Temporal trends in birth weight and its association with overweight/obesity in schoolchildren from a Brazilian city: a cross-sectional panels study (2002 to 2019)

Temporal trends in birth weight and its association with overweight/obesity in schoolchildren from a Brazilian city: a cross-sectional panels study (2002 to 2019) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal trends in birth weight and its association with overweight/obesity in schoolchildren from a Brazilian city: a cross-sectional panels study (2002 to 2019) Camila Elizandra Rossi, Bernardo Paz Barboza, Patrícia de Fragas Hinnig, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6335278/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background/Objectives : Birth weight is an important indicator of maternal and early childhood health and is associated with long-term metabolic and anthropometric issues. We aim to verify trends in the prevalence of low and high birth weight over 16 years and associate the trends of these variables with overweight/obesity status in 7 to 10-year-old schoolchildren in Florianópolis, in the South of Brazil. Methods : Data from four cross-sectional and probabilistic panels in 2002, 2007, 2012/2013, and 2018/2019 were analyzed. Multiple logistic regressions were used to assess the association between low and height birth weight and overweight/obesity in childhood. Analyzes included biological, socioeconomic and food intake variables in the adjusted models. Results: In the adjusted model, the association of high birth weight with overweight/obesity was statistically significant in the conjunct of all waves (OR=1.63; 95% CI = 1.32 - 2.00). Contrarily, children born with low and insufficient birth weight had less chance of presenting overweight/obesity (OR=0.74; 95% CI = 0.65 - 0.85). Conclusions : A statistically significant increase in the temporal trend of low-birth-weight prevalence from 2002 to 2018/2019 was observed among schoolchildren from Florianópolis. The high birth weight and overweight/obesity were directly associated, despite the influence of variables related to food consumption and the decrease of the high birth weight over time. Epidemiology Low birth weight High birth weight children overweight obesity. 1. Introduction Birth weight is an important maternal and early childhood health indicator. Regardless of their gestational age, newborns with less than 2500 grams are considered low birth weight (LBW) children [ 1 , 2 ]. Causal factors for LBW include prematurity, maternal undernutrition, or other gestational or intrauterine issues [ 1 – 3 ]. Data from 158 countries and geographic regions show an LBW rate of 14.7% in 2020, with Latin America and the Caribbean presenting lower rates − 9.6% [ 4 ]. In Brazil's capital and the 26 states of the nation, the indicator was stable between 1996 and 2011, totaling around 8% of the newborns each year [ 5 ]. On the other hand, high birth weight (HBW), when the newborn weighs more than 3999 grams [ 6 ], is also worrying because it indicates the previous occurrence of maternal risk factors such as gestational diabetes mellitus [ 7 , 8 ], pregestational obesity [ 9 ], or obesity during the pregnancy [ 10 ]. High birth weight prevalence in countries of the Northern Hemisphere varied from 5.1% in Spain and Tajikistan to 19.2% in Ireland [ 11 ]. In Brazil, estimates of HBW also varied according to the counties, with the cities of Recife (Northeastern Brazil) presenting 11.9% [ 12 ], Rio de Janeiro, 18.1% [ 13 ], and Florianópolis amounting to 7.4 to 7.9% [ 14 ]. In scientific literature, LBW is associated with long-term metabolic issues that may already appear in childhood, such as hypertriglyceridemia, dyslipidemia, fasting hyperglycemia, risk of type-2 diabetes mellitus related to obesity, and excessive body fat [ 1 , 14 – 17 ]. Long-term unwanted health events associated with high birth weight are, for instance, those related to overweight, obesity, and excess body fat [ 1 , 11 , 18 , 19 ], but also breast cancer, psychiatric disorders, hypertension, and type-1 and 2 diabetes [ 20 ]. When investigating the association of birth weight with overweight/obesity at ages seven to 17 in China, Shi et al. (2024) observed that the highest odds ratios for overweight were presented by those born with more than 3999g [ 21 ]. Temporal trends in low and high birth weight for different countries are not available in the international scientific literature. For Brazil, the analyses are scarce, with few studies evaluating the temporal trend in LBW for the country and its regions. Buriol et al. (2016) [ 5 ] brings data from 1996 to 2011, and Victor et al. (2022) brings data sequentially, from 2010 to 2020. The last data show an increase in proportions of LBW in the North, Northeast, and Centre-West regions while in the country and other regions the tendencies remained stable [ 22 ]. Similarly, the need for studies on temporal trends in HBW in scientific literature limits long-term analysis of efforts to improve the maternal and child health profile, making it difficult to assess the occurrence of undesirable prospective outcomes of birth weight in infancy. Thus, this article aimed to verify the temporal trends in the prevalence of low and high birth weight and its association with overweight/obesity in schoolchildren from seven to ten in Florianópolis, state of Santa Catarina, from 2002 to 2019. 2. Materials and Methods Data were collected through the Study on the Prevalence of Obesity in Children and Adolescents (Estudo da Prevalência da Obesidade em Crianças e Adolescentes, EPOCA) in Florianópolis, providing an analysis of four cross-sectional panels carried out in 2002, 2007, 2012/2013, and 2018/2019 [ 23 – 26 ]. EPOCA is a probabilistic school-based study that aims to investigate the prevalence trends of obesity and its associated factors among children and adolescents aged between seven and 14 years old [ 24 ] However, in the present study, only data from students aged seven to ten were used. Methodological strategy based on evidence from scientific literature, which states that the validity of the birth weight reported by relatives is usually high only in samples from high-income countries and tends to reduce when children are older [ 27 , 28 ]. The parameters used to select the sample were similar in each of the four waves. The first stage of the sampling process was randomly selecting the schools from clusters according to the geographic area (Centre, Continent, North, East, and South) and type of school (public or private). The second stage varied for each analyzed year. In 2002, all classes in each school were included and all the children in the 2nd to 5th grades were invited to participate, resulting in a sample of 2936 schoolchildren [ 25 , 26 , 29 ]. In 2007, students in each school were selected considering the ratios of schoolchildren registered in the 2004 School Census in Florianópolis (53595 individuals), and placed in the following categories: geographical location of the student's home, type of school, sex, and age group, resulting in 1232 schoolchildren [ 23 ]. In the 2012/2013 and 2018/2019 surveys, the sample was calculated based on information from the 2010 and 2017 School Censuses, considering the expected prevalence of overweight/obesity of 38% in 2012/2013 and 39% in 2018/2019, resulting in 1531 and 986 students, respectively [ 24 , 25 ]. In Table 1 we describe some sampling details from each wave. More details in each panel can be found in other publications [ 23 – 26 , 29 – 33 ]. Table 1 Sampling details of the EPOCA surveys in 2002, 2007, 2012/2013 and 2018/2019. Florianópolis, Brazil. Parameters 2002 a 2007 b 2012/2013 c 2018/2019 d Population of 7–10 years-old schoolchildren in the municipality 28 395 25 619 19 172 27 110 Total elementary schools in the municipality 122 87 85 82 Total of schools included in the survey† 16 17 30 30 Prevalence of Overweight/Obesity (%) 10.0 10.0 38.0 39.0 Sampling error (%) 2 2 3.5 (two-tailed) with a 95% CI 3.5 (two-tailed) with a 95% CI Outline effect 2 1.3 1.8 1.8 Additional (Losses/Refusals) - 10% 10% 10% Expected Final Sample - 1210 1440 1430 Total number of invited students 3313 1350 2234 3098 Total number of students investigated 2936 1232 1423 922 Participation rate 88.6% 91.2% 63.7% 29.8% † School units were drawn based on geographic region, type of administration (public and private), and number of students; a de Assis et al. (2005), Wagner et al., (2019); b Bernardo et al. (2012); c Gonzalez et al. (2017); d Pereira et al. (2023). All Brazilian schools have morning and afternoon shifts, and students attend classes in either one of these periods. Hence, the inclusion criteria were to attend school on the day of data collection, to have the Free and Informed Consent Form signed by parents or guardians, and to have the Free and Informed Assent Term signed by the student at the time of the data collection (only for wave 2018 /2019). The exclusion criteria were unavailable birth weight (2002: n = 651; 2007: n = 55; 2012/2013: n = 80; 2018/2019: n = 94), and unavailable weight and height data due to physical disabilities that did not allow anthropometric evaluation (2012/2013: n = 108; 2018/2019: n = 126)28–32. In the four waves, the projects were approved by the Humans Ethics Committee under nº. 037/02, 028/2006, CAAE 02713312000000121 and 87539718.1.0000.0121. In the four waves, the data on birth weight was self-reported by parents or others responsible for the child. The variable birth weight was categorized as follows: low < 2500g; insufficient ≥ 2500 and ≤ 2999g; adequate 3000-3999g (World Health Organization, 1995) [ 2 ]; or high ≥ 4000g [ 6 ]. In the four cross-sectional panels, weight and height measurements were collected by a previously trained team following the WHO protocol proposed by Lohman [ 34 ]. Children's weight status was classified according to the Body Mass Index (BMI) z-score for age into two categories: "not overweight or obese" (z-score ≤ + 1) and "overweight or obese" (z-score > + 1) [ 35 ]. Data on age, sex, and type of school (public or private) were obtained from a list provided by the schools in all the waves. In the 2002 cross-sectional panel, the children responded to a Typical Day Food Questionnaire (TDFQ), a pen-and-paper pictorial questionnaire (colored and printed on A4-size paper), in which they were supposed to check the 16 food illustrations consumed on a typical day, distributed in five meals organized chronologically (breakfast, morning snack, lunch, afternoon snack, and dinner) [ 26 ]. The food survey was validated