Empirical Classification Research Articles

Profiles and categorisation of perceptions and attitudes among European citizens regarding the just energy transition

Just energy transitions have re-emerged from their unionist roots to gain increasing momentum politically and scholarly, especially driven by the SDGs. In the movement from unionism to mainstream debate, the notion has acquired diverse nuances that determine its normative scope. Four major approaches have been theoretically proposed to classify views currently: statu quo, managerial, structural, and transformative. Implicitly, these approaches observe two dimensions: individualism versus collectivism, and green growth versus post-growth. Although this classification has been useful to study the positions of groups of individuals in international organisations, NGOs, and activist movements, this paper suggests testing if it remains operative in contrast with individuals’ attitudes and perceptions. Through basic statistics, clustering algorithms, and correspondence analysis applied to the most recent version of the European Social Survey (2020-2022), this contribution finds three key insights. First, although the empirical four-group classification resembles some of the theoretical traits, it does not fit the approaches. The individualism versus collectivism dimension is operational, but the environmental dimension is difficult to determine. Second, empirically, twenty-three optimal groups exist. Three groups congregate more than 90% of respondents. The remaining marginal but optimal groups point to the relevance of observing isolated profiles in the study and political planning of just energy transitions. Finally, human values show greater explanatory capacity than sociodemographic and political variables.

Stability Assessment of Rock Slopes Using the Q-Slope Classification System: A Reliability Analysis Employing Case Studies in Ecuador

Q-slope is one of the most recent empirical geomechanical classification systems and the least studied in South America. This study aims to expand the knowledge base regarding the Q-slope geomechanical method and demonstrate its applicability and reliability in rock slopes of Andean countries, such as Ecuador. To this end, thirty rocky slopes have been characterized considering (1) the physical visual approach—geographic location, climate, lithology, alteration, and stability (to obtain values of Jr, Ja, Jwice, and SRFa)—and (2) geomechanical stations and kinematic analysis (for the determination of the type of failure, Jn, O-factor, and SRFc for Q-slope). Field data were collected in contrasting environments (coastal, mountain, and forest), and different failure modes were considered (planar, wedge, block, and flexural topplings) to better understand the method. The results and main contributions of this research are (i) verifying the applicability of the Jwice parameters in different climatic settings and (ii) validating the Q-slope method by applying a confusion matrix to evaluate its reliability for slope stability assessment. The overall accuracy obtained is 80%, placing the Q-slope geomechanical method in the highest evaluation quartile and thus classifying it as very good for slope characterization.

On the statistical distribution of temperature and the classification of extreme events considering season and climate change—an application in Switzerland

With the increased occurrence of hot spells in recent years, there is growing interest in quantifying the recurrence of extreme temperature events. However, pronounced temperature anomalies occur all year round, and a reliable classification in terms of the time of occurrence in the year is needed. In this study, we present a novel approach to classifying daily air temperatures that take into account the seasonal cycle and climate change. We model the distribution of the daily Swiss temperatures using the skewed generalized error distribution with four time-varying parameters, thereby accounting for non-Gaussianity in daily air temperature, while the climatic trend is modeled linearly with smoothed northern hemisphere temperature as an explanatory variable. The daily observations are then transformed into a standard normal distribution. The resultant standardized temperature anomalies are comparable within a year and between years and are used for quantile-based empirical classification. The approach is suitable to classify historical and current extreme temperatures with respect to the temperature range expected at the time of the event. For example, a heat wave occurring at the end of June is classified as less likely to occur than a heat wave of similar intensity occurring in mid-July, as is shown for the two 7-day heat waves that struck Switzerland in the summer of 2019. Furthermore, climate change has increased the probability of hot events and decreased the probability of cold events in recent years. The presented approach thus allows a fair classification of extreme temperatures within a year and between years and offers new possibilities to analyze daily air temperature.

