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Implicit Value Judgments in the Measurement of Health Inequalities
Author(s) -
HARPER SAM,
KING NICHOLAS B.,
MEERSMAN STEPHEN C.,
REICHMAN MARSHA E.,
BREEN NANCY,
LYNCH JOHN
Publication year - 2010
Publication title -
the milbank quarterly
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 101
eISSN - 1468-0009
pISSN - 0887-378X
DOI - 10.1111/j.1468-0009.2010.00587.x
Subject(s) - normative , inequality , disadvantaged , value (mathematics) , health equity , psychology , social psychology , selection (genetic algorithm) , econometrics , positive economics , economics , health care , mathematics , statistics , political science , computer science , mathematical analysis , artificial intelligence , law , economic growth
Context: 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. Findings: 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.