47. Nkeiruka OA, Ogechi N. Infant feeding practices of mothers/caregivers and anthropometric indices of infants (0-12 months) in Etche local government area, Rivers state, Nigeria. *Int J Innov Res Sci Eng Technol.* (2018) 7:1074-80.

48. Danimoh MA, Idris SH, Dikko HG, Olorukooba AA, Mohammed A, Ayodeji OO. Nutritional status and its determinants among Fulani children aged 6-24 months in a rural Community of Kaduna State, Northwest Nigeria. *Eur J Nutr Food Saf.* (2020) 12:32-41. doi: 10.9734/ejnfs/2020/v12i630236

49. Amadi OF, Ezenwoso O, Odetunde OL. A cross-sectional study on nutritional status of preschool aged children in Enugu urban, Nigeria. *J Exp Res.* (2018) 6:19-26.

50. Osunmakinwa OO, Oladayin VO, Okunlola O, Ibirongbe D, Oyeyemi OT, Awosusi A, et al. Addressing malnutrition in children: an appraisal of the Ondo state nutrition program. *Cureus.* (2022) 14:e28595. doi: 10.7759/cureus.28595

51. Ogunnaike MG, Shittu AM, Fapojuwo, Oluwakemi E, Kehinde, Mojisola O. (2018). Effects of gender gaps in livelihood assets and adoption of climate amrt practices on nutritional outcomes of children in Nigeria. Available at: https://ageconsearch.umn.edu/record/273908

52. Ezeofor IO, Garcia AL, Wright CM. Criteria for undernutrition screening in hospitalised infants under 6 months: a diagnostic accuracy study in a resource-poor setting. *Arch Dis Child.* (2020) 105:524-9. doi: 10.1136/archdischild-2019-318313

53. Akande YO, Abata GD, Babalola AO, Abata EO. Nutritional status of under five children attending state hospital Okitipupa Ondo state. *Int J Res Sci Innov.* (2019) VI.

54. Atimati AO, Adam VY. Infant and young child feeding practices and nutritional status of children (0-24 months) in Egor local government area of Edo state, Nigeria. *Nigerian J Nut Sci.* (2019) 40:1-11.

55. Ezeh OK, Abir T, Zainol NR, Al MA, Milton AH, Haque MR, et al. Trends of stunting prevalence and its associated factors among nigerian children aged 0-59 months residing in the northern Nigeria, 2008-2018. *Nutrients.* (2021) 13:4312. doi: 10.3390/nu13124312

56. Seer-Uke EN, Samuel ES, Agajah ME, Ikpato VT, Tyoakaa AA, Abugu LI, et al. Nutritional status of children under age five in Benue state, Nigeria. *Afr J Food Agric Nutr Dev.* (2021) 21:18391-403.

57. Adeniyi OF, Ekure E, Olatona FA, Ajayi EO, Nworgu N. Nutritional assessment and maternal perception of toddler body size using toddler Silhouette scale in Nigeria a developing country. *Int J MCH AIDS.* (2018) 7:9. doi: 10.21106/ijma.217

58. Manyong VM, Abdoulaye T, Ojide M, Ogundapo A, Ayoola GB, Dashiell K, et al. Household hunger, poverty, and childcare in 5 states of Nigeria and their impacts on nutritional outcomes in preschool children. *Food Nutr Bull.* (2021) 42:188-209. doi: 10.1177/037957211211009482

59. Afolami I, Samuel F, Borgonjen-Van Den Berg K, Mwangi MN, Kalejaiye O, Sanusi RA, et al. The contribution of provitamin a biofortified cassava to vitamin a intake in Nigerian pre-schoolchildren. *Br J Nutr.* (2021) 126:1364. doi: 10.1017/S0007114521000039

60. Afolabi OFSaka AO, Ojuawo A, Biliaminu SA. Serum zinc levels of hospitalized children with acute diarrhea differ by the isolated viruses. *Int J Health Sci.* (2019) 13:4.

61. Ecker O, Comstock A, Babatunde RO, Andam KS. (2020). Poor dietary quality is Nigeria's key nutrition problem. doi: 10.2499/p15738coll2.133738

62. Isa H, Bashir M, Adedeti BA. Malaria prevalence and anemia status of children attending a health Facility in Yola. *J Adv Microbiol.* (2022) 8:25-31.

63. Ajakaye OG, Ibukunoluwa MR. Prevalence and risk of malaria, anemia and malnutrition among children in IDPs camp in Edo state, Nigeria. *Parasite Epid Cont.* (2020) 8:e00127. doi: 10.1016/j.parepi.2019.e00127

64. Hanson C, Lyden E, Anderson-Berry A, Kocmich N, Rezac A, Delair S, et al. Status of Retinoids and carotenoids and associations with clinical outcomes in maternal-infant pairs in Nigeria. *Nutrients.* (2018) 10:1286. doi: 10.3390/nu10091286

65. Afolami I, Mwangi MN, Samuel F, Boy E, Ilona P, Talsma EF, et al. Daily consumption of pro-vitamin a biofortified (yellow) cassava improves serum retinol concentrations in preschool children in Nigeria: a randomized controlled trial. *Am J Clin Nutr.* (2021) 113:221-31. doi: 10.1093/ajcn/nqa290

66. Owolabi AJ, Senbanjo IO, Oshikoya KA, Boekhorst J, Eijlander RT, Kortman GAM, et al. Multi-nutrient fortified dairy-based drink reduces anaemia without observed adverse effects on gut microbiota in anaemic malnourished nigerian toddlers: a randomised dose-response study. *Nutrients.* (2021) 13:1566. doi: 10.3390/nu13051566

67. Kennedy CU, Chukwuebuka NO, Uchenna E. Serum zinc levels in apparently healthy children in Nigeria: are they acceptable. *Niger Med J.* (2020) 61:291. doi: 10.4103/nmj.NMJ_20_20

68. Resnick D, Anigo K, Anjorin OM. (2021). Enabling environments for nutrition advocacy: A comparison of infant and young child feeding and food fortification in Nigeria [internet]. Available at: https://papers.ssrn.com/abstract=3955168

69. Iheme GO, Nzeagwu OC, Uzokwe CA, Matthew S, Edafioghor LO. A meta-analysis of compliance with revised WHO-infant and young child feeding indicators in data generation. *Clin Nutr ESPEN.* (2021) 43:169-73. doi: 10.1016/j.clnesp.2021.03.016

70. Samuel FO, Ibidapo EG. Complementary feeding practices and associated factors among nursing mothers in southwestern Nigeria. *Int J Mat Child Health AIDS.* (2020) 9:223-31. doi: 10.21106/ijma.363

71. Omotoye FE, Adesanmi RAS. Infant and young child-feeding practices in two local government areas in southwest, Nigeria. *J Food Sci Nutr Res.* (2019) 2:136-45.

72. United Nation Development Program (2023). Nigeria multidimensional poverty index [internet]. Available at: http://www.undp.org/nigeria-multidimensional-poverty-index [Accessed August 7, 2023].

73. Grantham-McGregor S, Ani C. A review of studies on the effect of Iron deficiency on cognitive development in children. *J Nutr.* (2001) 131:649S-68S. doi: 10.1093/jn/131.2.649S

74. Degarege A, Erko B, Negash Y, Animut A. Intestinal helminth infection, Anemia, undernutrition and academic performance among school children in northwestern Ethiopia. *Microorganisms.* (2022) 10:1353. doi: 10.3390/microorganisms10071353

75. Ramachandran P, Gopalan HS. Undernutrition & risk of infections in preschool children. *Indian J Med Res.* (2009) 130:579-83.

76. Muhoozi GKM, Atukunda P, Mwadime R, Iversen PO, Westerberg AC. Nutritional and developmental status among 6- to 8-month-old children in southwestern Uganda: a cross-sectional study. *Food Nutr Res.* (2016) 60:30270. doi: 10.3402/fnr.v60.30270

77. Matrins VJB, Toledo Florêncio TMM, Grillo LP, Franco M do CP, Martins PA, APG C, et al. Long-lasting effects of undernutrition. *Int J Environ Res Public Health.* (2011) 8:1817-46. doi: 10.3390/ijerph8061817

78. Lozoff B, Jimenez E, Hagen J, Mollen E, Wolf AW. Poorer behavioral and developmental outcome more than 10 years after treatment for iron deficiency in infancy. *Pediatrics.* (2000) 105:E51. doi: 10.1542/peds.105.4.e51

79. Ingram J. IFPRI Blog (2018). To address the triple burden of malnutrition, focus on food systems and demand | IFPRI.

80. Christian AK, Dake FAA. Profiling household double and triple burden of malnutrition in sub-Saharan Africa: prevalence and influencing household factors. *Public Health Nutr.* (2022) 25:1563-76. doi: 10.1017/S1368980021001750

81. Srivastava S, Upadhyay AK. A success story of reduction in childhood stunting and underweight in India: analysis of pooled data from three rounds of Indian demographic and health surveys (1998-2016). *J Biosoc Sci.* (2020) 54:106-23. doi: 10.1017/S002193202000070X

82. Angood C, Khara T, Dolan C, Berkley JA, Roberfroid D, Seal A, et al. Research priorities on the relationship between wasting and stunting. *PLoS One.* (2016) 11:e0153221. doi: 10.1371/journal.pone.0153221

83. Thurstan S, Sessions N, Dolan C, Sadler K, Cichon B, Isanaka S, et al. The relationship between wasting and stunting in young children: a systematic review. *Mat Child Nut.* (2022) 18:e13246. doi: 10.1111/mcn.13246

84. Dhami MV, Ogbo FA, Osuagwu UL, Agho KE. Prevalence and factors associated with complementary feeding practices among children aged 6-23 months in India: a regional analysis. *BMC Public Health.* (2019) 19:1-16. doi: 10.1186/s12889-019-7360-6

85. Loli S, Carcamo CP. Rotavirus vaccination and stunting: secondary data analysis from the Peruvian demographic and health survey. *Vaccine.* (2020) 38:8010-5. doi: 10.1016/j.vaccine.2020.10.044

86. Wilson AL, Bradley J, Kandeh B, Salami K, D'Alessandro U, Pinder M, et al. Is chronic malnutrition associated with an increase in malaria incidence? A cohort study in children aged under 5 years in rural Gambia. *Parasit Vectors.* (2018) 11:451. doi: 10.1186/s13071-018-3026-y

87. Islam MR, Rahman MS, Rahman MM, Nomura S, De Silva A, Lanerolle P, et al. Reducing childhood malnutrition in Bangladesh: the importance of addressing socio-economic inequalities. *Public Health Nutr.* (2020) 23:72-82. doi: 10.1017/S136898001900140X

88. Husseini M, Darboe MK, Moore SE, Nabwera HM, Prentice AM. Thresholds of socio-economic and environmental conditions necessary to escape from childhood malnutrition: a natural experiment in rural Gambia. *BMC Med.* (2018) 16:199. doi: 10.1186/s12916-018-1179-3

89. Gombart AF, Pierre A, Maggini S. A review of micronutrient and immune system. *Nutrients*, (2020) 12:236. doi: 10.3390/nu12010236

90. Ciosek Ż, Kot K, Kosik-Bogacka D, Łanocha-Arendarczyk N, Rotter I. The effects of calcium, magnesium, phosphorus, fluoride, and lead on bone tissue. *Biomolecules.* (2021) 11:506. doi: 10.3390/biom11040506

91. Harika R, Faber M, Samuel F, Kimiywe J, Mulugeta A, Eilander A. Micronutrient status and dietary intake of iron, vitamin a, iodine, folate and zinc in women of reproductive age and pregnant women in Ethiopia, Kenya, Nigeria and South Africa: a systematic review of data from 2005 to 2015. *Nutrients.* (2017) 9:1096. doi: 10.3390/nu9101096

92. Harika R, Faber M, Samuel F, Kimiywe J, Mulugeta A, Eilander A. (2017). Kenya, Nigeria and South Africa: a systematic review of data from 2005 to 2015. Mdpi.
<figure><img src="image_30.png" /></figure>

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EDITED BY
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Iran University of Medical Sciences, Iran

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RECEIVED 08 November 2023

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PUBLISHED 08 January 2024

CITATION

Liu S, Fan X, Jiang L and Liu T (2024) A cross-sectional study on the moderating effect of self-efficacy on the relationship between sociodemographic variables and nutrition literacy among older adults in rural areas of North Sichuan.
*Front. Nutr.* 10:1335008.
doi: 10.3389/fnut.2023.1335008

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A cross-sectional study on the
moderating effect of self-efficacy
on the relationship between
sociodemographic variables and
nutrition literacy among older
adults in rural areas of North
Sichuan

Shasha Liu¹,², Xiaomei Fan³, Li Jiang² and Tao Liu¹*

¹Department of Nursing, Jinzhou Medical University, Jinzhou, China, ²Chengdu BOE Hospital, Chengdu, China, ³First Affiliated Hospital of Chengdu Medical College, Chengdu, China

**Objective:** The purpose of this study is to examine the moderating role of self-efficacy among rural elderly individuals in northern Sichuan Province in the relationship between certain sociodemographic variables and nutritional literacy.

**Methods:** Convenience sampling was used to select 264 elderly individuals aged 60 and above from rural communities in Cangxi County, Guangyuan City, Yilong County, Nanchong City, and Bazhou District, Bazhong City, Sichuan Province. A self-designed questionnaire, including sociodemographic variables, the General Self-Efficacy Scale (GSES), and the Nutrition Literacy Questionnaire for the Elderly (NLQ-E), was administered through face-to-face interviews using a paper-based version. The relationships between sociodemographic variables, self-efficacy, and nutritional literacy in the elderly were analyzed using SPSS 26.0 and the Process plugin to examine the relationships between variables and to test for moderation effects.

**Results:** (1) There were significant differences in nutrition literacy scores among elderly people of different ages, genders, marital statuses, educational levels, personal monthly living expenses, dental conditions, and number of chronic diseases ($p < 0.05$). (2) When elderly individuals have lower self-efficacy, their nutritional literacy is lower as they become older, and they have poorer nutritional literacy with a higher number of chronic diseases.

**Conclusion:** General population demographic data has a significant impact on the nutritional literacy level of elderly people in rural areas of northern Sichuan. Self-efficacy plays a moderating role in the relationship between age and nutritional literacy, as well as the relationship between the number of chronic diseases and nutritional literacy.

KEYWORDS

elderly, demographic variables, nutrition literacy, self-efficacy, moderating effect
# 1 Introduction

Health is the cornerstone of human development, and nutrition is the cornerstone of maintaining health. Nutrition refers to the process by which the human body ingests, digests, and absorbs various nutrients from the outside world to maintain life activities and regulate the immune system to change the course of disease (1). With age, the body's metabolism slows down, and the physiological functions of various systems decline, making the elderly more prone to poor nutrition and overnutrition. According to statistics, the risk of malnutrition in Chinese elderly is as high as 48% (2). As of 2019, the population aged 65 and above in China reached 176.03 million, accounting for 12.6% of the total population; the population aged 80 and above reached 26 million; by 2050, the elderly population aged 65 and above will reach 365 million, accounting for 26.1% of the total population (3). Therefore, if effective measures are not implemented, the current situation 30 years later will be that about 1 in 12 people will be malnourished elderly. And due to the differences in conditions between rural and urban areas in China (4), this situation will be even more worrying. Therefore, reducing the risk of malnutrition and improving the current situation of malnutrition is essential for personal and social harmony and development.

Nutrition literacy is the ability of individuals to understand nutritional knowledge, their own nutritional status, and guide themselves in selecting foods to obtain nutrition, which is a part of health literacy (5, 6). At the same time, healthcare professionals also regard improving the nutrition literacy of chronic disease patients as an important part of their health guidance, and these measures play an important role in the prevention and treatment of nutrition and metabolism-related diseases such as hypertension, diabetes, obesity, and cancer (7-10). Therefore, from the concept and application, nutrition literacy is the upstream theory of nutrition, that is, an individual's level of nutrition literacy directly affects their nutritional status.

The differences in physiological and psychological status, living environment, cultural level, and economic level among the elderly lead to differences in their nutrition literacy (11). Aihara used a nutrition literacy scale suitable for the Japanese elderly based on the Japanese Dietary Guidelines to assess the elderly's understanding of nutrition-related knowledge. He found that there was a significant difference in nutrition literacy between genders, and lower cultural and economic status were related to limited nutrition literacy among elderly women (12). Prof. Zhang from the School of Public Health, Peking University, developed a nutrition literacy questionnaire suitable for Chinese elderly, and pointed out that elderly people with lower age, higher BMI, and higher cultural level have significantly higher nutrition literacy (2). In addition, the nutrition literacy of the elderly is also related to their oral health. A cross-sectional study from Finland found that elderly people who wear full dentures tend to choose unhealthy foods, leading to malnutrition (13).

Improving nutrition literacy is an important foundation for individuals to maintain a healthy state and an important means for the country to promote healthy aging. With the development of social psychology, the impact of positive psychological resources on health-promoting behaviors has received widespread attention, and more and more research has begun to pay attention to the relationship between elderly people's psychological changes and their health status (14, 15). In this field, research on the relationship between self-efficacy and health-promoting behaviors among the elderly involves more (16).

This relationship refers to whether the elderly can adopt adaptive behaviors to reduce the harm of events to their own health when facing stress or challenges in the environment, and self-efficacy is a psychological cognition related to this behavior (17-19). A survey on adult health literacy in Germany showed that demographic factors have an important impact on their health literacy level. But the effect size of these factors changed after adding self-efficacy to the regression model (20). The phenomenon of change occurring due to the addition of self-efficacy aligns with Bandura's theory of self-efficacy (21). Therefore, in formulating research on improving nutritional literacy in the elderly, it would be highly valuable to explore the relationship between sociodemographic factors and nutritional literacy, while incorporating Bandura's theory of self-efficacy.

Currently, nutrition literacy related research in the Chinese population mainly focuses on the development of scales (22). While the relationship between demographic data and nutrition literacy, as well as the underlying mechanisms, are not clearly understood. Therefore, this study aims to investigate the current status of nutrition literacy among elderly people in rural areas and promote local healthy aging. Based on the research objects and significance of the study, we proposed two hypotheses:

*Hypothesis 1:* The nutritional literacy of the elderly varies with differences in certain demographic variables.

*Hypothesis 2:* According to the theory of self-efficacy, this study assumes that general self-efficacy has a moderating effect on the relationship between certain demographic factors and nutrition literacy.

# 2 Materials and methods

The present study is based on the promotion effect of positive psychological resources on health behaviors. It explores the moderating effect of self-efficacy in rural elderly individuals on the relationship between sociodemographic variables and nutritional literacy. General sociodemographic data are used as independent variables, general self-efficacy is the moderating variable, and nutritional literacy is the dependent variable to examine the relationship among the three factors. The path relationships are presented in Figure 1.

## 2.1 Study population

This study used convenience sampling to select three rural communities in Guangyuan, Nanchong, and Bazhong cities in Sichuan Province, China. The selected participants were aged 60 years or older and had lived in the local rural areas for at least 1 year. The research team, accompanied by local community workers, we asked elderly individuals who voluntarily answered the questions and had normal cognitive function (not diagnosed with dementia or any mental illness by a doctor). Since the ultimate goal of this study was to investigate the nutrition self-management status of elderly people, elderly people who were completely unable to take care of themselves were excluded during the questionnaire survey stage. According to the Kendall sample calculation method, the sample size should be 5-10 times the maximum number of items on the scale. Taking into account a 20% sample loss,
<figure><img src="image_31.png" /><figcaption>FIGURE 1
Moderation effect pathway.</figcaption></figure>

the minimum sample size required for this study is 240. The final number of questionnaires collected that met the inclusion criteria is 264.

## 2.2 Selection of demographic variables

The survey questionnaire for this project included seven demographic variables, including five basic characteristics: age, gender, marital status, education level, and personal monthly living expenses. According to a systematic review published by the Department of Periodontology of the Federal University Rio Grande do Sul, elderly people with more teeth and functional teeth units have better nutritional status (23). Therefore, this study also added the dental status of elderly people in the local area as a basic characteristic in the questionnaire. It has been reported that elderly people with nutrition-related chronic diseases tend to a Mediterranean-style diet, while those who choose a high-calorie, high-fat, and high-salt diet have little knowledge about diet and disease (24). Therefore, the number of chronic diseases related to health in elderly people was also added to the basic information questionnaire in this study.

## 2.3 General self-efficacy scale

Self-efficacy is a core concept in Bandura's social cognitive theory. According to Bandura's theory, people with different levels of self-efficacy feel, think, and act differently (25). People with high self-efficacy tend to choose more challenging tasks, work harder once they start, persist longer, and quickly recover from setbacks. The General Self-Efficacy Scale (GSES) was revised by (26) and is a quantitative measure of an individual's psychological activity and self-confidence when facing setbacks or difficulties. The initial version consisted of 20 items, but it was later revised in 1997 to include 10 items and 1 dimension. The score of the GSES is obtained by summing the scores of the 10 items. The scores are rated on a 4-point Likert scale, where "1" means "completely incorrect," "2" means "what correct," "3" means "mostly correct," and "4" means "completely correct." The scale has been translated into 25 languages and is widely used. The Chinese version of the General Self-Efficacy Scale was used in this study, and its Cronbach's α coefficient was 0.89 (27).

## 2.4 Nutrition literacy questionnaire for the Chinese elderly

There are many different versions of nutrition literacy scales published both domestically and internationally. The Nutrition Literacy

Assessment Instrument (NLit) was designed by Gibbs (28). The scale covers all stages of obtaining nutrition and has been well validated in various populations. The Iranian version of FNLIT and the Turkish version of ANLS are nutrition and food literacy evaluation scales (29, 30). The questionnaire used in this study was the Chinese Elderly Nutrition Literacy Survey Questionnaire developed by Professor Zhang from the School of Public Health at Peking University (2). The questionnaire has 20 core items in three dimensions: the first 6 items evaluate the elderly's nutritional knowledge, the next 9 items evaluate the elderly's knowledge of healthy lifestyles and dietary behaviors, and the last 5 items evaluate the elderly's knowledge of promoting nutrition-related skills. The scores are rated on a 5-point Likert scale, where "1" means "strongly disagree," "2" means "disagree," "3" means "uncertain," "4" means "agree," and "5" means "strongly agree." The total score ranges from 20 to 100, with a higher score indicating higher nutrition literacy. The Cronbach's α coefficient for the scale used in this study is 0.926.