Diversity and the Hierarchical Taxonomy of Psychopathology (HiTOP)

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirically based, hierarchical model of the structure of psychopathology that was created in response to the limitations of traditional, categorical psychiatric classification frameworks. The HiTOP model has become increasingly popular in clinical psychology and psychiatry since its publication in 2017. In this Review, we consider the applicability of the HiTOP model to diverse, underrepresented and epistemically excluded populations. We first review the philosophy underlying psychopathology research in general to understand the impact of scientific norms on the inclusion of diverse populations within the research canon. We then review the HiTOP approach to modelling psychopathology, and how diverse populations have been included within HiTOP-related research to date. We conclude by highlighting ways for future research to increase the applicability of the HiTOP framework to diverse populations. Seriously engaging with the HiTOP model’s suitability for diverse, underrepresented and epistemically excluded populations is imperative in order to achieve the HiTOP consortium’s goal of delineating a fully empirical classification of psychopathology, and to provide a model that can guide the field of psychopathology research and training to increase representation. The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirically based, hierarchical model of the structure of psychopathology. In this Review, Rodriguez-Seijas et al. consider the applicability of the HiTOP model to diverse, underrepresented and epistemically excluded populations.

Study on the Classification and Identification Methods of Surrounding Rock Excavatability Based on the Rock-Breaking Performance of Tunnel Boring Machines

Rock mass conditions are extremely sensitive to tunnel boring machine (TBM) tunneling. Therefore, establishing a surrounding rock excavatability (SRE) classification system applicable to TBM tunnels. Accurately and intelligently identifying excavatability grades can also facilitate efficient TBM tunneling and intelligent construction. Specific excavation and penetration rates were used to evaluate SRE. Their correlations with geological and tunneling parameters were explored using the field data from two water conveyance tunnels in China with different lithologies. A high-precision empirical SRE classification system was constructed using TOPSIS for multi-objective decision-making, and it was verified using engineering cases. An intelligent identification model for SRE grades in the stable phase of a TBM excavation cycle was established using 12,382 TBM rock-breaking datasets and deep forest models. Ten characteristic parameters, e.g., total thrust, were selected as model input features. Hyperparameter optimization was achieved using the grid search method. Deep forest was compared with decision tree, random forest, support vector classifier, and deep neural network. The contribution of the model’s features was measured using random forest. The main conclusions are as follows: the proposed SRE classification method is feasible and matches well with the actual excavation. In the intelligent identification of SRE classification, the accuracy and F1 scores when using deep forest were 96.33% and 0.9581, respectively. Deep forest exhibited better grade identification performance than the four models. Among the ten input features, penetration is the most important feature for the model’s input, while the top shield cylinder rod’s chamber pressure is the least important. The findings can provide some references for SRE classification and prediction and intelligent TBM control.

Optimization design of short-circuit test platform for the distribution network of integrated power system based on improved K-means clustering

Short-circuit test platform can verify the correctness of the distribution network simulation model and the effectiveness of short-circuits protection. It is necessary to simplify the structure of the distribution network into a typical branch and build an equivalent test platform due to the system’s complexity. The equivalent system based on empirical classification has particular subjectivity and randomness, which will reduce the representativeness of the short-circuit test platform. Therefore, this paper adopts the improved K-means clustering algorithm to cluster the short-circuit impedance parameters from the typical short-circuit point of the distribution network to the power supply into different clusters. Each cluster center is used as the equivalent system’s characteristic impedance to realize the complex system’s simplification. The feasibility of the improved K-means clustering algorithm is verified by comparing the short-circuit simulation results with the original and the empirical classification equivalent system.

Evaluation of slope stability through rock mass classification and kinematic analysis of some major slopes along NH-1A from Ramban to Banihal, North Western Himalayas

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes, which is vital for India's socio-economic growth. Due to natural and artificial factors, frequency of slope instabilities along the networks has been increasing over last few decades. Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year. Several rock mass classification methods are generally used to assess the strength and deformability of rock mass. This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas. Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes. For evaluating the stability of these slopes, kinematic analysis was performed along with geological strength index (GSI), rock mass rating (RMR), continuous slope mass rating (CoSMR), slope mass rating (SMR), and Q-slope in the present study. The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable. The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating (SSR) for the factor of safety (FoS) of 1.2 and 1 respectively. Q-slope with probability of failure (PoF) 1% gives two slopes as stable slopes. Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS. The value ranges given by different empirical classifications were RMR (37–74), GSI (27.3–58.5), SMR (11–59), and CoSMR (3.39–74.56). Good relationship was found among RMR & SSR and RMR & GSI with correlation coefficient (R2) value of 0.815 and 0.6866, respectively. Lastly, a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

Back analysis of geomechanical parameters for rock mass under complex geological conditions using a novel algorithm

