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Original Articles: Hepatology and Nutrition

Secondary Malnutrition and Overweight in a Pediatric Referral Hospital: Associated Factors

Macías-Rosales, Rocío*; Vásquez-Garibay, Edgar M; Larrosa-Haro, Alfredo*,†; Rojo-Chávez, Marina*; Bernal-Virgen, Alicia*; Romo-Rubio, Hugo*

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Journal of Pediatric Gastroenterology and Nutrition: February 2009 - Volume 48 - Issue 2 - p 226-232
doi: 10.1097/MPG.0b013e31818de182
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The worldwide prevalence of primary malnutrition has decreased gradually during the last 2 decades (1); however, it is not clear whether secondary malnutrition prevalence is diminishing as well. Children with complex pathologies seen at pediatric referral hospitals share different secondary malnutrition mechanisms such as reduced energy intake, malabsorption, and increased energy expenditure, among others (2–12); these pathogenic mechanisms are determined by and interact with the underlying illness itself (13). It could be hypothesized that the prevalence of secondary malnutrition has not paralleled the trend of primary malnutrition because it is inherently linked to different types of primary illnesses.

The aims of our study were to establish the prevalence of malnutrition and overweight in a pediatric referral hospital and to achieve a multivariate model with nutritional status as the dependent variable and clinical and sociodemographic factors as independent variables.



Children from 1 month to 15 years old who were hospitalized or seen as outpatients in the 22 departments of the pediatric hospital's medical or surgical divisions were eligible for the study. The sample was selected by means of a random procedure; hospitalized patient and outpatient samples were obtained separately. The study was conducted from April through September 2004.

The sample size was calculated using a formula for cross-sectional studies; the probability of malnutrition was based on previous studies in pediatric hospitals (14–18). Each clinical department's sample was calculated using a formula for sample proportions. The estimated total sample size was 626 cases. The study measured nutritional status as diagnosed by anthropometry, with clinical and sociodemographic factors as independent variables. Our analyses included prevalence of acute malnutrition, chronic malnutrition, overweight risk, and overweight. We also established a crude odds ratio (OR) and confidence interval (CI) for each independent variable, and performed logistic regression with 4 bivariate dependent variables: height for age (<−2 SD as well as −2 to 2 SD), weight for height (<−2 SD as well as −2 to 2 SD), body mass index (BMI; 85th to 94th percentiles as well as 5th to 84th percentiles, and BMI ≥95th percentile as well as 5th to 84th percentiles) (19). Clinical and sociodemographic data were assigned as independent variables.

Clinical and Sociodemographic Variables

The demographic and socioeconomic variables were determined based on a validated instrument that previously had been applied in studies of primary malnutrition (20,21). Variables evaluated were sex, age groups in months (infants = 1–23, preschoolers = 24–71, school children = 72–143, adolescents = 144–192) (22), parents' age, parents' education (years attending school and degrees obtained), parents' employment, number of children, and family income (total monthly income, minimum wage income, families' daily food expenditure, and expense per person).

The clinical variables were the duration of the primary illness (<30 days or ≥30 days) and the clinical department. Medical divisions were allergy, cardiology, dermatology, endocrinology, gastroenterology and nutrition, hematology, infectious diseases, internal medicine, nephrology, neurology, pneumology, oncology, psychology, and psychiatry. Surgical divisions were cardiac surgery; ear, nose, and throat; pediatric surgery; maxillofacial surgery; plastic surgery; neurosurgery; ophthalmology; orthopedics and traumatology; and urology.


Before the data were collected, we performed an anthropometrical standardization trial with 20 pediatric patients. Consistency (intragroup individual measurements) and validity (intergroup comparison with a gold standard) were evaluated with Pearson bivariate correlations; when the r value was below 0.8, the anthropometrical technique was reviewed and corrected until the desired intra- and intergroup correlations were obtained.

Infants were weighed without clothing or diapers on a leveled pan scale with a beam and a movable weight. The patients were placed on the scale making sure that the weight was distributed equally on each side of the pan's center. Weight was recorded to the nearest 10 g. Children older than 2 years were weighed with a movable weight platform beam scale; subjects were measured with minimal clothing and without shoes. Weight was recorded to the nearest 100 g (23,24).