## 2.5 Statistical analysis

Before conducting statistical analysis, the first author reviewed all the included questionnaires again, and the data were entered independently by the first and second authors. SPSS26.0 was used for statistical. Since the sample data did not meet the normal distribution, median was used to describe the general information. The K-W test was used for intergroup comparison of nutrition literacy scores. The relationship between elderly self-efficacy and nutrition literacy was analyzed using Spearman's correlation. The moderating effect analysis was performed using PROCESS plug-in (by conducting 5,000 simulations using self-sampling, a 95% confidence interval is obtained), with chronic disease number and age as independent variables, self-efficacy score as moderating variable, and nutrition literacy score as dependent variable. Based on the results, a Johnson-Neyman plot was created (31). To ensure the accuracy of data analysis, data standardization, dummy variables, and multicollinearity diagnosis were performed before regression analysis. Considering that the questionnaires used in this study are widely used and established scales, no common method bias test was performed.

# 3 Results

## 3.1 Nutritional literacy score for the elderly based on demographic data

In this study, significant differences were found in the nutritional literacy scores of elderly individuals across various demographic
<table><caption>TABLE 1 Comparison of nutritional literacy scores among older adults based on demographic data (n = 264).</caption><thead><tr><th>Characteristics</th><th>n (%)</th><th>NLQ-E(median)</th><th>p</th></tr></thead><tbody><tr><td rowspan="3">Age</td><td>60-69</td><td>115 (43.6%)</td><td>80 (77, 97)</td><td rowspan="3">&lt;0.001</td></tr><tr><td>70-79</td><td>109 (41.3%)</td><td>77 (66, 80)</td></tr><tr><td>≥80</td><td>40 (15.2%)</td><td>62 (40, 72)</td></tr><tr><td rowspan="2">Gender</td><td>Male</td><td>131 (49.6%)</td><td>80 (76, 97)</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Female</td><td>133 (50.4%)</td><td>73 (63, 78)</td></tr><tr><td rowspan="3">Marriage status</td><td>Never married</td><td>1 (0.4%)</td><td>79</td><td rowspan="3">&lt;0.05</td></tr><tr><td>Married</td><td>200 (75.8%)</td><td>78 (71, 88)</td></tr><tr><td>Divorced/Widowhood</td><td>63 (23.9%)</td><td>72 (61, 80)</td></tr><tr><td rowspan="4">Education level</td><td>Illiteracy</td><td>97 (36.7%)</td><td>66 (51, 77)</td><td rowspan="4">&lt;0.001</td></tr><tr><td>Primary school diploma</td><td>94 (35.6%)</td><td>79 (73, 82)</td></tr><tr><td>Junior high school diploma</td><td>70 (26.5%)</td><td>88 (80, 98)</td></tr><tr><td>High school diploma or above</td><td>3 (1.1%)</td><td>100 (88,100)</td></tr><tr><td rowspan="4">living expenses CNY/month</td><td>≤500</td><td>101 (38.3%)</td><td>68 (53, 78)</td><td rowspan="4">&lt;0.001</td></tr><tr><td>501-999</td><td>112 (42.4%)</td><td>78 (73, 87)</td></tr><tr><td>1,000-1999</td><td>46 (17.4%)</td><td>97 (81, 100)</td></tr><tr><td>≥2000</td><td>5 (1.9%)</td><td>91 (77, 100)</td></tr><tr><td rowspan="4">Tooth condition</td><td>False tooth</td><td>100 (37.9%)</td><td>73 (66, 78)</td><td rowspan="4">&lt;0.001</td></tr><tr><td>Damaged teeth without dentures</td><td>90 (34.1%)</td><td>78 (66, 80)</td></tr><tr><td>Intact teeth</td><td>71 (26.9%)</td><td>84 (77, 97)</td></tr><tr><td>Toothless</td><td>3 (1.1%)</td><td>63 (40, 68)</td></tr><tr><td rowspan="4">Chronic diseases</td><td>0</td><td>117 (44.3%)</td><td>80 (77, 97)</td><td rowspan="4">&lt;0.001</td></tr><tr><td>1</td><td>113 (42.8%)</td><td>72 (66, 80)</td></tr><tr><td>2</td><td>32 (12.1%)</td><td>71 (52, 80)</td></tr><tr><td>≥3</td><td>2 (0.8%)</td><td>41 (40, 42)</td></tr></tbody></table>

<table><caption>TABLE 2 Spearman correlation coefficients between self-efficacy and nutritional literacy dimensions.</caption><thead><tr><th>Variables</th><th>Healthy lifestyle and dietary behavior</th><th>Healthy lifestyle and dietary behavior</th><th>Skill</th><th>Total</th></tr></thead><tbody><tr><td>GSES</td><td>0.779**</td><td>0.764**</td><td>0.754**</td><td>0.800**</td></tr></tbody></table>

variables, including age, gender, marital status, education level, monthly living expenses, tooth condition, and chronic diseases (*p* < 0.05). Please refer to Table 1 for detailed results. Additionally, comparative analysis was conducted within each demographic variable for all samples that exceeded the minimum required sample size. Further information can be found in the supplementary materials.

## 3.2 Correlation analysis between self-efficacy and nutrient literacy scores among rural elderly

As shown in Table 2, based on Spearman correlation, the correlation coefficients between self-efficacy scores and nutrient

literacy scores in each dimension ranged from 0.754 to 0.779 among the 264 elderly participants in this study. The correlation coefficient between self-efficacy scores and the total score of the nutrient literacy questionnaire was 0.800, indicating a strong positive correlation between self-efficacy and nutrient literacy among the population surveyed in this study.

## 3.3 Self-efficacy moderates the effects of sociodemographic variables on nutritional literacy among elderly individuals in rural areas

Before performing the moderation analysis, this study conducted a collinearity diagnosis on the included variables. The test results showed that the VIF < 5. The results are shown in Table 3, indicating that when the elderly residents' self-efficacy score is used as the moderating variable, there is a significant effect (*p* < 0.05) on the impact of age and the number of chronic diseases on their nutritional status. To demonstrate the moderating effect of self-efficacy among elderly residents, this study will quantify the impact of age and the number of chronic diseases on nutritional status under different self-efficacy scores, and present the results in two Johnson-Neyman graphs.

<footnote>*p < 0.05, **p < 0.01.</footnote>
### 3.3.1 Analysis of the impact of self-efficacy regulation age on nutritional literacy in older adults

As shown in Figure 2, when the self-efficacy score of elderly people is below 32 points, the effect of age on nutritional status is below 0, indicating a significant negative effect of age on nutritional status. While when the self-efficacy score is above 32 points (The confidence interval of the effect size includes 0) indicating that the effect size in this stage is meaningless. According to relevant literature, elderly people with self-efficacy below 30 points generally belong to

the group of poor self-efficacy (32). However, this study found a critical score of 32 points. Therefore, the study results indicate that elderly people with lower self-efficacy generally have lower nutritional status scores with increasing age, implying that this group of elderly people has lower ability to obtain nutrition.

### 3.3.2 The effect of self-efficacy in regulating the number of chronic diseases on the nutritional literacy score in elderly individuals was analyzed

As shown in Figure 3, the confidence intervals for the impact of the number of chronic diseases on nutritional status among older adults do not include zero when the self-efficacy score is below 30, and they are all below 0. This indicates that the effect of having fewer chronic diseases on higher nutritional status is significant only among older adults with self-efficacy scores below 30. However, among older adults with higher self-efficacy scores, the impact of the number of chronic diseases on nutritional status loses theoretical significance (The confidence interval of the effect size includes 0).

<table><caption>TABLE 3 Results of regression analysis on regulatory effectiveness.</caption><thead><tr><th></th><th></th><th>β</th><th>se</th><th>t</th><th>p</th><th colspan="2">95%CI</th></tr></thead><tbody><tr><td rowspan="5">Model 1</td><td>Constant</td><td>0.052</td><td>0.041</td><td>1.267</td><td>0.206</td><td>-0.029</td><td>0.133</td></tr><tr><td>Age</td><td>-0.130</td><td>0.043</td><td>-3.030</td><td>0.003</td><td>-0.214</td><td>-0.045</td></tr><tr><td>GSES</td><td>0.691</td><td>0.043</td><td>16.108</td><td>0.000</td><td>0.607</td><td>0.776</td></tr><tr><td>Int_1</td><td>0.107</td><td>0.039</td><td>2.738</td><td>0.007</td><td>0.030</td><td>0.184</td></tr><tr><td>R<sup>2</sup></td><td>0.651</td><td></td><td></td><td>0.000</td><td></td><td></td></tr><tr><td></td><td>ΔR<sup>2</sup></td><td>0.010</td><td></td><td></td><td>0.007</td><td></td><td></td></tr><tr><td rowspan="5">Model 2</td><td>Constant</td><td>0.044</td><td>0.041</td><td>1.070</td><td>0.286</td><td>-0.037</td><td>0.125</td></tr><tr><td>Chronic diseases</td><td>-0.099</td><td>0.042</td><td>-2.367</td><td>0.019</td><td>-0.182</td><td>-0.017</td></tr><tr><td>GSES</td><td>0.726</td><td>0.042</td><td>17.231</td><td>0.000</td><td>0.643</td><td>0.809</td></tr><tr><td>Int_1</td><td>0.097</td><td>0.040</td><td>2.454</td><td>0.015</td><td>0.019</td><td>0.175</td></tr><tr><td>R<sup>2</sup></td><td>0.641</td><td></td><td></td><td>0.000</td><td></td><td></td></tr><tr><td></td><td>ΔR<sup>2</sup></td><td>0.008</td><td></td><td></td><td>0.015</td><td></td><td></td></tr></tbody></table>

## 4 Discussion

### 4.1 Focus on the baseline characteristics of the elderly population and improve the nutritional literacy of key populations

The results of this study indicate that age is an important influencing factor in evaluating the nutritional literacy of the elderly. As age increases, the physiological indicators of the elderly decline, and their bodily functions decrease. Which may lead to the need for

<figure><img src="image_32.png" /><figcaption>FIGURE 2 Age has a significant negative effect on nutritional literacy when self-efficacy score is below 32.</figcaption></figure>
<figure><img src="image_33.png" /><figcaption>FIGURE 3
When the self-efficacy score is less than 30 points, the number of chronic diseases has a significant negative effect on nutritional literacy.</figcaption></figure>

more nutritional substances but also require more energy to obtain (33). However, they often lower their nutritional requirements due to lack of energy, forcing them to have insufficient ability to obtain nutrients, resulting in a significant decrease in nutritional literacy with increasing age. A study on the age distribution of nutritional and health knowledge among Chinese adults (34) conducted a multicenter large-sample survey. The results showed that as age increases, nutritional knowledge among Chinese adults declines significantly. These elderly individuals have not obtained more information about nutrition from healthcare providers to compensate for this knowledge gap (35). Therefore, this study explained the relationship between age and nutritional literacy from a more specific dimension.

However, there is a lack of multicenter large-sample studies on the impact of gender on the nutritional literacy of the elderly. A study in Taiwan, China, on the impact of gender on food choices among the study population (11) supports the view of this article that the nutritional literacy score of elderly males is higher than that of elderly females. That is, elderly males show significantly higher initiative in adopting health-related dietary behaviors than females. Another cross-sectional survey with a larger sample size from Japan showed that nutritional literacy shows gender differences in different dimensions of dietary quality and food choice value (36). Therefore, further research may be needed on the analysis of the impact of gender on nutritional literacy.

The results of this study showed that the nutritional status of married elderly people was significantly higher than that of divorced or widowed individuals. However, there is currently a lack of literature to support or refute the impact of marital status on the nutritional status of elderly people. Married elderly people may be more willing to invest more energy in obtaining better nutrition due to emotional support from their spouses and daily care. Previous literature has reported that elderly people living alone suffer more severe health impacts due to a lack of social support (37).

The nutritional status of the elderly people surveyed in this study showed significant differences in the cultural background level. A large-scale survey conducted in China showed that elderly people in low-culture rural areas had limited nutritional status due to their lack of knowledge about diet (38). Using the theory of knowledge, attitudes, and behavior, it can be inferred that a person's cultural level has a direct impact on their nutritional behavior (39). Therefore, the conclusion drawn in this study also confirms the reliability of this theoretical model. Another study on the nutritional status of elderly people in Spain, although using different questionnaires (40), also drew similar conclusions to this study: the nutritional status of elderly people with lower cultural education is significantly lower than that of college-educated elderly people, indicating that the nutritional status of elderly people increases with higher cultural education.

The economic source of rural elderly people surveyed in this study is mainly from farming (41), and the basic part of their daily diet is self-sufficient. A large part of their personal monthly living expenses is used to pay for additional nutritional supplements. Therefore, this study selected the personal monthly living expenses of elderly people as an economic indicator to measure their nutritional support expenditure. According to the results of this study, it is speculated that under a relatively low socioeconomic status, the reason why personal monthly living expenses have a significant impact on their nutritional status may be due to the differences in their purchasing power resulting in significant differences in the nutritional substances they choose. Currently, research has shown that guiding low-income people to choose nutritious and affordable foods can reduce the difference in nutritional status (42). The promotion of such intervention measures may have significant benefits in improving the nutritional status of low-income elderly people.

In this study, the nutritional status scores of elderly people with intact teeth and those with damaged teeth differed significantly, but
the difference in whether dentures were worn by elderly people with damaged teeth was not clear. The convincing reason for this conclusion comes from the fact that the oral environment, especially the condition of the teeth, has a significant impact on the food choices of most elderly people. The limited medical level in rural areas of China means that elderly people cannot choose more scientific treatment options after their teeth are damaged, and most elderly people's dentures cannot effectively chew food (43). The number of edentulous elderly people included in this study was limited, so effective statistical analysis was not carried out. The wearing of dentures was not further subdivided into more detailed categories, so it was not possible to make a more accurate judgment. However, research has shown that patients wearing full dentures tend to choose unhealthy foods (13), leading to a higher risk of malnutrition, which needs further exploration in future studies.

Elderly people with chronic diseases, especially those related to nutrition, often have dietary habits and lifestyles that are closely related to the occurrence and development of chronic diseases (44). The results of this survey show that the nutritional literacy scores of elderly people with chronic diseases are significantly lower than those of healthy elderly people, indicating a certain relationship between chronic diseases and nutritional literacy. An experimental study in the United States provided food resources and cooking skills for hypertensive patients, which can improve their self-management level and increase their nutritional literacy (45). Based on this idea, promoting healthy lifestyles, especially dietary-related skills, to the public may reduce the incidence of chronic diseases by improving nutritional literacy. Finally, this study also showed that there was no significant difference in the effect of the number of chronic diseases on the nutritional literacy of the elderly. There is currently no relevant evidence to prove this, which may be due to the insufficient sample size collected in this study and further research is needed to verify this.

## 4.2 Focus on the self-efficacy of the elderly population and assist in screening for nutritional literacy

The nutritional literacy of the elderly population varies not only due to their different baseline characteristics but also as a result of their psychological state (11). Correlation analysis in research results has shown a strong positive correlation between self-efficacy and nutritional literacy among the elderly, and multiple studies abroad have also demonstrated a strong correlation between self-efficacy and nutritional literacy (46, 47). Therefore, self-efficacy, as a positive psychological resource, can be used to assess the confidence of the elderly in their ability to obtain nutrition. Relevant studies have already confirmed that the self-efficacy of elderly people in rural China is relatively low and significantly different from that of elderly people in urban areas (48). So focusing on the self-efficacy of rural elderly populations will be an important measure to improve their nutritional literacy.

Self-efficacy theory is an important component of psychologist Bandura's social learning theory, which emphasizes the mediating role of self-efficacy in behavior (21). Based on this theory, the present research results show that self-efficacy, as a moderating variable, affects the predictive effect of age and the number of chronic diseases on the nutritional literacy of the elderly. A survey on adult health

literacy in Germany obtained results similar to those of this study (20), showing that the effect size of sociodemographic variables on nutritional literacy changed after self-efficacy was added to the regression model. Therefore, these studies suggest that when considering the impact of baseline characteristics on the nutritional literacy of the elderly, the position and role of self-efficacy in the model should be considered. Based on the Johnson-Neyman plot, the present research results show that when the self-efficacy of the surveyed elderly population is poor, age has a negative effect on nutritional literacy, that is, the nutritional literacy of the elderly decreases with age. Therefore, by using the self-efficacy score of rural elderly populations as a basis and dividing them into low self-efficacy score groups and other groups, the overall literacy level of rural elderly populations can be improved by implementing interventions to improve their nutritional literacy, especially for older elderly people. Similarly, it can be seen from the Johnson-Neyman plot that when self-efficacy is low, the number of chronic diseases also has a negative effect on nutritional literacy. Therefore, elderly people with chronic diseases in the low self-efficacy group can be screened out for targeted nutrition education and interventions (49). Therefore, this study suggests that when assessing the nutritional literacy of rural elderly populations, a general self-efficacy assessment scale can be used for auxiliary screening. On the one hand, self-efficacy itself has a positive predictive effect on nutritional literacy. And on the other hand, targeted interventions for elderly people with low self-efficacy scores who are older or have chronic diseases can significantly improve the overall nutritional literacy level. Additionally, the general self-efficacy assessment scale is simple and easy to evaluate (27), which is more convenient and effective for investigating rural elderly populations.

In summary, this article suggests that when providing elderly people with nutritional knowledge and other interventions, changes their psychological state should be considered in addition to the sociodemographic differences in the elderly population. The local authorities can strengthen the promotion of nutritional literacy, enhance the elderly's confidence in obtaining nutrition, and comprehensively improve the nutritional literacy level of the elderly population.

## 5 Conclusion

When conducting a nutritional literacy screening among rural elderly people, attention should be paid to the baseline characteristics of the elderly population: older age, elderly women, divorced or widowed, lower level of education, lower monthly living expenses, dental problems, and elderly people with chronic diseases tend to have lower nutritional literacy scores. Elderly people with low self-efficacy show a decline in nutritional literacy as they age, and those with multiple chronic diseases and low self-efficacy have lower nutritional literacy. Therefore, effective intervention measures should be taken for these key groups to promote a widespread improvement in nutritional literacy among the elderly population in rural western China.

## 6 Limitations

This study is the first to examine the nutritional status of elderly people in rural areas of northern Sichuan, and the sample
size is limited based on the local rural elderly population. However, according to Kendall's sample calculation method (50), the sample size of 264 cases meets the requirements by taking 5-10 times the number of entries in the most entries of the scale and considering a 20% sample loss rate. Furthermore, it is well known that people from different regions and cultural backgrounds have different health needs, which can lead to differences in nutritional status. The scale used in this study by Professor Zhang was selected to minimize the bias caused by geographic and cultural differences. Secondly, dietary habits and lifestyle are the basis for the development of nutritional status, and their nutritional status will ultimately move toward a direction favorable to chronic diseases. Since this study is a cross-sectional survey, it did not investigate the pre-nutrient status and course of chronic diseases of elderly people with these diseases, so more longitudinal studies are needed to explore the effect of chronic diseases on nutrient status. Finally, this study focused on rural elderly people in northern Sichuan, and the results cannot be generalized to other regions. Therefore, further research should expand the study population to comprehensively promote the improvement of nutritional status among rural elderly people.

## Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

## Ethics statement

The studies involving humans were approved by the Ethics Committee of Jinzhou Medical University (NO: JZMULL2022108). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

## References

1. Acar Tek N, Karaçil-Ermumcu M. Determinants of health related quality of life in home dwelling elderly population: appetite and nutritional status. *J Nutr Health Aging*. (2018) 22:996-1002. doi: 10.1007/s12603-018-1066-9

2. Aihemaitijiang S, Ye C, Halimulati M, Huang X, Wang R, Zhang Z. Development and validation of nutrition literacy questionnaire for the Chinese elderly. *Nutrients*. (2022) 14:1005. doi: 10.3390/nu14051005

3. Fang EF, Xie C, Schenkel JA, Wu C, Long Q, Cui H, et al. A research agenda for ageing in China in the 21st century (2nd edition): focusing on basic and translational research, Long-term care, policy and social networks. *Ageing Res Rev*. (2020) 64:101174. doi: 10.1016/j.arr.2020.101174

4. Sun H, Li X, Li W, Feng J. Differences and influencing factors of relative poverty of urban and rural residents in China based on the survey of 31 provinces and cities. *Int J Environ Res Public Health*. (2022) 19:9015. doi: 10.3390/ijerph19159015

5. Krause C, Sommerhalder K, Beer-Borst S, Abel T. Just a subtle difference? Findings from a systematic review on definitions of nutrition literacy and food literacy. *Health Promot Int*. (2018) 33:378-89. doi: 10.1093/heapro/daw084

6. Carbone ET, Zoellner JM. Nutrition and health literacy: a systematic review to inform nutrition research and practice. *J Acad Nutr Diet*. (2012) 112:254-65. doi: 10.1016/j.jada.2011.08.042

7. Krabbenborg I, de Roos N, van der Grinten P, Nap A. Diet quality and perceived effects of dietary changes in Dutch endometriosis patients: an observational study. *Reprod Biomed Online*. (2021) 43:952-61. doi: 10.1016/j.rbmo.2021.07.011

## Author contributions

SL: Conceptualization, Data curation, Investigation, Methodology, Project administration, Software, Writing – original draft. XF: Data curation, Supervision, Writing – review & editing. LJ: Investigation, Methodology, Writing – review & editing. TL: Formal analysis, Supervision, Writing – review & editing.

## Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Social Science Planning Foundation of Liaoning Province (L21CSH005).

