Key Points
Question
What is the global prevalence of overweight and obesity in children and adolescents?
Findings
In this systematic review and meta-analysis, we found high prevalence of overweight and obesity in children and adolescents. Various possible risk factors were identified, including inherent, dietary, and environmental factors.
Meaning
These findings suggest that excess weight commonly occurrs in children and adolescents, indicating a need for more control measures incorporating behavioral, environmental, and sociocultural factors.
This systematic review and meta-analysis estimates global prevalence of overweight and obesity in children and adolescents from 2000 to 2023 and evaluates potential risk factors associated therewith.
Abstract
Importance
Overweight and obesity in childhood and adolescence is a global health issue associated with adverse outcomes throughout the life course.
Objective
To estimate worldwide prevalence of overweight and obesity in children and adolescents from 2000 to 2023 and to assess potential risk factors for and comorbidities of obesity.
Data Sources
MEDLINE, Web of Science, Embase, and Cochrane.
Study Selection
The inclusion criteria were: (1) studies provided adequate information, (2) diagnosis based on body mass index cutoffs proposed by accepted references, (3) studies performed on general population between January 2000 and March 2023, (4) participants were younger than 18 years.
Data Extraction and Synthesis
The current study was performed in accordance with the Meta-analysis of Observational Studies in Epidemiology guidelines. DerSimonian-Laird random-effects model with Free-Tukey double arcsine transformation was used for data analysis. Sensitivity analysis, meta-regression, and subgroup analysis of obesity among children and adolescents were conducted.
Main Outcomes and Measures
Prevalence of overweight and obesity among children and adolescents assessed by World Health Organization, International Obesity Task Force, the US Centers for Disease Control and Prevention, or other national references.
Results
A total of 2033 studies from 154 different countries or regions involving 45 890 555 individuals were included. The overall prevalence of obesity in children and adolescents was 8.5% (95% CI 8.2-8.8). We found that the prevalence varied across countries, ranging from 0.4% (Vanuatu) to 28.4% (Puerto Rico). Higher prevalence of obesity among children and adolescents was reported in countries with Human Development Index scores of 0.8 or greater and high-income countries or regions. Compared to 2000 to 2011, a 1.5-fold increase in the prevalence of obesity was observed in 2012 to 2023. Substantial differences in rates of obesity were noted when stratified by 11 risk factors. Children and adolescents with obesity had a high risk of depression and hypertension. The pooled estimates of overweight and excess weight in children and adolescents were 14.8% (95% CI 14.5-15.1) and 22.2% (95% CI 21.6-22.8), respectively.
Conclusions and Relevance
This study’s findings indicated 1 of 5 children or adolescents experienced excess weight and that rates of excess weight varied by regional income and Human Development Index. Excess weight among children and adolescents was associated with a mix of inherent, behavioral, environmental, and sociocultural influences that need the attention and committed intervention of primary care professionals, clinicians, health authorities, and the general public.
Introduction
Overweight and obesity in children and adolescents is an emerging worldwide health concern. Estimates of the prevalence have shown heterogeneity across countries and regions, typically demonstrating a growing trend.1,2,3,4 The Global Burden of Disease Obesity Collaborators5 reported an overall prevalence of 5.0% for childhood obesity, with 107.7 million children having obesity globally in 2015, and data from the World Obesity Federation6 indicate that the rising trend has not yet been stopped, as it estimated that 158 million children and adolescents aged 5 to 19 years would experience obesity in 2020, 206 million in 2025, and 254 million in 2030. Awareness is growing that the epidemiological burden of childhood obesity has posed incremental expenses for both individuals and society.7
Obesity could result from multidimensional biological, behavioral, and environmental causes, and unbalanced diet and sedentary habits appearing to be the main drivers.8,9,10 Since obesity is a disease in and of itself, managing it becomes more difficult when it coexists with other pathological illnesses including diabetes, cardiovascular disease, and psychological disorders.11 Furthermore, childhood overweight and obesity have been shown to persist into adulthood,12 and their related adverse outcomes include not only certain health conditions in childhood, but also a greater risk and earlier onset of chronic disorders in later life.13,14,15 Hence, there is a demand for routine surveillance of weight status in children and adolescents.
There has been a dearth of studies into the prevalence of obesity among children and adolescents from global perspective since the Non-Communicable Diseases Risk Factor Collaboration16 reported an estimation of 5.6% of girls and 7.8% of boys with obesity in 2016. The present study pooled a larger and more recent set of national surveys than previously reported to estimate global prevalence as well as risk factors and comorbidities associated with overweight and obesity among children and adolescents under 18 years old from 2000 to 2023.
Methods
Search Strategy and Selection Criteria
The study followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline. A comprehensive literature search were performed in MEDLINE, Web of Science, Embase, and Cochrane databases between January 1, 2000, and March 31, 2023. The search strategy was structured to include terms pertaining to “overweight,” “obesity,” “excess weight,” “children,” “adolescent,” and “prevalence.” eTable 1 in Supplement 1 contains a full list of the search terms used. The study protocol was registered in PROSPERO (CRD42023483885).
Predefined inclusion criteria were cohort studies, case-control trials, and randomized clinical trials that (1) reported the prevalence of obesity, overweight, and excess weight (overweight and obesity) assessed by body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) cutoffs in children and adolescents younger than 18 years; (2) were conducted in the general population (defined as apparently healthy children or adolescents from school, community, or national demographic census); (3) used standardized instruments, self-reported questionnaires, or clinically structured interviews for assessment of overweight and obesity; and (4) completed data collection between January 2000 and March 2023. We excluded studies of hospitalized patients or a mix of hospitalized and general populations. Title and abstract screening were done by X.Z, J.L, K.L, and C.Y based on the selection criteria. If articles seemed relevant, then the full text was assessed for inclusion.
Data Extraction and Quality Assessment
Researchers reviewed and extracted data from included studies by using a data extraction form that included country or region, geographic region, publication year, study period, income of country or region, Human Development Index (HDI) of the country or region, study design, sample source, diagnostic reference, sample size, study quality, risk factors, and comorbidities. We also included race and ethnicity in subgroup analyses for comprehensive assessment, and the categories in this study were in accordance with our data sources, using a 4-level variable (Asian, Black, Hispanic, and White). Initial data extraction was done by X.Z, J.L, K.L, and C.Y. For quality assurance, data collected from all the included studies were validated by a second team member (Y.F, Q.N, B.S, or Y.N) for accuracy and completeness against the original source. All discrepancies were reviewed and resolved either by consensus or by a third team member if consensus was not reached. When duplicate data were identified, the duplicate with the smallest sample size or shortest duration of follow-up was excluded. We assessed the quality of included studies using an assessment scale based on the Joanna Briggs Institute Tool in accordance with previous published studies.15,16 Studies scoring 1 to 3 were defined as low quality, 4 to 6 as average quality, and 7 to 9 as high quality. Studies were not excluded regardless of their quality score to increase transparency and to ensure all available evidence in this area was reported.
Statistical Analysis
All data analysis was performed using R version 4.0.0 (R Foundation) with the meta and metafor statistical packages. A 95% CI was estimated using the Wilson score method, and the pooled prevalence was calculated using the DerSimonian-Laird random-effects model with Free-Tukey double arcsine transformation. Heterogeneity among the included studies was evaluated through the Cochran Q and I2 statistics. Given the anticipated heterogeneity in global data, a random-effects model was used to estimate the prevalence of obesity, overweight, and excess weight. Sensitivity analyses were conducted by performing a set of leave-1-out diagnostic tests focusing on the significant heterogeneity associated with obesity where individual studies were systematically removed from the meta-analysis and the pooled-effect estimate recalculated. The results were then verified by using a build-in function in metafor. As sensitivity analysis was unable to decrease the heterogeneity, meta-regression was performed by using a mixed-effects model. Univariable and multivariable meta-regression (multimodel inference) were performed by using the dmetar package in synthesizing evidence from multiple studies and exploring heterogeneity. The random-effects weighting method was used for assigning weights in meta-regression. To assess the potential confounding effects of heterogeneity, subgroup analyses were conducted. Characteristics of participants were compared with the prevalence of obesity to determine the pooled estimates of risk factors and comorbidities. IQR was defined as the difference between the first and the third quartile. P < .05 was considered as significant difference.
