Burden of disease attributable to risk factors in European countries: a scoping literature review.

OBJECTIVES: Within the framework of the burden of disease (BoD) approach, disease and injury burden estimates attributable to risk factors are a useful guide for policy formulation and priority setting in disease prevention. Considering the important differences in methods, and their impact on burden estimates, we conducted a scoping literature review to: (1) map the BoD assessments including risk factors performed across Europe; and (2) identify the methodological choices in comparative risk assessment (CRA) and risk assessment methods. METHODS: We searched multiple literature databases, including grey literature websites and targeted public health agencies websites. RESULTS: A total of 113 studies were included in the synthesis and further divided into independent BoD assessments (54 studies) and studies linked to the Global Burden of Disease (59 papers). Our results showed that the methods used to perform CRA varied substantially across independent European BoD studies. While there were some methodological choices that were more common than others, we did not observe patterns in terms of country, year or risk factor. Each methodological choice can affect the comparability of estimates between and within countries and/or risk factors, since they might significantly influence the quantification of the attributable burden. From our analysis we observed that the use of CRA was less common for some types of risk factors and outcomes. These included environmental and occupational risk factors, which are more likely to use bottom-up approaches for health outcomes where disease envelopes may not be available. CONCLUSIONS: Our review also highlighted misreporting, the lack of uncertainty analysis and the under-investigation of causal relationships in BoD studies. Development and use of guidelines for performing and reporting BoD studies will help understand differences, avoid misinterpretations thus improving comparability among estimates. REGISTRATION: The study protocol has been registered on PROSPERO, CRD42020177477 (available at: https://www.crd.york.ac.uk/PROSPERO/ ).

Effects of management strategies on animal welfare and productivity under heat stress: A synthesis.

Climate change includes different dramatic events, and among them, heat stress exposition is the strongest phenomenon affecting the livestock sector. The effects of heat stress events on animal welfare are complex and the economic impacts for the livestock sector are relevant. Management measures may contribute to improve the resilience to heat stress, but the extent to which they impact on livestock performances and management strategies depend on the magnitude of the stress conditions. Through a pioneering synthesis of existing knowledge from experiments conducted in controlled conditions, we show that management strategies, both adaptation and mitigation measures, halved the negative impacts on the ruminants' performances and welfare induced by heat stress, but the efficacy is low in extreme conditions, which in turn are more and more frequent. These novel findings emphasize the need to deepen research on more effective adaptation and mitigation measures.

Methodological considerations in injury burden of disease studies across Europe: a systematic literature review.

BACKGROUND: Calculating the disease burden due to injury is complex, as it requires many methodological choices. Until now, an overview of the methodological design choices that have been made in burden of disease (BoD) studies in injury populations is not available. The aim of this systematic literature review was to identify existing injury BoD studies undertaken across Europe and to comprehensively review the methodological design choices and assumption parameters that have been made to calculate years of life lost (YLL) and years lived with disability (YLD) in these studies. METHODS: We searched EMBASE, MEDLINE, Cochrane Central, Google Scholar, and Web of Science, and the grey literature supplemented by handsearching, for BoD studies. We included injury BoD studies that quantified the BoD expressed in YLL, YLD, and disability-adjusted life years (DALY) in countries within the European Region between early-1990 and mid-2021. RESULTS: We retrieved 2,914 results of which 48 performed an injury-specific BoD assessment. Single-country independent and Global Burden of Disease (GBD)-linked injury BoD studies were performed in 11 European countries. Approximately 79% of injury BoD studies reported the BoD by external cause-of-injury. Most independent studies used the incidence-based approach to calculate YLDs. About half of the injury disease burden studies applied disability weights (DWs) developed by the GBD study. Almost all independent injury studies have determined YLL using national life tables. CONCLUSIONS: Considerable methodological variation across independent injury BoD assessments was observed; differences were mainly apparent in the design choices and assumption parameters towards injury YLD calculations, implementation of DWs, and the choice of life table for YLL calculations. Development and use of guidelines for performing and reporting of injury BoD studies is crucial to enhance transparency and comparability of injury BoD estimates across Europe and beyond.

