared with the historical baseline. Some models are showing unrealistic amounts of JJA rainfall and underestimate drought risks in Indonesian Borneo and their teleconnections, owing to the underestimation of ENSO amplitude and overestimation of local convections. Our study strengthens the possibility of early warning triggers of fires and stresses the need for taking enhanced climate risk into consideration when formulating long‐term policies to eliminate fires.

Constructing Portfolio Optimization: Analysis in Indonesia Non-Cyclical Industry (Markowitz Approach and Skewness and Kurtosis)

Purpose: The purpose of this research is to examine the application of portfolio optimization in the context of real-world financial issues, particularly in light of the challenges posed by the COVID-19 pandemic. Traditional portfolio optimization strategies, such as those proposed by Markowitz, often rely on the assumption of normally distributed returns, which may not accurately capture the risks associated with extreme events like the COVID-19 crisis. This study aims to shed new light on portfolio optimization methods by exploring various approaches and considering the implications of non-normally distributed returns on portfolio construction. Methods: This research employs a quantitative approach to analyze portfolio optimization techniques in the face of non-normally distributed returns. Using data from financial markets impacted by the COVID-19 pandemic, the study investigates different portfolio construction methods, including risk-free rate for equally weighted portfolios, optimal risk portfolios, minimum variance weights, and maximum expected returns. Various risk metrics such as variance, Sharpe ratio, and standard deviation are considered to evaluate portfolio performance under different constraints. Results and discussion: The empirical findings highlight the limitations of traditional portfolio optimization techniques, particularly in accurately assessing and managing risk in the presence of non-normally distributed returns. Assets exhibit heavily tailed returns, leading to an underestimation of risk when using standard approaches. The study identifies that certain assets offer high returns but also entail significant risks, necessitating a nuanced approach to portfolio construction. By considering stable distribution models and optimizing for both maximum expected return and minimum variance weight, investors can build more profitable and diversified portfolios while managing risk effectively. Implications of the research: The research findings have important implications for investors and financial practitioners, particularly in navigating uncertain market conditions such as those brought about by the COVID-19 pandemic. By recognizing the limitations of traditional portfolio optimization methods and embracing more sophisticated approaches that account for non-normally distributed returns, investors can make more informed decisions and better manage portfolio risk. These insights can inform the development of robust investment strategies tailored to mitigate the impact of extreme events on portfolio performance. Originality/value: This research contributes to the literature by offering a fresh perspective on portfolio optimization under the backdrop of the COVID-19 pandemic. By systematically evaluating different portfolio construction methods and considering the implications of non-normally distributed returns, the study advances understanding in the field of financial risk management. The identification of stable distribution models and the emphasis on balancing maximum expected return with minimum variance weight provide practical guidance for investors seeking to build resilient and profitable portfolios in turbulent market environments. Overall, this research underscores the importance of adapting portfolio optimization strategies to address the realities of contemporary financial markets.

Autonomy and Its Limits

Autonomy and Its Limits

Unsmoothing Returns of Illiquid Funds

Abstract Funds investing in illiquid assets report returns with spurious autocorrelation. Consequently, investors need to unsmooth these funds’ returns when evaluating their risk exposures. We show that funds with similar investments share a common source of spurious autocorrelation not fully resolved by traditional unsmoothing methods and thereby leading to underestimation of systematic risk. Thus, we propose a generalized unsmoothing technique and apply it to hedge funds and private commercial real estate funds. Our method significantly improves the measurement of funds’ risk exposures and risk-adjusted performance, especially for highly illiquid funds. Overall, the average illiquid fund alpha is lower than previously thought. (JEL G11, G12, G23)

A search strategy for detecting duodenoscope-associated infections: a retrospective observational study

