Administrative data analysis of student attrition in Hungarian Medical Training

Background Even though dropout is a well-researched topic in tertiary education, it is still not clear which variables have an impact on it beyond individual attributes. There is numerous empirical evidence supporting that college students studying in STEM fields are characterized by a higher risk of attrition than their peers. Even though medicine is not traditionally considered to be part of STEM disciplines, some suggest to include it, as the field of medicine is an important area in research focusing on student attrition. Since Hungarian medical training attracts more and more international students every year, the issue of attrition in this field of study can have a global impact too. Methods In our study we examined the dropout behavior of all medical students who started their studies in 2010 in Hungary (N = 977) by analyzing longitudinal administrative data of the students between 2010 and 2017, which unlike self-reported questionnaires made it possible for us to analyse data that without any kind of distortion. Since we analyzed the data of all students studying medicine in this period in Hungary, we conducted descriptive statistics and revealed the risk and protective factors of drouput using bonary logistic regression. Results Our results indicate that the risk of dropout can be increased by a low number of credits and passive semesters and the tuition-based forms of finance, although dormitory placement can serve as a protective factor. Conclusions Relieving the rigidity of the training network, more educational attention, targeted mentoring in the case of learning difficulties and dormitory placement in support of learning communities can be formulated as a policy proposal.

Potentials, challenges, and limitations of the analysis of administrative data on vascular limb amputations in health care

Summary. Although more and more data on lower limb amputations are becoming available by leveraging the widening access to health care administrative databases, the applicability of these data for public health decisions is still limited. Problems can be traced back to methodological issues, how data are generated and to conceptual issues, namely, how data are interpreted in a multidimensional environment. The present review summarised all of the steps from converting the claims data of administrative databases into the analytical data and reviewed the wide array of sources of potential biases in the analysis of such data. The origins of uncertainty of administrative data analysis include uncontrolled confounding due to a lack of clinical data, the left- and right-censored nature of data collection, the non-standardized diagnosis/procedure-based data extraction methods (i.e., numerator/denominator problems) and additional methodological problems associated with temporal and spatial analyses. The existence of these methodological challenges in the administrative data-based analysis should not deter the analysts from using these data as a powerful tool in the armamentarium of clinical research. However, it must be done with caution and a thorough understanding and respect of the methodological limitations. In addition to this requirement, there is a profound need for pursuing further research on methodology and widening the search for other indicators (structural, process or outcome) that allow a deeper insight how the quality of vascular care may be assessed. Effective research using administrative data is based on strong collaboration in three domains, namely expertise in claims data handling and processing, the clinical field, and statistical analysis. The final interpretations of results and the countermeasures on the level of vascular care ought to be grounded on the integrity of research, open discussions and institutionalized mechanisms of science arbitration and honest brokering.

Repeatable Research Infrastructure Enabling Administrative Data Analysis

Background/RationaleLinked administrative datasets offer great potential for research, but also present major challenges—including the preparation of operational data into a form suitable for efficient research, complex and computationally demanding analysis, and the need to capture and share information about dataset contents and research methods. Main AimThe analytical services team in the Secure Anonymised Information Linkage (SAIL) Databank is creating interconnected tools and systems to automate the preparation and analysis of research data and to curate information about datasets and research methods. Our underlying goal is to make linked data research orders of magnitude faster and cheaper, as well as improve its consistency and quality. MethodsSeveral key developments are ongoing: Automation of data quality checking. Management of dataset metadata. Processing of raw source datasets into cleaned, research-ready data assets. The Concept Library, an application for creating, using, and sharing knowledge about research definitions and methods. A suite of R packages for analysis. Web Application Programming Interfaces will allow these pieces to work together as an integrated system enabling efficient research. ResultsInitial versions of dataset quality checking, cleaned datasets, and R code to implement common tasks are already in day-to-day use by researchers within SAIL. An advisory group has been convened to help guide the work. For example, shared library code that flags conditions within health data has been used across multiple projects; a cleaned dataset measuring follow-up within primary care has been used by more than 100 projects. ConclusionOur proof-of-concept work demonstrates the ability of shared code and cleaned data to meet needs across multiple projects, saving effort and standardizing results. Ongoing work to develop and integrate these tools should further streamline the research process, increasing the output and public benefit of SAIL and other data sources.