Results
The search identified 65 448 records, 39 243 of which were retained after removing duplicates. Titles and abstracts were screened, resulting in the exclusion of 33 417 ineligible records. Full texts of the remaining 5826 records were assessed for eligibility, and 3793 were excluded. Overall, 2033 eligible studies involving 45 890 555 children and adolescents from 154 countries or regions were included in the final analysis (Figure 1).
Figure 1. Flow Diagram of Study Selection Process.
Study Characteristics and Risk of Bias
The characteristics and quality assessment score of all 2033 included studies are presented in eTables 2-5 in Supplement 1. The sample size ranged from 30 to 3 190 300 participants. The cross-sectional design was used in most of the included research. The mean or median age and sex of participants was reported in 737 and 1090 studies. The median (IQR) age was 10.0 (7.1-12.5) years, and the median (IQR) proportion of participants who were female was 49.64% (48.1-51.5).
Prevalence of Obesity Among Children and Adolescents
The prevalence of obesity in children and adolescents was reported by 1668 studies comprising 44 414 245 individuals from 152 countries or regions (eTable 3 in Supplement 1). A total of 4 519 587 participants were diagnosed as having obesity with a pooled prevalence of 8.5% (95% CI, 8.2-8.8; I2, 99.9%). To gain a deeper understanding of the heterogeneity, we conducted a sensitivity analysis by performing a set of leave-1-out diagnostic tests (eTables 6-7 in Supplement 1). After removing the outliers, the pooled estimate of obesity for children and adolescents was 8.3% (95% CI, 8.0-8.6; I2, 99.9%). To further explore the source of heterogeneity, meta-regression analysis was performed. Our univariate meta-regression model indicated that country or region (R2, 66.6%; P < .001), geographic region (R2, 46.8%; P < .001), diagnostic reference (R2, 0; P < .001), HDI level (R2, 41.9%; P < .001), sample size (R2, 0.01%; P < .001), sample source (R2, 2.4%; P < .001), and publication year (R2, 1.4%; P = .02) were associated with heterogeneity, while study design was not (R2 = 4.4%; P = .63) (eTable 8 in Supplement 1). By performing multivariable meta-regression, it was found that the geographic region, income level of the country or region, sample sources, diagnostic reference, and sample size showed the highest predictor importance of 99.99% (eTable 9 in Supplement 1).
In subgroup analyses, prevalence of obesity varied substantially across different countries and regions, from 0.4% (Vanuatu, 95% CI, 0.1-0.8) to 28.4% (Puerto Rico, 95% CI, 23.6-33.4). Stratified data by geographic regions, the highest obesity prevalence was found in Polynesia with an estimated rate of 19.5% (95% CI, 16.1-23.1), and the lowest prevalence appeared in Middle Africa (2.4%; 95% CI, 1.8-3.0). The prevalence of obesity in countries and regions with HDI scores of 0.8 or greater was 9.5% (95% CI, 9.2-9.8), whereas countries and regions with HDI scores lower than 0.8 showed a significantly lower prevalence of 7.6% (95% CI, 7.3-7.9; P < .001). Likewise, there was a positive association between income of countries and regions and prevalence of children and adolescents’ obesity, with high-income countries showing the highest prevalence (9.3%; 95% CI, 9.0-9.6) and low-income countries exhibiting the lowest (3.6%; 95% CI, 2.5-4.8; P < .001). We also discovered significant disparity among race and ethnicity, with the highest prevalence appearing in the Hispanic population (23.55; 95% CI, 20.66-26.56) and the lowest appearing in the Asian population (10.0%; 95% CI 8.73-11.29; P < .001). Regarding sample sources, participants from medical institutions presented the highest prevalence of 13.6% (95% CI, 12.2-15.1), although sample sources drawn from databases contained most participants. Considering the diagnostic references for assessing obesity, 466 studies used the World Health Organization reference17 (8.6%; 95% CI, 7.9-9.3), 807 used the International Obesity Task Force reference18 (5.4%; 95% CI, 5.1-5.7), 453 used the US Centers for Disease Control and Prevention reference19 (14.5%; 95% CI, 13.6-15.3), and 282 studies used various national references (9.7%; 95% CI, 9.0-10.3). A pattern of decreased prevalence was found in studies having more than 5000 participants (7.7%; 95% CI, 7.1-8.2) than those with fewer than 5000 participants (8.7%; 95% CI, 8.4-9.1; P < .001). Moreover, studies performed from 2000 to 2011 showed significantly lower rates (7.1%; 95% CI, 6.8-7.3) than those performed from 2012 to 2023 (11.3%; 95% CI, 10.8-11.8; P < .001) (Table 1; eTable 10 in Supplement 1.
Table 1. Subgroup Analysis for Obesity Prevalence Among Children and Adolescents.
| Subgroup | Studies, No. | Events, No. | Total, No. | Prevalence (95% CI) | P value | I2, % |
|---|---|---|---|---|---|---|
| Country | ||||||
| Albania | 2 | 671 | 8069 | 8.72 (6.67-11.03) | <.001 | 90.1 |
| Algeria | 3 | 119 | 3837 | 3.53 (1.82-5.76) | <.001 | 89.0 |
| Argentina | 13 | 2516 | 25 261 | 11.53 (9.22-14.07) | <.001 | 95.8 |
| Australia | 57 | 14 105 | 220 141 | 5.96 (5.39-6.55) | <.001 | 96.6 |
| Austria | 4 | 315 | 8940 | 3.66 (1.78-6.15) | <.001 | 96.4 |