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1335008/full#supplementary-material

8. Shao JH, Chen SH. Development and evaluation of a dietary self-management Programme for older adults with low literacy and heart disease: pilot study of feasibility and acceptability. *J Adv Nurs*. (2016) 72:3015-9. doi: 10.1111/jan.13075

9. Shin NM, Choi J, Cho I, Park BJ. Self-management program for heart healthy behavior among middle- and old-aged Korean women at risk for metabolic syndrome. *J Cardiovasc Nurs*. (2017) 32:E8-e16. doi: 10.1097/jcn.0000000000000406

10. Donnelly HR, Collins CE, Haslam R, White D, Tehan PE. Perceptions of diet quality, advice, and dietary interventions in individuals with diabetes-related foot ulceration; a qualitative research study. *Nutrients*. (2022) 14:2457. doi: 10.3390/nu14122457

11. Shao JH, Chen SH. Who did it better? Gender differences in effects of a dietary self-management intervention for older community-dwelling adults. *J Women Aging*. (2021) 33:473-86. doi: 10.1080/08952841.2019.1707152

12. Aihara Y, Minai J. Barriers and catalysts of nutrition literacy among elderly Japanese people. *Health Promot Int*. (2011) 26:421-31. doi: 10.1093/heapro/dar005

13. Jauhiainen L, Männistö S, Ylöstalo P, Vehkalahti M, Nordblad A, Turunen AW, et al. Food consumption and nutrient intake in relation to denture use in 55- to 84-year-old men and women-results of a population based survey. *J Nutr Health Aging*. (2017) 21:492-500. doi: 10.1007/s12603-016-0793-z

14. Hajek A, König HH. The role of optimism, self-esteem, and self-efficacy in moderating the relation between health comparisons and subjective well-being: results
of a nationally representative longitudinal study among older adults. *Br J Health Psychol.* (2019) 24:547–70. doi: 10.1111/bjhp.12367

15. Lee LO, Grodstein F, Trudel-Fitzgerald C, James P, Okuzono SS, Koga HK, et al. Optimism, daily stressors, and emotional well-being over two decades in a cohort of aging men. *J Gerontol B Psychol Sci Soc Sci.* (2022) 77:1373–83. doi: 10.1093/geronb/gbac025

16. Whitehall L, Rush R, Górska S, Forsyth K. The general self-efficacy of older adults receiving care: a systematic review and Meta-analysis. *The Gerontologist*. (2021) 61:e302–17. doi: 10.1093/geront/gnaa036

17. Cybulski M, Cybulski L, Krajewska-Kulak E, Cwalina U. The level of emotion control, anxiety, and self-efficacy in the elderly in Bialystok, Poland. *Clin Intervent Aging*. (2017) 12:305–14. doi: 10.2147/cia.S128717

18. Su H, Zhou Y, Sun Y, Cai Y. The relationship between depression and subjective cognitive decline in older adults of China: the mediating role of general self-efficacy. *Psychol Health Med*. (2022) 28:1057–67. doi: 10.1080/13548506.2022.2125165

19. Tielemans NS, Schepers VP, Visser-Meily JM, Post MW, van Heugten CM. Associations of proactive coping and self-efficacy with psychosocial outcomes in individuals after stroke. *Arch Phys Med Rehabil*. (2015) 96:1484–91. doi: 10.1016/j.apmr.2015.04.009

20. Berens EM, Pelikan JM, Schaeffer D. The effect of self-efficacy on health literacy in the German population. *Health Promot Int.* (2022) 37:daab085. doi: 10.1093/heapro/daab085

21. Young MD, Plotnikoff RC, Collins CE, Callister R, Morgan PJ. Social cognitive theory and physical activity: a systematic review and Meta-analysis. *Obes Rev.* (2014) 15:983–95. doi: 10.1111/obr.12225

22. Zhang Y, Zhang Z, Xu M, Aihemaitijiang S, Ye C, Zhu W, et al. Development and validation of a food and nutrition literacy questionnaire for Chinese adults. *Nutrients*. (2022) 14:1933. doi: 10.3390/nu14091933

23. Toniazzo MP, Amorim PS, Muniz F, Weidlich P. Relationship of nutritional status and Oral health in elderly: systematic review with Meta-analysis. *Clin Nutr*. (2018) 37:824–30. doi: 10.1016/j.clnu.2017.03.014

24. Richard EL, Laughlin GA, Kritz-Silverstein D, Reas ET, Barrett-Connor E, McEvoy LK. Dietary patterns and cognitive function among older community-dwelling adults. *Nutrients*. (2018) 10:1088. doi: 10.3390/nu10081088

25. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. *Psychol Rev.* (1997) 84:191–215. doi: 10.1037/0033-295x.84.2.191

26. Schwarzer R, Jerusalem M. Measures in Health Psychology: A Users Portfolio. (1995)

27. Leung DY, Leung AY. Factor structure and gender invariance of the Chinese general self-efficacy scale among soon-to-be-aged adults. *J Adv Nurs*. (2011) 67:1383–92. doi: 10.1111/j.1365-2648.2010.05529.x

28. Gibbs H, Chapman-Novakofski K. Establishing content validity for the nutrition literacy assessment instrument. *Prev Chronic Dis.* (2013) 10:E109. doi: 10.5888/pcd10.120267

29. Doustmohammadian A, Keshavarz Mohammadi N, Omidvar N, Amini M, Abdollahi M, Eini-Zinab H, et al. Food and nutrition literacy (Fnlit) and its predictors in primary schoolchildren in Iran. *Health Promot Int.* (2019) 34:1002–13. doi: 10.1093/heapro/day050

30. Ayer Ç, Ergin A. Status of nutritional literacy in adolescents in the semi-rural area in Turkey and related factors. *Public Health Nutr.* (2021) 24:3870–8. doi: 10.1017/s1368980021002366

31. Hayes AF, Matthes J. Computational procedures for probing interactions in Ols and logistic regression: Spss and Sas implementations. *Behav Res Methods.* (2009) 41:924–36. doi: 10.3758/brm.41.3.924

32. Haugland T, Wahl AK, Hofoss D, DeVon HA. Association between general self-efficacy, social support, Cancer-related stress and physical health-related quality of life: a path model study in patients with neuroendocrine tumors. *Health Qual Life Outcomes*. (2016) 14:11. doi: 10.1186/s12955-016-0413-y

33. Whitelock E, Ensaff H. On your own: older Adults' food choice and dietary habits. *Nutrients*. (2018) 10:413. doi: 10.3390/nu10040413

34. Liu A, Ding C, Qiu Y, Yuan F, Wang Q, Hong H, et al. Age distribution of nutrition and health knowledge among Chinese adults in 2021. *J Hyg Res.* (2022) 51:876–80. doi: 10.19813/j.cnki.weishengyanjiu.2022.06.004

35. Liu X, Yu S, Mao Z, Li Y, Zhang H, Yang K, et al. Dyslipidemia prevalence, awareness, treatment, control, and risk factors in Chinese rural population: the Henan rural cohort study. *Lipids Health Dis.* (2018) 17:119. doi: 10.1186/s12944-018-0768-7

36. Murakami K, Shinozaki N, Livingstone MBE, Yuan X, Tajima R, Matsumoto M, et al. Associations of food choice values and food literacy with overall diet quality: a Nationwide cross-sectional study in Japanese adults. *Br J Nutr.* (2023) 130:1795–805. doi: 10.1017/s000711452300082x

37. Shah SJ, Fang MC, Wannier SR, Steinman MA, Covinsky KE. Association of Social Support with functional outcomes in older adults who live alone. *JAMA Intern Med.* (2022) 182:26–32. doi: 10.1001/jamainternmed.2021.6588

38. Wang S, Yang Y, Hu R, Long H, Wang N, Wang Q, et al. Trends and associated factors of dietary knowledge among Chinese older residents: results from the China health and nutrition survey 2004-2015. *Int J Environ Res Public Health.* (2020) 17:8029. doi: 10.3390/ijerph17218029

39. Schlüter K, Vamos S, Wacker C, Welter VDE. A conceptual model map on health and nutrition behavior (CMM<sup>HB/NB</sup>). *Int J Environ Res Public Health.* (2020) 17:7829. doi: 10.3390/ijerph17217829

40. Lobo E, Tamayo M, Sanclemente T. Nutrition literacy and healthy diet: findings from the validation of a short seniors-oriented screening tool, the Spanish myths-NL. *Int J Environ Res Public Health.* (2021) 18:12107. doi: 10.3390/ijerph182212107

41. Zhou Y, Guo Y, Liu Y. Health, income and poverty: evidence from china's rural household survey. *International journal for equity in health.* (2020) 19(1):36. Epub 2020/03/18. doi: 10.1186/s12939-020-1121-0

42. Bessems K, Linssen E, Lomme M, van Assema P. The effectiveness of the good affordable food intervention for adults with low socioeconomic status and small incomes. *Int J Environ Res Public Health.* (2020) 17:2535. doi: 10.3390/ijerph17072535

43. Gao YB, Hu T, Zhou XD, Shao R, Cheng R, Wang GS, et al. Dental caries in Chinese elderly people: findings from the 4th National Oral Health Survey. *Chinese J Dent Res.* (2018) 21:213–20. doi: 10.3290/j.cjdr.a41077

44. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. *Curr Opin Lipidol.* (2002) 13:3–9. doi: 10.1097/00041433-200202000-00002

45. Rivera RL, Adams M, Dawkins E, Carter A, Zhang X, Tu W, et al. Delivering food resources and kitchen skills (forks) to adults with food insecurity and hypertension: a pilot study. *Nutrients.* (2023) 15:1452. doi: 10.3390/nu15061452

46. Guntzviller LM, King AJ, Jensen JD, Davis LA. Self-efficacy, health literacy, and nutrition and exercise behaviors in a low-income, Hispanic population. *J Immigr Minor Health.* (2017) 19:489–93. doi: 10.1007/s10903-016-0384-4

47. Guttersrud Ø, Petterson KS. Young Adolescents' engagement in dietary behaviour - the impact of gender, socio-economic status, self-efficacy and scientific literacy. *Methodological aspects of constructing measures in nutrition literacy research using the Rasch model*. *Public Health Nutr.* (2015) 18:2565–74. doi: 10.1017/s1368980014003152

48. Aierken A, Ding X, Pan Y, Chen Y, Li Y. Association between dependency on community resources and social support among elderly people living in rural areas in China: a cross-sectional study. *BMC Geriatr.* (2022) 22:589. doi: 10.1186/s12877-022-03247-5

49. Chau PH, Ngai HH, Leung AY, Li SF, Yeung LO, Tan-Un KC. Preference of food saltiness and willingness to consume low-sodium content food in a Chinese population. *J Nutr Health Aging.* (2017) 21:3–10. doi: 10.1007/s12603-016-0732-z

50. Preacher KJ, Kelley K. Effect size measures for mediation models: quantitative strategies for communicating indirect effects. *Psychol Methods.* (2011) 16:93–115. doi: 10.1037/a0022658
<figure><img src="image_34.png" /></figure>

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EDITED BY
Norbert Amougou,
UMR7206 Eco Anthropologie et
Ethnobiologie (EAE), France

REVIEWED BY
Eskezyiaw Agedew,
Debre Markos University, Ethiopia
Catherine Nkirote Kunyanga,
University of Nairobi, Kenya

*CORRESPONDENCE
Fahmi H. Kakamad
fahmi.hussein@univsul.edu.iq

RECEIVED 16 October 2023
ACCEPTED 29 January 2024
PUBLISHED 14 February 2024

CITATION
Mhamad HJ, Najmadden ZB, Hama Salih KH, Hama DA, Abdullah HO, Hasan KM, Kareem HO, Mohammed BA, Fattah FH, Abdalla BA, Kakamad FH and Mohammed SH (2024) Prevalence and predictive factors associated with stunting in preschool children in a governorate of Iraq: a community-based cross-sectional study. *Front. Nutr.* 11:1322625.
doi: 10.3389/fnut.2024.1322625

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© 2024 Mhamad, Najmadden, Hama Salih, Hama, Abdullah, Hasan, Kareem, Mohammed, Fattah, Abdalla, Kakamad and Mohammed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Prevalence and predictive factors associated with stunting in preschool children in a governorate of Iraq: a community-based cross-sectional study

Huda J. Mhamad¹, Zana B. Najmadden², Kaihan H. Hama Salih¹, Dlkhwaz A. Hama¹, Hiwa O. Abdullah³,⁴, Karzan M. Hasan³, Honar O. Kareem³, Bilal A. Mohammed³, Fattah H. Fattah³,⁵, Berun A. Abdalla³,⁴, Fahmi H. Kakamad³,⁴,⁵* and Shvan H. Mohammed⁴

¹Food Science and Quality Control, Technical College of Applied Science, Sulaimani Polytechnic University, Sulaimani, Iraq, ²Research Center, University of Halabja, Halabja, Iraq, ³Smart Health Tower, Sulaimani, Iraq, ⁴Kscien Organization for Scientific Research, Sulaimani, Iraq, ⁵College of Medicine, University of Sulaimani, Sulaimani, Iraq

**Introduction:** The prevalence and risk factors of stunting in various geographical regions have been well investigated. However, not enough data exists regarding the communities in Iraq. This study investigated the prevalence and risk factors of stunting in preschool children in Halabja governorate.

**Methods:** The required data for the study was collected through a structured questionnaire form from the children's parents. Then, the height and weight of the children were measured. According to the World Health Organization Child Growth Standards and using the WHO Anthro Survey Analyser software, children were classified as "stunted" when their height-for-age z-score was below two standard deviations.

**Results:** A total of 646 children were included, of which 310 (48%) were male and 336 (52%) were female. The gestational age of 556 (86%) children was 9 months, while 84 (13%) were born between 7-9 months, and 6 (1%) were born in 7 months. Regarding feeding during the first 2 years of life, 229 children (35.4%) were exclusively breastfed, 93 (14.4%) were bottle-fed, and 324 (50.2%) had mixed feeding. The prevalence of stunting was 7.9% in the sample pool, with 4.6% of females and 3.3% of males. Among stunted children, 6.35% were term babies, and 1.55% were preterm babies. None of the studied factors had a significant association with stunting.

**Conclusion:** The prevalence of stunting in the studied population was 7.9%. However, we could not find any significant association between the studied factors and stunting. Thus, the factors that may significantly affect stunting in our area of study, especially the historical chemical warfare side effects, need to be more extensively investigated in future studies.

KEYWORDS

child health care, growth retardation, malnutrition, nutritional status, stunting, survey
# Introduction

The future prospects of infants and young children critically hinge on their optimal growth and development during the early stages of life. Stunting is a growth defect in which children have a lower height relative to their age and constitutes a serious health problem among children under the age of five in numerous low- and middle-income countries (1, 2). Stunting in children under the age of five can cause compromised physical development and may have an enduring effect on cognitive development, educational attainment, maternal reproductive outcomes, and economic productivity during adulthood (1, 2). As per United Nations estimates, one in eight Iraqi children dies before reaching 5 years of age. Nearly 30% experience malnutrition, a quarter are born underweight, and an additional 25% lack access to safe water (3).

The prevalence and risk factors of stunting in various geographical regions have been well investigated (1, 2, 4–6). However, not enough data exists regarding the communities in Iraq (3, 7, 8). The ongoing instability in Iraq since 2003 continues to have a profound impact on the physical and mental health of its residents (8). In a study conducted by Gabriela Guerrero-Serdan in Iraq to assess the effects of war on nutrition and health, it was hypothesized that those born after the war in high-intensity conflict areas were found to have lower height-for-age z-scores compared to those born in less violent areas (9).

This cross-sectional study explores the prevalence and risk factors of stunting in children aged four to five in the governorate of Halabja, which experienced chemical warfare in 1988.

# Method

## Study location and population

The study covered the urban areas in the Halabja Governorate of the Kurdistan Region, Iraq, with a total population of 121,793 people based on the Kurdistan Region Statistics Office.<fnref n="1" /> The study was specific to the kindergartens located inside the city.

## Study design

This was a survey-based cross-sectional study that enrolled kindergarten children aged 4 and 5 years old in Halabja governorate. The study lasted one year, from July 2022 to June 2023, and was ethically approved. Written informed consent was obtained from the parents or families of the children.

## Setting and data collection

A multistage cluster sampling method was employed to collect the study sample. The city's geography was categorized into four distinct regions: north, south, east, and west. Within each of these regions, a total of 2–3 kindergartens were selected, resulting in a comprehensive

selection of 10 kindergartens representing the entirety of the city. The required data for the study was collected through a structured questionnaire form from the children's parents including the following questions: demographics of children and parents, gestational age, type of feeding during the first two years of life, complementary feeding time, educational level of parents, family size, and socio-economic status. The questionnaire was distributed among families of children in all public and private kindergartens. We implemented quality control measures and adhered to best practices, which included training our data collection team, conducting pre-tests on processes and materials, and closely monitoring data collection in the field. To determine the necessary sample size for the population, we utilized the following equation from OpenEpi (Version 3), an open-source calculator: $n = [DEFF*Np(1-p)]/ [(d2/Z21-α/2*(N-1)+p*(1-p))].$ According to that calculation, a sample size of 383 individuals was needed at a confidence level of 95%. However, we collected a larger sample size than this requirement to ensure greater accuracy.

## Anthropometric measurements

The height and weight of the children were measured. An electronic body scale (TCS-200-RT) was used for measuring the height. The children were taught to stand on the baseboard with their feet slightly apart. The back of the head, shoulder blades, buttocks, calves, and heels all touched the vertical board. This alignment was impossible for obese children, so they were helped to stand on the board with one or more contact points touching the board. The headboard was placed on the highest point of the head with enough strength to compress the hair. Anthropometric measurements were acquired in duplicate by the research team, with the final measurements being calculated as the mean between the two readings based on the protocols advised by WHO (10). According to the WHO Child Growth Standards and using the WHO Anthro Survey Analyser software, children were classified as “stunted” when their height-for-age z-score was below two standard deviations from the median of the same age and sex group (11).

## Eligibility criteria

Children aged 4 to 5 years old in public and private kindergartens in Halabja governorate were included. Children with skeletal deformities, genetic diseases like achondroplasia, growth hormone deficiency, and familial short stature were excluded. Additionally, children whose parents were unable to complete the questionnaire or did not complete it properly were also excluded.

## Statistical analysis

The data was collected and encoded using Microsoft Excel (2019), and then it was analyzed using the Statistical Package for Social Sciences (SPSS Version 22). Frequencies and percentages were calculated for categorical variables. The binary logistic regression test was used to compare proportions and show the possible association between stunting and the studied factors. The significant level was considered at a p-value < 0.05.

<footnote n="1">1 https://krso.gov.krd/en/indicator</footnote>
# Resource assessment

For assessing the available resources, all cited studies have been checked for credibility (fully peer-reviewed) (12).

# Results

## Social demographics of the children

There were 646 children in the study. Of these, 310 (48%) were boys, and 336 (52%) were girls. Most participants (87.9%) were 5 years old, while the remaining 12.1% were 4 years old. Overall, 86% were born at 9 months of gestational age, 13% were born between 7-9 months, and 1% were born at 7 months. Regarding feeding during the first 2 years of life, 35.4% of children were exclusively breastfed, 14.4% were bottle-fed, and 50.2% had mixed feeding. Also, 20% of children started complementary feeding before 6 months, while 80% started it after 6 months (Table 1).

## Stunting and children-related factors

In the sample pool, the stunting prevalence was 7.9%, with 4.6% of females and 3.3% of males. Most stunted children were 5 years old (7.6%). Among stunted children, 3.6% were breastfed, 3.1% were mixed-fed, and 1.2% were bottle-fed. The majority of stunted children (6.7%) started complementary feeding after 6 months, while 1.2% started before that. Stunting affected 6.35% of term babies and 1.55% of preterm babies. When comparing age, gender, type of feeding, complementary feeding, and gestational age with stunting, no statistically significant correlation was found (p-value > 0.05) (Table 2).

## Stunting and parent-related factors

Children whose parents completed primary and secondary school had a higher likelihood of being affected by stunting (3.7% for mothers and 3.4% for fathers), followed by those with bachelor's degrees (2% for mothers and 2.2% for fathers) and diplomas (1.7%). However, this wasn't statistically confirmed (p-value > 0.05). No significant association was found between stunting and age, occupation of mothers, family size, or family socioeconomic status (p-value > 0.05) (Table 3).

# Discussion

Stunting is an indicative factor for potential risks, as it reflects the comprehensive state of development marked by impoverished conditions, low socioeconomic status, and the incidence of chronic disorders (13). According to data compiled by the United Nations Children's Fund (UNICEF) and WHO in 2018, approximately 149 million children under the age of 5 experienced stunting (14). This nutrition problem primarily impacts developing countries, and the highest rates of stunting in preschool children were observed in Eastern (45%), Middle (39%), and Western Africa (38%) (15).

