Food Insecurity Reduces the Chance of Following a Nutrient-Dense Dietary Pattern by Brazilian Adults: Insights from a Nationwide Cross-Sectional Survey
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Food Security Data
2.3. Dietary Data Collection
2.4. Foods Grouping
2.5. Covariates
2.6. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- FAO. World Food Summit. Rome Declaration on World Food Security and World Food Summit Plan of Action. 1996. Available online: http://www.fao.org/3/w3613e/w3613e00.htm (accessed on 6 March 2022).
- Simelane, K.S.; Worth, S. Food and Nutrition Security Theory. Food Nutr. Bull. 2020, 41, 367–379. [Google Scholar] [CrossRef] [PubMed]
- FAO; IFAD; UNICEF; WFP; WHO. The State of Food Security and Nutrition in the World 2020. Transforming Food Systems for Affordable Healthy Diets; FAO: Rome, France, 2020; Available online: https://reliefweb.int/sites/reliefweb.int/files/resources/SOFI2020_EN_web.pdf (accessed on 6 March 2022).
- Saccone, D. Can the Covid19 pandemic affect the achievement of the ‘Zero Hunger’ goal? Some preliminary reflections. Eur. J. Health Econ. 2021, 2, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Moradi, S.; Mirzababaei, A.; Dadfarma, A.; Rezaei, S.; Mohammadi, H.; Jannat, B.; Mirzaei, K. Food insecurity and adult weight abnormality risk: A systematic review and meta-analysis. Eur. J. Nutr. 2019, 58, 45–61. [Google Scholar] [CrossRef] [PubMed]
- Gregory, C.A.; Coleman-Jensen, A. Food Insecurity, Chronic Disease, and Health among Working-Age Adults. USDA Economic Research Service. Available online: https://www.ers.usda.gov/webdocs/publications/84467/err-2235.pdf?v=0 (accessed on 6 March 2022).
- Hanson, K.L.; Connor, L.M. Food insecurity and dietary quality in US adults and children: A systematic review. Am. J. Clin. Nutr. 2014, 100, 684–692. [Google Scholar] [CrossRef] [Green Version]
- Morais, D.C.; Dutra, L.V.; Franceschini, S.C.C.; Priore, S.E. Food insecurity and anthropometric, dietary and social indicators in Brazilian studies: A systematic review. Cien. Saúde Colet. 2014, 19, 1475–1488. [Google Scholar] [CrossRef] [Green Version]
- Taylor, C.A.; Spees, C.K.; Markwordt, A.M.; Watowicz, R.P.; Clark, J.K.; Hooker, N.H. Differences in US Adult Dietary Patterns by Food Security Status. J. Consum. Aff. 2017, 51, 549–565. [Google Scholar] [CrossRef]
- Araújo, M.L.; Mendonça, R.D.; Lopes Filho, J.D.; Lopes, A.C.S. Association between food insecurity and food intake. Nutrition 2018, 54, 54–59. [Google Scholar] [CrossRef]
- Moeller, S.M.; Reedy, J.; Millen, A.E.; Dixon, L.B.; Newby, P.K.; Tucker, K.L.; Krebs-Smith, S.M.; Guenther, P.M. Dietary patterns: Challenges and opportunities in dietary patterns research an Experimental Biology workshop. J. Am. Diet. Assoc. 2006, 107, 1233–1239. [Google Scholar] [CrossRef]
- Ntwenya, J.E.; Kinabo, J.; Msuya, J.; Mamiro, P.; Majili, Z.S. Dietary Patterns and Household Food Insecurity in Rural Populations of Kilosa District, Tanzania. PLoS ONE 2015, 10, e0126038. [Google Scholar] [CrossRef] [Green Version]
- Rezazadeh, A.; Omidvar, N.; Eini-Zinab, H.; Ghazi-Tabatabaie, M.; Majdzadeh, R.; Ghavamzadeh, S.; Nouri-Saeidlou, S. Major dietary patterns in relation to demographic and socio-economic status and food insecurity in two Iranian ethnic groups living in Urmia, Iran. Public Health Nutr. 2016, 19, 3337–3348. [Google Scholar] [CrossRef] [Green Version]
- Galindo, E.; Teixeira, M.A.; de Araújo, M.; Motta, R.; Pessoa, M.; Mendes, L.; Renno, L. Effects of the Covid-19 Pandemic on Food Consumption and Food Security in Brazil. Available online: https://www.lai.fu-berlin.de/en/forschung/food-for-justice/publications/Publikationsliste_Working-Paper-Series/Working-Paper-4/index.html (accessed on 6 March 2022).