| Bahamas | 1 | 279 | 1308 | 21.33 (19.15-23.59) | <.001 | NA |
| Bahrain | 3 | 295 | 3350 | 9.74 (5.48-15.04) | <.001 | 94.4 |
| Bangladesh | 8 | 966 | 18 088 | 7.76 (3.96-12.68) | <.001 | 98.9 |
| Barbados | 1 | 214 | 1504 | 14.23 (12.51-16.04) | <.001 | NA |
| Belgium | 15 | 870 | 39 466 | 2.22 (1.48-3.10) | <.001 | 96.2 |
| Benin | 2 | 33 | 3398 | 1.30 (0.11-3.62) | <.001 | 92.8 |
| Bhutan | 1 | 2 | 392 | 0.51 (0.01-1.53) | <.001 | NA |
| Bolivia | 4 | 446 | 7020 | 3.87 (1.45-7.35) | <.001 | 97.0 |
| Bosnia and Herzegovina | 2 | 619 | 6108 | 10.52 (4.05-19.54) | <.001 | 99.0 |
| Botswana | 1 | 35 | 707 | 4.95 (3.46-6.68) | <.001 | NA |
| Brazil | 92 | 31 043 | 333 397 | 8.65 (7.59-9.77) | <.001 | 99.1 |
| Brunei Darussalam | 1 | 319 | 1824 | 17.49 (15.78-19.27) | <.001 | NA |
| Bulgaria | 6 | 1130 | 14 734 | 6.29 (2.53-11.57) | <.001 | 99.2 |
| Burkina Faso | 3 | 185 | 8431 | 2.20 (0.66-4.57) | <.001 | 94.9 |
| Burundi | 1 | 52 | 3493 | 1.49 (1.11-1.92) | <.001 | NA |
| Cameroon | 7 | 334 | 13 385 | 2.32 (1.74-2.97) | <.001 | 79.2 |
| Canada | 47 | 380 348 | 3 478 991 | 10.43 (9.26-11.66) | <.001 | 99.5 |
| Chile | 13 | 4894 | 173 378 | 15.94 (10.03-22.91) | <.001 | 99.7 |
| China mainland | 148 | 410 959 | 5 986 764 | 7.77 (7.11-8.45) | <.001 | 99.9 |
| Colombia | 6 | 889 | 25 937 | 4.70 (3.02-6.73) | <.001 | 96.5 |
| Comoros | 1 | 173 | 2699 | 6.41 (5.52-7.37) | <.001 | NA |
| Congo | 2 | 286 | 12 922 | 1.95 (0.81-3.56) | <.001 | 96.7 |
| Costa Rica | 1 | 49 128 | 347 366 | 14.14 (14.03-14.26) | <.001 | NA |
| Cote d’Ivoire | 2 | 71 | 4545 | 1.76 (0.72-3.22) | <.001 | 88.1 |
| Croatia | 8 | 1486 | 19 623 | 5.77 (2.28-10.71) | <.001 | 99.3 |
| Cyprus | 8 | 1522 | 21 867 | 6.54 (5.42-7.76) | <.001 | 90.7 |
| Czech | 7 | 1488 | 48 743 | 4.35 (1.87-7.78) | <.001 | 99.4 |
| Denmark | 12 | 2047 | 68 426 | 3.07 (1.58-5.02) | <.001 | 99.3 |
| Djibouti | 2 | 226 | 3249 | 6.93 (3.75-11) | <.001 | 94.2 |
| Dominican Republic | 1 | 117 | 954 | 12.26 (10.26-14.42) | <.001 | NA |
| East Timor | 1 | 20 | 1631 | 1.23 (0.74-1.82) | <.001 | NA |
| Ecuador | 4 | 1435 | 12 962 | 12.28 (4.03-24.12) | <.001 | 98.9 |
| Egypt | 10 | 1608 | 15 845 | 13.33 (10.72-16.17) | <.001 | 95.2 |
| El Salvador | 1 | 10 087 | 111 991 | 9.01 (8.84-9.18) | <.001 | NA |
| Estonia | 4 | 188 | 10 275 | 2.09 (0.97-3.60) | <.001 | 94.4 |
| Ethiopia | 11 | 336 | 18 012 | 2.70 (1.61-4.06) | <.001 | 94.7 |
| Fiji | 3 | 684 | 12 257 | 6.07 (4.52-7.84) | <.001 | 89.8 |
| Finland | 8 | 838 | 31 278 | 2.88 (2.45-3.34) | <.001 | 76.5 |
| France | 23 | 5411 | 162 311 | 3.93 (3.17-4.76) | <.001 | 97.9 |
| French Polynesis | 1 | 420 | 1902 | 22.08 (20.25-23.97) | <.001 | NA |
| Gabon | 1 | 129 | 3482 | 3.70 (3.10-4.36) | <.001 | NA |
| Gambia | 1 | 114 | 3360 | 3.39 (2.81-4.03) | <.001 | NA |
| Georgia | 2 | 281 | 3226 | 8.60 (7.65-9.61) | <.001 | 0 |
| Germany | 39 | 7349 | 168 736 | 4.35 (3.74-5.01) | <.001 | 97.3 |
| Ghana | 13 | 686 | 20 425 | 7.16 (4.06-11.04) | <.001 | 98.7 |
| Greece | 59 | 33 519 | 418 004 | 8.19 (7.54-8.86) | <.001 | 97.5 |
| Greenland | 2 | 34 | 1501 | 2.18 (1.30-3.28) | <.001 | 39.8 |
| Guatemala | 1 | 71 | 363 | 19.56 (15.63-23.81) | <.001 | NA |
| Guinea | 1 | 80 | 3216 | 2.49 (1.98-3.06) | <.001 | NA |
| Honduras | 1 | 112 | 2554 | 4.39 (3.62-5.22) | <.001 | NA |
| Hong Kong | 11 | 13 180 | 256 924 | 5.32 (4.59-6.11) | <.001 | 96.0 |
| Hungary | 11 | 3129 | 43 224 | 6.30 (4.36-8.58) | <.001 | 98.7 |
| Iceland | 4 | 438 | 14 284 | 2.67 (1.66-3.90) | <.001 | 92.4 |
| India | 89 | 14 355 | 318 874 | 5.63 (4.92-6.39) | <.001 | 98.7 |
| Indonesia | 12 | 10 682 | 186 391 | 10.18 (8.71-11.76) | <.001 | 98.0 |
| Iran | 79 | 169 562 | 460 7462 | 8.28 (7.83-8.75) | <.001 | 99.6 |
| Iraq | 5 | 617 | 21 340 | 5.09 (2.86-7.91) | <.001 | 98.4 |
| Ireland | 15 | 4289 | 65 512 | 5.78 (5.00-6.60) | <.001 | 93.5 |
| Israel | 9 | 24 855 | 612 186 | 6.41 (4.82-8.22) | <.001 | 99.8 |
| Italy | 55 | 30 477 | 282 659 | 8.49 (6.77-10.38) | <.001 | 99.6 |
| Jamaica | 1 | 107 | 1061 | 10.08 (8.34-11.97) | <.001 | NA |
| Japan | 14 | 3071 | 86 053 | 3.9 0(2.84-5.12) | <.001 | 98.5 |
| Jordan | 12 | 1508 | 14 367 | 9.09 (6.49-12.08) | <.001 | 97.1 |
| Kazakhstan | 2 | 241 | 6388 | 2.51 (0-9.86) | <.001 | 99.4 |
| Kenya | 4 | 143 | 2826 | 5.48 (3.91-7.29) | <.001 | 70.5 |
| Kiribati | 1 | 117 | 1582 | 7.40 (6.16-8.74) | <.001 | NA |
| Kuwait | 11 | 4506 | 64 261 | 20.49 (11.68-31.01) | <.001 | 99.8 |
| Kyrgyzstan | 1 | 161 | 5958 | 2.70 (2.31-3.13) | <.001 | NA |
| Laos | 1 | 36 | 1644 | 2.19 (1.53-2.96) | <.001 | NA |
| Latvia | 5 | 537 | 13 122 | 2.85 (0.32-7.59) | <.001 | 99.2 |
| Lebanon | 7 | 1440 | 20 131 | 6.84 (5.32-8.52) | <.001 | 92.2 |
| Liberia | 1 | 55 | 3259 | 1.69 (1.27-2.16) | <.001 | NA |
| Libya | 4 | 1146 | 9251 | 10.22 (7.54-13.26) | <.001 | 90.3 |
| Lithuania | 5 | 912 | 19 529 | 3.82 (0.90-8.66) | <.001 | 99.5 |
| Luxemburg | 1 | 90 | 3904 | 2.31 (1.86-2.80) | <.001 | NA |
| Macedonia | 4 | 508 | 11 931 | 5.86 (1.98-11.60) | <.001 | 99.2 |
| Malawi | 2 | 298 | 7134 | 2.74 (0.01-9.72) | <.001 | 99.3 |
| Malaysia | 20 | 14 446 | 126 080 | 10.90 (9.84-12.01) | <.001 | 96.1 |
| Mali | 1 | 101 | 4591 | 2.20 (1.79-2.65) | <.001 | NA |
| Malta | 5 | 970 | 6904 | 12.67 (9.28-16.51) | <.001 | 94.7 |