<table><caption>TABLE 1 The baseline characteristics of the participants.</caption><thead><tr><th>Variables</th><th>No. cases/frequency</th></tr></thead><tbody><tr><td>Demographics</td><td></td></tr><tr><td>Age</td><td></td></tr><tr><td>4 years</td><td>78 (12.1%)</td></tr><tr><td>5 years</td><td>568 (87.9%)</td></tr><tr><td>Sex</td><td></td></tr><tr><td>Male</td><td>310 (48.0%)</td></tr><tr><td>Female</td><td>336 (52.0%)</td></tr><tr><td>Gestational age</td><td></td></tr><tr><td>9 months</td><td>556 (86.0%)</td></tr><tr><td>&gt;7-&lt;9 months</td><td>84 (13.0%)</td></tr><tr><td>7 months</td><td>6 (1.0%)</td></tr><tr><td>Type of feeding</td><td></td></tr><tr><td>Breastfeeding</td><td>229 (35.4%)</td></tr><tr><td>Bottle feeding</td><td>93 (14.4%)</td></tr><tr><td>Mixed feeding</td><td>324 (50.2%)</td></tr><tr><td>Complementary feeding</td><td></td></tr><tr><td>Before 6 months</td><td>129 (20.0%)</td></tr><tr><td>After 6 months</td><td>517 (80.0%)</td></tr><tr><td>Stunting statue</td><td></td></tr><tr><td>Yes</td><td>51 (7.9%)</td></tr><tr><td>Male</td><td>21 (3.3%)</td></tr><tr><td>Female</td><td>30 (4.6%)</td></tr><tr><td>No</td><td>595 (92.1%)</td></tr><tr><td>Mothers' age group</td><td></td></tr><tr><td>15-24</td><td>20 (3.1%)</td></tr><tr><td>24-35</td><td>360 (55.7%)</td></tr><tr><td>35-45</td><td>234 (36.2%)</td></tr><tr><td>≥45</td><td>32 (5.0%)</td></tr><tr><td>Mothers' education</td><td></td></tr><tr><td>Illiterate</td><td>39 (6.0%)</td></tr><tr><td>Primary and Secondary school</td><td>281 (43.5%)</td></tr><tr><td>Diploma</td><td>167 (25.9%)</td></tr><tr><td>Bachelor</td><td>152 (23.5%)</td></tr><tr><td>Higher education</td><td>7 (1.1%)</td></tr><tr><td>Fathers' education</td><td></td></tr><tr><td>Illiterate</td><td>12 (1.9%)</td></tr><tr><td>Primary and Secondary school</td><td>341 (52.8%)</td></tr><tr><td>Diploma</td><td>128 (19.8%)</td></tr><tr><td>Bachelor</td><td>149 (23.0%)</td></tr><tr><td>Higher education</td><td>16 (2.5%)</td></tr><tr><td>Mothers' occupation</td><td></td></tr><tr><td>Housewife</td><td>436 (67.5%)</td></tr><tr><td>Employee</td><td>200 (31.0%)</td></tr><tr><td>Unknown</td><td>10 (1.5%)</td></tr><tr><td>Family size</td><td></td></tr><tr><td>3-5 person</td><td>516 (79.9%)</td></tr><tr><td>6-9 person</td><td>129 (20.0%)</td></tr><tr><td>≥9 person</td><td>1 (0.1%)</td></tr><tr><td>Family socio-economic status</td><td></td></tr><tr><td>Low</td><td>111 (17.2%)</td></tr><tr><td>Medium</td><td>373 (57.7%)</td></tr><tr><td>High</td><td>162 (25.1%)</td></tr></tbody></table>

No., number.
<table><caption>TABLE 2 Association between nutritional status (assessed by HAZ) and gender, age groups, type of feeding, complementary feeding, and gestational age.</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">HAZ*</th><th rowspan="2">p-value</th></tr><tr><th>Stunting<br>No. (%)</th><th>Normal<br>No. (%)</th><th>Total<br>No. (%)</th></tr></thead><tbody><tr><td><strong>Gender</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>21 (3.3)</td><td>289 (44.7)</td><td>310 (48)</td><td rowspan="3">1.00</td></tr><tr><td>Female</td><td>30 (4.6)</td><td>306 (47.4)</td><td>336 (52)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td></tr><tr><td><strong>Children age group</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>4 years</td><td>2 (0.3)</td><td>76 (11.8)</td><td>78 (12.1)</td><td rowspan="3">1.00</td></tr><tr><td>5 years</td><td>49 (7.6)</td><td>519 (80.3)</td><td>568 (87.9)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td></tr><tr><td><strong>Type of feeding</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Breastfeeding</td><td>23 (3.6)</td><td>206 (31.9)</td><td>229 (35.4)</td><td rowspan="4">0.491</td></tr><tr><td>Bottle feeding</td><td>8 (1.2)</td><td>85 (13.2)</td><td>93 (14.4)</td></tr><tr><td>Mixed feeding</td><td>20 (3.1)</td><td>304 (47)</td><td>324 (50.2)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td></tr><tr><td><strong>Complementary feeding</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Before 6 months</td><td>8 (1.2)</td><td>121 (18.7)</td><td>129 (20)</td><td rowspan="3">0.715</td></tr><tr><td>After 6 months</td><td>43 (6.7)</td><td>474 (73.4)</td><td>517 (80)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td></tr><tr><td><strong>Gestational age</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>9 months</td><td>41 (6.35)</td><td>515 (79.7)</td><td>556 (86.1)</td><td rowspan="4">0.266</td></tr><tr><td>&gt;7–&lt;9 months</td><td>10 (1.55)</td><td>74 (11.5)</td><td>84 (13)</td></tr><tr><td>7 months</td><td>0 (0)</td><td>6 (0.9)</td><td>6 (0.9)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td></tr></tbody></table>

Iraq, as a developing country, confronts challenges pertaining to economic and social development. Moreover, it contends with a multitude of environmental factors that impede the physical growth of children prior to puberty, including diseases, inadequate sanitation facilities, poor hygiene practices, limited access to adequate healthcare coverage and resources, and deficient food consumption. According to estimates from United Nations agencies, one out of eight Iraqi children die before reaching the age of five. Almost one-third of the children may suffer from malnutrition, and one-quarter are born underweight. In addition, another one-quarter lack access to safe water (3). This study was set out with the aim of assessing the risk factors and prevalence of stunting among preschool children in the Halabja governorate. The results showed that the prevalence of stunting among 646 children was 7.9%, indicating a high rate. Overall, the rate of stunting was higher than that found in Kosovo (16), yet considerably lower than in previous local studies from Baghdad (3, 8) and China (14), Nigeria (1), and Indonesia (2). The stunting prevalence in this study was similar to that of Brazil (9.9%), Vietnam (9.53%), Ethiopia (8.9%), and Iran (8.2%) (3–6).

Regarding the role of gender in stunting, there have been different outcomes (1–3, 17). Agho et al. reported that gender was a robust predictor of stunting in children aged 0–23 and 0–59 months. During infancy and childhood, girls were found to be less susceptible to

stunting and severe stunting compared to boys (2). In contrast, another study revealed that, despite not being statistically confirmed, girls were more susceptible to stunting than boys, indicating that girl's growth might be more influenced by environmental factors like infectious diseases (3). A lack of gender roles in stunting has also been reported (17). Our study supported the latter assumption.

In the literature, numerous factors associated with stunting have been discussed, including environmental factors, number of family meals per day, age, gender, socioeconomic status, parental occupation, parental education, perceived birth size, diarrhea, breastfeeding duration, complementary feeding, maternal body mass index, family size, and residency (1–3, 18–20). Akombi et al. found a significant association between the duration of breastfeeding and stunting. Children breastfed for more than 12 months were found to have a higher likelihood of experiencing stunting and severe stunting compared to those breastfed for less than 12 months. They claimed that this outcome could be attributed to various factors, including cultural influences, exclusive breastfeeding practices, socioeconomic conditions, the time of initiating complementary feeding, the quality of complementary foods, and the educational status of the mothers (1). In the present study, there was no statistically significant association between stunting and age. The highest percentage of stunted children were breastfed children

<footnote>*HAZ, height-age-Z score.</footnote>
<table><caption>TABLE 3 Association between nutritional status (assessed by HAZ) and parents' level of education, mothers' occupation, mothers' age, family size, and family socio-economic status.</caption><thead><tr><th rowspan="2">Variables</th><th colspan="3">HAZ*</th><th rowspan="2">p-value</th></tr><tr><th>Stunting<br>No. (%)</th><th>Normal<br>No. (%)</th><th>Total<br>No. (%)</th></tr></thead><tbody><tr><td><strong>Mothers' education</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Illiterate</td><td>3 (0.5)</td><td>36 (5.6)</td><td>39 (6)</td><td rowspan="5">1.00</td></tr><tr><td>Primary and secondary school</td><td>24 (3.7)</td><td>257 (39.8)</td><td>281 (43.5)</td></tr><tr><td>Diploma</td><td>11 (1.7)</td><td>156 (24.1)</td><td>167 (25.9)</td></tr><tr><td>Bachelor</td><td>13 (2)</td><td>139 (21.5)</td><td>152 (23.5)</td></tr><tr><td>Higher education</td><td>0 (0)</td><td>7 (1.1)</td><td>7 (1.1)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td><td></td></tr><tr><td><strong>Fathers' education</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Illiterate</td><td>3 (0.5)</td><td>9 (1.4)</td><td>12 (1.9)</td><td rowspan="5">1.00</td></tr><tr><td>Primary and secondary school</td><td>22 (3.4)</td><td>319 (49.4)</td><td>341 (52.8)</td></tr><tr><td>Diploma</td><td>11 (1.7)</td><td>117 (18.1)</td><td>128 (19.8)</td></tr><tr><td>Bachelor</td><td>14 (2.2)</td><td>135 (20.9)</td><td>149 (23)</td></tr><tr><td>Higher education</td><td>1 (0.1)</td><td>15 (2.3)</td><td>16 (2.5)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td><td></td></tr><tr><td><strong>Mothers' occupation</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Housewife</td><td>37 (5.7)</td><td>399 (61.8)</td><td>436 (67.5)</td><td rowspan="4">1.00</td></tr><tr><td>Employee</td><td>14 (2.2)</td><td>186 (28.8)</td><td>200 (31)</td></tr><tr><td>Unknown</td><td>0 (0)</td><td>10 (1.5)</td><td>10 (1.5)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td></tr><tr><td><strong>Mothers' age group</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>15-24</td><td>1 (0.1)</td><td>19 (2.94)</td><td>20 (3.1)</td><td rowspan="5">1.00</td></tr><tr><td>24-35</td><td>27 (4.2)</td><td>333 (51.55)</td><td>360 (55.7)</td></tr><tr><td>35-45</td><td>20 (3.1)</td><td>214 (33.1)</td><td>234 (36.2)</td></tr><tr><td>≥45</td><td>3 (0.5)</td><td>29 (4.5)</td><td>32 (5)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td></tr><tr><td><strong>Family size</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>3-5 person</td><td>37 (5.7)</td><td>479 (74.1)</td><td>516 (79.9)</td><td rowspan="4">1.00</td></tr><tr><td>6-9 person</td><td>13 (2)</td><td>116 (18)</td><td>129 (20)</td></tr><tr><td>≥9 person</td><td>1 (0.2)</td><td>0 (0)</td><td>1 (0.1)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td></tr><tr><td><strong>Family socio-economic status</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>11 (1.7)</td><td>100 (15.5)</td><td>111 (17.2)</td><td rowspan="4">1.00</td></tr><tr><td>Medium</td><td>27 (4.2)</td><td>346 (53.5)</td><td>373 (57.7)</td></tr><tr><td>High</td><td>13 (2)</td><td>149 (23.1)</td><td>162 (25.1)</td></tr><tr><td>Total</td><td>51 (7.9)</td><td>595 (92.1)</td><td>646 (100)</td></tr></tbody></table>

(3.3%). Stunting is a reflection of chronic malnutrition, so this finding may be explained by improper nutrition of the mother during pregnancy and the lactation period. Additionally, the high percentage of stunting in those who started complementary feeding after 6 months may be due to the malpractice of giving food, as the family thought it was time to give everything to their child, but this may be a misconception, consequently leading to stunting.

Moreover, this study showed that children born at full term had a higher percentage of stunting than those born prematurely; this is in contrast to the study of Sullivan et al. (21). The explanation for this in our study may be that families of preterm babies took care of their babies more seriously than those of full-term babies and more carefully followed the pediatrician's advice because of the babies' condition.

<footnote>*HAZ, height-age-Z score.</footnote>
It has also been found that infants who were perceived by their mothers as being of smaller or average size at birth displayed a greater propensity for experiencing stunted and severely stunted growth in comparison to larger newborns. This diminished birth size may be attributed to inadequate maternal nutrition during the gestational period. Throughout this critical period, the child is entirely reliant on the mother for nourishment through the placenta. Consequently, any deprivation of nutrition by the mother can have detrimental effects on the child's growth and optimal development (1). Furthermore, the study presented a significant association between the mother's BMI and stunting in children under 5 years old. Mothers with a BMI below 18.5 kg/m² were more prone to have stunted children when compared to mothers with a BMI of 25 kg/m² or higher (1). Unfortunately, in this study, we could not represent any data regarding that, but another reason for such a higher incidence in full-term babies might be due to the dual mentioned factors.

Akombi et al., in a study on the Nigerian population, found that children from economically disadvantaged households faced a higher risk of stunting compared to children from more affluent households. This phenomenon could be attributed to the limited financial resources available to poorer families, resulting in inadequate spending on proper nutrition and making them more vulnerable to infectious diseases due to not reaching basic health care services (1). Comparable findings were derived from cross-sectional studies conducted in Iran (7) and Nepal (18). However, Sarma et al. demonstrated that children of rich families may also be affected by stunting (20).

Offspring of educated fathers have shown a reduced likelihood of experiencing stunting in comparison to children born to fathers with limited education. Moreover, children born to educated mothers who were breastfed demonstrated a decreased probability of severe stunting in contrast to children born to uneducated mothers who were not breastfed. These findings underscore a positive correlation between breastfeeding and parental education in fostering the development of their children (1, 18, 22). Another study showed that children whose mothers had bachelor's degrees were less likely to be affected by stunting compared to those whose mothers had diplomas or lower educational attainment. They explained that mothers with higher levels of education had greater knowledge about their children's health and nutrition, leading to improved childcare practices, increased utilization of health services, and better attention to hygiene and sanitation (7). Despite all of that, other studies have found that fathers' education was not correlated with being affected by stunting or not (7, 23). In addition, it has been reported that children with educated mothers showed a higher likelihood of being stunted than those whose mothers had no formal education. This was explained by the possibility that educated mothers were more likely to be employed, which might have led to reduced time available for childcare responsibilities (5). Giao et al. also revealed that housewife mothers and mothers with seller occupations were less likely to have stunted children than mothers employed in the public sector (6). Regarding the relationship between stunting and family education, this study showed a high prevalence of stunting in children whose mothers completed primary and secondary school, followed by those with bachelor's degrees and diplomas. These results may be explained by the fact that illiterate mothers in our community easily follow the instructions and advice given by experts in the field compared to those who have access to getting steps by themselves. In our study, the

father's education played a reverse role in comparison to the previously mentioned studies (1, 18, 22). This may be related to the fact that families with illiterate fathers still have traditional feeding practices and easily follow instructions.

Our findings showed that a high percentage of the mothers of stunted children were housewives, although this may be due to the fact that most mothers in our region were housewives or families with employed mothers were economically more stable. The mothers of the stunted children were mostly young, similar to the study by Giao et al. (6). Children from middle-income families had a higher incidence than those from high- and low-income families. This finding might be due to the inaccuracy of the information given about family income in our sample data.

The major limitations of this study can be attributed to the nature of the study design, which is subjected to bias by respondents providing inaccurate data, and the collection of data in a small geographical area. However, this study can be a starting point for further studies with larger samples occupying a wider geographical area to accurately point out the factors and prevalence of the disease in the Iraqi population. Research to examine whether the historical chemical warfare has enduring effects on growth in both recent and future generations could be intriguing.

## Conclusion

The prevalence of stunting in the studied population was 7.9%. However, we could not find any significant association between the studied factors and stunting. Thus, the factors that may significantly affect stunting in our area of study, especially the historical chemical warfare side effects, need to be more extensively investigated in future studies.

## Data availability statement

The datasets presented in this article are not readily available because if needed, after that, it will be uploaded. Requests to access the datasets should be directed to fahmi.hussein@univsul.edu.iq.

## Ethics statement

The study involved humans and was approved by the University of Halabja, Halabja, Kurdistan, Iraq. The study was conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants' parents.

## Author contributions

HM: Data curation, Investigation, Writing – review & editing.
ZN: Conceptualization, Formal analysis, Writing – original draft.
KHH: Methodology, Validation, Visualization, Writing – review & editing.
DH: Data curation, Investigation, Validation, Writing – review & editing.
HA: Conceptualization, Validation, Visualization, Writing – original draft, Writing – review & editing.
KMH:
Conceptualization, Data curation, Validation, Writing – review & editing. HK: Data curation, Investigation, Methodology, Writing – review & editing. BM: Conceptualization, Methodology, Validation, Visualization, Writing – review & editing. FF: Writing – review & editing. BA: Data curation, Methodology, Supervision, Validation, Writing – review & editing. FK: Supervision, Writing – review & editing. SM: Data curation, Formal analysis, Validation, Visualization, Writing – review & editing.

## Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

## References

1. Akombi BJ, Agho KE, Hall JJ, Merom D, Astell-Burt T, Renzaho AM. Stunting and severe stunting among children under-5 years in Nigeria: a multilevel analysis. *BMC Pediatr.* (2017) 17:1–6. doi: 10.1186/s12887-016-0770-z

2. Agho KE, Inder KJ, Bowe SJ, Jacobs J, Dibley MJ. Prevalence and risk factors for stunting and severe stunting among under-fives in North Maluku province of Indonesia. *BMC Pediatr.* (2009) 9:1–10. doi: 10.1186/1471-2431-9-64

3. Al Saffar AJ. Stunting among primary-school children: a sample from Baghdad, Iraq. *East Mediterr Health J.* (2009) 15:322–9. doi: 10.26719/2009.15.2.322

4. Souza OF, Benício MH, Castro TG, Muniz PT, Cardoso MA. Malnutrition among children under 60 months of age in two cities of the state of acre, Brazil: prevalence and associated factors. *Rev Bras Epidemiol.* (2012) 15:211–21. doi: 10.1590/S1415-790X2012000100019

5. Mesfin F, Berhane Y, Worku A. Prevalence and associated factors of stunting among primary school children in eastern Ethiopia. *Nutr Diet Suppl.* (2015) 18:61–8. doi: 10.2147/NDS.S80803

6. Giao H, Le An P, Truong Vien N, Van Khanh T, Quang VB. Stunting and overweight among 12-24-month-old children receiving vaccination in Ho Chi Minh City, Vietnam. *BioMed Res Int.* (2019) 2019:1547626. doi: 10.1155/2019/1547626

7. Kavosi E, Rostami ZH, Kavosi Z, Nasihatkon A, Moghadami M, Heidari M. Prevalence and determinants of under-nutrition among children under six: a cross-sectional survey in Fars province, Iran. *Int J Health Policy Manag.* (2014) 3:71–6. doi: 10.15171/ijhpm.2014.63

8. Ghazi HF, Mustafa J, Aljunid S, Isa ZM, Abdalqader MA. Malnutrition among 3 to 5 years old children in Baghdad city, Iraq: a cross-sectional study. *J Health Popul Nutr.* (2013) 31:350–5. doi: 10.3329/jhpn.v3i13.16827

9. Serdan GG. The effects of the war in Iraq on nutrition and health: an analysis using anthropometric outcomes of children. Available at SSRN: https://ssrn.com/abstract=1359161.

10. World Health Organization. Physical status: The use and interpretation of anthropometric indicators of nutritional status. Geneva: World Health Organization (1995). (WHO – Technical Report Series 854). Available at: https://www.who.int/publications/i/item/9241208546.

11. United Nations Children's Fund WHO, World Bank Group. Levels and Trends in Child Malnutrition—UNICEF/WHO/World Bank Group joint child malnutrition estimates. In: UNICEF/WHO/World Bank Group New York, NY (2017). Available at: https://data.unicef.org/wp-content/uploads/2017/06/JME-2017_brochure_June-25.pdf.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

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12. Muhialdeen AS, Ahmed JO, Baba HO, Abdullah IY, Hassan HA, Najar KA, et al. Kscien's List; A new strategy to discourage predatory journals and publishers (second version). *Barw Med J.* (2023) 1:1–3. doi: 10.58742/bmj.v1i1.14

13. Jesmin A, Yamamoto SS, Malik AA, Haque MA. Prevalence and determinants of chronic malnutrition among preschool children: a cross-sectional study in Dhaka City, Bangladesh. *J Health Popul Nutr.* (2011) 29:494–9. doi: 10.3329/jhpn.v29i5.8903

14. Li H, Yuan S, Fang H, Huang G, Huang Q, Wang H, et al. Prevalence and associated factors for stunting, underweight and wasting among children under 6 years of age in rural Hunan Province, China: a community-based cross-sectional study. *BMC Public Health.* (2022) 22:483–2. doi: 10.1186/s12889-022-12875-w

15. De Onis M, Blössner M, Borghi E. Prevalence and trends of stunting among pre-school children, 1990–2020. *Public Health Nutr.* (2012) 15:142–8. doi: 10.1017/S1368980011001315

16. Rysha A, Gjergji TM, Ploeger A. Nutritional status of preschool children attending kindergartens in Kosovo. *J Health Popul Nutr.* (2017) 36:26–8. doi: 10.1186/S41043-017-0105-1

17. Mushtaq MU, Gull S, Khurshid U, Shahid U, Shad MA, Siddiqui AM. Prevalence and socio-demographic correlates of stunting and thinness among Pakistani primary school children. *BMC Public Health.* (2011) 11:1–2. doi: 10.1186/1471-2458-11-790

18. Tiwari R, Ausman LM, Agho KE. Determinants of stunting and severe stunting among under-fives: evidence from the 2011 Nepal demographic and health survey. *BMC Pediatr.* (2014) 14:1–5. doi: 10.1186/1471-2431-14-239

19. Ikeda N, Irie Y, Shibuya K. Determinants of reduced child stunting in Cambodia: analysis of pooled data from three demographic and health surveys. *Bull World Health Organ.* (2013) 91:341–9. doi: 10.2471/BLT.12.113381

20. Sarma H, Khan JR, Asaduzzaman M, Uddin F, Tarannum S, Hasan MM, et al. Factors influencing the prevalence of stunting among children aged below five years in Bangladesh. *Food Nutr Bull.* (2017) 38:291–301. doi: 10.1177/0379572117710103

21. Sullivan MC, McGrath MM, Hawes K, Lester BM. Growth trajectories of preterm infants: birth to 12 years. *J Pediatr Health Care.* (2008) 22:83–93. doi: 10.1016/j.pedhc.2007.02.008

22. Rahman A, Chowdhury S. Determinants of chronic malnutrition among preschool children in Bangladesh. *J Biosoc Sci.* (2007) 39:161–73. doi: 10.1017/S0021932006001295

23. Janevic T, Petrovic O, Bjelic I, Kubera A. Risk factors for childhood malnutrition in Roma settlements in Serbia. *BMC Public Health.* (2010) 10:1–8. doi: 10.1186/1471-2458-10-509
<figure><img src="image_35.png" /></figure>

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EDITED BY
Emmanuel Cohen,
UMR7206 Eco Anthropologie et
Ethnobiologie (EAE), France

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Diana Nur Afifah,
Diponegoro University, Indonesia
Despina Sdrali,
Harokopio University, Greece

*CORRESPONDENCE

Hamed Rafiee

<figure><img src="image_36.png" /></figure>

hamedrafiee@ut.ac.ir

RECEIVED 20 July 2023

ACCEPTED 22 January 2024

PUBLISHED 15 February 2024

CITATION

Roustaee R, Eini-Zinab H, Ghodsi D, Mehrparvar Hosseini E, Omidvar N, Hosseini H, Hosseini Mousavi SO and Rafiee H (2024) A 30-year trend of dairy consumption and its determinants among income groups in Iranian households. *Front. Public Health* 12:1261293. doi: 10.3389/fpubh.2024.1261293

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© 2024 Roustaee, Eini-Zinab, Ghodsi, Mehrparvar Hosseini, Omidvar, Hosseini, Hosseini Mousavi and Rafiee. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A 30-year trend of dairy consumption and its determinants among income groups in Iranian households

Roshanak Roustaee¹, Hassan Eini-Zinab¹, Delaram Ghodsi²,
Elham Mehrparvar Hosseini³, Nasrin Omidvar¹, Hedayat Hosseini⁴,
Seyed Omid Hosseini Mousavi¹ and Hamed Rafiee³*

¹Department of Community Nutrition, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran, ²Department of Nutrition Research, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran, ³Department of Agricultural Economics, University of Tehran, Tehran, Iran, ⁴Department of Food Science and Technology, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran

**Introduction:** Milk and dairy products provide essential nutrients and have the potential to prevent chronic diseases, thus reducing healthcare costs. However, there is a lack of consistent and updated data on dairy consumption trends in Iran. This study aims to analyze the trends in dairy consumption among Iranian households from 1991 to 2021, focusing on household-level determinants across different expense groups.

