Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Factors associated with the double burden of malnutrition among adolescents, National Adolescent School-Based Health Survey (PENSE 2009 and 2015)

Factors associated with the double burden of malnutrition among adolescents, National Adolescent School-Based Health Survey (PENSE 2009 and 2015)

  • Júlia Caffé Oliveira Uzêda, 
  • Rita de Cássia Ribeiro-Silva, 
  • Natanael de Jesus Silva, 
  • Rosemeire L. Fiaccone, 
  • Débora C. Malta, 
  • Naiá Ortelan, 
  • Maurício L. Barrato


27 Jun 2019: Uzêda JCO, Ribeiro-Silva RdC, Silva NdJ, Fiaccone RL, Malta DC, et al. (2019) Correction: Factors associated with the double burden of malnutrition among adolescents, National Adolescent School-Based Health Survey (PENSE 2009 and 2015). PLOS ONE 14(6): e0219315. View correction



To assess the sociodemographic factors associated with the double burden of malnutrition (DBM) among Brazilian adolescents.


This was a descriptive study based on data from 59,637 and 10,770 students who participated in the National Adolescent School-Based Health Survey (PeNSE), 2009 and 2015 editions, respectively. Weight and height measurements were obtained to evaluate nutritional status. DBM was classified as follows: adolescents with high BMI-for-age and low height-for-age (BMI/A: Z-score > +1 and H/A: Z-score < -2). Sociodemographic data on the participants were also collected. A multinomial logistic regression analysis was used to detect associations of interest.


The prevalence of DBM in the 2009 and 2015 editions of the PeNSE was 0.4% and 0.3%, respectively. In the 2009 edition, the chance of DBM was lower among boys (OR = 0.60; 95% CI = 0.45–0.81) and higher among those over 14 years old (OR = 2.40; 95% CI = 1.80–3.20), living in the country’s north and northeast regions (OR = 2.01; 95% CI = 1.49–2.84), and from families with a low maternal education level (OR = 1.48; 95% CI = 1.07–2.04). In the 2015 edition, no significant associations were found regarding the DBM outcome.


The results indicate the presence of socioeconomic inequalities in the occurrence of DBM in the 2009 edition of the PeNSE. Simultaneous interventions in the area of equity are necessary to prevent the advancement of nutrition-related problems.


The epidemiological transition phenomenon, particularly in the context of low- and medium-income countries (LMICs), has been accompanied by another process called the nutritional transition [1]. This phenomenon is characterised by important changes in the patterns of food consumption [1], [2] combined with an increase in sedentarism in the population [3]. This scenario is reflected in the nutritional status, including changes in mean height and body composition [4], [5]. The nutritional transition in LMICs has been marked by an accelerated decrease in malnutrition among children and adults, but still includes unsettling incidence rates among the poorest. Simultaneously, an increase in overweight and obesity has been observed in all life stages [6], [1]. Hence, apparently antagonistic health-related problems have come to exist simultaneously in the population.

The coexistence of stunting and overweight in the same child or adolescent reflects the poor quality of diet and morbidity in the first two years of life, followed by excess energy consumption at a later life stage [7], [8], [3]. This condition is due to underlying determinants to which the adolescents are exposed, such as unfavourable environmental, social and economic factors [9], [10]. It is known that, among countries with similar economic levels, those with higher levels of social inequality have lower levels of health [11].

In Brazil, the improvement of health indicators has not been achieved homogeneously by all population groups, contributing to the maintenance of a persistent burden of morbidity, even to problems in which reductions have been observed. For example, undernutrition is still prevalent among the poorest, difficulting this burden to be reduced below certain levels. Recently, overweight, obesity and diet-related NCDs are rapidly increasing in the country (14). However, the way that socioeconomic disparities relate to DBM are not very well understood yet (15). The double burden phenomenon has been very well documented at household and community level in the various life stages [12] [3], [13]. However, very little is still known on the DBM at individual level, especially among adolescents [14]. Therefore, the aim of the present study was to measure and evaluate the sociodemographic factors associated with this phenotype in adolescents who participated in the 2009 and 2015 editions of the National Adolescent School-Based Health Survey (Pesquisa Nacional de Saúde do Escolar—PeNSE), in Brazil.