Duodenoscope-associated infections (DAIs) are exogenous infections resulting from the use of contaminated duodenoscopes. Though numerous outbreaks of DAI have involved multidrug-resistant micro-organisms (MDROs), outbreaks involving non-MDROs are also likely to occur. Detection challenges arise as these infections often resolve before culture or because causative strains are not retained for comparison with duodenoscope strains. To identify and analyse DAIs spanning a seven-year period in a tertiary care medical centre. This was a retrospective observational study. Duodenoscope cultures positive for gastrointestinal flora between March 2015 and September 2022 were paired with duodenoscope usage data to identify patients exposed to contaminated duodenoscopes. Analysis encompassed patients treated after a positive duodenoscope culture and those treated within the interval from a negative to a positive culture. Patient identification numbers were cross-referenced with a clinical culture database to identify patients developing infections with matching micro-organisms within one year of their procedure. A 'pair' was established upon a species-level match between duodenoscope and patient cultures. Pairs were further analysed via antibiogram comparison, and by whole-genome sequencing (WGS) to determine genetic relatedness. Sixty-eight pairs were identified; of these, 21 exhibited matching antibiograms which underwent WGS, uncovering two genetically closely related pairs categorized as DAIs. Infection onset occurred up to two months post procedure. Both causative agents were non-MDROs. This study provides crucial insights into DAIs caused by non-MDROs and it highlights the challenge of DAI recognition in daily practice. Importantly, the delayed manifestation of the described DAIs suggests a current underestimation of DAI risk.

Congruency and its related factors between patients' fall risk perception and nurses' fall risk assessment in acute care hospitals.

Inpatients need to recognize their fall risk accurately and objectively. Nurses need to assess how patients perceive their fall risk and identify the factors that influence patients' fall risk perception. This study aims to explore the congruency between nurses' fall risk assessment and patients' perception of fall risk and identify factors related to the non-congruency of fall risk. A descriptive and cross-sectional design was used. The study enrolled 386 patients who were admitted to an acute care hospital. Six nurses assessed the participants' fall risk. Congruency was classified using the Morse Fall Scale for nurses and the Fall Risk Perception Questionnaire for patients. The nurses' fall risk assessments and patients' fall risk perceptions were congruent in 57% of the participants. Underestimation of the patient's risk of falling was associated with gender (women), long hospitalization period, department (orthopedics), low fall efficacy, and history of falls before hospitalization. Overestimation of fall risk was associated with age group, gender (men), department, and a high health literacy score. In the multiple logistic regression, the factors related to the underestimation of fall risk were hospitalization period and department, and the factors related to the overestimation of fall risk were health literacy and department. Nurses should consider the patient's perception of fall risk and incorporate it into fall prevention interventions. Nurses need to evaluate whether patients perceive the risk of falling consistently. For patients who underestimate or overestimate their fall risk, it may be helpful to consider clinical and fall-related characteristics together when evaluating their perception of fall risk.

Studying Microbial Ecology of Diabetic Foot Infections: Significance of PCR Analysis for Prudent Antimicrobial Stewardship.

This study presents a comprehensive investigation into the microbial ecology of diabetic foot infections (DFIs), using molecular-polymerase chain reaction (PCR) analysis to accurately identify the causative agents. One hundred DFI patients were recruited and classified using the Depth Extent Phase and Associated Etiology (DEPA) score according to their severity. Results revealed polymicrobial infections in 75% of cases, predominantly featuring Staphylococcus epidermidis (83%) and Staphylococcus aureus (63%). Importantly, 20% of samples exhibited facultative anaerobes Bacteroides fragilis or Clostridium perfringens, exclusively in high DEPA score ulcers. Candida albicans coinfection was identified in 19.2% of cases, underscoring the need for mycological evaluation. Empirical antimicrobial therapy regimens were tailored to DEPA severity, yet our findings highlighted a potential gap in methicillin-resistant Staphylococcus aureus (MRSA) coverage. Despite an 88% prevalence of methicillin-resistant Staphylococci, vancomycin usage was suboptimal. This raises concerns about the underestimation of MRSA risk and the need for tailored antibiotic guidelines. Our study demonstrates the efficacy of molecular-PCR analysis in identifying diverse microbial communities in DFIs, influencing targeted antibiotic choices. The results advocate for refined antimicrobial guidelines, considering regional variations in microbial patterns and judiciously addressing multidrug-resistant strains. This research contributes crucial insights for optimizing DFIs management and helps the physicians to have a fast decision in selection the suitable antibiotic for each patient and to decrease the risk of bacterial resistance from the improper use of broad-spectrum empirical therapies.