Length was measured and recorded to the nearest 0.1 cm, in a recumbent position using an infantometer in children younger than 3 years old. An assistant held the infant's head while the examiner straightened the infant's legs, holding the feet with toes pointed up while moving the footboard against the feet. In children older than 3 years, height was measured and recorded to the nearest 0.1 cm using a stadiometer with a movable block. The subjects were measured while standing (without shoes, heels together, back as straight as possible, and arms hanging freely); the head was positioned in the Frankfurt horizontal plane and the movable block was brought down until it touched the head (23–25).

Height for age and weight for height z scores were calculated using the Centers for Disease Control and Prevention and Frisancho reference patterns. BMI percentiles were calculated with the Frisancho reference pattern (23–25). When z scores were between −2 and 2 SD, nutritional status was considered normal. Subjects with a z score of less than −2 SD were considered malnourished (26). Subjects were classified as having acute malnutrition if the weight for height z score was less than −2, and as having chronic malnutrition if in addition the height z score was less than −2. Children older than 24 months of age and adolescents were classified as at risk for overweight if the BMI was between the 85th and 94th percentiles and they were classified as overweight if the BMI was at the 95th percentile for age or higher (19).


Informed written consent was obtained from the parents or guardians. The study's protocol was approved by the hospital's research and ethics committee (No. 2001-251-040).


Sociodemographic Variables

The subjects consisted of 641 patients, with a mean age of 7.1 ± 4.9 years; 56% were male. There was no difference in age by sex. The age distribution was 137 (21.4%) infants, 171 (26.7%) preschoolers, 235 (36.7%) schoolchildren, and 98 (15.3%) adolescents.

In mothers, the mean age was 33.4 ± 7.3 years and in fathers 36.6 ± 8.7 years, P < 0.001. For the mothers, the number of school years since attending elementary school was 9.2 ± 4.2 and for fathers 8.7 ± 3.7, with no significant difference between them. The illiteracy proportion was 2.1% for mothers and 3.2% for fathers. Thirty-seven mothers (9.8%) and 55 fathers (14.8%) had a university degree (P = 0.047).

Two thirds of the mothers were housewives and most of them did not have a job; in addition to their household activities, one fifth of the mothers had office jobs or were in sales. The fathers' most frequent occupations were office employees, factory workers, and/or self-employed professionals.

Of the parents, 81% were legally married, most of them with both a civil and a religious formal procedure. Of the families, 80.7% reported being a nuclear family (parents and children); the remaining families were made up of parents, children, and other relatives. Among the families, 76.7% had 3 children or less and families with more than 5 children were less than 1% of the sample.

When this survey was taken, the families' monthly income was $502.70 US; of that, $283.40 (56.4%) went to feeding the family. The money spent for feeding 1 person per day was calculated at $2.30 US.

Clinical Variables

Of the cases, 262 (40.9%) were hospitalized and 379 (59.1%) were cared for as outpatients. Overall, 412 (64.3%) belonged to the medical and 229 (35.7%) to the surgical divisions. The number of subjects studied per clinical department varied from 23 to 35. The duration of the primary illness was ≤30 days in 143 cases (22.3%) and >30 days in 498 (77.7%).

Nutritional Status

Of the patients, 47.4% (304) had normal nutritional status, 17.0% chronic malnutrition, 8% acute malnutrition, 15.4% overweight risk, and 12.2% overweight. Frequencies and percentages of cases with malnutrition and overweight by age groups are presented in Tables 1 and 2.

Prevalence of chronic malnutrition (height for age z score <−2 SD) and acute malnutrition (weight for height z score <−2 SD) in 641 patients from a pediatric referral hospital
Prevalence of overweight risk* and overweight in 641 patients from a pediatric referral hospital

The risk of acute malnutrition was higher in infants and adolescents when compared with preschoolers and schoolchildren (Table 1). The proportion of cases with weight for height less than −2 SD within each age group was 13.9% in infants, 5.8% in preschoolers, 3.8% in schoolchildren, and 13.2% in adolescents.

The risk for chronic malnutrition was higher in infants when compared with preschoolers and schoolchildren (Table 1). The proportion of cases with height for age less than −2 SD within each age group was 27.7% in infants, 15.8% in preschoolers, 11.1% in schoolchildren, and 18.4% in adolescents.

