Gender and socio-economic stratification of ultra-processed and deep-fried food consumption among rural adolescents: A cross-sectional study from Bangladesh

Abstract

Background

Although consumption of ultra-processed and deep-fried foods among adolescents is a global health concern, little is known about its gender and socio-economic stratification in rural settings of low- and middle-income countries. We, thus, aimed to describe ultra-processed and deep-fried food consumption among rural adolescents by gender and socio-economic factors, and to explore their relative importance in shaping consumption.

Methods

This cross-sectional study drew on data from a household survey in Matlab, a rural sub-district in Bangladesh. The analytic sample comprised 2463 adolescents. We assessed consumption of four ultra-processed food groups: ready-to-eat or “instant” foods; confectionery, sweets and similar packaged products; savory snacks; sugar-sweetened beverage; and of deep-fried foods with a 24-hour, qualitative recall. Asset scores were constructed. Proportion of consumption was calculated and compared by gender and household wealth. Logistic regression models were fitted to isolate socio-demographic variables associated with consumption.

Results

Approximately 83% (81.5–84.4) adolescents consumed at least one ultra-processed or deep-fried item. Confectioneries were the most consumed (53.5%), whereas sugar-sweetened beverage was the least consumed (12%) group. Boys had greater odds of consumption than girls for all food groups. The association was strongest for sugar-sweetened beverage (adjusted odds ratio = 2.57; 95% CI: 1.97, 3.37), followed by deep-fried foods (adjusted odds ratio = 1.96; 95% CI: 1.66, 2.32) and ready-to-eat foods (adjusted odds ratio = 1.85; 95% CI: 1.45, 2.38). Belonging to the richest households was associated with ready-to-eat food consumption (adjusted odds ratio = 1.55; 95% CI: 1.12, 2.16). Adolescents with higher educational attainment had lower odds of consuming sugar-sweetened beverage (adjusted odds ratio = 0.73; 95% CI: 0.54, 0.98).

Conclusion

Consumption of packaged confectioneries, savory snacks, and deep-fried foods appeared common, while SSB consumption was relatively low. Role of gender was pre-eminent as consumption was more likely among boys across the food groups. This may disproportionately expose them to the risk of diet-related non-communicable diseases.

Introduction

Ultra-processed foods (UPFs) refer to multi-ingredient, industrial formulations composed of such food-derived substances as starch, fat, oil, sugar, casein, etcetera; or synthesized through complex processing of food constituents like whey, gluten, soya protein isolate, maltodextrin, and corn syrup [1, 2]. UPFs contribute to suboptimal diets, a global health concern that accounted for an estimated 11 million deaths and 255 million disability-adjusted life-years (DALYs) among adults in 2017 [3]. Suboptimal diets typically have a higher share of refined sugar, added salt and trans-fat with a decreasing proportion of whole grains, fruits and vegetables, nuts and seeds [4]. These attributes are related with a global shift away from diets based on homemade meals to those dominated by energy-dense, highly processed food and drink products [1].

Monteiro and collaborators proposed the NOVA (non-acronym) classification in 2010 and coined the term UPF. The word “ultra-processed” signifies the multitude of industrial processes required to generate these hyper-palatable, highly profitable, packaged products containing little to no whole food [1, 5, 6]. Consumption of UPFs has escalated across the world with their dietary share reaching as high as 57% of the daily energy intake in some settings [7]. After dominating the food systems in high-income countries, availability of mass-produced UPFs is rapidly growing in low- and middle-income countries (LMICs) [8]. Epidemiological studies consistently link UPF consumption with greater energy intake; higher consumption of refined sugar, added salt, saturated and trans-fats, and lowering of diet quality [9–14]. Unsurprisingly, observational research has associated UPFs with obesity [15–18], heightened non-communicable disease (NCD) risk [8, 19–21], and all-cause mortality [22].

Deep-fried foods represent another category of unhealthy foods. Oil frying increases energy density and improves flavor profile of foods [23]. Owing to palatability and affordability, deep-fried foods are popular among adolescents and often consumed together with ultra-processed products like sugar-sweetened beverage (SSB). A major increase in vegetable oil production in the developing world [24] underlies the wide availability of deep-fried foods. Energy density of deep-fried foods drives increased energy intake. Accordingly, prospective studies capture a positive association of frequent consumption of deep-fried foods with obesity and type 2 diabetes [25]. Deep frying also leads to formation of oil degradation products that have been linked to NCDs including cancer [26]. Nonetheless, policy responses to minimize consumption of these unhealthy foods remain patchy in LMICs [27].