Ethnicity-specific blood pressure thresholds based on cardiovascular and renal complications: a prospective study in the UK Biobank.

The appropriateness of hypertension thresholds for triggering action to prevent cardiovascular and renal complications among non-White populations in the UK is subject to question. Our objective was to establish ethnicity-specific systolic blood pressure (SBP) cutoffs for ethnic minority populations and assess the efficacy of these ethnicity-specific cutoffs in predicting adverse outcomes. We analyzed data from UK Biobank, which included 444,418 participants from White, South Asian, Black Caribbean, and Black African populations with no history of cardiorenal complications. We fitted Poisson regression models with continuous SBP and ethnic groups, using Whites as the referent category, for the composite outcome of atherosclerotic cardiovascular disease, heart failure, and chronic kidney disease. We determined ethnicity-specific thresholds equivalent to the risks observed in Whites at SBP levels of 120, 130, and 140mm Hg. We adjusted models for clinical characteristics, sociodemographic factors, and behavioral factors. The performance of ethnicity-specific thresholds for predicting adverse outcomes and associated population-attributable fraction (PAF) was assessed in ethnic minority groups. After a median follow-up of 12.5years (interquartile range, 11.7-13.2), 32,662 (7.4%) participants had incident composite outcomes. At any given SBP, the predicted incidence rate of the composite outcome was the highest for South Asians, followed by White, Black Caribbean, and Black African. For an equivalent risk of outcomes observed in the White population at an SBP level of 140mm Hg, the SBP threshold was lower for South Asians (123mm Hg) and higher for Black Caribbean (156mm Hg) and Black African (165mm Hg). Furthermore, hypertension defined by ethnicity-specific thresholds was a stronger predictor and resulted in a larger PAF for composite outcomes in South Asians (21.5% [95% CI, 2.4,36.9] vs. 11.3% [95% CI, 2.6,19.1]) and Black Africans (7.1% [95% CI, 0.2,14.0] vs. 5.7 [95% CI, -16.2,23.5]) compared to hypertension defined by guideline-recommended thresholds. Guideline-recommended blood pressure thresholds may overestimate risks for the Black population and underestimate risks for South Asians. Using ethnicity-specific SBP thresholds may improve risk estimation and optimize hypertension management toward the goal of eliminating ethnic disparities in cardiorenal complications.

Effects of elastic anisotropic models on the prediction of horizontal stresses and hydraulic fracture geometry

Abstract Stress barriers play a key role in the propagation of hydraulic fractures. They are local maxima in the stress field that constrain vertical fracture propagation. The development of stress barriers is influenced by rock mechanical properties, pore pressure and tectonic stresses. However, stress prediction models are highly sensitive to available geophysical measurements and assumptions made on rock constitutive models. We compare stress estimations performed with elastic isotropic, anisotropic and viscoplastic models using Thomsen's notation ( ɛ , δ , γ ) to quantify anisotropy and its effects on hydraulic fracture geometry. Using a single depth for principal stress calibration, we compare stress distributions and simulate hydraulic fracture geometries along simple vertical and horizontal well sections. Prediction errors stemming from isotropic models along anisotropic intervals increase when tectonic stresses increase. Errors generated by either over- or underestimation of δ increase for tectonically passive environments, while errors generated by either over- or underestimation of ɛ increase for tectonically active environments. Additional corrections, but also uncertainties, can be introduced by considering viscoelastic rock behaviour. Because of stress shadowing and fracture interaction, the risk of stress barrier underestimation is higher when estimating hydraulic fracture geometries along the various stages of horizontal wells.

Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014-2021.

Ticks are an important driver of veterinary health care, causing irritation and sometimes infection to their hosts. We explored epidemiological and geo-referenced data from > 7 million electronic health records (EHRs) from cats and dogs collected by the Small Animal Veterinary Surveillance Network (SAVSNET) in Great Britain (GB) between 2014 and 2021 to assess the factors affecting tick attachment in an individual and at a spatiotemporal level. EHRs in which ticks were mentioned were identified by text mining; domain experts confirmed those with ticks on the animal. Tick presence/absence records were overlaid with a spatiotemporal series of climate, environment, anthropogenic and host distribution factors to produce a spatiotemporal regression matrix. An ensemble machine learning spatiotemporal model was used to fine-tune hyperparameters for Random Forest, Gradient-boosted Trees and Generalized Linear Model regression algorithms, which were then used to produce a final ensemble meta-learner to predict the probability of tick attachment across GB at a monthly interval and averaged long-term through 2014-2021 at a spatial resolution of 1km. Individual host factors associated with tick attachment were also assessed by conditional logistic regression on a matched case-control dataset. In total, 11,741 consultations were identified in which a tick was recorded. The frequency of tick records was low (0.16% EHRs), suggesting an underestimation of risk. That said, increased odds for tick attachment in cats and dogs were associated with younger adult ages, longer coat length, crossbreeds and unclassified breeds. In cats, males and entire animals had significantly increased odds of recorded tick attachment. The key variables controlling the spatiotemporal risk for tick attachment were climatic (precipitation and temperature) and vegetation type (Enhanced Vegetation Index). Suitable areas for tick attachment were predicted across GB, especially in forests and grassland areas, mainly during summer, particularly in June. Our results can inform targeted health messages to owners and veterinary practitioners, identifying those animals, seasons and areas of higher risk for tick attachment and allowing for more tailored prophylaxis to reduce tick burden, inappropriate parasiticide treatment and potentially TBDs in companion animals and humans. Sentinel networks like SAVSNET represent a novel complementary data source to improve our understanding of tick attachment risk for companion animals and as a proxy of risk to humans.

A novel bioaccessibility-based probabilistic risks assessment of potentially toxic elements (PTEs) in earthworm.

Introduction: Early risk assessment studies usually based on total heavy metal (loid) contents, inevitably leading to an overestimation of the health risks. In addition, inputs are represented as single-point estimates in deterministic models, leading to underestimation or overestimation of the health risks. Methods: To overcome these barriers, a novel probabilistic risk assessment strategy based on the combinational use of bioaccessibility and Monte Carlo simulation was developed to assess heavy metal (loid) associated health risks of earthworms in this study. To obtain a realistic and robust probabilistic risk assessment, heavy metal (loid) exposure duration and frequency were determined using our questionnaire data. Results: As a result, the mean gastrointestinal bioaccessibility was in the order: Cd > As > Cu > Hg. The mean hazard index (HI) values for investigated metal (loid)s were 0.65 and 0.59 for male and female, respectively, demonstrating an acceptable health risk in an average community. However, the 90th percentile of HI values was 1.87 and 1.65 for male and female, respectively. And the total non-cancer risks of heavy metal (loid) exposure exceeded the acceptable threshold for 19.9% and 17.8% of male and female, respectively. In addition, the total cancer risk (TCR) value through co-exposure to As and Cd suggested that the carcinogenic risks may be of concern for average exposure population. Sensitivity analyses revealed that the exposure frequency and bioaccessible As concentration were the dominant contributors to the total risk variance, which provided meaningful implications for environmental management. Conclusion: Altogether, the refined strategy based on bioaccessibility and Monte Carlo simulation is the first of its kind, such effort attempts to scientifically guide the rational clinic use of TCM and the improvement of population-health.