against the 24-hour recall method and showed moderate agreement in a sample of schoolchildren from Florianópolis [ 36 ]. In the 2007 and 2012/2013 waves, data on food consumption were collected using the Previous Day Questionnaire (PDQ), an analog to the TDFQ, previously validated for collecting data in this age group [ 37 ]. The questionnaire provides data on 21 items or groups, including one more meal than the TDFQ, an evening snack before bed (de Assis et al., 2009) [ 29 ]. In the 2018/2019 panel, data regarding food consumption were collected through the Web-CAAFE (Consumo Alimentar e Atividade Física de Escolares - Food Consumption and Physical Activities of Schoolchildren) questionnaire. The Web-CAAFE was developed based on the previous pencil-and-paper instruments and was subjected to reproducibility, usability, and validity tests [ 38 – 40 ]. The food consumption section consists of the six meals previously defined, and the presence of an animated character (avatar) helped the child identify which meal was being addressed at that time. For example, the avatar explains: "Breakfast is the first meal we have in the day, right after waking up". For each meal, children can select 31 images of items or food groups on the computer screen [ 41 ]. The questionnaires on food consumption were all qualitative and applied throughout the year's four seasons. The application covered all days with classes [ 29 , 38 , 39 ]. Variables related to food consumption were grouped considering the markers of healthy and unhealthy food consumption cited on the Brazilian Food Guidelines published by the Ministry of Health [ 40 ], resulting in the following variables responded as yes/no: a) healthy food consumption - consumption of fruits, consumption of legumes or vegetables; b) unhealthy food consumption - consumption of sweets and soft drinks. In this article, as a methodological analysis procedure, we considered that the schoolchildren consumed the food when they entered or indicated the consumption of at least one of the items of each food group, by filling out the survey instruments [ 26 , 40 ]. Birth weight was considered the main independent variable while overweight/obesity was considered as the outcome variable. As the biological characteristics and the behaviors adopted by each child might directly influence the weight status, variables related to food consumption and sex were considered control variables. Regarding socioeconomic characteristics, it is known that social determinants are related to access to health services and information on this subject. Curi and Filho (2010) observed that 80% of Brazilian children from wealthier families go to private schools, and the monthly family income is directly associated with the chance of being in a private school [ 41 ]. Thus, given this characteristic of Brazilian schoolchildren and because the "family income" variable had few answers in our study, the variable "type of school" was included in the multivariate model as a control variable and as a proxy for family income. The statistical analysis was performed using STATA version 14.0. For this study, the variables that characterize the sample, such as sex, type of school, birth weight, and weight status (BMI-for-age) were described as absolute and relative frequencies using the "svy" command, which considers the sample weight of each cross-sectional wave. The temporal trends for the birth weight categories were described using 95% Confidence Intervals (95% CI), and the overlapping of the 95% CI indicates no differences between the prevalences over the years. To assess the association between the exposition and the outcome variables, a fusion was made between categories of birth weight. LBW and IBW (insufficient birth weight) were aggregated into one category named LBW, for simplicity. Bivariate logistic regression was carried out between birth weight, the control variables, and weight status (outcome) for each wave and the waves as a group. Subsequently, a multiple logistic regression was performed to analyze the adjusted association between birth weight and overweight/obesity for all waves. 3. Results After applying the exclusion criteria, samples of 2243, 1177, 1446, and 828 schoolchildren were obtained in 2002, 2007, 2012/2013, and 2018/2019, respectively, resulting in a final sample with 5694 observations. The composition of each of the samples' waves was similar: a majority of female schoolchildren enrolled in public schools, with approximately one-third of the children being overweight or obese. In the 2007 wave, a statistically significant reduction in the participation of students in private schools required a greater inclusion of students enrolled in public schools (Table 2 ). Table 2 Description of the sample in the years 2002, 2007, 2012/2013 and 2018/2019. EPOCA Project, Florianópolis, SC, Brazil. Characteristic 2002 (n = 2243) 2007 (n = 1177) 2012/ 2013 (n = 1446) 2018/ 2019 (n = 828) n % 95% CI a n % 95% CI n % 95% CI n % 95% CI Sex Male 1094 48.8 46.7–50.8 572 48.6 45.7–51.4 680 47.0 44.5–49.6 366 44.2 40.9–47.6 Female 1149 51.2 49.2–53.3 605 51.4 48.5–54.3 766 53.0 50.4–55.5 462 55.8 52.4–59.2 Type of school Public 1406 62.7 60.7–64.7 902 76.6 74.1–79.0 930 64.3 61.8–66.7 498 60.1 56.8–63.4 Private 837 37.3 35.3–39.3 275 23.4 21.0–25.9 516 35.7 33.3–38.2 330 39.9 36.6–43.2 Birth weight (g) (Mean, SD, min.; max.) b 3307 546 650; 5500 3273 555.8 0.9; 5500 3212 564 700; 5630 3230 590 550; 5500 Birth weight c LBW 134 6.0 5.1–7.0 97 8.2 6.8–10.0 141 9.7 8.3–11.4 79 9.5 7.7–11.7 IBW 417 18.6 17.0–20.3 226 19.2 17.1–21.1 309 21.4 19.4–23.6 168 20.3 17.7–23.2 ABW 1488 66.3 64.4–68.3 772 65.6 62.8–68.3 918 63.5 61.0–65.9 526 63.5 60.2–66.7 HBW 204 9.1 8.0–10.4 82 7.0 5.6–8.6 78 5.4 4.3–6.7 55 6.6 5.1–8.6 Weight status d Without OW/OB 1534 68.4 66.4–70.3 782 66.4 63.7–69.1 916 63.7 61.2–66.2 539 65.1 61.8–68.3 With OW/OB 709 31.6 29.7–33.6 395 33.6 30.9–36.3 521 36.3 33.8–38.8 289 34.9 31.7–38.2 a 95% CI = 95% confidence interval. b SD = standard deviation; min. = minimum value; max. = maximum value; c LBW = low birth weight; IBW = insufficient birth weight; ABW = adequate birth weight; HBW = high birth weight; d OW = overweight; OB = obesity; Data available for the variable weight status was 1437 in 2012/2013. The increase in rates of low birth weight was statistically significant, going from 6.0 to 9.7% between 2002 and 2012/2013, representing an increase of 61.7%. The prevalence slightly decreased in 2018/2019 but remained significantly higher in statistical terms compared to the 2002 wave, with an addition of 58.3%. High birth weight, contrarily, presented a statistically significant decrease between the same waves where the low birth weight decreased (2002 and 2012/2013), with a 40.7% reduction (Table 2 ). Bivariate analyses showed a significant association between HBW and overweight/obesity in the conjunct of all waves (OR = 2.21; 95% CI = 1.77–2.77), results that come from the sample evaluated in 2002 (OR = 2.40; 95% CI = 1.53–3.75) when the highest prevalence of HBW was found. Beyond this association, it is possible to verify that food consumption is also associated with overweight/obesity as an actual influence on schoolchildren's lives. Fruit consumption, for example, is inversely related to the outcome in two waves (2002 and 2018/2019) and when all waves are analyzed together (OR = 0.89; 95% CI = 0.80–0.98). The consumptions of sugary drinks and sweets were inversely associated with overweight/obesity, which may indicate that children with these conditions are avoiding these food items (OR = 0.85; 95% CI = 0.77–0.95 for sugary drinks in the conjunct of all waves; and OR = 0.64; 95% CI = 0.47–0.87, and OR = 0.74; 95% CI = 0.67–0.82 for sweets in 2002 and for the conjunct of all waves, respectively) (Table 3 ). Sex was associated with the outcome in the conjunct of all waves, and type of school was associated with the outcome in two waves and for the conjunct of waves. Both sex and type of school were included as control variables in the multivariate model of analysis. Table 3 Bivariate associations between overweight/obesity and exposition variable, and between overweight/obesity and food consumption and other control variables in the years 2002 to 2018/2019. EPOCA Project, Florianópolis, SC, Brazil. 2002 2007 2012/2013 2018/2019 All waves (n = 5694) Variables OR p-value (CI 95%) OR p-value (CI 95%) OR p-value (CI 95%) OR p-value (CI 95%) OR p-value (CI 95%) Birth weight LBW/IBW 1.00 1.00 1.00 1.00 1.00 ABW 1.40 0.064 (0.96–2.06) 1.19 0.461 (0.60–2.36) 1.63 0.054 (0.98–2.71) 1.28 0.266 (0.79–2.05) 1.35 < 0.001 (1.19–1.54) HBW 2.40 0.008 (1.53–3.75) 2.02 0.163 (0.59–6.84) 1.95 0.245 (0.44–8.58) 1.27 0.374 (0.70–2.30) 2.21 < 0.001 (1.77–2.77) Sex Male 1.00 1.00 1.00 1.00 1.00 Female 0.77 0.116 (0.53–1.12) 0.84 0.528 (0.40–1.77) 0.81 0.156 (0.57–1.15) 0.59 0.074 (0.33–1.06) 0.71 < 0.001 (0.64–0.79) Fruit consumption No 1.00 1.00 1.00 1.00 1.00 Yes 0.88 0.001 (0.86–0.90) 1.21 0.417 (0.62–2.36) 0.98 0.892 (0.69–1.38) 0.68 0.011 (0.52–0.89) 0.89 0.031 (0.80–0.98) Vegetables/Legumes consumption No 1.00 1.00 1.00 Yes 0.99 0.985 (0.80–1.24) 0.94 0.402 (0.79–1.12) 1.09 0.744 (0.48–2.44) 0.60 0.058 (0.36–1.02) 0.90 0.066 (0.81–1.00) Sugary drinks consumption No 1.00 1.00 1.00 1.00 1.00 Yes 0.75 0.108 (0.51–1.11) 0.85 0.322 (0.56–1.29) 0.95 0.805 (0.52–1.72) 1.07 0.520 (0.83–1.38) 0.85 0.005 (0.77–0.95) Table 3 Continuation. 2002 2007 2012/2013 2018/2019 All waves (n = 5694) Variables OR p-value (CI 95%) OR p-value (CI 95%) OR p-value (CI 95%) OR p-value (CI 95%) OR p-value (CI 95%) Sweets consumption No 1.00 1.00 1.00 1.00 1.00 Yes 0.64 0.019 (0.47–0.87) 0.64 0.105 (0.34–1.18) 0.90 0.596 (0.53–1.52) 0.87 0.545 (0.55–1.39) 0.74 < 0.001 (0.67–0.82) Type of school Public 1.00 1.00 1.00 1.00 1.00 Private 1.50 0.003 (1.29–1.73) 1.27 0.018 (1.08–1.49) 1.41 0.048 (1.00–1.98) 0.87 0.250 (0.69–1.11) 1.23 < 0.001 (1.10–1.37) Year of study 2002 - - - - - - - - 1.00 2007 - - - - - - - - 1.17 0.030 (1.01–1.34) 2012 - - - - - - - - 1.31 < 0.001 (1.14–1.49) 2018 - - - - - - - - 1.25 0.004 (1.07–1.46) OR = Odds ratios; CI = Confidence Interval; LBW/IBW = Low birth weight/ Insufficient birth weight; ABW = Adequate birth weight; HBW = high birth weight. In Table 4 , although the model was adjusted for the food consumption variables, the association of HBW with overweight/obesity remained statistically significant in the conjunct of all waves (OR = 1.63; 95% CI = 1.32–2.00). LBW, on the other hand, shows an inverse association with the outcome, indicating that children born with low and insufficient birth weight have less chance of being overweight/obese (OR = 0.74; 95% CI = 0.65–0.85). Table 4 Adjusted associations between birth weight and overweight/obesity in the years 2002 to 2018/2019. EPOCA Project, Florianópolis, SC, Brazil. Variables Overweight/ Obesity OR 95% CI p-value Birth weight Adequate birth weight 1.00 Low/Insufficient birth weight 0.74 0.65–0.85 < 0.001 High birth weight 1.63 1.32–2.00 < 0.001 Adjusted model including as control variables: fruit consumption, vegetable/legumes consumption, sugary drinks consumption, sweets consumption, sex, year of survey, and type of school. All variables obtained a value of p < 0.20 in the simple models. Bold values denote statistical significance at the p < 0.05 level. 