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- Krulichova, I. S., et al.
(författare)
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Comparison of drug prescribing before and during the COVID-19 pandemic: A cross-national European study
- 2022
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Ingår i: Pharmacoepidemiology and Drug Safety. - : Wiley. - 1053-8569 .- 1099-1557. ; 31:10, s. 1046-1055
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The COVID-19 pandemic had an impact on health care, with disruption to routine clinical care. Our aim was to describe changes in prescription drugs dispensing in the primary and outpatient sectors during the first year of the pandemic across Europe. Methods We used routine administrative data on dispensed medicines in eight European countries (five whole countries, three represented by one region each) from January 2017 to March 2021 to compare the first year of the COVID-19 pandemic with the preceding 3 years. Results In the 10 therapeutic subgroups with the highest dispensed volumes across all countries/regions the relative changes between the COVID-19 period and the year before were mostly of a magnitude similar to changes between previous periods. However, for drugs for obstructive airway diseases the changes in the COVID-19 period were stronger in several countries/regions. In all countries/regions a decrease in dispensed DDDs of antibiotics for systemic use (from -39.4% in Romagna to -14.2% in Scotland) and nasal preparations (from -34.4% in Lithuania to -5.7% in Sweden) was observed. We observed a stockpiling effect in the total market in March 2020 in six countries/regions. In Czechia the observed increase was not significant and in Slovenia volumes increased only after the end of the first lockdown. We found an increase in average therapeutic quantity per pack dispensed, which, however, exceeded 5% only in Slovenia, Germany, and Czechia. Conclusions The findings from this first European cross-national comparison show a substantial decrease in dispensed volumes of antibiotics for systemic use in all countries/regions. The results also indicate that the provision of medicines for common chronic conditions was mostly resilient to challenges faced during the pandemic. However, there were notable differences between the countries/regions for some therapeutic areas.
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- Shakibfar, S., et al.
(författare)
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Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review
- 2023
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Ingår i: Frontiers In Public Health. - : Frontiers Media SA. - 2296-2565. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources. Study eligibility criteria: Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data sources: Articles recorded in OvidMEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction: We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment: A bias assessment of AI models was done using PROBAST. Participants: Patients tested positive for COVID-19. Results: We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions: A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.
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- Montgomery, Scott M., 1961-, et al.
(författare)
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Multiple sclerosis and risk of young-adult-onset Hodgkin lymphoma
- 2016
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Ingår i: Neurology. - Philadelphia, USA : Lippincott Williams & Wilkins. - 2332-7812. ; 3:3
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To determine whether there is an association between multiple sclerosis (MS) and young-adult-onset Hodgkin lymphoma (YAHL) as this will signal etiologic similarities relevant both to inherited characteristics and environmental exposures in childhood.Methods: Swedish general population registers identified a cohort of 29,617 with an MS diagnosis between 1968 and 2012, matched with a cohort of 296,164 without MS. Cox regression was used to assess the association of MS with subsequent YAHL (defined as onset between ages 15 and 39 years; n = 20), with adjustment, for age/period, sex, county of residence, and level of education.Results: The adjusted hazard ratio (and 95% confidence interval) for the association of MS with YAHL is 3.30 (1.01-10.73), resulting from 4 and 16 events in the MS and non-MS cohorts, respectively. All 4 of the YAHL diagnoses in MS occurred in women, and the association of MS with YAHL has a hazard ratio of 4.04 (1.17-13.94) among women. There was no notable association of MS with older-onset Hodgkin lymphoma.Conclusion: There may be common risks for YAHL and MS, consistent with an etiologic role in MS for early-life exposures, such as to infectious agents.
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