BackgroundAdverse drug events, the unintended and harmful effects of medications, are important outcome measures in health services research. Yet no universally accepted set of International Classification of Diseases (ICD) revision 10 codes or coding algorithms exists to ensure their consistent identification in administrative data. Our objective was to synthesize a comprehensive set of ICD-10 codes used to identify adverse drug events.MethodsWe developed a systematic search strategy and applied it to five electronic reference databases. We searched relevant medical journals, conference proceedings, electronic grey literature and bibliographies of relevant studies, and contacted content experts for unpublished studies. One author reviewed the titles and abstracts for inclusion and exclusion criteria. Two authors reviewed eligible full-text articles and abstracted data in duplicate. Data were synthesized in a qualitative manner.ResultsOf 4241 titles identified, 41 were included. We found a total of 827 ICD-10 codes that have been used in the medical literature to identify adverse drug events. The median number of codes used to search for adverse drug events was 190 (IQR 156–289) with a large degree of variability between studies in the numbers and types of codes used. Authors commonly used external injury (Y40.0–59.9) and disease manifestation codes. Only two papers reported on the sensitivity of their code set.ConclusionsSubstantial variability exists in the methods used to identify adverse drug events in administrative data. Our work may serve as a point of reference for future research and consensus building in this area.

BackgroundAdverse reactions and medication errors are complications of drug use. Spontaneous reporting systems and pharmacoepidemiological studies incompletely detect the occurrence of these events in daily hospital care. In this study, the frequency and type of drug-related admissions and hospital-acquired adverse drug events (ADE) in Germany were assessed using routinely collected hospital data.MethodsThe study was based on aggregated hospital routine data covering the period 2003 to 2007 and annually recorded as part of the further development of the German Diagnosis-Related Groups. The 505 ICD-10-codes indicating an ADE were categorized in seven groups according to their certainty. Primary diagnoses were considered as a proxy for drug-related admissions, and secondary diagnoses as a proxy for hospital-acquired ADE.ResultsAmong all hospital admissions, 5% were found to be at least possibly drug-induced and 0.7% very likely drug-induced. There was a significant increase in the overall rate of drug-related admissions over time (p < 0.038). Enterocolitis due to Clostridium difficile infection was the most frequent cause of a drug-related admission. About 4.5% of in-patients had experienced a hospital-acquired ADE. In addition, over the course of the study period, the overall frequency of hospital-acquired ADEs significantly increased (p < 0.001).ConclusionsIn Germany, more than 5% of hospital episodes are either caused or complicated by an ADE. Between 2003 and 2007, there was a statistically significant increase in the overall rate and in some of the subcategories defined by the list of ICD-10-codes suspected to be indicative of an ADE. Before the use of routine data in pharmacovigilance and patient safety can be fully exploited, a further tailoring of both the ICD and the available variable set is needed.

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Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP). Results The data quality framework comprises 34 data quality indicators. These target four aspects of data quality: compliance with pre-specified structural and technical requirements (integrity); presence of data values (completeness); inadmissible or uncertain data values and contradictions (consistency); unexpected distributions and associations (accuracy). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses but the developments are also of relevance beyond research.

BackgroundAdverse drug events are a frequent cause of emergency department presentations. Administrative data could be used to identify patients presenting with adverse drug events for post-market surveillance, and to conduct research in patient safety and in drug safety and effectiveness. However, such data sources have not been evaluated for their completeness with regard to adverse drug event reporting. Our objective was to determine the proportion of adverse drug events to outpatient medications diagnosed at the point-of-care in emergency departments that were documented in administrative data.MethodsWe linked the records of patients enrolled in a prospective observational cohort study on adverse drug events conducted in two Canadian tertiary care emergency departments to their administrative data. We compared the number of adverse drug events diagnosed and recorded at the point-of-care in the prospective study with the number of adverse drug events recorded in the administrative data.ResultsAmong 1574 emergency department visits, 221 were identified as adverse drug event-related in the prospective database. We found 15 adverse drug events documented in administrative records with ICD-10 codes clearly indicating an adverse drug event, indicating a sensitivity of 6.8% (95% CI 4.0–11.2%) of this code set. When the ICD-10 code categories were broadened to include codes indicating a very likely, likely or possible adverse event to a medication, 62 of 221 events were identifiable in administrative data, corresponding to a sensitivity of 28.1% (95% CI 22.3-34.6%).ConclusionsAdverse drug events to outpatient medications were underreported in emergency department administrative data compared to the number of adverse drug events diagnosed and recorded at the point-of-care.

BackgroundAdverse drug events (ADEs) are frequent in hospitals, occurring either in patients before admission or as a nosocomial event, and either as a drug reaction or as a consequence of a medication error. Routine data primarily recorded for reimbursement purposes are increasingly being used on a national level both in pharmacoepidemiological studies and in trigger tools. The aim of this study was to compare the prevalence rates of coded ADEs in hospitals on a transnational level.MethodsHospital data for England and the USA were obtained for the fiscal or calendar year 2006. German data for 2006 were accessed via teleprocessing with the Federal Statistical Office. The datasets from England and the USA were adapted to the German data. About 6 million (England), 7 million (USA), and 16 million (Germany) inpatients could be included. ADEs were identified through a list of codes used in the national diagnosis classifications.ResultsThe overall prevalence rate (and 95% confidence interval, CI) of coded ADEs was 3.22% (3.20–3.23%) for England, 4.78% (4.73–4.83%) for Germany, and 5.64% (5.63–5.66%) for the USA. Most of the English ADE cases occurred in patients admitted as emergency. A non-surgical status and a longer length of stay were consistently associated with the occurrence of an ADE. Enterocolitis caused by Clostridium difficile was the most frequent ADE in all countries.ConclusionsAccording to routine data, the overall ADE prevalence rates for England, Germany, and the USA are different. However, the differences are narrower than those determined from the rates of ADEs or adverse drug reactions inferred from prospective or retrospective pharmacoepidemiological studies. Since the ADEs in the countries examined in this study share several characteristics, the use of routine data for transnational research on ADEs is feasible.

This is one of the first administrative data-based analyses calculating the economic consequences of ADEs in Germany. Further efforts are necessary to improve pharmacotherapy and relieve health care payers of preventable treatment costs.

Paper-based and electronic patient records generally are used in parallel to support different tasks. Many studies comparing their quality do not report sufficiently on the methods used. Few studies refer to the patient. Instead, most regard the paper record as the gold standard. Focusing on quality criteria, the current study compared the two records patient by patient, presuming that each might hold unique advantages. For surgical patients at a nonuniversity hospital, diagnosis and procedure codes from the hospital's electronic patient record (EPR set) were compared with the paper records (PPR set). Diagnosis coding from the paper-based patient record resulted in minor qualitative advantages. The EPR documentation showed potential advantages in both quality and quantity of procedure coding. As in many previous studies, the current study relied on a single individual to extract and transform contents from the paper record to compare PPR with EPR. The exploratory study, although limited, supports previous views of the complementary nature of paper and electronic records. The lessons learned from this study are that medical professionals should be cognizant of the possible discrepancies between paper and electronic information and look toward combining information from both records whenever appropriate. The inadequate methodology (transformations done by a single individual) used in the authors' study is typical of other studies in the field. The limited generalizability and restricted reproducibility of this commonly used approach emphasize the need to improve methods for comparing paper-based with electronic versions of a patient's chart.