Geomechanical properties of rock mass are of vital importance for design optimization and stability evaluation in geotechnical engineering. Traditionally, they are estimated by laboratory experiments, in situ tests, and empirical classification, which ignore the scale effect, and may be not representative, leading to uncertainties of parameters. Back analysis method based on the monitoring displacement data provides the state-of-the-art tool for the estimation of geomechanical parameters. This paper proposed a novel back analysis method, which combined the optimized particle swarm optimization (OPSO) algorithm with the support vector machine (SVM) algorithm. And then, we cooperate the method with finite element method (FEM) forming the comprehensive method named OPSO-SVM-ABAQUS. This method has developed the standard PSO algorithm from two aspects for more powerful optimization capability and higher optimization precision. The OPSO algorithm is then used for the estimation of the SVM hyperparameters and the optimal values of geomechanical parameters. Furthermore, the performance of the proposed algorithm is compared with other six algorithms. Finally, the geomechanical parameters of the interlayered soft and hard rocks are calculated accurately, with the average error between the back-analyzed and assumed actual parameters to be 0.91 %. And the parametric investigations are implemented with the trained model. It dominates that the proposed algorithm is feasible for reasonable parameters in geotechnical engineering under complex geological conditions.

A Hybrid Methodology of Rock Support Design for Poor Ground Conditions in Hard Rock Tunnelling

The design of rock support in hard rock tunnelling is often assisted by the use of empirical engineering rock mass classification systems. This has involved support design in a wide variety of geological- and rock mechanical-conditions, including poor ground conditions like fault zones and weak rocks. For most of the encountered situations, the empirical support designs have performed well. However, the empirical classification systems pose several limitations to describe ground behavior and derive optimal rock support design in poor ground conditions as discussed in several publications during the last decades. In the present study, an analysis of 118 case records, mostly from monitored Norwegian tunnel sites, has been undertaken to investigate the performance of the current empirical design practice with the Q-system when exposed to poor ground conditions. The results have revealed significant improvement possibilities which relate to a wide variety of ground conditions. Among others, weak rocks, anisotropic rock, and rock mass conditions in the transition from self-bearing rock masses to rock masses requiring load-bearing support in the tunnel, and typically with rock mass quality Q < 1. As a result, a set of improvements and design recommendations have been developed and an integrated or hybrid design methodology specific for poor ground conditions proposed. The hybrid approach combines the main components and advantages of different design methodologies and provides guidance for design optimization where the empirical approach presents limitations.

Cyclin A2 and Ki-67 proliferation markers could be used to identify tumors with poor prognosis in African American women with breast cancer.

Diagnosed invasive breast carcinomas in African American patients are more aggressive compared with those in Caucasian patients and diagnosed at later stages of the disease with higher grade tumors. Despite advances in breast cancer systemic treatment, new prognostic and predictive biomarkers are still needed. Therefore, potential biomarkers were chosen to correlate with different subtypes, recurrence, and survival of invasive breast cancer in a cohort of African American women. Eight protein biomarkers (ER, PR, HER2, Cyclin A2, Cytokeratin 5, Vimentin, Bcl2, and Ki-67) were evaluated using tissue microarrays (TMAs) and immunohistochemistry (IHC). The IHC results from TMAs were analyzed by both supervised and unsupervised clustering methods. The predictive clusters for the supervised and unsupervised methods were compared for agreement with the empirical classification. Kappa values were used to determine the overall percent correct clusters and agreement between specific clusters. Chi-square statistics was used to examine the association between hierarchical and multinomial logistic clustering methods. Five subtypes of breast tumors with distinct protein expression patterns were identified among the studied 166 breast tumors. Luminal B tumors have been distinguished from luminal A tumors by staining for cell cycle proteins Cyclin A2 and Ki-67, which promote cell proliferation. Forty-nine percent were stained positive for Cyclin A2, 39.2% positive for Ki-67, and 37% positive for both Cyclin A2 and Ki-67. The age of patients did not show any significant effect whether five (p-value= 0.576) or eight (p-value= 0.605) biomarkers were used, which indicating that age did not have any influence on the classification of the subtypes. Ninety percent of the thirty triple negative tumors were positive for Cyclin A2 or Ki-67 or both. Six-year overall survival was better for luminal A tumors (76%) than luminal B tumors (71%). Likewise, six-year relapse-free survival was better for luminal A tumors (76%) than luminal B tumors (29%). Discovery of molecular markers such as Cyclin A2 and Ki-67, and subtypes that are most prevalent in African Americans could lead to a better understanding of the factors contributing to higher morbidity and mortality in this group and to aid in decision-making to offer earlier treatment.