| Mauritania | 1 | 69 | 2028 | 3.40 (2.65-4.24) | <.001 | NA |
| Mauritius | 3 | 235 | 2996 | 6.87 (4.09-10.29) | <.001 | 87.7 |
| Mexico | 37 | 11 205 | 69 829 | 16.56 (14.05-19.22) | <.001 | 98.7 |
| Mongolia | 1 | 67 | 3707 | 1.81 (1.40-2.26) | <.001 | NA |
| Montenegro | 3 | 583 | 6999 | 9.26 (4.09-16.22) | <.001 | 98.3 |
| Morocco | 5 | 1241 | 14 974 | 7.78 (2.87-14.80) | <.001 | 99.4 |
| Mozambique | 1 | 408 | 9721 | 4.20 (3.81-4.60) | <.001 | NA |
| Multiple countries | 15 | 12178 | 210 258 | 6.36 (4.31-8.78) | <.001 | 99.7 |
| Namibia | 2 | 79 | 3781 | 2.09 (1.65-2.57) | <.001 | 0 |
| Nepal | 7 | 212 | 7945 | 3.36 (0.80-7.48) | <.001 | 98.0 |
| the Netherlands | 25 | 7303 | 252 778 | 3.23 (2.38-4.19) | <.001 | 99.2 |
| New Zealand | 10 | 36 378 | 226 167 | 15.33 (10.95-20.28) | <.001 | 99.8 |
| Niger | 1 | 179 | 5123 | 3.49 (3.01-4.01) | <.001 | NA |
| Nigeria | 13 | 1672 | 49 525 | 4.02 (2.67-5.62) | <.001 | 98.0 |
| Norway | 18 | 1187 | 53 178 | 2.37 (2.06-2.70) | <.001 | 78.2 |
| Pakistan | 12 | 1515 | 24 011 | 10.37 (7.89-13.15) | <.001 | 96.8 |
| Palestine | 8 | 488 | 7463 | 5.68 (3.19-8.82) | <.001 | 96.2 |
| Peru | 4 | 35 815 | 2 341 760 | 6.25 (2.06-12.47) | <.001 | 99.4 |
| Philippines | 1 | 173 | 6162 | 2.81 (2.41-3.24) | <.001 | NA |
| Poland | 39 | 3675 | 94 598 | 4.32 (3.55-5.15) | <.001 | 97.1 |
| Portugal | 38 | 9655 | 121 395 | 8.39 (7.22-9.64) | <.001 | 98.2 |
| Puerto Rico | 4 | 1339 | 5211 | 28.35 (23.57-33.39) | <.001 | 90.6 |
| Qatar | 3 | 700 | 11 824 | 9.13 (5.22-13.99) | <.001 | 98.1 |
| Republic of Marshall Islands | 1 | 167 | 3271 | 5.11 (4.38-5.89) | <.001 | NA |
| Romania | 10 | 6096 | 55 265 | 6.46 (3.56-10.15) | <.001 | 99.6 |
| Russia | 5 | 358 | 19 758 | 2.28 (0.63-4.88) | <.001 | 98.9 |
| Rwanda | 1 | 95 | 4116 | 2.31 (1.87-2.79) | <.001 | NA |
| Samoa | 1 | 467 | 2418 | 19.31 (17.76-20.91) | <.001 | NA |
| San Marino | 1 | 37 | 303 | 12.21 (8.75-16.15) | <.001 | NA |
| Saudi Arabia | 29 | 9734 | 72 356 | 16.93 (13.7-20.42) | <.001 | 99.3 |
| Senegal | 1 | 49 | 6062 | 0.81 (0.60-1.05) | <.001 | NANA |
| Serbia | 10 | 2035 | 32 643 | 8.21 (6.18-10.49) | <.001 | 97.7 |
| Seychelles | 4 | 1759 | 30 478 | 6.60 (3.51-10.52) | <.001 | 98.3 |
| Sierra Leone | 1 | 446 | 4698 | 9.49 (8.67-10.35) | <.001 | NA |
| Singapore | 3 | 649 | 9870 | 6.55 (6.07-7.05) | <.001 | 0 |
| Slovakia | 2 | 55 | 5078 | 1.23 (0.44-2.39) | <.001 | 87.6 |
| Slovenia | 11 | 2501 | 46 166 | 4.97 (3.78-6.32) | <.001 | 97.4 |
| Solomon Islands | 1 | 38 | 1421 | 2.67 (1.89-3.58) | <.001 | NA |
| South Africa | 24 | 2565 | 45 509 | 6.20 (4.49-8.16) | <.001 | 98.3 |
| South Korea | 26 | 633 630 | 5 644 482 | 8.39 (7.68-9.14) | <.001 | 99.8 |
| Spain | 54 | 392 129 | 2 821 506 | 9.28 (8.27-10.33) | <.001 | 99.5 |
| Sri Lanka | 4 | 667 | 15 077 | 3.34 (1.53-5.80) | <.001 | 94.7 |
| Sudan | 3 | 168 | 2344 | 7.11 (2.84-13.07) | <.001 | 95.4 |
| Suriname | 1 | 167 | 1453 | 11.49 (9.9-13.19) | <.001 | NA |
| Sweden | 33 | 12 744 | 1 131 530 | 3.18 (2.45-4.01) | <.001 | 99.4 |
| Switzerland | 10 | 965 | 31 991 | 3.24 (2.08-4.64) | <.001 | 97.5 |
| Syria | 3 | 1231 | 7292 | 10.99 (3.39-22.19) | <.001 | 99.1 |
| Taiwan | 21 | 8752 | 70 568 | 12.05 (9.99-14.28) | <.001 | 98.6 |
| Tajikistan | 1 | 42 | 2822 | 1.49 (1.07-1.97) | <.001 | NA |
| Tanzania | 7 | 505 | 12 740 | 4.88 (3.35-6.67) | <.001 | 92.8 |
| Thailand | 15 | 3775 | 42 954 | 9.80 (7.75-12.07) | <.001 | 98.1 |
| Togo | 2 | 41 | 3862 | 1.26 (0.46-2.44) | <.001 | 76.7 |
| Tonga | 3 | 998 | 4602 | 18.33 (12.1-25.52) | <.001 | 96.8 |
| Trinidad and Tobago | 2 | 450 | 2699 | 13.05 (5.47-23.22) | <.001 | 95.4 |
| Tunisia | 2 | 123 | 2138 | 5.75 (4.80-6.78) | <.001 | 0 |
| Turkey | 55 | 10 879 | 152 633 | 6.97 (5.79-8.25) | <.001 | 98.9 |
| Turkmenistan | 1 | 1055 | 9768 | 10.80 (10.19-11.42) | <.001 | NA |
| Uganda | 1 | 55 | 4212 | 1.31 (0.98-1.67) | <.001 | NA |
| Ukraine | 5 | 1406 | 40 633 | 2.56 (1.11-4.57) | <.001 | 99.0 |
| United Arab Emirates | 7 | 5422 | 31 845 | 15.62 (13.86-17.45) | <.001 | 82.1 |
| United Kingdom | 53 | 163 750 | 1 294 718 | 7.63 (6.40-8.95) | <.001 | 99.6 |
| US | 262 | 1 849 465 | 10, 411 152 | 18.57 (18.03-19.12) | <.001 | 99.8 |
| Vanuatu | 1 | 4 | 1119 | 0.36 (0.08-0.81) | <.001 | NA |
| Vietnam | 13 | 2617 | 30 800 | 6.91 (3.85-10.77) | <.001 | 99.2 |
| Yemen | 1 | 975 | 10 924 | 8.93 (8.40-9.47) | <.001 | NA |
| Zambia | 1 | 397 | 11 677 | 3.40 (3.08-3.74) | <.001 | NA |
| Zimbabwe | 2 | 207 | 5379 | 5.05 (1.44-10.64) | <.001 | 97.3 |
| Geographic region | ||||||
| Southern Europe | 248 | 474 682 | 3 770 379 | 8.42 (7.84-9.01) | <.001 | 99.6 |
| Northern Africa | 27 | 4405 | 48 389 | 9.22 (7.32-11.3) | <.001 | 98.2 |
| South America | 133 | 76 759 | 2 914 148 | 9.38 (8.24-10.59) | <.001 | 99.8 |
| Australia and New Zealand | 67 | 50 483 | 446 308 | 6.99 (5.74-8.36) | <.001 | 99.6 |
| Western Europe | 124 | 24 042 | 687 349 | 3.79 (3.38-4.22) | <.001 | 98.7 |
| Caribbean | 10 | 2506 | 12 737 | 19.22 (15.1-23.7) | <.001 | 97.3 |
| Western Asia | 152 | 62 734 | 1 040 310 | 9.94 (9.03-10.88) | <.001 | 99.5 |
| Southern Asia | 125 | 18 163 | 391 407 | 5.79 (5.17-6.45) | <.001 | 98.6 |
| Western Africa | 43 | 3781 | 122 523 | 3.95 (3.13-4.87) | <.001 | 98.2 |
