Childhood Overweight and Obesity Survey: An Overlooked Public Health Issue

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The 6th Health Administrative Region (ADM) of Greece reports the highest childhood obesity rates nationally (18.8%), however, comprehensive local-level data using standardized methodology are lacking. Methods: A cross-sectional survey was conducted between Novermber 2024 and January 2025 across healthcare facilities in the region of 6 th Health ADM, to estimate children’s weight status. Trained healthcare professionals collected standardized anthropometric measurements from 2,730 children (3-16 years). Body weight status was classified using International Obesity Task Force (IOTF) standards. Data were analyzed by region, age group, sex, and degree of urbanization using descriptive statistics, ANOVA, and logistic regression. Results: Overall prevalence of overweight and obesity was 21.6% and 7.0%, respectively, with significant regional and sex variations. The highest prevalence was found in Western Greece (24.6%), while Peloponnese had the highest obesity rates (9.1%). Boys demonstrated higher rates in both weight categories. A significant age-related obesity gradient was observed, increasing from 3.4% in toddlers to 8.5% in adolescents (p=0.021). No significant differences were found by degree of urbanization. Conclusion: Survey findings provide essential baseline data for an integrated surveillance protocol within existing healthcare infrastructure, facilitating future longitudinal monitoring across this diverse administrative region. childhood obesity surveillance Greece regional disparities public health IOTF standards Figures Figure 1 Figure 2 1. Introduction In 2010 overweight and obesity were estimated to cause 3.4 million deaths, 3.9% of years of life lost, and 3.8% of DALYs globally 1 , a proportion that in 2019 increased by 55,7% for YLL’s and 69.7% for DALY’s 2 . This alarming trend begins in childhood with total prevalence reaching 29% (13% obesity) and 27% (9% obesity) in 6–9-year-old boys and girls, respectively. Based on the NCD-Risk documentation, child obesity trends have increased from 1975 to 2016 and is now an important public health problem in the European region 3 , however the between country distribution greatly varies as documented by NCD-RisC study, after data harmonization 3 . Specifically in Northwestern Europe, the prevalence has plateaued at approximately 18% (total or obesity only?) since the year 2000, although small and moderate increases continue in Eastern & Central Europe. The WHO European Childhood Obesity Surveillance Initiative (COSI), the largest childhood obesity surveillance initiative globally, was established with the aim to provide comparable, timely data on children's body weight status in the WHO European Region 4 . Results confirmed that there is a wide between-country variation, with a higher prevalence found in Southern Europe 5 . Some Mediterranean countries, including Greece, reported very high rates, showing that 4 in 10 children are living with overweight and 2 in 10 with obesity 6 . Higher child obesity rates in Europe are more likely in low-income areas and with parents of lower educational level 7 . As such, localized geographical data in a specific country is also of great importance, and regular monitoring of overweight and obesity prevalence trends are warranted, through screening or surveillance. BMI surveillance programs can be used to describe trends in body weight status over time, identify high-risk subgroups overall and/or in geographic areas for targeting interventions, and to raise awareness about childhood overweight/obesity 8 . For such goals the use of aggregate data through surveillance programs are generally preferred and more sensible than specific individual child measures, as they protect confidentiality and do not involve communicating with parents about children’s weight status or recommending follow-up care 9 . Overall height and weight data routinely collected are valuable and important public health resources 10 , 11 for childhood obesity surveillance at the local level, although this process is often underused and overlooked. These anthropometric measurements, often gathered through school health programs, primary care visits, or national surveillance systems, provide essential information for tracking trends in overweight and obesity, evaluating interventions, and informing public health policy 12 , 13 . While there is adequate overall data on child prevalence of overweight and obesity in Europe 14 , 15 and in the North America 16 , child status information from specific areas is limited. These are required, however, since within-country differences can occur due to socioeconomic, health, and other environmental factor differences. This has been documented in the US, where large between States variations in child obesity prevalence have been observed by socioeconomic level 17 ,18 10 . Socially determined inequalities are recognised as important barriers in preventing childhood overweight and obesity and baseline obesity levels should also be known. Locally collected anthropometric data, therefore, could provide important information of community-specific obesity trends to establish public health priorities and targeted interventions 11 . Child obesity is a growing epidemic, with Greece ranking in the top three European countries (WHO 2022). The highest prevalence of childhood obesity has been found in 6th Health administrative region (ADM) (18.8%), an area with urban-suburban and rural areas/island and social inequalities. However, community level data in this region, following a standardized approach, has not been collected to date from all areas of the 6th Health ADM region to our knowledge. This is, therefore, essential to provide information on baseline prevalence data and identify vulnerable regions, for targeted public health interventions. Additionally, it is crucial to compare baseline anthropometry parameters and children obesity burden between the area of 6th Health ADM and remaining Greece – taking into consideration that most epidemiological studies focus on Athens (capital). The aim of this study was to survey the children’s body weight status in the 6th Health ADM region, by region, age groups, sex and degree of urbanization, to identify high-risk groups and sub-groups in specific geographic areas. 2. Materials and Methods 2.1. The area Greece is separated into 13 regions, of which 4 belong to the 6th Health ADM including Epirus, Western Greece, the Ionian islands, and the Peloponnese. The total child population (0–19 years of age) that resided in this area in 2021 was 255,609 (25.5% of total residing in Greece) based on the latest census, for which to date adequate information has not been collected regarding weight and height. All measurements were conducted following the ethical principles outlined in the World Medical Association (WMA) Declaration of Helsinki. The distribution per region for with percent total from the 6th ADM and from total can be viewed in Table 1 . Table 1 Child (0–19 years) distribution data based on 2021 census and total sampled, in 6th Health ADM, in Greece. Region Total children (0–19 years) % of ADM % of total children Total sampled % from total sampled children Epirus 56440 18.1 5.6 1214 44.5 Ionian Islands 37962 12.2 3.8 66 2.4 Western Greece 123971 39.7 12.4 300 11.0 Peloponnese 93676 30.0 9.3 1150 42.1 Total child population (0–19 years) residing in 6th Health ADM* 312049 31.1 - 2730 - *ADM: Administrative Region; Total child population residing in Greece = 1,002,640 2.2. The tools An official communication document was sent from the Greek Ministry of Health's 6th Health Region Administration covering Peloponnese, Ionian Islands, Epirus, and Western Greece, to health professionals at Health Centers and Local Health Units (TOMY) to collect anonymous statistical data on children aged 3–16 who visit their facilities. The age ranges were selected because BMI cut-offs are valid for ages above 2 years old and children in Greece are seen by pediatricians until 16 years of age, after which they are followed by General Practitioners. The document emphasized the importance of the documentation to address childhood obesity in these areas. This systematic data collection formed part of a region-wide initiative to develop evidence-based interventions for childhood obesity, with data being aggregated and analyzed at the regional level prior to implementation of comprehensive interventions. The anonymized data was collected from all children aged 3–16 years who visited the healthcare facilities between November 2024 and January 2025 in these regions. Each eligible participant's data, including age, sex, height, weight, geographical area, and postal code, was recorded once in a standardized format, specifically avoiding duplicate entries from follow-up visits. Children less than 3 years, above 16 years, and those that resided in other areas of Greece, as assessed by the postal code, were excluded from the final analysis. A specific sampling frame was not used for data collection since the aim was to record all available informative data from the children visiting the health care facilities in the region. The time interval for data collection (November to January) was selected to establish baseline prevalence data before intervention rollout (Health4EUkids program; grant number 101082462). It was also chosen to maximize the data collection, since this period corresponds to when children have stable programs due to school attendance, and healthcare facility visits are more frequent due to seasonal illnesses such as influenza and other viruses, thereby potentially decreasing selection bias. Trained health care professionals, mostly nurses, health visitors and pediatricians, were asked