Consumption of ultra-processed and deep-fried foods among adolescents in LMICs is a multi-dimensional challenge. Changes in food environment accompanying nutrition transition have occurred at an unprecedented pace in LMICs [24]. With growth of UPF sales stalling in high-income countries, Asian LMICs are observing marked increase in UPF availability [28]. Stunting, thinness, and food insecurity affect a significant proportion of adolescents in these settings [29]. Consequently, access to inexpensive calories through these foods may expose them to a disproportionately high NCD risk in ensuing adulthood [30, 31]. Moreover, adolescents are particularly susceptible to highly charged ideas and images employed in pervasive marketing of UPFs through electronic and social media [32, 33]. Evidence also suggests that energy intake among adolescents in LMICs has spiralled, whereas their diet quality remained poor [34]. While the trend of adolescent overweight and obesity has plateaued in high-income countries, it has accelerated in South Asia [35]. Similar to other parts of South Asia, Bangladesh is also experiencing a growing burden of overweight and obesity among adolescents [36]. Ultra-processed and deep-fried foods are known dietary drivers of increasing overweight and obesity [15, 18]. Nevertheless, socio-demographic correlates of consumption of these unhealthy foods among rural, Bangladeshi adolescents remain unexplored. Hence, we aimed to describe ultra-processed and deep-fried food consumption among rural adolescents by gender and socio-economic factors, and to explore their relative importance in shaping consumption.

Methods

Participants, data collection and study site

This cross-sectional study utilized data collected during the 15-year follow-up of MINIMat trial (Maternal and Infant Nutrition Interventions in Matlab, reg#ISRCTN16581394). MINIMat was a factorial, randomized trial that recruited 4436 pregnant women from Matlab, between November 2001 and October 2003 to test the effects of food and micronutrient supplementation for pregnant women. The sampling, interventions and results have been reported elsewhere [37]. The cohort of children born to the participating mothers has been repeatedly followed up [38]. The latest, 15-year follow-up was conducted from September 2017 to June 2019. Eligible adolescents (n = 3267) were tracked using unique identification numbers allotted during the trial. Trained enumerators interviewed the adolescents and their mothers/guardians with a pre-tested, structured questionnaire.

Matlab is a low-lying, rural sub-district that is part of Chandpur district. The International Center for Diarrheal Disease Research, Bangladesh (icddr,b) has been running a Health and Demographic Surveillance System (HDSS) in Matlab since 1966. The HDSS currently covers more than 230,000 people in 142 villages [39]. The community is agrarian and rice farming is the commonest occupation.

Assessment of ultra-processed and deep-fried food consumption

NOVA classifies foods based on the extent and purpose of industrial processing into four categories: (i) natural or minimally processed, (ii) processed culinary ingredients, (iii) processed, and (iv) ultra-processed. Operationalization of this classification and identifying characteristics of UPFs have been published extensively [1, 2, 6, 17]. Common UPFs include biscuits or cookies, chips (crisps) and various savoury or sweet, packaged snacks; ice cream, chocolate and candies; carbonated and energy drinks; “instant” noodles and soup; packaged breads, buns and muffins; frozen meals among others. UPFs available in Matlab were identified in accordance with NOVA and listed systematically during the formative phase of the 15-year follow-up. As recommended by Monteiro et al. [2], we considered industrial formulation and presence of multiple ingredients (typically >5) to identify the UPFs. The locally available UPFs were grouped as follows: (i) ready-to-eat or “instant” foods, (ii) confectionery, sweets and similar packaged products, (iii) savory snacks, and (iv) SSBs (including energy drinks). Foods that were submerged in extremely hot oil during the cooking process were considered deep-fried. S1 Table shows the grouping of ultra-processed and deep-fried foods.

Consumption was assessed at individual level through a single, qualitative recall of all ultra-processed and deep-fried foods consumed in the past 24 hours of the survey [40]. Reported consumption of roughly one tablespoonful or more of one or more items qualified the participant as consumer of that particular group of ultra-processed or deep-fried food [41]. The enumerators probed for unreported consumption after the initial recall using list and pictorial aid for minimizing recall bias. The pictorial aid displayed photographs of ultra-processed and deep-fried foods available in Matlab, listed and taken during the extensive formative phase of the 15-year follow-up.

Socio-demographic variables

Socio-demographic variables included in the analysis were gender, household wealth, maternal and adolescents’ education. Gender was a dichotomous variable (girl/boy). Household wealth was ascertained with asset score [42]; computed for each household from principal component analysis (PCA) of ownership of a set of durable assets (e.g., mobile phone, radio, television, refrigerator, bicycle, etcetera), access to electricity and sanitary latrine, and nature of fuel used. PCA yields factor scores for each variable and eigenvectors for the principal components. The asset score variable, with a mean of zero and a standard deviation of one, was created using the factor scores for variables within the first principal component as weights. Asset scores arranged in ascending order were divided into tertiles with the lowest, intermediate and highest representing the poorest, the middle-status and the richest households, respectively. Educational status was categorized according to completed years of formal education: none, primary (1–5 years), and secondary (6–12 years) for adolescents or secondary and above (≥ 6 years) for mothers. Because of the low number of adolescents in the category of primary education (n = 62), we combined this category with that of no education during analysis.

Statistical analysis

All analyses were performed in R, version 3.6.3 (The R Foundation, Vienna, Austria). Sample characteristics are presented as frequency with percentage or mean with standard deviation (SD). We calculated consumption proportion across categories of gender and household wealth, and differences between groups were tested with Pearson´s Chi-squared Test. We fitted binary logistic regression models to explore association of socio-demographic variables with consumption of ultra-processed and deep-fried foods. The multivariable models adjusted for all four socio-demographic variables simultaneously. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) are reported. Visual examination of the plots of quantile residuals and Hosmer-Lemeshow Test were done to assess adequacy of the models. Variance inflation factors (VIF) were retrieved, and none exceeded 2.5. Two-sided P-values <0·05 and 95% CIs that exclude 1 were considered statistically significant.