The proportion of overweight risk within each age group was infants 4.4%, preschoolers 19.9%, schoolchildren 20.4%, and adolescents 11.2%. Preschoolers, schoolchildren, and adolescents had an increased probability of overweight risk than infants. Schoolchildren had a significantly increased OR when compared with adolescents (Table 2).

The proportion of overweight was significantly higher in preschoolers, schoolchildren, and adolescents when compared with infants (Table 2). The percentages of overweight within age groups were infants 6.6%, preschoolers 11.7%, schoolchildren 14%, and adolescents 16.3%.

The subspecialties presenting higher frequencies of height for age less than −2 SD were endocrinology (33.3%), pediatric surgery (29.4%), hematology (29.4%), oncology (25%), and neurology (20%). Comparing the chronic malnutrition proportions of these departments with the remaining subspecialties proved significant (OR 2.3, CI 1.5–3.6).

The subspecialties with higher frequencies of weight for height less than −2 SD were internal medicine (19.2%), nephrology (15.6%), gastroenterology (13.9%), neurology (13.8%), and cardiology (13.3%). Comparisons between the proportion of acute malnutrition in these departments and the remaining subspecialties were significant (OR 3.3, CI 1.7–6.4).

The departments with higher frequencies of overweight risk and overweight were psychology (50%), allergy and immunology (45.5%), urology (41.2%), ophthalmology (40.9%), and endocrinology (36.8%). When the above proportions of overweight risk plus overweight were compared with the rest of the departments, the OR was 2.5 (CI 1.6–3.8).

More than 250 different diagnoses were recorded in the 641 patients studied. The 5 diagnoses with the highest proportion of height for age less than −2 SD were chronic renal failure, cerebral palsy, biliary atresia, Fallot tetralogy, and bronchopulmonary dysplasia. The frequencies and percentages of the 10 diagnoses with the higher proportion of height for age less than −2 SD are described in Table 3; they comprise 35 of 109 (32.1%) cases with chronic malnutrition. In the remaining 14 subspecialties, the distribution of cases with height for age and/or weight for height less than −2 SD did not show a characteristic distribution. No association was found between the observed frequencies of acute and chronic malnutrition, overweight risk, and overweight with the diagnoses.

Diagnoses with the highest observed frequencies of height for age <−2 SD and their corresponding < −2 SD weight for height frequencies and percentages in patients from 10 subspecialties

Height for age less than −2 SD was more frequent in hospitalized patients (20.6%) when compared with outpatients (14.5%); P = 0.043. Weight for height less than −2 SD was also more frequent in hospitalized patients (12.2%) than in outpatients (5%); P = 0.003. Rates of overweight risk, overweight, or both together were not different in hospitalized patients versus outpatients (P = 0.466).

Multivariate Analyses

The statistically significant independent variables ascertained by adjusted analysis are summarized in Table 4. For height for age, crude analysis of each independent variable showed an increased risk and significant CIs for age groups, illness duration, fathers' age group (20–30 vs 31–45 years: OR 1.77, CI 1.07–2.92), fathers' education (elementary vs high school: OR 1.77, CI 1.03–3.05), mothers' education (elementary vs high school: OR 1.73, CI 1.01–2.99), marital status (civil vs civil plus religious: OR 2.87, CI 1.21–67.3), and number of siblings (<1 vs ≥2, OR 2.09, CI 1.24–3.52). When these variables were included in the logistic regression model, age groups and illness duration remained significant without interaction between them; the other variables were not significant and were discarded.

Logistic regression models achieved in 641 patients from a pediatric referral hospital for the anthropometrical indicators of chronic malnutrition, acute malnutrition, overweight risk, and overweight as dependent variables with clinical and sociodemographic factors as predictor variables

For weight for height, crude analysis of each independent variable showed an increased risk and a significant CI for age groups (infants vs school children: OR 3.0, CI 1.12–8.41). The other variables associated with weight for height less than −2 SD are presented in Table 3. In the adjusted analyses, acute malnutrition was predicted by clinical variables relating to the patients' location (hospitalization in medical departments) and number of children. Among these variables neither interaction nor confounding was found.

