1 Introduction
Obesity in children is increasing in most regions and countries.[1] [2] [3,4] [5] [6] [7]
Obesity among children has a multifactorial nature involving environmental factors that include poor eating habits and a lack of physical activity.[10]
Health promoting programs implemented within the context of schools and families are critical for treatment and prevention of childhood obesity. Preventive programs on obesity recommend the systematic assessment of body mass index (BMI) and environmental factors including eating and physical activity habits.[8,9] [10]
The purpose of this observational study was to examine the prevalence of obesity in children of 6 to 8 years of age from primary public schools over a period of 6 years and the associated environmental and metabolic health risk factors.
2 Methods
2.1 Study design and settings
This was a cohort observational study to investigate the prevalence of obesity in children ages 6 to 8 years from 14 state primary schools in Vinhedo municipality located at Metropolitan Region of Campinas—Sao Paulo state—Brazil. Environmental and metabolic health risk factors for obesity were investigated in a cross-sectional survey. The population of Vinhedo in 2015 was 72,550.
Participation in the study was on a voluntary basis after informing the children's parents about the study, its objectives and methods, and that all investigative procedures were cost-free to patient. This study was conducted according to the Declaration of Helsinki guidelines. All procedures involving human subjects/patients were approved by the Ethics Committee of the São Leopoldo Mandic School, Campinas—SP (CAAE: 51643715.3.0000.5374). Written informed consent was obtained by the parents/guardians of all the participating students. The Vinhedo Municipal Department of Health authorized the use of data.
The screening program comprised of:
a) cost-free to patient clinical and laboratory analysis to detect overweight/obesity children and associated risk factors of metabolic syndrome; and
b) a survey questionnaire on environmental risks factors.
In the first phase of the program, weight and height for BMI, waist circumference, blood pressure, and fasting glucose, insulin, triglycerides, HDL and low-density lipoprotein (LDL) were evaluated.
In the second phase of the program, the parents of the children participating in the study were invited to complete a survey questionnaire on environmental risk for childhood obesity. This questionnaire was recommended by the Municipal Health Department (Viuniski, N. Obesidade em adultos, um desafio pediátrico? Nutrição Brasil. Ano XIII, n.74, p.9–10, set/out, 2005.).
2.2 Screening of obesity and primary outcomes
Weight, height, and waist circumference were measured in light-weight sports clothing using a calibrated anthropometric mechanical balance (Welmy, up to 150 Kg), a stadiometer, and a non-elastic flexible tape at the level of the navel, respectively. Obesity was assessed in accordance with the indications of the International Obesity Task Force (IOTF), defined with reference to the BMI threshold values for boys and girls ages 2 to 18 years, calculated by Cole et al.[11] [7]
Systemic arterial pressure was measured in a sitting position, at rest, using a sphygmomanometer with a cuff suitable for the child (width 9 cm, length 18 cm, brachial circumference 22 cm), according to current guidelines. If the brachial circumference exceeded the above value, cuff was replaced to improve suitability (width 10 cm, length 24 cm, arm circumference 26 cm).
On the day of retreatment, the parents/guardians received a previously prepared form with laboratory tests requested for collection (hematocrit, hemoglobin, glucose, insulin, total cholesterol, HDL, LDL, triglycerides) and the 12-hour fasting orientation. The blood sampling was made available in all basic health units, and the laboratory analysis was done by the Municipal Laboratory, allowing the families to perform at the nearest place of their residence. The results of the exams were sent directly to the Municipal Health Department, where the team participating in the project carried out the data analysis. Subsequently, the first individual telephone consultation with the student and family member with the nutritionist was performed.
The questionnaire for the assessment of environmental risk for childhood obesity contained 22 questions, 9 of which were related to nutrition, 5 to physical activity, 6 to emotional aspects and 2 to school meals.
2.3 Statistical analysis
The percentage distribution of obesity prevalence was calculated. Comparison of proportions was performed with ‘N − 1’ Chi-squared test (the K. Pearson chi-squared test but with N replaced by N − 1).[12,13] www.graphpad.com
3 Results
The cohort in the year 2012 included 1045 children ages 6 to 8 years attending the 9 municipal elementary schools present on the days prearranged for evaluation. Of this cohort, 2.6% were underweight, 66.4% were eutrophic, 15.3% were overweight and 15.7% with obesity (Table 1 ).
Table 1: Prevalence of obesity among children.
