Hospital Antibiotic Consumption before and during the COVID-19 Pandemic in Hungary

Ruzsa, Roxána; Benkő, Ria; Hambalek, Helga; Papfalvi, Erika; Csupor, Dezső; Nacsa, Róbert; Csatordai, Márta; Soós, Gyöngyvér; Hajdú, Edit; Matuz, Mária

doi:10.3390/antibiotics13010102

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…A retrospective drug-utilization study in Campania, southwestern Italy, found a higher prevalence of this third-generation cephalosporins (ATC J01DD) in COVID-19 positive adults under 40 and above 80, compared to the general population [ 32 ]. Additionally, a Hungarian study on hospital antibiotic consumption revealed a noteworthy 63.7% increase in the utilization of ATC J01DD from 2010 to 2019, with a substantial 70.46% surge during the pandemic years from 2019 to 2020 [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Benny,

Giacobini,

Costa

et al. 2024

BMC Med Res Methodol

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Background Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions. Methods The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling. Results From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68). Conclusion Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts.

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Section: Discussionmentioning

confidence: 99%

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Benny,

Giacobini,

Costa

et al. 2024

BMC Med Res Methodol

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Measuring hospital antibiotic consumption in EU/EEA countries: comparison of different metrics, 2017 to 2021

Rubinić,

Leung,

Högberg

et al. 2024

Eurosurveillance

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Background Antibiotic resistance poses a considerable public health threat, with data-driven stewardship a main prevention measure. While quantifying antibiotic consumption is a key component of antibiotic stewardship programmes, the choice of denominator for calculating this metric can impact comparative analyses and trend evaluations substantially, influencing targeted stewardship interventions. Aim We aim to evaluate how using hospital sector-specific antibiotic consumption rate denominators at country level impacts country rankings and trends, addressing the limitations of the commonly used 'defined daily doses (DDD) per 1,000 inhabitants per day' metric. Methods Hospital antibiotic consumption data from ESAC-Net and denominator data from Eurostat (‘inhabitants,’ ‘bed-days’ and ‘discharges’) for 2017–2021 were used to calculate hospital antibiotic consumption rates for 24 reporting European Union/ European Economic Area (EU/EEA) countries. Countries were ranked by their consumption rates and trends were analysed to assess the effects of using different denominators. Results Country rankings and 5-year trend analyses varied depending on the denominator used. Antibiotic consumption rates were more similar when using hospital activity-based denominators ‘bed-days’ and ‘discharges’ compared with the population-based ‘inhabitants’ denominator. Differences in country rankings and trends were also seen among rates derived using ‘bed-days’ and ‘discharges’. Conclusion The study underscores the importance of using hospital activity-based denominators such as ‘bed-days’ and ‘discharges’ when evaluating hospital antibiotic consumption. ESAC-Net’s historical reliance on only ‘DDD per 1,000 inhabitants per day’ is challenged, advocating for the use of multiple hospital activity-based denominators. Corresponding hospital activity denominators for ESAC-Net data will more effectively inform national hospital antibiotic stewardship interventions.

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Environmental Antimicrobial Resistance: Implications for Food Safety and Public Health

Ifedinezi,

Nnaji,

Anumudu

et al. 2024

Antibiotics

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Antimicrobial resistance (AMR) is a serious global health issue, aggravated by antibiotic overuse and misuse in human medicine, animal care, and agriculture. This study looks at the different mechanisms that drive AMR, such as environmental contamination, horizontal gene transfer, and selective pressure, as well as the severe implications of AMR for human and animal health. This study demonstrates the need for concerted efforts across the scientific, healthcare, agricultural, and policy sectors to control the emergence of AMR. Some crucial strategies discussed include developing antimicrobial stewardship (AMS) programs, encouraging targeted narrow-spectrum antibiotic use, and emphasizing the significance of strict regulatory frameworks and surveillance systems, like the Global Antimicrobial Resistance and Use Surveillance System (GLASS) and the Access, Watch, and Reserve (AWaRe) classification. This study also emphasizes the need for national and international action plans in combating AMR and promotes the One Health strategy, which unifies environmental, animal, and human health. This study concludes that preventing the spread of AMR and maintaining the effectiveness of antibiotics for future generations requires a comprehensive, multidisciplinary, and internationally coordinated strategy.

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Hospital Antibiotic Consumption before and during the COVID-19 Pandemic in Hungary

Cited by 3 publications

References 23 publications

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

Measuring hospital antibiotic consumption in EU/EEA countries: comparison of different metrics, 2017 to 2021

Environmental Antimicrobial Resistance: Implications for Food Safety and Public Health

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