Study design, population and sampling method (PeNSE 2009–2015)

Data from the PeNSE 2009 and 2015 editions were used for the present study. These are national surveys conducted by the Brazilian Institute of Geography and Statistics (Instituto Nacional de Geografia e Estatística–IBGE) in collaboration with the Ministry of Health, which aimed to identify and measure the risk and protective factors of health among Brazilian adolescents. The PeNSE data are public and can be accessed freely at the IBGE website ( The PeNSE is a school-based epidemiological survey, and the target population is students from public and private Brazilian schools [15]. Each edition has peculiarities regarding the sampling process. In the 2009 edition [16], the study was limited to 9th grade students in public and private schools located in the 26 Brazilian state capitals and in the Federal District. The schools were grouped according to their public or private status. The primary sampling unit was the school, and the secondary sampling unit was the class. Each stratum was subjected to systematic sampling by lottery with a probability proportional to the number of schools in the respective stratum (n = 63,411 respondents; valid sample n = 63,411). The PeNSE 2015 [17] was composed of two independent probabilistic samples (1 and 2). In the present study, only sample 2 was included, which is composed of students in the 13- to 17-year-old age range (from 6th grade to the last grade of high school) of public and private schools located in the 26 state capitals of the five Brazilian macroregions and in the capital of Brazil (n = 16,608 respondents; valid sample n = 10,926). Further methodological details are described in the respective reports of each survey [16], [17], [15].

In all editions of the PeNSE, the respective projects were submitted to and approved by the National Commission for Ethics in Research (Comissão Nacional de Ética em Pesquisa–CONEP) under opinion n. 11.537/2009, n. 16.805/2012 and n. 1.006.467/2015 and were in agreement with Resolution no. 196 of October 10, 1996 from the National Health Council (Centro Nacional de Saúde–CNS).

Data collection

Student-related data were collected via a self-administered questionnaire with electronic devices, i.e., personal digital assistants (PDAs) in 2009 and smartphones in 2015. The IBGE surveyors distributed the devices to the students on the day of the interviews and acquainted them with how to use the devices. In addition to the questionnaire, anthropometric measurements were also collected to calculate body mass index (BMI). In 2009 and 2015, data on student weight and height were collected. In 2009, this encompassed the entire sample, whereas in 2015, these data were only collected in the sample used in the present study. Further information on the measurements are available in the reports on the 2009 and 2015 editions of the PeNSE [16], [17].

Anthropometric indicators

Weight and height were used to assess the adolescents’ nutritional status by means of the height-for-age (H/A) and BMI-for-age (BMI/A) indices as Z-scores. BMI is defined as the ratio between weight (kg) and height in metres squared (m2). The values proposed by the World Health Organization (WHO) for children and adolescents between 5 and 19 years of age were used as references [18]. Data were calculated with the programmes WHO AnthroPlus, v.1.0.4. Outliers were excluded from the analysis [i.e., H/A (Z-score < -6 or > +6) and BMI/A (Z-score < -5 or > +5)] [19].

The outcome of interest was ranked into the following four categories:

  • Category 0 (reference): adolescents with normal BMI and height-for-age (BMI: Z-score ≤ +1 and H/A: Z-score ≥ -2);
  • category 1 (stunting): adolescents with normal BMI-for-age and stunting (BMI: Z-score ≤ +1 and H/A: Z-score < -2);
  • category 2 (overweight): adolescents with high BMI-for-age and normal height-for-age (BMI: Z-score > +1 and H/A: Z-score ≥ -2);
  • category 3 (defined as a double burden of malnutrition): adolescents with high BMI-for-age and stunting (BMI: Z-score > +1 and H/A: Z-score < -2).

Sociodemographic variables

For the present study, variables with similar questions in both editions of the PeNSE (2009 and 2015) were harmonised. These sociodemographic variables were as follows: age (≤ 14 years/ >14 years); sex (male/female); race/ethnicity (white/non-white); maternal education level (non-educated or incomplete primary education/completed primary education or incomplete secondary education/completed secondary education or incomplete higher education/completed higher education/did not know); school type (public/private); region (south, southeast and centre-west/north and northeast). The household assets and services index was based on the possession of a landline telephone, computer (desktop, laptop, tablet, etc.), car and/or motorcycle, number of bathrooms with a shower, home internet access, whether the adolescent had her own cell phone and whether the household had a housekeeper (at least three days a week). The household assets and services index was constructed according to a previously described method [20].