For BMI and overweight risk, crude analysis identified 3 variables significantly associated with overweight risk: age, the father's level of education, and family income greater than 4 times the minimum wage (OR 1.7, CI 1.1–2.7). In the adjusted analyses, the latter variable was excluded. A synergistic interaction between ages 36 months or older and the father's education (college and university) increased the estimated overweight risk (OR 3.1, CI 1.7–5.8).

For overweight, age (1–35 and ≥36 months) and sex (boys) were identified as variables associated with overweight by both crude and adjusted analyses. A significant antagonistic interaction between these 2 variables was observed (OR 1.5, CI 1.1–2.0).


In more than one-fourth of the infants studied, height for age was less than −2 SD. This obviously has clinical implications for their long-term growth. It is not clear whether this condition could be prevented or reversed with nutritional intervention. The first step in dealing with this problem would be to diagnose height impairment and identify the risk factors involved. The rate of height for age less than −2 SD in our series is comparable to those in studies reported in the last 30 years (14,16–18,27–33).

Chronic malnutrition was predicted with a model of 2 biological factors: age (infants) and illness duration (>30 days). Since the earliest publications on secondary malnutrition, the increased risk of chronic malnutrition in infants and adolescents and its relation to rapid growth during these periods have been discussed (14). As a result, in almost two-thirds of the overall sample, the association of height for age less than −2 SD with illness duration (>6 months) makes sense.

When our weight data were classified with the Waterlow criteria (34) and compared with previous nutritional surveys in pediatric referral hospitals, higher rates of low weight were observed. There is no simple explanation for these differential rates, but it is possible that our patients' global socioeconomic situation is lower than that of children in others series from developed countries. According to the 1999 and 2006 National Nutrition and Health Surveys, our relatively high rate of malnutrition contrasts with the decreasing trend of acute malnutrition in Mexico (35–37).

Acute malnutrition was not associated with age or illness duration, but rather with specific situations such as hospitalization and attention in nonsurgical departments. Hospitalized patients in nonsurgical departments have a higher probability of acute malnutrition, which seems to be related to the severity and type of the primary illness. A third variable included in the regression model was related to young, single mothers with only 1 child; these mothers worked long shifts as factory workers or employees and spent little time with their children. This socioeconomic factor is clearly associated with specific medical situations that lead to acute malnutrition, which could be modified with appropriate day care centers or nurseries.

Because being overweight was not an issue in previous decades, the high rates of at risk for overweight and overweight found in this study modify the traditional nutrition status profile of ambulatory or hospitalized patients in pediatric referral hospitals. Our study's rates resemble the proportions found in the 1999 and 2006 Health and Nutrition Surveys in Mexico. In our data, schoolchildren and adolescents were the groups with a higher risk for becoming overweight. These rates resemble population studies already mentioned. We believe that these rates reflect the already described nutrition transition in Mexico during the last 2 decades, and that they are not related to the primary illness.

Both overweight risk and overweight were associated with sex and age older than 36 months; the overweight regression model added the fathers' education (college and university) and almost significantly the family income to these variables. In contrast with the variables included in the acute malnutrition models, which are mainly clinical and biological, the variables associated with overweight and obesity were sociodemographic. Eating habits in school-age children and adolescents partly are determined by social eating patterns, and we believe that age and sex were identified as social factors rather than associated biological factors.

Contrary to the known association between multiple demographic and socioeconomic variables to primary malnutrition, our analysis identified mainly clinical and biological factors in cases with secondary malnutrition (38). The population under study is covered by the Instituto Mexicano del Seguro Social and is made up of small nuclear and integrated families with a stable income and a mean high school education level, in which the estimated rate of primary malnutrition is low (39). Therefore, we believe that the rates of acute and chronic malnutrition found in the present study exemplify secondary malnutrition, which is associated with biological conditions such as pediatric periods of accelerated growth and increased nutritional requirements in infancy and adolescence, rather than to a severe illness requiring hospitalization or to the duration of the primary disease. These results could be extrapolated to a large proportion of the Mexican population covered by the Instituto Mexicano del Seguro Social, who share similar sociodemographic conditions.

