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dc.contributor.authorGROSS, Christiane
dc.contributor.authorSCHÜBEL, Thomas
dc.contributor.authorHOFFMANN, Rasmus
dc.date.accessioned2015-01-28T16:56:52Z
dc.date.available2015-01-28T16:56:52Z
dc.date.issued2015
dc.identifier.citationHealth policy, 2015, Vol. 119, No. 4, pp. 549–557en
dc.identifier.issn0168-8510
dc.identifier.issn1872-6054
dc.identifier.urihttp://hdl.handle.net/1814/34410
dc.descriptionPublished online: 24 November 2014en
dc.description.abstractThis 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.en
dc.description.sponsorshipThe authors acknowledge the financial support from the European Research Council for the research project "Socioeconomic Status and Health: Disentangling causal pathways in a life course perspective" (SESandHealth, grant number 313532) .
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/313532
dc.relation.ispartofHealth policyen
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titlePicking up the pieces : applying the disease filter to health dataen
dc.typeArticleen
dc.identifier.doi10.1016/j.healthpol.2014.11.011
dc.identifier.volume119en
dc.identifier.startpage549en
dc.identifier.endpage557en
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dc.identifier.issue4en
dc.twitterfalse


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