- Rede PENSSAN, Rede Brasileira de Pesquisa em Soberania e Segurança Alimentar. VIGISAN Inquérito Nacional sobre Insegurança Alimentar no Contexto da Pandemia da Covid-19 no Brasil. 2021. Available online: http://olheparaafome.com.br/VIGISAN_Inseguranca_alimentar.pdf (accessed on 6 March 2022).
- Instituto Brasileiro de Geografia e Estatística (IBGE). Coordenação de Trabalho e Rendimento, 2019. Pesquisa de Orçamentos Familiares 2017–2018: Primeiros Resultados; IBGE: Rio de Janeiro, Brazil, 2019. Available online: https://biblioteca.ibge.gov.br/visualizacao/livros/liv101670.pdf (accessed on 6 March 2022).
- Instituto Brasileiro de Geografia e Estatística (IBGE). Coordenação de Trabalho e Rendimento, 2020. Pesquisa de Orçamentos Familiares 2017–2018: Análise do Consumo Alimentar Pessoal no Brasil; IBGE: Rio de Janeiro, Brazil, 2020. Available online: https://biblioteca.ibge.gov.br/visualizacao/livros/liv101742.pdf (accessed on 6 March 2022).
- Instituto Brasileiro de Geografia e Estatística (IBGE). Coordenação de Trabalho e Rendimento, 2020. Pesquisa de Orçamentos Familiares 2017–2018: Análise da Segurança Alimentar no Brasil; IBGE: Rio de Janeiro, Brazil, 2020. Available online: https://biblioteca.ibge.gov.br/visualizacao/livros/liv101749.pdf (accessed on 6 March 2022).
- Segall-Corrêa, A.M.; Marin-León, L.; Melgar-Quiñonez, H.; Pérez-Escamilla, R. Refinement of the Brazilian Household Food Insecurity Measurement Scale: Recommendation for a 14-item EBIA. Rev Nutr. 2014, 27, 241–251. [Google Scholar] [CrossRef] [Green Version]
- Moshfegh, A.J.; Rhodes, D.G.; Baer, D.J.; Murayi, T.; Clemens, J.C.; Rumpler, W.V.; Paul, D.R.; Sebastian, R.S.; Kuczynski, K.J.; Ingwersen, L.A.; et al. The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am. J. Clin. Nutr. 2008, 88, 324–332. [Google Scholar] [CrossRef] [PubMed]
- Vilela, A.A.F.; Sichieri, R.; Pereira, R.A.; Cunha, D.B.; Rodrigues, P.R.M.; Gonçalves-Silva, R.M.V.; Ferreira, M.G. Dietary patterns associated with anthropometric indicators of abdominal fat in adults. Cad. Saúde Publica 2014, 30, 502–510. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sichieri, R.; Castro, J.F.G.; Moura, A.S. Factors associated with dietary patterns in the urban Brazilian population. Cad. Saúde Pública 2003, 19 (Suppl. S1), S47–S53. [Google Scholar] [CrossRef] [Green Version]
- Castro, M.A.; Baltar, V.T.; Marchioni, D.M.; Fisberg, R.M. Examining associations between dietary patterns and metabolic CVD risk factors: A novel use of structural equation modelling. Br. J. Nutr. 2016, 115, 1586–1597. [Google Scholar] [CrossRef] [Green Version]
- Harttig, U.; Haubrock, J.; Knüppel, S.; Boeing, H.; EFCOVAL Consortium. The MSM program: Web-based statistics package for estimating usual dietary intake using the Multiple Source Method. Eur. J. Clin. Nutr. 2011, 65 (Suppl. S1), S87–S91. [Google Scholar] [CrossRef] [Green Version]
- World Health Organization. Physical Status: The Use of and Interpretation of Anthropometry, Report of a WHO Expert Committee. 1995. Available online: https://apps.who.int/iris/handle/10665/37003 (accessed on 6 March 2022).
