Table 1.
Prevalence of severe obesity in children and adolescents
| Author, reference, country Years presented in the table | Age-group, years | Children with severe obesity, % | Type of study | Classification | Participant in the presented years, n | |
|---|---|---|---|---|---|---|
| Skinner et al. [13] USA 2015–2016 | Class II | Class III | Nationally representative data | >120% of 95th percentile and >140% of 95th percentile CDC cut-off | 3,340 | |
| 2–19 | 6 (4.3, 7.6) | 1.9 (1, 2.9) | ||||
| 2–5 | 1.8 (0.6, 3.0) | 0.2 (−0.1, 0.4) | ||||
| 6–8 | 5.1 (3.2, 7.1) | 1.4 (0.5, 2.3) | ||||
| 9–11 | 5.3 (2.9, 7.7) | 1.0 (0.4, 1.7) | ||||
| 12–15 | 7.5 (4.2, 10.8) | 2.2 (0.9, 3.4) | ||||
| 16–19 | 9.5 (5.8, 13.1) | 4.5 (1.7, 7.4) | ||||
|
| ||||||
| Pan et al. [10] USA 2014 | 2–4 | 1.96 | Serial cross-sectional national data | ≥120% of 95th percentile, CDC cut-off | 3,016,487 | |
| 2 | 1.13 | |||||
| 3 | 2.10 | |||||
| 4 | 3.12 | |||||
|
| ||||||
| Marcus et al. [27] USA 2006 | 12 | 6.9 | School-based screening | >99th percentile CDC cut-off | 6,365 | |
|
| ||||||
| Skelton et al. [32] USA 1999–2004 | 2–19 | 3.8 | Nationally representative data | >99th percentile CDC cut-off | 12,384 | |
| 2–5 | 4.2 | |||||
| 6–11 | 4.0 | |||||
| 12–19 | 3.4 | |||||
|
| ||||||
| Flores and Lin [26] USA 2007–2008 | 5.6±0.03 | 5.7 | The National Center for Education Statistics Institute of Education Sciences (IES) | >99th percentile CDC cut-off | 14,000 | |
|
| ||||||
| Robbins et al. [11] Philadelphia 2012–2013 | 5–18 | 7.3 | Philadelphia public schoolchildren | ≥120% of 95th percentile, CDC cutoff | 88,798 | |
|
| ||||||
| Lohrmann et al. [33] Pennsylvania | Pre k–5G | 12.5 | National data | BMI ≥97 percentile CDC cut-off | 212,055 | |
| 6G–8 | 14.9 | |||||
| G9–12 | 13.9 | |||||
|
| ||||||
| Nguyen et al. [1] Philadelphia 2010 |
3–17 | 7.7 (5.8–9.9) | 8 public community health centers | >120% of 95th percentile, CDC cut-off | 691 | |
| 3–5 | 6 (3–11) | |||||
| 6–8 | 7 (3–13) | |||||
| 9–12 | 8 (5–13) | |||||
| 13–17 | 8 (5–13) | |||||
|
| ||||||
| Kharofa et al. [6] Cincinnati OH 2012–2014 | Class II | Class III | Chart review of children | >120% of 95th percentile and 140% of 95th percentile CDC cut-off | 217,037 | |
| 2–18 | 4.7 | 2.7 | ||||
| 2–5 | 1.6 | 0.7 | ||||
| 6–11 | 5.0 | 2.4 | ||||
| 12–18 | 6.3 | 4.3 | ||||
|
| ||||||
| Day et al. [3] New York City 2010–2011 | 5–14 | 5.7 | NYC public school students | >120% of 95th percentile CDC cut-off | 635,257 | |
| 5–6 | 3.7 | |||||
| 7–10 | 6.0 | |||||
| 11–14 | 6.5 | |||||
|
| ||||||
| Lo et al. [8] Northern California 2007–2010 | 3–5 | 1.6 | Kaiser Permanente Northern California | >120% of 95th percentile CDC cut-off | 42,559 | |
|
| ||||||
