Assessing Equivalence: An Alternative to the Use of Difference Tests for Measuring Disparities in Vaccination Coverage
Author(s) -
Lawrence Barker
Publication year - 2002
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwf149
Subject(s) - equivalence (formal languages) , vaccination , ethnic group , public health , significant difference , demography , population , medicine , age groups , statistics , social psychology , mathematics , econometrics , psychology , environmental health , immunology , political science , sociology , pathology , discrete mathematics , law
Eliminating health disparities in vaccination coverage among various groups is a cornerstone of public health policy. However, the statistical tests traditionally used cannot prove that a state of no difference between groups exists. Instead of asking, "Has a disparity--or difference--in immunization coverage among population groups been eliminated ?," one can ask, "Has practical equivalence been achieved?" A method called equivalence testing can show that the difference between groups is smaller than a tolerably small amount. This paper demonstrates the method and introduces public health considerations that have an impact on defining tolerable levels of difference. Using data from the 2000 National Immunization Survey, the authors tested for statistically significant differences in rates of vaccination coverage between Whites and members of other racial/ethnic groups and for equivalencies among Whites and these same groups. For some minority groups and some vaccines, coverage was statistically significantly lower than was seen among Whites; however, for some of these groups and vaccines, equivalence testing revealed practical equivalence. To use equivalence testing to assess whether a disparity remains a threat to public health, researchers must understand when to use the method, how to establish assumptions about tolerably small differences, and how to interpret the test results.
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