International Journal of Public Health

, Volume 61, Issue 9, pp 1069–1077 | Cite as

Socioeconomic predictors of dietary patterns among Guatemalan adults

  • Ana-Lucia Mayén
  • Silvia Stringhini
  • Nicole D. Ford
  • Reynaldo Martorell
  • Aryeh D. Stein
  • Fred Paccaud
  • Pedro Marques-VidalEmail author
Original Article



We aimed to assess the associations of socioeconomic factors with dietary patterns in a Guatemalan population.


Cross-sectional data of 1076 participants (42 % men, mean age 32.6 ± 4.2 years) collected between 2002 and 2004 in four rural villages in Guatemala. Dietary patterns were derived using principal component analysis. Chi-square and Poisson regression models were used to assess associations between socioeconomic factors and dietary patterns.


Three dietary patterns were identified: “Western” (high in processed foods), “traditional” (high in traditional foods) and “coffee and sugar”, explaining 11, 7 and 6 % of the variance, respectively. Annual expenditures were associated with a higher adherence to the “Western” pattern: prevalence ratios [(PR) (95 % confidence interval)] 1.92 (1.17–3.15) for the highest vs. lowest expenditure group in men and 8.99 (3.57–22.64) in women. A borderline significant (p = 0.06) negative association was found between the “traditional” pattern and higher household expenditures [0.71 (0.49–1.02) in men] and with schooling [0.23 (0.05–1.02)] in women (p = 0.05).


Dietary patterns in Guatemala are predicted by socioeconomic factors. In particular, high annual expenditures are associated with a more westernized, less traditional diet.


Socioeconomic Diet patterns Guatemala Expenditures 


Compliance with ethical standards

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Procedures were approved by the institutional review boards at INCAP, Emory University, and the International Food Policy Research Institute. An informed consent was obtained from all individual participants included in the study.

Conflict of interest



Data were collected using funding provided by the US National Institutes of Health (R01 TW-05598: PI Martorell). Ana-Lucia Mayén-Chacón is a recipient of a Swiss Excellence Government scholarship. Silvia Stringhini is supported by an Ambizione Grant (No. PZ00P3_147998) from the Swiss National Science Foundation (SNSF). Funders had no role in the design, analysis or writing of this article.

Supplementary material

38_2016_863_MOESM1_ESM.docx (35 kb)
Supplementary material 1 (DOCX 35 kb)