4. Discussion The prevalence of low birth weight among the schoolchildren from Florianópolis in 2002 was lower than the Brazilian rate (6.0% versus 8.0% [ 5 ]). However, the temporal trend in the city showed a progressively and statistically significant increase over 10 years, with an addition of 61.7% from 2002 to 2012/2013, exceeding the national indicators [ 6 ]. Low birth weight prevalence slightly decreased in 2018/2019 but remained higher than in the wave 2002 in statistically significant terms, with an addition of 58.3%. On the other hand, high birth weight prevalence in Florianópolis is lower than in other Brazilian cities, and it presented a statistically significant decrease over the same ten-year period (2002 to 2012/2014), falling 40.7%. Regarding overweight/obesity, approximately one-third of the schoolchildren presented this outcome over the 16 years evaluated, with no statistically significant changes. Associations between birth weight and overweight/obesity showed a statistically significant relationship between HBW and the outcome, despite the influence of variables related to food consumption. On the other hand, children born with low or insufficient birth weight had lower chances of being overweight/obese in childhood. Compared to South American and Brazilian prevalences in 2012, children evaluated in Florianópolis showed a higher rate of low birth weight (8.6% and 8.3% versus 9.7% in that year, respectively) [ 1 , 5 ]. Trombe et al. (2021) found a prevalence of 7.7% of children with low birth weight in Ribeirão Preto, state of São Paulo, in 2010 [ 10 ]. The present study found a lower prevalence only in the first wave, 2002. The higher prevalence and the increase in its rates in Florianópolis require that researchers and policymakers know if major efforts have been made to preserve the lives of small and preterm newborns. Pinheiro et al. (2010) analyzed survival rates in the first year of life in 90153 newborn children in the cities of Florianópolis and São José, finding a rate of 98.8% survival, high when compared to national and international standards [ 42 ]. Data on the National System of Liveborn strengthen this hypothesis because despite the increase in the prevalence of LBW in Florianópolis, mortality rates in children above 1 year from 2002 to 2018/2019 showed a continuous decrease (from 9.8 deaths by 1000 alive born in 2002 to 6.6 deaths by 1000 in 2018/2019) [ 43 ]. Regardless, causes of increasing low birth weight cannot be neglected and demand more careful monitoring of maternal health during pregnancy. This care should consider the premises of the Brazilian Policy for Integral Attention to the Child on the HealthCare System, which has one strategic action targeting the enhancement of obstetric and neonatal attention [ 44 ]. The significant reduction in high birth weight prevalence in Florianópolis for ten years (9.1–5.4%) could indicate that maternal health during pregnancy has focused on controlling glycemia and body weight. Data from 9,047,145 singletons from South Korea from the year 2000 to 2020 also showed a reduction, from 3.7–2.5% [ 45 ]. The prevalence of HBW in Florianópolis is higher than in South Korea. However, its decrease achieved in 2018 is expressive lower if compared to national data found in the United States of America (8.9%) [ 46 ]. In Brazil, pregnant women with diabetes are considered at risk in the public health care service and may receive more frequent and specialized treatment [ 47 ]. Marano et al. (2024) developed a study evaluating 12712 recent mothers in Brazilian hospitals. They found that diabetic mothers received adequate and more than adequate prenatal care more frequently than mothers in other risk categories (74.1% versus 65.0%, respectively) [ 48 ]. The association between HBW and overweight/obesity was also found in other samples from Brazilian and foreign studies. In Santa Cruz do Sul (South of Brazil), being born with more than 3000 grams increased by 18% the chance of being overweight/obese at the ages of seven to fourteen, despite controlling for other biological variables and socioeconomic and behavioral factors [ 49 ]. In another sample from Santa Cruz do Sul, Brand et al. (2022) evaluated 1562 children and adolescents aged between six and 17 and found the same positive association between birth weight and overweight (ß=0.84; 95% CI = 0.08–1.60) in models adjusted for the children's and mother's age, sex, sexual maturation, skin color/ethnicity, and education level [ 50 ]. Analyzing schoolchildren from 9 to 11 years living in São Caetano do Sul (Southeast of Brazil), Dos Santos et al. (2022) found a positive and statistically significant association between birth weight and body mass index and birth weight and body fat (ß=0.001; 95% CI:0.001–0.002, and ß=0.002; 95% CI:0.001–0.003, respectively) in a model adjusted for sex, age, type of school, family income, and levels of physical activity [ 51 ]. Despite the inverse association between low/insufficient birth weight and overweight/obesity, children born with more than 3000 grams are at risk of developing other chronic diseases. In a meta-analysis of 28 studies, Martín-Calvo et al. (2021) found that children and adolescents born small for gestational age had a 2.33-fold higher risk of presenting type 2 diabetes (95%CI = 1.05–5.17). This meta-analysis also found that both LBW and being born small for the gestational age were associated with higher insulin resistance [ 52 ]. The findings related to the temporal trends show a statistically significant reduction in high birth weight prevalence over ten years and stability of overweight/obesity prevalence among schoolchildren aged seven to ten. However, the association between high birth weight and overweight/obesity remained statistically significant in adjusted models over the sixteen years. This means that the reduction in the prevalence of high birth weight over time was insufficient to reduce the outcome populationally. The efforts to prevent and treat the causes of being born with 4 or more kilograms must be taken in terms of public health, especially regarding gestational diabetes and gestational obesity. The study's limitations include the lower participation rate in the 2018/2019 wave, mainly due to the difficulty in obtaining parental consent and the long questionnaire parents had to fill in, which could represent a selection bias. Furthermore, the cross-sectional design does not allow assessing cause and effect associations. 5. Conclusions This study observed an important increase in the temporal trend of low-birth-weight prevalence from 2002 to 2012/2013 among schoolchildren from Florianópolis. Despite a discrete decrease in the next six years, the rise in low-birth-weight prevalence from 2002 to 2018/2019 remained statistically significant. On the other hand, high birth weight presented a statistically significant decrease over the ten years from 2002 to 2012/2013. Associations between birth weight and overweight/obesity showed a statistically significant relationship between HBW and the outcome, despite the influence of variables related to food consumption and the decrease of HBW over time. More public health efforts are needed to prevent macrosomia, given its long-term relationship with overweight and obesity. Declarations Declaration of generative AI and AI-assisted technologies in the writing process While preparing this work, the author(s) used Grammarly solely to improve the formal English language. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the publication's content. Author Contributions Designed the study: CER and FAGV; Collection of data: PFH; Performed the analyses: CER and BPB; Wrote the article: CER; Reviewed the manuscript: PFH AND FAGV. All authors contributed to the final version of the manuscript and approved the manuscript. 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Lancet 403:1071–1080 Buriol VCS, Hirakata V, Goldani MZ et al (2016) Temporal evolution of the risk factors associated with low birth weight rates in Brazilian capitals (1996–2011). Popul Health Metrics 14:15. 10.1186/s12963-016-0086-0 Wilcox AJ (2001) On the importance - and the unimportance - of birthweight. Int J Epidemiol 130:1233–1241 Nicolosi BF, Vernini JM, Costa RA et al (2020) Maternal factors associated with hyperglycemia in pregnancy and perinatal outcomes: a Brazilian reference center cohort study. Diabetol Metab Syndr 12:49 Ye W, Luo C, Huang J et al (2022) Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis. BMJ 377:e067946 Madi SRC, Garcia RMR, de Souza VC et al (2017) Effect of Obesity on Gestational and Perinatal Outcomes. Rev Bras Ginecol Obstet 39:330–336 Trombe KSD, Rodrigues LS, Nascente LMP et al (2021) Is birth weight associated with pregestational maternal BMI? BRISA Cohort, Ribeirão Preto, Brazil. Braz J Med Biol Res 54(1):e10037 Rito AI, Buoncristiano M, Spinelli A et al (2019) Association between Characteristics at Birth, Breastfeeding and Obesity in 22 Countries: The WHO European Childhood Obesity Surveillance Initiative – COSI 2015/2017. Obes Facts 12:226–243 Gomes KBA, Leal VS, Oliveira J et al (2021) Birth weight and overweight in adolescents: the Erica Project in the city of Recife. Pernambuco Rev Paul Pediatr 39:e2019380 Farias DR, Poston L, Franco-Sena AB et al (2017) Maternal lipids and leptin concentrations are associated with large-for-gestational-age births: a prospective cohort study. Sci Rep 7:804 Rossi CE, Vasconcelos FAG (2014) Relationship between birth weight and overweight/obesity among students in Florianópolis, Santa Catarina, Brazil: a retrospective cohort study. Sao Paulo Med J 132(5):273–281 Feng C, Osgood ND, Dyck RF (2018) Low Birth Weight, Cumulative Obesity Dose, and the Risk of Incident Type 2 Diabetes. J Diabetes Res Article ID 8435762, 9 pages. Umer A, Hamilton C, Cottrell L et al (2020) Association between birth weight and childhood cardiovascular disease risk factors in West Virginia. J Dev Origins Health Disease 11:86–95 Wei X, Hu J, Liu Y et al (2022) Association between Marginally Low Birth Weight and Obesity-Related Outcomes and Indirect Effects via Attention-Deficit. Hyperactivity Disorder and Abnormal Eating. Obes Facts 15:197–208 Zhou J, Zeng L, Wang D et al (2020) Effects of birth weight on body composition and overweight/obesity at early school age. Clin Nutr 39:1778e1784 He X, Shao Z, Jing J et al (2022) Secular trends of birth weight and its associations with obesity and hypertension among Southern Chinese children and adolescents. J Pediatr Endocrinol Metab 35(12):1487–1496 Magnusson Å, Laivuori H, Loft A et al (2021) The Association Between High Birth Weight and Long-Term Outcomes - Implications for Assisted Reproductive Technologies: A Systematic Review and Meta-Analysis. Front Pediatr 9:675775 Shi J, Guo Q, Fang H et al The Relationship between Birth Weight and the Risk of Overweight and Obesity among Chinese Children and Adolescents Aged 7–17 Years. Nutrients 2024;16, 715 Victor A, Aguiar IWO, Flores-Ortiz R, Mahoche M, Gotine ARM, Falcão I, Vasco MD, Ferreira A, Xavier SP, Omenka M, Antunes JLF, Rondo PH (2024) Social Inequalities in Child Development: Analysis of Low-Birth-Weight Trends in Brazil, 2010–2020. Journal of prevention (2022), 45(4), 545–555. https://doi.org/10.1007/s10935-024-00768-0 Bernardo CO, Pudla KJ, Longo GZ et al (2012) Factors associated with nutritional status of 7–10 year-old schoolchildren: sociodemographic variables, dietary and parental nutritional status. Revista Brasileira de Epidemiologia 15(3):651–661 Wagner KJP, de Fragas Hinnig P, Rossi CE et al (2020) Time trends in the prevalence of breastfeeding among schoolchildren from public and private schools in Florianópolis, Southern Brazil: From 2002 to 2013. Am J Hum Biol ;e23386 Pereira LJ, Vieira FGK, Belchor ALL et al (2022) Methodological aspects and characteristics of participants in the Study on the Prevalence of Obesity in Children and Adolescents in Florianópolis, Southern Brazil, 2018–2019: EPOCA study. Ann Epidemiol. 10.1016/J.ANNEPIDEM.2022.10.017 Kupek E, Liberali R, de Assis MAA (2022) Time trend estimation of food consumption in repeated studies with different versions of food questionnaire among Brazilian schoolchildren aged 7 to 11 years. Ciênc saúde coletiva 27(2):665–676 Shenkin SD, Zhang MG, Der G et al (2017) Validity of recalled v. recorded birth weight: a systematic review and meta-analysis. J Dev Origins Health Disease 8(2):137–148 Araujo CL, Dutra CL, Hallal PC (2007) Validity of maternal report on birth weight 11 years after delivery: the 1993 Pelotas Birth Cohort Study, Rio Grande do Sul State, Brazil. Cad Saude Publica 23(10):2421–2427 de Assis MAA, Rolland-Cachera MF, Grosseman S et al (2005) Obesity, overweight and thinness in schoolchildren of the city of Florianópolis, Southern Brazil. Eur J Clin Nutr 59:1015–1021 Leal DB, de Assis MAA, Conde WL et al (2018) Individual characteristics and public or private schools predict the body mass index of Brazilian children: a multilevel analysis. Cadernos de saúde pública 34(5):e00053117 Lobo AS, de Assis MAA, Leal DB et al (2019) Empirically derived dietary patterns through latent profile analysis among Brazilian children and adolescents from Southern Brazil, 2013–2015. PLoS ONE 14(1):e0210425 Motter AF, Vasconcelos FAG, Correa EN et al (2015) Retail food outlets and the association with overweight/obesity in schoolchildren from Florianopolis, Santa Catarina State, Brazil. Cadernos de Saúde Publica 31(3):620–632 D’Avila GL, Silva DAS, Vasconcelos FAG (2016) Association between dietary intake, physical activity, socioeconomic factors and body fat percentage among schoolchildren. Ciênc saúde coletiva 21(4):1071–1081 Lohman T, Roche A, Martorell R (1988) Anthropometric Standardization Reference Manual. Human Kinetics Books, Champaign WHO (2006) WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-forage: methods and development. World Health Organisation, 1–312 Barros MVG, Assis MAA, Pires MC et al (2007) Validity of physical activity and food consumption questionnaire for children aged seven to ten years old. Rev Bras Saude Mater Infant 7(4):437–448 Davies VF, Kupek E, de Assis MA et al (2015) Validation of a web-based questionnaire to assess the dietary intake of Brazilian children aged 7–10 years. J Hum Nutr Dietetics 28(s1):93–102 Jesus GM, de Assis MAA, Kupek E (2017) Validity and reproducibility of an Internet-based questionnaire (Web-CAAFE) to evaluate the food consumption of students aged 7 to 15 years. Cadernos de saúde pública 33(5):e00163016 Perazi FM, Kupek E, de Assis MAA et al (2020) Efeito do dia e do número de dias de aplicação na reprodutibilidade de um questionário de avaliação do consumo alimentar de escolares. Revista Brasileira de Epidemiologia 23:E200084 Brazil, Ministry of Health. Secretariat of Health Care. Department of Primary Care (2014). Food Guide for the Brazilian Population. [ Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. Departamento de Atenção Básica. Guia Alimentar para a População Brasileira]. – 2. ed., 1. reimpr. – Brasília: Ministério da Saúde , 156 p. Available: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://bvsms.saude.gov.br/bvs/publicacoes/guia_alimentar_populacao_brasileira_2ed.pdf . Accessed on 15 mar 2024 Curi AZ, Filho NAM (2010) Determinants of education spending in Brazil. [ Determinantes dos gastos com educação no Brasil ]. Pesquisa Planej Econômico 40(1):1–40 Pinheiro CEA, Peres MA, D’ Orsi E (2010) Increased survival among lower-birthweight children in Southern Brazil. Rev Saúde Pública 44(5):1–8 Directorate of Epidemiological Surveillance (2024) Mortality/live births. [Diretoria de Vigilância Epidemiológica. Mortalidade / nascidos vivos]. Available at: http://tabnet.dive.sc.gov.br/ . Accessed on 21 Brazil, Ministry of Health (2015). Ordinance No. 1,130 of August 5, Institutes the National Policy for Comprehensive Child Health Care within the scope of the Unified Health System (SUS). [Ministério da Saúde. Portaria nº 1.130 de 5 de agosto de 2015. Institui a Política Nacional de Atenção Integral à Saúde da Criança no âmbito do Sistema Único de Saúde (SUS)]. Available at: https://bvsms.saude.gov.br/bvs/saudelegis/gm/2015/prt1130_05_08_2015.html Hur Y (2023) Secular Trends of Birth Weight in Twins and Singletons in South Korea from 2000 to 2020. Twin Res Hum Genet 26:171–176 Scifres CM (2021) Short - and Long-Term Outcomes Associated with Large for Gestational Age Birth Weight. Obstet Gynecol Clin N Am 48:325–337 Ministry of Women, Racial Equality and Human Rights. Monitoring and follow-up of the National Policy for Comprehensive Health Care for Women and the National Policy Plan for Women. [Ministério das Mulheres, da Igualdade Racial e dos Direitos Humanos. Monitoramento e acompanhamento da Política Nacional de Atenção Integral à Saúde da Mulher e do Plano Nacional de Políticas para as Mulheres] (2015) 50 p. Available at: https://www.gov.br/mdh/pt-br/navegue-por-temas/politicas-para-mulheres/arquivo/central-de-conteudos/publicacoes/publicacoes/2015/pnaism_pnpm-versaoweb.pdf . Accessed on 21 mar 2024 Marano D, Magalhães CAS, Moreira MEL, Dias MAB (2024) Neonatal adverse outcomes and associated factors among pregnant women with gestational and usual risk of diabetes mellitus. Demetra 19:e73514 Reuter CP, de Mello ED, da Silva PT et al Overweight and Obesity in Schoolchildren: Hierarchical Analysis of Associated Demographic, Behavioral, and Biological Factors. J Obes 2018;Article ID 6128034, 6 pages Brand C, Fochesatto CF, Villa-González E et al (2022) From pregnancy to breastfeeding: adequate maternal body mass index is essential to prevent a high body mass index in your children. J Pediatr Endocrinol Metabolism 35(8):1033–1040 dos Santos MD, Ferrari G, Drenowatz C et al (2022) Association between breastfeeding, parents' body mass index and birth weight with obesity indicators in children. BMC Pediatr 22:604 Martín-Calvo N, Goni L, Tur JA, Martínez JA (2022) Low birth weight and small for gestational age are associated with complications of childhood and adolescence obesity: Systematic review and meta-analysis. Obes Rev 23(S1):e13380 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6335278","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435767529,"identity":"f0f3175b-f3eb-4bfe-bd91-f7ae0a8a6696","order_by":0,"name":"Camila Elizandra Rossi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYJCCAw/AFGPjAwYbORDLgIGxgYCWBIiWZgOGNGPitDBAtDCwSRClhb/9dOKBhJp7cvL9h9uqeRIM7BnYm7dJMO64h1OLxJncDQcSjhUbG9xIbLsN1JLYwHOsTILxTDFOLQYMIC1sCYkbJBjbbvP++JPAIJFjBmQn4NbC/xao5V9C4vz+g23FYIfJvyGgRQJoS2JbQmIDkGQGamFskODBr0XiBtCWxL4EkF+aJecA/dLGk1ZskXgGtxb+/tzNHz58SwCG2PGHH94AHcbPfnjjjY87cGvBBGwgghQNo2AUjIJRMAowAQAGqFYWLS+8xAAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal da Fronteira Sul","correspondingAuthor":true,"prefix":"","firstName":"Camila","middleName":"Elizandra","lastName":"Rossi","suffix":""},{"id":435767530,"identity":"1b8260ad-e917-4e62-b1cc-dd9a19c933b4","order_by":1,"name":"Bernardo Paz Barboza","email":"","orcid":"","institution":"Universidade Federal de Santa Catarina","correspondingAuthor":false,"prefix":"","firstName":"Bernardo","middleName":"Paz","lastName":"Barboza","suffix":""},{"id":435767531,"identity":"00b113b9-2fa9-45e2-9393-a26efad78202","order_by":2,"name":"Patrícia de Fragas Hinnig","email":"","orcid":"","institution":"Universidade Federal de Santa Catarina","correspondingAuthor":false,"prefix":"","firstName":"Patrícia","middleName":"de Fragas","lastName":"Hinnig","suffix":""},{"id":435767532,"identity":"fbf3cc5c-b8a3-4fb2-a891-0ad19ec1e2a0","order_by":3,"name":"Francisco de Assis Guedes de Vasconcelos","email":"","orcid":"","institution":"Universidade Federal de Santa Catarina","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"de Assis Guedes","lastName":"de Vasconcelos","suffix":""}],"badges":[],"createdAt":"2025-03-29 17:04:13","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6335278/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6335278/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79648971,"identity":"8adc01c4-362e-4557-905e-6eaf819e3846","added_by":"auto","created_at":"2025-04-01 07:35:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1186568,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6335278/v1/25ba92e3-e241-49f6-81b0-ce117428e355.