Statistical analysis

The sample was characterized using a descriptive analysis for complex sample. The association between sociodemographic variables and nutritional outcomes was verified by the Pearson’s χ2 test. In order to identify the sociodemographic variables that are associated with the nutritional outcomes, Odds Ratio (OR) and 95%CI were obtained by multinomial logistic regression analysis. Fitted models were adjusted for all covariates, including age. To account for the complex sampling design, weighting was applied to the estimates be representative of the intended population. The svy command in Stata software version 12.1 (StataCorp LP, College Station, USA) was used to consider this effect.


The prevalence of overweight, stunting and DBM was 21.7% (n = 12,787; 95% CI 21.3–22.0), 3.1% (n = 1,819; 95% CI 2.9–3.2) and 0.4% (n = 214; 95% CI 0.32–0.41), respectively, in the PeNSE 2009. In the 2015 edition, the prevalence of the above outcomes was 24.8% (n = 2,668; 95% CI 24.0–25.6) for overweight, 2.5% (n = 270; 95% CI 2.2–2.8) for stunting and 0.3% (n = 37; 95% CI 0.23–0.45) for DBM (Fig 1).

Fig 1. Prevalence of nutritional outcomes in adolescents. PeNSE (2009–2015).

2009 –Stunting (n = 1,582), overweight (n = 12,553) and double burden (n = 214). 2015 –Stunting (n = 2,631), overweight (n = 233) and double burden (n = 37).

The data from the PeNSE 2009 edition show higher rates of female adolescents (53.4%), 14-year-olds (45.8%), mixed ethnicity (42.0%), studying at public schools (76.0%), families with a low maternal education level (26.5%), living in the north and northeast region of the country (33.4%) and a low household assets and services score (33.4%). The data from the PeNSE 2015 edition show higher rates of male adolescents (50.6%), 16- and 17-year-olds (34.6%), mixed ethnicity (41.7%), studying at public schools (76.7%), families with a low maternal education level (25.3%), living in the north and northeast region (21.1%) and a low household assets and services score (33.5%) (Table 1).

Table 1. Description of the study population according to sociodemographic characteristics.

PeNSE (2009–2015).

The data further indicate significant differences between all of the studied sociodemographic predictors and the nutritional status of the adolescents in both the 2009 and 2015 editions of the PeNSE (p<0.05), except for sex and race/skin color that were not statistically significant in the PeNSE 2015 (Table 2).

Table 2. Analysis of associations between sociodemographic predictors and nutritional outcomes of adolescents.

PeNSE (2009–2015).

The results of the multinomial logistic regression analysis indicate that in both periods of the PeNSE (2009–2015), the chance of overweight was lower, respectively, among adolescents over 14 years old [(OR = 0.78, 95% CI = 0.74–0.82), (OR = 0.90, 95% CI = 0.86–0.94)], residing in the north and northeast region [(OR = 0.89, 95% CI = 0.86–0.93), (OR = 0.79, 95% CI = 0.72–0.86)], from public schools [(OR = 0.76, 95% CI = 0.72–0.80), (OR = 0.82, 95% CI = 0.73–02.92)] and with the lowest household assets score [(OR = 0.72, 95% CI = 0.68–0.76), (OR = 0.87, 95% CI = 0.78–0.99)]. In both survey periods, the chance of stunting was higher, respectively, among boys [(OR = 1.49, 95% CI = 1.34–1.65), (OR = 1.38, 95% CI = 1.05–1.81)], those residing in the north and northeast region [(OR = 1.84, 95% CI = 1.64–2.07), (OR = 1.54, 95% CI = 1.17–2.02)] and those from public schools [(OR = 1.99, 95% CI = 1.64–2.41), (OR = 2.08, 95% CI = 1.28–3.39)] (Table 3).

Table 3. Multinomial logistic regression analysis between sociodemographic predictors and nutritional outcomes of adolescents.

PeNSE (2009–2015).

In the PeNSE 2009, the chance of DBM was lower among boys (OR = 0.60, 95% CI = 0.45–0.81) and higher among those over 14 years old (OR = 2.40, 95% CI = 1.80–3.20), those living in the north and northeast regions (OR = 2.01, 95% CI = 1.49–2.84) and from families with a low maternal education level (OR = 1.48, 95% CI = 1.07–2.04). In the 2015 edition, no significant associations were found for this outcome (Table 3).