to measure the heights and weights of all children and adolescents that attended their facility, following standardized anthropometric methodology 19 . All children were measured in morning clinics (08:00 am -14:00 pm). The tools used were calibrated medical scales and stadiometers with a horizontal headspace, found in their setting. All medical equipment used was standardized and harmonized to national standards 5 . Children were asked to be weighed wearing light clothing and barefoot. For the height, children were asked to stand with their backs straight at the stadiometer (still barefoot). The measure was taken following a deep breath and exhale at the highest point of the child’s head and was recorded to the nearest tenth of a centimeter (0.01 cm). A standardized spreadsheet was constructed and provided for all healthcare professionals to input the data. Information on age (date of birth), biological sex, weight in kilos, height in centimeters, area of residence, postal code, and health care center. The postal code was used to distinguish socioeconomic levels when these differed by region of residence. All information was pseudonymized prior to submitting (no names or initials were kept). Data collected were harmonized and merged in one main dataset. Health centers were categorized by region, including Epirus, Western Greece, Ionian islands and Peloponnese. Children were categorized by age group into toddlers (3–5 year-olds), school aged children (6–12 year-olds) and adolescents (+ 13 years). Weight status was assessed using the International Obesity Task Force standards (IOTF), where the children’s calculated Body Mass Index (BMI) is related by month, to the corresponding adult BMI 20 . The IOTF standards were used to maintain consistency with European growth standards, and avoid discrepancies due to cutoff choices and different criteria in sample selection process between IOTF and WHO standards 21 . For the degree of urbanization categorization, all major cities, like Patras in Peloponnese, Ioannina in Epirus, and Korinthos in Western Greece, were classified as urban, while smaller towns and villages were classified as semi-urban or rural as per their population density, after reviewing area of residence and postal codes. 2.3. Statistical Analysis Descriptive analyses were carried out to provide insights on regional and age-group differences between childhood overweight and obesity prevalence. All statistical analyses were performed using STATA 18.0 (Texas Ltd.). Normality distribution was examined using k-density and probability plots. Normally distributed continuous variables are presented as means and standard deviations (SD), while categorical variables are presented as percentages (%). Between-group differences of continuous variables were tested using either one-way Analysis of Variance (ANOVA) for more than two group comparisons or the independent samples T-test (for 2 groups). The significance of the association between categorical variables was examined using the chi-squared (χ 2 ) test. To address potential bias from unequal participation, sensitivity analyses were conducted by facility type, and results interpretation was adjusted to account for geographic disparities. More specifically, a sensitivity analysis on the relative frequencies of the children’s weight status was conducted by type of facility and area to address potential enrollment and/or measurement reporting bias and decrease sampling error. 3. Results 3.1. Demographics Data from a total of 2730 children were collected, the majority of which were from the Epirus area (44.5%), followed by Peloponnese (42.1%). The overall mean age was 10.2 (SD 3.8) years, with most participants being of school age. Most children measured resided in urban areas (64.7%), followed by rural (18.6%) and semi-urban (16.7%). In relation to the latest census in the overall child population of Greece, Epirus and Western Greece are substantial, a relatively proportional sample was obtained from the Peloponnese area, but a small, underrepresented sample was obtained from the Ionian Islands (Table 1). Table 2 presents the degree of urbanization distribution by area. The mean age of the population was 10.2 years, ranging from 9.1 (Ionian Islands) to 10.6 (Western Greece) years (Table 2). In all areas most the children measured were of school age (6-12 years), but no specific sex differences were present. 3.2. Weight status categories data The children’s weight status categorization can also be seen in Table 2. A total of 21.7% (n=588) and 7% (n=190) of children were categorized with overweight status and obesity, respectively. Weight status significantly differed by area, with the highest prevalence of overweight status found in Western Greece (24.6%) and obesity in Peloponnese (9.1%). This also differed by age group with overweight status ranging from 20.9% in primary school aged children to 22.2 % and 22.5%, in adolescents and toddlers, respectively (Figure 1). Table 2: Baseline characteristics of sampled children in total and by region. Weight status categorized based on children’s calculated Body Mass Index (BMI) following the International Obesity Task Force (IOTF) cut-offs by age and sex. Region Epirus Western Greece Ionian Islands Peloponnese Total p value of Significance Test* Total children (3-16 years), (N) 1,214 (44.5%) 300 (11.0%) 66 (2.4%) 1,150 (42.1%) 2,730 (100.0%) Age in years, mean (SD) 10.1 (3.7) 10.6 (3.9) 9.1 (3.8) 10.2 (3.8) 10.2 (3.8) 0.011 Age Group 3-5 year-old 162 (13.4%) 38 (12.7%) 12 (18.5%) 172 (15.0%) 384 (14.1%) 0.048 6-12 year-old 660 (54.4%) 146 (48.7%) 39 (60.0%) 573 (49.9%) 1,418 (52.0%) 13+ year-old 391 (32.2%) 116 (38.7%) 14 (21.5%) 404 (35.2%) 925 (33.9%) Biological Sex Males 609 (50.2%) 141 (47.2%) 37 (56.1%) 556 (48.3%) 1,343 (49.2%) 0.470 Females 605 (49.8%) 158 (52.8%) 29 (43.9%) 594 (51.7%) 1,386 (50.8%) Weight Status Normal weight 875 (72.5%) 211 (71.0%) 46 (73.0%) 803 (70.1%) 1,935 (71.3%) 0.021 With Overweight 263 (21.8%) 73 (24.6%) 13 (20.6%) 239 (20.9%) 588 (21.7%) With Obesity 69 (5.7%) 13 (4.4%) 4 (6.3%) 104 (9.1%) 190 (7.0%) Degree of urbanization Urban 821 (67.6%) 77 (25.8%) 0 (0.0%) 868 (75.5%) 1,766 (64.7%) <0.001 Semi-Urban 209 (17.2%) 103 (34.4%) 26 (39.4%) 118 (10.3%) 456 (16.7%) Rural 184 (15.2%) 119 (39.8%) 40 (60.6%) 164 (14.3%) 507 (18.6%) *Analyses based on one way analysis of variance (ANOVA) for continuous data and chi square test for categorical; significance set at alpha 5%. 3.3. Analyses Based on the regional differences, observed that the main regions that contributed to obesity were Peloponnese (54.7%) and Epirus (36.3%), from the total children classified with obesity (Figure 2), however, the above could be related to sampling. A significant difference in weight status distribution across the different age groups, mostly for obesity (Figure 1), was also found. Specifically, although it was relatively steady for children 3-5, 6-12, and 13-18year old, with slight fluctuations (22.5%, 20.9%, 22.2%, respectively), an increasing linear trend was observed in obesity status, from 3.4% in toddlers to 8.5% in adolescents (p=0.021). Lastly, when data were analyzed by biological sex, a greater percentage of overweight and obesity was observed among boys with a total of 27.3% and 8.5% compared to 16.1% and 5.5% in girls, respectively, which led to the stratification of the logistic regression analysis by sex. Table 3 shows that no differences were found by level of urbanization or age group in the total sample, but the age-related gradient in weight status was significant in girls. Specifically, 13+ year-olds girls were 5.4 times more likely to be with overweight and 8% more likely to be with obesity than 3-5 year-olds, although with a large variability (OR=5.4, 95% CI: 2.725-10.886; and OR=8.0, 95% CI: 3.989-16.165, respectively). Table 3: Multiple Logistic Regression of weight status by degree of urbanization and biological sex. Total sample* By sex Boys Girls OR 95% CI OR 95% CI OR 95% CI Degree of urbanization Urban ref** ref** ref** Semi-Urban 1.0 0.806 - 1.283 1.1 0.789 - 1.484 0.9 0.660 - 1.329 Rural 1.0 0.817 - 1.273 1.0 0.769 - 1.375 1.1 0.752 - 1.511 Age group 3-5 year old ref** ref ** ref** 6-12 year old 1.1 0.870 - 1.463 0.6 0.446 - 0.852 5.4 2.725 - 10.886 13+ year old 1.3 0.976 - 1.680 0.6 0.398 - 0.790 8.0 3.989 - 16.165 *Significance at alpha 5%; adjusted by sex as well. ** Reference level 4. Discussion This survey aimed to provide surveillance obesity prevalence data at a single time point enrolling a large number of children aged 3–16 years of age throughout the 6th Health ADM region. The study revealed a high overweight and obesity problem in the 6th Health ADM area. with 3 in 10 children being classified either with overweight or obesity, but significant regional differences demographic variations were highlighted. The survey included a well-representative sample from Epirus, Western Greece and Peloponnese, but the Ionian islands were underrepresented. Overall, the study showed relatively stable overweight rates across age groups, but pronounced sex differences were observed, with boys showing higher rates of both overweight and obesity status than girls (in all ages). As per regional disparities, Western Greece showed the highest overweight prevalence while obesity levels were highest in the Peloponnese and Epirus regions. Geographically, Peloponnese and Epirus contributed disproportionately to obesity cases, suggesting regional clustering of childhood obesity that warrants targeted public health interventions. An age-related obesity gradient was also found that remained significant only in girls when degree of urbanization and age were accounted for simultaneously. An important observation was the age-related gradient specifically within the female population despite the higher rates of overweight