Picking up the pieces—Applying the DISEASE FILTER to health data. Issue 4 (April 2015)

• Record Type: Journal Article
• Authors: Gross, Christiane; Schübel, Thomas; Hoffmann, Rasmus
• Is Part Of: Health policy. Volume 119:Issue 4(2015)
• Page Start: 549
• Page End: 557
• Publication Date: 2015-04
• Subjects: Health data -- Register data -- Survey data -- Bias; Medical education -- Periodicals; Medical policy -- Periodicals; Delivery of Health Care -- Periodicals; Education, Medical -- Periodicals; Health Education -- Periodicals; Health Planning -- Periodicals; Public Policy -- Periodicals; Enseignement médical -- Périodiques; Politique sanitaire -- Périodiques; Medical education; Medical policy; Periodicals; Electronic journals; Electronic journals; 362.1
• DOI: 10.1016/j.healthpol.2014.11.011
• Languages: English
• ISSNs: 0168-8510

Abstract

Highlights: Health data includes systematic bias, which varies by data type and in the way that health outcomes are operationalized. Our heuristic model – the DISEASE FILTER – is based on previous research results on these biases and describes them in detail. By applying the DISEASE FILTER to your health data, biases can be identified more easily. Suggestions are given on how to avoid and how to deal with biases in (health) data. 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.