Prevalence of dichotic deficits in adjudicated adolescents: Implications for language skills and competency within the juvenile justice system

This study reports the prevalence of dichotic listening weaknesses in 10-18-year-old adjudicated adolescents and its potential impact on their language skills. The findings reveal that 35% of adjudicated adolescents exhibited matched dichotic deficit patterns sufficient to diagnose an APD, a prevalence rate that is higher than the 19% rate among age-matched typically developing children but lower than the 45% rate among age-matched children evaluated clinically for an auditory processing disorder (APD). These rates for all groups are significantly higher than the 2-7% prevalence rate commonly reported for APD in the pediatric population. The study also identified a link between low scores on dichotic listening tests and language deficits, suggesting potentially negative consequences for successful navigation through the juvenile justice system for these adolescents. These results challenge the sustained underestimation of auditory processing risks in children and advocate for early identification and treatment of common processing deficits, emphasizing the need for consensus among researchers and clinicians in identifying and treating them. The implications of untreated auditory processing deficit on educational achievement, language skills, and behavior in adjudicated adolescents are discussed, emphasizing how they contribute to the schools-to-prison pipeline in the United States (U.S.). The study concludes by suggesting that early intervention through an evidence-based short-term auditory training protocol can improve dichotic listening and may alleviate some long-term educational underachievement and dysregulated behaviors associated with juvenile incarceration. The findings underscore the need for increased awareness, screening, and intervention for dichotic listening deficits in general and particularly in the at-risk, underserved pediatric populations world-wide.

Assessment of Subclinical Cardiovascular Disease Risk by Measuring Carotid Intima Media Thickness in Rheumatoid Arthritis Patients: A Cross-sectional Study

Introduction: Rheumatoid Arthritis (RA) is a chronic inflammatory disease that affects both synovial joints and extra-articular regions. Patients with RA have an increased risk of Cardiovascular Disease (CVD), leading to premature mortality. Aim: To assess subclinical atherosclerosis in RA patients using Carotid Intima Media Thickness (CIMT) and compare it with age-matched healthy individuals. Materials and Methods: This cross-sectional study included 50 RA patients and 50 healthy age- and sex-matched controls. Ultrasound was used to measure CIMT, which serves as an indicator of subclinical atherosclerosis. Framingham, Atherosclerotic CVD (ASCVD) risk scores, and QRISK3 scores were used to estimate the 10-year CVD risk. Student’s t-test and Chi-square test were used to identify statistically significant differences. Results: RA patients had significantly higher mean CIMT values compared to controls (p-value<0.001), indicating an increased burden of atherosclerosis. While ASCVD scores were comparable, Framingham scores were significantly lower (p-value=0.028), and QRISK3 scores were significantly higher (p-value=0.007) in RA patients. This suggests an underestimation of CVD risk by Framingham and potentially better prediction by QRISK3. Conclusion: The study highlights that RA patients have a higher burden of atherosclerotic disease compared to healthy adults of the same age when CIMT is used as a marker of atherosclerosis. These findings indicate the need for early CVD risk assessment and intervention in RA patients using appropriate risk-scoring systems and tools. Further research with larger sample sizes and longitudinal follow-up is warranted to confirm these findings.

Влияние фактора полиморбидности на прогноз пожизненных радиационных рисков смертности от злокачественных новообразований