Ethics statement

This study was conducted in strict accordance with the Declaration of Helsinki. The Ethical Review Committee at icddr,b in Dhaka, Bangladesh, approved the 15-year follow-up of MINIMat trial (reference: PR–17029). Written informed consent and assent were obtained from the participating mothers and adolescents, respectively; after full disclosure of the purpose, methods, risks, and benefits of the study.

Results

Adolescents born to MINIMat mothers as singletons with valid birth anthropometrics were eligible to participate. Out of 3267 eligible adolescents, 2465 (75.45%) completed the household survey of 15-year follow-up. The reasons for loss to follow-up were: outmigration (n = 656), child death (n = 94), and refusal to participate (n = 52). After exclusion of two participants for missing data, the analytic sample comprised 2463 adolescents. Table 1 presents their socio-demographic, household and basic anthropometric attributes. Girls constituted slightly more than half of the sample (51.2%), and participants’ mean age was 15.03 years (standard deviation 0.16). The vast majority of adolescents had secondary education (80.1%), whereas 44.6% of the mothers attained similar or higher educational status. Most of the households accessed drinking water from tube-wells (92.9%) and had electricity coverage (87.9%). Approximately 52% of the households owned farming land.

[Figure omitted. See PDF.]

Table 2 shows the consumption proportions for four groups of UPF and for deep-fried food by gender and household wealth. About 83% of the adolescents reportedly consumed at least one ultra-processed or deep-fried food in the past 24 hours. Confectionery, sweets and similar packaged products was the most consumed UPF group (53.5%, 51.5–55.4), while SSB was the least consumed (12%, 10.7–13.2). Deep-fried foods were consumed by 41.3% adolescents. Consumption proportion was higher among boys than girls for all five food groups with the highest difference observed for deep-fried food (49.7 versus 33.4%, P < 0.001), followed by SSB (17.1 versus 7.0%, P < 0.001). While 10.8% of those from the poorest households consumed ready-to-eat or “instant” foods, this increased to 13.1% and 15.1% among their peers from the middle-status and richest households, respectively (P = 0.037). Proportion of adolescents consuming at least one item from any of the five groups increased along household wealth: from 78.1% among those from the poorest to 86.6% among those from the richest households (Table 2).

[Figure omitted. See PDF.]

Table 3 presents the adjusted odds ratios from multivariable binary logistic regression analyses that accounted for gender, household wealth, maternal and adolescents´ education simultaneously. Gender was associated with ultra-processed and deep-fried food consumption. The odds of reported consumption of all four groups of UPF and of deep-fried food were higher among boys compared to girls. The most pronounced gender difference was observed for SSB (adjusted odds ratio (AOR) for boys = 2.57; 95% CI: 1.97, 3.37). Boys also had 1.85 times higher odds of consuming ready-to-eat or “instant” foods (AOR = 1.85; 95% CI: 1.45, 2.38). The odds of deep-fried food consumption were nearly two times higher among boys (AOR = 1.96; 95% CI: 1.66, 2.32).

[Figure omitted. See PDF.]

Household wealth was associated with the consumption of ready-to-eat or “instant” foods and SSB, but not with consumption of the other three groups. Compared to adolescents from the poorest households, those from the richest households had significantly higher odds of consuming ready-to-eat or “instant” foods (AOR = 1.55; 95% CI: 1.12, 2.16) and of consuming SSB (AOR = 1.44; 95% CI: 1.02, 2.03). Maternal education was not associated with consumption of any of the five food groups (odds ratios not shown). Adolescents’ educational status was inversely associated with SSB consumption with the odds being lower among those with secondary education compared to those with lower educational attainment (AOR = 0.73; 95% CI: 0.54, 0.98). All P-values from Hosmer-Lemeshow Test were >0.05, excluding inadequate fit (Table 3). The complete regression analyses with both the crude and adjusted ORs can be found in S2 Table.

Discussion

The objective of the present study was to describe and analyze the gender and socio-economic stratification of ultra-processed and deep-fried food consumption in a cohort of rural adolescents. The results showed that proportion of adolescents consuming the four groups of UPF and the deep-fried foods ranged from 12% to 53.5%. Packaged confectioneries were the most commonly consumed food group, followed by deep-fried foods and savory snacks. Consumption of SSB and ready-to-eat or “instant” foods was fairly low. While prevalence of consumption varied by gender for all food groups, it varied by wealth status only for ready-to-eat or “instant” foods. Boys were more likely to consume ultra-processed and deep-fried foods than girls. Household wealth was positively associated with consumption of ready-to-eat or “instant” foods and SSB, whereas educational status of adolescents was inversely associated with SSB consumption.