The cohort in the year 2015 included 1930 children ages 6 to 8 years attending the 14 municipal elementary schools (5 new established schools) present on the days prearranged for evaluation. The increase in the number of schools, from 9 in 2012 to 14 in 2015, occurred due to the City Hall's investment in education, allowing a greater number of students to be enrolled. Of this cohort, 1.5% (29) were with underweight, 59.5% (1148) of eutrophic, 18.2% (352) were overweight and 20.8% (401) were children with obesity (Table 1 ).
A significant increase in the prevalence of obesity occurred from 2012 to 2015 (difference 5.1%, 95.0%CI 2.1–8.0, P <.001). The obesity prevalence per studied year was similar between girls and boys (Fig. 1 ).
Figure 1: Obesity prevalence in children, girls and boys between 2012 and 2015. Values are percent prevalence. Comparison of proportions by Chi-squared test ∗ P <.05, # P <.001 in comparison to group proportions in 2012.
Of the 2015 cohort of children with obesity, only 34.3% (138) completed the complementary clinical and laboratory investigation program for the screening of metabolic and environmental health risk factors. We identified school and family environmental risk factors associated with childhood obesity. This present study revealed 74.0% of children with obesity consumed fried foods and sweets at school (Fig. 2 ). Of this group, 44.8% were girls who consumed fried foods and sweets. Parents who considered school meal programs to be healthy totaled 26.0%, and similarly, 27.0% of the children with obesity liked and ate lunch every day at school (Fig. 2 ). Of this group, 38.7% were girls’ parents who considered school meal healthy and similarly 39.4% girls ate lunch regularly at school. This present study revealed 84.0% of children with obesity consumed snacks and soft drinks at home, of which 48.1% were girls. Also, 43.0% of children with obesity ate at the table without any activity (Fig. 2 ), equally distributed among girls and boys (49.1% vs 50.9%, respectively).
Figure 2: Spearman correlation between BMI and metabolic risk factors waist circumference, fasting glucose, insulin resistance, triglycerides, HDL and LDL. Values are Spearman's ρ coefficient, ∗ P <.005, # P <.0001. BMI = body mass index, HDL = high-density lipoprotein, LDL = low-density lipoprotein.
Physical activity was less than 3 hours per week in 93.0% at school and 85.0% at home (Fig. 3 ), of which 42.7% and 44.3% were girls. The majority stayed more than 2 hours per day watching television (64.0%) but spent less than an hour with computer or videogaming (56.0%). Videogaming was played significantly more by boys than by girls (2.1 ± 0.1 vs 1.3 ± 0.1 hours/day respectively; mean ± SEM, P <.001).
Figure 3: Diet and eating behavior in children with obesity at school and home.
In this cohort of children with obesity, the BMI had a significant Spearman correlation with waist circumference, insulin, and triglycerides (Fig. 4 ). There was a prevalence of increased: waist circumference in 84.9% (39.6% girls), fasting glucose in 11.1% (4.4% girls), insulin resistance in 84.5% (40.5% girls), fasting triglycerides in 23.3% (11.1% girls) and blood pressure in 19.5% (13.0% girls), and reduced HDL in 37.8% (18.9% girls).
Figure 4: Physical activity in children with obesity at school and home.
4 Discussion
School-based obesity screening and care management are recommended to reduce prevalence of obesity, albeit confronting challenges in encouraging students and parents to participate in such programs.[14]
In the studied cohort of school children with obesity, there was a prevalence of increased metabolic risk factors. We identified both school and family environmental risk factors. These included diet compositions and reduced physical activity programs at school and home and also feeding practices at home. A challenge for this screening program for childhood obesity represented the parental involvement, which represents a key barrier preventing students from making sustainable changes in health-related behaviors beyond the school intervention setting.[15] [16,17] [18]
The scientific investigation of obesity risk environments is very much in its early stages and requires new tools that are adapted to physical, economic, political, and sociocultural contexts. Thus, since community intervention programs are contextual interventions by nature, community decision-makers have a distinct role in developing “health in all policies”-strategies, as suggested by Schneider et al[19] [19] [20] [21] [22] [23]
The BMI had a significant Spearman correlation with waist circumference, and fasting insulin and triglycerides. The strong association of obesity and insulin was also found in the Filippou et al study.[24] [25] [26] [27,28] [29]
This study has some limitations. These findings on the interaction between childhood obesity and metabolic risk and environmental factors are derived from a cross-sectional study, thus impeding causal interpretation. While the observations resulting from the present study are of public health interest, evidence from longitudinal randomized controlled trials is needed to indicate causality. Relatively low number of children and parents enrolled in the study could underestimate associations between obesity and metabolic and environmental risk factors. Also, voluntary response sampling methods are biased as compared to random sampling. Another limitation may be that for the multicultural society of Brazil, the race of students was not considered in this study or the socio-economic status. No results were obtained from the 3rd step of the educational program containing the parental engagement agenda. This points out to the importance of improving the training of health and schools’ professionals as strategic partners in the implementation of such community/school educational programs. Gaps in intervention design and methodology to implement these parental engagement programs remain to be identified.