Multiple instance learning for lung pathophysiological findings detection using CT scans.

Lung diseases affect the lives of billions of people worldwide, and 4 million people, each year, die prematurely due to this condition. These pathologies are characterized by specific imagiological findings in CT scans. The traditional Computer-Aided Diagnosis (CAD) approaches have been showing promising results to help clinicians; however, CADs normally consider a small part of the medical image for analysis, excluding possible relevant information for clinical evaluation. Multiple Instance Learning (MIL) approach takes into consideration different small pieces that are relevant for the final classification and creates a comprehensive analysis of pathophysiological changes. This study uses MIL-based approaches to identify the presence of lung pathophysiological findings in CT scans for the characterization of lung disease development. This work was focus on the detection of the following: Fibrosis, Emphysema, Satellite Nodules in Primary Lesion Lobe, Nodules in Contralateral Lung and Ground Glass, being Fibrosis and Emphysema the ones with more outstanding results, reaching an Area Under the Curve (AUC) of 0.89 and 0.72, respectively. Additionally, the MIL-based approach was used for EGFR mutation status prediction - the most relevant oncogene on lung cancer, with an AUC of 0.69. The results showed that this comprehensive approach can be a useful tool for lung pathophysiological characterization.

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.

Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and "motivate" the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.

Burden of non-communicable disease studies in Europe: a systematic review of data sources and methodological choices.

BACKGROUND: Assessment of disability-adjusted life years (DALYs) resulting from non-communicable diseases (NCDs) requires specific calculation methods and input data. The aims of this study were to (i) identify existing NCD burden of disease (BoD) activities in Europe; (ii) collate information on data sources for mortality and morbidity; and (iii) provide an overview of NCD-specific methods for calculating NCD DALYs. METHODS: NCD BoD studies were systematically searched in international electronic literature databases and in grey literature. We included all BoD studies that used the DALY metric to quantify the health impact of one or more NCDs in countries belonging to the European Region. RESULTS: A total of 163 BoD studies were retained: 96 (59%) were single-country or sub-national studies and 67 (41%) considered more than one country. Of the single-country studies, 29 (30%) consisted of secondary analyses using existing Global Burden of Disease (GBD) results. Mortality data were mainly derived (49%) from vital statistics. Morbidity data were frequently (40%) drawn from routine administrative and survey datasets, including disease registries and hospital discharge databases. The majority (60%) of national BoD studies reported mortality corrections. Multimorbidity adjustments were performed in 18% of national BoD studies. CONCLUSION: The number of national NCD BoD assessments across Europe increased over time, driven by an increase in BoD studies that consisted of secondary data analysis of GBD study findings. Ambiguity in reporting the use of NCD-specific BoD methods underlines the need for reporting guidelines of BoD studies to enhance the transparency of NCD BoD estimates across Europe.

An Interpretable Approach for Lung Cancer Prediction and Subtype Classification using Gene Expression.

Lung cancer is the deadliest form of cancer, accounting for 20% of total cancer deaths. It represents a group of histologically and molecularly heterogeneous diseases even within the same histological subtype. Moreover, accurate histological subtype diagnosis influences the specific subtype's target genes, which will help define the treatment plan to target those genes in therapy. Deep learning (DL) models seem to set the benchmarks for the tasks of cancer prediction and subtype classification when using gene expression data; however, these methods do not provide interpretability, which is great concern from the perspective of cancer biology since the identification of the cancer driver genes in an individual provides essential information for treatment and prognosis. In this work, we identify some limitations of previous work that showed efforts to build algorithms to extract feature weights from DL models, and we propose using tree-based learning algorithms that address these limitations. Preliminary results show that our methods outperform those of related research while providing model interpretability.Clinical Relevance: The machine learning methods used in this work are interpretable and provide biological insight. Two sets of genes were extracted: a set that differentiates normal tissue from cancerous tissue (cancer prediction), and a set of genes that distinguishes LUAD from LUSC samples (subtype classification).