| Southern Africa | 27 | 2679 | 49 997 | 5.76 (4.24-7.50) | <.001 | 98.2 |
| South-Eastern Asia | 146 | 202 279 | 5 014 818 | 8.71 (8.26-9.17) | <.001 | 99.6 |
| Eastern Europe | 85 | 17 337 | 322 033 | 4.58 (3.75-5.50) | <.001 | 99.3 |
| Eastern Africa | 34 | 4384 | 105 992 | 4.12 (3.30-5.04) | <.001 | 97.9 |
| Middle Africa | 10 | 749 | 29 789 | 2.36 (1.83-2.96) | <.001 | 89.1 |
| Northern America | 311 | 2 229 847 | 13 891 644 | 17.17 (16.59-17.75) | <.001 | 99.9 |
| Eastern Asia | 221 | 1 069 659 | 12 048 498 | 7.78 (7.24-8.32) | <.001 | 99.9 |
| Central America | 41 | 70 603 | 532 103 | 15.85 (14.23-17.55) | <.001 | 99.3 |
| Northern Europe | 157 | 186 930 | 2 701 852 | 4.55 (3.57-5.63) | <.001 | 99.9 |
| Melanesia | 5 | 726 | 14 797 | 3.79 (1.84-6.40) | <.001 | 97.5 |
| Polynesia | 5 | 1885 | 8922 | 19.45 (16.06-23.07) | <.001 | 94.2 |
| Central Asia | 12 | 2004 | 37 676 | 4.28 (2.46-6.58) | <.001 | 98.9 |
| Micronesia | 10 | 772 | 12 316 | 5.80 (3.95-7.98) | <.001 | 95.4 |
| Not applicable | 15 | 12 178 | 210 258 | 6.36 (4.31-8.78) | <.001 | 99.7 |
| HDI | ||||||
| <0.8 | 1047 | 871 742 | 17 166 470 | 7.56 (7.28-7.85) | <.001 | 99.8 |
| ≥0.8 | 946 | 3 635 667 | 27 037 517 | 9.50 (9.19-9.82) | <.001 | 99.9 |
| Not applicable | 15 | 12 178 | 210 258 | 6.36 (4.31-8.78) | <.001 | 99.7 |
| Country or region income | ||||||
| High income | 1129 | 3 692 176 | 28 815 921 | 9.29 (8.95-9.64) | <.001 | 99.9 |
| Upper-middle income | 495 | 588 196 | 9 709 961 | 8.50 (8.02-8.99) | <.001 | 99.9 |
| Lower-middle income | 333 | 221 821 | 5 562 640 | 6.35 (6.09-6.62) | <.001 | 99.2 |
| Low income | 36 | 5216 | 115 465 | 3.60 (2.54-4.83) | <.001 | 99.1 |
| Not applicable | 15 | 12 178 | 210 258 | 6.36 (4.31-8.78) | <.001 | 99.7 |
| Race and ethnicitya | ||||||
| Asian | 23 | 9414 | 91 834 | 9.97 (8.73-11.29) | <.001 | 91.6 |
| Black | 53 | 21 917 | 129 800 | 16.64 (14.06-19.39) | <.001 | 99.2 |
| Hispanic | 35 | 250 747 | 1 141 081 | 23.55 (20.66-26.56) | <.001 | 99.9 |
| White | 66 | 61 705 | 505 895 | 12.28 (11.19-13.42) | <.001 | 98.8 |
| Sample source | ||||||
| Database | 681 | 3 079 529 | 27 464 011 | 7.40 (7.01-7.79) | <.001 | 99.9 |
| School | 1026 | 1 073 740 | 8 985 888 | 8.66 (8.23-9.10) | <.001 | 99.8 |
| Community | 192 | 233 742 | 6 721 847 | 8.90 (8.32-9.50) | <.001 | 99.8 |
| Medical institution | 109 | 132 576 | 1 242 499 | 13.59 (12.18-15.05) | <.001 | 99.8 |
| Study design | ||||||
| Cross-sectional | 1855 | 3 797 742 | 38 296 246 | 8.38 (8.10-8.67) | .004 | 99.9 |
| Longitudinal | 44 | 492 403 | 3 216 207 | 9.70 (8.15-11.36) | .004 | 99.9 |
| Cohort | 86 | 64 556 | 1 618 514 | 9.53 (7.82-11.40) | .004 | 99.9 |
| Randomized clinical trial | 12 | 2161 | 16 279 | 10.99 (8.06-14.30) | .004 | 97.3 |
| Prospective | 10 | 162 687 | 1 266 740 | 9.98 (5.27-15.96) | .004 | 99.9 |
| Case-control | 1 | 38 | 259 | 14.67 (10.61-19.26) | .004 | NA |
| Diagnostic reference | ||||||
| WHO | 466 | 730 348 | 8 703 325 | 8.59 (7.94-9.26) | <.001 | 99.9 |
| IOTF | 807 | 661 187 | 8 215 613 | 5.41 (5.11-5.73) | <.001 | 99.7 |
| CDC | 453 | 1 877 278 | 14 592 246 | 14.46 (13.63-15.32) | <.001 | 100.0 |
| National reference | 282 | 1 250 774 | 12 903 061 | 9.67 (9.02-10.33) | <.001 | 99.9 |
| Sample size | ||||||
| ≤5000 | 1558 | 195 777 | 2 356 611 | 8.74 (8.41-9.07) | <.001 | 98.8 |
| >5000 | 450 | 4 323 810 | 42 057 634 | 7.67 (7.12-8.23) | <.001 | 100.0 |
| Study period | ||||||
| 2000-2011 | 1232 | 1 318 508 | 21 086 914 | 7.05 (6.80-7.32) | <.001 | 99.8 |
| 2012-2023 | 681 | 211 0031 | 15 956 095 | 11.31 (10.81-11.81) | <.001 | 99.9 |
Abbreviations: CDC, US Centers for Disease Control and Prevention; HDI, Human Development Index; IOTF, International Obesity Task Force; NA, not applicable; WHO, World Health Organization.
Race and ethnicity data were collected via in accordance with the data sources and reported for comprehensive assessment.
Analysis of Risk Factors Associated With Obesity Among Children and Adolescents
To gain a more comprehensive view of obesity in children and adolescents, further analysis regarding potential risk factors were performed (Table 2; eTable 11 in Supplement 1). Results indicated that a significant difference in the prevalence of obesity was found in the pooled estimate by age (0-5, 6-12, or 13-18 years; 8.5% vs 9.4% vs 6.9%, respectively; P < .001), sex (male or female; 9.4% vs 7.5%, respectively; P < .001), school type (public or private; 6.5% vs 11.6%, respectively; P < .001), maternal weight status (obesity or nonobesity; 15.9% vs 8.1%, respectively; P = .001), breakfast (having breakfast daily or usually skipping breakfast; 7.1% vs 10.0%, respectively; P = .03), numbers of meals per day (>3 or ≤3; 3.3% vs 11.6%, respectively; P = .008), hours of playing on the computer per day (≥2 or <2 hours; 11.9% vs 5.5%, respectively; P = .01), maternal smoking in pregnancy (smoking or never; 7.7% vs 4.7%, respectively; P = .006), birth weight (low, normal, or high; 6.2% vs 9.2% vs 12.8%, respectively; P = .005), physical activity (regular or irregular; 7.7% vs 12.1%, respectively; P = .006), and nightly sleep duration (<10 or ≥10 hours; 13.7% vs 7.2%, respectively; P = .03). Minimal differences were observed among other factors.