The majority of the adolescents reported consumption of packaged confectioneries and similar products. Matlab is a rural area without super- and hyper-markets and transnational fast food chains. Instead, small-scale, independent retailers are the common distribution channel for UPFs in Matlab. They operate from small stores–rural equivalent of “corner stores” in high-income, urban settings [43]–mostly selling cheap UPFs available in small packets. Price of these match the affordability of rural consumers or scant pocket money that the adolescents receive from their parents [44]. This may explain the highest consumption proportion observed for packaged confectioneries and similar products. Evidence suggests that these alternative retailers play a critical role in expanding sales of UPFs in low-income, Asian settings [28]. These mass-produced, packaged confectioneries are loaded with refined carbohydrate, added sugar, salt and saturated fat, and additives, and serve as important source of inexpensive calories [1]. More than 40% of the participants consumed deep-fried foods. Household wealth status did not predict consumption of deep-fried foods in our study. We could not ascertain whether this reflected uniform affordability of deep-fried foods across wealth categories, or existing perception of “instant” items and SSB being expensive and extravagant compared to deep-fried foods. Moreover, there may have been behavioral factors [45] influencing deep-fried food consumption–meal skipping, snacking, eating away from home, screen time, etcetera–that we could not capture. Deep frying is considered a health-compromising way of food preparation [46]. Such aspects as type of oil used, type of food being fried, and frying mode (deep versus pan frying) are relevant in this regard [25]. Cost largely determines the type of frying oil used in low-income settings and repeated use of frying oil is a common practice [26]. Palm and soybean oils are commonly used for frying in Bangladesh [47]. Palm oil contains 45–50% saturated fatty acids and concentration of trans fat in partially hydrogenated soybean oil can reach up to 20% [25]. Consumption of deep-fried foods, thereby, may predispose the participants to obesity.

Consumption of SSB appeared low among MINIMat adolescents. Studies demonstrate that SSB consumption is more prevalent in urban than rural settings [48, 49]. In an urban sample of 475 Bangladeshi participants (age range: 18–25 years), Muktadir and colleagues found self-reported SSB consumption to be 78.9% [50]. Prevalence was higher in other urban settings as well: 88.7% in the preceding month of survey in Quebec (n = 311) [51], and 71.4–79.8% in the preceding week of survey in Guatemala City (n = 1042) [52]. Similarly, contrasting with ready-to-eat items’ marked contribution to overall UPF intake in high-income settings [12, 13], we observed low consumption of ready-to-eat preparations. Low consumption of these two UPF groups in our study could be an indication of limited purchasing ability. The positive association between wealth status and consumption of ready-to-eat foods and SSB suggests that adolescents from relatively wealthier families in Matlab can afford these products. Contrary to this finding, lower socio-economic status aligns with higher UPF consumption in some settings [53–56]. A couple of nation-wide surveys from high-income countries, however, reveals no association of wealth status with UPF consumption [57, 58].

Qualitative research from Matlab suggests that mothers perceive empty-calorie, UPFs like “instant” noodles, mass-produced biscuits and muffins available in sealed packets to be healthier alternatives to unhygienic street foods [44]. This is accompanied by a lack of understanding of the inherently unbalanced nutrient profile of UPFs and negative impact of industrial processing on dietary pattern [44]. Additionally, we demonstrated limited dietary diversity with a monotonous diet dominated by rice and lower consumption of fruits, vegetables and animal-source foods in the same cohort [59]. UPF consumption concurrent with such marginal diet quality, labelled “double burden of suboptimal diet”, has been documented in a low-income, rural setting elsewhere [60]. Nonetheless, its health implication among adolescents remains to be studied. Consumption of ultra-processed and deep-fried foods has been associated with several clinical outcomes including metabolic syndrome, hypertension, dyslipidemia and coronary artery disease [25, 61]. These associations were observed predominantly among adults, yet the underlying cardio-metabolic alterations may commence during adolescence [62]. Besides, the habit of consuming unhealthy foods established during adolescence can track into adulthood [63]. Accordingly, the gender stratification observed in this study might play a role in exposing the boys to a higher cardio-metabolic risk in upcoming adulthood.

Gender difference in UPF consumption appears variable: some studies reporting higher consumption among men and boys [54, 57], some documenting the opposite [64, 65], and others finding no association [10–12]. Although prevalence of SSB consumption was low in our analysis, SSB consumption showed a clear gender stratification. The predominance of boys as consumers of SSB agrees with earlier studies [14, 66, 67] and the qualitative study conducted in Matlab [44]. Gender association in the present study may have resulted from taste preference, gender norms allowing boys greater access than girls to retailers or vendors as boys stay outdoors more [44], and avoidance of UPFs by girls because of perceiving those fattening [68]. Nevertheless, examination of these themes was beyond the scope of the study. We found an inverse association between adolescents’ education and consumption of SSB but no other food groups. This contradicts the positive association of UPF consumption and education shown in a Brazilian cohort, albeit from an urban setting with widespread UPF availability [64]. A recent study among adults of NutriNet-Sante´ cohort also reports those in the highest quartile of UPF consumption having higher educational status [69]. How adolescents with different educational attainment interact with the food environment in Matlab needs to be explored to understand this discrepant association.