Attention Based Deep Multiple Instance Learning Approach for Lung Cancer Prediction using Histopathological Images.

Deep Neural Networks using histopathological images as an input currently embody one of the gold standards in automated lung cancer diagnostic solutions, with Deep Convolutional Neural Networks achieving the state of the art values for tissue type classification. One of the main reasons for such results is the increasing availability of voluminous amounts of data, acquired through the efforts employed by extensive projects like The Cancer Genome Atlas. Nonetheless, whole slide images remain weakly annotated, as most common pathologist annotations refer to the entirety of the image and not to individual regions of interest in the patient's tissue sample. Recent works have demonstrated Multiple Instance Learning as a successful approach in classification tasks entangled with this lack of annotation, by representing images as a bag of instances where a single label is available for the whole bag. Thus, we propose a bag/embedding-level lung tissue type classifier using Multiple Instance Learning, where the automated inspection of lung biopsy whole slide images determines the presence of cancer in a given patient. Furthermore, we use a post-model interpretability algorithm to validate our model's predictions and highlight the regions of interest for such predictions.

The Impact of Interstitial Diseases Patterns on Lung CT Segmentation.

Lung segmentation represents a fundamental step in the development of computer-aided decision systems for the investigation of interstitial lung diseases. In a holistic lung analysis, eliminating background areas from Computed Tomography (CT) images is essential to avoid the inclusion of noise information and spend unnecessary computational resources on non-relevant data. However, the major challenge in this segmentation task relies on the ability of the models to deal with imaging manifestations associated with severe disease. Based on U-net, a general biomedical image segmentation architecture, we proposed a light-weight and faster architecture. In this 2D approach, experiments were conducted with a combination of two publicly available databases to improve the heterogeneity of the training data. Results showed that, when compared to the original U-net, the proposed architecture maintained performance levels, achieving 0.894 ± 0.060, 4.493 ± 0.633 and 4.457 ± 0.628 for DSC, HD and HD-95 metrics, respectively, when using all patients from the ILD database for testing only, while allowing a more effficient computational usage. Quantitative and qualitative evaluations on the ability to cope with high-density lung patterns associated with severe disease were conducted, supporting the idea that more representative and diverse data is necessary to build robust and reliable segmentation tools.

Ensemble Strategies for EGFR Mutation Status Prediction in Lung Cancer.

Lung cancer treatments that are accurate and effective are urgently needed. The diagnosis of advanced-stage patients accounts for the majority of the cases, being essential to provide a specialized course of treatment. One emerging course of treatment relies on target therapy through the testing of biomarkers, such as the Epidermal Growth Factor Receptor (EGFR) gene. Such testing can be obtained from invasive methods, namely through biopsy, which may be avoided by applying machine learning techniques to the imaging phenotypes extracted from Computerized Tomography (CT). This study aims to explore the contribution of ensemble methods when applied to the prediction of EGFR mutation status. The obtained results translate in a direct correlation between the semantic predictive model and the outcome of the combined ensemble methods, showing that the utilized features do not have a positive contribution to the predictive developed models.

Sharing Biomedical Data: Strengthening AI Development in Healthcare.

Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data.

Cognitive aging in migraine sufferers is associated with more subjective complaints but similar age-related decline: a 5-year longitudinal study.

OBJECTIVES AND BACKGROUND: The effect of headache on cognitive performance is controversial, due to conflicting results obtained from studies in clinical or population settings. We aimed to understand if migraine and other headaches modify the rates of decline on different cognitive measures, during a 5-year interval. DESIGN AND METHOD: A cohort of community dwelling adults (> 50 years) with migraine (MH), non-migraine headaches (NMH) and controls without headache (WoH), was assessed by a comprehensive neuropsychological battery with tests of memory, language and executive functions, repeated 5 years apart. Change in performance between baseline and reevaluation was compared between groups, and controlled for age, gender, literacy and depressive symptoms. RESULTS: A total of 275 participants (78.5% WoH, 12.7% MH, 8.7% NMH) were reevaluated (average age 70.40 + 8.34 years, 64% females). Cognitive decline or dementia occurred in 11.4%, with a similar proportion among the three groups. Although MH participants had significantly more subjective cognitive complaints (p = 0.030, 95%CI:]-3.929,-0.014[), both MH and NMH subjects showed an age-associated decline identical to controls. Furthermore, migraine features (disease and attack duration, frequency and aura) were unrelated with cognitive performance. CONCLUSION: Migraine and non-migraine headache are not associated with increasing risk of dementia or cognitive decline at an older age although subjects with migraine have more cognitive complaints. Longer longitudinal studies are necessary to understand if this pattern persists for more than 5 years.

Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images.

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.

[Portuguese Consensus on the Diagnosis and Management of Lewy Body Dementia (PORTUCALE)]. / Consenso Português para o Diagnóstico e Gestão Clínica da Demência com Corpos de Lewy (PORTUCALE).

Lewy body dementia is a common cause of dementia leading to the progressive deterioration of cognitive function and motor skills, behavioral changes, and loss of autonomy, impairing the quality of life of patients and their families. Even though it is the second leading cause of neurodegenerative dementia, diagnosis is still challenging, due to its heterogenous clinical presentation, especially in the early stages of the disease. Accordingly, Lewy body dementia is often misdiagnosed and clinically mismanaged. The lack of diagnostic accuracy has important implications for patients, given their increased susceptibility to the adverse effects of certain drugs, such as antipsychotics, which may worsen some symptoms associated with Lewy body dementia. Therefore, a specialist consensus based on the analysis of the most updated and relevant literature, and on clinical experience, is useful to all professionals involved in the care of these patients. This work aims to inform and provide recommendations about the best diagnostic and therapeutic approaches in Lewy body dementia in Portugal. Moreover, we suggest some strategies in order to raise the awareness of physicians, policy makers, and the society at large regarding this disease.

A demência com corpos de Lewy é uma causa comum de demência, provocando a perda progressiva de funções cognitivas e capacidades motoras, alterações comportamentais, e perda de autonomia, com compromisso da qualidade de vida dos doentes e seus familiares. Apesar de ser a segunda causa mais frequente de demência neurodegenerativa, o diagnóstico mantém-se um desafio, devido à sua apresentação clínica heterogénea, sobretudo nas fases iniciais da doença. Por conseguinte, a demência com corpos de Lewy é frequentemente mal diagnosticada e clinicamente gerida de forma insuficiente. A falta de acuidade diagnóstica tem implicações significativas para os doentes, dada a maior suscetibilidade aos efeitos adversos de determinados fármacos, tais como os antipsicóticos, que podem agravar alguns sintomas associados à demência com corpos de Lewy. Por conseguinte, um consenso de especialistas, baseado na análise da literatura mais atual e relevante, e na experiência clínica, é útil para todos os profissionais envolvidos no cuidado destes doentes. O objetivo deste trabalho é informar e gerar recomendações acerca das melhores abordagens diagnóstica e terapêutica da demência com corpos de Lewy em Portugal. Além disso, sugerimos estratégias para aumentar a sensibilização dos médicos, dos decisores políticos e da sociedade em geral em relação a esta doença.

Predictors of cognitive stability or decline during aging: A longitudinal study in primary care.

We aimed to identify the early predictors of cognitive decline, and primary care physicians' (PCP) ability to diagnose cognitively impaired subjects, in a cohort of individuals recruited in primary care centers. Independent adults, aged ≥50 years at inception, with an overall low level of education, undertook a prospective clinical and cognitive evaluation targeting memory, attention and executive functions. At follow-up subjects were classified as cognitively normal (CN) or impaired (CI). Of 275 subjects (70.4 ± 8.3 years old, 176 females, 7.5 ± 4.4 education, 162 with MRI), 31 (11.2%) presented CI 4.9 years later, the majority (64.5%) presenting subjective cognitive complaints. PCP could correctly identify 40% of CI individuals, particularly if they presented current cognitive complaints. Male sex (OR = 3.117; CI95%: 1.007-9.645), age (OR = 1.063; CI95%: 1.004-1.126) and baseline scores on TMT-B (OR = 0.225; CI95%: 0.073-0.688) and Vocabulary (OR = 0.940; 95% CI: 0.894-0.986) predicted CI. This study shows that measures indicating poor cognitive reserve and low executive performance (as shown by low vocabulary and executive test scores, respectively) can be early indicators of the risk of decline, stressing the role of cognitive assessment as part of prevention/early intervention programs. The results also underline the need to help PCP to improve the detection of subjects with cognitive decline.