This is a novel study aimed at measuring DBM and its socioeconomic determinants in the adolescent population of Brazil in 2009 and 2015. Despite the comparative limitations of the sample, it seems that overweight increased, stunting reduced and DBM seemed to be stable in the studied period. However, the DBM rates were well below those found in the study of Caleyachett et al. (2018) [14] who used health data from 57 LMICs between 2003 and 2013, which included 129,276 adolescents from 12 to 15 years old. In that study [14], the DBM phenomenon was found in 2.0% (95%CI = 1.7%, 2.5%) of the adolescents. The reason for this phenomenon is not yet fully understood: if there is sufficient energy for children to gain excess weight, then why do the children not reach their linear growth potential and remain stunted? Some studies suggest that this phenomenon is particularly related to inadequate nutrition during the period between conception, resulting in restricted intrauterine growth, and age below two years—which might entail retarded linear growth—followed by exposure to “obesogenic environments” at more advanced ages [21], [22], [8], [23], [3]. Other possible explanations for this phenomenon have been explored [23]. Some studies also suggest that retarded growth can change normal metabolic processes and thus increase insulin sensitivity and reduce fat oxidation, which increases the risk of excess weight gain at a later life stage [24], [25].

The results of the multinomial logistic regression analysis clearly show an increased prevalence of stunting among adolescents with an unfavourable socioeconomic status (in public school, from a family with a low maternal education level, from the north and northeast regions, and with worse household assets scores) in both evaluated editions (PeNSE 2009–2015). By contrast, the highest prevalence of overweight was found among adolescents from private schools, from families with a higher maternal education level, from the South, Southeast and Centre-West regions and in a more favourable economic situation. The effect of improved economic situation on the favourable trajectory of overweight/obesity is associated with the socioeconomic expansion and the processes of urbanization and globalization of the country, which result in negative changes in dietary and physical activity patterns [26]. These phenomena have been exhaustively discussed in the national [27] and international [28] literature in an isolated manner.

The main finding of the present study concerns the socioeconomic factors associated with the DBM phenotype [short and fat, according to Varela et al. (2012) [29]]. The data indicated higher chances of DBM among adolescents living under more adverse conditions. That is, DBM is positively associated with predictors of poverty and social inequality [23]. These more structuring factors are possibly acting through different chains of determination of both undernutrition and overnutrition, thus affecting the balance between the amount of nutrients and energy required by the body and between the amount of nutrients and energy consumed [30]. Many studies consider this phenomenon typical in economically developing countries that are passing through nutritional transition, such as Brazil and other LMICs [23]. The predominant feature of the nutritional transition in these regions is the epidemic emergence of overweight and, particularly, of obesity, as an event of greater epidemiological visibility and implications associated with the patterns of morbidity and mortality [31]. This has been accompanied by a decrease in deficiency diseases, such as stunting and micronutrient deficiencies, although these still persist in the scenario of morbidities in Brazil [6], [1].

Hence, health-related problems that are apparently of antagonistic origin occur simultaneously in the population. Thus, controlling DBM poses an important challenge for most developing countries [8]. Recently, the WHO published suggested actions to control DBM (which include interventions, programmes and policies) with the potential to simultaneously reduce the risk of undernutrition and overweight, obesity and diet-related NCDs [32]. One of the key principles of the WHO is to ensure that the actions planned to address one form of malnutrition do not unintentionally increase the risk of other form. These actions aim at providing easy access to healthy and nutritious foods and thereby to promote a healthy weight. This is also the objective of the United Nations Sustainable Development Goal (SDG) 2, which aims at ending hunger, ensuring safe, nutritious food and promoting sustainable agriculture [33].


Since the PeNSE 2009 and 2015 are cross-sectional surveys, this study has some limitations such as the determination of causality. Some precaution is necessary in the comparison of results between the editions, considering the changes sampling plans of the PeNSE. In 2009, the target-population of PeNSE was formed by students enrolled in the 9th grade of Elementary School; in 2015, students enrolled from the 6th grade of elementary school to the 3rd grade of high school were also included with the objective of having a more representative research for students aged from 13 to 17 years old. Furthermore, it is important to note that the sampled population excluded young individuals who are not in school. Nevertheless, the school-based methodology has been adopted in European countries and worldwide because of the easy access to the adolescent population and because of the benefits that arise from the study, which enable integrated planning by the health and education sectors for the target population. Furthermore, the coverage of the educational system in Brazil has increased, thus approaching universalisation. Additionally, the fact that the survey sampled two strata (public and private school students) extends the representativeness of the target population. A further limitation is the fact that the questionnaire used to obtain the household assets and services score is self-reported, being then subject to information bias. The use of BMI as a measure of adiposity among adolescents also seems to be a further limitation of the study. According to many researchers, BMI must be used specially in epidemiological studies since it requires only the collection of simple anthropometric measures that are non-invasive, easily collected, low-cost and well-accepted by the general population [34]. However, its interpretation as an index of adiposity must be made with caution. Nevertheless, we believe that our results are consistent for the following reasons. First, the PeNSE is the widest-ranging survey of students in Brazil, in terms of both the sample size and the surveyed topics. The data from the different editions of the PeNSE are considered high-quality because they were obtained by following standardised procedures, and, in most cases, such surveys are the only source of data on student health that some countries have for surveillance, particularly in developing economies and those in transition. Second, in general, the findings corroborate those of similar studies and were analysed using appropriate statistical methods.


