BackgroundA ubiquitous issue in research is that of selecting a representative sample from the study population. While random sampling strategies are the gold standard, in practice, random sampling of participants is not always feasible nor necessarily the optimal choice. In our case, a selection must be made of 12 hospitals (out of 89 Dutch hospitals in total). With this selection of 12 hospitals, it should be possible to estimate blood use in the remaining hospitals as well. In this paper, we evaluate both random and purposive strategies for the case of estimating blood use in Dutch hospitals.MethodsAvailable population-wide data on hospital blood use and number of hospital beds are used to simulate five sampling strategies: (1) select only the largest hospitals, (2) select the largest and the smallest hospitals (‘maximum variation’), (3) select hospitals randomly, (4) select hospitals from as many different geographic regions as possible, (5) select hospitals from only two regions. Simulations of each strategy result in different selections of hospitals, that are each used to estimate blood use in the remaining hospitals. The estimates are compared to the actual population values; the subsequent prediction errors are used to indicate the quality of the sampling strategy.ResultsThe strategy leading to the lowest prediction error in the case study was maximum variation sampling, followed by random, regional variation and two-region sampling, with sampling the largest hospitals resulting in the worst performance. Maximum variation sampling led to a hospital level prediction error of 15 %, whereas random sampling led to a prediction error of 19 % (95 % CI 17 %-26 %). While lowering the sample size reduced the differences between maximum variation and the random strategies, increasing sample size to n = 18 did not change the ranking of the strategies and led to only slightly better predictions.ConclusionsThe optimal strategy for estimating blood use was maximum variation sampling. When proxy data are available, it is possible to evaluate random and purposive sampling strategies using simulations before the start of the study. The results enable researchers to make a more educated choice of an appropriate sampling strategy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-015-0089-8) contains supplementary material, which is available to authorized users.

Background: While the number of hospitalized patients in Dutch hospitals has increased since 1997, the demand for red blood cell units (RBCs) has simultaneously decreased. This implies a dramatic change in transfusion practice toward fewer blood transfusions on average per patient. Objectives: In order to explain the RBC reduction, different patient groups (surgical, medical, obstetrical, and specific age groups) were studied retrospectively in relation to RBC use. In addition, the use of combinations of RBCs, fresh frozen plasma, and platelets during a transfusion episode was examined for trends over time. Materials and methods: Data from the PROTON database, containing information on all transfusions in twelve Dutch hospitals in the period 1996-2005, including corresponding patient data (age, diagnosis, treatment, and hospitalizations) and blood unit data (type, amount, and date) were analyzed. Results: The proportion of RBCs used for surgical patients declined from 50% in 1996 to 40% in 2005, whereas medical use increased from 47% to 58% (the remaining 2%-3% went to obstetrical patients). Changes were more marked in the higher age groups. Also, a trend was observed toward the use of only one or two RBCs during a transfusion episode rather than three or more. Among surgical patients who received blood, the use of combinations of blood units, as compared to RBCs only, increased from 32% to 39%. Conclusion: The results suggest a more restrictive transfusion policy for surgical patients as well as an increase in medical indications for transfusion. This fits well with the current focus on more cost-effective transfusion policies.

BackgroundAlthough data from electronic health records (EHR) are often used for research purposes, systematic validation of these data prior to their use is not standard practice. Existing validation frameworks discuss validity concepts without translating these into practical implementation steps or addressing the potential influence of linking multiple sources. Therefore we developed a practical approach for validating routinely collected data from multiple sources and to apply it to a blood transfusion data warehouse to evaluate the usability in practice.MethodsThe approach consists of identifying existing validation frameworks for EHR data or linked data, selecting validity concepts from these frameworks and establishing quantifiable validity outcomes for each concept. The approach distinguishes external validation concepts (e.g. concordance with external reports, previous literature and expert feedback) and internal consistency concepts which use expected associations within the dataset itself (e.g. completeness, uniformity and plausibility). In an example case, the selected concepts were applied to a transfusion dataset and specified in more detail.ResultsApplication of the approach to a transfusion dataset resulted in a structured overview of data validity aspects. This allowed improvement of these aspects through further processing of the data and in some cases adjustment of the data extraction. For example, the proportion of transfused products that could not be linked to the corresponding issued products initially was 2.2% but could be improved by adjusting data extraction criteria to 0.17%.ConclusionsThis stepwise approach for validating linked multisource data provides a basis for evaluating data quality and enhancing interpretation. When the process of data validation is adopted more broadly, this contributes to increased transparency and greater reliability of research based on routinely collected electronic health records.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0504-7) contains supplementary material, which is available to authorized users.