Contribution of Food to the Human Health Burden of Antimicrobial Resistance.

The impact of foodborne antimicrobial resistance (AMR) on the human health burden of AMR infections is unknown. The aim of this review was to evaluate and summarize the scientific literature investigating all potential sources of human AMR infections related to food. A literature search was conducted in Embase (Ovid) and MEDLINE (Ovid) databases to identify appropriate studies published between 2010 and 2023. The results of the search were reviewed and categorized based on the primary subject matter. Key concepts from each category are described from the perspective of food safety as a public health objective. The search yielded 3457 references, 1921 remained after removal of duplicates, abstracts, editorials, comments, notes, retractions, and errata. No properly designed source attribution studies were identified, but 383 journal articles were considered relevant and were classified into eight subcategories and discussed in the context of four streams of evidence: prevalence data, epidemiological studies, outbreak investigations and human health impact estimates. There was sufficient evidence to conclude that AMR genes, whether present in pathogenic or nonpathogenic bacteria, constitute a foodborne hazard. The level of consumer risk owing to this hazard cannot be accurately estimated based on the data summarized here. Key gaps in the literature are noted.

Creating a Medication Therapy Observational Research Database from an Electronic Medical Record: Challenges and Data Curation.

BACKGROUND: Observational research has shown its potential to complement experimental research and clinical trials by secondary use of treatment data from hospital care processes. It can also be applied to better understand pediatric drug utilization for establishing safer drug therapy. Clinical documentation processes often limit data quality in pediatric medical records requiring data curation steps, which are mostly underestimated. OBJECTIVES: The objectives of this study were to transform and curate data from a departmental electronic medical record into an observational research database. We particularly aim at identifying data quality problems, illustrating reasons for such problems and describing the systematic data curation process established to create high-quality data for observational research. METHODS: Data were extracted from an electronic medical record used by four wards of a German university children's hospital from April 2012 to June 2020. A four-step data preparation, mapping, and curation process was established. Data quality of the generated dataset was firstly assessed following an established 3 × 3 Data Quality Assessment guideline and secondly by comparing a sample subset of the database with an existing gold standard. RESULTS: The generated dataset consists of 770,158 medication dispensations associated with 89,955 different drug exposures from 21,285 clinical encounters. A total of 6,840 different narrative drug therapy descriptions were mapped to 1,139 standard terms for drug exposures. Regarding the quality criterion correctness, the database was consistent and had overall a high agreement with our gold standard. CONCLUSION: Despite large amounts of freetext descriptions and contextual knowledge implicitly included in the electronic medical record, we were able to identify relevant data quality issues and to establish a semi-automated data curation process leading to a high-quality observational research database. Because of inconsistent dosage information in the original documentation this database is limited to a drug utilization database without detailed dosage information.