**Methods:** The study uses data from the Iranian Household Expenditure and Income Survey conducted annually from 1991 to 2021 to analyze households' dairy consumption. The data includes values and expenses of food and non-food items purchased in the previous month, as well as demographic characteristics of household members. The households were categorized into 10 deciles based on their gross expense. The econometric model used weighted mean *per capita* milk, yogurt, cheese, and total dairy consumption based on milk equivalent for each decile. The model takes into account changes in income, prices, household composition, education level, occupation, and residency area using panel data. Data preparation and model estimation were performed using RStudio and STATA17 software.

**Results:** Based on the findings, in 1991, *per capita* milk, yogurt, and cheese consumption were 26.77 kg, 16.63 kg, and 2.42 kg, respectively. By 2021, these figures changed to 22.68 kg, 11.06 kg, and 3.79 kg, reflecting a decrease in milk and yogurt consumption but an increase in cheese consumption. Family size was positively correlated with yogurt consumption and head of the household spouse's job score were positively correlated with milk, yogurt and cheese consumption. Also, the presence of under five-year-old children and older adults members (over the age of 60) in the household was inversely related with yogurt and cheese consumption. Female-headed households tended to purchase more cheese, while their milk purchase level was significantly lower. Residing in urban areas was negatively related to milk, while cheese and total dairy consumption was higher in urban areas.

**Discussion:** The findings highlight the importance of targeted dairy subsidy interventions and educational programs to improve dairy consumption in Iranian households, especially among vulnerable groups. This will require urging policymakers and food system stakeholders for effective strategies that address macro-level factors to promote dairy consumption.
the entire milk and dairy products system from production to consumption (16). Studies have shown that subjective norms and social groups, including family, relatives, co-workers/colleagues, and other individuals with whom consumers associate, played a crucial role in influencing Iranian consumers' choice of dairy products (17, 18). It is important to note that Iran's economy has undergone several years of double-digit inflation and economic shocks, which are likely to have had an impact on food prices and household consumption, particularly among lower socioeconomic level households. However, the exact magnitude and severity of these changes remain unclear due to the lack of valid and up-to-date data. For this purpose, various data sources such as Food Balance Sheets, Household Budget Surveys (HBS), or Individual Dietary Surveys (IDS) may be utilized, each with its own advantages and limitations. Typically, national statistical offices conduct HBS to gather nationally representative data on household expenditure, including food, primarily to construct cost-of-living indicators. While HBS data includes information on the quantities of different types of food purchased and their consumption, it represents the food provided at the household level and is often used as a proxy to estimate household food consumption (19). Given that studies on dairy consumption in Iran are generally cross-sectional and have limited sample sizes, the objective of this study is to assess the trends in dairy consumption among Iranian households from 1991 to 2021 and explore household-level determinants. In addition to understanding the current situation, identifying these determinants is necessary to develop and implement appropriate, evidence-based policies and programs aimed at reversing the declining trend in dairy consumption.

# 2 Materials and methods

## 2.1 Data preparation

This panel study utilized data derived from the Iranian Household Expenditure and Income Survey which is conducted annually between 1991 and 2021 by the Statistical Center of Iran. The data include households' food and non-food expenses in urban and rural areas of Iran. The food expenses database contains information on 223 food items purchased by households during the previous month, including their amounts, prices, and expenses. Additionally, demographic characteristics, including age, gender, education level, occupation, and marital status of household members are also collected.

To determine households' total dairy consumption as the dependent variable, data was extracted from households that had consumed at least one dairy item each year. The dairy items included in this study were pasteurized and non-pasteurized milk, dried milk, cream, pasteurized and non-pasteurized yogurt, non-pasteurized doogh (yogurt drink), pasteurized and non-pasteurized cheese, and keshk until 2013. From 2014 onwards, some changes were applied to the questionnaires, and other types of milk, pasteurized cream, mixed yogurt, pizza cheese, and mixed cheese were added to the survey. Butter and ice cream were excluded since butter is regarded as fat from a nutrition perspective, and for ice cream only expense data was available. All monthly amounts of dairy items purchased were converted to kilograms per year by multiplying by 12. To calculate the total amount of dairy consumed, it is not sufficient to simply sum up the values of different dairy products. Instead, we employed a

conversion method that involved determining the average milk equivalents for each dairy item. This conversion represents the amount of milk required to produce a given dairy product. To establish accurate conversion coefficients for dairy products into milk, we considered the variability in production coefficients based on factors such as the characteristics of the milk consumed and the technology employed in production. To ensure the reliability of these coefficients, we sought the expertise of both our research team and a panel of experts from the Iranian Dairy Association Society. The resulting conversion coefficients, which account for these factors, are presented in Table 1. Non-consumer households were assigned a value of zero for their dairy consumption. Family size for each household was also extracted, and per capita consumption was calculated by dividing the consumption value by the family size. Total household expenses were calculated by summing all expense categories, and households were categorized into 10 deciles each year based on their gross expenses. The weight of each household in the sample was provided by the Statistical Center of Iran relative to the population each year. Finally, the weighted mean per capita milk, yogurt, cheese, and total dairy consumption for each decile was calculated and entered into the model. All data extraction and preparation were performed using Rstudio software, version 3.5.1.

## 2.2 Econometric model

The present study applies consumer demand theory to identify factors that influence the demand for dairy foods among Iranian households. The theory implies that changes in food demand result from changes in income and prices, which can be influenced by policies (20). In Iran, due to the increasing inflation rate, prices of food and non-food commodities have been continuously raised (21), and household real income is also affected by several exchange rate shocks (22). As a result, households' food demand has changed considerably; depending on the household expense deciles. For example, per capita dairy consumption in the 10th decile of household expenditure has been 3.7 and 2.7 times more than the first decile in 2010 and 2011, respectively (23).

Studies have also shown that Iranian consumers prefer milk, yogurt, and cheese to other dairy items, and these items have the highest consumption among the dairy food group (9, 24). However since the same items show different price elasticity (25, 26), separate equations were used to estimate for household per-capita milk, yogurt, cheese, and total dairy intakes.

Family structure and composition, i.e., age and gender of family members, can also affect their dairy food preferences and consumption. Additionally, household size, occupation, and education

<table><caption>TABLE 1 Conversion coefficients of dairy products into milk.</caption><thead><tr><td>Product</td><td>Coefficient</td><td>Product</td><td>Coefficient</td></tr></thead><tbody><tr><td>Pasteurized milk</td><td>1.01</td><td>UF cheese</td><td>5.5</td></tr><tr><td>Dried milk</td><td>11</td><td>Mixed cheese</td><td>5.5</td></tr><tr><td>Yogurt</td><td>1.11</td><td>Pizza cheese</td><td>4.5</td></tr><tr><td>Mixed yogurt</td><td>0.9</td><td>Doogh</td><td>0.5</td></tr><tr><td>Brine cheese</td><td>7.5</td><td>Keshk</td><td>2</td></tr></tbody></table>
are considered some of the other determinants of food choice at the household level (24, 27–31). The variables that entered the model and their definitions are shown in Table 2.

This study used panel data to estimate the econometrics model for assessing dairy demand function. To avoid false regression in estimating the model, the stationarity test of Levin, Lin & Chui (LLC) was used (32) and it was confirmed that some variables are integrated in the first level. Therefore, no risk of false regression was seen in the variables used in this model.

The baseline regression model is as follows:

$$Y_{it} = \beta_0 + \beta_1 C_{it} + \beta_2 D_{it} + \beta_3 E_{it} + \beta_4 U_{it} + \beta_5 P_{it} + \varepsilon_{it}$$

In the above equation, Y indicates the weighted mean of *per capita* milk, yogurt, cheese, and total dairy consumption in household expense decile of *i* in year *t*, *Cit* is a vector of family composition, *D*<sub>it</sub> is other demographic characteristics of households, *E*<sub>it</sub> is households' economic characteristics, *U* is urban residence *P*<sub>it</sub> is the price of the product and *ε*<sub>it</sub> is residual. Regressions were estimated by STATA17 software.

## 2.3 Ethical considerations

This study protocol was approved by the ethics committee of the National Nutrition and Food Technology Research Institute (No IR.SBMU.nnftri.Rec.1400.059).

## 3 Results

This study analyzed data on dairy food group consumption of Iranian households from 1991 to 2021. Figures 1–4 show weighted means of *per capita* milk, yogurt, cheese, and total dairy consumption, among Iranian households over thirty years. For clarity and a better understanding of the trends, findings are presented in tertiles instead of deciles.

<table><caption>TABLE 2 The variables entered the model as determinants of household dairy consumption and their definition.</caption><thead><tr><td>Variable</td><td>Definition</td></tr></thead><tbody><tr><td>Price</td><td>Weighted mean of milk, yogurt, and cheese prices</td></tr><tr><td>Sex of Household's head</td><td>As the weighted ratio of women-headed households</td></tr><tr><td>Presence of children under 5 years old in the family</td><td>As the weighted ratio of households with children under 5 years old</td></tr><tr><td>Presence of adults over 60 years old in the family</td><td>As the weighted ratio of households with adults over years old</td></tr><tr><td>Household education score</td><td>Weighted mean of education scores of adult members. Education scores were: 0 = illiterate, 6 = elementary school, 11 = high school and diploma, 14 = under-graduation university degrees, and 18 = graduate degrees</td></tr><tr><td>Mothers' occupation</td><td>Weighted mean of mothers' job scores. Scores were: 0 = housewives, 5 = unemployed but have income and 10 = employed</td></tr><tr><td>Residency area</td><td>The weighted ratio of the urban population</td></tr></tbody></table>

According to Figure 1, higher-income households consume more milk and dairy products than lower-income households. There has been an overall decreasing trend in milk consumption across all tertile groups over time, although the rate of decrease varies somewhat between the income groups. In 2021, the mean milk consumption for the 3rd tertile was 27.05 kg *per capita*, as opposed to 16.50 kg *per capita* in the 1st tertile in the same year. There have also been some fluctuations in mean milk consumption; for instance, there was a slight increase in consumption in the early 2000s followed by a sharp decrease in the late 2000s and another decrease in 2010. The increasing slope of the 3rd tertile was more pronounced between 2000–2005, and the decreasing slope of consumption was slower in the 3rd tertile around 2012 compared to the other two tertiles. However, the decreasing trend in milk consumption has accelerated in both the 2nd and 3rd tertiles since 2018.

Figure 2, depicts a declining trend in yogurt consumption across all income groups, with the weighted mean of yogurt consumption dropping from 16.63 kg *per capita* in 1991 to 11.06 kg *per capita* in 2021. In terms of yogurt consumption, there was a decreasing trend until 1996, followed by a slight increase up to 2004 in the 1st and 2nd tertiles and up to 2005 in the 3rd tertile. However, there is a drop in consumption again, reaching the lowest level for the first three deciles in 2009, with a lower slope in the 3rd tertile. There has been an increasing trend since then, until 2012, when a decrease started and has continued since then.

Higher-income households tend to consume more yogurt than lower-income. Over time, the gap between the mean yogurt consumption for the 1st and 3rd tertiles has increased somewhat, from a mean difference of 2.9 kg in 1991 to 4.79 kg in 2021.

Based on Figure 3, despite the decreasing trend in *per capita* cheese consumption since 2012 for the 2nd and 3rd tertiles and 2014 for the 1st tertile, which has continued until 2021, *per capita* cheese consumption has increased over time across all tertiles of households' expense. For example, in 1991, the *per capita* cheese consumption for the 1st tertile was 1.96, while in 2021, it increased to 3.08. Similarly, the *per capita* cheese consumption for the 3rd tertile was 4.05 in 1991 and 4.25 in 2021.

In terms of differences in cheese consumption across different tertiles, the data indicate that higher-income households tend to consume more cheese than their lower-income counterparts. For example, the 10th decile had the highest *per capita* cheese consumption in most years, while the 1st decile had the lowest *per capita* cheese consumption. The increasing trend of cheese consumption shows a higher slope in the 1st and 2nd tertiles from 1996 to 2004. Additionally, the decreasing slope of consumption since 2014 in the 1st tertile is lower compared to the 3rd tertile. The gap between the 1st and 3rd tertiles of households' expenses has decreased (from 2.09 kg in 1991 to 1.17 kg in 2021).

Figure 4, illustrates an overall increasing trend in dairy consumption across all tertiles until 2005, followed by a decrease with two sharp drops. The first drop occurred in all tertiles since 2011, with the most significant decrease in the 3rd tertile. The second drop was observed around 2018, which had a steeper slope in the 2nd and 3rd tertiles. For instance, the total dairy consumption for the 1st tertile decreased from 59.64 kg in 1991 to 49.04 kg in 2021, while the total dairy consumption for the 3rd tertile decreased from 84.88 kg in 1991 to 81.74 kg in 2021. The weighted mean of *per*
Trend Of Weighted Mean Of Percapita Milk
Consumption(Kg/Yr) In Tertiles Of Households' Expenses

<figure><img src="image_37.png" /><figcaption>FIGURE 1
Trend of weighted mean per capita consumption (Kg/Yr) of milk in households' expenses tertiles during 1991–2021.</figcaption></figure>

Trend Of Weighted Mean Of Percapita Yogurt
Consumption(kg/Yr) In Tertiles Of Households' Expenses

<figure><img src="image_38.png" /><figcaption>FIGURE 2
Trend of weighted mean per capita yogurt consumption (Kg/Yr) in tertiles of households' expenses during 1991–2021.</figcaption></figure>

capita total dairy consumption increased from 63.39 in 1991 to 68.34 kg in 2021.

As shown in Figure 5, the mean per capita consumption of milk and yogurt has decreased and cheese has increased over time, from 26.77 kg, 16.63 kg, and 2.42 kg in 1991 to 22.68 kg, 11.06 kg, and 3.79 kg in 2021, respectively. It is also apparent that up to 2015, yogurt has been a substitute for milk in most years, and its consumption increased while milk consumption decreased.

However, since then, yogurt consumption has also been on a decreasing trend, similar to milk. On the other hand, cheese consumption shows a similar trend to milk, except that, unlike milk, its overall trend is still increasing.

Findings of the estimated demand function for milk, yogurt, cheese, and total dairy consumption based on milk equivalent are presented in Table 3. This highlights that household expense was positively associated with the amount of all types of dairy
Trend Of Weighted Mean Of Percapita Cheese
Consumption(kg/Yr) In Tertiles Of Households' Expenses

<figure><img src="image_39.png" /><figcaption>FIGURE 3
Trend of weighted mean per capita cheese consumption (Kg/Yr) in tertiles of households' expenses during 1991-2021.</figcaption></figure>

Trend Of Weighted Mean Of Percapita Dairy
Consumption Based on Milk Equivalent (kg/Yr) In
Tertiles Of Households' Expenses

<figure><img src="image_40.png" /><figcaption>FIGURE 4
Trend of the weighted mean of per capita dairy consumption based on milk equivalent (Kg/Yr) in tertiles of households' expenses during 1991-2021.</figcaption></figure>

consumption, while product price had a negative association. Family
size showed a positive relationship between yogurt and total dairy
consumption but a negative relationship with cheese consumption.
Family education score had a significant positive relationship with
milk, but a negative association with cheese and total dairy
consumption. Housewives' job scores showed a statistically significant
positive relationship with milk and cheese consumption. The share

of families with children under 5 years old had a significant negative
relationship with all dairy items, but not with total dairy, while the
share of families with older adults members (over 60 years old) had
an inverse relationship with yogurt and cheese, and total dairy
consumption. Living in urban areas was negatively associated with
milk consumption but positively associated with cheese and total
dairy consumption.
Comparison Of The Trends Of Weighted Mean Per Capita (Kg/Yr)
Consumption Of Milk, Yogurt, Cheese and Total dairy Milk Equivalent
During 1991-2021

<figure><img src="image_41.png" /><figcaption>FIGURE 5
Comparison of the trends of weighted mean per capita consumption of milk, yogurt, cheese and total dairy based on milk equivalent during 1991–2021.</figcaption></figure>

4 Discussion

The present study showed that despite an increasing trend in dairy
consumption from 1997 to 2005 overall dairy consumption among
Iranian households had a declining trend, with two significant drops
in 2011 and 2019. There is also obvious that all tertiles have decreased
their consumption since 2007, but 2nd and 3rd tertiles are less affected
at least till 2018.

As shown in Figure 6, Iran began subsidizing pasteurized milk in (33). After the revolution, with the start of the Iraq war, the government introduced a voucher system to provide some main food items such as cheese. By the end of the war, due to the adoption of expansionary policies for the prosperity of production, the inflation rate increased and reached a peak of 50% (34). Despite the US imposing economic sanctions at that time (35, 36), the inflation rate began to decline. Meanwhile, the government implemented policies such as the guaranteed purchase of raw milk (37) and the school milk program (38) to safeguard producers and consumers, contributing to an uptrend in dairy consumption until 2005. However, with the change of government in 2005 and Iran's nuclear program's escalation, leading to its referral to the Security Council in 2006 inflation and sanctions intensified (34). The government implemented price stabilization measures (39), to counter inflation's effects while termination of the household cheese voucher program. But a slight reduction in dairy consumption is obvious. In 2010, the United Nations Security Council (UNSC) approved resolution number (40), further intensifying previous sanctions. Meanwhile, at the beginning of 2011, following government economic reforms and the Targeted Subsidies Law (41), subsidies were removed for goods such as gasoline, oil, water, and milk, substantially increasing milk and dairy prices during five years, and the inflation rate starts its ascending trend. The

government distributed some of its revenue to the public as cash
transfers to combat price hikes, but this led to significant inflation.
This is the first considerable drop in dairy consumption. The trend of
declining dairy consumption slowed after 2014, coinciding with a
decrease in inflation rates and the JCPOA's approval in 2015. However,
the US's withdrawal from the JCPOA in 2018, coupled with the
COVID-19 pandemic in 2019, led to a second drop in dairy
consumption due to an increase in inflation rates. Despite the
government's efforts to control dairy prices by granting preferential
exchange rates for livestock feed imports, dairy consumption
continued to decline.

Other cross-sectional and longitudinal studies also mention the
low consumption of milk and dairy products among Iranians and its
decreasing trend (42–44) despite the increase in dairy production
(45). For example, in a cross-sectional study on a subsample of a
population-based cohort study of the “Tehran Lipid and Glucose
Study” with 827 subjects (357 men and 470 women) aged 18–74 years,
the mean (±SD) consumption of milk, yogurt, and cheese was
0.7 ± 0.2, 1.06 ± 0.6, and 0.9 ± 0.3 servings/day, respectively (46). The
Iran National Comprehensive Study on Household Food
Consumption Pattern and Nutritional Status 2001–2003 also showed
that the *per capita* means daily consumption of milk and dairy groups
in urban and rural households was 142.3 ± 1.8 and 134.1 ± 2.7 grams/
day, respectively, with a total of 139.6 ± 1.5 grams/day (around 51 kg/
year) (11) which is lower than the amount reported in the present
study at that. This could be because the Household Expenditure and
Income Survey used in the present study is a reliable proxy for
household food consumption but does not consider food loss, waste,
or food used for other reasons such as guests (19).