Programs could be delivered in a school setting by teachers to build on a teacher-parent partnership to increase effectiveness of both school and parenting practices in order to strengthen positive behavior support and behavior management at school and at home. Such program is ParentCorps that builds on the strengths of culturally-diverse families and helps schools engage parents as partners in helping children succeed.[30]
5 Conclusion
Both school and family aspects appear to contribute as health environmental risk factors for obesity in school children. Childhood obesity was associated with metabolic syndrome. The lack of parents’ awareness of childhood obesity and its risk factors represents a substantial barrier to lifestyle counseling. The present study indicates that health quality improvement projects should target both school and family health promotion interventions. Further community programs including controlled trials, specific for the pediatric populations, are necessary to tackle obesity and other conditions associated.
Acknowledgments
We acknowledge the contribution of the municipal health and schools’ professional teams for contributing to the implementation of the screening strategy and therapeutic education sessions for children with obesity in the Vinhedo municipal schools. We also thank the Municipality of Vinhedo (Municipal Department of Education and the Municipal Health Department) for permission to publish the study.
We thank the reviewers for their thoughtful review of the manuscript. They raised important issues and their inputs were very helpful for improving the manuscript.
Author contributions
Study conception and design: PS, LC, and OB. Performed the study: PS, LC, and OB. Interpretation of the data, writing of the manuscript, critical revision of the manuscript regarding the important intellectual content: PS, AP, RO, RZ, LC, OB.
Conceptualization: Priscilla Bueno Rocha Sentalin, Luciana Aparecida Campos, Ovidiu Constantin Baltatu.
Data curation: Priscilla Bueno Rocha Sentalin, Robson Rocha de Oliveira, Luciana Aparecida Campos, Ovidiu Constantin Baltatu.
Formal analysis: Andreia de Oliveira Pinheiro, Renato Amaro Zângaro, Luciana Aparecida Campos, Ovidiu Constantin Baltatu.
Investigation: Priscilla Bueno Rocha Sentalin.
Methodology: Priscilla Bueno Rocha Sentalin, Ovidiu Constantin Baltatu.
Project administration: Priscilla Bueno Rocha Sentalin, Luciana Aparecida Campos, Ovidiu Constantin Baltatu.
Supervision: Luciana Aparecida Campos, Ovidiu Constantin Baltatu.
Validation: Andreia de Oliveira Pinheiro, Robson Rocha de Oliveira, Renato Amaro Zângaro, Luciana Aparecida Campos, Ovidiu Constantin Baltatu.
Visualization: Andreia de Oliveira Pinheiro, Robson Rocha de Oliveira, Renato Amaro Zângaro, Luciana Aparecida Campos, Ovidiu Constantin Baltatu.
Writing – original draft: Luciana Aparecida Campos, Ovidiu Constantin Baltatu.
Writing – review & editing: Priscilla Bueno Rocha Sentalin, Andreia de Oliveira Pinheiro, Robson Rocha de Oliveira, Renato Amaro Zângaro.
Ovidiu Constantin Baltatu orcid: 0000-0001-9732-6039.
References
[1]. NCD Risk Factor Collaboration (NCD-RisC). 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. Lancet 2017;390:2627–42.
[2]. Llewellyn A, Simmonds M, Owen CG, et al. Childhood obesity as a predictor of morbidity in adulthood: a systematic review and meta-analysis. Obes Rev 2016;17:56–67.
[3]. Friedemann C, Heneghan C, Mahtani K, et al. Cardiovascular disease risk in healthy children and its association with body mass index: systematic review and meta-analysis. BMJ 2012;345:e4759.
[4]. Brady TM. Obesity-related hypertension in children. Front Pediatr 2017;5:197eCollection 2017.
[5]. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome–a new worldwide definition. Lancet 2005;366:1059–62.
[6]. Reinehr T, Sousa G de, Toschke AMM, et al. Comparison of metabolic syndrome prevalence using eight different definitions: a critical approach. Arch Dis Child 2007;92:1067–72.
[7]. Bussler S, Penke M, Flemming G, et al. Novel insights in the metabolic syndrome in childhood and adolescence. Horm Res Paediatr 2017;88:181–93.
