Gross C, Schübel T, Hoffmann R 2015, Health Policy 119:549-57.
This contribution presents systematic biases in the process of generating health data byusing a step-by-step explanation of the DISEASE FILTER, a heuristic instrument that wedesigned in order to better understand and evaluate health data. The systematic bias inhealth data generally varies by data type (register versus survey data) and the operational-ization of health outcomes. Self-reported subjective health and disease assessments, forinstance, underlie a different selectivity than do data based on medical examinations orhealth care statistics. Although this is obvious, systematic approaches used to better under-stand the process of generating health data have been missing until now. We begin with thedefinitions and classifications of diseases that change (e.g. over time), describe the selectivenature of access to and use of medical health care (e.g. depending on health insurance andgender), present biases in diagnoses (e.g. by gender and professional status), report thesebiases 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. viamobility and age). We then show how to apply the DISEASE FILTER to health data and discussthe benefits and shortcomings of our heuristic model. Finally, we give some suggestions onhow to deal with biases in health data and how to avoid them.