A Model-Based Approach to Identify Classes and Respective Cutoffs of the Brazilian Household Food Insecurity Measurement Scale.
The Brazilian Household Food Insecurity Measurement Scale (EBIA) is the main tool for assessing household food insecurity (FI) in Brazil, assisting in monitoring and improving national public policies to promote food security. Based on the sum of item scores, households have been classified into 4 levels of FI, with the use of cutoffs arising from expert discussions informed by psychometric analyses and policy considerations.



This study aimed to identify homogeneous latent groups corresponding to levels of FI, examine whether such subgroups could be defined from discriminant cutoffs applied to the overall EBIA raw score, and compare these cutoffs against those currently used.



A cross-sectional population-based study with a representative sample of 1105 households from a low-income metropolitan area of Rio de Janeiro was conducted. Latent class factor analysis (LCFA) models were applied to the answers to EBIA's items to identify homogeneous groups, obtaining the number of latent classes for FI measured by the scale. Based on this and a thorough classification agreement evaluation, optimal cutoffs for discriminating between different severity levels of FI were ascertained. Model-based grouping and the official EBIA classification cutoffs were also contrasted.



LCFA identified 4 homogeneous groups with a very high degree of class separation (entropy = 0.906), endorsing the classification of EBIA as a 4-level measure of FI. Two sets of cutoffs were identified to separate such groups according to household type: 1/2, 5/6, and 10/11 in households with children and adolescents (score range: 0-14); and 1/2, 3/4, and 5/6 in adult-only households (score range: 0-7).



Although roughly classifying EBIA as in previous studies, the current approach suggests that, in terms of raw score, households endorsing only one item of the scale would be better classified by being placed in the same stratum as those remaining negative on all items.
Department of Epidemiology, Institute of Social Medicine, Rio de Janeiro State University, Rio de Janeiro, Brazil;
Michael E
ME
Reichenheim
Department of Epidemiology, Institute of Social Medicine, Rio de Janeiro State University, Rio de Janeiro, Brazil;
Gabriela S
GS
Interlenghi
Department of Epidemiology, Institute of Social Medicine, Rio de Janeiro State University, Rio de Janeiro, Brazil; Brazil and Family Health Program, Estácio de Sá University, Rio de Janeiro, Brazil;
Claudia L
CL
Moraes
Department of Collective Health, University of Campinas, São Paulo, Brazil;
Ana M
AM
Segall-Corrêa
Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT; and.
Rafael
R
Pérez-Escamilla
Department of Social and Applied Nutrition, Josué de Castro Nutrition Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil rosana@nutricao.ufrj.br.
Rosana
R
Salles-Costa
D001938:Brazil; D002947:Cities; D003430:Cross-Sectional Studies; D005191:Family Characteristics; D005502:Food / Q000191:economics*; D005523:Food Supply; D006801:Humans; D008962:Models, Theoretical; D011204:Poverty Areas; D012959:Socioeconomic Factors
food insecurity; psychometric; questionnaires; statistical model; surveys