- Drewnowski, A.; Henderson, S.A.; Driscoll, A.; Rolls, B.J. The Dietary Variety Score: Assessing diet quality in healthy young and older adults. J. Am. Diet. Assoc. 1997, 97, 266–271. [Google Scholar] [CrossRef]
- Hatløy, A.; Torheim, L.E.; Oshaug, A. Food variety—A good indicator of nutritional adequacy of the diet? A case study from an urban area in Mali, West Africa. Eur. J. Clin. Nutr. 1998, 52, 891–898. [Google Scholar] [CrossRef] [Green Version]
- Hair, J.F.; Black, B.; Babin, B.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis, 6th ed.; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2006; p. 899. [Google Scholar]
- Selem, S.S.; Castro, M.A.; César, C.L.; Marchioni, D.M.; Fisberg, R.M. Associations between dietary patterns and self-reported hypertension among Brazilian adults: A cross-sectional population-based study. J. Acad. Nutr. Diet. 2014, 114, 1216–1222. [Google Scholar] [CrossRef]
- Castro, M.A.; Baltar, V.T.; Selem, S.S.; Marchioni, D.M.; Fisberg, R.M. Empirically derived dietary patterns: Interpretability and construct validity according to different factor rotation methods. Cad. Saúde Pública 2015, 31, 298–310. [Google Scholar] [CrossRef]
- DiStefano, C.; Zhu, M.; Mîndrilã, D. Understanding and Using Factor Scores: Considerations for the Applied Researcher. PARE 2009, 14, 20. [Google Scholar] [CrossRef]
- Newby, P.K.; Tucker, K.L. Empirically derived dietary patterns using factor or cluster analysis: A review. Nutr. Rev. 2004, 62, 177–203. [Google Scholar] [CrossRef] [PubMed]
- Morais, D.C.; Lopes, S.O.; Priore, S.E. Indicadores de avaliação da Insegurança Alimentar e Nutricional e fatores associados: Revisão sistemática. Cienc. Saúde Colet 2020, 25, 2687–2700. [Google Scholar] [CrossRef] [PubMed]
- Herforth, A.; Arimond, M.; Álvarez-Sánchez, C.; Coates, J.; Christianson, K.; Muehlhoff, E. A Global Review of Food-Based Dietary Guidelines. Adv. Nutr. 2019, 10, 590–605. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Instituto Brasileiro de Geografia e Estatística (IBGE). Coordenação de Trabalho e Rendimento. Pesquisa Nacional por Amostra de Domicílios. Segurança Alimentar 2004/2009; IBGE: Rio de Janeiro, Brazil, 2010. Available online: https://biblioteca.ibge.gov.br/visualizacao/livros/liv47241.pdf (accessed on 6 March 2022).
- Instituto Brasileiro de Geografia e Estatística (IBGE). Coordenação de Trabalho e Rendimento. Pesquisa Nacional por Amostra de Domicílios. Segurança Alimentar 2013; IBGE: Rio de Janeiro, Brazil, 2014. Available online: https://biblioteca.ibge.gov.br/visualizacao/livros/liv91984.pdf (accessed on 6 March 2022).
- De Oliveira Santos, R.; Fisberg, R.M.; Marchioni, D.M.; Baltar, V.T. Dietary patterns for meals of Brazilian adults. Br. J. Nutr. 2015, 114, 822–828. [Google Scholar] [CrossRef] [Green Version]
- Pereira, J.L.; Castro, M.A.; Hopkins, S.; Gugger, C.; Fisberg, R.M.; Fisberg, M. Proposal for a breakfast quality index for Brazilian population: Rationale and application in the Brazilian National Dietary Survey. Appetite 2017, 111, 12–22. [Google Scholar] [CrossRef]
- Antunes, A.B.S.; Cunha, D.B.; Baltar, V.T.; Steluti, J.; Pereira, R.A.; Yokoo, E.M.; Sichieri, R.; Marchioni, D.M. Padrões alimentares de adultos brasileiros em 2008–2009 e 2017–2018. Rev. Saúde Pública 2021, 55 (Suppl. S1), 8s. [Google Scholar] [CrossRef]
- Bessada, S.M.F.; Barreira, J.C.M.; Oliveira, M.B.P.P. Pulses and food security: Dietary protein, digestibility, bioactive and functional properties. Trends Food Sci. Technol. 2019, 93, 53–68. [Google Scholar] [CrossRef]