The forecast of lifetime radiation risks in exposed cohorts is currently carried out without considering the heterogeneity of cohort members in terms of their degree of polymorbidity. Polymorbidity is characterized by a reduction in the life expectancy of a person in the presence of several chronic diseases. Therefore, considering the polymorbidity factor can significantly affect the estimate of lifetime radiation risk. The purpose of this study is to investigate the effect of the polymorbidity factor of patients on the estimates of lifetime radiation risks of mortality from malignant neoplasms, as well as to compare the impact of polymorbidity factors and uncertainty of radiation doses on these estimates. To calculate the lifetime attributable radiation risk (LAR) per 1 mSv, the radiation risk models recommended by Publication 103 of the International Commission on Radiological Protection were used. The calculations are based on official medical and statistical data of 2021 for the Russian population. The effect of polymorbidity on human lifespan and on LAR estimates was considered using the classic Charlson Comorbidity Index (CCI). To assess the effect of the uncertainty of radiation doses on the LAR value, an example of a lognormal dose distribution with a standard geometric deviation of 2.2 (i.e., with a 90% uncertainty factor of 3.6) was chosen, which is typical for the population living in the areas of the Kaluga and Bryansk regions of Russia contaminated as a result of the Chernobyl accident. Simulation modeling was used to assess the effect of radiation dose uncertainty. It is shown that the variability of estimates of the lifetime radiation risk of mortality from malignant neoplasms due to the polymorbidity factor is comparable to the variability of this radiation risk due to the uncertainty of doses with an 90% uncertainty factor of 3.6. Thus, when calculating radiation risks in persons exposed to radiation, it is necessary to consider the presence or absence of chronic diseases, since this significantly affects the final assessments of radiation risks. Underestimation or overestimation of risk can be from 1.5 times or more.

Long-term second primary cancer risk in adolescent and young adult (15-39 years) cancer survivors: a population-based study in the Netherlands between 1989 and 2018

Few studies have comprehensively investigated the long-term second cancer risk among adolescent and young adult (AYA, aged 15-39 years) cancer survivors. This study investigated the long-term second cancer risk by including the full range of first and second cancer combinations with at least 10 observations in the Netherlands between 1989 and2018. First and second primary cancer data of all 6-month AYA cancer survivors were obtained from the nationwide population-based Netherlands Cancer Registry. Excess cancer risk compared to the general population was assessed with standardized incidence ratio (SIR) and absolute excess risk (AER) statistics up to 25 years after diagnosis. Cumulative incidences were estimated, using death as a competing risk factor. Analyses were carried out with and without applying multiple cancer rules. The cohort included 99 502 AYA cancer survivors. Male survivors had a 2-fold higher risk of developing any cancer compared to the general population, whereas this was around 1.3-fold in females. AERs were 17.5 and 10.1 per 10 000 person-years for males and females. The long-term excess risk of cancer was significantly higher for most first and second primary cancer combinations, but comparable and lower risk estimates were also observed. Application of the multiple cancer rules resulted in a noticeable risk underestimation in melanoma, testicular, and breast cancer survivors. Risk outcomes remained similar in most cases otherwise. The cumulative incidence of second cancer overall increased over time up to 8.9% in males and 10.3% in females at 25 years' follow-up. Highest long-term cumulative incidences were observed among lymphoma survivors (13.3% males and 18.9% females). AYA cancer survivors have a higher cancer risk compared to the general population for most cancers up to 25 years after their initial cancer diagnosis. Additional studies that investigate risk factors for the specific cancer type combinations are needed to develop personalized follow-up strategies.

Discrepancy between Tumor Size Assessed by Full-Field Digital Mammography or Ultrasonography (cT) and Pathology (pT) in a Multicenter Series of Breast Metaplastic Carcinoma Patients.

A retrospective cohort study was conducted on databases encompassing records of BC patients (2012-2022) at the National Research Institutes of Oncology (Warsaw, Gliwice and Krakow Branches). Inclusion criteria comprised confirmed diagnosis in postsurgical pathology reports with tumor size details (pT) and availability of tumor size from preoperative US and/or FFDM. Patients subjected to neoadjuvant systemic treatment were excluded. Demographics and clinicopathological data were gathered. Forty-five females were included. A total of 86.7% were triple-negative. The median age was 66 years (range: 33-89). The median pT was 41.63 mm (6-130), and eight patients were N-positive. Median tumor size assessed by US and FFDM was 31.81 mm (9-100) and 34.14 mm (0-120), respectively. Neither technique demonstrated superiority (p > 0.05), but they both underestimated the tumor size (p = 0.002 for US and p = 0.018 for FFDM). Smaller tumors (pT1-2) were statistically more accurately assessed by any technique (p < 0.001). Only pT correlated with overall survival. The risk of underestimation in tumor size assessment with US and FFDM has to be taken into consideration while planning surgical procedures for BC-Mp.