Differential symptom weighting in estimating empirical thresholds for underlying PTSD severity: Toward a "platinum" standard for diagnosis?

Symptom counts as the basis for Post-Traumatic Stress Disorder(PTSD) diagnoses in the DSM presume each symptom is equally reflective of underlying disorder severity. However, the "equal weight" assumption fails to fit PTSD symptom data when tested. The present study developed an enhanced PTSD diagnosis based on (a) a conventional PTSD diagnosis from a clinical interview and (b) an empirical classification of full PTSD that reflected the relative clinical weights of each symptom. Baseline structured interview data from Project Harmony (N=2658) was used. An enhanced diagnosis for full PTSD was estimated using an empirical threshold from moderated nonlinear factor analysis (MNLFA) latent PTSD scale scores, in combination with a full conventional PTSD diagnosis based on interview data. One in 4 patients in the sample had a PTSD diagnosis that was inconsistent with their empirical PTSD grouping, such that the enhanced diagnostic standard reduced the diagnostic discrepancy rate by 20%. Veterans, and in particular female Veterans, were at greatest odds for discrepancy between their underlying PTSD severity and DSM diagnosis. Psychometric methodologies that differentially weight symptoms can complement DSM criteria and may serve as a platform for symptom prioritization for diagnoses in future editions of DSM.

Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks

Decision forests, in particular random forests and gradient boosting trees have demonstrated state-of-the-art accuracy compared to other methods in many supervised learning scenarios. Forests dominate other methods in tabular data, that is, when the feature space is unstructured, so that the signal is invariant to a permutation of the feature indices. However, in structured data lying on a manifold—such as images and time-series—deep networks, specifically convolutional deep networks (ConvNets), tend to outperform forests. We conjecture that it is in part due to networks not simply analyzing feature magnitudes, but also their indices. In contrast, naïve forest implementations fail to explicitly consider feature indices. A recent approach demonstrates that forests, for each node, implicitly sample a random matrix from some specific distribution. These forests, like some networks, learn by partitioning the feature space into convex polytopes corresponding to linear functions. We build on that approach with Manifold Oblique Random Forests (Morf) that chooses distributions in a manifold-aware fashion to incorporate feature locality. Morf runs fast and maintains interpretability and theoretical justification. Morf also has excellent empirical classification performance on simulated data and real images and multivariate time-series. It outperforms non-neural network approaches that ignore feature space structure and challenges the performance of ConvNets in some cases.

Anger profiles among individuals seeking treatment for maladaptive anger: Associations with emotion regulation.

Individuals who experience problems with anger represent a heterogeneous group. Identifying clinically relevant subtypes of anger may advance treatment research. In the current study, latent profile analysis (LPA) was conducted to explore if distinct subtypes of anger could be identified within a sample of individuals seeking treatment for maladaptive anger (N = 538; 55.4% females; mean age = 39.78, SD = 12.28). Furthermore, the utility of the empirical classification was examined based on differences in usage of emotion regulation strategies (cognitive reappraisal, expressive suppression, mindful emotion awareness and relaxation) across subtypes. Four anger profiles were identified in the best-fitted LPA model: Low Anger (n = 153, 28.4%), Anger In (n = 91, 16.9%), Moderate Anger (n = 193, 35.9%) and High Anger (n = 101, 35.5%). Results from the multinominal regression revealed that the use of emotion regulation differed across all profiles. Participants from the High Anger and Anger In profiles exhibited distinct patterns of dysfunctional emotion regulation. The results add to the increasing amount of evidence demonstrating a link between emotion regulation strategies and maladaptive anger. Clinical implications on how to tailor treatments for individuals with maladaptive anger are discussed.

Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences.

Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides made of 6-9 amino acids to identify potent antimicrobial peptides. The pipeline consists of trainable machine-learning modules (for performing empirical selection, classification, ranking and regression tasks) assembled sequentially following a coarse-to-fine design principle to gradually narrow down the search space. The leading three antimicrobial hexapeptides identified by the pipeline showed strong activities against a wide range of clinical isolates of multidrug-resistant pathogens. In mice with bacterial pneumonia, aerosolized formulations of the identified peptides showed therapeutic efficacy comparable to penicillin, negligible toxicity and a low propensity to induce drug resistance. The machine-learning pipeline may accelerate the discovery of new functional peptides.