Certain limitations of this study need to be considered for proper interpretation of the findings. We did not objectively ascertain the extent of industrial processing and few items might have been misclassified. Despite trained enumerators adopting a two-pronged approach–free recall followed by probing with list and pictorial aid–that has been recommended [70], recall bias is unavoidable to some extent. Self-reported consumption is also prone to social desirability bias. While adolescents’ tendency to under-report consumption of sweet and savory snacks and beverages in 24-hour recall has been documented in LMICs, a recent validation study shows the degree of under-estimation to be acceptable [71]. The findings are generalizable to adolescents in Matlab, given the area-wide recruitment protocol adopted in the initial trial [37]. The findings can also be extended to adolescents in other rural, agrarian settings in Bangladesh taking into account the similarity in socio-cultural context.

Conclusion

We explored ultra-processed and deep-fried food consumption in a rural birth cohort, and observed a strong influence of gender in shaping consumption. Ultra-processed confectioneries and deep-fried foods were commonly consumed. Widespread consumption of these empty-calorie foods is of concern from a public health nutrition perspective. Boys had a greater likelihood of consumption across the food groups. This may disproportionately expose them to the risk of diet-related NCDs. Household wealth was associated with consumption of ready-to-eat or “instant” foods and SSB, but not the other three food groups. This may indicate a role of purchasing ability in the rural setting. Public health initiatives for promoting healthy diet among adolescents should take into account the gendered consumption of unhealthy foods. Strategies to improve adolescents’ understanding of the health-compromising features of ultra-processed and deep-fried foods are also needed.

Supporting information

S1 Table. Food items belonging to the four groups of ultra-processed foods and one group of deep-fried foods for which consumption was assessed during the household survey.

https://doi.org/10.1371/journal.pone.0272275.s001

(DOCX)

S2 Table. Logistic regression analyses of association between socio-demographic variables and consumption of ultra-processed and deep-fried foods.

https://doi.org/10.1371/journal.pone.0272275.s002

(DOCX)

Acknowledgments

We thank Shams El Arifeen, Senior Director, Maternal and Child Health, icddr,b, for his continued support. We are immensely thankful to the MINIMat adolescents and their mothers for their participation.

Citation: Islam MR, Rahman SM, Rahman MM, Pervin J, Rahman A, Ekström E-C (2022) Gender and socio-economic stratification of ultra-processed and deep-fried food consumption among rural adolescents: A cross-sectional study from Bangladesh. PLoS ONE 17(7): e0272275. https://doi.org/10.1371/journal.pone.0272275

About the Authors:

Mohammad Redwanul Islam

Roles: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

E-mail: [email protected]

Affiliation: Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden

https://orcid.org/0000-0001-6005-0884

Syed Moshfiqur Rahman

Roles: Data curation, Investigation, Project administration, Writing – review & editing

Affiliations Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden, International Center for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh

Md. Monjur Rahman

Roles: Data curation, Investigation, Writing – review & editing

Affiliation: International Center for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh

Jesmin Pervin

Roles: Data curation, Investigation, Writing – review & editing

Affiliation: International Center for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh

https://orcid.org/0000-0002-8082-4178

Anisur Rahman

Roles: Data curation, Funding acquisition, Investigation, Project administration, Writing – review & editing

Affiliations Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden, International Center for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh

Eva-Charlotte Ekström

Roles: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – review & editing

Affiliation: Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden

https://orcid.org/0000-0001-5464-7756

References

1. Monteiro CA, Cannon G, Moubarac J-C, Levy RB, Louzada MLC, Jaime PC. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutrition. 2018;21: 5–17. pmid:28322183

2. Monteiro CA, Cannon G, Levy RB, Moubarac J-C, Louzada ML, Rauber F, et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019;22: 936–941. pmid:30744710

3. Afshin A, Sur PJ, Fay KA, Cornaby L, Ferrara G, Salama JS, et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2019;393: 1958–1972. pmid:30954305

4. Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet. 2020;396: 1223–1249. pmid:33069327

5. Monteiro CA, Levy RB, Claro RM, Castro IRR de, Cannon G. A new classification of foods based on the extent and purpose of their processing. Cadernos de Saúde Pública. 2010;26: 2039–2049. pmid:21180977

6. Monteiro CA, Cannon G, Levy R, Moubarac J-C, Jaime P, Martins AP, et al. NOVA. The star shines bright. World Nutrition. 2016;7: 28–38.

7. Martini D, Godos J, Bonaccio M, Vitaglione P, Grosso G. Ultra-Processed Foods and Nutritional Dietary Profile: A Meta-Analysis of Nationally Representative Samples. Nutrients. 2021;13: 3390. pmid:34684391

8. Vandevijvere S, Jaacks LM, Monteiro CA, Moubarac J-C, Girling‐Butcher M, Lee AC, et al. Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obesity Reviews. 2019;20: 10–19. pmid:31099480

9. Moubarac J-C, Martins APB, Claro RM, Levy RB, Cannon G, Monteiro CA. Consumption of ultra-processed foods and likely impact on human health. Evidence from Canada. Public Health Nutrition. 2013;16: 2240–2248. pmid:23171687