Corporate climate risk disclosure: assessing materiality and stakeholder expectations for sustainable value creation

PurposeThis study aims to evaluate the adequacy of climate risk disclosure by providing empirical evidence on whether corporate disclosure meets rising stakeholders’ demand for risk disclosure concerning climate change.Design/methodology/approachDrawing on a triangulated approach for collecting data from multiple sources in a longitudinal study, we perform a panel regression analysis on a sample of multinational firms between 2007 and 2021. Inspired by the Global Reporting Initiative (GRI) principles, our innovative and inclusive model of measuring firm-level climate risks underscores the urgent need to redefine materiality from a broader value creation (rather than only financial) perspective, including the impact on sustainable development.FindingsThe findings of this study provide evidence of limited corporate climate risk disclosure, indicating that organisations have yet to accept the reality of climate-related risks. An additional finding supports the existence of a nexus between higher corporate environmental disclosure and higher corporate resilience to material financial and environmental risks, rather than pervasive sustainability risk disclosure.Practical implicationsWe argue that a mechanical process for climate-related risk disclosure can limit related disclosure variability, risk reporting priority selection, thereby broadening the short-term perspective on financial materiality assessment for disclosure.Social implicationsThis study extends recent literature on the adequacy of corporate risk disclosure, highlighting the importance of disclosing material sustainability risks from the perspectives of different stakeholder groups for long-term success. Corporate management should place climate-related risks at the centre of their disclosure strategies. We argue that reducing the systematic underestimation of climate-related risks and variations in their disclosure practices may require regulations that enhance corporate perceptions and responses to these risks.Originality/valueThis study emphasises the importance of reconceptualising materiality from a multidimensional value creation standpoint, encapsulating financial and sustainable development considerations. This novel model of assessing firm-level climate risk, based on the GRI principles, underscores the necessity of developing a more comprehensive approach to evaluating materiality.

Value‐at‐Risk under Measurement Error

We propose a method for estimating Value‐at‐Risk that corrects for the effect of measurement errors in stock prices. We show that the presence of measurement errors might pose serious problems for estimating risk measures. In particular, when stock prices are contaminated, existing estimators of Value‐at‐Risk are inconsistent and might lead to an underestimation of risk, which can result in extreme leverage ratios within the held portfolios. Using a Fourier transform and a deconvolution kernel estimator of the probability distribution function of actual latent prices, we derive a robust estimator of Value‐at‐Risk in the presence of measurement errors. Monte Carlo simulations and real data analysis illustrate satisfactory performance of the proposed method.

A physiological approach for assessing human survivability and liveability to heat in a changing climate

Most studies projecting human survivability limits to extreme heat with climate change use a 35 °C wet-bulb temperature (Tw) threshold without integrating variations in human physiology. This study applies physiological and biophysical principles for young and older adults, in sun or shade, to improve current estimates of survivability and introduce liveability (maximum safe, sustained activity) under current and future climates. Our physiology-based survival limits show a vast underestimation of risks by the 35 °C Tw model in hot-dry conditions. Updated survivability limits correspond to Tw~25.8–34.1 °C (young) and ~21.9–33.7 °C (old)—0.9–13.1 °C lower than Tw = 35 °C. For older female adults, estimates are ~7.2–13.1 °C lower than 35 °C in dry conditions. Liveability declines with sun exposure and humidity, yet most dramatically with age (2.5–3.0 METs lower for older adults). Reductions in safe activity for younger and older adults between the present and future indicate a stronger impact from aging than warming.