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTemporal trends in birth weight and its association with overweight/obesity in schoolchildren from a Brazilian city: a cross-sectional panels study (2002 to 2019)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBirth weight is an important maternal and early childhood health indicator. Regardless of their gestational age, newborns with less than 2500 grams are considered low birth weight (LBW) children [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Causal factors for LBW include prematurity, maternal undernutrition, or other gestational or intrauterine issues [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Data from 158 countries and geographic regions show an LBW rate of 14.7% in 2020, with Latin America and the Caribbean presenting lower rates \u0026minus;\u0026thinsp;9.6% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Brazil's capital and the 26 states of the nation, the indicator was stable between 1996 and 2011, totaling around 8% of the newborns each year [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. On the other hand, high birth weight (HBW), when the newborn weighs more than 3999 grams [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], is also worrying because it indicates the previous occurrence of maternal risk factors such as gestational diabetes mellitus [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], pregestational obesity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], or obesity during the pregnancy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. High birth weight prevalence in countries of the Northern Hemisphere varied from 5.1% in Spain and Tajikistan to 19.2% in Ireland [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Brazil, estimates of HBW also varied according to the counties, with the cities of Recife (Northeastern Brazil) presenting 11.9% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], Rio de Janeiro, 18.1% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and Florian\u0026oacute;polis amounting to 7.4 to 7.9% [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn scientific literature, LBW is associated with long-term metabolic issues that may already appear in childhood, such as hypertriglyceridemia, dyslipidemia, fasting hyperglycemia, risk of type-2 diabetes mellitus related to obesity, and excessive body fat [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Long-term unwanted health events associated with high birth weight are, for instance, those related to overweight, obesity, and excess body fat [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], but also breast cancer, psychiatric disorders, hypertension, and type-1 and 2 diabetes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. When investigating the association of birth weight with overweight/obesity at ages seven to 17 in China, Shi et al. (2024) observed that the highest odds ratios for overweight were presented by those born with more than 3999g [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTemporal trends in low and high birth weight for different countries are not available in the international scientific literature. For Brazil, the analyses are scarce, with few studies evaluating the temporal trend in LBW for the country and its regions. Buriol et al. (2016) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] brings data from 1996 to 2011, and Victor et al. (2022) brings data sequentially, from 2010 to 2020. The last data show an increase in proportions of LBW in the North, Northeast, and Centre-West regions while in the country and other regions the tendencies remained stable [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, the need for studies on temporal trends in HBW in scientific literature limits long-term analysis of efforts to improve the maternal and child health profile, making it difficult to assess the occurrence of undesirable prospective outcomes of birth weight in infancy.\u003c/p\u003e \u003cp\u003eThus, this article aimed to verify the temporal trends in the prevalence of low and high birth weight and its association with overweight/obesity in schoolchildren from seven to ten in Florian\u0026oacute;polis, state of Santa Catarina, from 2002 to 2019.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eData were collected through the Study on the Prevalence of Obesity in Children and Adolescents (Estudo da Preval\u0026ecirc;ncia da Obesidade em Crian\u0026ccedil;as e Adolescentes, EPOCA) in Florian\u0026oacute;polis, providing an analysis of four cross-sectional panels carried out in 2002, 2007, 2012/2013, and 2018/2019 [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. EPOCA is a probabilistic school-based study that aims to investigate the prevalence trends of obesity and its associated factors among children and adolescents aged between seven and 14 years old [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] However, in the present study, only data from students aged seven to ten were used. Methodological strategy based on evidence from scientific literature, which states that the validity of the birth weight reported by relatives is usually high only in samples from high-income countries and tends to reduce when children are older [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe parameters used to select the sample were similar in each of the four waves. The first stage of the sampling process was randomly selecting the schools from clusters according to the geographic area (Centre, Continent, North, East, and South) and type of school (public or private). The second stage varied for each analyzed year. In 2002, all classes in each school were included and all the children in the 2nd to 5th grades were invited to participate, resulting in a sample of 2936 schoolchildren [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In 2007, students in each school were selected considering the ratios of schoolchildren registered in the 2004 School Census in Florian\u0026oacute;polis (53595 individuals), and placed in the following categories: geographical location of the student's home, type of school, sex, and age group, resulting in 1232 schoolchildren [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the 2012/2013 and 2018/2019 surveys, the sample was calculated based on information from the 2010 and 2017 School Censuses, considering the expected prevalence of overweight/obesity of 38% in 2012/2013 and 39% in 2018/2019, resulting in 1531 and 986 students, respectively [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e we describe some sampling details from each wave. More details in each panel can be found in other publications [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSampling details of the EPOCA surveys in 2002, 2007, 2012/2013 and 2018/2019. Florian\u0026oacute;polis, Brazil.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2002\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2007\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012/2013\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2018/2019\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation of 7\u0026ndash;10 years-old schoolchildren in the municipality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal elementary schools in the municipality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal of schools included in the survey\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevalence of Overweight/Obesity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling error (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5 (two-tailed) with a 95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5 (two-tailed) with a 95% CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutline effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional (Losses/Refusals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpected Final Sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of invited students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of students investigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026dagger; School units were drawn based on geographic region, type of administration (public and private), and number of students; \u003csup\u003ea\u003c/sup\u003e de Assis et al. (2005), Wagner et al., (2019); \u003csup\u003eb\u003c/sup\u003e Bernardo et al. (2012); \u003csup\u003ec\u003c/sup\u003e Gonzalez et al. (2017); \u003csup\u003ed\u003c/sup\u003e Pereira et al. (2023).\u003c/p\u003e \u003cp\u003eAll Brazilian schools have morning and afternoon shifts, and students attend classes in either one of these periods. Hence, the inclusion criteria were to attend school on the day of data collection, to have the Free and Informed Consent Form signed by parents or guardians, and to have the Free and Informed Assent Term signed by the student at the time of the data collection (only for wave 2018 /2019). The exclusion criteria were unavailable birth weight (2002: n\u0026thinsp;=\u0026thinsp;651; 2007: n\u0026thinsp;=\u0026thinsp;55; 2012/2013: n\u0026thinsp;=\u0026thinsp;80; 2018/2019: n\u0026thinsp;=\u0026thinsp;94), and unavailable weight and height data due to physical disabilities that did not allow anthropometric evaluation (2012/2013: n\u0026thinsp;=\u0026thinsp;108; 2018/2019: n\u0026thinsp;=\u0026thinsp;126)28\u0026ndash;32. In the four waves, the projects were approved by the Humans Ethics Committee under n\u0026ordm;. 037/02, 028/2006, CAAE 02713312000000121 and 87539718.1.0000.0121.\u003c/p\u003e \u003cp\u003eIn the four waves, the data on birth weight was self-reported by parents or others responsible for the child. The variable birth weight was categorized as follows: low\u0026thinsp;\u0026lt;\u0026thinsp;2500g; insufficient\u0026thinsp;\u0026ge;\u0026thinsp;2500 and \u0026le;\u0026thinsp;2999g; adequate 3000-3999g (World Health Organization, 1995) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]; or high\u0026thinsp;\u0026ge;\u0026thinsp;4000g [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the four cross-sectional panels, weight and height measurements were collected by a previously trained team following the WHO protocol proposed by Lohman [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Children's weight status was classified according to the Body Mass Index (BMI) z-score for age into two categories: \"not overweight or obese\" (z-score\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;1) and \"overweight or obese\" (z-score\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;1) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Data on age, sex, and type of school (public or private) were obtained from a list provided by the schools in all the waves.