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Gender Differences in Years of Life Lost resulting from the 2015 Gorkha Earthquake in Nepal
No full textAssessing mental health from registry data: What is the best proxy?
No full textObjective
Medical registries frequently underestimate the prevalence of health problems compared with surveys. This study aimed to determine the registry variables that can serve as a proxy for variables studied in a mental health survey.
Materials and methods
Prevalences of depressive symptoms, anxiety and psychoactive medication use from the 2018 Belgian Health Interview Survey (HIS) were compared with same-year prevalences from INTEGO, a Belgian primary care registry. Participants aged 15 and above were included. We assessed correlation using Spearman’s rho (SR), and agreement using the intraclass correlation coefficient (ICC). We also calculated the limits of agreement (LOAs) for each comparison. HIS questions about depressive symptoms, anxiety and psychoactive medication use were compared with the following variables from INTEGO: symptom codes, diagnosis codes, free text, antidepressant/benzodiazepine prescriptions and the combinations symptom + diagnosis codes and symptom + diagnosis codes + free text, wherever relevant.
Results and discussion
Correlation between the HIS and INTEGO was generally high, except for anxiety. Agreement ranged from fair to poor, but increased when combining certain variables, by including free text, or by increasing the prescription frequency to resemble chronic use. Agreement remained poor when comparing questions about anxiety. Prevalences from INTEGO were mostly underestimates.
Conclusion
The external validity of medical registries can be poor, especially compared with survey data. A considerate choice of variables and prescription chronicity is needed to accurately use a registry as a surveillance tool for mental health.</p
Monitoring community antibiotic consumption in Belgium: reimbursement versus retail data (2013–22)
No full textBackground and objectives
In Belgium, monitoring antibiotic consumption relies on reimbursement data, which is obtained with a time delay and does not account for over-the-counter or nonreimbursed products. This study aims to bridge this gap by comparing reimbursement and retail data for primary care to understand variations and assess the accuracy of current surveillance methods.
Method
Reimbursement data were obtained from the National Institute for Health and Disability Insurance, and retail data were obtained from IQVIA for the period 2013–22. The community consumption of systemic antibiotics was expressed in defined daily doses (DDD—WHO ATC/DDD Index 2023) per inhabitants per day (DID). Relative differences in DID (RDs) based on the two data sets were computed and validated through Bland–Altman plots and correlation analysis.
Results
The sales of antibiotics declined from 22.89 DID (2013) to 20.50 (2022), with a steep drop during the COVID-19 pandemic—from 21.31 DID in 2019 to 16.55 DID in 2020—and a subsequent rebound. Reimbursement data slightly underestimated consumption compared to retail data, with RDs ranging from 2% (2013) to 9% (2022) when including quinolones and from 2% to 4% when excluding them. Bland–Altman plots showed high agreement between reimbursement and retail estimates, identifying quinolones as outliers.
Conclusion
Our findings suggest that reimbursement data are generally reliable for monitoring antibiotic consumption, but incorporating retail data is crucial for accurate assessments. The use of retail data can facilitate timely interventions and inform public health strategies to effectively address antimicrobial resistance.</p
Healthcare-associated infections and antimicrobial use in Belgian acute care hospitals: Comparison of the 2017 and 2022 point prevalence survey results
No full textFT-IR spectroscopy as a rapid method for Salmonella spp. typing
No full textIntroduction
Salmonella is a major foodborne pathogen, leading cause of non-typhoidal human salmonellosis. Eggs and egg products are most often implicated, although it can contaminate many food products. Fourier transform (FT-IR) spectroscopy allows for a rapid and cost-effective typing method of this bacterium at the subspecies levela, which is crucial for the early management of foodborne outbreaks. However, implementing it in a routine laboratory requires the development of a standardized protocol due to the high sensitivity of the method.
Materials and Methods
115 Salmonella isolates fully characterized by whole genome sequencing were included in the study. The strains, originating from food, feed and primary production, were collected during official controls by the FASFC in 2023. Of those 76 belonged to the serogroup O:4: Salmonella Typhimurium (n=19), S. Monophasic Typhimurium (n=19), S. Derby (n=18), S. Paratyphi B var Java (n=20); 15 to O:7 (S. Infantis) and 24 to O:9 (S. Enteritidis). The selected serotypes are under particular surveillance in the European Unionb. The portion of the spectrum studied reflects the carbohydrate composition of the outer membrane (1200 to 900 cm-1). Three biological replicates were tested for each single isolate, each of these was analyzed three times. Cluster analysis was done from the means of the spectra represented by scatter plots and dendrograms using principal component analysis (PCA) or linear discriminant analysis (LDA). Additionally, the classification capacity of the serogroups by the software’s integrated classifier was evaluated.
Discussion
Cluster analysis revealed a discrimination power of 88,7% (102/115 isolates), of serogroups O:4, O:7, and O:9 using LDA. This can be explained by the selected spectral region, which reflects the carbohydrate composition, the main components of the somatic O-antigens determining the serogroup. Discriminating serotypes within serogroup O:4 would require more in-depth analysis to increase the performance. S. Infantis and S. Enteritidis, being the only representatives of the O:7 and O:9 serogroup respectively, do not allow for conclusions. The classifier’s accuracy is 85.2% at O-group level (84,2% for O:4; 93,3% for O:7; 83,3% for O:9). Serotypes that displayed the higher rate of misclassification were S. Typhimurium serotype and its monophasic variant (both 26,3%). Whether through cluster analysis or serogroup determination by the classifier, 13 isolates were misclassified (11,3%). Further investigations are ongoing.</p
Consensus process for a data quality and utility label
No full textEnsuring high-quality health data is critical for effective decision-making and interoperability within the European Health Data Space (EHDS). As part of the QUANTUM project, this study developed a comprehensive framework for assessing data quality and utility through a modified Delphi method. The present study builds upon the results from QUANTUM Task 1.1, which involved an extensive and systematic literature review along with individual expert consultations. This process produced a set of 54 data quality (DQ) dimensions, which were the input for the actual study. A modified Delphi method, utilizing the RAND/UCLA appropriateness method, was conducted to reach consensus among experts on the most relevant dimensions. This iterative process involved two rounds, leveraging a 9-point Likert scale and statistical measures such as the Interpercentile Range Adjusted for Symmetry (IPRAS) to evaluate agreement. The study highlights critical considerations, including representativeness of respondents, potential biases, and methodological rigour. Results reveal a refined set of 12 prioritized data quality dimensions essential for creating a robust data quality and utility labelling tool. This paper discusses the methodology, key findings, and implications for future developments in data quality assessment for the EHDS.</p
Azithromycin resistance in Escherichia coli and Salmonella from food-producing animals and meat in Europe.