  1. Araujo MC, Verly Junior E, Junger WL, Sichieri R (2014) Independent associations of income and education with nutrient intakes in Brazilian adults: 2008–2009 national dietary survey. Public Health Nutr 17(12):2740–2752. doi: 10.1017/s1368980013003005 CrossRefPubMedGoogle Scholar
  2. Arruda SP, da Silva AA, Kac G, Goldani MZ, Bettiol H, Barbieri MA (2014) Socioeconomic and demographic factors are associated with dietary patterns in a cohort of young Brazilian adults. BMC Public Health 14:654. doi: 10.1186/1471-2458-14-654 CrossRefPubMedPubMedCentralGoogle Scholar
  3. Barquera S, Campos I, Rivera JA (2013) Mexico attempts to tackle obesity: the process, results, push backs and future challenges. Obes Rev 14(Suppl 2):69–78. doi: 10.1111/obr.12096 CrossRefPubMedGoogle Scholar
  4. Barros AJ, Hirakata VN (2003) Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol 3:21. doi: 10.1186/1471-2288-3-21 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Blouin C, Chopra M, van der Hoeven R (2009) Trade and social determinants of health. Lancet 373(9662):502–507. doi: 10.1016/S0140-6736(08)61777-8 CrossRefPubMedGoogle Scholar
  6. Campos-Ortiz F, Oviedo-Pacheco M (2013) Study on the competitiveness of the Mexican sugar industry. In: Banco de México (ed) vol 2013–16. Banco de MéxicoGoogle Scholar
  7. Cardoso I, Bovet P, Viswanathan B, Luke A, Marques-Vidal P (2013) Nutrition transition in a middle-income country: 22-year trends in the Seychelles. Eur J Clin Nutr 67(2):135–140. doi: 10.1038/ejcn.2012.199 CrossRefPubMedGoogle Scholar
  8. Central Bank of Guatemala (2014) Central Bank of Guatemala monthly indicators page. Accessed 25 Apr 2016
  9. Darmon N, Drewnowski A (2008) Does social class predict diet quality? Am J Clin Nutr 87(5):1107–1117PubMedGoogle Scholar
  10. de Castro MBT et al (2016) Sociodemographic characteristics determine dietary pattern adherence during pregnancy. Public Health Nutr 19(07):1245–1251. doi: 10.1017/S1368980015002700 CrossRefPubMedGoogle Scholar
  11. Dekker LH et al (2015) Socio-economic status and ethnicity are independently associated with dietary patterns: the HELIUS-dietary patterns study. Food Nutr Res 59:26317. doi: 10.3402/fnr.v59.26317 CrossRefPubMedGoogle Scholar
  12. Di Cesare M et al (2013) Inequalities in non-communicable diseases and effective responses. Lancet 381(9866):585–597CrossRefPubMedGoogle Scholar
  13. Fernandez-Alvira JM et al (2014) Country-specific dietary patterns and associations with socioeconomic status in European children: the IDEFICS study. Eur J Clin Nutr 68(7):811–821. doi: 10.1038/ejcn.2014.78 CrossRefPubMedGoogle Scholar
  14. Ferrante D et al (2011) Feasibility of salt reduction in processed foods in Argentina. Rev Panam Salud Publica 29(2):69–75CrossRefPubMedGoogle Scholar
  15. Filmer D, Pritchett L (2001) Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of India. Demography 38:115–132PubMedGoogle Scholar
  16. USDA Foreign Agricultural Service, GAIN (2013) Guatemala MY 2012–2013 Sugar Production Record High. In: USDA. Accessed 20 Jan 2016
  17. Grajeda R, Behrman JR, Flores R, Maluccio JA, Martorell R, Stein AD (2005) The human capital study 2002–04: tracking, data collection, coverage, and attrition. Food Nutr Bull 26(2 Suppl 1):S15–S24CrossRefPubMedPubMedCentralGoogle Scholar
  18. Howe L, Hargreaves J, Huttly S (2008) Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries. Emerg Themes Epidemiol 5(1):1–14. doi: 10.1186/1742-7622-5-3 CrossRefGoogle Scholar
  19. Kesse-Guyot E et al (2009) Dietary patterns and their sociodemographic and behavioural correlates in French middle-aged adults from the SU.VI.MAX cohort. Eur J Clin Nutr 63(4):521–528. doi: 10.1038/sj.ejcn.1602978 CrossRefPubMedGoogle Scholar
  20. Kline P (1994) An easy guide to factor analysis. Routledge, LondonGoogle Scholar
  21. Lako JV, Nguyen VC (2001) Dietary patterns and risk factors of diabetes mellitus among urban indigenous women in Fiji. Asia Pac J Clin Nutr 10(3):188–193CrossRefPubMedGoogle Scholar
  22. Luger E, Aspalter R, Luger M, Longin R, Rieder A, Dorner TE (2016) Changes of dietary patterns during participation in a web-based weight-reduction programme. Public Health Nutr 19(7):1211–1221. doi: 10.1017/s1368980015002852 CrossRefPubMedGoogle Scholar