and obesity found in boys. It has been recommended that the simultaneous implementation of a screening program with other measures to fight childhood obesity necessitates that researchers and interventionists expand how they define success for the screening programs and tailor interventions accordingly 22 . Therefore, identifying high risk areas is essential for targeting interventions and to raise awareness of child overweight and obesity rates in school districts, and in the communities 8 . Research also indicates that when local health departments effectively utilize specific community-level data, they can identify at-risk populations, evaluate the impact of public health initiatives, and allocate resources more efficiently to address childhood obesity disparities. Overweight and obesity prevalence found in our study are closely aligned with the other study findings for developed countries, where approximately 23.8% of boys and 22.6% of girls were overweight or obese in 2013, mirroring the global observation of rising obesity prevalence with age 1 , 15 . In Serbia, being overweight was strongly associated with poor local community development and lower level of urbanization 15 . The overall prevalence of overweight (23.1%, including obesity) and obesity (6.9%) in Serbian primary-school children seem to be comparable to the high rates found in our study. Furthermore, the global systematic review explicitly acknowledged that crucial subnational information was missing, hence adding to the study's conclusion that “there are no national success stories over the past 33 years”, which our study aimed to address. The increase in obesity trend from toddlers to adolescents reported in our study may be the result of the carry over effect of the higher prevalence in overweight in school aged children, seen the past years mostly in girls 3 , 16 , 23 , however, this trend is aligned with other study findings 1 and underline the need for regular systematic surveillance of children’s weight status and possible interventions to reduce the burden of overweight and obesity when entering adolescence. The standardized assessment of BMI data collected by trained personnel is more valid and reliable than the self- or parent-reported data used for most national estimates. 9 , 24 Geographical variations in childhood overweight and obesity can be attributed to multiple factors including sociodemographic, environmental, and lifestyle determinants 5 , 6 . A recent scoping review 25 addressed Energy Balance Related Behaviors (EBRB) among vulnerable children in Greece and highlighted the scarcity in data available in this area, with only 7 studies being identified. These studies showed that EBRB are affected by lower socioeconomic status as well, in addition to other known risk factors. This scoping review supports the regional variations reported in our study and provides context for understanding the higher obesity rates in certain geographic areas where vulnerable populations are concentrated. Feel4Diabetes study, that included 9847 children from Europe, also reported that dietary intake as well as overweight and obesity prevalence varied highly between included countries 26 . No differences were found by degree of urbanization in this study. Overall, the 6th Health ADM, is a large and diverse administrative region in Greece, and likely reflects the broader socioeconomic challenges faced by the country, including lower Gross Domestic Product (GDP) per capita and higher poverty/unemployment rates compared to the EU average. Also, the regions within the 6th Health ADM include both urban centers, like Patras in Western Greece, and extensive rural and island areas, like the Ionian islands and parts of Epirus and the Peloponnese. Results, however, in relation to degree of urbanization must be interpreted with caution since in this study, although large, most children measured resided in urban areas and may be skewed towards more urbanized settings within the 6th ADM region. This study that assessed weight status in the 6th Health ADM region contributes to the gaps in knowledgeable information of children in need, following a standardized evaluation through a surveillance program in an understudied area. This is the first study taking place in this region, with the aim to conduct follow-ups at regular yearly time intervals to monitor long-term trends in overweight and obesity in children and set the basis for surveillance. Although the survey the aim of the study was not to specifically examine risk factors, we should report it as a potential limitation, because it has been suggested that an effective surveillance system should also collect weight-related behaviors 11 . This is important since most interventions and obesity prevention education programs focus mostly on energy balance, which is at least an oversimplification of the obesity epidemic observed since the 20th century. Although dietary interventions and reduced energy density, can effectively improve adiposity-related outcomes 27 , the effect of nutrient density 26 , 28 and dietary patterns 29 seem to be as effective, if potentially of greater importance, and should therefore not be neglected. This study includes potential limitations that should be considered when interpreting the results. Selection bias may have been introduced since the sample was collected at the healthcare level as children visiting health centres may not be representative of the general child population in the 6th Health ADM region, especially children from the rural and semi-urban areas. However, this approach was chosen to ensure standardized anthropometric measurements by trained professionals and to integrate the baseline for surveillance protocol within the existing healthcare infrastructure, facilitating future longitudinal monitoring across this diverse administrative region. Also, the geographic distribution was low for the Ionian Islands, potentially affecting the generalizability of results in this region. The degree of urbanization results may have been affected by the sampling method. Furthermore, to capture seasonal variations in weight status, data collection should be performed at regular time intervals, and future next steps should be formulated. 5. Conclusions This study reports the community-level disparities that occur between regions, and provides comprehensive surveillance data for targeted interventions, policy decisions, and resource allocation that can potentially address the disproportionate burden of childhood obesity in this diverse and socioeconomically challenged region. Baseline data can serve as a valuable tool to assess future trends and evaluate the effect of specific future obesity interventions in the 6th Health ADM region. Having baseline measures is particularly important to implement specific intervention child obesity prevention programs and to evaluate long term effectiveness of these, hence helping to develop regional and national policies. Abbreviations ADM Administrative region IOTF International Obesity Task Force DALY Disability-Adjusted Life Year YLL Years of life lost from mortality NCD Non-Communicable Diseases WHO World Health Organization COSI European Childhood Obesity Surveillance Initiative BMI Body Mass Index CDC Centers for Disease Control and Prevention TOMY Local Health Units in Greece SD Standard Deviation OR Odds Ration CI Confidence Interval EBRB Energy Balance Related Behaviors GDP Gross Domestic Product Declarations Author contribution Conceptualization: EM, AV; Investigation: EM, AV, GK.; Writing – Original Draft: EM, EP, AV; Writing – Review & Editing: EM, EP, GC, AV; Supervision: AV. All authors have read and approved the final manuscript. Funding This research was funded by European Union, EU4Health program. (See methods section). Data Availability Statement All data supporting this study’s findings are available upon reasonable request from the corresponding author. Additionally, the data will be made publicly accessible on the Open Science Framework (OSF) after a provisional period of one year. Acknowledgments We would like to thank all Pediatricians that selected and documented the information required. Special thanks to the whole team in the 6 th Heath ADM. Conflicts of Interest The authors declare no conflicts of interest Ethical approval Ethical approval was obtained from Bioethics Committee of University of Patras and the Regional Directorate of Primary and Secondary Education of Western Greece (number of approval: 304; date: 26/10/2023). All measurements were conducted following the ethical principles outlined in the World Medical Association (WMA) Declaration of Helsinki. References Ng M, Fleming T, Robinson M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766–81. 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The Role of Energy Balance-Related Behaviors (EBRBs) and their Determinants on the Prevalence of Overweight and Obesity in Children in Need, in Greece: A Scoping Review. Curr Nutr Rep. 2024;14(1):2. Mahmood L, Moreno LA, Schwarz P, et al. A snapshot of country-specific dietary habits and obesity in European children: the Feel4Diabetes study. Eur J Pediatr. 2025;184(3):214. Hassapidou M, Duncanson K, Shrewsbury V, et al. EASO and EFAD Position Statement on Medical Nutrition Therapy for the Management of Overweight and Obesity in Children and Adolescents. Obes Facts. 2023;16(1):29–52. Kostopoulou E, Tsekoura E, Fouzas S, Gkentzi D, Jelastopulu E, Varvarigou A. Association of lifestyle factors with a high prevalence of overweight and obesity in Greek children aged 10–16 years. Acta Paediatr. 