Table 2. Analysis of Risk Factors Associated With Obesity in Children and Adolescents.
| Risk factor | Studies, No. | Events, No. | Total, No. | Prevalence (95% CI) | P value | I2, % |
|---|---|---|---|---|---|---|
| Age, y | ||||||
| 0-5 | 246 | 524 593 | 7 839 060 | 8.46 (7.64-9.32) | <.001 | 99.9 |
| 6-12 | 816 | 931 446 | 8 322 894 | 9.36 (8.88-9.85) | 99.8 | |
| 13-18 | 515 | 10,22 690 | 10 787 040 | 6.92 (6.51-7.34) | 99.8 | |
| Sex | ||||||
| Male | 1070 | 955 224 | 9 909 202 | 9.38 (8.95-9.81) | <.001 | 99.8 |
| Female | 1093 | 616 419 | 9 365 604 | 7.50 (7.17-7.83) | 99.8 | |
| Residential location | ||||||
| Rural | 74 | 39 680 | 1 388 709 | 6.25 (5.12-7.48) | .14 | 99.8 |
| Urban | 83 | 1 146 682 | 3 005 017 | 8.12 (6.75-9.60) | 99.9 | |
| Suburban | 14 | 9979 | 152 193 | 7.94 (4.72-11.90) | 99.8 | |
| School type | ||||||
| Public | 42 | 5583 | 121 865 | 6.53 (4.94-8.32) | <.001 | 99.0 |
| Private | 40 | 4622 | 84 325 | 11.63 (9.64-13.78) | 98.5 | |
| Paternal weight status | ||||||
| Obesity | 13 | 1677 | 8378 | 12.22 (6.94-18.69) | .11 | 98.4 |
| Nonobesity | 13 | 3241 | 32 375 | 7.06 (3.97-10.93) | 99.3 | |
| Maternal weight status | ||||||
| Obesity | 26 | 3362 | 22 637 | 15.92 (12.24-19.97) | .001 | 98.2 |
| Nonobesity | 26 | 14 385 | 146 859 | 8.06 (5.38-11.23) | 99.7 | |
| Maternal diabetes | ||||||
| With diabetes | 6 | 233 | 7351 | 13.29 (3.80-26.87) | .32 | 96.1 |
| Without diabetes | 6 | 7503 | 945 998 | 7.52 (2.48-14.93) | 99.7 | |
| Gestational diabetes | ||||||
| With gestational diabetes | 5 | 411 | 2004 | 17.83 (9.89-27.44) | .43 | 95.1 |
| Without gestational diabetes | 5 | 3612 | 19 981 | 13.83 (8.32-20.45) | 99.1 | |
| Paternal diabetes | ||||||
| With diabetes | 2 | 254 | 13 044 | 11.43 (0-49.08) | .73 | 98.8 |
| Without diabetes | 2 | 6800 | 931 876 | 6.20 (0-30.41) | 99.3 | |
| Maternal education | ||||||
| Less than secondary | 30 | 2098 | 22 838 | 8.81 (6.12-11.87) | .63 | 97.4 |
| Secondary | 29 | 6942 | 49 769 | 9.83 (7.79-12.06) | 97.4 | |
| Tertiary | 33 | 7916 | 47 855 | 11.60 (9.04-14.43) | 98.6 | |
| Paternal education | ||||||
| Less than secondary | 22 | 941 | 13 490 | 6.78 (4.83-9.01) | .52 | 92.7 |
| Secondary | 21 | 1204 | 16 386 | 8.73 (6.71-10.98) | 93.4 | |
| Tertiary | 20 | 1495 | 28 047 | 8.27 (5.66-11.32) | 97.8 | |
| Mother occupation | ||||||
| Employed | 20 | 5601 | 44 643 | 9.80 (6.99-13.02) | .79 | 98.3 |
| Unemployed | 20 | 2537 | 26 267 | 8.32 (5.16-12.05) | 98.6 | |
| Father occupation | ||||||
| Employed | 7 | 882 | 18 430 | 6.50 (3.67-10.05) | .85 | 98.1 |
| Unemployed | 7 | 71 | 1069 | 5.42 (1.87-10.42) | 85.7 | |
| Parental marriage status | ||||||
| Married or cohabiting | 4 | 4050 | 580 120 | 2.43 (1.28-3.92) | .92 | 99.1 |
| Never married, widowed, or divorced | 4 | 3328 | 391 895 | 1.35 (0.17-3.22) | 92.1 | |
| Caregivers | ||||||
| Parents | 2 | 58 | 662 | 10.09 (4.79-16.98) | .79 | 67.1 |
| Grandparents | 2 | 35 | 217 | 6.80 (0-35.40) | 93.7 | |
| No. of children in the family | ||||||
| 1 | 12 | 2525 | 14 718 | 10.32 (5.50-16.39) | .28 | 98.9 |
| >1 | 12 | 3292 | 38 658 | 6.88 (3.78-10.78) | 99.4 | |
| Birth order | ||||||
| First born | 7 | 923 | 24 389 | 5.28 (2.63-8.77) | .63 | 98.9 |
| Not first born | 7 | 924 | 29 578 | 4.41 (2.60-6.66) | 98.1 | |
| Socioeconomic status of family | ||||||
| Low | 33 | 4691 | 96 335 | 6.91 (5.15-8.90) | .71 | 98.4 |
| Middle | 30 | 5498 | 114 863 | 7.96 (6.52-9.53) | 98.0 | |
| High | 30 | 5510 | 109 564 | 7.56 (6.13-9.12) | 96.6 | |
| Way of going to school | ||||||
| Walk | 12 | 399 | 6295 | 6.40 (3.98-9.31) | .16 | 93.9 |
| Bike | 4 | 48 | 832 | 5.94 (2.92-9.87) | 74.1 | |
| Car | 9 | 335 | 3014 | 11.29 (6.66-16.92) | 94.5 | |
| Breakfast | ||||||
| Daily | 24 | 3902 | 54 224 | 7.08 (5.20-9.23) | .03 | 98.7 |
| Usually skipped | 24 | 1332 | 14 133 | 9.96 (7.94-12.16) | 92.6 | |
| No. of meals per day | ||||||
| >3 | 4 | 75 | 2870 | 3.26 (1.32-5.97) | .008 | 88.3 |
| ≤3 | 5 | 169 | 1823 | 11.64 (5.77-19.16) | 94.6 | |
| Watching TV while eating | ||||||
| Usually | 6 | 364 | 1640 | 28.17 (5.37-59.71) | .56 | 99.3 |
| Never | 6 | 162 | 1819 | 18.29 (4.48-38.35) | 98.5 | |
| Hours of watching TV per d | ||||||
| ≥2 h | 19 | 5970 | 42 021 | 18.61 (13.58-24.23) | .06 | 99.3 |
| <2 h | 19 | 10 419 | 87 570 | 12.57 (9.16-16.44) | 98.9 | |
| Hours of playing on the computer per d | ||||||
| ≥2 h | 3 | 593 | 4466 | 11.88 (8.20-16.12) | .01 | 89.9 |
| <2 h | 3 | 1025 | 13 258 | 5.48 (2.94-8.73) | 96.6 | |
| Passive smoking | ||||||
| Exposed | 8 | 3431 | 30 454 | 9.68 (6.02-14.09) | .27 | 98.5 |
| Not exposed | 8 | 3902 | 70 516 | 6.50 (3.11-11.01) | 99.7 | |
| Maternal smoking in pregnancy | ||||||
| Smoking | 22 | 855 | 11 101 | 7.66 (5.75-9.80) | .006 | 92.5 |
| Never | 22 | 3297 | 66 990 | 4.70 (3.50-6.07) | 98.3 | |
| Gestational weight gain | ||||||
| Inadequate | 4 | 807 | 6561 | 11.09 (6.49-16.70) | .63 | 96.3 |
| Adequate | 4 | 2538 | 17 038 | 11.19 (6.21-17.37) | 98.5 | |
| Excessive | 4 | 5553 | 31 198 | 15.85 (7.75-26.12) | 99.6 | |
| Maternal age at birth, y | ||||||
| ≥25 | 7 | 1294 | 37 181 | 5.75 (2.90-9.47) | .37 | 99.0 |
| <25 | 7 | 1077 | 16 680 | 7.95 (4.73-11.90) | 98.3 | |
| Term of delivery | ||||||
| Premature | 14 | 4025 | 25 546 | 10.92 (8.45-13.66) | .65 | 95.4 |
| Full term | 14 | 38 255 | 260 305 | 10.32 (8.09-12.77) | 99.6 | |
| Type of delivery | ||||||
| Cesarean | 14 | 1451 | 17 265 | 9.94 (7.51-12.67) | .29 | 96.3 |
| Vaginal | 14 | 1714 | 21 852 | 8.35 (6.79-10.05) | 94.1 | |
| Birth weight | ||||||
| Low (≤2499 g) | 17 | 1958 | 18 413 | 6.22 (4.26-8.50) | .005 | 95.8 |
| Normal (2500-4000 g) | 17 | 37 096 | 251 645 | 9.16 (7.14-11.40) | 99.6 | |
| High (≥4001 g) | 15 | 5345 | 24 954 | 12.82 (9.56-16.46) | 97.6 | |
| Duration of breastfeeding | ||||||
| ≥3 mo | 10 | 2553 | 21 255 | 8.51 (5.00-12.82) | .68 | 98.9 |
| <3 mo | 7 | 1768 | 18 803 | 9.37 (8.26-10.55) | 79.5 | |
| Breastfeeding amount | ||||||
| None (full formula) | 12 | 5113 | 38 862 | 10.45 (7.25-14.13) | .29 | 99.0 |
| Mixed | 8 | 9030 | 83 335 | 10.07 (6.95-13.67) | 99.5 | |
