Application of the mixture item response theory model to the Self-Administered Food Security Survey Module for Children

Autoři: Isabel Maia aff001;  Milton Severo aff001;  Ana Cristina Santos aff001
Působiště autorů: EPIUnit—Instituto de Saúde Pública, Universidade do Porto, Rua das Taipas, Porto, Portugal aff001;  Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Alameda Prof. Hernâni Monteiro, Porto, Portugal aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0228099



The Self-Administered Food Security Survey Module for Children was developed to assess food insecurity of individual children and has not been used in Portugal. We aimed to apply the mixture item response theory model to the Self-Administered Food Security Survey Module for Children, to assess its reliability and validity, and to estimate the cut-offs of the food security status for Portuguese children.


The scale was self-administered to 2132 children of the Generation XXI birth cohort. The internal consistency was assessed using Cronbach’s alpha. We evaluated dimensionality and/or clustering, and Latent Class Analysis, Latent Trait Analysis and Mixture Latent Trait Analysis were tested. The number of classes and/or traits were defined according to the Akaike Information Criterion, Bayesian Information Criterion, Adjusted Bayesian Information Criterion, Vuong-Lo-Mendell-Rubin Likelihood Ratio Test, Bootstrapped Likelihood Ratio Test and Entropy. Construct validity was explored using socio-demographic characteristics. The classification tree was used to define cut-offs to predict cluster membership.


The best model was a Mixture Latent Trait Analysis with 1 factor and 2 classes (food security and food insecurity), assuming class variant item parameters (for items 1 and 3). Based on the estimated posterior probabilities, the food insecurity prevalence was 17.6%. Cronbach’s alpha was 0.617. A higher proportion of less-educated mothers and low-income households was observed in the food insecurity class. The classification tree showed an accuracy of 100.0% by identifying the food security and food insecurity groups.


Our results supported that the Self-Administered Food Security Survey Module for Children provides a valid and reliable measure, which allows the identification of food insecurity among Portuguese children.

Klíčová slova:

Cohort studies – Decision trees – Eating – Children – Information entropy – Portuguese people – Schools – Surveys


1. Anderson SA. Core indicators of nutritional state for difficult-to-sample populations. J Nutr. 1990;120(suppl_11):1555–1600. doi: 10.1093/jn/120.suppl_11.1555 2243305

2. Zaslow M, Bronte-Tinkew J, Capps R, Horowitz A, Moore KA, Weinstein D. Food security during infancy: implications for attachment and mental proficiency in toddlerhood. Matern Child Health J. 2009;13(1):66–80. Epub 2008/03/05. doi: 10.1007/s10995-008-0329-1 18317892.

3. Melchior M, Chastang JF, Falissard B, Galera C, Tremblay RE, Cote SM, et al. Food insecurity and children's mental health: a prospective birth cohort study. PLoS One. 2012;7(12):e52615. Epub 2013/01/10. doi: 10.1371/journal.pone.0052615 23300723; PubMed Central PMCID: PMC3530436.

4. Bernal J, Frongillo EA, Herrera HA, Rivera JA. Food insecurity in children but not in their mothers is associated with altered activities, school absenteeism, and stunting. J Nutr. 2014;144(10):1619–1626. Epub 2014/08/22. doi: 10.3945/jn.113.189985 25143373.

5. Ke J, Ford-Jones EL. Food insecurity and hunger: A review of the effects on children's health and behaviour. Paediatr Child Health. 2015;20(2):89–91. Epub 2015/04/04. doi: 10.1093/pch/20.2.89 25838782; PubMed Central PMCID: PMC4373582.

6. Mangini LD, Hayward MD, Dong YQ, Forman MR. Household food insecurity is associated with childhood asthma. J Nutr. 2015;145(12):2756–2764. Epub 2015/10/23. doi: 10.3945/jn.115.215939 26491120.

7. Knowles M, Rabinowich J, Ettinger de Cuba S, Cutts DB, Chilton M. "Do you wanna breathe or eat?": Parent perspectives on child health consequences of food insecurity, trade-offs, and toxic stress. Matern Child Health J. 2016;20(1):25–32. Epub 2015/07/15. doi: 10.1007/s10995-015-1797-8 26156827.

8. Marques ES, Reichenheim ME, de Moraes CL, Antunes MM, Salles-Costa R. Household food insecurity: a systematic review of the measuring instruments used in epidemiological studies. Public Health Nutr. 2015;18(5):877–892. Epub 2014/06/26. doi: 10.1017/S1368980014001050 24963759.

9. United States Department of Agriculture. U.S. Household Food Security Survey Module: Three-Stage Design, With Screeners US Department of Agriculture, Economic Research Service; 2012.

