Suitability of the eight-item version of the Brazilian Household Food Insecurity Measurement Scale to identify risk groups: evidence from a nationwide representative sample

Public Health Nutr. 2019 Apr;22(5):776-784. doi: 10.1017/S1368980018003592. Epub 2018 Dec 27.

Abstract

Objective: The Brazilian Household Food Insecurity Measurement Scale (EBIA) has eight general/adult items applied in all households and six additional items exclusively asked in households with children and/or adolescents (HHCA). Continuing an investigation programme on the adequacy of model-based cut-off points for EBIA, the present study aims to: (i) explore the capacity of properly stratifying HHCA according to food insecurity (FI) severity level by applying only the eight 'generic' items; and (ii) compare it against the fourteen-item scale.

Design: Latent class factor analysis (LCFA) models were applied to the answers to the eight general/adult items to identify latent groups corresponding to FI levels and optimal group-separating cut-off points. Analyses involved a thorough classification agreement evaluation and were performed at the national level and by macro-regions.

Setting: Data derived from the cross-sectional Brazilian National Household Sample Survey of 2013.

Participants: A nationally representative sample of 116 543 households.

Results: In all households and investigated domains, LCFA detected four distinct household food (in)security groups (food security and three levels of severity of FI) and the same set of cut-off points (1/2, 4/5 and 6/7). Misclassification in the aggregate data was 0·66 % in adult-only households and 1·06 % in HHCA. Comparison of the scale reduced to eight items with the 'original' fourteen-item scale demonstrated consistency in the classification. In HHCA, the agreement between both classifications was 96·2 %.

Conclusions: Results indicate the eight 'generic' items in HHCA can be reliably used when it is not possible to apply the fourteen-item scale.

Keywords: Brazil; Classification analysis; Factor mixture model; Food insecurity; Psychometrics; Scales; Surveys and questionnaires.