title: A framework for identifying similarities among countries to improve cross-national comparisons of health systems.
abstract: Cross-national research on health system performance can yield important findings for public policy purposes. We seek to further this research by examining the problem of selection bias, an important methodological issue that investigators initially should consider. Because of the logistical difficulties and enormous expense involved in collecting voluminous data from many countries, researchers often must rely on information contained in data sets of international organizations, such as the World Health Organization (WHO) and the Organization for Economic Cooperation and Development. Under the circumstances, the comparisons that researchers can make will depend to a great extent on the availability and richness of data for certain measures. This situation raises the potential for selection or experimenter bias. We use multivariate statistics to group countries with similar characteristics, an approach that we believe will mitigate the problem. We perform a cluster analysis of 186 countries using principal components derived from 7 demographic variables and 27 mortality and burden of disease variables. Our analysis produced six clusters that we believe represent suitable groupings for comparative purposes.
authors.affiliation: Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA. dtb5f@virginia.edu
authors.forename: David T
authors.initials: DT
authors.lastname: Bauer
authors.forename: Carl F
authors.initials: CF
authors.lastname: Ameringer
mesh_terms: D000293:Adolescent; D000328:Adult; D015982:Bias; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D003695:Delivery of Health Care / Q000592:standards*; D005260:Female; D014943:Global Health; D006305:Health Status Indicators; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D011640:Public Policy; D055815:Young Adult