10. Bickel G, Nord M, Price C, Hamilton W, Cook J. Guide to Measuring Household Food Security, Revised 2000. Alexandria VA: US Department of Agriculture, Food and Nutrition Service; 2000.

11. Nord M, Hopwood H. Recent advances provide improved tools for measuring children's food security. J Nutr. 2007;137(3):533–536. Epub 2007/02/22. doi: 10.1093/jn/137.3.533 17311935.

12. Nalty CC, Sharkey JR, Dean WR. Children’s reporting of food insecurity in predominately food insecure households in Texas border colonias. Nutr J. 2013;12:15. doi: 10.1186/1475-2891-12-15 PMC3598463. 23356877

13. Connell CL, Nord M, Lofton KL, Yadrick K. Food security of older children can be assessed using a standardized survey instrument. J Nutr. 2004;134(10):2566–2572. Epub 2004/10/07. doi: 10.1093/jn/134.10.2566 15465749.

14. United States Department of Agriculture. Self-Administered Food Security Survey Module for Children Ages 12 Years and Older: US Department of Agriculture, Economic Research Service; 2006.

15. Cohen B, Parry J, Yang K, Solutions I. Household Food Security in the United States, 1998 and 1999—Detailed Statistical Report. 2002.

16. Muthen B, Asparouhov T. Item response mixture modeling: application to tobacco dependence criteria. Addict Behav. 2006;31(6):1050–66. Epub 2006/05/06. doi: 10.1016/j.addbeh.2006.03.026 16675147.

17. Gollini I, Murphy TB. Mixture of latent trait analyzers for model-based clustering of categorical data. Stat Comput. 2014;24(4):569–588. doi: 10.1007/s11222-013-9389-1

18. Conway C, Hammen C, Brennan P. A comparison of latent class, latent trait, and factor mixture models of DSM-IV borderline personality disorder criteria in a community setting: implications for DSM-5. J Pers Disord. 2012;26(5):793–803. doi: 10.1521/pedi.2012.26.5.793 23013346.

19. Alves E, Correia S, Barros H, Azevedo A. Prevalence of self-reported cardiovascular risk factors in Portuguese women: a survey after delivery. Int J Public Health. 2012;57(5):837–47. Epub 2012/02/09. doi: 10.1007/s00038-012-0340-6 22314542.

20. Larsen PS, Kamper-Jorgensen M, Adamson A, Barros H, Bonde JP, Brescianini S, et al. Pregnancy and birth cohort resources in Europe: a large opportunity for aetiological child health research. Paediatr Perinat Epidemiol. 2013;27(4):393–414. Epub 2013/06/19. doi: 10.1111/ppe.12060 23772942.

21. Akaike H. A New Look at the Statistical Model Identification. IEEE T Automat Contr. 1974;19(6):716–723.

22. Schwarz G. Estimating the Dimension of a Model. Ann Stat. 1978;6(2):461–464.

23. Vuong QH. Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses. Econometrica. 1989;57(2):307–333. doi: 10.2307/1912557

24. Nylund KL, Asparouhov T, Muthén BO. Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Struct Equ Modeling. 2007;14(4):535–569. doi: 10.1080/10705510701575396

25. Li F, Cohen AS, Kim S-H, Cho S-J. Model Selection Methods for Mixture Dichotomous IRT Models. Appl Psychol Meas. 2009;33(5):353–373. doi: 10.1177/0146621608326422

26. Muthen B, Brown CH, Masyn K, Jo B, Khoo ST, Yang CC, et al. General growth mixture modeling for randomized preventive interventions. Biostatistics. 2002;3(4):459–75. Epub 2003/08/23. doi: 10.1093/biostatistics/3.4.459 12933592.

27. Celeux G, Soromenho G. An entropy criterion for assessing the number of clusters in a mixture model. J Classif. 1996;13(2):195–212. doi: 10.1007/bf01246098

28. Cattarello AM. Community-level influences on individuals' social bonds, peer associations, and delinquency: A multilevel analysis. Justice Q. 2000;17(1):33–60. doi: 10.1080/07418820000094471

29. Gray A. Definitions of crowding and the effects of crowding on health: A literature review. New Zealand: Ministry of Social Policy, 2001.

30. Breiman L, Friedman J, Olshen RA, Stone CJ. Classification and Regression Trees. United States of America: CRC Press, 1984.

31. Therneau TM, Atkinson B, Ripley B. rpart. recursive partitioning 2011. Available from:

32. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis. 7th ed. United States of America: Prentice Hall; 2010.