Discrimination of debris flow in narrow-steep type and wide-gentle type gullies in Wenchuan meizoseismal area

The debris flow disasters in the Wenchuan meizoseismal area are dominantly triggered by the gully-type debris flow. Research on its classification method can be of great theoretical value and practical significance for developing targeted prevention measures. The current empirical classification method has some disadvantages, such as inconsistent discrimination criteria and poor practicability. In this paper, in order to overcome these drawbacks, the topography, rainfall, and source characteristics data of 176 gully-type debris flows in the Wenchuan “5.12” meizoseismal area since 2008 were collected and divided into the narrow-steep, transitional, and wide-gentle types based on field investigation. The narrow-steep type gullies are mainly concentrated in small catchments with severe erosion. In contrast, the wide-gentle type gullies are often characterized by big catchments, gentle vertical slopes, and debris flows movement dominated by deposition. An empirical discrimination method for debris flow gullies is proposed based on the characters of the gullies in the meizoseismal area, and a mathematical discrimination model named Gully Geomorphology Index (GGI) is also constructed. The results from existing cases indicated that both methods were accurate to discriminate between the narrow-steep and wide-gentle debris flow gullies. According to the empirical discrimination method, among the 176 channel-type debris flows, the numbers of narrow-steep, transitional, and wide-gentle channel types are 105 (59.66%), 12 (6.82%), and 59 (33.52%), respectively. While for the GGI method, the value 0.05 and 0.10 were defined as the threshold of the three types, and the distribution of the results is 104 (59.09%) for the narrow-steep type, 16 (9.09%) for the transitional type, and 56 (31.82%) for the narrow-steep type, which can better classify the transitional type gullies and is more practical. We hope that the discrimination methods proposed in this paper will help better understand the disaster-causing mechanism and improve the prevention measures of debris flow in the Wenchuan meizoseismal area.

EDUCATIONAL ENVIRONMENT AS A FACTOR OF PSYCHOLOGICAL WELL-BEING OF PRE-SCHOOL CHILDREN

The relevance of the study lies in the existing contradiction between the signifi cance of psychological well-being of the child for successful socialization and development of higher mental functions and insuffi cient development of this problem at the pre-school stage. Taking into account the infl uence of negative trends of sociocultural transformations, as well as qualitatively other conditions of life of modern children is essential. The purpose is to study the infl uence of the educational environment on the psychological well-being of children of late preschool age. Methods include organizational, empirical (observation, survey, psychological experiment, quasi-experiment, physcho-diagnostic method, including tests and surveys), quantitative and qualitative methods of data processing, interpretive methods. The sample consisted of  older pre-school children of pre-school institutions in Belarus. The number of participants in the formative stage was determined on the basis of the naturally established  groups of pre-school institutions. In the course of the study, the author’s interpretation and operationalization of the concept of “psychological well-being of the child” in relation to pre-school age were carried out. Based on the empirical classifi cation, three types of psychological well-being of children of senior pre-school age have been singled out and characterised in a meaningful way: well-off, conditionally well-off, and eventually disadvantaged. The relationship of indicators of child well-being and components of educational environment has been determined. A programme to optimize the educational environment in the pre-school education institution group has been developed and tested. The results presented in the study make it possible to speak about the characteristics of social, spatial and subject and technological components as determinants of psychological well-being of the child with a high probability.

The rock mass and the development of joints during the tunnel excavation

The term joint has been historically used imprecisely when carrying out geomechanical characterization of rock mass. The current classification systems consider the different fracture planes simply as they are observed visually, assuming independence of dimensions and other fixed patterns with depth. That can lead to over-design underground works. Joint patterns in a rock mass depend on the change of the stress field acting on the rock from its original confinement state. The stress field on the rock mass is not constant and varies with changing distance in any underground excavation. In tunnel analyses, empirical classifications consider the parameters related to the joints in the rock mass as constant variables. Those classifications, constitute more than 75% of the valuation without considering the effect of the development and frequencies of joints according to the change of the stress field. In this work, it is considered the necessity to broaden the definition of joints as non-moving fractures for which the development of the fracture plane depends on the change of the stress field in the rock mass. Regarding the discontinuity term, it is important to differentiate the joints from the stratification and foliation planes, which are terms associated with the classification of rocks according to its origin, particularly for sedimentary and metamorphic, respectively. These are made up of continuous planes that control the mechanical behavior according to the stresses acting in the rock mass. Considering a tunnel excavated in an igneous rock mass, the decompression due to the opening effect differs from a stratified or foliated massif. The first one depends on the development of joints, and the second, mainly on the phenomenon of bending of layers within the rock mass, according to the relative dimensions of layer thickness to tunnel diameter. In this article, several practical examples are presented which support the hypothesis that joints develop as a function of the change in the stress field. Also, by means of examples applying numerical methods, it is shown that the deformations of a rock mass with constant parameters are bigger than those when a zoned rock mass is considered, including changes in the geomechanical characteristics related to the distance in the tunnel excavation. The results have an impact on the thickness of supporting elements, yielding lower construction costs.

