Spatial Analysis of Determinants of COVID-19 Vaccine Hesitancy in Portugal.

Vaccine hesitancy tends to exhibit geographical patterns and is often associated with social deprivation and migrant status. We aimed to estimate COVID-19 vaccination hesitancy in a high-vaccination-acceptance country, Portugal, and determine its association with sociodemographic risk factors. We used the Registry of National Health System Users to determine the eligible population and the Vaccination Registry to determine individuals without COVID-19 vaccine doses. Individuals older than five with no COVID-19 vaccine dose administered by 31 March 2022 were considered hesitant. We calculated hesitancy rates by municipality, gender, and age group for all municipalities in mainland Portugal. We used the spatial statistical scan method to identify spatial clusters and the Besag, Yorke, and Mollié (BYM) model to estimate the effect of age, gender, social deprivation, and migrant proportion across all mainland municipalities. The eligible population was 9,852,283, with 1,212,565 (12%) COVID-19 vaccine-hesitant individuals. We found high-hesitancy spatial clusters in the Lisbon metropolitan area and the country's southwest. Our model showed that municipalities with higher proportions of migrants are associated with an increased relative risk (RR) of vaccine hesitancy (RR = 8.0; CI 95% 4.6; 14.0). Social deprivation and gender were not associated with vaccine hesitancy rates. We found COVID-19 vaccine hesitancy has a heterogeneous distribution across Portugal and has a strong association with the proportion of migrants per municipality.

Assessment of the Implementation of the International Health Regulations during the COVID-19 Pandemic: Portugal as a Case Study.

INTRODUCTION: The International Health Regulations (IHR) were developed to prepare countries to deal with public health emergencies. The spread of SARS-CoV-2 underlined the need for international coordination, although few attempts were made to evaluate the integrated implementation of the IHR's core capacities in response to the COVID-19 pandemic. The aim of this study was to evaluate whether IHR shortcomings stem from non-compliance or regulatory issues, using Portugal as a European case study due to its size, organization, and previous discrepancies between self-reporting and peer assessment of the IHR's core capacities. METHODS: Fifteen public health medical residents involved in contact tracing in mainland Portugal interpreted the effectiveness of the IHR's core capabilities by reviewing the publicly available evidence and reflecting on their own field experience, then grading each core capability according to the IHR Monitoring Framework. The assessment of IHR enforcement considered efforts made before and after the onset of the pandemic, covering the period up to July 2021. RESULTS: Four out of nine core IHR capacities (surveillance; response; risk communication; and human resource capacity) were classified as level 1, the lowest. Only two were graded level 3 (preparedness; and laboratory), the highest. The remaining three) (national legislation, policy & financing; coordination and national focal point communication; and points of entry) were classified as level 2. CONCLUSION: Portugal exemplifies the extent to which implementation of the IHR was not fully achieved, which has resulted in the underperformance of several core capacities. There is a need to improve preparedness and international cooperation in order to harmonize and strengthen the global response to public health emergencies, with better political, institutional, and financial support.

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.