Task Force for a rapid response to an outbreak of severe acute hepatitis of unknown aetiology in children in Portugal in 2022.

On 5 April 2022, the United Kingdom reported an increase of cases of severe acute hepatitis of unknown aetiology in children, several needing hospitalisation and some required liver transplant or died. Thereafter, 35 countries reported probable cases, almost half of them in Europe. Facing the alert, on 28 April, Portugal created a multidisciplinary Task Force (TF) for rapid detection of probable cases and response. The experts of the TF came from various disciplines: clinicians, laboratory experts, epidemiologists, public health experts and national and international communication. Moreover, Portugal adopted the European Centre for Disease Prevention and Control (ECDC) and the World Health Organization (WHO) case definition and recommendations. By 31 December 2022, 28 probable cases of severe acute hepatitis of unknown aetiology were reported: 16 male and 17 aged under 2 years. Of these cases, 23 were hospitalised but none required liver transplant or died. Adenovirus was detected from nine of 26 tested cases. No association was observed between adenovirus infection and hospital admission after adjusting for age, sex and region in a binomial regression model. The TF in Portugal may have contributed to increase awareness among clinicians, enabling early detection and prompt management of the outbreak.

Real-Time Classification of Causes of Death Using AI: Sensitivity Analysis.

BACKGROUND: In 2021, the European Union reported >270,000 excess deaths, including >16,000 in Portugal. The Portuguese Directorate-General of Health developed a deep neural network, AUTOCOD, which determines the primary causes of death by analyzing the free text of physicians' death certificates (DCs). Although AUTOCOD's performance has been established, it remains unclear whether its performance remains consistent over time, particularly during periods of excess mortality. OBJECTIVE: This study aims to assess the sensitivity and other performance metrics of AUTOCOD in classifying underlying causes of death compared with manual coding to identify specific causes of death during periods of excess mortality. METHODS: We included all DCs between 2016 and 2019. AUTOCOD's performance was evaluated by calculating various performance metrics, such as sensitivity, specificity, positive predictive value (PPV), and F1-score, using a confusion matrix. This compared International Statistical Classification of Diseases and Health-Related Problems, 10th Revision (ICD-10), classifications of DCs by AUTOCOD with those by human coders at the Directorate-General of Health (gold standard). Subsequently, we compared periods without excess mortality with periods of excess, severe, and extreme excess mortality. We defined excess mortality as 2 consecutive days with a Z score above the 95% baseline limit, severe excess mortality as 2 consecutive days with a Z score >4 SDs, and extreme excess mortality as 2 consecutive days with a Z score >6 SDs. Finally, we repeated the analyses for the 3 most common ICD-10 chapters focusing on block-level classification. RESULTS: We analyzed a large data set comprising 330,098 DCs classified by both human coders and AUTOCOD. AUTOCOD demonstrated high sensitivity (≥0.75) for 10 ICD-10 chapters examined, with values surpassing 0.90 for the more prevalent chapters (chapter II-"Neoplasms," chapter IX-"Diseases of the circulatory system," and chapter X-"Diseases of the respiratory system"), accounting for 67.69% (223,459/330,098) of all human-coded causes of death. No substantial differences were observed in these high-sensitivity values when comparing periods without excess mortality with periods of excess, severe, and extreme excess mortality. The same holds for specificity, which exceeded 0.96 for all chapters examined, and for PPV, which surpassed 0.75 in 9 chapters, including the more prevalent ones. When considering block classification within the 3 most common ICD-10 chapters, AUTOCOD maintained a high performance, demonstrating high sensitivity (≥0.75) for 13 ICD-10 blocks, high PPV for 9 blocks, and specificity of >0.98 in all blocks, with no significant differences between periods without excess mortality and those with excess mortality. CONCLUSIONS: Our findings indicate that, during periods of excess and extreme excess mortality, AUTOCOD's performance remains unaffected by potential text quality degradation because of pressure on health services. Consequently, AUTOCOD can be dependably used for real-time cause-specific mortality surveillance even in extreme excess mortality situations.

Mumps outbreak among fully vaccinated school-age children and young adults, Portugal 2019/2020.

On 16-17 January 2020, four suspected mumps cases were reported to the local Public Health Authorities with an epidemiological link to a local school and football club. Of 18 suspected cases identified, 14 were included in this study. Laboratory results confirmed mumps virus as the cause and further sequencing identified genotype G. Our findings highlight that even with a high MMR vaccine coverage, mumps outbreaks in children and young adults can occur. Since most of the cases had documented immunity for mumps, we hypothesise that waning immunity or discordant mumps virus strains are likely explanations for this outbreak.