- McCrory, M.A.; Hamaker, B.R.; Lovejoy, J.C.; Eichelsdoerfer, P.E. Pulse consumption, satiety, and weight management. Adv. Nutr. 2010, 1, 17–30. [Google Scholar] [CrossRef] [Green Version]
- Dos Passos, K.E.; Bernardi, J.R.; Mendes, K.G. Analysis of the nutritional composition of the Brazilian Staple Foods Basket. Cad. Saúde Pública 2014, 19, 1623–1630. [Google Scholar] [CrossRef]
- Beigrezaei, S.; Ghiasvand, R.; Feizi, A.; Iraj, B. Relationship between Dietary Patterns and Incidence of Type 2 Diabetes. Int. J. Prev. Med. 2019, 10, 122. [Google Scholar] [CrossRef]
- Strate, L.L.; Keeley, B.R.; Cao, Y.; Wu, K.; Giovannucci, E.L.; Chan, A.T. Western dietary pattern increases, and Prudent dietary pattern decreases, risk of incident diverticulitis in a Prospective Cohort Studies. Gastroenterology 2017, 152, 1023–1030. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gao, M.; Jebb, S.A.; Aveyard, P.; Ambrosini, G.L.; Perez-Cornago, A.; Carter, J.; Sun, X.; Piernas, C. Associations between dietary patterns and the incidence of total and fatal cardiovascular disease and all-cause mortality in 116,806 individuals from the UK Biobank: A prospective cohort study. BMC Med. 2021, 19, 83. [Google Scholar] [CrossRef] [PubMed]
- Egg, S.; Erler, J.; Perktold, B.; Hasenegger, V.; Rust, P.; Ramoner, R.; König, J.; Purtscher, E. Traditional v. modern dietary patterns among a population in western Austria: Associations with body composition and nutrient profile. Public Health Nutr. 2018, 22, 455–465. [Google Scholar] [CrossRef] [PubMed]
- Leung, C.W.; Epel, E.S.; Ritchie, L.D.; Crawford, P.B.; Laraia, B.A. Food insecurity is inversely associated with diet quality of lower-income adults. J. Am. Diet. Assoc. 2014, 114, 1943–1953. [Google Scholar] [CrossRef] [PubMed]
- World Bank. COVID Crisis Is FUELING Food Price Rises for World’s Poorest. 2021. Available online: https://blogs.worldbank.org/voices/covid-crisis-fueling-food-price-rises-worlds-poorest (accessed on 6 March 2022).
- Kershaw, K.N.; Klikuszowian, E.; Schrader, L.; Siddique, J.; Van Horn, L.; Womack, V.Y.; Zenk, S.N. Assessment of the influence of food attributes on meal choice selection by socioeconomic status and race/ethnicity among women living in Chicago, USA: A discrete choice experiment. Appetite 2019, 139, 19–25. [Google Scholar] [CrossRef]
- Livingstone, K.M.; Abbott, G.; Lamb, K.E.; Dullaghan, K.; Worsley, T.; McNaughton, S.A. Understanding Meal Choices in Young Adults and Interactions with Demographics, Diet Quality, and Health Behaviors: A Discrete Choice Experiment. J. Nutr. 2021, 24, 2361–2371. [Google Scholar] [CrossRef]
- Almeida, C.A.N.; Almeida, C.C.J.N.; João, C.A.; João, C.R. Food cost in Brazil: Assessment and implications. Rev. Nutrologia 2008, 1, 53–56. (In Portuguese) [Google Scholar]
- Maia, E.G.; dos Passos, C.M.; Levy, R.B.; Martins, A.P.B.; Mais, L.A.; Claro, R.M. What to expect from the price of healthy and unhealthy foods over time? The case from Brazil. Public Health Nutr. 2020, 23, 579–588. [Google Scholar] [CrossRef] [Green Version]
- Siqueira, K.; Borges, C.; Binoti, M.; Pilati, A.; da Silva, P.; Gupta, S.; Drewnowski, A. Nutrient density and affordability of foods in Brazil by food group and degree of processing. Public Health Nutr. 2020, 24, 4564–4571. [Google Scholar] [CrossRef]
- Harriman, C. Shrinking the Price Gap for Whole Grains. AACC International 2013. Available online: https://www.aaccnet.org/publications/plexus/cfwplexus/library/books/Documents/WholeGrainsSummit2012/CPLEX-2013-1001-17B.pdf (accessed on 6 March 2022).