\u003c/p\u003e \u003cp\u003eIn the 2002 cross-sectional panel, the children responded to a Typical Day Food Questionnaire (TDFQ), a pen-and-paper pictorial questionnaire (colored and printed on A4-size paper), in which they were supposed to check the 16 food illustrations consumed on a typical day, distributed in five meals organized chronologically (breakfast, morning snack, lunch, afternoon snack, and dinner) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The food survey was validated against the 24-hour recall method and showed moderate agreement in a sample of schoolchildren from Florian\u0026oacute;polis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In the 2007 and 2012/2013 waves, data on food consumption were collected using the Previous Day Questionnaire (PDQ), an analog to the TDFQ, previously validated for collecting data in this age group [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The questionnaire provides data on 21 items or groups, including one more meal than the TDFQ, an evening snack before bed (de Assis et al., 2009) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the 2018/2019 panel, data regarding food consumption were collected through the Web-CAAFE (Consumo Alimentar e Atividade F\u0026iacute;sica de Escolares - Food Consumption and Physical Activities of Schoolchildren) questionnaire. The Web-CAAFE was developed based on the previous pencil-and-paper instruments and was subjected to reproducibility, usability, and validity tests [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The food consumption section consists of the six meals previously defined, and the presence of an animated character (avatar) helped the child identify which meal was being addressed at that time. For example, the avatar explains: \"Breakfast is the first meal we have in the day, right after waking up\". For each meal, children can select 31 images of items or food groups on the computer screen [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The questionnaires on food consumption were all qualitative and applied throughout the year's four seasons. The application covered all days with classes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVariables related to food consumption were grouped considering the markers of healthy and unhealthy food consumption cited on the Brazilian Food Guidelines published by the Ministry of Health [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], resulting in the following variables responded as yes/no: a) healthy food consumption - consumption of fruits, consumption of legumes or vegetables; b) unhealthy food consumption - consumption of sweets and soft drinks. In this article, as a methodological analysis procedure, we considered that the schoolchildren consumed the food when they entered or indicated the consumption of at least one of the items of each food group, by filling out the survey instruments [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBirth weight was considered the main independent variable while overweight/obesity was considered as the outcome variable. As the biological characteristics and the behaviors adopted by each child might directly influence the weight status, variables related to food consumption and sex were considered control variables. Regarding socioeconomic characteristics, it is known that social determinants are related to access to health services and information on this subject. Curi and Filho (2010) observed that 80% of Brazilian children from wealthier families go to private schools, and the monthly family income is directly associated with the chance of being in a private school [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Thus, given this characteristic of Brazilian schoolchildren and because the \"family income\" variable had few answers in our study, the variable \"type of school\" was included in the multivariate model as a control variable and as a proxy for family income.\u003c/p\u003e \u003cp\u003eThe statistical analysis was performed using STATA version 14.0. For this study, the variables that characterize the sample, such as sex, type of school, birth weight, and weight status (BMI-for-age) were described as absolute and relative frequencies using the \"svy\" command, which considers the sample weight of each cross-sectional wave. The temporal trends for the birth weight categories were described using 95% Confidence Intervals (95% CI), and the overlapping of the 95% CI indicates no differences between the prevalences over the years.\u003c/p\u003e \u003cp\u003eTo assess the association between the exposition and the outcome variables, a fusion was made between categories of birth weight. LBW and IBW (insufficient birth weight) were aggregated into one category named LBW, for simplicity. Bivariate logistic regression was carried out between birth weight, the control variables, and weight status (outcome) for each wave and the waves as a group. Subsequently, a multiple logistic regression was performed to analyze the adjusted association between birth weight and overweight/obesity for all waves.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eAfter applying the exclusion criteria, samples of 2243, 1177, 1446, and 828 schoolchildren were obtained in 2002, 2007, 2012/2013, and 2018/2019, respectively, resulting in a final sample with 5694 observations. The composition of each of the samples' waves was similar: a majority of female schoolchildren enrolled in public schools, with approximately one-third of the children being overweight or obese. In the 2007 wave, a statistically significant reduction in the participation of students in private schools required a greater inclusion of students enrolled in public schools (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the sample in the years 2002, 2007, 2012/2013 and 2018/2019. EPOCA Project, Florian\u0026oacute;polis, SC, Brazil.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2243)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1177)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e2012/ 2013\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1446)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e2018/ 2019\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;828)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.7\u0026ndash;50.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.7\u0026ndash;51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e44.5\u0026ndash;49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e40.9\u0026ndash;47.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.2\u0026ndash;53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.5\u0026ndash;54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50.4\u0026ndash;55.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e52.4\u0026ndash;59.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eType of school\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.7\u0026ndash;64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e76.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e74.1\u0026ndash;79.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e61.8\u0026ndash;66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e56.8\u0026ndash;63.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.3\u0026ndash;39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e23.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e21.0\u0026ndash;25.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.3\u0026ndash;38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e36.6\u0026ndash;43.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBirth weight (g)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(Mean, SD, min.; max.)\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e650; 5500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e555.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9; 5500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e700; 5630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e550; 5500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBirth weight\u003c/em\u003e \u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.1\u0026ndash;7.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.8\u0026ndash;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e9.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e8.3\u0026ndash;11.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e9.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e7.7\u0026ndash;11.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.0\u0026ndash;20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.1\u0026ndash;21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.4\u0026ndash;23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e17.7\u0026ndash;23.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.4\u0026ndash;68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e62.8\u0026ndash;68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e61.0\u0026ndash;65.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e60.2\u0026ndash;66.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8.0\u0026ndash;10.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.6\u0026ndash;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e5.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e4.3\u0026ndash;6.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.1\u0026ndash;8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWeight status\u003c/em\u003e \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout OW/OB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.4\u0026ndash;70.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.7\u0026ndash;69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e61.2\u0026ndash;66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e65.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e61.8\u0026ndash;68.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith OW/OB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.7\u0026ndash;33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.9\u0026ndash;36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.8\u0026ndash;38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e31.7\u0026ndash;38.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e 95% CI\u0026thinsp;=\u0026thinsp;95% confidence interval. \u003csup\u003eb\u003c/sup\u003e SD\u0026thinsp;=\u0026thinsp;standard deviation; min. = minimum value; max. = maximum value; \u003csup\u003ec\u003c/sup\u003e LBW\u0026thinsp;=\u0026thinsp;low birth weight; IBW\u0026thinsp;=\u0026thinsp;insufficient birth weight; ABW\u0026thinsp;=\u0026thinsp;adequate birth weight; HBW\u0026thinsp;=\u0026thinsp;high birth weight; \u003csup\u003ed\u003c/sup\u003e OW\u0026thinsp;=\u0026thinsp;overweight; OB\u0026thinsp;=\u0026thinsp;obesity; Data available for the variable weight status was 1437 in 2012/2013.