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Health Policy. 2015 Apr;119(4):549-57. doi: 10.1016/j.healthpol.2014.11.011. Epub 2014 Nov 24.

Picking up the pieces--applying the DISEASE FILTER to health data.

Author information

1
University of Erlangen-Nuremberg, Faculty of Law and Economics, Findelgasse 7, 90402 Nuremberg, Germany. Electronic address: christiane.gross@fau.de.
2
Deutsches Jugendinstitut, Nockherstr. 2, 81541 Munich, Germany. Electronic address: thomas.schuebel@fau.de.
3
European University Institute, Department of Political and Social Sciences, Via dei Roccettini 9, 50014 San Domenico di Fiesole, Italy. Electronic address: rasmus.hoffmann@eui.eu.

Abstract

This contribution presents systematic biases in the process of generating health data by using a step-by-step explanation of the DISEASE FILTER, a heuristic instrument that we designed in order to better understand and evaluate health data. The systematic bias in health data generally varies by data type (register versus survey data) and the operationalization of health outcomes. Self-reported subjective health and disease assessments, for instance, underlie a different selectivity than do data based on medical examinations or health care statistics. Although this is obvious, systematic approaches used to better understand the process of generating health data have been missing until now. We begin with the definitions and classifications of diseases that change (e.g. over time), describe the selective nature of access to and use of medical health care (e.g. depending on health insurance and gender), present biases in diagnoses (e.g. by gender and professional status), report these biases in relation to the decision for or against various treatment (e.g. by age and income), and finally outline the determinants of the treatments (ambulant versus stationary, e.g. via mobility and age). We then show how to apply the DISEASE FILTER to health data and discuss the benefits and shortcomings of our heuristic model. Finally, we give some suggestions on how to deal with biases in health data and how to avoid them.

KEYWORDS:

Bias; Health data; Register data; Survey data

PMID:
25481023
DOI:
10.1016/j.healthpol.2014.11.011
[Indexed for MEDLINE]

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