| Full (no formula) | 8 | 2521 | 31 312 | 7.88 (6.05-9.93) | 96.3 | |
| Antibiotic exposure | ||||||
| Exposed | 6 | 29 079 | 218 911 | 8.66 (5.15-12.96) | .84 | 99.1 |
| Not exposed | 6 | 20 079 | 160 943 | 7.51 (2.38-14.94) | 99.6 | |
| Physical activity | ||||||
| Regular exercise | 21 | 3118 | 32 947 | 7.65 (5.65-9.92) | .006 | 97.7 |
| No regular exercise | 21 | 2742 | 24 289 | 12.08 (9.88-14.48) | 96.3 | |
| Nightly sleep duration | ||||||
| <10 h | 14 | 4217 | 28 615 | 13.68 (8.99-19.15) | .03 | 99.2 |
| ≥10 h | 10 | 1404 | 16 849 | 7.23 (4.42-10.66) | 98.2 |
Comorbidities of Obesity Among Children and Adolescents
Eight comorbidities associated with obesity among children and adolescents were investigated (Table 3; eTable 12 in Supplement 1). There were 26 studies reporting on hypertension in children and adolescents with obesity, with a pooled rate of 28.0% (95% CI, 20.2-36.6). In addition, 13 studies documented dental caries (17.9%; 95% CI, 12.6-23.8), 8 included vitamin D deficiency (11.6% 95% CI, 5.4-19.9), 7 included asthma (18.8%; 95% CI, 12.5-26.2), 3 reported on diabetes (1.2%; 95% CI, 0.2-3.0), 3 included flatfoot (26.1%; 95% CI, 6.7-52.2), 2 reported on anxiety (25.1%; 95% CI, 0-94.2), and 2 included depression (35.2%; 95% CI, 0.4-87.0).
Table 3. Analysis of Comorbidities for Obesity in Children and Adolescents.
| Comorbidity | Studies, No. | Events, No. | Total, No. | Prevalence (95% CI) | I2, % |
|---|---|---|---|---|---|
| Hypertension | 26 | 5583 | 195,22 | 28.02 (20.16-36.61) | 99.2 |
| Dental caries | 13 | 36 472 | 531 470 | 17.88 (12.61-23.83) | 99.8 |
| Vitamin D deficiency | 8 | 705 | 4546 | 11.63 (5.36-19.86) | 98.3 |
| Asthma | 7 | 844 | 7696 | 18.84 (12.46-26.16) | 97.7 |
| Diabetes | 3 | 62 | 5916 | 1.23 (0.23-2.98) | 95.1 |
| Flatfoot | 3 | 69 | 354 | 26.08 (6.69-52.19) | 95.8 |
| Anxiety | 2 | 316 | 1173 | 25.08 (0-94.18) | 99.9 |
| Depression | 2 | 379 | 1166 | 35.24 (0.44-86.90) | 99.7 |
Prevalence of Overweight and Excess Weight in Children and Adolescents
We further performed analyses on the prevalence of overweight and excess weight in children and adolescents. In total, 5 621 782 participants were diagnosed as having overweight with a pooled prevalence of 14.8% (95% CI, 14.5-15.1; I2, 99.8%), and 5 621 782 participants were diagnosed as having excess weight with a pooled prevalence of 22.2% (95% CI, 21.6-22.8; I2, 100.0%) (Figure 2). Details on subgroup analyses for overweight and excess weight are listed in eTables 13-14 in Supplement 1.
Figure 2. Global Prevalence of Excess Weight in Children and Adolescents.
Discussion
This systematic review and meta-analysis provided a comprehensive analysis of the global epidemiology of overweight and obesity from 2000 to 2023 in children and adolescents younger than 18 years. The overall prevalence of pediatric obesity, overweight, and excess weight was 8.5%, 14.8%, and 22.2%, respectively. According to our findings, there were notable regional variations, with Polynesia exhibiting the highest prevalence across all 3 categories and Middle and Western Africa displaying the lowest rates. Furthermore, a number of factors demonstrated a noteworthy association with the prevalence of pediatric obesity, including age, sex, school type, maternal obesity, having breakfast, number of meals per day, hours of playing on the computer per day, maternal smoking in pregnancy, birth weight, regular exercise, and sleep duration. Besides, children and adolescents with obesity are more likely to experience mental and physical comorbidities, such as depression and hypertension.
The Non-Communicable Diseases Risk Factor Collaboration16 provided data on global prevalence of obesity in children and adolescents aged 5 to 19 years from 1975 to 2016 and found the prevalence had grown for both boys and girls, from 0.9% to 7.8% and 0.7% to 5.6%, respectively. Their key finding was that, although the prevalence of obesity in high-income nations had plateaued around the year 2000, in other parts of Asia it was still rising. Our findings reconfirmed that obesity was more common in boys than girls. More importantly, we found a sharply increased prevalence of obesity from 2012 to 2023 to 2000 to 2011. Even though obesity is growing more widespread globally, there are still notable regional differences to be aware of. According to previous studies, Polynesia, the Caribbean, Northern America, and Central America have the highest rates of obesity (above 15%).16,20 Apart from the fact that many countries in these regions, such as the US, are well developed, which may contribute to the high prevalence of childhood obesity, it is noteworthy that most of these regions are adjacent to each other geographically, indicating that the genetic traits and unique diet habits of the habitants may also be potential drivers. Interestingly, the lowest prevalence (under 4%) appeared in Western European, Middle Africa, Melanesia, and Western Africa, covering highly developed countries as well as a large number of the least-developed countries. While the prevalence in Western Europe may be attributed to the quality of the health care system and health-conscious lifestyle choices, the similar prevalence in Middle Africa, Melanesia, and Western Africa were mainly due to their poverty. Furthermore, current findings revealed that pediatric obesity prevalence was closely linked to country development and national or regional income, which is in line with prior research.20 Notably, even among nations in similar economic strata, there are differences in the estimates of prevalence. For example, the prevalence of pediatric obesity in the US is 18.6%, while that in Japan, another high-income country, is 3.9%. Differences in dietary habits may play a role in this disparity. European countries and the US often embrace a diet preference of processed food, which are typically abundant in unhealthy fats, added sugars, and refined carbohydrates. In contrast, diets rich in whole grains and vegetables, which are generally regarded as healthier options, have historically been prioritized in Southeast Asian countries.