| Lo et al. [7] Northern California 2007–2010 | 6–17 | 5.6 | Kaiser Permanente Northern California | >120% of 95th percentile CDC cut-off | 117,618 | |
| 6–11 | 5.3 | |||||
| 12–17 | 5.8 | |||||
|
| ||||||
| Koebnick [47] Southern California 2007–2008 | 2–19 | 6.4 | Kaiser Permanente Southern California | >120% of 95th percentile, CDC cut-off | 710,949 | |
| 2–5 | 2.5 | |||||
| 6–11 | 7.4 | |||||
| 12–19 | 7.7 | |||||
|
| ||||||
| Ball et al. [17] Edmonton Calgary Alberta, Canada 2017 |
4–6 | 2.16 | BMI Z >3SD WHO cut-off | 16,595 | ||
|
| ||||||
| Carsley et al. [18] Toronto, Canada 2009–2015 | 0–6 | 1.0 | Practice-based research network in | BMI Z >3SD WHO cut-off [≥99.9th] |
6,364 | |
| 0–2 | 0.5 | |||||
| 2–5 | 1.3 | |||||
| 5–6 | 2.1 | |||||
|
| ||||||
| Carsley et al. [34] Ontario, Canada 2014–2015 | <4 | 0.9 (0.7–1.0) | Electronic Medical Records Administrative data Linked Database (EMRALD) | BMI Z >3SD WHO cut-off [≥99.9th] |
31,272 | |
| 5–9 | 2.7 (2.3–3.1) | |||||
| 10–14 | 2.9 (2.4–3.3) | |||||
| 15–18 | 3.7 (3.1–4.3) | |||||
|
| ||||||
| Satkunam et al. [12] Ontario, Canada 2014–2016 | 1.5–2 | 0.3 (0.2–0.6) | The Applied Research Group for Kids (TARGet Kids!) and BORN Ontario | 120% of 95th percentile CDC cut-off | 4,481 | |
|
| ||||||
| Jimenez Cruz et al. [35] Mexico Tijuana and Ensenada 2007 | 6–12 | 5.2 | Survey | ≥99th percentile CDC cut-off | 2,690 | |
|
| ||||||
| Shackleton et al. [38] New Zealand 2015–2016 | 4 | 2.9 (2.9, 3.0) | A national screening program | ≥99.7th percentile WHO cut-off | 56,541 | |
|
| ||||||
| Farrant et al. [21] New Zealand 2007 | 13–18 | 2.5 (1.9–3.1) | Nationally representative sample | BMI >35 kg/m2 | 9,107 | |
| <13 | 2.6 (1.7–3.5) | IOTF cut-off | ||||
| 14 | 2.1 (1.1–3.2) | |||||
| 15 | 2.6 (1.8–3.5) | |||||
| 16 | 2.5 (1.7–3.5) | |||||
| >17 | 2.7 (1.6–3.8) | |||||
|
| ||||||
| Utter et al. [22] New Zealand 2012 | 13–18 | 3.7 (2.5, 5.0) | Nationally representative sample | BMI >35 kg/m2 IOTF cut-off | 8,372 | |
|
| ||||||
| Garnett et al. [5] Australia 2012 |
7–15 | Class II 2.0 | Class III 0.5 | Australian cross-sectional surveys | >120% of 95th percentile and > 140% of 95th percentile CDC cut-off | 2,940 |
|
| ||||||
| Xu et al. [23] Australia 2014 |
7–15 | 1.9 | Cross-sectional Australian database | BMI >35 kg/m2 IOTF cut-off [≥99.8th] | 2,079 | |
|
| ||||||
| Cho et al. [29] Korea 2007–2014 |
10–18 | Class II 5.9 (5.2, 6.6) |
Class III 0.9 (0.6, 1.1) | National Health and Nutrition Examination Surveys | >120% of 95th percentile and > 140% of 95th percentile Korean growth curve |
7,197 |
|
| ||||||
| Nam et al. [9] | 2–19 | 2.1 (1.6–2.7) | Korea Centers for Disease | >120% of 95th percentile | 3,226 | |
| Korea | 2–9 | 0.6 (0.3–1.3) | Control and Prevention | CDC cut-off | ||
| 2013–2014 | 10–19 | 3.0 (2.2–4.0) | ||||
