Categorical and anti-categorical approaches to US racial/ethnic groupings: revisiting the National 2009 H1N1 Flu Survey (NHFS)

Intersectionality theory calls for the understanding of race/ethnicity, sex/gender and class as interlinked. Intersectional analysis can contribute to public health both through furthering understanding of power dynamics causing health disparities, and by pointing to heterogeneities within, and overlap between, social groups. The latter places the usefulness of social categories in public health under scrutiny. Drawing on McCall we relate the first approach to categorical and the second to anti-categorical intersectionality. Here, we juxtapose the categorical approach with traditional between-group risk calculations (e.g. odds ratios) and the anti-categorical approach with the statistical concept of discriminatory accuracy (DA), which is routinely used to evaluate disease markers in epidemiology. To demonstrate the salience of this distinction, we use the example of racial/ethnic identification and its value for predicting influenza vaccine uptake compared to other conceivable ways of organizing attention to social differentiation. We analyzed data on 56,434 adults who responded to the NHFS. We performed logistic regressions to estimate odds ratios and computed the area under the receiver operating characteristic curve (AU-ROC) to measure DA. Above age, the most informative variables were education and household poverty status, with race/ethnicity providing minor additional information. Our results show that the practical value of standard racial/ethnic categories for making inferences about vaccination status is questionable, because of the high degree of outcome variability within, and overlap between, categories. We argue that, reminiscent of potential tension between categorical and anti-categorical perspectives, between-group risk should be placed and understood in relationship to measures of DA, to avoid the lure of misguided individual-level interventions.