Wildfire hazard mapping in the eastern Mediterranean landscape

Background Wildfires are a growing threat to many ecosystems, bringing devastation to human safety and health, infrastructure, the environment and wildlife. Aims A thorough understanding of the characteristics determining the susceptibility of an area to wildfires is crucial to prevention and management activities. The work focused on a case study of 13 countries in the eastern Mediterranean and southern Black Sea basins. Methods A data-driven approach was implemented where a decade of past wildfires was linked to geoclimatic and anthropic descriptors via a machine learning classification technique (Random Forest). Empirical classification of fuel allowed linking of fire intensity and hazard to environmental drivers. Key results Wildfire susceptibility, intensity and hazard were obtained for the study area. For the first time, the methodology is applied at a supranational scale characterised by a diverse climate and vegetation landscape, relying on open data. Conclusions This approach successfully allowed identification of the main wildfire drivers and led to identification of areas that are more susceptible to impactful wildfire events. Implications This work demonstrated the feasibility of the proposed framework and settled the basis for its scalability at a supranational level.

Determination of sublevel stoping layout using a network flow algorithm and the MRMR classification system

Reduced access to shallow mineral resources, increased demand for minerals, and advances in technology have led to the development of deep underground mines. The location of the stopes and, finally, the determination of the mine layout is one of the problems in the design of any underground mine. In this study, a network flow algorithm was used to optimize the underground mine layout where sublevel stoping was chosen as the mining method. Due to the geomechanical constraints, the empirical Mining Rock Mass Rating classification system was used to determine the maximum stable width of the mineable stopes in the mine layout of each mining level. The model was run on a copper deposit and optimized for one mining level. The results obtained from running the model showed that 40 mineable stopes with a total recovery of 97% could be achieved. For validity check, the results were compared with those obtained from the algorithm of maximum value neighborhood. Application of the proposed model into the example block model revealed approximately 7% more economic value for the network flow algorithm than the maximum value neighborhood algorithm.

Management of the Medico-Legal Dispute of Healthcare-Related SARS-CoV-2 Infections: Evaluation Criteria and Case Study in a Large University Hospital in Northwest Italy from 2020 to 2021

Healthcare-related SARS-CoV-2 infection is an issue of particular concern during the pandemic. It has important repercussions on the National Health System, which represents a source of medical-legal health disputes. In the healthcare context, there are reports of negative screening at hospital admission (via nasopharyngeal swabs) and subsequent diagnosis of SARS-CoV-2 infection during hospitalization. Such cases cannot be considered a priori of healthcare-related infections but require extensive in-depth evaluation. In this study, we propose an empirical classification to frame cases of SARS-CoV-2 infection diagnosed in the hospital (first negative admission swab, with subsequent positive test during hospitalization). The classification is based on five categories: nosocomial, probably nosocomial, indeterminate, probably community, and community cases. We analyzed patients who died after testing positive for SARS-CoV-2 during hospitalization (with initial negative screening) in the largest hospital in Northwest Italy from February 2020 to 31 December 2021. A total of 383 cases were tracked and are listed as follows: 41 cases (11%) were classified as nosocomial (i.e., 3.2% of COVID-19 deaths). In contrast, 71 cases (19%) were classified as probably nosocomial, 69 (18%) were indeterminate (i.e., the clinical, radiological, and laboratory characteristics did not provide information on the genesis of the infection), 166 (43%) were classified as probably community cases, and 36 (9%) were defined as community cases. Deceased patients with nosocomial SARS-CoV-2 infection constituted the following: 3.23% (41/1266) with respect to the total number of COVID-19 deaths, 1.1% (41/3789) with respect to those who entered the hospital with a negative swab and 0.82% (41/4672) with respect to the total of deaths from any cause of death. In this paper we discuss the topic and issues of nosocomial COVID-19 in hospitalized patients and address the medicolegal implications.