An analytical framework for assessing data for health services research

Abstract

Abstract This presentation will provide an overview of the conceptual framework we used as a basis for the analysis of the case studies. The framework distinguishes between data sets with care-relevant data i) at the individual level (i.e. microdata) and ii) at the non-individual level, and iii) from three or four large content blocks (health data, health care data, socio-demographic or economic data, environmental data). Using health data as an example, individual-level health data includes individual patient data, such as laboratory and clinical results, vital signs (body temperature, pulse rate, and respiration date), as well as diagnoses and health behavior. Non-individual level data includes aggregated data in areas such as life expectancy, years of life lost (YLL), years lost to disability (YLD), disability-adjusted life years (DALY), as well as population characteristics such as prevalence of risk factors and chronic illness. The framework we have developed shows linking possibilities that are available by either storing the data in common databases (e.g., based on an electronic health record) or by linking them via a unique personal characteristic (e.g., patient identifier). The country case studies selected in the research - largely within the European region but also Australia, Canada, the Republic of Korea, New Zealand, and the United States - are all evaluated using the same conceptual framework.