10. Marrón-Ponce JA, Sánchez-Pimienta TG, Louzada ML da C, Batis C. Energy contribution of NOVA food groups and sociodemographic determinants of ultra-processed food consumption in the Mexican population. Public Health Nutrition. 2018;21: 87–93. pmid:28937354

11. Cediel G, Reyes M, da Costa Louzada ML, Martinez Steele E, Monteiro CA, Corvalán C, et al. Ultra-processed foods and added sugars in the Chilean diet (2010). Public Health Nutrition. 2018;21: 125–133. pmid:28625223

12. Vandevijvere S, De Ridder K, Fiolet T, Bel S, Tafforeau J. Consumption of ultra-processed food products and diet quality among children, adolescents and adults in Belgium. European Journal of Nutrition. 2018 [cited 20 Jan 2019]. pmid:30511164

13. Martínez Steele E, Popkin BM, Swinburn B, Monteiro CA. The share of ultra-processed foods and the overall nutritional quality of diets in the US: evidence from a nationally representative cross-sectional study. Population Health Metrics. 2017;15.

14. Cunha DB, da Costa THM, da Veiga GV, Pereira RA, Sichieri R. Ultra-processed food consumption and adiposity trajectories in a Brazilian cohort of adolescents: ELANA study. Nutr & Diabetes. 2018;8: 28. pmid:29795367

15. Louzada ML da C, Baraldi LG, Steele EM, Martins APB, Canella DS, Moubarac J-C, et al. Consumption of ultra-processed foods and obesity in Brazilian adolescents and adults. Preventive Medicine. 2015;81: 9–15. pmid:26231112

16. Juul F, Martinez-Steele E, Parekh N, Monteiro CA, Chang VW. Ultra-processed food consumption and excess weight among US adults. British Journal of Nutrition. 2018;120: 90–100. pmid:29729673

17. Nardocci M, Leclerc B-S, Louzada M-L, Monteiro CA, Batal M, Moubarac J-C. Consumption of ultra-processed foods and obesity in Canada. Canadian Journal of Public Health. 2019;110: 4–14. pmid:30238324

18. Monteiro CA, Moubarac J-C, Levy RB, Canella DS, Louzada ML da C, Cannon G. Household availability of ultra-processed foods and obesity in nineteen European countries. Public Health Nutrition. 2018;21: 18–26. pmid:28714422

19. Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Méjean C, Andrianasolo RM, et al. Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé). BMJ. 2019;365: l1451. pmid:31142457

20. Mendonça R de D, Lopes ACS, Pimenta AM, Gea A, Martinez-Gonzalez MA, Bes-Rastrollo M. Ultra-Processed Food Consumption and the Incidence of Hypertension in a Mediterranean Cohort: The Seguimiento Universidad de Navarra Project. American Journal of Hypertension. 2017;30: 358–366. pmid:27927627

21. Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, Méjean C, et al. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ. 2018;360: k322. pmid:29444771

22. Rico-Campà A, Martínez-González MA, Alvarez-Alvarez I, Mendonça R de D, de la Fuente-Arrillaga C, Gómez-Donoso C, et al. Association between consumption of ultra-processed foods and all cause mortality: SUN prospective cohort study. BMJ. 2019; l1949. pmid:31142450

23. Chang C, Wu G, Zhang H, Jin Q, Wang X. Deep-fried flavor: characteristics, formation mechanisms, and influencing factors. Critical Reviews in Food Science and Nutrition. 2020;60: 1496–1514. pmid:30740987

24. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutrition Reviews. 2012;70: 3–21. pmid:22221213

25. Gadiraju T, Patel Y, Gaziano J, Djoussé L. Fried Food Consumption and Cardiovascular Health: A Review of Current Evidence. Nutrients. 2015;7: 8424–8430. pmid:26457715

26. Ganesan K, Sukalingam K, Xu B. Impact of consumption of repeatedly heated cooking oils on the incidence of various cancers- A critical review. Critical Reviews in Food Science and Nutrition. 2019;59: 488–505. pmid:28925728

27. Huse O, Reeve E, Baker P, Hunt D, Bell C, Peeters A, et al. The nutrition transition, food retail transformations, and policy responses to overnutrition in the East Asia region: A descriptive review. Obesity Reviews. 2022;23: e13412. pmid:34981877

28. Baker P, Friel S. Food systems transformations, ultra-processed food markets and the nutrition transition in Asia. Global Health. 2016;12: 80. pmid:27912772

29. Akseer N, Al-Gashm S, Mehta S, Mokdad A, Bhutta ZA. Global and regional trends in the nutritional status of young people: a critical and neglected age group: Global and regional trends in the nutritional status of young people. Annals of the New York Academy of Sciences. 2017;1393: 3–20. pmid:28436100

30. Santos CD da L, Clemente APG, Martins VJB, Albuquerque MP, Sawaya AL. Adolescents with Mild Stunting Show Alterations in Glucose and Insulin Metabolism. Journal of Nutrition and Metabolism. 2010;2010: 1–6. pmid:21318152

31. Mandy M, Nyirenda M. Developmental Origins of Health and Disease: the relevance to developing nations. Int Health. 2018;10: 66–70. pmid:29528398