The results of the PeNSE 2009 and 2015 indicate the epidemic emergence of overweight and a reduction in stunting, although it still persists as a morbidity among the poorest adolescents of the country. Cases where both conditions (overweight and stunting) are simultaneously present in the same individual characterise DBM, which was observed at low rates in both of the studied survey editions. The results from the PeNSE 2009 also indicate the presence of socioeconomic inequalities in the occurrence of DBM and the remaining studied outcomes. Undoubtedly, the social determinants of health have a strong influence on the opportunities and the ways Brazilians eat and on the risks related to the nutritional status of individuals and communities, which indicates a need for answers beyond the health sector. Thus, we suggest that simultaneous interventions in the area of equity be conducted to prevent the advancement of nutritional problems.


  1. 1. Prentice AM. The Double Burden of Malnutrition in Countries Passing through the Economic Transition. Annals of nutrition & metabolism. 2018;72 Suppl 3:47–54.
  2. 2. Popkin BM. Nutrition Transition and the Global Diabetes Epidemic. Current diabetes reports. 2015;15(9):64. pmid:26209940
  3. 3. Min J, Zhao Y, Slivka L, Wang Y. Double burden of diseases worldwide: coexistence of undernutrition and overnutrition-related non-communicable chronic diseases. Obesity reviews: an official journal of the International Association for the Study of Obesity. 2018;19(1):49–61.
  4. 4. Ngaruiya C, Hayward A, Post L, Mowafi H. Obesity as a form of malnutrition: over-nutrition on the Uganda "malnutrition" agenda. Pan Afr Med J. 2017;28:49. pmid:29184601
  5. 5. White AH, James SA, Paulson SW, Beebe LA. Sugar Sweetened Beverage Consumption Among Adults With Children in the Home. Frontiers in nutrition. 2018;5:34. pmid:29780809
  6. 6. Kac G, Vel squez-Mel ndez G. [The nutritional transition and the epidemiology of obesity in Latin America]. Cadernos de saude publica. 2003;19 Suppl 1:S5, S4.
  7. 7. Piernas C, Wang D, Du S, Zhang B, Wang Z, Su C, et al. The double burden of under- and overnutrition and nutrient adequacy among Chinese preschool and school-aged children in 2009–2011. Eur J Clin Nutr. 2015;69(12):1323–9.
  8. 8. Abdullah A. The Double Burden of Undernutrition and Overnutrition in Developing Countries: an Update. Current obesity reports. 2015;4(3):337–49. pmid:26627492
  9. 9. Gillespie S, Haddad L, Mannar V, Menon P, Nisbett N, Maternal, et al. The politics of reducing malnutrition: building commitment and accelerating progress. Lancet. 2013;382(9891):552–69. pmid:23746781
  10. 10. Maternal, Child Nutrition Study G, Group m, Black RE, Alderman H, Bhutta ZA, et al. Maternal and child nutrition: building momentum for impact. Lancet. 2013;382(9890):372–5. pmid:23746778
  11. 11. Carmo EH, Barreto ML, Silva JBd Jr.. Mudanças nos padrões de morbimortalidade da população brasileira: os desafios para um novo século. Epidemiologia e Serviços de Saúde. 2003;12:63–75.
  12. 12. Gea-Horta T, Silva Rde C, Fiaccone RL, Barreto ML, Velasquez-Melendez G. Factors associated with nutritional outcomes in the mother-child dyad: a population-based cross-sectional study. Public Health Nutr. 2016;19(15):2725–33. pmid:27121979
  13. 13. Hauqe SE, Sakisaka K, Rahman M. Examining the relationship between socioeconomic status and the double burden of maternal over and child under-nutrition in Bangladesh. European journal of clinical nutrition. 2018.
  14. 14. Caleyachetty R, Thomas GN, Kengne AP, Echouffo-Tcheugui JB, Schilsky S, Khodabocus J, et al. The double burden of malnutrition among adolescents: analysis of data from the Global School-Based Student Health and Health Behavior in School-Aged Children surveys in 57 low- and middle-income countries. The American journal of clinical nutrition. 2018.