2021;110(12):3356–64. Kosti RI, Kanellopoulou A, Fragkedaki E, et al. The Influence of Adherence to the Mediterranean Diet among Children and Their Parents in Relation to Childhood Overweight/Obesity: A Cross-Sectional Study in Greece. Child Obes. 2020;16(8):571–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Nov, 2025 Read the published version in Journal of Health, Population and Nutrition → Version 1 posted Editorial decision: Revision requested 09 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviews received at journal 04 Sep, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 29 Jul, 2025 Editor assigned by journal 25 Jul, 2025 Submission checks completed at journal 25 Jul, 2025 First submitted to journal 22 Jul, 2025 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. 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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-7189294","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492662993,"identity":"70e3fe24-393e-4df3-bcff-c399a1902823","order_by":0,"name":"Emmanuella Magriplis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYNACAwYZNgbmA6Rp4WFjYEsgzR4eIDIgTim/2OHHrysK7vDw8Z/5+JmnhsFuewMBLZKz08wszxg842GTyN0szXOMIXnOAQJaDG4nmBk2GBwGauHdIA30UbIEIYfZ307/BtHCf+bxb55/RGgxkM4xfgjWwpDDJs3bxmBHUIvE7ZwyxgawX4CemtsnkUBQC//s9M0fG/7ckZPvP/z4xptvNvYEtQABG1DRAbitiQ1EaGH+gKSFwZ4IHaNgFIyCUTDCAADx8Dl3/UyWDwAAAABJRU5ErkJggg==","orcid":"","institution":"Agricultural University of Athens","correspondingAuthor":true,"prefix":"","firstName":"Emmanuella","middleName":"","lastName":"Magriplis","suffix":""},{"id":492662995,"identity":"d537bef2-5f1b-4ec5-8186-93fa22f4d937","order_by":1,"name":"Eleni Papachatzi","email":"","orcid":"","institution":"University General Hospital of Patras","correspondingAuthor":false,"prefix":"","firstName":"Eleni","middleName":"","lastName":"Papachatzi","suffix":""},{"id":492662997,"identity":"c5f1678a-c7e7-4ff9-a15c-2180e260b377","order_by":2,"name":"George Karydas","email":"","orcid":"","institution":"6th Health ADM","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Karydas","suffix":""},{"id":492662999,"identity":"6e017b30-536b-4921-97e1-cfcf132cf202","order_by":3,"name":"Georgios Chrousos","email":"","orcid":"","institution":"National and Kapodistrian University of Athens","correspondingAuthor":false,"prefix":"","firstName":"Georgios","middleName":"","lastName":"Chrousos","suffix":""},{"id":492663000,"identity":"a94a5e51-013c-41cb-bc39-4deb1b3787e2","order_by":4,"name":"Apostolos Vantarakis","email":"","orcid":"","institution":"University of Patras","correspondingAuthor":false,"prefix":"","firstName":"Apostolos","middleName":"","lastName":"Vantarakis","suffix":""}],"badges":[],"createdAt":"2025-07-22 16:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7189294/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7189294/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s41043-025-01132-6","type":"published","date":"2025-11-10T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88004108,"identity":"e8892896-ffc2-4a7f-a48c-9876aaa641a5","added_by":"auto","created_at":"2025-07-31 10:35:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49902,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Weight Status Across Age Groups.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7189294/v1/aff0961965ed70896a423974.jpg"},{"id":88005513,"identity":"e8f268a0-62a7-4746-ae92-704aa3fb30a0","added_by":"auto","created_at":"2025-07-31 10:43:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47163,"visible":true,"origin":"","legend":"\u003cp\u003eRegional Contribution to Weight Status Categories.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7189294/v1/fd779064a18e52fcc3ab2481.jpg"},{"id":96105208,"identity":"27b43527-2038-4208-b9b9-9d0a96af13f6","added_by":"auto","created_at":"2025-11-17 16:10:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":781157,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7189294/v1/11c576b0-d3d3-4aef-b946-f91f1235c9e8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Childhood Overweight and Obesity Survey: An Overlooked Public Health Issue","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn 2010 overweight and obesity were estimated to cause 3.4\u0026nbsp;million deaths, 3.9% of years of life lost, and 3.8% of DALYs globally \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, a proportion that in 2019 increased by 55,7% for YLL\u0026rsquo;s and 69.7% for DALY\u0026rsquo;s \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This alarming trend begins in childhood with total prevalence reaching 29% (13% obesity) and 27% (9% obesity) in 6\u0026ndash;9-year-old boys and girls, respectively. Based on the NCD-Risk documentation, child obesity trends have increased from 1975 to 2016 and is now an important public health problem in the European region \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, however the between country distribution greatly varies as documented by NCD-RisC study, after data harmonization \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Specifically in Northwestern Europe, the prevalence has plateaued at approximately 18% (total or obesity only?) since the year 2000, although small and moderate increases continue in Eastern \u0026amp; Central Europe. The WHO European Childhood Obesity Surveillance Initiative (COSI), the largest childhood obesity surveillance initiative globally, was established with the aim to provide comparable, timely data on children's body weight status in the WHO European Region \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Results confirmed that there is a wide between-country variation, with a higher prevalence found in Southern Europe \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Some Mediterranean countries, including Greece, reported very high rates, showing that 4 in 10 children are living with overweight and 2 in 10 with obesity \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Higher child obesity rates in Europe are more likely in low-income areas and with parents of lower educational level \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. As such, localized geographical data in a specific country is also of great importance, and regular monitoring of overweight and obesity prevalence trends are warranted, through screening or surveillance.\u003c/p\u003e\u003cp\u003eBMI surveillance programs can be used to describe trends in body weight status over time, identify high-risk subgroups overall and/or in geographic areas for targeting interventions, and to raise awareness about childhood overweight/obesity \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. For such goals the use of aggregate data through surveillance programs are generally preferred and more sensible than specific individual child measures, as they protect confidentiality and do not involve communicating with parents about children\u0026rsquo;s weight status or recommending follow-up care \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Overall height and weight data routinely collected are valuable and important public health resources \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e for childhood obesity surveillance at the local level, although this process is often underused and overlooked. These anthropometric measurements, often gathered through school health programs, primary care visits, or national surveillance systems, provide essential information for tracking trends in overweight and obesity, evaluating interventions, and informing public health policy \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile there is adequate overall data on child prevalence of overweight and obesity in Europe \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and in the North America \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, child status information from specific areas is limited. These are required, however, since within-country differences can occur due to socioeconomic, health, and other environmental factor differences. This has been documented in the US, where large between States variations in child obesity prevalence have been observed by socioeconomic level \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,18 10\u003c/sup\u003e. Socially determined inequalities are recognised as important barriers in preventing childhood overweight and obesity and baseline obesity levels should also be known. Locally collected anthropometric data, therefore, could provide important information of community-specific obesity trends to establish public health priorities and targeted interventions \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eChild obesity is a growing epidemic, with Greece ranking in the top three European countries (WHO 2022). The highest prevalence of childhood obesity has been found in 6th Health administrative region (ADM) (18.8%), an area with urban-suburban and rural areas/island and social inequalities. However, community level data in this region, following a standardized approach, has not been collected to date from all areas of the 6th Health ADM region to our knowledge. This is, therefore, essential to provide information on baseline prevalence data and identify vulnerable regions, for targeted public health interventions. Additionally, it is crucial to compare baseline anthropometry parameters and children obesity burden between the area of 6th Health ADM and remaining Greece \u0026ndash; taking into consideration that most epidemiological studies focus on Athens (capital).\u003c/p\u003e\u003cp\u003eThe aim of this study was to survey the children\u0026rsquo;s body weight status in the 6th Health ADM region, by region, age groups, sex and degree of urbanization, to identify high-risk groups and sub-groups in specific geographic areas.