When the analysis focused on the subspecialties and diagnoses, we found a trend to higher rates of malnutrition in departments in which patients shared multiple mechanisms of secondary malnutrition. However, probably due to the large number of subspecialties and diagnoses, these variables did not become significant in the logistic regression model. As expected, overweight risk and overweight rates were higher in departments that do not see patients with severe health problems. It is interesting that overweight risk and overweight rates are similar in hospitalized patients and outpatients; this finding most likely reflects the Mexican nutrition transition identified in the last decade more than it does an association with the primary illness that brought our patients to a pediatric referral hospital in the first place.

In summary, the overall acute and chronic malnutrition prevalence in certain age groups was established at 1 of the main Mexican pediatric referral hospitals. Age, sex, and some clinical factors were associated with height and weight less than −2 SD. Except for the number of siblings, the demographic and socioeconomic variables evaluated were not associated with malnutrition. In a large sample of patients from a pediatric referral hospital, secondary malnutrition was identified as a clinically significant and associated condition. Overweight risk and overweight have emerged as a novel finding in the nutritional profile of pediatric referral hospitals in Mexico and probably reflect a global nutrition transition.


The authors thank Joyce Jackson for technical language assistance and Richard B. Colletti, MD, of the University of Vermont College of Medicine, for reviewing the manuscript in detail.