- Mello, A.V.; Sarti, F.M.; Pereira, J.L.; Goldbaum, M.; Cesar, C.L.G.; Alves, M.C.G.P.; Fisberg, R.M. Determinants of inequalities in the quality of Brazilian diet: Trends in 12-year population-based study (2003–2015). Int. J. Equity Health 2018, 17, 72. [Google Scholar] [CrossRef] [PubMed]
- Amin, M.D.; Badruddoza, S.; McCluskey, J.J. Predicting access to healthful food retailers with machine learning. Food Policy 2021, 99, 101985. [Google Scholar] [CrossRef] [PubMed]
- Vilar-Compte, M.; Burrola-Méndez, S.; Lozano-Marrufo, A.; Ferré-Eguiluz, I.; Flores, D.; Gaitán-Rossi, P.; Teruel, G.; Pérez-Escamilla, R. Urban poverty and nutrition challenges associated with accessibility to a healthy diet: A global systematic literature review. Int. J. Equity Health 2021, 20, 40. [Google Scholar] [CrossRef] [PubMed]
- Ministry of Education. National Education Development Fund—FNDE. National School Feeding Booklet. 2015. Available online: https://www.fnde.gov.br/index.php/centrais-de-conteudos/publicacoes/category/230-controle-social-acae?download=13182:cartilha_nacional_da_alimentacao_escolar_2015 (accessed on 6 March 2022).
- Brazil. Law # 6.321, 1976. Provides for the Deduction of Taxable Income for Corporate Income Tax Purposes of Double the Expenses Incurred in Worker Food Programs. Union Official Journal. 1976. Available online: https://www.camara.leg.br/proposicoesWeb/prop_mostrarintegra;jsessionid=7F09379F5A217178C8F43E7BEAA13BA0.proposicoesWeb1?codteor=349581&filename=LegislacaoCitada+-PL+6088/2005 (accessed on 6 March 2022).
- Bhat, S.; Coyle, D.H.; Trieu, K.; Neal, B.; Mozaffarian, D.; Marklund, M.; Wu, J.H.Y. Healthy Food Prescription Programs and their Impact on Dietary Behavior and Cardiometabolic Risk Factors: A Systematic Review and Meta-Analysis. Adv. Nutr. 2021, 12, 1944–1956. [Google Scholar] [CrossRef]
- Kennedy, E.; Webb, P.; Block, S.; Griffin, T.; Mozaffarian, D.; Kyte, R. Transforming Food Systems: The Missing Pieces Needed to Make Them Work. Curr. Dev. Nutr. 2020, 5, nzaa177. [Google Scholar] [CrossRef]
- Tucker, K.L. Assessment of usual dietary intake in population studies of gene-diet interaction. Nutr. Metab. Cardiovasc. Dis. 2007, 17, 74–81. [Google Scholar] [CrossRef]
- Trivellato, P.T.; Morais, D.C.; Lopes, S.O.; Miguel, E.S.; Franceschini, S.C.C.; Priore, S.E. Food and nutritional insecurity in families in the Brazilian rural environment: A systematic review. Cien. Saúde Colet. 2019, 24, 865–874. [Google Scholar] [CrossRef]
Characteristics | Food Security | Food Insecurity 1 | p-Value 2 | ||||
---|---|---|---|---|---|---|---|
n | % 3 | 95% CI | n | % | 95% CI | ||
Total | 15,878 | 59.52 | (58.13–60.89) | 12,275 | 40.48 | (39.11–41.87) | - |
Characteristics of the household members | |||||||
Age group, years | |||||||
20–29 | 3544 | 57.94 | (55.65–60.2) | 3121 | 42.06 | (39.8–44.35) | <0.001 |
30–39 | 4193 | 57.44 | (55.21–59.63) | 3405 | 42.56 | (40.37–44.79) | |
40–49 | 4147 | 59.7 | (57.57–61.8) | 3127 | 40.3 | (38.2–42.43) | |
50–59 | 3994 | 63.52 | (61.4–65.59) | 2622 | 36.48 | (34.41–38.6) | |
Sex | |||||||
Male | 7635 | 60.41 | (58.91–61.9) | 5703 | 39.59 | (38.1–41.09) | 0.004 |