The findings of the study also showed that household expenses (as
a proxy of income) have a positive relation and food price shows a
<table><caption>TABLE 3 Results of estimating milk, yogurt, cheese, and total dairy demand function.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Milk</th><th colspan="2">Yogurt</th><th colspan="2">Cheese</th><th colspan="2">Total dairy<br>(Milk equivalent)</th></tr><tr><th>β</th><th>t</th><th>β</th><th>t</th><th>B</th><th>t</th><th>β</th><th>t</th></tr></thead><tbody><tr><td>Family size</td><td>-1.873</td><td>-1.360</td><td>2.345***</td><td>3.740</td><td>-0.422***</td><td>-3.530</td><td>15.07**</td><td>2.340</td></tr><tr><td>Family education score</td><td>1.452*</td><td>2.030</td><td>-0.355</td><td>-0.850</td><td>-0.176**</td><td>-2.080</td><td>-7.22**</td><td>-2.190</td></tr><tr><td>Housewife job score</td><td>12.607***</td><td>9.060</td><td>-0.466</td><td>-0.940</td><td>0.297**</td><td>3.100</td><td>-27.89**</td><td>-2.630</td></tr><tr><td>Share of pasteurized milk</td><td>47.167***</td><td>16.270</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Household expense</td><td>6.83E-09*</td><td>1.870</td><td>4.54E-09**</td><td>2.590</td><td>2.14E-09***</td><td>6.270</td><td>2.58E-08*</td><td>1.760</td></tr><tr><td>Product price</td><td>-0.051***</td><td>-6.140</td><td>-0.001*</td><td>1.860</td><td>-1.23E-04**</td><td>-2.880</td><td>-2.46E-04**</td><td>-2.120</td></tr><tr><td>Share of women-headed households</td><td>-60.036**</td><td>-4.130</td><td></td><td></td><td>9.754***</td><td>8.520</td><td>-76.366**</td><td>-5.10</td></tr><tr><td>Share of families with children under 5 yr</td><td>-14.267**</td><td>-4.910</td><td>-7.282**</td><td>-3.130</td><td>-3.966***</td><td>-8.120</td><td>-28.79</td><td>-0.860</td></tr><tr><td>Share of families with members more than 60 yr</td><td>3.247</td><td>0.140</td><td>-19.307***</td><td>-4.170</td><td>-11.303***</td><td>-8.550</td><td>-223.43**</td><td>-2.450</td></tr><tr><td>Urban living</td><td>-55.220***</td><td>-5.890</td><td>2.924</td><td>0.920</td><td>1.176*</td><td>1.900</td><td>80.98*</td><td>1.850</td></tr><tr><td>Intercepts</td><td>41.604</td><td>3.170</td><td>10.167*</td><td>1.770</td><td>7.226***</td><td>6.630</td><td>87.198*</td><td>1.850</td></tr><tr><td>F</td><td>237.220**</td><td></td><td>41.26**</td><td></td><td>-0.639**</td><td></td><td>47.99**</td><td></td></tr><tr><td>R<sup>2</sup></td><td>0.891</td><td></td><td>0.531</td><td></td><td>0.865</td><td></td><td>0.012</td><td></td></tr><tr><td>F- limer</td><td>5.17</td><td>p-value: 0.00</td><td>6.720***</td><td>p-value: 0.000</td><td>4.540***</td><td>p-value: 0.000</td><td>13.01***</td><td>p-value: 0.001</td></tr><tr><td>Hausman test</td><td>43.05</td><td>p-value: 0.00</td><td>51.310***</td><td>p-value: 0.000</td><td>33.040***</td><td>p-value: 0.000</td><td>259.69***</td><td>p-value: 0.089</td></tr></tbody></table>

significant inverse relation with milk, yogurt, and cheese consumption, although, among them, cheese seems less affected while milk was the most affected dairy items. Additionally, these studies show higher amounts of yogurt consumption, while milk consumption purchase is higher in the present study. This is probably due to the culture of home yogurt making, mainly in rural areas, e.g., in Turkey, where consumers of non-pasteurized milk, buy milk to make yogurt (47).

Iran's economic sanctions have resulted in austerity policies causing inflation, stagnation, and recession, resulting in lower household incomes and rising commodity prices, and likely leading to a sharp decline in dairy consumption (17). Targeting subsidies also led to a significant increase in milk and dairy prices, as production costs rose due to the elimination of subsidies as well as removing milk subsidy and high inflation due to the adoption of this policy. From 2010 to 2014, the price of milk increased by approximately four times based on the author's calculation.

As mentioned before, inflation reduces household real income, while studies indicate that economic factors such as price and income affect dairy consumption (48–51). For example, Maitah and Smutka noted an increase of about 68% in milk consumption in the Middle East and North Africa during 1990–2008, with country- level *per capita* income being the most significant factor for this rise (52). Bousbia identifies income level and price as major factors impacting the variation of dairy product consumption (53). In Brazil, the cost of dairy products has been an important factor related to their low intake (54). It is worth noting that all income groups are not affected by increasing the price or inflation in the same value. Low-income groups are more sensitive to income and price fluctuations as allocate a larger share of their income to food than higher-income households (55, 56). On the other hand, low consumption levels respondents are more

responsive to changes in income (57). As household income increases, the possibility of consuming dairy products increases. In other words, a more diversified diet using more expensive animal food sources like milk and dairy products is possible with increasing household income or decreasing the prices of these products.

However, the effect of income is not uniform on all dairy products. The findings show that although cheese consumption (mainly white cheese) has a slower decreasing trend, its overall trend indicates an increase. Most Iranians consume cheese with bread for breakfast, making cheese consumption a part of their food culture (58). This could justify the slower decreasing trend, and the study's findings align with those of other studies (58, 25). In 2019, another drop in dairy consumption was observed, mainly in the 2nd and 3rd tertiles of income, which could be attributed to the COVID-19 pandemic and high inflation rates of 31.2, 41.2, and 47.1% during 2019–2021. A nationwide cross-sectional survey by Nikooyeh et al. found a significant decrease in dairy product consumption, particularly milk, and yogurt, in a high proportion of households during the COVID-19 epidemic lockdown (58). A similar decreasing trend in dairy consumption due to COVID-19 has also been reported for other countries such as China mainly due to the price increases, income loss, and insufficient savings combination (59). However, some others reported increasing dairy products consumption in the same period compared with that before the COVID-19 outbreak due to consumer health concerns (60).

The present study demonstrated that factors such as household composition and size have an impact on total dairy consumption. It seems that larger families tend to prefer yogurt consumption to cheese. Yekta et al., reported that household size has a positive and statistically important effect on yogurt consumption and large families

<footnote>*, **, *** are significance levels of 90, 95, and 99 percent, respectively. Results of the Fixed effect model for milk, yogurt, and cheese and the random effect model for Total dairy are presented.</footnote>
<figure><img src="image_42.png" /><figcaption>FIGURE 6
Total dairy consumption trend based on milk equivalent in comparison with inflation trend and timeline of major dairy food system and contextual events.</figcaption></figure>

consume more yogurt than small families. Other studies have also found a positive association between household size and yogurt consumption (47). It seems that family size exerts its effect on dairy consumption in two ways. As Phuong et al. (61) concluded, family size has a positive effect on milk product buying decisions, as families perceive dairy as part of their children's diet (62); however, it has a negative effect on household's dairy expenditure on a *per capita* basis. Radam et al., also showed that consumers from households with four or more people, including children, tended to be more price-sensitive and less likely to pay for expensive food (63). Hagh et al., found that households in the lowest-income groups consumed significantly lower amounts of livestock and dairy food items as the family size increased (64). Also, Hannan et al. reported that as the family size increases, the budget shares for milk will decrease (65). Based on the Iranian cultural preference for yogurt consumption (66) and the perceived health benefits associated with yogurt, it can be concluded that family size has a significant impact on dairy consumption, particularly yogurt.

Family education score was negatively related to yogurt, cheese and total dairy consumption. Vakili et al., also showed that People with a high school diploma and lower levels of education consumed substantially more dairy products than more educated ones (67). Contrary to these findings, some other cross-sectional studies in Iran (66), indicated that higher education level increases the probability of

choosing dairy products. Streeter and colleagues also showed that in China, better-educated respondents favor lower-calorie diets with more animal foods, fruit, and dairy but less cereals (57). Similar results were observed in Turkey where dairy product consumption was higher in those with a higher level of education (68). In recent years, there has been an international discourse about the potential association between high dairy consumption and an increased risk of certain non-communicable diseases (69, 70). As educated individuals often have greater access to information (71), they may be more aware of such concerns, which could have influenced their consumption patterns. Furthermore, there has been significant negative publicity surrounding the Iranian processed dairy industry, such as the unauthorized use of palm oil by the dairy industry being brought to the media's attention by the Minister of Health in 2015, or the discussion on aflatoxin contamination of milk on a national television show by an expert. However, it is important to note that there is also evidence highlighting safety concerns related to dairy consumption (72), as well. Considering that individuals with higher education often exhibit more sustainable and conscious eating behaviors (73), these factors may influence their dairy consumption.

Our study revealed a positive correlation between the job score of mothers and consumption of milk and cheese, while showed a negative correlation with overall dairy consumption. These findings
align with the research conducted by Kaheni et al., who also
demonstrated a positive relationship (p = 0.03) between maternal
employment and milk consumption among children aged 6–11 in
Birjand (74), Iran. Another study by Widodo et al. (75) reported
higher dairy consumption in Indonesian children whose mothers had
permanent jobs (75). However, these findings contrast with Afrin
et al.'s systematic review, which indicated that maternal employment
tends to have more adverse effects than favorable on children's dietary
patterns. The review concluded that in dual-income families,
maternal employment may provide the financial means to afford
healthier food options. Nevertheless, it also highlighted that
employment can impose time constraints that affect meal-related
behaviors (76). Therefore, the increased consumption of milk and
cheese observed in our study could be attributed to greater resources
provided by working women. However, the lower overall dairy
consumption might be a result of time constraints, reduced
supervision, and less caregiving by these women toward their families
and possibly themselves. Interestingly, our study also revealed that
female-headed households tend to consume more cheese and total
dairy while have lower milk consumption. This finding is in contrast
with other studies that have shown female-headed households have
lower dairy consumption (58, 77) probably due to their limited
resources. However, our study also found a positive relationship
between female-headed households and cheese consumption. This
may be due to the role of cheese in Iranian food culture, where cheese
and bread can serve as a meal and may also be seen as a valuable
source of protein for the households. Due to cheese's substantial
weight in converting to milk equivalent, it is probable that it also
influences overall dairy consumption.

We found a significant negative relationship with having under 5 age children and all dairy items but not total dairy. In contrast to the present study, several studies indicated that the presence of young children in the household positively affects household dairy consumption (78-81). Radam et al. showed that households with children less than 12 years of age were generally less concerned about price and more interested in purchasing safe and wholesome food (63). It may be rooted in economic issues as studies show there is a well-established relationship between household poverty and having children under the age of five. In general, households living in poverty are more likely to have young children. This is because poverty is often associated with a lack of access to education, healthcare, and family planning services, which can lead to higher fertility rates (82, 83).

The present study demonstrated that the presence of family
members over 60 years in households was also inversely related to
yogurt, cheese and total dairy consumption. In contrast, Possa et al.
(54) showed that older adults subjects had the highest total dairy
intake, milk, and cheese in Brazil (54). In another study on global,
national, and regional estimates of milk and dairy beverage
consumption, higher values were reported for the older adults (84).
Align with the present study, a study in Switzerland reported that
adults and the older adults did not consume the recommended
number of dairy products per day. Besides, 25.0% of the individuals
reduced dairy consumption to decrease fat or cholesterol intake (85).
Although the amount of consumption is much less than in this study
in Iran, geriatric health concerns may also affect older adults dairy
consumption. For example, traditional Iranian medicine considers
dairy food as cold temperament foods that exacerbate bone and joint

pains that should be avoided (86). On the other hand, medical costs,
in particular, can be a significant burden for older adults, particularly
those who have chronic health conditions or require long-term care
which may result in inappropriate economic conditions which affect
their consumption as well (87).

The present study showed that residing in urban areas was negatively related to milk and positively related to cheese and total dairy consumption. In contrast with these findings, several studies showed that dairy product consumption among urban households is much more than in rural ones (57, 61, 79, 88). The difference is due to the different methodologies. In other words, these studies evaluate consumption while Household Expenditure and Income Survey studies assess household purchases. It seems that in rural areas people mainly buy milk and using home processing convert it to dairy products while urban people purchase less milk and instead, buy and consume more cheese. This difference may be attributed to rural households' access to raw milk as well as market and dairy products. A study in rural Ethiopia demonstrated that household cow ownership increases children's milk consumption and this ownership is less important where there is good access to local markets (89). Moreover, regarding the urban-rural income gap (90, 91), the economic situation may also negatively affect cheese consumption among rural households (77).

Strengths of the current study included the use of panel data to
consider crosses effects (expenditure deciles) in addition to time series
to draw a picture of Iranian dairy consumption using recent and large
nationally representative data. Assessing the role of demographic and
socio-economic factors in dairy consumption allows for deep problem
identification and adoption of appropriate policy interventions unique
to these households. It is also important to consider the limitations of
the Household Expenditure and Income Survey analysis. One of the
most important is the lack of information about the food allocation
within the family. Also, the present study did not consider other
determinants of dairy consumption such as the role of habits,
nutritional knowledge, perceptions, and hedonic attributes of
dairy products.

5 Conclusion

In conclusion, this study provides insight into the trends and
determinants of dairy consumption in Iranian households. It also
showed that the determinants of consumption vary for different
dairy items, and it is more appropriate to evaluate them
individually rather than as a group. This is particularly evident
when converting dairy to milk equivalent, as the consumption
pattern sometimes follows the trend of cheese due to its high
weight in the conversion process. Therefore, using milk equivalent
as a method to evaluate overall dairy consumption may not be a
perfect approach. The findings indicate a declining trend in
overall dairy consumption, with household income and product
price showing significant positive and inverse relationships with
milk, yogurt, and cheese consumption. Family size, family
education score, and head of the HH spouse's job score were found
to be positively correlated with dairy consumption, while the
presence of children under the age of five and older adult members
(over the age of 60) had inverse relationships with yogurt and
cheese consumption. Female-headed households tend to purchase
more cheese, while their milk consumption is significantly lower. Residing in urban areas was negatively related to milk consumption, while cheese consumption was higher in urban areas. Based on the findings, it is necessary for policymakers to adopt appropriate economic policies at the macro level and take necessary measures to improve the economic situation of the society. Additionally, implementing supportive policies and allocating subsidies to promote dairy consumption can be achieved through interventions such as food support programs for female-headed households or the older adults. Furthermore, implementing suitable educational programs at the community level, along with supportive programs to enhance knowledge and attitudes toward dairy consumption, can also help to improve the consumption of this food group. However, further research is needed to explore the role of other determinants of dairy consumption, such as dietary habits, nutritional knowledge, and perceptions of dairy products.

## Data availability statement

The data presented in this study is not publicly available due to privacy restrictions. Requests to view these datasets should be directed to the corresponding author.

## Author contributions

RR: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Writing – original draft, Writing – review & editing. HE-Z: Conceptualization, Methodology, Supervision, Writing – review & editing. DG: Data curation, Formal analysis, Methodology, Writing – review & editing. EM: Data curation, Formal analysis, Investigation, Software, Writing – review & editing. NO: Formal analysis, Writing – review & editing. HH: Formal analysis, Writing – review & editing. SH: Data curation, Software, Writing – review & editing. HR: Conceptualization, Methodology, Supervision, Writing – review & editing.

## References

1. Elwood PC, Givens DI, Beswick AD, Fehily AM, Pickering JE, Gallacher J. The survival advantage of milk and dairy consumption: an overview of evidence from cohort studies of vascular diseases, diabetes and cancer. *J Am Coll Nutr.* (2008) 27:723s-34s. doi: 10.1080/07315724.2008.10719750

2. Evershed RP, Payne S, Sherratt AG, Copley MS, Coolidge J, Urem-Kotsu D, et al. Earliest date for milk use in the near east and southeastern Europe linked to cattle herding. *Nature*. (2008) 455:528–31. doi: 10.1038/nature07180

3. Rozenberg S, Body J-J, Bruyere O, Bergmann P, Brandi ML, Cooper C, et al. Effects of dairy products consumption on health: benefits and beliefs—a commentary from the Belgian bone Club and the European Society for Clinical and Economic Aspects of osteoporosis, osteoarthritis and musculoskeletal diseases. *Calcif Tissue Int.* (2016) 98:1–17. doi: 10.1007/s00223-015-0062-x

4. Weaver CM. How sound is the science behind the dietary recommendations for dairy? *Am J Clin Nutr.* (2014) 99:1217S–22S. doi: 10.3945/ajcn.113.073007

5. Department of Community Nutrition. (2015). Food-based dietary guidelines – Iran [online]. Ministry of Health and Medical Education. Available at: http://www.fao.org/ nutrition/education/food-dietary-guidelines/regions/countries/iran/en/

6. Thorning TK, Raben A, Tholstrup T, Soedamah-Muthu SS, Givens I, Astrup A. Milk and dairy products: good or bad for human health? An assessment of the totality of scientific evidence. *Food Nutr Res.* (2016) 60:32527. doi: 10.3402/fnr.v60.32527

## Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research conduction funded by the National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Grant No. 26700; approved on 21 November 2021).

## Acknowledgments

The authors wish to acknowledge the National Nutrition and Food Technology Research Institute for their financial support of this study. Additionally, the authors express their gratitude to the Statistical Center of Iran for providing the necessary data.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2024.1261293/full#supplementary-material

7. Community Nutrition Improvement Office, M. O. H., Treatment and Medical Education. *Desirable food basket for the Iranian population*. Qom: Andishe Mandegar (2013).

8. Kimiagar M. *Comprehensive project on food consumption and nutrition in Iran* (1991–96) [*in Persian*]. Tehran: National Nutrition and Food Technology Research Institute (2000).

9. Kalantari N, Ghaffarpoor M, Houshyar-Rad A, Kianfar H. *National Comprehensive Study on household food consumption patterns and nutritional status of I. R Iran, 2001-2003*. Tehran: Shaheed Beheshti University of Medical Sciences, Ministry of Health (2005).

10. Roghanchi M. Per capita milk consumption in urban and rural areas of Iran during 2004–2014. *Statistics*. (2015) 3:4.

11. Abdollahi M, Mohammadi-Nasrabadi F, Houshiarrad A, Ghaffarpur M, Ghodsi D, Kalantari N. Socio-economic differences in dietary intakes: the comprehensive study on household food consumption patterns and nutritional status of I.R. Iran Nutrit Food Sci Res. (2014) 1:19–26.

12. Javanbakht M, Jamshidi AR, Baradaran HR, Mohammadi Z, Mashayekhi A, Shokraneh F, et al. Estimation and prediction of avoidable health care costs of cardiovascular diseases and type 2 diabetes through adequate dairy food consumption: a systematic review and micro simulation modeling study. *Arch Iran Med.* (2018) 21:213–22.
13. Alipour V, Meshkani Z, Rezapour A, Aboutorabi A, Bagherzadeh R, Saber N. Medical costs of osteoporosis in the Iranian elderly patients. *Med J Islam Repub Iran.* (2020) 34:37. doi: 10.34171/mjiri.34.37

14. FAO. Milk and dairy product in human nutrition. In: E Muehlhoff, A Bennett, D Mcmahon, editors. Rome: Food and Agriculture Organization of the United Nations (2013).

15. Mohammadi-Nasrabadi F, Ghodsi D. *Assessment and analysis of food market in the recent decade in Iran* (from 2006–2016). Tehran: Shahid Beheshti University of Medical Sciences (2019).

16. Mozaffarian D, Angell SY, Lang T, Rivera JA. Role of government policy in nutrition—barriers to and opportunities for healthier eating. *BMJ*. (2018) 361:2426. doi: 10.1136/bmj.k2426

17. Adham-Maleki E. Resisting economy, dairy products consumption and public health policy: current status & future planning. *Int J Med Rev.* (2014) 1:53.

18. Rahnama H, Rajabpour S. Factors for consumer choice of dairy products in Iran. *Appetite*. (2017) 111:46–55. doi: 10.1016/j.appet.2016.12.004

19. Hawkesworth S, Dangour AD, Johnston D, Lock K, Poole N, Rushton J, et al. Feeding the world healthily: the challenge of measuring the effects of agriculture on health. *Philos Trans R Soc Lond Ser B Biol Sci.* (2010) 365:3083–97. doi: 10.1098/rstb.2010.0122

20. Babu SC, Gajanan SN, Hallam JA. Chapter 6 – consumer theory and estimation of demand for food In: SC Babu, SN Gajanan and JA Hallam, editors. *Nutrition economics*. San Diego: Academic Press (2017)

21. Ture E. H., Khazaei A. R. (2022). Determinants of inflation in Iran and policies to curb it. Imf working paper no. 2022/181. Available at: https://ssrn.com/abstract=4224069

22. Kiani M, Khalilian S, Najafi AlAMDarlo H, Vakilpor MH. Evaluating the impact of rising food prices on the cost of living of Iranian households. *J Econ Res.* (2022) 56:771–802. doi: 10.22059/JTE.2022.332477.1008563

23. Mostafavi-Dehzooei M. H., Salehi-Isfahani D., Heshmatpour M. (2020). *26th Annual Conference of the Economic Research Forum*. Cairo, Egypt.

24. Kaliji SA, Mojaverian SM, Amirnejad H, Canavari M. Factors affecting consumers' dairy products preferences. *Agris on-line Papers Econ Informat.* (2019) 11:3–11. doi: 10.7160/aol.2019.110201

25. Chizari A, Shokouhi Z, Salami H, Hosseini S-S. Existence of consumption habits and empirical analysis of demand: a case study of demand for dairy products of urban households in Iran. *Agric Econ.* (2015) 26:19–35.

26. Pishbahar E, Kheiri Netaj Firoozjah M. Evaluation the dairy demand of Iran using almost ideal demand system (AIDS). *Iranian J Agric Econ Dev Res.* (2014) 45:693–701. doi: 10.22059/ijaedr.2014.53843

27. Bytyqi N, Muji S, Rexhepi A. Consumer behavior for milk and dairy products as daily consumption products in every household—the case of Kosovo. *Open J Bus Manag.* (2020) 8:997–1003. doi: 10.4236/ojbm.2020.82063

28. Kurajdová K, Táborecká-Petrovičová J, Kaščáková A. Factors influencing Milk consumption and purchase behavior – evidence from Slovakia. *Proc Econ Finan.* (2015) 34:573–80. doi: 10.1016/S2212-5671(15)01670-6

29. Rabiei S, Zahedi M, Abtahi M, Doustmohammadian A, Dadkhah M, Zoghi T, et al. Consumption of milk and dairy products in Iranian population; barriers and facilitators. *Clin Nutr Open Sci.* (2021) 38:1–23. doi: 10.1016/j.nutos.2021.05.002

30. Świąder K, Banach R, Tan F-J. Dairy product consumption and preferences of polish and Taiwanese students—Npd case study. *Appl Sci.* (2022) 12:10138. doi: 10.3390/app121910138

31. Vargas-Bello Pérez E, Enriquez Hidalgo D, Toro Mujica P, Fellenberg Plaza MA, Ibáñez Alfaro RI, Schnettler B. Factors affecting consumption of retail milk in Chile. *Agric Food Sci.* (2018):310–9. doi: 10.15567/mljekarstvo.2018.0406

32. Levin A, Lin C-F, James Chu C-S. Unit root tests in panel data: asymptotic and finite-sample properties. *J Econ.* (2002) 108:1–24. doi: 10.1016/S0304-4076(01)00098-7

33. Bureau of Fundamental Studies. (2004). *A comparative strategic analysis of the status of production, per capita consumption, trade, and pricing of dairy products in Iran and other countries around the world*. Islamic parliament research center, Bureau of Fundamental Studies.