1. De Onis M, Blössner M, Borghi E, et al. Estimates of global prevalence of childhood underweight in 1990 and 2015. JAMA 2004; 291:2600–2606.
2. Martin E, Belleton F, Lallemand Y, et al. Malnutrition in pediatric oncology: prevalence and screening. Arch Pediatr 2006; 13:352–357.
3. Rashid R, Neill E, Maxwell H, et al. Growth and body composition in children with chronic kidney disease. Br J Nutr 2007; 97:232–238.
4. Brem AS, Lambert C, Hill C, et al. Prevalence of protein malnutrition in children maintained on peritoneal dialysis. Pediatr Nephrol 2002; 17:527–530.
5. Villasís-Keever MA, Aquiles Pineda-Cruz R, Halley-Castillo E, et al. Frequency and risk factors associated with malnutrition in children with congenital cardiopathy. Salud Pública Mex 2001; 43:313–323.
6. Cameron JW, Rosenthal A, Olson AD. Malnutrition in hospitalized children with congenital heart disease. Arch Pediatr Adolesc Med 1995; 149:1098–1102.
7. Rogers B. Feeding method and health outcomes of children with cerebral palsy. J Pediatr 2004; 145(Suppl 2):S28–S32.
8. Gangil A, Patwari AK, Aneja S, et al. Feeding problems in children with cerebral palsy. Indian Pediatr 2001; 38:839–846.
9. Durie PR, Pencharz PB. Cystic fibrosis: nutrition. Br Med Bull 1992; 48:823–846.
10. Kelly DA. Nutrition and growth in patients with chronic liver disease. Indian J Pediatr 1995; 6:533–544.
11. Shepherd RW, Chin SE, Cleghorn GJ. Malnutrition in children with chronic liver disease accepted for liver transplantation: clinical profile and effect on outcome. J Paediatr Child Health 1991; 27:295–299.
12. Sokol RJ. Fat-soluble vitamins and their importance in patients with cholestatic liver diseases. Gastroenterol Clin North Am 1994; 23:673–705.
13. Fuchs G. Secondary malnutrition in children. In: Suskind RM, Suskind LL, editors. The Malnourished Child. New York: Raven Press; 1990. pp. 23–36.
14. Merrit RJ, Suskind RM. Nutritional survey of hospitalized pediatric patients. Am J Clin Nutr 1979; 32:1320–1325.
15. Fleiss JL. Statistical Methods for Rates and Proportions. 2nd ed. New York: Wiley; 1981: 38–45.
16. Vásquez-Garibay EM, Sandoval-Galindo D, Kumazawa-Ichikawa M, et al. Estado de nutrición del niño que ingresa al Nuevo Hospital Civil de Guadalajara. Bol Med Hosp Infant Mex 1993; 50:383–393.
17. Hendricks KM, Duggan C, Gallagher L, et al. Malnutrition in hospitalized pediatric patients: current prevalence. Arch Pediatr Adolesc Med 1995; 149:1118–1122.
18. Leleiko NS, Luder E, Fridman N, et al. Nutritional assessment of pediatric patients admitted to an acute-care pediatric service utilizing anthropometric measurements. J Parenter Enteral Nutr 1986; 10:166–168.
19. Centers for Disease Control, National Center for Health Statistics. 2000 CDC growth charts. Accessed August 18, 2008.
20. Vásquez-Garibay EM, Nápoles-Rodríguez F, Romero-Velarde E. Interpretación epidemiológica de los indicadores antropométricos en áreas marginadas. Bol Med Hosp Infant Mex 1991; 48:637–642.
21. Vásquez-Garibay EM, Navarro-Lozano ME, Romero-Velarde E, et al. Características secundarias socioeconómicas de la desnutrición proteico calórica primaria y secundaria grave. Bol Med Hosp Infant Mex 1999; 56:641–647.
22. US National Library of Medicine-National Institutes of Health. Age groups (updated December 20, 2008). Accessed December 20, 2008.
23. Frisancho AR. Methods and materials and anthropometric standards. In: Frisancho AR, editor. Anthropometric Standards for the Assessment of Growth and Nutritional Status. Ann Arbor, MI: University of Michigan Press; 1990. pp. 9–64.
24. Frisancho AR. New norms of upper limb fat and muscle areas for assessment of nutritional status. Am J Clin Nutr 1981; 34:2540–2545.
25. Fomon SJ, Nelson SE. Size and growth. In: Fomon SJ, editor. Nutrition of Normal Infants. St Louis: Mosby-Year Book; 1993. pp. 36–83.
26. World Health Organization Expert Committee. Physical Status: The Use and Interpretation of Anthropometry. WHO Technical Report Series 854. Geneva: WHO; 1995. pp. 6–9.
27. Vázquez-Cortés R, Flores-Nava G, Cuatecontzi-Pérez C. Frecuencia de desnutrición en niños de un hospital pediátrico de tercer nivel. Rev Mex Pediatr 1995; 62:131–133.
28. Dogan Y, Erkan T, Yalvac S, et al. Nutritional status of patients hospitalized in pediatric clinic. Turk J Gastroenterol 2005; 16:212–216.
29. O'Connor J, Youde LS, Allen JR, et al. Obesity and under-nutrition in a tertiary paediatric hospital. J Paediatr Child Health 2004; 40:299–304.
30. Ratanachu-Ek S. The nutritional status of outpatients at Queen Sirikit National Institute of Child Health. J Med Assoc Thai 2003; 86(Suppl 3):S583–S589.
31. Rocha GA, Rocha EJ, Martins CV. The effects of hospitalization on the nutritional status of children. J Pediatr (Rio J) 2006; 82:70–74.
32. Marek A, Plata-Nazar K, Furtak J, et al. Undernutrition in hospitalized children. Part I. Med Wieku Rozwoj 2004; 8:439–443.
33. Tienbonn P. Nutrition problems of hospitalised children in a developing country: Thailand. Asia Pac J Clin Nutr 2002; 11:258–262.
34. Waterlow JC. Classification and definition of protein calorie malnutrition. BMJ 1972; 3:566–569.
35. Sepúlveda-Amor J, Lezana MA, Tapia-Conyer R, et al. Estado nutricional de preescolares y mujeres en México: resultados de una encuesta probabilística nacional. Gaceta Med Mex 1990; 126:207–224.
36. Rivera-Dommarco J, Shamah LT, Villalpando S González CT, et al. Encuesta Nacional de Nutrición 1999. Estado Nutricio de Niños y Mujeres en México. Cuernavaca Morelos, México: Instituto Nacional de Salud Pública; 2001.
37. Olaiz-Fernández G, Rivera-Dommarco J, Shamah-Levy T, et al. Encuesta Nacional de Salud y Nutrición 2006. Cuernavaca, México: Instituto Nacional de Salud Pública; 2006.
38. Torun B, Chew F. Protein-energy malnutrition. In: Shils M, Olson J, Shike M, et al., editors. Modern Nutrition in Health and Disease. 9th ed. Baltimore, MD: Williams & Wilkins; 1999. pp. 963–988.
39. González-Rico JL, Vásquez-Garibay EM, Sánchez-Talamantes E, et al. Dinámica familiar y otros factores asociados al retardo en el crecimiento de niños de 12 a 24 meses que acuden a una unidad de atención primaria. Bol Med Hosp Infant Mex 2007; 64:143–152.

Malnutrition; Overweight; Sociodemographic factors

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