Female | 8243 | 58.63 | (57.11–60.14) | 6572 | 41.37 | (39.86–42.89) | |
Ethnicity | |||||||
White and yellow | 7119 | 70.09 | (68.25–71.86) | 3377 | 29.91 | (28.14–31.75) | <0.001 |
Black, brown and indigenous | 8747 | 51.45 | (49.8–53.09) | 8889 | 48.55 | (46.91–50.2) | |
Education level, years | |||||||
0–4 | 1693 | 41.66 | (38.88–44.48) | 2450 | 58.34 | (55.52–61.12) | <0.001 |
5–9 | 4406 | 52.26 | (50.32–54.18) | 4153 | 47.74 | (45.82–49.68) | |
10–12 | 5744 | 59.1 | (57.19–60.98) | 4277 | 40.9 | (39.02–42.81) | |
≥13 | 4035 | 78.48 | (76.27–80.53) | 1395 | 21.52 | (19.47–23.73) | |
Lifestyle characteristics | |||||||
Body mass index | |||||||
Underweight | 316 | 53.95 | (46.44–61.29) | 288 | 46.05 | (38.71–53.56) | 0.055 |
Healthy weight | 6723 | 58.78 | (57.02–60.53) | 5349 | 41.22 | (39.47–42.98) | |
Overweight | 6204 | 61 | (59.19–62.78) | 4521 | 39 | (37.22–40.81) | |
Obese | 2635 | 58.68 | (56.23–61.09) | 2117 | 41.32 | (38.91–43.77) | |
Followed a specific diet | |||||||
Yes | 2183 | 61.42 | (58.98–63.79) | 1595 | 38.58 | (36.21–41.02) | 0.092 |
No | 13,695 | 59.23 | (57.76–60.7) | 10,680 | 40.77 | (39.3–42.24) | |
Food variety score (FVS) | |||||||
Tertile 1 (2–10 food items) | 6125 | 51.68 | (49.69–53.68) | 6314 | 48.32 | (46.32–50.31) | <0.001 |
Tertile 2 (11–12 food items) | 4279 | 60.74 | (58.7–62.75) | 3235 | 39.26 | (37.25–41.3) | |
Tertile 3 (13 food items) | 5474 | 69.26 | (67.29–71.17) | 2726 | 30.74 | (28.83–32.71) | |
Number of meals | |||||||
1–3 | 1482 | 54.24 | (49.97–58.44) | 1306 | 45.76 | (41.56–50.03) | 0.010 |
4–6 | 9958 | 60.18 | (58.56–61.77) | 7644 | 39.82 | (38.23–41.44) | |
≥7 | 4438 | 60.11 | (58.03–62.16) | 3325 | 39.89 | (37.84–41.97) | |
Main meals | |||||||
3 | 12,986 | 59.46 | (58.01–60.88) | 10,073 | 40.54 | (39.12–41.99) | 0.115 |
2 | 2665 | 60.78 | (58.26–63.24) | 1996 | 39.22 | (36.76–41.74) | |
≤1 | 227 | 50.59 | (39.71–61.42) | 206 | 49.41 | (38.58–60.29) | |
Evaluation of the standard of living in relation to diet | |||||||
Good | 11,374 | 72.01 | (70.37–73.6) | 5004 | 27.99 | (26.4–29.63) | <0.001 |
Satisfactory | 4331 | 45.17 | (42.99–47.37) | 5880 | 54.83 | (52.63–57.01) | |
Bad | 173 | 12.92 | (9.83–16.78) | 1391 | 87.08 | (83.22–90.17) | |
Household characteristics | |||||||
Area | |||||||
Urban | 12,530 | 61.19 | (59.64–62.73) | 9333 | 38.81 | (37.27–40.36) | <0.001 |
Rural | 3348 | 48.97 | (46.39–51.56) | 2942 | 51.03 | (48.44–53.61) | |
Region | |||||||
North | 1692 | 37.96 | (34.18–41.89) | 2440 | 62.04 | (58.11–65.82) | <0.001 |
Northeast | 4517 | 46.59 | (44.72–48.46) | 5200 | 53.41 | (51.54–55.28) | |
Southeast | 4531 | 64.98 | (62.28–67.6) | 2498 | 35.02 | (32.4–37.72) | |
South | 2859 | 76.88 | (74.12–79.41) | 840 | 23.12 | (20.59–25.88) | |
Midwest | 2279 | 63.3 | (59.88–66.59) | 1297 | 36.7 | (33.41–40.12) | |
Family income per capita 4 | |||||||
≤1 minimum wage | 5133 | 39.49 | (37.59–41.42) | 8085 | 60.51 | (58.58–62.41) | <0.001 |
>1 and ≤3 minimum wages | 7883 | 67.35 | (65.3–69.33) | 3779 | 32.65 | (30.67–34.7) | |
>3 minimum wages | 2862 | 89.17 | (86.82–91.14) | 411 | 10.83 | (8.86–13.18) | |