Frontotemporal Dementia Caused by the P301L Mutation in the MAPT Gene: Clinicopathological Features of 13 Cases from the Same Geographical Origin in Barcelona, Spain.

BACKGROUND/AIMS: We identified and studied 13 patients carrying the P301L mutation in the MAPT gene from the same area (Baix Llobregat County) in Barcelona, Spain. METHODS: The demographic and clinical features were reviewed retrospectively. Detailed neuropathological characterization was obtained in 9 subjects. To investigate the origin of the P301L mutation in these families, 20 single nucleotide polymorphisms (SNPs) in the MAPT gene were analyzed. RESULTS: The mean age at disease onset was 51 years and the mean disease duration was 7 years. The most common initial symptoms were behavioral changes (54%), followed by language disturbances (31%) and memory loss (15%). 46% developed parkinsonism. Neuropathology showed an extensive neuronal and glial 4-repeat (4R) tauopathy with "mini-Pick"-like bodies in the dentate gyrus as the characteristic underlying pathology in all cases. In 1 subject, additional 4R globular glial inclusions were observed. All the mutation carriers showed the same haplotype for the SNPs analyzed, suggesting a common ancestor. CONCLUSION: These findings suggest a relative homogeneous clinicopathological phenotype in P301L MAPT mutation carriers in our series. This phenotype might help in the differential diagnosis from other tauopathies and be a morphological hint for genetic testing. The haplotype analysis results suggest a founder effect of the P301L mutation in this area.

Avian influenza.

Previous introductions of highly pathogenic avian influenza virus (HPAIV) to the EU were most likely via migratory wild birds. A mathematical model has been developed which indicated that virus amplification and spread may take place when wild bird populations of sufficient size within EU become infected. Low pathogenic avian influenza virus (LPAIV) may reach similar maximum prevalence levels in wild bird populations to HPAIV but the risk of LPAIV infection of a poultry holding was estimated to be lower than that of HPAIV. Only few non-wild bird pathways were identified having a non-negligible risk of AI introduction. The transmission rate between animals within a flock is assessed to be higher for HPAIV than LPAIV. In very few cases, it could be proven that HPAI outbreaks were caused by intrinsic mutation of LPAIV to HPAIV but current knowledge does not allow a prediction as to if, and when this could occur. In gallinaceous poultry, passive surveillance through notification of suspicious clinical signs/mortality was identified as the most effective method for early detection of HPAI outbreaks. For effective surveillance in anseriform poultry, passive surveillance through notification of suspicious clinical signs/mortality needs to be accompanied by serological surveillance and/or a virological surveillance programme of birds found dead (bucket sampling). Serosurveillance is unfit for early warning of LPAI outbreaks at the individual holding level but could be effective in tracing clusters of LPAIV-infected holdings. In wild birds, passive surveillance is an appropriate method for HPAIV surveillance if the HPAIV infections are associated with mortality whereas active wild bird surveillance has a very low efficiency for detecting HPAIV. Experts estimated and emphasised the effect of implementing specific biosecurity measures on reducing the probability of AIV entering into a poultry holding. Human diligence is pivotal to select, implement and maintain specific, effective biosecurity measures.

Avian influenza overview October 2016-August 2017.

