Implicit value judgments in the measurement of health inequalities.
BACKGROUND
Quantitative estimates of the magnitude, direction, and rate of change of health inequalities play a crucial role in creating and assessing policies aimed at eliminating the disproportionate burden of disease in disadvantaged populations. It is generally assumed that the measurement of health inequalities is a value-neutral process, providing objective data that are then interpreted using normative judgments about whether a particular distribution of health is just, fair, or socially acceptable.


METHODS
We discuss five examples in which normative judgments play a role in the measurement process itself, through either the selection of one measurement strategy to the exclusion of others or the selection of the type, significance, or weight assigned to the variables being measured.


RESULTS
Overall, we find that many commonly used measures of inequality are value laden and that the normative judgments implicit in these measures have important consequences for interpreting and responding to health inequalities.


CONCLUSIONS
Because values implicit in the generation of health inequality measures may lead to radically different interpretations of the same underlying data, we urge researchers to explicitly consider and transparently discuss the normative judgments underlying their measures. We also urge policymakers and other consumers of health inequalities data to pay close attention to the measures on which they base their assessments of current and future health policies.
Harper
Sam
S
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada. sam.harper@mcgill.ca
King
Nicholas B
NB
Meersman
Stephen C
SC
Reichman
Marsha E
ME
Breen
Nancy
N
Lynch
John
J
D000328:Adult; D002648:Child; D002675:Child, Preschool; D019049:Developed Countries; D003906:Developing Countries; D005260:Female; D014943:Global Health; D006297:Health Services Accessibility / Q000706:statistics & numerical data*; D054624:Health Status Disparities; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D011203:Poverty / Q000706:statistics & numerical data*; D012042:Registries / Q000706:statistics & numerical data; D012307:Risk Factors; D012923:Social Class; D012935:Social Justice; D012959:Socioeconomic Factors; D014481:United States / Q000453:epidemiology; D035862:Vulnerable Populations / Q000706:statistics & numerical data*; D055815:Young Adult