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. The area\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eGreece is separated into 13 regions, of which 4 belong to the 6th Health ADM including Epirus, Western Greece, the Ionian islands, and the Peloponnese. The total child population (0\u0026ndash;19 years of age) that resided in this area in 2021 was 255,609 (25.5% of total residing in Greece) based on the latest census, for which to date adequate information has not been collected regarding weight and height. All measurements were conducted following the ethical principles outlined in the World Medical Association (WMA) Declaration of Helsinki. The distribution per region for with percent total from the 6th ADM and from total can be viewed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\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\u003eChild (0\u0026ndash;19 years) distribution data based on 2021 census and total sampled, in 6th Health ADM, in Greece.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal children\u003c/p\u003e\u003cp\u003e(0\u0026ndash;19 years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of ADM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e% of total children\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal sampled\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e% from total sampled children\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIonian Islands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Greece\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e123971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeloponnese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal child population (0\u0026ndash;19 years) residing in 6th Health ADM*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e312049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003e*ADM: Administrative Region; Total child population residing in Greece\u0026thinsp;=\u0026thinsp;1,002,640\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. The tools\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e An official communication document was sent from the Greek Ministry of Health's 6th Health Region Administration covering Peloponnese, Ionian Islands, Epirus, and Western Greece, to health professionals at Health Centers and Local Health Units (TOMY) to collect anonymous statistical data on children aged 3\u0026ndash;16 who visit their facilities. The age ranges were selected because BMI cut-offs are valid for ages above 2 years old and children in Greece are seen by pediatricians until 16 years of age, after which they are followed by General Practitioners. The document emphasized the importance of the documentation to address childhood obesity in these areas. This systematic data collection formed part of a region-wide initiative to develop evidence-based interventions for childhood obesity, with data being aggregated and analyzed at the regional level prior to implementation of comprehensive interventions. The anonymized data was collected from all children aged 3\u0026ndash;16 years who visited the healthcare facilities between November 2024 and January 2025 in these regions. Each eligible participant's data, including age, sex, height, weight, geographical area, and postal code, was recorded once in a standardized format, specifically avoiding duplicate entries from follow-up visits. Children less than 3 years, above 16 years, and those that resided in other areas of Greece, as assessed by the postal code, were excluded from the final analysis. A specific sampling frame was not used for data collection since the aim was to record all available informative data from the children visiting the health care facilities in the region. The time interval for data collection (November to January) was selected to establish baseline prevalence data before intervention rollout (Health4EUkids program; grant number 101082462). It was also chosen to maximize the data collection, since this period corresponds to when children have stable programs due to school attendance, and healthcare facility visits are more frequent due to seasonal illnesses such as influenza and other viruses, thereby potentially decreasing selection bias.\u003c/p\u003e\u003cp\u003eTrained health care professionals, mostly nurses, health visitors and pediatricians, were asked to measure the heights and weights of all children and adolescents that attended their facility, following standardized anthropometric methodology \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. All children were measured in morning clinics (08:00 am -14:00 pm).\u003c/p\u003e\u003cp\u003eThe tools used were calibrated medical scales and stadiometers with a horizontal headspace, found in their setting. All medical equipment used was standardized and harmonized to national standards\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Children were asked to be weighed wearing light clothing and barefoot. For the height, children were asked to stand with their backs straight at the stadiometer (still barefoot). The measure was taken following a deep breath and exhale at the highest point of the child\u0026rsquo;s head and was recorded to the nearest tenth of a centimeter (0.01 cm).\u003c/p\u003e\u003cp\u003eA standardized spreadsheet was constructed and provided for all healthcare professionals to input the data. Information on age (date of birth), biological sex, weight in kilos, height in centimeters, area of residence, postal code, and health care center. The postal code was used to distinguish socioeconomic levels when these differed by region of residence. All information was pseudonymized prior to submitting (no names or initials were kept).\u003c/p\u003e\u003cp\u003eData collected were harmonized and merged in one main dataset. Health centers were categorized by region, including Epirus, Western Greece, Ionian islands and Peloponnese. Children were categorized by age group into toddlers (3\u0026ndash;5 year-olds), school aged children (6\u0026ndash;12 year-olds) and adolescents (+\u0026thinsp;13 years). Weight status was assessed using the International Obesity Task Force standards (IOTF), where the children\u0026rsquo;s calculated Body Mass Index (BMI) is related by month, to the corresponding adult BMI \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The IOTF standards were used to maintain consistency with European growth standards, and avoid discrepancies due to cutoff choices and different criteria in sample selection process between IOTF and WHO standards \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor the degree of urbanization categorization, all major cities, like Patras in Peloponnese, Ioannina in Epirus, and Korinthos in Western Greece, were classified as urban, while smaller towns and villages were classified as semi-urban or rural as per their population density, after reviewing area of residence and postal codes.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistical Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDescriptive analyses were carried out to provide insights on regional and age-group differences between childhood overweight and obesity prevalence. All statistical analyses were performed using STATA 18.0 (Texas Ltd.). Normality distribution was examined using k-density and probability plots. Normally distributed continuous variables are presented as means and standard deviations (SD), while categorical variables are presented as percentages (%). Between-group differences of continuous variables were tested using either one-way Analysis of Variance (ANOVA) for more than two group comparisons or the independent samples T-test (for 2 groups). The significance of the association between categorical variables was examined using the chi-squared (χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) test.\u003c/p\u003e\u003cp\u003eTo address potential bias from unequal participation, sensitivity analyses were conducted by facility type, and results interpretation was adjusted to account for geographic disparities. More specifically, a sensitivity analysis on the relative frequencies of the children\u0026rsquo;s weight status was conducted by type of facility and area to address potential enrollment and/or measurement reporting bias and decrease sampling error.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Demographics\u003c/p\u003e\n\u003cp\u003eData from a total of 2730 children were collected, the majority of which were from the Epirus area (44.5%), followed by Peloponnese (42.1%). The overall mean age was 10.2 (SD 3.8) years, with most participants being of school age. Most children measured resided in urban areas (64.7%), followed by rural (18.6%) and semi-urban (16.7%). In relation to the latest census in the overall child population of Greece, Epirus and Western Greece are substantial, a relatively proportional sample was obtained from the Peloponnese area, but a small, underrepresented sample was obtained from the Ionian Islands (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 2 presents the degree of urbanization distribution by area. The mean age of the population was 10.2 years, ranging from 9.1 (Ionian Islands) to 10.6 (Western Greece) years (Table 2). In all areas most the children measured were of school age (6-12 years), but no specific sex differences were present.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2. Weight status categories data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe children\u0026rsquo;s weight status categorization can also be seen in Table 2. A total of 21.7% (n=588) and 7% (n=190) of children were categorized with overweight status and obesity, respectively. Weight status significantly differed by area, with the highest prevalence of overweight status found in Western Greece (24.6%) and obesity in Peloponnese (9.1%). \u0026nbsp; This also differed by age group with overweight status ranging from 20.9% in primary school aged children to 22.2 % and 22.5%, in adolescents and toddlers, respectively (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Baseline characteristics of sampled children in total and by region.