\u003c/p\u003e \u003cp\u003eThe increase in rates of low birth weight was statistically significant, going from 6.0 to 9.7% between 2002 and 2012/2013, representing an increase of 61.7%. The prevalence slightly decreased in 2018/2019 but remained significantly higher in statistical terms compared to the 2002 wave, with an addition of 58.3%. High birth weight, contrarily, presented a statistically significant decrease between the same waves where the low birth weight decreased (2002 and 2012/2013), with a 40.7% reduction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBivariate analyses showed a significant association between HBW and overweight/obesity in the conjunct of all waves (OR\u0026thinsp;=\u0026thinsp;2.21; 95% CI\u0026thinsp;=\u0026thinsp;1.77\u0026ndash;2.77), results that come from the sample evaluated in 2002 (OR\u0026thinsp;=\u0026thinsp;2.40; 95% CI\u0026thinsp;=\u0026thinsp;1.53\u0026ndash;3.75) when the highest prevalence of HBW was found. Beyond this association, it is possible to verify that food consumption is also associated with overweight/obesity as an actual influence on schoolchildren's lives. Fruit consumption, for example, is inversely related to the outcome in two waves (2002 and 2018/2019) and when all waves are analyzed together (OR\u0026thinsp;=\u0026thinsp;0.89; 95% CI\u0026thinsp;=\u0026thinsp;0.80\u0026ndash;0.98). The consumptions of sugary drinks and sweets were inversely associated with overweight/obesity, which may indicate that children with these conditions are avoiding these food items (OR\u0026thinsp;=\u0026thinsp;0.85; 95% CI\u0026thinsp;=\u0026thinsp;0.77\u0026ndash;0.95 for sugary drinks in the conjunct of all waves; and OR\u0026thinsp;=\u0026thinsp;0.64; 95% CI\u0026thinsp;=\u0026thinsp;0.47\u0026ndash;0.87, and OR\u0026thinsp;=\u0026thinsp;0.74; 95% CI\u0026thinsp;=\u0026thinsp;0.67\u0026ndash;0.82 for sweets in 2002 and for the conjunct of all waves, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sex was associated with the outcome in the conjunct of all waves, and type of school was associated with the outcome in two waves and for the conjunct of waves. Both sex and type of school were included as control variables in the multivariate model of analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate associations between overweight/obesity and exposition variable, and between overweight/obesity and food consumption and other control variables in the years 2002 to 2018/2019. EPOCA Project, Florian\u0026oacute;polis, SC, Brazil.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2012/2013\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2018/2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eAll waves (n\u0026thinsp;=\u0026thinsp;5694)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBirth weight\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBW/IBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.064 (0.96\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.461 (0.60\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.054 (0.98\u0026ndash;2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.266 (0.79\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (1.19\u0026ndash;1.54)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008 (1.53\u0026ndash;3.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.163 (0.59\u0026ndash;6.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.245 (0.44\u0026ndash;8.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.374 (0.70\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (1.77\u0026ndash;2.77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.116 (0.53\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.528 (0.40\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.156 (0.57\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.074 (0.33\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (0.64\u0026ndash;0.79)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFruit consumption\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001 (0.86\u0026ndash;0.90)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.417 (0.62\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.892 (0.69\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.011 (0.52\u0026ndash;0.89)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.031 (0.80\u0026ndash;0.98)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVegetables/Legumes consumption\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.985 (0.80\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.402 (0.79\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.744 (0.48\u0026ndash;2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.058 (0.36\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.066 (0.81\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSugary drinks consumption\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108 (0.51\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.322 (0.56\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.805 (0.52\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.520 (0.83\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.005 (0.77\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContinuation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2012/2013\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e2018/2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eAll waves (n\u0026thinsp;=\u0026thinsp;5694)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003ep-value (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSweets consumption\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019 (0.47\u0026ndash;0.87)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.105 (0.34\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.596 (0.53\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.545 (0.55\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (0.67\u0026ndash;0.82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eType of school\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003 (1.29\u0026ndash;1.73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.018 (1.08\u0026ndash;1.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.048 (1.00\u0026ndash;1.98)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.250 (0.69\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (1.10\u0026ndash;1.37)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear of study\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.030 (1.01\u0026ndash;1.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (1.14\u0026ndash;1.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e1.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.004 (1.07\u0026ndash;1.46)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOR\u0026thinsp;=\u0026thinsp;Odds ratios; CI\u0026thinsp;=\u0026thinsp;Confidence Interval; LBW/IBW\u0026thinsp;=\u0026thinsp;Low birth weight/ Insufficient birth weight; ABW\u0026thinsp;=\u0026thinsp;Adequate birth weight; HBW\u0026thinsp;=\u0026thinsp;high birth weight.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, although the model was adjusted for the food consumption variables, the association of HBW with overweight/obesity remained statistically significant in the conjunct of all waves (OR\u0026thinsp;=\u0026thinsp;1.63; 95% CI\u0026thinsp;=\u0026thinsp;1.32\u0026ndash;2.00). LBW, on the other hand, shows an inverse association with the outcome, indicating that children born with low and insufficient birth weight have less chance of being overweight/obese (OR\u0026thinsp;=\u0026thinsp;0.74; 95% CI\u0026thinsp;=\u0026thinsp;0.65\u0026ndash;0.85).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted associations between birth weight and overweight/obesity in the years 2002 to 2018/2019. EPOCA Project, Florian\u0026oacute;polis, SC, Brazil.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOverweight/ Obesity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBirth weight\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow/Insufficient birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.65\u0026ndash;0.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.32\u0026ndash;2.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdjusted model including as control variables: fruit consumption, vegetable/legumes consumption, sugary drinks consumption, sweets consumption, sex, year of survey, and type of school. All variables obtained a value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in the simple models. Bold values denote statistical significance at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe prevalence of low birth weight among the schoolchildren from Florian\u0026oacute;polis in 2002 was lower than the Brazilian rate (6.0% versus 8.0% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]). However, the temporal trend in the city showed a progressively and statistically significant increase over 10 years, with an addition of 61.7% from 2002 to 2012/2013, exceeding the national indicators [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Low birth weight prevalence slightly decreased in 2018/2019 but remained higher than in the wave 2002 in statistically significant terms, with an addition of 58.3%. On the other hand, high birth weight prevalence in Florian\u0026oacute;polis is lower than in other Brazilian cities, and it presented a statistically significant decrease over the same ten-year period (2002 to 2012/2014), falling 40.7%. Regarding overweight/obesity, approximately one-third of the schoolchildren presented this outcome over the 16 years evaluated, with no statistically significant changes. Associations between birth weight and overweight/obesity showed a statistically significant relationship between HBW and the outcome, despite the influence of variables related to food consumption. On the other hand, children born with low or insufficient birth weight had lower chances of being overweight/obese in childhood.