Number of household members | |||||||
≤3 members | 9231 | 66.17 | (64.59–67.71) | 5533 | 33.83 | (32.29–35.41) | <0.001 |
4 to 6 members | 6159 | 54.18 | (51.88–56.45) | 5771 | 45.82 | (43.55–48.12) | |
≥7 members | 488 | 31.09 | (25.66–37.1) | 971 | 68.91 | (62.9–74.34) | |
Children <5 years | |||||||
Yes | 2707 | 48.96 | (45.93–51.99) | 2961 | 51.04 | (48.01–54.07) | <0.001 |
No | 13,171 | 62.15 | (60.63–63.64) | 9314 | 37.85 | (36.36–39.37) | |
Individuals > 60 years | |||||||
Yes | 2986 | 60.04 | (57.2–62.82) | 2185 | 39.96 | (37.18–42.8) | 0.6883 |
No | 12,892 | 59.41 | (57.88–60.92) | 10,090 | 40.59 | (39.08–42.12) | |
Sex of the household reference person | |||||||
Male | 10452 | 63.31 | (61.62–64.98) | 6933 | 36.69 | (35.02–38.38) | <0.001 |
Female | 5426 | 52.72 | (50.61–54.81) | 5342 | 47.28 | (45.19–49.39) | |
Age of the household reference person, years | |||||||
≤39 | 4884 | 57.49 | (55.03–59.9) | 4129 | 42.51 | (40.1–44.97) | 0.0694 |
40 to 59 | 9050 | 60.62 | (58.82–62.39) | 6686 | 39.38 | (37.61–41.18) | |
≥60 | 1944 | 60.3 | (57.01–63.49) | 1460 | 39.7 | (36.51–42.99) | |
Ethnicity of the household reference person | |||||||
White and yellow | 7004 | 69.97 | (67.84–72.03) | 3273 | 30.03 | (27.97–32.16) | <0.001 |
Black, brown and indigenous | 8855 | 51.73 | (49.98–53.48) | 8995 | 48.27 | (46.52–50.02) | |
Education level of the household reference person, years | |||||||
0–4 | 2453 | 43.65 | (41.03–46.31) | 3383 | 56.35 | (53.69–58.97) | <0.001 |
5–9 | 5060 | 54 | (51.59–56.4) | 4445 | 46 | (43.6–48.41) | |
10–12 | 5054 | 61.78 | (59.37–64.13) | 3336 | 38.22 | (35.87–40.63) | |
≥13 | 3311 | 78.81 | (76.05–81.33) | 1111 | 21.19 | (18.67–23.95) |
Factor Scores | Cluster 1 (n = 4098) | Cluster 2 (n = 13,346) | Cluster 3 (n = 6050) | Cluster 4 (n = 4659) | Prob > F 2 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | (SE) | p-Value 1 | Mean | (SE) | p-Value | Mean | (SE) | p-Value | Mean | (SE) | p-Value | ||
Factor score—Factor 1 | −0.32 | (0.02) | - | −0.42 | (0.01) | - | 1.42 | (0.02) | - | −0.24 | (0.02) | - | <0.001 |
Food security | −0.34 | (0.03) | 0.122 | −0.43 | (0.01) | 0.278 | 1.42 | (0.03) | 0.865 | −0.24 | (0.02) | 0.737 | <0.001 |
Food insecurity | −0.26 | (0.04) | −0.41 | (0.01) | 1.41 | (0.03) | −0.25 | (0.03) | <0.001 | ||||
Factor score—Factor 2 | −0.66 | (0.03) | - | 0.28 | (0.02) | - | 0.06 | (0.02) | - | −0.43 | (0.02) | - | <0.001 |
Food security | −0.68 | (0.04) | 0.151 | 0.30 | (0.02) | 0.283 | 0.09 | (0.03) | 0.123 | −0.46 | (0.03) | 0.151 | <0.001 |
Food insecurity | −0.59 | (0.05) | 0.26 | (0.02) | 0.02 | (0.03) | −0.38 | (0.04) | <0.001 | ||||
Factor score—Factor 3 | −0.40 | (0.02) | - | −0.29 | (0.01) | - | −0.10 | (0.01) | - | 1.63 | (0.03) | - | <0.001 |
Food security | −0.36 | (0.03) | 0.006 | −0.21 | (0.01) | <0.001 | −0.04 | (0.02) | <0.001 | 1.63 | (0.03) | 0.999 | <0.001 |
Food insecurity | −0.51 | (0.04) | −0.38 | (0.01) | −0.17 | (0.02) | 1.63 | (0.05) | <0.001 | ||||
Factor score—Factor 4 | 1.66 | (0.03) | - | −0.38 | (0.01) | - | −0.04 | (0.02) | - | −0.12 | (0.02) | - | <0.001 |