  23. Maluccio JA, Martorell R, Ramirez LF (2005a) Household expenditures and wealth among young Guatemalan adults. Food Nutr Bull 26(2 Suppl 1):S110–S119CrossRefPubMedGoogle Scholar
  24. Maluccio JA, Murphy A, Yount KM (2005b) Research note: a socioeconomic index for the INCAP longitudinal study 1969–77. Food Nutr Bull 26(2 Suppl 1):S120–S124CrossRefPubMedGoogle Scholar
  25. Mayén A-L, Marques-Vidal P, Paccaud F, Bovet P, Stringhini S (2014) Socioeconomic determinants of dietary patterns in low- and middle-income countries: a systematic review. Am J Clin Nutr. doi: 10.3945/ajcn.114.089029 PubMedGoogle Scholar
  26. Menchú M, Méndez H, Lemus J (2000) Tabla de composición de alimentos de centroamerica. 2012 edn. INCAP, GuatemalaGoogle Scholar
  27. Ministerio de Salud Pública y Asistencia Social, Instituto Nacional de Estadística, Universidad del Valle de Guatemala (2011) V Encuesta Nacional de Salud Materno Infantil 2008–2009. GuatemalaGoogle Scholar
  28. Monteiro CA, Moura EC, Conde WL, Popkin BM (2004) Socioeconomic status and obesity in adult populations of developing countries: a review. Bull World Health Organ 82(12):940–946. doi: 10.1590/S0042-96862004001200011 PubMedGoogle Scholar
  29. Neuman M, Finlay JE, Davey Smith G, Subramanian SV (2011) The poor stay thinner: stable socioeconomic gradients in BMI among women in lower- and middle-income countries. Am J Clin Nutr 94(5):1348–1357. doi: 10.3945/ajcn.111.018127 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Ni Mhurchu C, Blakely T, Jiang Y, Eyles HC, Rodgers A (2010) Effects of price discounts and tailored nutrition education on supermarket purchases: a randomized controlled trial. Am J Clin Nutr 91(3):736–747. doi: 10.3945/ajcn.2009.28742 CrossRefPubMedGoogle Scholar
  31. Noel SE, Newby PK, Ordovas JM, Tucker KL (2009) A traditional rice and beans pattern is associated with metabolic syndrome in Puerto Rican older adults. J Nutr 139(7):1360–1367. doi: 10.3945/jn.109.105874 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Olinto MT, Willett WC, Gigante DP, Victora CG (2011) Sociodemographic and lifestyle characteristics in relation to dietary patterns among young Brazilian adults. Public Health Nutr 14(1):150–159. doi: 10.1017/s136898001000162x CrossRefPubMedGoogle Scholar
  33. Pérez-Grovas V, Cervantes E, Burstein J (2001) Case study of the coffee sector in Mexico. Cornell University, New YorkGoogle Scholar
  34. Popkin BM (2006) Global nutrition dynamics: the world is shifting rapidly toward a diet linked with noncommunicable diseases. Am J Clin Nutr 84(2):289–298PubMedGoogle Scholar
  35. Popkin BM, Adair LS, Ng SW (2012) Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev 70(1):3–21. doi: 10.1111/j.1753-4887.2011.00456.x CrossRefPubMedPubMedCentralGoogle Scholar
  36. Public Health Ministry et al. (2009) Guatemala reproductive health survey 2008–2009. GuatemalaGoogle Scholar
  37. Sofianou A, Fung TT, Tucker KL (2011) Differences in diet pattern adherence by nativity and duration of US residence in the Mexican–American population. J Am Diet Assoc 111(10):1563–1569. doi: 10.1016/j.jada.2011.07.005 (e2) CrossRefPubMedGoogle Scholar
  38. Waterlander WE, Steenhuis IH, de Boer MR, Schuit AJ, Seidell JC (2013) Effects of different discount levels on healthy products coupled with a healthy choice label, special offer label or both: results from a web-based supermarket experiment. Int J Behav Nutr Phys Act 10:59CrossRefPubMedPubMedCentralGoogle Scholar
  39. Winham DM (2009) Culturally tailored foods and CVD prevention. Am J Lifestyle Med 3(1):64S–68S. doi: 10.1177/1559827609335552 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Swiss School of Public Health (SSPH+) 2016

Authors and Affiliations

  1. 1.Institute of Social and Preventive Medicine (IUMSP)Lausanne University HospitalLausanneSwitzerland
  2. 2.Nutrition and Health Sciences, Laney Graduate SchoolEmory UniversityAtlantaGeorgia
  3. 3.Hubert Department of Global Health, Rollins School of Public HealthEmory UniversityAtlantaGeorgia
  4. 4.Department of Medicine, Internal MedicineLausanne University HospitalLausanneSwitzerland

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