The means by which patients acquire their medications differ between countries, and a knowledge of this is essential when conducting and interpreting pharmacoepidemiological studies. The aim of this paper is to provide an overview of how patients obtain medicines in Denmark, to relate these to nationwide registries available for research and to discuss the implications for research. Health services are predominantly tax-funded in Denmark, with dentistry and some medicine bought at community pharmacies being exceptions, involving partial reimbursement of charges. The paper gives an overview of prescription medicines acquired from community pharmacies (including magistral preparations), over-the-counter medicines, vaccinations and in-hospital medicine including so-called "free medicine" (in Danish: "vederlagsfri medicin"). "Free medicine" is medicines for a defined list of diseases and indications that is provided free of charge to patients in outpatient clinics. The paper also describes the content of the various Danish data sources about medicine use, summarizes their strengths and limitations, and exemplifies the ways of evaluating their completeness. An example is provided of the regional variation in the means by which medicines are acquired. K E Y W O R D S drug prescriptions (MeSH), electronic health records (MeSH), in-hospital drug use, nonprescription drugs (MeSH), pharmacoepidemiology (MeSH) | 47 JENSEN Et al.
The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.
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