  15. 15. Oliveira MM, Campos MO, Andreazzi MAR, Malta DC. Characteristics of the National Adolescent School-based Health Survey—PeNSE, Brazil. Epidemiologia e servicos de saude: revista do Sistema Unico de Saude do Brasil. 2017;26(3):605–16.
  16. 16. Instituto Brasileiro de Geografia e Estatísitca. Pesquisa nacional de saúde do escolar: 2009. Rio de Janeiro: IBGE; 2009. 140 p.
  17. 17. Instituto Brasileiro de Geografia e Estatística. Pesquisa nacional de saúde do escolar, 2015. Rio de Janeiro: IBGE; 2016. 126 p.
  18. 18. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85(9):660–7. pmid:18026621
  19. 19. World Health Organization. Physical status: the use of and interpretation of anthropometry, report of a WHO expert committee. Genebra: World Health Organization; 1995.
  20. 20. Levy RB, Castro IR, Cardoso Lde O, Tavares LF, Sardinha LM, Gomes Fda S, et al. [Food consumption and eating behavior among Brazilian adolescents: National Adolescent School-based Health Survey (PeNSE), 2009]. Ciencia & saude coletiva. 2010;15 Suppl 2:3085–97.
  21. 21. Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 2013;382(9890):427–51. pmid:23746772
  22. 22. Conde WL, Monteiro CA. Nutrition transition and double burden of undernutrition and excess of weight in Brazil. Am J Clin Nutr. 2014;100(6):1617S–22S. pmid:25411303
  23. 23. Tzioumis E, Adair LS. Childhood dual burden of under- and overnutrition in low- and middle-income countries: a critical review. Food and nutrition bulletin. 2014;35(2):230–43. pmid:25076771
  24. 24. Walker SP, Chang SM, Powell CA. The association between early childhood stunting and weight status in late adolescence. International journal of obesity. 2007;31(2):347–52. pmid:16718285
  25. 25. Sawaya AL. Desnutrição: conseqüências em longo prazo e efeitos da recuperação nutricional. Estudos Avançados. 2006;20:147–58.
  26. 26. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012;70(1):3–21. pmid:22221213
  27. 27. Araujo C, Toral N, Silva AC, Velasquez-Melendez G, Dias AJ. [Nutritional status of adolescents and its relation with socio-demographics variables: National Adolescent School-based Health Survey (PeNSE), 2009]. Cien Saude Colet. 2010;15 Suppl 2:3077–84.
  28. 28. Galloway R. Global Nutrition Outcomes at Ages 5 to 19. In: rd Bundy DAP, Silva N, Horton S, Jamison DT, Patton GC, editors. Child and Adolescent Health and Development. Washington (DC)2017.
  29. 29. Varela-Silva MI, Dickinson F, Wilson H, Azcorra H, Griffiths PL, Bogin B. The nutritional dual-burden in developing countries—how is it assessed and what are the health implications? Coll Antropol. 2012;36(1):39–45. pmid:22816196
  30. 30. Haddad L, Cameron L, Barnett I. The double burden of malnutrition in SE Asia and the Pacific: priorities, policies and politics. Health Policy Plan. 2015;30(9):1193–206. pmid:25324529
  31. 31. Kac G, Perez-Escamilla R. Nutrition transition and obesity prevention through the life-course. International journal of obesity supplements. 2013;3(Suppl 1):S6–8. pmid:27152157
  32. 32. WHO. World Health Organization. The double burden of malnutrition. Policy brief. Geneva: World Health Organization, 2017.
  33. 33. International Food Policy Research Institute (IFPRI). Global Nutrition Report 2016: From Promise to Impact: Ending Malnutrition by 2030. Washington: IFPRI; 2016.
  34. 34. Alves CA Junior, Mocellin MC, Goncalves ECA, Silva DA, Trindade EB. Anthropometric Indicators as Body Fat Discriminators in Children and Adolescents: A Systematic Review and Meta-Analysis. Advances in nutrition. 2017;8(5):71827. pmid:28916572