34. Inflation rate and the Consumer Price Index (CPI) for Goods and Services. (2023) Tehran: Central bank of Ir Iran. Available at: https://www.cbi.ir/Inflation/Inflation_Fa.aspx

35. United States. *Executive Office of the President William J. Clinton. Executive order 12959: Prohibiting certain transactions with respect to Iran. Executive order 12959, Federal Register*. (1995). 60:24757–9.

36. United States. (1995). *Executive Office of the President William J. Clinton. Executive order 12957: Prohibiting certain transactions with respect to the development of Iranian petroleum resources. Executive order 12957, Federal Register*. 60:14615–16.

37. Islamic Consultative Assembly. (1993). *The law on amending the law on guaranteeing the purchase of basic agricultural products, enacted in 1368 (1989).* Islamic Republic of Iran.

38. Supreme Economic Council. (2001). *Free milk distribution in elementary schools and kindergartens under the coverage of the state welfare organization and the Ministry of Education*. Resolution no. 34/1464, Islamic Republic of Iran.

39. Islamic Consultative Assembly. (2005). *The law amending article (3) of the fourth economic, social, and cultural development plan of the Islamic Republic of Iran*. Islamic Republic of Iran. (2005).

40. United Nations Security Council (UNSC). (2010). *Res 1929 (9 June 2010)*, UN Doc S/RES/1929.

41. Islamic Consultative Assembly. (2009). *The law on targeting subsidies*. Islamic Republic of Iran.

42. Esfarjani F, Mohammadi-Nasrabadi F, Roustaee R, Khalafi M, Alikhanian H, Nouri-Saeidlou S, et al. Household milk consumption and its socio-economic associates in west Azarbayjan province, north-West Iran. *Nutr Food Sci Res.* (2015) 2:21–7.

43. Jessri M, Mirmiran P, Golzarand M, Rashidkhani B, Hosseini-Esfahani F, Azizi F. Comparison of trends in dietary pattern in Iran, middle eastern and north African countries from 1961 to 2005. *Pajoohande*. (2011) 16:1–10.

44. Naeeni MM, Jafari S, Fouladgar M, Heidari K, Farajzadegan Z, Fakhri M, et al. Nutritional knowledge, practice, and dietary habits among school children and adolescents. *Int J Prev Med.* (2014) 5:S171–8. doi: 10.4103/2008-7802.157687

45. Information and Communication Technology Center, Vice President of Statistics. *2021 Agricultural statistics*. Iran: Ministry of Jihad Agriculture (2021).

46. Azadbakht L, Mirmiran P, Esmaillzadeh A, Azizi F. Dairy consumption is inversely associated with the prevalence of the metabolic syndrome in Tehranian adults. *Am J Clin Nutr.* (2005) 82:523–30. doi: 10.1093/ajcn/82.3.523

47. Gezginc Y, Akbay C. An analysis of household's yogurt consumption in Turkey. *J Food Nutr Res.* (2015) 3:285–9. doi: 10.12691/jfnr.3-4-9

48. De Alwis A, Edirisinghe J, Athauda A. Analysis of factors affecting fresh milk consumption among the mid-country consumers. *Trop Agric Res Ext.* (2009) 12:103–9. doi: 10.4038/tare.v12i2.2799

49. French SA, Tangney CC, Crane MM, Wang Y, Appelhans BM. Nutrition quality of food purchases varies by household income: the shopper study. *BMC Public Health.* (2019) 19:231. doi: 10.1186/s12889-019-6546-2

50. Headey D. Can dairy help solve the malnutrition crisis in developing countries? An economic analysis. *Anim Front.* (2023) 13:7–16. doi: 10.1093/af/vfac083

51. Ilie DM, Lădaru G-R, Diaconeasa MC, Stoian M. Consumer choice for Milk and dairy in Romania: does income really have an influence? *Sustain For.* (2021) 13:12204. doi: 10.3390/su132112204

52. Maitah M, Smutka L. Economic analysis of milk production and consumption in the Middle East and North Africa. *Acta Univ Agric et Silvic Mendelianae Brunensis*. (2012) 60:245–54. doi: 10.11118/actaun201260040245

53. Bousbia A, Boudalia S, Chelia S, Oudaifa K, Amari H, Benidir M, et al. Analysis of factors affecting consumer behavior of dairy products in Algeria: a case study from the region of Guelma. *Int J Agric Res.* (2017) 12:93–101. doi: 10.3923/ijar.2017.93.101

54. Possa G, Castro MAD, Sichieri R, Fisberg RM, Fisberg M. Dairy products consumption in Brazil is associated with socioeconomic and demographic factors: results from the National Dietary Survey 2008–2009. *Rev Nutr.* (2017) 30:79–90. doi: 10.1590/1678-98652017000100008

55. Blisard N., Stewart H. (2007). USDA Economic Research Service, Economic Information Bulletin No. (EIB-23). 115:2003–04.

56. Wu B, Shang X, Chen Y. Household dairy demand by income groups in an urban Chinese province: a multistage budgeting approach. *Agribusiness*. (2021) 37:629–49. doi: 10.1002/agr.21681

57. Streeter JL. Socioeconomic factors affecting food consumption and nutrition in China: empirical evidence during the 1989–2009 period. *Chin Econ.* (2017) 50:168–92. doi: 10.1080/10971475.2017.1297653

58. Nikooyeh B, Rabiei S, Amini M, Ghodsi D, Rasekhi H, Doustmohammadian A, et al. Covid-19 epidemic lockdown-induced remarkable decrease in dairy products consumption of Iran population: does it really matter? National Food and nutrition surveillance. *BMC Nutr.* (2022) 8:122. doi: 10.1186/s40795-022-00612-w

59. Shen Q, Zhong T. Did household income loss have an immediate impact on animal-source foods consumption during the early stage of the Covid-19 pandemic? *Food Secur.* (2023) 12:1424. doi: 10.3390/foods12071424

60. Xu J, Wang J, Li C. Impact of consumer health awareness on dairy product purchase behavior during the Covid-19 pandemic. *Sustain For.* (2022) 14:314.

61. Phuong N, Cuong T, Mergenthaler M. Effects of household characteristics on expenditure for dairy products in Vietnam. *Int J Res Stud Agric Sci.* (2015) 1:1–13.

62. Ortez M, Bir C, Olynk Widmar N, Townsend J. Dairy product purchasing in households with and without children. *JDS Commun.* (2021) 2:7–12. doi: 10.3168/jdsc.2020-19305
63. Radam A, Yacob MR, Bee TS, Selamat J. Consumers' perceptions, attitudes and willingness to pay towards food products with" no added Msg" labeling. *Int J Market Stud.* (2010) 2:65. doi: 10.5539/ijms.v2n1p65

64. Haq S, Boz I, Shahbaz P, Murtaza M. Rural families food intake behaviour in relation to livelihood diversification, income differences and family size. *Int J Consum Stud.* (2020) 44:481–98. doi: 10.1111/ijcs.12580

65. Hannan M, Dutta A, Kabir H. Household demand for dairy products in Bangladesh: an application of aids model. *J Bangladesh Agric Univ.* (2010) 8:121–6.

66. Ahmadi Kaliji S., Mojaverian S. M., Amirnejad H., Canavari M. (2019). Factors affecting Consumers' dairy products preferences. Agris on-line papers in economics and informatics. Available at: http://ageconsearch.umn.edu/record/294150/files/410_agris-on-line-2019-2-ahmadi-kaliji-mojaverian-amirnejad-canavari.pdf (Accessed June 30, 2019).

67. Vakili V, Vakili K, Zamiri Bidari M, Azarshab A, Vakilzadeh MM, Kazempour K. Effect of social beliefs on consumption of dairy products and its predicting factors based on the Transtheoretical model: a population-based study. *J Environ Public Health*. (2023) 2023:5490068. doi: 10.1155/2023/5490068

68. Onurlubas E, Yilmaz N. The factors affecting milk consumption preferences of the consumers in Edirne Keşan township. *J Food Agric Environ.* (2013) 11:516–8.

69. Willett WC, Ludwig DS. Milk and health. *N Engl J Med.* (2020) 382:644–54. doi: 10.1056/NEJMra1903547

70. Zhang X, Chen X, Xu Y, Yang J, Du L, Li K, et al. Milk consumption and multiple health outcomes: umbrella review of systematic reviews and meta-analyses in humans. *Nutr Metabol.* (2021) 18:7. doi: 10.1186/s12986-020-00527-y

71. Machado Nardi VA, Teixeira R, Ladeira WJ, De Oliveira Santini F. A meta-analytic review of food safety risk perception. *Food Control.* (2020) 112:107089. doi: 10.1016/j.foodcont.2020.107089

72. Abedi A-S, Nasseri E, Esfarjani F, Mohammadi-Nasrabadi F, Hashemi Moosavi M, Hoseini H. A systematic review and meta-analysis of lead and cadmium concentrations in cow milk in Iran and human health risk assessment. *Environ Sci Pollut Res.* (2020) 27:10147–59. doi: 10.1007/s11356-020-07989-w

73. Klink U, Mata J, Frank R, Schüz B. Socioeconomic differences in animal food consumption: education rather than income makes a difference. *Front Nutr.* (2022) 9. doi: 10.3389/fnut.2022.993379

74. Kaheni F, Kaheni S, Sharifzadeh G, Nasiri Foorg A, Avan M. Consumption amount of milk and dairy products in school children of 6-11 year olds in Birjand during 2007. *J Birjand Univ Med Sci.* (2009) 16:61–7.

75. Widodo Y, Sandjaja S, Sumedi E, Khouw I, Deurenberg P. The effect of socio-demographic variables and dairy use on the intake of essential macro- and micronutrients in 0.5-12-year-old Indonesian children. *Asia Pac J Clin Nutr.* (2016) 25:356–67. doi: 10.6133/apjcn.2016.25.2.09

76. Afrin S, Mullens AB, Chakrabarty S, Bhoumik L, Biddle SJH. Dietary habits, physical activity, and sedentary behaviour of children of employed mothers: a

systematic review. *Prev Med Rep.* (2021) 24:101607. doi: 10.1016/j.pmedr.2021.101607

77. Rossini G., Vicentin J., Depetris E. (2015). EAAE-AAEA Joint Seminar: Consumer Behavior in a Changing World: Food, Culture, Society. Naples, Italy.

78. Kapaj A., Deci E. (2017). Chapter 7 - World milk production and socio-economic factors affecting its consumption. In: RR Watson, RJ Collier, VR Preedy, editors. *Dairy in Human Health and Disease Across the Lifespan*. Academic Press. 107–15.

79. Murphy MM, Barraj LM, Toth LD, Harkness LS, Bolster DR. Daily intake of dairy products in Brazil and contributions to nutrient intakes: a cross-sectional study. *Public Health Nutr.* (2016) 19:393–400. doi: 10.1017/S1368980015001780

80. Trung TQ, Giam DQ, Hai VT, Thao LP, Hang NTT, Son L, et al. Factor influencing milk consumption of rural household in northern Vietnam. *Greener J Bus Manag Stud.* (2014) 4:31–40. doi: 10.15580/GJBMS.2014.2.031714148

81. Nguyen VP, Mergenthaler M, Tran CH. *Effects of socio-economic and demographic variables on Vietnamese households' expenditure for dairy products*. Tropentag Symposium: Stuttgart (2013).

82. Birdsall NM, Griffin CC. Fertility and poverty in developing countries. *J Policy Model.* (1988) 10:55–9. doi: 10.1016/0161-8938(88)90034-8

83. Traeger B. Poverty and fertility in India: some factors contributing to a positive correlation. *Global Majority E-J.* (2011) 2:87–98.

84. Singh GM, Micha R, Khatibzadeh S, Shi P, Lim S, Andrews KG, et al. Global, regional, and national consumption of sugar-sweetened beverages, fruit juices, and milk: a systematic assessment of beverage intake in 187 countries. *PLoS One.* (2015) 10:e0124845. doi: 10.1371/journal.pone.0124845

85. Chollet M, Gille D, Piccinali P, Bütikofer U, Schmid A, Stoffers H, et al. Dairy consumption among middle-aged and elderly adults in Switzerland. *J Dairy Sci.* (2014) 97:5387–92. doi: 10.3168/jds.2014-8193

86. Mokabberinezhad R, Saeidi A, Ahmadi N, Mashhadi M. A review of treatment protocols for cold and rheumatic joint pains from the perspective of traditional Iranian medicine. *J Med History.* (2017) 9:83–114.

87. World Health Organization. *World report on aging and health*. Geneva: World Health Organization (2015).

88. Akaichi F, Revoredo-Giha C. The demand for dairy products in Malawi. *African J Agric Resour Econ.* (2014)

89. Hoddinott J, Headey D, Dereje M. Cows, missing Milk markets, and nutrition in rural Ethiopia. *J Dev Stud.* (2015) 51:958–75. doi: 10.1080/00220388.2015.1018903

90. Falahati ALI, Goli Y. Estimation the share of inefficient wage inequality between rural-urban in Iran. *J Appl Econ Stud.* (2017) 6:123–43. doi: 10.22084/AES.2017.11959.2299

91. Khodadad Kashi F, Shahikitash MN. Measuring the intensity of poverty in Iranian rural households: a comparison with urban households. *Village Dev.* (2012) 15:41–55.
<figure><img src="image_43.png" /></figure>

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OPEN ACCESS

EDITED BY
Emmanuel Cohen,
UMR7206 Eco Anthropologie et
Ethnobiologie (EAE), France

REVIEWED BY
Katarzyna Lewtak,
Medical University of Warsaw, Poland
Cinzia Barbieri,
University of Turin, Italy

*CORRESPONDENCE
Katarzyna Magdalena Brukato
kbrukalo@sum.edu.pl

RECEIVED 23 July 2023
ACCEPTED 16 January 2024
PUBLISHED 04 March 2024

CITATION
Brukato KM, Nowak J, Pietrzykowska A, Fras N, Ožbolt P, Kowalski O and Blenkuš MG (2024) Public food procurement as a tool of sustainable food and nutrition policy—fat products.
*Front. Sustain. Food Syst.* 8:1265745.
doi: 10.3389/fsufs.2024.1265745

COPYRIGHT
© 2024 Brukato, Nowak, Pietrzykowska, Fras, Ožbolt, Kowalski and Blenkuš. This is an open-access article distributed under the terms of the *Creative Commons Attribution License (CC BY)*. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Public food procurement as a tool of sustainable food and nutrition policy—fat products

Katarzyna Magdalena Brukato<fnref n="1" />*, Justyna Nowak²,
Aleksandra Pietrzykowska³, Neza Fras<fnref n="4" />, Petra Ožbolt<fnref n="4" />,
Oskar Kowalski<fnref n="5" /> and Mojca Gabrijelčič Blenkuš<fnref n="4" />

**Introduction:** Ensuring a sustainable and responsible diet, particularly in the procurement of fatty products, holds paramount importance for early childhood development. Dietary fats significantly influence children's growth and well-being, both in the short and long term. Schools and kindergartens play a pivotal role in shaping children's dietary habits. This study aims to quantitatively and qualitatively analyze public procurement orders conducted by educational institutions.

**Materials and methods:** Out of 1,126 public procurement orders, 197 met inclusion criteria, leading to the identification of 1,248 products categorized as sources of fats in children's diets. The study conducted both quantitative and qualitative analyses on the identified products.

**Results:** Criteria commonly employed by purchasers were derived from product descriptions. While product composition, especially fat content and the absence of certain additives, received due attention, organoleptic characteristics criteria were frequently overlooked. Sustainable procurement criteria were given the least consideration. The study highlights a notable reliance on vegetable oils, predominantly rapeseed oil. However, it reveals a worrisome prevalence of animal-derived fats, including butter, mayonnaise, pork belly, and lard. Although plant-based fats constitute around 52.77% of total orders, the substantial presence of animal fats poses challenges to maintaining a balanced and healthy diet for children.

**Conclusion:** The study underscores the necessity of establishing specific criteria for evaluating the quality of delivered products, especially fatty items, in educational settings. Standardized guidelines are crucial to promote healthier food choices, encourage sustainable diets, and ultimately enhance the overall health and well-being of children.

KEYWORDS

public food procurement, food policy, fat products, schools and kindergartens, nutrition policy, sustainability

<footnote n="1">¹Faculty of Public Health in Bytom, Health Policy Department, Medical University of Silesia, Katowice, Poland, ²Department of Metabolic Disease Prevention, Faculty of Public Health in Bytom, Medical University of Silesia, Katowice, Poland, ³Student Scientific Circle, Department of Metabolic Disease Prevention, Faculty of Public Health in Bytom, Medical University of Silesia, Katowice, Poland,</footnote>

<footnote n="4">⁴Healthy Lifestyle Promotion Department, National Institute for Public Health, Ljubljana, Slovenia,</footnote>

<footnote n="5">⁵Department of Human Nutrition, Faculty of Public Health in Bytom, Medical University of Silesia, Katowice, Poland</footnote>
Introduction

Rational nutrition is defined as systematically providing the body
with an optimal amount of energy and all necessary nutrients,
including proteins, carbohydrates, fats, vitamins, and minerals. A
healthy nutritious provides beneficial nutrients (e.g., vitamins,
minerals, essential amino acids, essential fatty acids, dietary fibre) and
minimizes potentially harmful elements (e.g., sugars, saturated fats,
anti-nutrients, quantities of sodium). Consuming these
macronutrients and micronutrients in appropriate proportions,
according to the body's needs, enables normal functioning and
significantly influences one's health. Proper nutrition ensures proper
growth and development in children. On the other hand, unhealthy
nutrition leads to numerous lifestyle diseases. Both excess and
deficiency of nutrients can cause serious health disorders, such as
obesity or malnutrition (Neufeld et al., 2021).

It is crucial to create an environment in schools that promotes the development of healthy habits, including dietary habits, in a proper and supportive manner during children's education. Incorporating appropriate child nutrition into a healthy lifestyle is essential for ensuring proper development, preventing childhood diseases, and reducing the risk of diet-related illnesses in later stages of life. The reported obesity prevalence data pertains specifically to children aged 1-3 years old. Currently, approximately 10% of this age group is identified as overweight or obese, while 18.4% are deemed at risk of excessive body weight. The issue of overweight and obesity also affects nearly one in three 8-year-olds. Among adolescents aged 10-16 years old, one in five children is affected by excessive body weight. The significant increase in the prevalence of obesity among children and adolescents in Poland is very important as a problem of public health. Research shows that over a span of 30 years in Warsaw, the prevalence of obesity among boys aged 11-15 years old has tripled, while among girls of the same age group, it has increased tenfold (National Institute of Food and Nutrition, 2023a,b).

According to Polish recommendations for the nutrition of children and adolescents, a daily diet should provide proteins, fats, carbohydrates, as well as vitamins and minerals in appropriate proportions (National Center for Health Education, n.d.). The diet should include a small amount of plant-based fats (vegetable oils and products such as nuts, seeds, or kernels). It is important for health to limit the consumption of products such as cookies, candy bars, salty snacks, and fast food, as they contain harmful trans fats in addition to high amounts of salt or sugar. Excessive intake of animal fats, which contain saturated fatty acids, is a cause of many diseases, especially cardiovascular diseases and certain types of cancer. On the other hand, vegetable oils are the richest source of mono- and polyunsaturated fatty acids, which protect against these diseases. Therefore, it is recommended to replace animal fats with vegetable oils, with the exception of coconut and palm oil. The best choice is rapeseed oil, which has the most favorable fatty acid composition. It is important to note that cold-pressed oils should only be consumed raw, as an excellent addition to salads and raw vegetable dishes. For frying, it is best to use refined rapeseed oil or olive oil (however, it is advisable to limit the consumption of fried foods) (Food Pyramid for Children, 2022).

One of the effective tools for promoting proper dietary choices and proper patterns in schools and kindergartens are public food procurement, on which the entire mass catering system in educational

units is based. Either ready meals or products and semi-finished
products are ordered, from which dishes are made on site. It is
important that the products used for school nutrition meet certain
requirements and are of the highest quality, and on the other hand,
they fit into a sustainable diet.

In Poland, public procurement of food is not subject to any special regulations, and the regulation on nutrition in schools focuses more on products available for sale, vending machines and menu composition rules (Regulation of the Minister of Health, 2016). This type of gap, on the one hand, leaves a lot of freedom to the stewards, but on the other hand, it can lead to the selection of inferior products if the only criterion is price.

All Polish public procurements are collected in the Public Procurement Bulletin (in the form of an application), from which documents are removed 3 months after the end of the procedure (only general information remains).

Aim of the study

The aim of the study was to evaluate the use of public procurement as an investment in health promotion and a tool for sustainable and responsible food/nutrition policy. For this purpose, a quantitative and qualitative analysis of food products classified as sources of fats in children's nutrition was carried out and qualitative criteria for product composition, organoleptic characteristics and sustainable public procurement were defined and verified.

Materials and methods

In Poland, the E-procurement Platform has been in operation since 2022. Each entity performing public procurement must log in and enter all relevant documents.

An integral part of the E-Procurement Platform is the Public Procurement Bulletin, which allows you to search for and obtain information about a specific public procurement. In accordance with the rules of operation of the E-Procurement Platform, all documents related to a specific public procurement are deleted within 3 months of its completion.

According to Polish legislation, educational units (kindergartens, primary schools and preschool teams) are obliged to provide at least one hot meal during the day. They can fulfill this obligation in two ways – by providing a catering service or preparing meals. In order to collect information on public tenders carried out by educational units for food products ordered for 2023, data collection was carried out between 15 November 2022 and 15 March 2023.

In order to obtain information on tenders concerning the purchase of food products, the resources of the database were searched on the basis of CPV codes (15000000-8-Food, beverages, tobacco and related products and all fats related to it, a detailed list is included in Annex 1).

On this basis, a database was created covering 1,126 items – public procurement, which in a given period were processed by the above-mentioned educational units in the scope of the indicated CPV codes.

The final criterion for inclusion in the analysis was the first procurement (those in which part of the procedure was repeated due to the invalidity of one of the parts was excluded) and the availability
of full documentation to guarantee the reliability of the analysis carried out.

In the end, 197 public contracts were extracted, which together met the following criteria:

- have been processed by an educational unit (kindergarten, primary school or school-preschool complex),

- were processed for the first time (covered the full commodity demand of individual units),

- had complete documentation of public procurement process.