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWeight status categorized based on children\u0026rsquo;s calculated Body Mass Index (BMI) following the International Obesity Task Force (IOTF) cut-offs by age and sex. \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 359px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpirus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWestern Greece\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIonian Islands\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeloponnese\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003ep value of Significance Test*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTotal children (3-16 years), (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1,214 (44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e300 (11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;(2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1,150\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2,730 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge in years, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e10.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e10.6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e10.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10.2 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; 3-5 year-old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e162 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e38\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12 (18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e172\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e384 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; 6-12 year-old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e660 (54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e146 (48.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e39 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e573\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(49.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,418 (52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; 13+ year-old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e391 (32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e116 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e14 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e404\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e925 (33.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBiological Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; Males\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e609 (50.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e141 (47.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37 (56.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e556\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,343 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; Females\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e605 (49.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e158 (52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e29 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e594\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,386 (50.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWeight Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e875 (72.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e211 (71.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e46\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(73.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e803\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(70.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,935 (71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWith Overweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e263 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e73\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e13\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e239\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e588 (21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWith Obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e69 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e13\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e104\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e190 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 690px;\"\u003e\n \u003cp\u003eDegree of urbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e821 (67.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e77 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e868\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1,766 (64.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; Semi-Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e209 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e103 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26 (39.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e118\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e456 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e184 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e119 (39.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e40 (60.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e164\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e507 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*Analyses based on one way analysis of variance (ANOVA) for continuous data and chi square test for categorical; significance set at alpha 5%.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3. Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBased on the regional differences, observed that the main regions that contributed to obesity were Peloponnese (54.7%) and Epirus (36.3%), from the total children classified with obesity (Figure 2), however, the above could be related to sampling.\u003c/p\u003e\n\u003cp\u003eA significant difference in\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eweight status distribution across the different age groups, mostly for obesity (Figure 1), was also found. Specifically, although it was relatively steady for children 3-5, 6-12, and 13-18year old, with slight fluctuations (22.5%, 20.9%, 22.2%, respectively), an increasing linear trend was observed in obesity status, from 3.4% in toddlers to 8.5% in adolescents (p=0.021).\u003c/p\u003e\n\u003cp\u003eLastly, when data were analyzed by biological sex, a greater percentage of overweight and obesity was observed among boys with a total of 27.3% and 8.5% compared to 16.1% and 5.5% in girls, respectively, which led to the stratification of the logistic regression analysis by sex. Table 3 shows that no differences were found by level of urbanization or age group in the total sample, but the age-related gradient in weight status was significant in girls. Specifically, 13+ year-olds girls were 5.4 times more likely to be with overweight and 8% more likely to be with obesity than 3-5 year-olds, although with a large variability (OR=5.4, 95% CI: 2.725-10.886; and OR=8.0, 95% CI: 3.989-16.165, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Multiple Logistic Regression of weight status by degree of urbanization and biological sex.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"707\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eTotal sample*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 367px;\"\u003e\n \u003cp\u003eBy sex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Boys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Girls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp; 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003eDegree of urbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003eUrban\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;ref**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eref**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eref**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSemi-Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.806 - 1.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.789 - 1.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.660 - 1.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.817 - 1.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.769 - 1.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.752 - 1.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3-5 year old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eref**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;ref **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eref**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6-12 year old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.870 - 1.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.446 - 0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2.725 - 10.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e13+ year old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.976 - 1.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.398 - 0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3.989 - 16.