\u003c/p\u003e \u003cp\u003eCompared to South American and Brazilian prevalences in 2012, children evaluated in Florian\u0026oacute;polis showed a higher rate of low birth weight (8.6% and 8.3% versus 9.7% in that year, respectively) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Trombe et al. (2021) found a prevalence of 7.7% of children with low birth weight in Ribeir\u0026atilde;o Preto, state of S\u0026atilde;o Paulo, in 2010 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The present study found a lower prevalence only in the first wave, 2002. The higher prevalence and the increase in its rates in Florian\u0026oacute;polis require that researchers and policymakers know if major efforts have been made to preserve the lives of small and preterm newborns. Pinheiro et al. (2010) analyzed survival rates in the first year of life in 90153 newborn children in the cities of Florian\u0026oacute;polis and S\u0026atilde;o Jos\u0026eacute;, finding a rate of 98.8% survival, high when compared to national and international standards [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Data on the National System of Liveborn strengthen this hypothesis because despite the increase in the prevalence of LBW in Florian\u0026oacute;polis, mortality rates in children above 1 year from 2002 to 2018/2019 showed a continuous decrease (from 9.8 deaths by 1000 alive born in 2002 to 6.6 deaths by 1000 in 2018/2019) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Regardless, causes of increasing low birth weight cannot be neglected and demand more careful monitoring of maternal health during pregnancy. This care should consider the premises of the Brazilian Policy for Integral Attention to the Child on the HealthCare System, which has one strategic action targeting the enhancement of obstetric and neonatal attention [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe significant reduction in high birth weight prevalence in Florian\u0026oacute;polis for ten years (9.1\u0026ndash;5.4%) could indicate that maternal health during pregnancy has focused on controlling glycemia and body weight. Data from 9,047,145 singletons from South Korea from the year 2000 to 2020 also showed a reduction, from 3.7\u0026ndash;2.5% [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The prevalence of HBW in Florian\u0026oacute;polis is higher than in South Korea. However, its decrease achieved in 2018 is expressive lower if compared to national data found in the United States of America (8.9%) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In Brazil, pregnant women with diabetes are considered at risk in the public health care service and may receive more frequent and specialized treatment [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Marano et al. (2024) developed a study evaluating 12712 recent mothers in Brazilian hospitals. They found that diabetic mothers received adequate and more than adequate prenatal care more frequently than mothers in other risk categories (74.1% versus 65.0%, respectively) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association between HBW and overweight/obesity was also found in other samples from Brazilian and foreign studies. In Santa Cruz do Sul (South of Brazil), being born with more than 3000 grams increased by 18% the chance of being overweight/obese at the ages of seven to fourteen, despite controlling for other biological variables and socioeconomic and behavioral factors [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In another sample from Santa Cruz do Sul, Brand et al. (2022) evaluated 1562 children and adolescents aged between six and 17 and found the same positive association between birth weight and overweight (\u0026szlig;=0.84; 95% CI\u0026thinsp;=\u0026thinsp;0.08\u0026ndash;1.60) in models adjusted for the children's and mother's age, sex, sexual maturation, skin color/ethnicity, and education level [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Analyzing schoolchildren from 9 to 11 years living in S\u0026atilde;o Caetano do Sul (Southeast of Brazil), Dos Santos et al. (2022) found a positive and statistically significant association between birth weight and body mass index and birth weight and body fat (\u0026szlig;=0.001; 95% CI:0.001\u0026ndash;0.002, and \u0026szlig;=0.002; 95% CI:0.001\u0026ndash;0.003, respectively) in a model adjusted for sex, age, type of school, family income, and levels of physical activity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the inverse association between low/insufficient birth weight and overweight/obesity, children born with more than 3000 grams are at risk of developing other chronic diseases. In a meta-analysis of 28 studies, Mart\u0026iacute;n-Calvo et al. (2021) found that children and adolescents born small for gestational age had a 2.33-fold higher risk of presenting type 2 diabetes (95%CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;5.17). This meta-analysis also found that both LBW and being born small for the gestational age were associated with higher insulin resistance [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings related to the temporal trends show a statistically significant reduction in high birth weight prevalence over ten years and stability of overweight/obesity prevalence among schoolchildren aged seven to ten. However, the association between high birth weight and overweight/obesity remained statistically significant in adjusted models over the sixteen years. This means that the reduction in the prevalence of high birth weight over time was insufficient to reduce the outcome populationally. The efforts to prevent and treat the causes of being born with 4 or more kilograms must be taken in terms of public health, especially regarding gestational diabetes and gestational obesity.\u003c/p\u003e \u003cp\u003eThe study's limitations include the lower participation rate in the 2018/2019 wave, mainly due to the difficulty in obtaining parental consent and the long questionnaire parents had to fill in, which could represent a selection bias. Furthermore, the cross-sectional design does not allow assessing cause and effect associations.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study observed an important increase in the temporal trend of low-birth-weight prevalence from 2002 to 2012/2013 among schoolchildren from Florian\u0026oacute;polis. Despite a discrete decrease in the next six years, the rise in low-birth-weight prevalence from 2002 to 2018/2019 remained statistically significant. On the other hand, high birth weight presented a statistically significant decrease over the ten years from 2002 to 2012/2013. Associations between birth weight and overweight/obesity showed a statistically significant relationship between HBW and the outcome, despite the influence of variables related to food consumption and the decrease of HBW over time. More public health efforts are needed to prevent macrosomia, given its long-term relationship with overweight and obesity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile preparing this work, the author(s) used Grammarly solely to improve the formal English language. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the publication\u0026apos;s content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesigned the study: CER and FAGV; Collection of data: PFH; Performed the analyses: CER and BPB; Wrote the article: CER; Reviewed the manuscript: PFH AND FAGV. All authors contributed to the final version of the manuscript and approved the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Children\u0026rsquo;s Fund (UNICEF) (2019) World Health Organization (WHO). UNICEF-WHO Low birthweight estimates: Levels and trends 2000\u0026ndash;2015. World Health Organization, Geneva\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO) (1995) Physical status: the use and interpretation of anthropometry. 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BMC Pediatr 22:604\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;n-Calvo N, Goni L, Tur JA, Mart\u0026iacute;nez JA (2022) Low birth weight and small for gestational age are associated with complications of childhood and adolescence obesity: Systematic review and meta-analysis. Obes Rev 23(S1):e13380\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidade Federal de Santa Catarina","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Low birth weight, High birth weight, children, overweight, obesity.","lastPublishedDoi":"10.21203/rs.3.rs-6335278/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6335278/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objectives\u003c/strong\u003e: Birth weight is an important indicator of maternal and early childhood health and is associated with long-term metabolic and anthropometric issues. We aim to verify trends in the prevalence of low and high birth weight over 16 years and associate the trends of these variables with overweight/obesity status in 7 to 10-year-old schoolchildren in Florianópolis, in the South of Brazil.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Data from four cross-sectional and probabilistic panels in 2002, 2007, 2012/2013, and 2018/2019 were analyzed. Multiple logistic regressions were used to assess the association between low and height birth weight and overweight/obesity in childhood. Analyzes included biological, socioeconomic and food intake variables in the adjusted models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In the adjusted model, the association of high birth weight with overweight/obesity was statistically significant in the conjunct of all waves (OR=1.63; 95% CI = 1.32 - 2.00). Contrarily, children born with low and insufficient birth weight had less chance of presenting overweight/obesity (OR=0.74; 95% CI = 0.65 - 0.85).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: A statistically significant increase in the temporal trend of low-birth-weight prevalence from 2002 to 2018/2019 was observed among schoolchildren from Florianópolis. The high birth weight and overweight/obesity were directly associated, despite the influence of variables related to food consumption and the decrease of the high birth weight over time.\u003c/p\u003e","manuscriptTitle":"Temporal trends in birth weight and its association with overweight/obesity in schoolchildren from a Brazilian city: a cross-sectional panels study (2002 to 2019)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 07:27:00","doi":"10.21203/rs.3.rs-6335278/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"766148ab-94d4-4b83-a9e2-bf3ad961b81d","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46399848,"name":"Epidemiology"}],"tags":[],"updatedAt":"2025-04-01T07:27:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 07:27:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6335278","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6335278","identity":"rs-6335278","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}