Food security | 1.71 | (0.03) | 0.001 | −0.27 | (0.01) | <0.001 | 0.07 | (0.03) | <0.001 | −0.06 | (0.03) | <0.001 | <0.001 |
Food insecurity | 1.49 | (0.05) | −0.49 | (0.01) | −0.17 | (0.02) | −0.26 | (0.04) | <0.001 |
Food Security Status | Cluster 1 (n = 4098) | Cluster 2 (n = 13,346) | Cluster 3 (n = 6050) | Cluster 4 (n = 4659) | p-Value 1 | ||||
---|---|---|---|---|---|---|---|---|---|
‘Fruits, Vegetables, and Whole Grains’ Pattern | ‘Brazilian Traditional Staple Foods’ Pattern | ‘Brazilian Breakfast Style’ Pattern | ‘Beverages, Ready-to-Eat and Convenience Foods’ Pattern | ||||||
% | (95% CI) | % | (95% CI) | % | (95% CI) | % | (95% CI) | ||
Food security | 21.30 | (19.93–22.74) | 35.14 | (33.75–36.55) | 20.73 | (19.57–21.94) | 22.83 | (21.51–24.22) | <0.001 |
Food insecurity | 9.58 | (8.67–10.57) | 49.82 | (48.02–51.62) | 25.28 | (23.80–26.82) | 15.33 | (13.91–16.85) |
Models (n = 28,127) 1 | OR | SE | 95% CI | p-Value |
---|---|---|---|---|
Model 1—univariate model | ||||
Cluster 2—‘Brazilian Traditional staple foods’ pattern | 1.00 (ref.) | |||
Cluster 1—‘Fruits, vegetables, and whole grains’ pattern | 0.32 | 0.02 | (0.27–0.37) | <0.001 |
Cluster 3—‘Brazilian breakfast style’ pattern | 0.86 | 0.05 | (0.77–0.96) | 0.008 |
Cluster 4—‘Beverages, ready-to-eat and convenience foods’ pattern | 0.47 | 0.03 | (0.41–0.55) | <0.001 |
Model 2—model 1 + lifestyle variables 2 | ||||
Cluster 2—‘Brazilian Traditional staple foods’ pattern | 1.00 (ref.) | |||
Cluster 1—‘Fruits, vegetables, and whole grains’ pattern | 0.43 | 0.03 | (0.36–0.5) | <0.001 |
Cluster 3—‘Brazilian breakfast style’ pattern | 0.93 | 0.06 | (0.83–1.06) | 0.227 |
Cluster 4—‘Beverages, ready-to-eat and convenience foods’ pattern | 0.56 | 0.04 | (0.48–0.66) | <0.001 |
Model 3—model 2 + sociodemographic variables 3 | ||||
Cluster 2—‘Brazilian Traditional staple foods’ pattern | 1.00 (ref.) | |||
Cluster 1—‘Fruits, vegetables, and whole grains’ pattern | 0.75 | 0.06 | (0.64–0.89) | 0.001 |
Cluster 3—‘Brazilian breakfast style’ pattern | 0.98 | 0.06 | (0.86–1.11) | 0.723 |
Cluster 4—‘Beverages, ready-to-eat and convenience foods’ pattern | 0.93 | 0.08 | (0.79–1.09) | 0.370 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Castro, M.A.d.; Fontanelli, M.d.M.; Nogueira-de-Almeida, C.A.; Fisberg, M. Food Insecurity Reduces the Chance of Following a Nutrient-Dense Dietary Pattern by Brazilian Adults: Insights from a Nationwide Cross-Sectional Survey. Nutrients 2022, 14, 2126. https://doi.org/10.3390/nu14102126
Castro MAd, Fontanelli MdM, Nogueira-de-Almeida CA, Fisberg M. Food Insecurity Reduces the Chance of Following a Nutrient-Dense Dietary Pattern by Brazilian Adults: Insights from a Nationwide Cross-Sectional Survey. Nutrients. 2022; 14(10):2126. https://doi.org/10.3390/nu14102126
Chicago/Turabian StyleCastro, Michelle Alessandra de, Mariane de Mello Fontanelli, Carlos Alberto Nogueira-de-Almeida, and Mauro Fisberg. 2022. "Food Insecurity Reduces the Chance of Following a Nutrient-Dense Dietary Pattern by Brazilian Adults: Insights from a Nationwide Cross-Sectional Survey" Nutrients 14, no. 10: 2126. https://doi.org/10.3390/nu14102126