33. Bauer KW, MacLehose R, Loth KA, Fisher JO, Larson NI, Neumark-Sztainer D. Eating- and weight-related parenting of adolescents in the context of food insecurity. J Acad Nutr Diet. 2015;115(9):1408–1416. Epub 2015/04/01. doi: 10.1016/j.jand.2015.01.011 25824114.

34. Huang J, Kim Y, Birkenmaier J. Unemployment and household food hardship in the economic recession. Public Health Nutr. 2015;19(3):511–519. Epub 06/01. doi: 10.1017/S1368980015001603 26028335

35. Cook JT, Black M, Chilton M, Cutts D, Ettinger de Cuba S, Heeren TC, et al. Are food insecurity's health impacts underestimated in the U.S. population? Marginal food security also predicts adverse health outcomes in young U.S. children and mothers. Adv Nutr. 2013;4(1):51–61. Epub 2013/01/16. doi: 10.3945/an.112.003228 23319123; PubMed Central PMCID: PMC3648739.

36. Carter MA, Dubois L, Tremblay MS, Taljaard M. Local social environmental factors are associated with household food insecurity in a longitudinal study of children. BMC Public Health. 2012;12:1038–1038. doi: 10.1186/1471-2458-12-1038 23190743.

37. Markwick A, Ansari Z, Sullivan M, McNeil J. Social determinants and lifestyle risk factors only partially explain the higher prevalence of food insecurity among Aboriginal and Torres Strait Islanders in the Australian state of Victoria: a cross-sectional study. BMC Public Health. 2014;14:598. Epub 2014/06/14. doi: 10.1186/1471-2458-14-598 24924598; PubMed Central PMCID: PMC4076758.

38. Ramsey R, Giskes K, Turrell G, Gallegos D. Food insecurity among Australian children: potential determinants, health and developmental consequences. J Child Health Care. 2011;15(4):401–16. Epub 2011/12/27. doi: 10.1177/1367493511423854 22199175.

39. Loewenthal KM. An Introduction to Psychological Tests and Scales. 2nd ed. Hove, UK: Psychology Press; 2004.

40. Ursachi G, Horodnic IA, Zait A. How Reliable are Measurement Scales? External Factors with Indirect Influence on Reliability Estimators. Procedia Econ Financ. 2015;20:679–686.

41. Interlenghi GS, Reichenheim ME, Segall-Correa AM, Perez-Escamilla R, Moraes CL, Salles-Costa R. Modeling optimal cutoffs for the Brazilian household food insecurity measurement scale in a nationwide representative sample. J Nutr. 2017;147(7):1356–1365. Epub 2017/06/02. doi: 10.3945/jn.117.249581 28566526.

42. Reichenheim ME, Interlenghi GS, Moraes CL, Segall-Correa AM, Perez-Escamilla R, Salles-Costa R. A Model-Based Approach to Identify Classes and Respective Cutoffs of the Brazilian Household Food Insecurity Measurement Scale. J Nutr. 2016;146(7):1356–64. Epub 2016/06/10. doi: 10.3945/jn.116.231845 27281803.

43. Shankar-Krishnan N, Penelo E, Fornieles Deu A, Sanchez-Carracedo D. Spanish adaptation and validation of the Child Food Security Survey Module (CFSSM-S). Public Health Nutr. 2018;21(15):2753–2761. Epub 2018/07/13. doi: 10.1017/S1368980018001672 29996952.

44. Godrich S, Lo J, Davies C, Darby J, Devine A. Prevalence and socio-demographic predictors of food insecurity among regional and remote Western Australian children. Aust N Z J Public Health. 2017;41(6):585–590. Epub 2017/09/15. doi: 10.1111/1753-6405.12716 28906569.

45. Ruiz-Castell M, Muckle G, Dewailly E, Jacobson JL, Jacobson SW, Ayotte P, et al. doi: 10.2105/AJPH.2014.302290 25602890. Am J Public Health. 2015;105(3):e122–32. Epub 2015/01/21. PubMed Central PMCID: PMC4330833.

46. Fram MS, Frongillo EA, Jones SJ, Williams RC, Burke MP, DeLoach KP, et al. Children are aware of food insecurity and take responsibility for managing food resources. J Nutr. 2011;141(6):1114–9. Epub 2011/04/29. doi: 10.3945/jn.110.135988 21525257.

47. Lemon SC, Roy J, Clark MA, Friedmann PD, Rakowski W. Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression. Ann Behav Med. 2003;26(3):172–181. doi: 10.1207/S15324796ABM2603_02 14644693

48. Frongillo EA Jr. Validation of measures of food insecurity and hunger. J Nutr. 1999;129(2S Suppl):506s–509s. Epub 1999/03/04. doi: 10.1093/jn/129.2.506S 10064319.

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