32. Brownbill AL, Miller CL, Braunack‐Mayer AJ. The marketing of sugar-sweetened beverages to young people on Facebook. Australian and New Zealand Journal of Public Health. 2018;42: 354–360. pmid:29972262

33. Scully M, Wakefield M, Niven P, Chapman K, Crawford D, Pratt IS, et al. Association between food marketing exposure and adolescents’ food choices and eating behaviors. Appetite. 2012;58: 1–5. pmid:22001023

34. Ochola S, Masibo PK. Dietary Intake of Schoolchildren and Adolescents in Developing Countries. Annals of Nutrition and Metabolism. 2014;64: 24–40. pmid:25341871

35. Abarca-Gómez L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, Acuin C, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet. 2017;390: 2627–2642. pmid:29029897

36. Biswas T, Islam A, Islam MdS, Pervin S, Rawal LB. Overweight and obesity among children and adolescents in Bangladesh: a systematic review and meta-analysis. Public Health. 2017;142: 94–101. pmid:28057205

37. Persson LÅ, Arifeen S, Ekström E-C, Rasmussen KM, Frongillo EA, Yunus M, et al. Effects of Prenatal Micronutrient and Early Food Supplementation on Maternal Hemoglobin, Birth Weight, and Infant Mortality Among Children in Bangladesh: The MINIMat Randomized Trial. JAMA. 2012;307: 2050–2059. pmid:22665104

38. Arifeen SE, Ekström E-C, Frongillo EA, Hamadani J, Khan AI, Naved RT, et al. Cohort Profile: The Maternal and Infant Nutrition Interventions in the Matlab (MINIMat) Cohort in Bangladesh. International Journal of Epidemiology. 2018 [cited 9 Aug 2018]. pmid:29868907

39. Alam N, Ali T, Razzaque A, Rahman M, Zahirul Haq M, Saha SK, et al. Health and Demographic Surveillance System (HDSS) in Matlab, Bangladesh. International Journal of Epidemiology. 2017;46: 809–816. pmid:28637343

40. Shim J-S, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiol Health. 2014;36: 1–8. pmid:25078382

41. Arimond M, Wiesmann D, Becquey E, Carriquiry A, Daniels MC, Deitchler M, et al. Simple Food Group Diversity Indicators Predict Micronutrient Adequacy of Women’s Diets in 5 Diverse, Resource-Poor Settings. J Nutr. 2010;140: 2059S–2069S. pmid:20881077

42. Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy and Planning. 2006;21: 459–468. pmid:17030551

43. Borradaile KE, Sherman S, Vander Veur SS, McCoy T, Sandoval B, Nachmani J, et al. Snacking in Children: The Role of Urban Corner Stores. Pediatrics. 2009;124: 1293–1298. pmid:19822591

44. Islam Trenholm, Rahman Pervin, Ekström Rahman. Sociocultural Influences on Dietary Practices and Physical Activity Behaviors of Rural Adolescents—A Qualitative Exploration. Nutrients. 2019;11: 2916. pmid:31810284

45. Ferreira NL, Claro RM, Mingoti SA, Lopes ACS. Coexistence of risk behaviors for being overweight among Brazilian adolescents. Preventive Medicine. 2017;100: 135–142. pmid:28412185

46. Minihane AM, Harland JI. Impact of Oil used by the Frying Industry on Population Fat Intake. Critical Reviews in Food Science and Nutrition. 2007;47: 287–297. pmid:17453925

47. Fiedler JL, Lividini K, Guyondet C, Bermudez OI. Assessing Alternative Industrial Fortification Portfolios: A Bangladesh Case Study. Food Nutr Bull. 2015;36: 57–74. pmid:25898716

48. Monge-Rojas R, Colón-Ramos U, Chinnock A, Smith-Castro V, Reyes-Fernández B. Gender-based eating norms, the family environment and food intake among Costa Rican adolescents. Public Health Nutrition. undefined/ed; 1–35. pmid:33602372

49. Pengpid S, Peltzer K. Prevalence and socio-behavioral factors associated with sugar-sweetened beverages consumption among 15 years and older persons in South Africa. Diabetes Metab Syndr Obes. 2019;12: 937–945. pmid:31417294

50. Muktadir MHA, Islam M, Amin N, Ghosh S, Siddiqui S, Debnath D, et al. Nutrition transition—Pattern IV: Leads Bangladeshi youth to the increasing prevalence of overweight and obesity. Diabetes Metab Syndr. 2019;13: 1943–1947. pmid:31235119

51. Beaulieu D, Vézina-Im L-A, Turcotte S, Guillaumie L, Boucher D, Douville F, et al. Correlates of sugar-sweetened beverages consumption among adolescents. Public Health Nutrition. 2020;23: 2145–2154. pmid:32383422

52. Godin KM, Chacón V, Barnoya J, Leatherdale ST. The school environment and sugar-sweetened beverage consumption among Guatemalan adolescents. Public Health Nutr. 2017;20: 2980–2987. pmid:28803573

53. D’Avila HF, Kirsten VR. Energy intake from ultra-processed foods among adolescents. Rev Paul Pediatr. 2017;35: 54–60. pmid:28977317

