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.
Social Housing exit points, outcomes and future pathways: An administrative data analysis