The A(H5N8) highly pathogenic avian influenza (HPAI) epidemic occurred in 29 European countries in 2016/2017 and has been the largest ever recorded in the EU in terms of number of poultry outbreaks, geographical extent and number of dead wild birds. Multiple primary incursions temporally related with all major poultry sectors affected but secondary spread was most commonly associated with domestic waterfowl species. A massive effort of all the affected EU Member States (MSs) allowed a descriptive epidemiological overview of the cases in poultry, captive birds and wild birds, providing also information on measures applied at the individual MS level. Data on poultry population structure are required to facilitate data and risk factor analysis, hence to strengthen science-based advice to risk managers. It is suggested to promote common understanding and application of definitions related to control activities and their reporting across MSs. Despite a large number of human exposures to infected poultry occurred during the ongoing outbreaks, no transmission to humans has been identified. Monitoring the avian influenza (AI) situation in other continents indicated a potential risk of long-distance spread of HPAI virus (HPAIV) A(H5N6) from Asia to wintering grounds towards Western Europe, similarly to what happened with HPAIV A(H5N8) and HPAIV A(H5N1) in previous years. Furthermore, the HPAI situation in Africa with A(H5N8) and A(H5N1) is rapidly evolving. Strengthening collaborations at National, EU and Global levels would allow close monitoring of the AI situation, ultimately helping to increase preparedness. No human case was reported in the EU due to AIVs subtypes A(H5N1), A(H5N6), A(H7N9) and A(H9N2). Direct transmission of these viruses to humans has only been reported in areas, mainly in Asia and Egypt, with a substantial involvement of wild bird and/or poultry populations. It is suggested to improve the collection and reporting of exposure events of people to AI.

Ad hoc method for the assessment on listing and categorisation of animal diseases within the framework of the Animal Health Law.

The European Commission has requested EFSA to assess animal diseases according to the criteria as laid down in Articles 5, 7, 8 and Annex IV for the purpose of categorisation of diseases in accordance with Article 9 of the Regulation (EU) No 2016/429 (Animal Health Law). This scientific opinion addresses the ad hoc method developed for assessing any animal disease for the listing and categorisation of diseases within the Animal Health Law (AHL) framework. The assessment of individual diseases is addressed in distinct scientific opinions that are published separately. The assessment of Articles 5, 8 and 9 criteria is performed on the basis of the information collected according to Article 7 criteria. For that purpose, Article 7 criteria were structured into parameters and the information was collected at parameter level. The resulting fact sheets on the profile and impact of each disease were compiled by disease scientists. A mapping was developed to identify which parameters from Article 7 were needed to inform each Article 5, 8 and 9 criterion. Specifically, for Articles 5 and 9 criteria, a categorical assessment was performed, by applying an expert judgement procedure, based on the mapped information. The judgement was performed by EFSA Panel experts on Animal Health and Welfare in two rounds, individual and collective judgement. The output of the expert judgement on the criteria of Articles 5 and 9 for each disease is composed by the categorical answer, and for the questions where no consensus was reached, the different supporting views are reported.

Assessment of listing and categorisation of animal diseases within the framework of the Animal Health Law (Regulation (EU) No 2016/429): Aujeszky's disease.

Aujeszky's disease has been assessed according to the criteria of the Animal Health Law (AHL), in particular criteria of Article 7 on disease profile and impacts, Article 5 on the eligibility of Aujeszky's disease to be listed, Article 9 for the categorisation of Aujeszky's disease according to disease prevention and control rules as in Annex IV and Article 8 on the list of animal species related to Aujeszky's disease. The assessment has been performed following a methodology composed of information collection and compilation, expert judgement on each criterion at individual and, if no consensus was reached before, also at collective level. The output is composed of the categorical answer, and for the questions where no consensus was reached, the different supporting views are reported. Details on the methodology used for this assessment are explained in a separate opinion. According to the assessment performed, Aujeszky's disease can be considered eligible to be listed for Union intervention as laid down in Article 5(3) of the AHL. The disease would comply with the criteria as in sections 4 and 5 of Annex IV of the AHL, for the application of the disease prevention and control rules referred to in points (d) and (e) of Article 9(1). The animal species to be listed for Aujeszky's disease according to Article 8(3) criteria are mainly the species of the family of Suidae as susceptible species, although almost all mammals can be infected, and Sus scrofa as reservoir species.