54. Rauber F, Steele EM, Louzada ML da C, Millett C, Monteiro CA, Levy RB. Ultra-processed food consumption and indicators of obesity in the United Kingdom population (2008–2016). PLoS ONE. 2020;15: e0232676. https://doi.org/10.1371/journal.pone.0232676 pmid:32357191

55. Barrett P, Imamura F, Brage S, Griffin SJ, Wareham NJ, Forouhi NG. Socio-demographic, lifestyle and behavioural factors associated with consumption of sweetened beverages among adults in Cambridgeshire, UK–the Fenland Study. Public Health Nutr. 2017;20: 2766–2777. pmid:28789721

56. Schnabel L, Buscail C, Sabate J-M, Bouchoucha M, Kesse-Guyot E, Allès B, et al. Association Between Ultra-Processed Food Consumption and Functional Gastrointestinal Disorders: Results From the French NutriNet-Santé Cohort: American Journal of Gastroenterology. 2018;113: 1217–1228. pmid:29904158

57. Moubarac J-C, Batal M, Louzada ML, Martinez Steele E, Monteiro CA. Consumption of ultra-processed foods predicts diet quality in Canada. Appetite. 2017;108: 512–520. pmid:27825941

58. Adams J, White M. Characterisation of UK diets according to degree of food processing and associations with socio-demographics and obesity: cross-sectional analysis of UK National Diet and Nutrition Survey (2008–12). Int J Behav Nutr Phys Act. 2015;12. https://doi.org/10.1186/s12966-015-0317-y

59. Islam MR, Rahman SM, Tarafder C, Rahman MdM, Rahman A, Ekström E-C. Exploring Rural Adolescents’ Dietary Diversity and Its Socioeconomic Correlates: A Cross-Sectional Study from Matlab, Bangladesh. Nutrients. 2020;12: 2230. pmid:32722644

60. Contreras M, Blandón EZ, Persson L-Å, Hjern A, Ekström E-C. Socio-economic resources, young child feeding practices, consumption of highly processed snacks and sugar-sweetened beverages: a population-based survey in rural northwestern Nicaragua. BMC Public Health. 2015;15.

61. Elizabeth L, Machado P, Zinöcker M, Baker P, Lawrence M. Ultra-Processed Foods and Health Outcomes: A Narrative Review. Nutrients. 2020;12: 1955. pmid:32630022

62. Laitinen TT, Pahkala K, Magnussen CG, Viikari JSA, Oikonen M, Taittonen L, et al. Ideal Cardiovascular Health in Childhood and Cardiometabolic Outcomes in Adulthood: The Cardiovascular Risk in Young Finns Study. Circulation. 2012;125: 1971–1978. pmid:22452832

63. Cruz F, Ramos E, Lopes C, Araújo J. Tracking of food and nutrient intake from adolescence into early adulthood. Nutrition. 2018;55–56: 84–90. pmid:29980092

64. Bielemann RM, Motta JVS, Minten GC, Horta BL, Gigante DP. Consumption of ultra-processed foods and their impact on the diet of young adults. Revista de Saúde Pública. 2015;49: 1–10.

65. Monteles N L, Santos O K dos, Gomes KRO, Pacheco R MT, Gonçalves F K de M. The impact of consumption of ultra-processed foods on the nutritional status of adolescents. Rev chil nutr. 2019;46: 429–435.

66. Sim E, Sohn W, Choi E-S, Noh H. Sugar-sweetened beverage consumption frequency in Korean adolescents: based on the 2015 Youth Risk Behavior Web-Based Survey. International Dental Journal. 2019;69: 376–382. https://doi.org/10.1111/idj.12485 pmid:31077367

67. Gui Z-H, Zhu Y-N, Cai L, Sun F-H, Ma Y-H, Jing J, et al. Sugar-Sweetened Beverage Consumption and Risks of Obesity and Hypertension in Chinese Children and Adolescents: A National Cross-Sectional Analysis. Nutrients. 2017;9. pmid:29189729

68. Mallick N, Ray S, Mukhopadhyay S. Eating Behaviours and Body Weight Concerns among Adolescent Girls. Advances in Public Health. 2014;2014: 1–8.

69. Beslay M, Srour B, Méjean C, Allès B, Fiolet T, Debras C, et al. Ultra-processed food intake in association with BMI change and risk of overweight and obesity: A prospective analysis of the French NutriNet-Santé cohort. Souza Lopes AC, editor. PLoS Med. 2020;17: e1003256. pmid:32853224

70. Martin-Prevel Y, Becquey E, Arimond M. Food Group Diversity Indicators Derived from Qualitative List-Based Questionnaire Misreported Some Foods Compared to Same Indicators Derived from Quantitative 24-Hour Recall in Urban Burkina Faso. The Journal of Nutrition. 2010;140: 2086S–2093S. pmid:20881076

71. Arsenault JE, Moursi M, Olney DK, Becquey E, Ganaba R. Validation of 24‐h dietary recall for estimating nutrient intakes and adequacy in adolescents in Burkina Faso. Matern Child Nutr. 2020;16: e13014. pmid:32337835