|
| ||||||
| Chen et al. [36] Xiamen, China 2017 | 2–7 | 0.28 | Cross-sectional survey, kindergarten | Weight-for height >50% reference population WHO cut-off |
21,883 | |
|
| ||||||
| Zhang [24] | Girls | Boys | National surveys | BMI >35 kg/m2 | 9,719 | |
| China | 7–18 | 1.29 | 2.73 | IOTF cut-off | ||
| 2014 | 7–12 | 1.95 | 3.93 | |||
| 13–18 | 0.63 | 1.51 | ||||
|
| ||||||
| Ells et al. [30] | Girls | Boys | NCMP | 99.9th percentile of the | 1,076,824 | |
| United Kingdom | 4–5 | 1.1 | 1.5 | British 1990 (UK90) growth | ||
| 2012–2013 | 10–11 | 1.2 | 1.5 | reference | ||
|
| ||||||
| Beynon and Bailey [40] Walles | 4–5 | 3.1 (3.0–3.2) | CMP | 99.6th percentile Royal College of Paediatrics | 34,163 | |
| 2017–2018 | ||||||
|
| ||||||
| Bohn et al. [42] Germany 2015 | <21 | 12.7 | APR | ≥99.5th percentile for age and sex | 4,196 | |
|
| ||||||
| Segna et al. [39] Austria 2003–2004 | 2–16 | 2.1 | Viennese sample of children and adolescents | BMI ≥99.5th percentile of German national reference | 24,989 | |
| 2–4 | 1.8 | |||||
| 4–7 | 2.4 | |||||
| 7–10 | 2.5 | |||||
| 10–1 | 1.3 | |||||
| 13–16 | 1.2 | |||||
|
| ||||||
| Cadenas-Sanchez et al. [41] Spain 2014–2015 | 4.6±0.9 | Class II | Class III | PREFIT project | >120% of 95th percentile | 3,178 |
| 1.2 | 1.3 | and > 140% of 95th percentile WHO cut-off | ||||
|
| ||||||
| van Dommelen et al. [15] Holland 1980–2009 | 2–18 | Girls | Boys | National Dutch Growth Studies | >120% of 95th percentile | 10,894 |
| 2–5 | 1.00 | 0.53 | WHO cut-off | |||
| 6–11 | 0.63 | 0.63 | ||||
| 12–18 | 0.17 | 0.60 | ||||
|
| ||||||
| Twig et al. [14] | 17 | Girls | Boys | National data | >120% of 95th percentile and > 140% of 95th percentile | 63,652 |
| Israel | Class II | Class II | ||||
| 2015 | 1.2 | 1.9 | ||||
| Class III | Class III | CDC cut-off | ||||
| 0.4 | 0.5 | |||||
|
| ||||||
| El Mouzan et al. [19] Saudi Arabia 2005 | 5–18 | 2 | Cross-sectional sample from a stratified listing based on the population census | BMI >3 SDS | 19,317 | |
| 5–12 | 1.5 | WHO cut-off | ||||
| 13–18 | 2.4 | |||||
|
| ||||||
| AlBlooshi et al. [25] | 3–6 | 3.3 | Population-based study | BMI ≥99th percentile | 44,942 | |
| Ras Al-Khaimah | 7–10 | 3.8 | CDC cut-off | |||
| United Arab Emirates | 11–14 | 5.7 | ||||
| 2014–2015 | 15–18 | 8.8 | ||||
| Total, N | 6,542,161 | |||||
The CDC cut-off of > 120% of the 95th percentile and WHO cut-off of zBMI >3, applied to the age and sex standardized. zBMI scores from WHO growth reference charts were used to estimate prevalence of severe obesity. IOTF, International Obesity Task Force; NCMP, National Child Measurement Programme; CMP, Childhood Measurement Programme; APV, Adiposity Patients Registry; BORN, Better Outcomes Registry and Network.