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J Nutr. 2017 Jul;147(7):1356-1365. doi: 10.3945/jn.117.249581. Epub 2017 May 31.

Modeling Optimal Cutoffs for the Brazilian Household Food Insecurity Measurement Scale in a Nationwide Representative Sample.

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Department of Epidemiology, Institute of Social Medicine, Rio de Janeiro State University, Rio de Janeiro, Brazil;
Department of Epidemiology, Institute of Social Medicine, Rio de Janeiro State University, Rio de Janeiro, Brazil.
Department of Collective Health, State University of Campinas, Campinas, São Paulo, Brazil.
Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT.
Family Health Program, Estácio de Sá University, Rio de Janeiro, Brazil; and.
Department of Social and Applied Nutrition, Nutrition Institute Josué de Castro, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.


Background: This is the second part of a model-based approach to examine the suitability of the current cutoffs applied to the raw score of the Brazilian Household Food Insecurity Measurement Scale [Escala Brasileira de Insegurança Alimentar (EBIA)]. The approach allows identification of homogeneous groups who correspond to severity levels of food insecurity (FI) and, by extension, discriminant cutoffs able to accurately distinguish these groups.Objective: This study aims to examine whether the model-based approach for identifying optimal cutoffs first implemented in a local sample is replicated in a countrywide representative sample.Methods: Data were derived from the Brazilian National Household Sample Survey of 2013 (n = 116,543 households). Latent class factor analysis (LCFA) models from 2 to 5 classes were applied to the scale's items to identify the number of underlying FI latent classes. Next, identification of optimal cutoffs on the overall raw score was ascertained from these identified classes. Analyses were conducted in the aggregate data and by macroregions. Finally, model-based classifications (latent classes and groupings identified thereafter) were contrasted to the traditionally used classification.Results: LCFA identified 4 homogeneous groups with a very high degree of class separation (entropy = 0.934-0.975). The following cutoffs were identified in the aggregate data: between 1 and 2 (1/2), 5 and 6 (5/6), and 10 and 11 (10/11) in households with children and/or adolescents <18 y of age (score range: 0-14), and 1/2, between 4 and 5 (4/5), and between 6 and 7 (6/7) in adult-only households (range: 0-8). With minor variations, the same cutoffs were also identified in the macroregions. Although our findings confirm, in general, the classification currently used, the limit of 1/2 (compared with 0/1) for separating the milder from the baseline category emerged consistently in all analyses.Conclusions: Nationwide findings corroborate previous local evidence that households with an overall score of 1 are more akin to those scoring negative on all items. These results may contribute to guide experts' and policymakers' decisions on the most appropriate EBIA cutoffs.


EBIA; food insecurity; psychometrics; statistical model; surveys

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