A comprehensive database of 1,248 ordered products from the group of animal or vegetable fats was created based on the collected documentation. This group encompasses various fats such as: oil, olive oil, frying grease, butter, margarine, lard, pork fat, pork belly, bacon, and dewlap. This group also includes mayonnaise, avocados, and nuts, which are primary sources of fat in the diet. Subsequently, a quantitative analysis was carried out for each product.

Quantitative data were supplemented with a qualitative analysis – taking into account the criteria that are given by the Purchaser for individual food products classified in this group.

Based on the preliminary review of the collected descriptions of individual products, the following quality criteria were identified:

- in terms of product composition:

o fat content, monounsaturated fatty acids [JNKT] and polyunsaturated fatty acids [WNKT],

o without artificial colours, flavour enhancers and flavourings,

o without preservatives,

o without thickeners and stabilizers,

o without added salt/sugar,

o without the addition of palm oil,

o free of antioxidants;

- in terms of organoleptic characteristics:

o taste,

o smell,

o colour,

o appearance/consistency;

- in the field of sustainable public procurement:

o locality of products,

o organic products,

o GMO-free products.

To the best of the authors' knowledge, this is the first detailed and comprehensive analysis of public procurement of a specific group of food products.

## Results

Based on the analysis of the volume of ordered fats, they were divided into 13 subgroups (Table 1):

<table><caption>TABLE 1 Type and quantity of ordered fats for schools and kindergartens in 2023 (according to forecasts).</caption><thead><tr><th rowspan="2">Type of fats</th><th colspan="2">Number of products</th><th colspan="2">Order volume</th></tr><tr><th>Number of products (n)</th><th>% of all products</th><th>Order volume [kg]</th><th>% of order volume</th></tr></thead><tbody><tr><td>Oil</td><td>239</td><td>19.15%</td><td>21,442,851,32 [l]</td><td>99.73%</td></tr><tr><td>Olive oil</td><td>94</td><td>7.53%</td><td>1516.50 [l]</td><td>0.01%</td></tr><tr><td>Frying grease</td><td>4</td><td>0.32%</td><td>130.50 [l]</td><td>0.00%</td></tr><tr><td>Avocados</td><td>37</td><td>2.96%</td><td>401.29</td><td>0.00%</td></tr><tr><td>Nuts</td><td>194</td><td>15.54%</td><td>2086.34</td><td>0.01%</td></tr><tr><td>Mayonnaise</td><td>156</td><td>12.50%</td><td>4511.50</td><td>0.02%</td></tr><tr><td>Butter</td><td>228</td><td>18.27%</td><td>36577.70</td><td>0.17%</td></tr><tr><td>Margarine</td><td>91</td><td>7.29%</td><td>2,826.65</td><td>0.01%</td></tr><tr><td>Lard</td><td>20</td><td>1.60%</td><td>219.95</td><td>0.00%</td></tr><tr><td>Pork fat</td><td>27</td><td>2.16%</td><td>511.00</td><td>0.00%</td></tr><tr><td>Pork belly</td><td>149</td><td>11.94%</td><td>9254.40</td><td>0.04%</td></tr><tr><td>Bacon</td><td>2</td><td>0.16%</td><td>20.00</td><td>0.00%</td></tr><tr><td>Dewlap</td><td>7</td><td>0.56%</td><td>167.00</td><td>0.00%</td></tr><tr><td>Total</td><td>1,248</td><td>100.00%</td><td>21,501,074,14</td><td>100.00%</td></tr></tbody></table>

The main source of fat in products ordered by educational institutions was oil (21442851.32 L, 99.73%). It should be noted that subsequent places were occupied by animal fats: butter (36577.70 kg, 0.17%) and pork belly (9254.40 kg, 0.04%).

In terms of the number of products, butter (228 products, 18.27%), oil (239 products, 19.15%) and nuts (194 products, 15.54%) were ordered the most.

Detailed analysis of each of the groups is presented below.

## Vegetable fats—vegetable oil

239 products were classified into the oil category, of which the most numerous group was rapeseed oil. A detailed summary is presented in the Table 2.

The ordering pattern indicated a predominant preference for rapeseed oil, with 194 products (81.17% of vegetable oils) and a substantial volume of 21,433,247.66 liters (99.96% of vegetable oils). The second large group consisted of products categorized under the general name “oil,” because the Purchaser did not specify in any way what type of product it expects. The least frequently ordered oils were coconut oil (1 product, 0.42%) and sesame oil (1 product, 0.42%).

The Purchaser mainly chose refined oil (113 products, 47.28%), virgin oil (132 products, 55.23%) and cold filtered oil (91 products, 38.08%). In the description of 100 products (41.84%), the contracting authority also specified the expected content of monounsaturated fatty acids (minimum 50%), and in the description of 96 products (40.17%) also polyunsaturated fatty acids (less than 40%). For 12 products (5.02%), specific proportions of fat content in 10 g were determined:
saturated fatty acids—0.7 g, monounsaturated fatty acids 6.5 g, polyunsaturated acids 2.8 g, cholesterol 0 mg.

In the case of 62 products (25.94%), it was indicated that the ordered oil was suitable for both frying and salads. In addition, according to the description, 61 products (25.52%) classified in the group of oils should not contain preservatives, artificial dyes and flavour enhancers in their composition, and 2 products (0.84%) should be zero-erucic.

No criteria on the composition or characteristics of the oil were taken into account for 64 products (26.78%). Organoleptic features have not been described with any product.

## Other vegetable fats

### Olive oil

The educational units ordered only olive oil (94 products, 7.53%). This oil should be virgin (40 products, 42.55%), free of preservatives, flavour enhancers and artificial colours (3 products, 3.19%), and cold-pressed (2 products, 2.13%).

No quality criteria were defined for 50 products (53.19%). Organoleptic features have not been described with any product.

### Frying grease

The frying grease was ordered only by 4 educational units (4 products, 0.00%) in the amount of 130.5 L (0.00%). The temperature of smoking was expected to be at least 232°C.

### Avocados

Avocados were ordered 37 times (2.96%), of which in 3 cases (8.10%) it was indicated that a variety of Hass was desirable. The only criteria indicated in this product group concerned organoleptic characteristics. The ordered avocado should be ripe and without damage (14 products, 37.83%), without signs of rotting and mold (13 products, 35.14%), without stains (12 products, 32.43%) and fresh (10 products, 27.03%), firm (6 products, 16.22%), healthy (5 products, 13.51%) and green (4 products, 10.81%).

### Nuts

Nuts, although in terms of quantity they constitute a small part of the ordered fats (2086.34 kg, 0.01%), it is necessary to pay attention to their wide volume in public procurement of educational units (Table 3).

The most frequently ordered are almonds (56 products, 28.87%) and walnuts (52 products, 26.80%), while the least frequently ordered are peanuts (3 products, 1.55%), pistachios (1.03%) and Brazil nuts (1 product, 0.52%) and nut cream (1 product, 0.52%).

In the largest quantities, walnuts, hazelnuts and cashews are ordered. In total, they account for 71.44% of all ordered nuts (1490.4 kg). The Purchaser expects these nuts to be husked (40 products, 20.62%), without the addition of salt (34 products, 17.53%) and/or without the addition of sugar (33 products, 17.01%).

Nuts should also not contain preservatives, artificial colors and flavor enhancers, as well as palm oil (27 products, 13.92%).

In addition, the description of 2 products indicates that these products should be marked with the bio symbol, and in the case of the next two, that they should come from the European Union.

For 147 products (75.77%) no quality criteria were defined.

<table><caption>TABLE 2 Type and quantity of oil ordered for schools and kindergartens in 2023 (according to forecasts).</caption><thead><tr><th rowspan="2">Type of oil</th><th colspan="2">Number of products</th><th colspan="2">Order volume</th></tr><tr><th>Number of products (n)</th><th>% of all products</th><th>Order volume [l]</th><th>% of order volume</th></tr></thead><tbody><tr><td>Oil</td><td>18</td><td>7.53%</td><td>9030.65</td><td>0.04%</td></tr><tr><td>Coconut oil</td><td>1</td><td>0.42%</td><td>8</td><td>0.00%</td></tr><tr><td>Flaxseed oil</td><td>9</td><td>3.77%</td><td>150</td><td>0.00%</td></tr><tr><td>Rice oil</td><td>2</td><td>0.84%</td><td>104</td><td>0.00%</td></tr><tr><td>Rapeseed oil</td><td>194</td><td>81.17%</td><td>21433247.66</td><td>99.96%</td></tr><tr><td>Sesame oil</td><td>1</td><td>0.42%</td><td>2.5</td><td>0.00%</td></tr><tr><td>Sunflower oil</td><td>3</td><td>1.26%</td><td>178</td><td>0.00%</td></tr><tr><td>Pumpkin seed oil</td><td>2</td><td>0.84%</td><td>4</td><td>0.00%</td></tr><tr><td>Grape seed oil</td><td>9</td><td>3.77%</td><td>115.5</td><td>0.00%</td></tr><tr><td>Total</td><td>239</td><td>100.00%</td><td>21,442,840.31</td><td>100.00%</td></tr></tbody></table>

<table><caption>TABLE 3 Type and quantity of nuts ordered for schools and kindergartens in 2023 (according to forecasts).</caption><thead><tr><th rowspan="2">Type of nuts</th><th colspan="2">Number of products</th><th colspan="2">Order volume</th></tr><tr><th>Number of products (n)</th><th>% of all products</th><th>Order volume [kg]</th><th>% of order volume</th></tr></thead><tbody><tr><td>Coconut</td><td>25</td><td>12.89%</td><td>96.8</td><td>4.64%</td></tr><tr><td>Nut cream</td><td>1</td><td>0.52%</td><td>2</td><td>0.10%</td></tr><tr><td>Peanut butter</td><td>11</td><td>5.67%</td><td>61.92</td><td>2.97%</td></tr><tr><td>Almonds</td><td>56</td><td>28.87%</td><td>390.22</td><td>18.70%</td></tr><tr><td>Brazil nuts</td><td>1</td><td>0.52%</td><td>30</td><td>1.44%</td></tr><tr><td>Hazelnuts</td><td>26</td><td>13.40%</td><td>495.04</td><td>23.73%</td></tr><tr><td>Cashew nuts</td><td>17</td><td>8.76%</td><td>470.2</td><td>22.54%</td></tr><tr><td>Walnuts</td><td>52</td><td>26.80%</td><td>525.16</td><td>25.17%</td></tr><tr><td>Peanuts</td><td>3</td><td>1.55%</td><td>11.5</td><td>0.55%</td></tr><tr><td>Pistachios</td><td>2</td><td>1.03%</td><td>3.5</td><td>0.17%</td></tr><tr><td>Total</td><td>194</td><td>100.00%</td><td>2086.34</td><td>100.00%</td></tr></tbody></table>

In terms of organoleptic characteristics, the main focus was on the shape and texture (softness/hardness) of nuts (9 products, 4.64%).

### Mayonnaise

Mayonnaise was ordered 156 times (12.50%) by educational institutions in the amount of 4,511.50 kg (0.02%). The description of
15 products (9.62%) indicated that it was decorative mayonnaise, and the description of 9 products (5.77%) indicated that it was salad mayonnaise.

For every fifth product (31 products, 19.87%), the desired fat content was defined (at a minimum of 70%), and for 20 products (12.82%), the expected egg content was determined (at a minimum of 4%) or specifically egg yolk (38 products, 24.36%, at a minimum of 6%).

Mayonnaise was most commonly expected to be free of preservatives (85 products, 54.49%), stabilizers (62 products, 39.74%), artificial colors (62 products, 39.74%), antioxidants (50 products, 32.05%), and flavor enhancers (59 products, 37.82%). In addition, its composition should not include the addition of citric acid (26 products, 16.67%), as well as sugar (6 products, 3.85%), sweeteners (5 products, 3.21%) and salt (1 product, 0.64%).

For every third mayonnaise ordered (48 products, 30.77%), a specific permissible composition is given in the description. There were also individual specific records, such as without E385 (EDTA), starch, phosphoric acid (E338), powdered eggs, xanthan gum or glucose-fructose syrup.

In terms of organoleptic characteristics, the consistency was specified, which should be dense (3 products, 1.92%) or creamy (1 product, 0.64%), color and taste (1 product, 0.64%).

In the case of 6 products (3.85%), it was indicated that mayonnaise should be produced using free-range eggs.

For 58 products (37.18%) no criteria were defined (neither in terms of composition nor in terms of organoleptic characteristics).

## Hydrogenated vegetable fats—margarine

A specific type of fat and a semi-synthetic product of plant origin is margarine. It was ordered 91 times to schools and kindergartens (7.29%) (Table 4).

Among margarines, vegetable butter (27 products, 29.67%) is the most frequently ordered, while soft margarine (12 products, 13.19%) and milk margarine (7 products, 7.69%) are the least frequently ordered. For 27 products (29.67%), the expected fat content was determined (at the level of a minimum of 60%).

Margarines should be free of preservatives (22 products, 24.18%), flavor enhancers (21 products, 23.08%), artificial colors (18 products, 19.78%) antioxidants (10 products, 10.99%) and stabilizers (2 products, 2.2%).

In addition, they should also be free of genetically modified organisms (6 products, 6.59%) and the addition of palm fat (1 product, 1.1%).

None of the descriptions referred to the organoleptic characteristics of margarine.

No criteria were identified for 59 products (64.84%).

## Animal fats—butter

Butter is the product of the fat group that was ordered most often (228 products, 18.27%) (Table 5).

Traditional butter was most often ordered, the so-called “extra butter” (168 products, 73.68%), and one in ten butter ordered by

<table><caption>TABLE 4 Type and quantity of ordered margarine for schools and kindergartens in 2023 (according to forecasts).</caption><thead><tr><th rowspan="2">Type of margarine</th><th colspan="2">Number of products</th><th colspan="2">Order volume</th></tr><tr><th>Number of products (n)</th><th>% of all products</th><th>Order volume [kg]</th><th>% of order volume</th></tr></thead><tbody><tr><td>Margarine</td><td>26</td><td>28.57%</td><td>616.5</td><td>21.81%</td></tr><tr><td>Soft margarine</td><td>12</td><td>13.19%</td><td>244.15</td><td>8.64%</td></tr><tr><td>Milk margarine</td><td>7</td><td>7.69%</td><td>410</td><td>14.50%</td></tr><tr><td>Hard margarine</td><td>19</td><td>20.88%</td><td>411.25</td><td>14.55%</td></tr><tr><td>Vegetable butter</td><td>27</td><td>29.67%</td><td>1,144.75</td><td>40.50%</td></tr><tr><td>Grand total</td><td>91</td><td>100.00%</td><td>2,826.65</td><td>100.00%</td></tr></tbody></table>

<table><caption>TABLE 5 Type and quantity of butter ordered for schools and kindergartens in 2023 (according to forecasts).</caption><thead><tr><th rowspan="2">Type of butter</th><th colspan="2">Number of products</th><th colspan="2">Order volume</th></tr><tr><th>Number of products (n)</th><th>% of all products</th><th>Order volume [kg]</th><th>% of order volume</th></tr></thead><tbody><tr><td>Butter</td><td>168</td><td>73.68%</td><td>33475.90</td><td>91.52%</td></tr><tr><td>Lactose-free butter</td><td>13</td><td>5.70%</td><td>164.50</td><td>0.45%</td></tr><tr><td>Clarified butter</td><td>25</td><td>10.96%</td><td>798.00</td><td>2.18%</td></tr><tr><td>Butter mix</td><td>1</td><td>0.44%</td><td>90.00</td><td>0.25%</td></tr><tr><td>Pat of butter</td><td>6</td><td>2.63%</td><td>614.90</td><td>1.68%</td></tr><tr><td>Cream butter</td><td>15</td><td>6.58%</td><td>1434.40</td><td>3.92%</td></tr><tr><td>Grand Total</td><td>228</td><td>100.00%</td><td>36577.70</td><td>100.00%</td></tr></tbody></table>

educational units was clarified butter (25 products, 1.96%). In 13 cases (5.70%) schools and kindergartens also ordered lactose-free butter.

For 179 products (78.5%), the expected fat content was specified (for cream butter it was at least 60%, and for traditional butter it was 82% /although for 2 products it was 73%). In addition, butter should be without vegetable additives (56 products, 24.56%) and without salt (34 products). Butter should also not include preservatives (42 products, 18.42%), artificial colors (38 products, 16.67%) and antioxidants (11 products, 4.82%) and stabilizers (11 products, 4.82%). In relation to 12 products (5.26%), the Ordering Party specified the specific required composition.

In terms of organoleptic characteristics for the Purchasers, the most important was the consistency, which should be: uniform, compact and lubricating and slightly hard or slightly greasy (30 products, 13.16%), carefully formed (27 products, 11.84%) with a smooth surface (17 products, 74.56%). The smell should
be characteristic of butter (16 products, 7.02%), slightly sour taste with a specific taste of pasteurization (19 products, 8.33%), and the color should be uniform and golden (16 products, 7.02%).

47 products (20.61%) had no defined quality or organoleptic criteria.

## Other animal fats

### Lard

Lard for schools and kindergartens was ordered 20 times (1.60%) in the amount of 219.95 kg. Only for 1 product (5.00%) it was indicated that it should consist of 100% bacon, without the addition of preservatives and without foreign smells.

### Pork fat

Of the analysed products, 27 (2.16%) were pork fat. In the case of 4 products (14.81%), the Purchaser indicated that it should be raw, and in the case of 3 products (11.11%), that it should be smoked. This pork fat is supposed to be fresh (6 products, 22.22%) and unfrozen (2 products, 7.41%), but can be minced (2 products, 7.41%). In addition, it should be without skin (4 products, 14.81%) and without bone shards (1 product, 3.7%) and without any additives (2 products, 7.41%). It should have a thickness of 2–4 cm (2 products, 7.41%), a white to light cream color (2 products, 7.41%) and a specific smell (1 product, 3.7%).

### Pork belly

Pork belly was included in public procurement of educational units 149 times (11.94%). The most frequently ordered pork belly smoked (115 products, 77.18%) or steamed (43 products, 37.39%), less raw (27 products, 18.12%), fresh (23 products, 15.44%), unfrozen (4 products, 2.68%) or cooked (2 products, 1.34%).

The description of 12 products (8.05%) specifies the expected meat content in the product (above 70%), and for 2 products (1.34%) the expected fat content (below 30%).

These products should be free of ribs (48 products, 32.21%), skin (37 products, 24.83%) and bones (14 products, 9.4%). They should not contain preservatives (9 products, 6.04%), flavor enhancers (9 products, 6.04%), emulsifiers (5 products, 3.36%), antioxidants (3 products, 2.01%), stabilizers (3 products, 2.01%), acidity regulators (3 products, 2.01%), protein additives (3 products, 2.01%) and artificial flavors (3 products, 2.01%).

The taste (24 products, 16.11%) and smell (28 products, 18.79%) should be characteristic for a given product type, and the color (17 products, 11.41%) should be pale pink to red. The consistency (19 products, 12.75%) should be moist and soft, but at the same time firm and elastic.

With regard to 2 products (1.34%), it was indicated that they were of Polish origin.

68 products (45.64%) did not have any criteria.

### Bacon

These were only 2 products (0.16%), smoked, with no specific criteria for composition or organoleptic characteristics.

### Dewlap

Dewlap schools and kindergartens ordered 7 times (0.56%). It was supposed to be a fresh, unfrozen, smoked and skinless product.

## Collective sheet

The matrix summarizing the occurrence of the identified criteria in the descriptions of individual groups of products from the fat group is presented below.

The most frequently taken into account criteria are those for the composition of the product (taking into account the specific fat content and the absence of the addition of preservatives, artificial colours and flavour enhancers). The criteria for organoleptic characteristics are quite often overlooked and the criteria for sustainable public procurement are the least taken into account.

The group of products that is described in the most detail (the largest number of products has specific criteria) is butter, mayonnaise and oil.

A detailed summary is presented in the Table 6.

## Discussion

Dietary fats are all lipids present in plant and animal tissues that are consumed as food. They primarily serve as a source of energy necessary for proper growth and maintenance of the body's vital functions. Additionally, they provide essential unsaturated fatty acids and serve as a source of fat-soluble vitamins. Fats play various important roles in the human body. To maintain good health, it is crucial to ensure the intake of fats of appropriate quality. Excessive fat intake in the diet increases the risk of developing overweight, obesity, and other metabolic disorders and nutrition-related diseases. It is important for maintaining good health to consume an appropriate level of total fat while reducing the intake of Saturated Fatty Acids (SFA) and Trans Fatty Acid isomer (TFA). Saturated fatty acids contribute to an increase in total cholesterol and LDL cholesterol levels in the blood serum and are risk factors for the development of cardiovascular diseases. They also increase the risk of developing diseases such as colorectal cancer and breast cancer. Saturated fatty acids have prothrombotic effects, increasing the risk of strokes. The consumption of trans fatty acid isomers should be limited as they are primarily formed during the industrial partial hydrogenation of vegetable oils. They lead to an elevation of LDL cholesterol levels in the blood serum, decrease HDL cholesterol levels, and are recognized risk factors for cardiovascular diseases, stroke, and type 2 diabetes. Replacing saturated fatty acids with polyunsaturated fatty acids (PUFAs) reduces the risk of ischemic heart disease. Monounsaturated fatty acids (MUFAs) can play a protective role in the prevention of atherosclerosis and heart disease when they replace saturated fats as a component of the diet [g]. Based on Polish recommendation percentage of fat in total daily energy for children and adolescents aged 4–18 years is 20–35%. Intake of Saturated Fatty Acid and Trans Fatty Acid in daily diet should be as lower as is possible for healthy and balanced diet (but not more than 10% among 1–9 years group and 5–6% among adolescents aged 10–18 years) (Jarosz et al., 2020).

According to Polish observation (2007–2011) among children and adolescents there has been a shift in the structure of its sources in favor of plant-based fats. However, the consumption of animal fats remains too high. Among animal fats, butter dominates and is used both as a spread on bread and as an addition to dishes. Cream is also frequently added. Other animal fats such as lard and bacon are also found in the diets of children and adolescents, although they should practically be eliminated from the diet. High consumption of total fats