Prevalence of obesity in children and adolescents shows disparities across different ages. Our results revealed a lower prevalence of obesity in adolescents than that of preschool and school-age children, which is largely in accordance with prior studies.20 This decline in obesity prevalence could be mainly attributed to the hormone shifts as boys and girls approach puberty.21 Besides, teenagers tend to be more conscious about their appearance, thus making more effort toward weight control. Furthermore, heavier pressure from middle and high school could partly contribute to weight loss in adolescents.
Early life is a pivotal period for childhood obesity development.22 Prior analyses have linked preconception and prenatal environmental exposures to childhood obesity, including high maternal prepregnancy BMI,23 gestational weight gain,24 gestational diabetes,25 and maternal smoking,26 potentially through effects on the environment in uterus. The current study determined maternal obesity and smoking in pregnancy as risk factors for childhood and adolescent obesity, while maternal diabetes, gastrointestinal diabetes and gestational weight gain exhibited positive yet modest impact on it. Although prior studies considered paternal obesity to be a risk factor for childhood obesity, our findings revealed otherwise.27,28 Furthermore, our results revealed low birthweight was associated with lowest prevalence of obesity. However, Yuan et al29 claimed that children weighing less than 1500 g were most likely to be centrally obese. This mismatch may be due to the fact that we used BMI to quantify general obesity, whereas central obesity is measured by sex-specific waist to height ratio. Additionally, different infant feeding strategies, such as breastfeeding duration and formula addition, exhibit varying effects on childhood obesity in several meta-analyses.30,31,32 Nevertheless, our findings showed no discernible impact from these parameters.
The rise in prevalence of obesity has been profoundly influenced by environmental and behavioral factors,33 including dietary patterns,34,35 physical activity level,36 and use of technology.37 The current study revealed that skipping breakfast was associated with an increased risk of pediatric obesity, which was consistent with previous research.38 Surprisingly, having more than 3 meals per day was associated with a lower risk of being obese, which might be explained by the theory that having several small meals throughout a day is healthier than 3 large ones.39,40 As previously noted, children with obesity tend to participate in less physical activity than their peers without obesity,36 and decreasing levels of exercise as well as increasing sedentary behaviors contribute to obesity development. Our findings also showed that children with regular exercise had a much lower chance of obesity. Moreover, we observed that playing on the computer for more than 2 hours a day was associated with an increase in risk of excess weight, and time spent watching TV also showed a positive correlation, yet not significant. A connection between screen time and obesity in the pediatric population was initially demonstrated in studies of TV viewing,41,42 while mobile and gaming devices are gaining more and more attention.43,44 Screen exposure may raise the risk of obesity via increased exposure to food marketing, increased mindless eating while watching screens, displacement of time spent in physical activities, reinforcement of sedentary behaviors, and reduced sleep duration.
All body systems can be affected by obesity in the short or long term, depending upon age and obesity severity. Plenty of previous studies have discussed potential comorbidities of multiple system related to childhood obesity.45,46,47,48,49,50,51 According to a systematic analysis, children and adolescents with obesity have a 1.4 times higher likelihood of developing prediabetes, 1.7 times higher likelihood of developing asthma, 4.4 times higher likelihood of developing high blood pressure, and 26.1 times higher likelihood of developing fatty liver disease compared to those who are of a healthy weight.52 Likewise, our research disclosed high prevalence of comorbidities in children and adolescents with obesity. The highest pooled prevalence was found in depression, which approximately 1 in 3 children with obesity might experience, followed by hypertension, with a pooled prevalence of 28.0%. Compared to previously reported incidence in general population, which is approximately 25% for depression53 and 4% for hypertension,54 children and adolescents with obesity seemed to be more vulnerable to those health condition. The association between obesity and mentioned comorbidities had been shown to be bidirectional.55,56 In the management of childhood and adolescent obesity, it is pivotal that comorbidities are assessed and treated alongside to prevent progression of both.
Limitations
There are some limitations in the present research. To our knowledge, this is the most comprehensive study to date, covering all geographic regions, but some countries and regions had limited data, making it challenging to accurately estimate. Besides, different criteria for recognizing overweight and obesity in children may influence the accuracy of the estimation. Moreover, limited studies concerning comorbidities were included in our analysis, since we focused on the epidemiology in the literature search process. Additionally, we simply divided the study period into 2 categories, namely 2000 to 2011 and 2012 to 2023, which resulted in less detailed information of the time trajectory of prevalence for childhood and adolescent obesity.
Conclusions
In conclusion, the current study provided new epidemiological insights of overweight and obesity among children and adolescents worldwide. Our findings indicated high prevalence of overweight and obesity in children and adolescent with a pooled estimation of 8.5% and 14.8%, meaning approximately 1 of every 5 children or adolescents experience excess weight. Various risk factors, including inherent, dietary, and environmental factors, were significantly associated with the prevalence of pediatric obesity. It is noteworthy that children and adolescents with obesity were at high risk of mental and physical comorbidities. Global coordinated action and national control program are paramount to comprehend, prevent, and manage childr and adolescent obesity.
eTable 1. Searching strategy for prevalence of overweight and obesity in children and adolescents
eTable 2. Quality assessment for including studies
eTable 3. Characteristics of the studies for prevalence of obesity in children and adolescents
eTable 4. Characteristics of the studies for prevalence of overweight in children and adolescents
eTable 5. Characteristics of the studies for prevalence of excess weight in children and adolescents
eTable 6. Sensitivity analysis and leave-one-out results performed in Metafor package
eTable 7. Sensitivity analysis performed by using a built-in function
eTable 8. Univariate meta-regression
eTable 9. Multi-variable meta-regression
eTable 10. Subgroup analysis for obesity in children and adolescents
eTable 11. Analysis of risk factors for obesity in children and adolescents
eTable 12. Analysis of comorbidities for obesity in children and adolescents
eTable 13. Subgroup analysis for overweight in children and adolescents
eTable 14. Subgroup analysis for excess weight in children and adolescents.
Data sharing statement
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eTable 1. Searching strategy for prevalence of overweight and obesity in children and adolescents
eTable 2. Quality assessment for including studies
eTable 3. Characteristics of the studies for prevalence of obesity in children and adolescents
eTable 4. Characteristics of the studies for prevalence of overweight in children and adolescents
eTable 5. Characteristics of the studies for prevalence of excess weight in children and adolescents
eTable 6. Sensitivity analysis and leave-one-out results performed in Metafor package
eTable 7. Sensitivity analysis performed by using a built-in function
eTable 8. Univariate meta-regression
eTable 9. Multi-variable meta-regression
eTable 10. Subgroup analysis for obesity in children and adolescents
eTable 11. Analysis of risk factors for obesity in children and adolescents
eTable 12. Analysis of comorbidities for obesity in children and adolescents
eTable 13. Subgroup analysis for overweight in children and adolescents
eTable 14. Subgroup analysis for excess weight in children and adolescents.
Data sharing statement