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significance at alpha 5%; adjusted by sex as well. ** Reference level\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis survey aimed to provide surveillance obesity prevalence data at a single time point enrolling a large number of children aged 3\u0026ndash;16 years of age throughout the 6th Health ADM region. The study revealed a high overweight and obesity problem in the 6th Health ADM area. with 3 in 10 children being classified either with overweight or obesity, but significant regional differences demographic variations were highlighted. The survey included a well-representative sample from Epirus, Western Greece and Peloponnese, but the Ionian islands were underrepresented. Overall, the study showed relatively stable overweight rates across age groups, but pronounced sex differences were observed, with boys showing higher rates of both overweight and obesity status than girls (in all ages). As per regional disparities, Western Greece showed the highest overweight prevalence while obesity levels were highest in the Peloponnese and Epirus regions. Geographically, Peloponnese and Epirus contributed disproportionately to obesity cases, suggesting regional clustering of childhood obesity that warrants targeted public health interventions. An age-related obesity gradient was also found that remained significant only in girls when degree of urbanization and age were accounted for simultaneously. An important observation was the age-related gradient specifically within the female population despite the higher rates of overweight and obesity found in boys.\u003c/p\u003e\u003cp\u003eIt has been recommended that the simultaneous implementation of a screening program with other measures to fight childhood obesity necessitates that researchers and interventionists expand how they define success for the screening programs and tailor interventions accordingly \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Therefore, identifying high risk areas is essential for targeting interventions and to raise awareness of child overweight and obesity rates in school districts, and in the communities \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Research also indicates that when local health departments effectively utilize specific community-level data, they can identify at-risk populations, evaluate the impact of public health initiatives, and allocate resources more efficiently to address childhood obesity disparities.\u003c/p\u003e\u003cp\u003eOverweight and obesity prevalence found in our study are closely aligned with the other study findings for developed countries, where approximately 23.8% of boys and 22.6% of girls were overweight or obese in 2013, mirroring the global observation of rising obesity prevalence with age \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In Serbia, being overweight was strongly associated with poor local community development and lower level of urbanization \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The overall prevalence of overweight (23.1%, including obesity) and obesity (6.9%) in Serbian primary-school children seem to be comparable to the high rates found in our study. Furthermore, the global systematic review explicitly acknowledged that crucial subnational information was missing, hence adding to the study's conclusion that \u0026ldquo;there are no national success stories over the past 33 years\u0026rdquo;, which our study aimed to address. The increase in obesity trend from toddlers to adolescents reported in our study may be the result of the carry over effect of the higher prevalence in overweight in school aged children, seen the past years mostly in girls \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, however, this trend is aligned with other study findings \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and underline the need for regular systematic surveillance of children\u0026rsquo;s weight status and possible interventions to reduce the burden of overweight and obesity when entering adolescence. The standardized assessment of BMI data collected by trained personnel is more valid and reliable than the self- or parent-reported data used for most national estimates. \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eGeographical variations in childhood overweight and obesity can be attributed to multiple factors including sociodemographic, environmental, and lifestyle determinants \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A recent scoping review \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e addressed Energy Balance Related Behaviors (EBRB) among vulnerable children in Greece and highlighted the scarcity in data available in this area, with only 7 studies being identified. These studies showed that EBRB are affected by lower socioeconomic status as well, in addition to other known risk factors. This scoping review supports the regional variations reported in our study and provides context for understanding the higher obesity rates in certain geographic areas where vulnerable populations are concentrated. Feel4Diabetes study, that included 9847 children from Europe, also reported that dietary intake as well as overweight and obesity prevalence varied highly between included countries \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNo differences were found by degree of urbanization in this study. Overall, the 6th Health ADM, is a large and diverse administrative region in Greece, and likely reflects the broader socioeconomic challenges faced by the country, including lower Gross Domestic Product (GDP) per capita and higher poverty/unemployment rates compared to the EU average. Also, the regions within the 6th Health ADM include both urban centers, like Patras in Western Greece, and extensive rural and island areas, like the Ionian islands and parts of Epirus and the Peloponnese. Results, however, in relation to degree of urbanization must be interpreted with caution since in this study, although large, most children measured resided in urban areas and may be skewed towards more urbanized settings within the 6th ADM region.\u003c/p\u003e\u003cp\u003eThis study that assessed weight status in the 6th Health ADM region contributes to the gaps in knowledgeable information of children in need, following a standardized evaluation through a surveillance program in an understudied area. This is the first study taking place in this region, with the aim to conduct follow-ups at regular yearly time intervals to monitor long-term trends in overweight and obesity in children and set the basis for surveillance. Although the survey the aim of the study was not to specifically examine risk factors, we should report it as a potential limitation, because it has been suggested that an effective surveillance system should also collect weight-related behaviors \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This is important since most interventions and obesity prevention education programs focus mostly on energy balance, which is at least an oversimplification of the obesity epidemic observed since the 20th century. Although dietary interventions and reduced energy density, can effectively improve adiposity-related outcomes \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, the effect of nutrient density \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and dietary patterns \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e seem to be as effective, if potentially of greater importance, and should therefore not be neglected.\u003c/p\u003e\u003cp\u003eThis study includes potential limitations that should be considered when interpreting the results. Selection bias may have been introduced since the sample was collected at the healthcare level as children visiting health centres may not be representative of the general child population in the 6th Health ADM region, especially children from the rural and semi-urban areas. However, this approach was chosen to ensure standardized anthropometric measurements by trained professionals and to integrate the baseline for surveillance protocol within the existing healthcare infrastructure, facilitating future longitudinal monitoring across this diverse administrative region. Also, the geographic distribution was low for the Ionian Islands, potentially affecting the generalizability of results in this region. The degree of urbanization results may have been affected by the sampling method. Furthermore, to capture seasonal variations in weight status, data collection should be performed at regular time intervals, and future next steps should be formulated.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study reports the community-level disparities that occur between regions, and provides comprehensive surveillance data for targeted interventions, policy decisions, and resource allocation that can potentially address the disproportionate burden of childhood obesity in this diverse and socioeconomically challenged region.\u003c/p\u003e\u003cp\u003eBaseline data can serve as a valuable tool to assess future trends and evaluate the effect of specific future obesity interventions in the 6th Health ADM region. Having baseline measures is particularly important to implement specific intervention child obesity prevention programs and to evaluate long term effectiveness of these, hence helping to develop regional and national policies.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eADM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eAdministrative region\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eIOTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eInternational Obesity Task Force\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eDALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eDisability-Adjusted Life Year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eYLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eYears of life lost from mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eNCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eNon-Communicable Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eCOSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eEuropean Childhood Obesity Surveillance Initiative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eCDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eTOMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eLocal Health Units in Greece\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eOdds Ration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eEBRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eEnergy Balance Related Behaviors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eGross Domestic Product\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: EM, AV; Investigation: EM, AV, GK.; Writing \u0026ndash; Original Draft: EM, EP, AV; Writing \u0026ndash; Review \u0026amp; Editing: EM, EP, GC, AV; Supervision: AV. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by European Union, EU4Health program. (See methods section).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting this study\u0026rsquo;s findings are available upon reasonable request from the corresponding author. Additionally, the data will be made publicly accessible on the Open Science Framework (OSF) after a provisional period of one year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all Pediatricians that selected and documented the information required. Special thanks to the whole team in the 6\u003csup\u003eth\u003c/sup\u003e Heath ADM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from Bioethics Committee of University of Patras and the Regional Directorate of Primary and Secondary Education of Western Greece (number of approval: 304; date: 26/10/2023). All measurements were conducted following the ethical principles outlined in the World Medical Association (WMA) Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg M, Fleming T, Robinson M, et al. 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Prev Chronic Dis. 2012;9:E145.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHardy LL, Mihrshahi S. Elements of Effective Population Surveillance Systems for Monitoring Obesity in School Aged Children. Int J Environ Res Public Health. 2020;17(18):6812.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e(CDC) CfDCaP. Childhood Obesity Facts. Retrieved from:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/obesity/data/childhood.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/obesity/data/childhood.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2022. Accessed May 31, 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e(WHO) WHO. \u003cem\u003eSurveillance of childhood obesity: A toolkit for action.\u003c/em\u003e 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuoncristiano M, Spinelli A, Williams J et al. Childhood overweight and obesity in Europe: Changes from 2007 to 2017. \u003cem\u003eObesity Reviews.\u003c/em\u003e 2021;22(S6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDjordjic V, Radisavljevic S, Milanovic I, et al. WHO European Childhood Obesity Surveillance Initiative in Serbia: a prevalence of overweight and obesity among 6-9-year-old school children. J Pediatr Endocrinol Metab. 2016;29(9):1025\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOgden CL, Carroll MD, Lawman HG, et al. Trends in Obesity Prevalence Among Children and Adolescents in the United States, 1988\u0026ndash;1994 Through 2013\u0026ndash;2014. JAMA. 2016;315(21):2292\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDay SE, Konty KJ, Leventer-Roberts M, Nonas C, Harris TG. Severe Obesity Among Children in New York City Public Elementary and Middle Schools, School Years 2006\u0026ndash;07 Through 2010\u0026ndash;11. Prev Chronic Dis 2014;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKharofa RY, Klein JA, Khoury P, Siegel RM. Severe Obesity Decreasing in Children in Cincinnati, Ohio. Clin Pediatr (Phila). 2017;56(8):752\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorton K. Standards for Anthropometry Assessment. In:2018:68\u0026ndash;137.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes. 2012;7(4):284\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMonasta L, Lobstein T, Cole TJ, Vignerov\u0026aacute; J, Cattaneo A. Defining overweight and obesity in pre-school children: IOTF reference or WHO standard? Obes Rev. 2011;12(4):295\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuggieri DG, Bass SB. A comprehensive review of school-based body mass index screening programs and their implications for school health: do the controversies accurately reflect the research? J Sch Health. 2015;85(1):61\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuoncristiano M, Spinelli A, Williams J, et al. Childhood overweight and obesity in Europe: Changes from 2007 to 2017. Obes Rev. 2021;22:e13226.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHimes JH. Challenges of accurately measuring and using BMI and other indicators of obesity in children. Pediatrics. 2009;124(Suppl 1):S3\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMannino A, Halilagic A, Argyropoulou M, et al. The Role of Energy Balance-Related Behaviors (EBRBs) and their Determinants on the Prevalence of Overweight and Obesity in Children in Need, in Greece: A Scoping Review. Curr Nutr Rep. 2024;14(1):2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahmood L, Moreno LA, Schwarz P, et al. A snapshot of country-specific dietary habits and obesity in European children: the Feel4Diabetes study. Eur J Pediatr. 2025;184(3):214.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHassapidou M, Duncanson K, Shrewsbury V, et al. EASO and EFAD Position Statement on Medical Nutrition Therapy for the Management of Overweight and Obesity in Children and Adolescents. Obes Facts. 2023;16(1):29\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKostopoulou E, Tsekoura E, Fouzas S, Gkentzi D, Jelastopulu E, Varvarigou A. Association of lifestyle factors with a high prevalence of overweight and obesity in Greek children aged 10\u0026ndash;16 years. Acta Paediatr. 2021;110(12):3356\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKosti RI, Kanellopoulou A, Fragkedaki E, et al. The Influence of Adherence to the Mediterranean Diet among Children and Their Parents in Relation to Childhood Overweight/Obesity: A Cross-Sectional Study in Greece. Child Obes. 2020;16(8):571\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"childhood obesity, surveillance, Greece, regional disparities, public health, IOTF standards ","lastPublishedDoi":"10.21203/rs.3.rs-7189294/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7189294/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Childhood obesity has reached epidemic proportions in Europe, with Greece ranking among the top three countries for prevalence. The 6th Health Administrative Region (ADM) of Greece reports the highest childhood obesity rates nationally (18.8%), however, comprehensive local-level data using standardized methodology are lacking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional survey was conducted between Novermber 2024 and January 2025 across healthcare facilities in the region of 6\u003csup\u003eth\u003c/sup\u003e Health ADM, to estimate children’s weight status. Trained healthcare professionals collected standardized anthropometric measurements from 2,730 children (3-16 years). Body weight status was classified using International Obesity Task Force (IOTF) standards. Data were analyzed by region, age group, sex, and degree of urbanization using descriptive statistics, ANOVA, and logistic regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Overall prevalence of overweight and obesity was 21.6% and 7.0%, respectively, with significant regional and sex variations. The highest prevalence was found in Western Greece (24.6%), while Peloponnese had the highest obesity rates (9.1%). Boys demonstrated higher rates in both weight categories. A significant age-related obesity gradient was observed, increasing from 3.4% in toddlers to 8.5% in adolescents (p=0.021). No significant differences were found by degree of urbanization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Survey findings provide essential baseline data for an integrated surveillance protocol within existing healthcare infrastructure, facilitating future longitudinal monitoring across this diverse administrative region.\u003c/p\u003e","manuscriptTitle":"Childhood Overweight and Obesity Survey: An Overlooked Public Health Issue","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 10:35:09","doi":"10.21203/rs.3.rs-7189294/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-09T07:25:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T19:50:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T19:22:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81235508023044076883715799195971760260","date":"2025-08-27T06:13:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202769699842598582215202072687097676548","date":"2025-08-22T11:56:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44104187341678182631510709899120679497","date":"2025-07-31T07:33:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110258408528736722036277522319547644338","date":"2025-07-30T15:57:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-29T07:06:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-25T18:39:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-25T18:38:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2025-07-22T16:46:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08df58ea-a3ea-4790-b64c-0522dd7706ed","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:05:40+00:00","versionOfRecord":{"articleIdentity":"rs-7189294","link":"https://doi.org/10.1186/s41043-025-01132-6","journal":{"identity":"journal-of-health-population-and-nutrition","isVorOnly":false,"title":"Journal of Health, Population and Nutrition"},"publishedOn":"2025-11-10 15:58:00","publishedOnDateReadable":"November 10th, 2025"},"versionCreatedAt":"2025-07-31 10:35:09","video":"","vorDoi":"10.1186/s41043-025-01132-6","vorDoiUrl":"https://doi.org/10.1186/s41043-025-01132-6","workflowStages":[]},"version":"v1","identity":"rs-7189294","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7189294","identity":"rs-7189294","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}