[8]. Stok FM, Hoffmann S, Volkert D, et al. The DONE framework: creation, evaluation, and updating of an interdisciplinary, dynamic framework 2.0 of determinants of nutrition and eating. PLoS ONE 2017;12:e0171077.
[9]. Atkin AJ, Foley L, Corder K, et al. Sluijs EM van. Determinants of three-year change in children's objectively measured sedentary time. PLoS ONE 2016;11:e0167826.
[10]. Gauthier KI, Krajicek MJ. Obesogenic environment: a concept analysis and pediatric perspective. J Spec Pediatr Nurs 2013;18:202–10.
[11]. Cole TJ, Bellizzi MC, Flegal KM, et al. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000;320:1240–3.
[12]. Campbell I. Chi-squared and Fisher-Irwin tests of two-by-two tables with small sample recommendations. Stat Med 2007;26:3661–75.
[13]. Richardson JT. The analysis of 2 × 2 contingency tables–yet again. Stat Med 2011;30:890.
[14]. Bailey-Davis L, Peyer KL, Fang Y, et al. Effects of enhancing school-based body mass index screening reports with parent education on report utility and parental intent to modify obesity risk factors. Child Obes 2017;13:164–71.
[15]. Wilson DK. Horn ML Van, Kitzman-Ulrich H, Saunders R, Pate R, Lawman HG, et al. Results of the “Active by Choice Today” (ACT) randomized trial for increasing physical activity in low-income and minority adolescents. Health Psychol 2011;30:463–71.
[16]. Park MH. Half of parents underestimate the weight of their overweight or obese children. Evid Based Nurs 2015;18:47.
[17]. Lundahl A, Kidwell KM, Nelson TD. Parental underestimates of child weight: a meta-analysis. Pediatrics 2014;133:e689–703.
[18]. Ash T, Agaronov A, Young T, et al. Family-based childhood obesity prevention interventions: a systematic review and quantitative content analysis. Int J Behav Nutr Phys Act 2017;14:113.
[19]. Schneider S, Diehl K, Görig T, et al. Contextual influences on physical activity and eating habits -options for action on the community level. BMC Public Health 2017;17:760.
[20]. Dias PC, Henriques P, Anjos LADA, et al. Obesity and public policies: the Brazilian government's definitions and strategies. Cad Saude Publica 2017;33:e00006016.
[21]. Queiroz de Medeiros AC, Pedrosa L de FCF, Yamamoto ME. Food cravings among Brazilian population. Appetite 2017;108:212–8.
[22]. Caetano LCGC, Teixeira-Salmela LF, Samora GARA, et al. Cross-cultural adaptation and measurement properties of the Brazilian version of the Exercise Preference Questionnaire (stroke). Braz J Phys Ther 2017;21:336–43.
[23]. Gicevic S, Aftosmes-Tobio A, Manganello JA, et al. Parenting and childhood obesity research: a quantitative content analysis of published research 2009–2015. Obes Rev 2016;17:724–34.
[24]. Filippou C, Moschonis G, Katsadoura A, et al. Investigation of the relationship between obesity, insulin resistance and vitamin D insufficiency in prepubertal-school aged children. Clin Nutr ESPEN 2016;13:e60.
[25]. Lo K, Wong M, Khalechelvam P, et al. Waist-to-height ratio, body mass index and waist circumference for screening paediatric cardio-metabolic risk factors: a meta-analysis. Obes Rev 2016;17:1258–75.
[26]. Buchan DS, McLellan G, Donnelly S, et al. Diagnostic performance of body mass index, waist circumference and the waist-to-height ratio for identifying cardiometabolic risk in Scottish pre-adolescents. Ann Hum Biol 2017;44:297–302.
[27]. Bacha F, Saad R, Gungor N, et al. Are obesity-related metabolic risk factors modulated by the degree of insulin resistance in adolescents? Diabetes Care 2006;29:1599–604.
[28]. Fujii C, Sakakibara H. Association between insulin resistance, cardiovascular risk factors and overweight in Japanese schoolchildren. Obes Res Clin Pract 2012;6:e1–90.
[29]. Rajjo T, Almasri J, Nofal A Al, et al. The association of weight loss and cardiometabolic outcomes in obese children: systematic review and meta-regression. J Clin Endocrinol Metab 2017;102:758–62.
[30]. Hajizadeh N, Stevens ER, Applegate M, et al. Potential return on investment of a family-centered early childhood intervention: a cost-effectiveness analysis. BMC Public Health 2017;17:796.