No full textOBJECTIVES: To characterize the genetic basis of azithromycin resistance in Escherichia coli and Salmonella collected within the EU harmonized antimicrobial resistance (AMR) surveillance programme in 2014-18 and the Danish AMR surveillance programme in 2016-19.
METHODS: WGS data of 1007 E. coli [165 azithromycin resistant (MIC > 16 mg/L)] and 269 Salmonella [29 azithromycin resistant (MIC > 16 mg/L)] were screened for acquired macrolide resistance genes and mutations in rplDV, 23S rRNA and acrB genes using ResFinder v4.0, AMRFinder Plus and custom scripts. Genotype-phenotype concordance was determined for all isolates. Transferability of mef(C)-mph(G)-carrying plasmids was assessed by conjugation experiments.
RESULTS: mph(A), mph(B), mef(B), erm(B) and mef(C)-mph(G) were detected in E. coli and Salmonella, whereas erm(C), erm(42), ere(A) and mph(E)-msr(E) were detected in E. coli only. The presence of macrolide resistance genes, alone or in combination, was concordant with the azithromycin-resistant phenotype in 69% of isolates. Distinct mph(A) operon structures were observed in azithromycin-susceptible (n = 50) and -resistant (n = 136) isolates. mef(C)-mph(G) were detected in porcine and bovine E. coli and in porcine Salmonella enterica serovar Derby and Salmonella enterica 1,4, [5],12:i:-, flanked downstream by ISCR2 or TnAs1 and associated with IncIγ and IncFII plasmids.
CONCLUSIONS: Diverse azithromycin resistance genes were detected in E. coli and Salmonella from food-producing animals and meat in Europe. Azithromycin resistance genes mef(C)-mph(G) and erm(42) appear to be emerging primarily in porcine E. coli isolates. The identification of distinct mph(A) operon structures in susceptible and resistant isolates increases the predictive power of WGS-based methods for in silico detection of azithromycin resistance in Enterobacterales.</p
Real-time cluster detection of Listeria to enhance outbreak investigation in Belgium
No full textIntroduction
In Belgium, the annual incidence of Listeria monocytogenes infections reported at the National Reference Centre (NRC) ranges between 0.65 and 0.75 cases/100 000 inhabitants over the last ten years. Although fluctuations in total numbers were usually related to fluctuations in serotype ½a prevalence, since 2019 this trend has changed to fluctuations in serotype 4b. The main cause of these fluctuations are small and large outbreaks. Therefore, it is key to closely monitor the genetic relatedness of clinical strains to enable real-time tracking of potential outbreaks and initiate further investigations promptly.
Materials and Methods
Since 2020, each clinical isolate sent to the NRC is sequenced on a weekly basis by Next-Generation sequencing. The obtained data are analysed using an in-house Listeria pipeline (v1.2) in Galaxy, which amongst other analyses performs cgMLST (core genome MultiLocus Sequence Typing) based on the scheme of Moura et al 2016. Cluster analyses are performed after uploading the profiles in BioNumerics v8. A genetic cluster is defined from the moment that at least two isolates have a maximum of 7 allelic differences (AD) within a 12-month timeframe. All genetically related strains that fall outside this timeframe, are subsequently added to the cluster.
Discussion
From January 2020 until June 2024, a total of 26 ‘active’ genetic clusters of Belgian clinical isolates were identified; involving 168 cases, 10 reported deaths and 8 reported fatal pregnancy outcomes. Thirteen of the clusters are of serotype 4b (92 cases), while 11 clusters are of serotype ½a (65 cases). Twenty clusters contain a smaller number of cases (2-6), whereas 6 clusters consist of a larger number of cases (8 to 35). Additionally, 12 clusters span a period of more than 2 years (range 2-13 years), indicating a persistent source. Seven of the clusters (of which 6 persistent, and 2 notified by our NRC) are part of a cross-border event/investigation, with smoked salmon, soft cheese, and pig meat as potential sources. For 2 of the 4 clusters reported internationally by our NRC, no closely related international isolates were detected, suggesting a national food source, although it remains unidentified.
The four outbreaks with the highest number of cases were (1) cluster 2020_Beta: 35 cases of CC4 (2020, 2021, 2022), no internationally related cases, potential link to meat salad but the direct source was not identified; (2) cluster 2023_Alpha: 22 cases of CC1 (2019, 2020, 2023, 2024), 6 international cases involved, source identified as pig meat of Belgian origin; (3) cluster 2017_Alpha: 17 cases of CC155 (2016-2023), 10 countries involved, related to smoked fish products of Lithuanian origin; (4) cluster 2014_Alpha: 14 cases of CC14 (2014, 2018, 2019, 2020, 2021), 9 Swedish cases involved, related to smoked Salmon of Norwegian origin.</p