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This report presents the revised growth charts for the United States. It summarizes the history of the 1977 National Center for Health Statistics (NCHS) growth charts, reasons for the revision, data sources and statistical procedures used, and major features of the revised charts.
Data from five national health examination surveys collected from 19...
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BACKGROUND AND OBJECTIVES
The 2000 CDC growth charts for the United States, a revision of the National Center for Health Statistics/World Health Organization (NCHS/WHO) growth charts, were released in 2002 to replace the NCHS/WHO charts. We evaluated the differences between the CDC growth charts and the Saudi 2005 reference to determine the implica...
This study evaluated the agreement of growth charts proposed by the National Center for Health Statistics (NCHS/1977), Centers for Disease Control and Prevention (CDC/2000) and World Health Organization (WHO/2006).
Were assessed children between 0 and 5 years old, hospitalized in the pediatric wards of a Brazilian school hospital. Z-score indexes:...
For ages 5-19 years, the World Health Organization (WHO) publishes reference charts based on 'core data' from the US National Center for Health Statistics (NCHS), collected from 1963-75 on 22,917 US children. To promote the use of body mass index in older children, weight-for-age was omitted after age 10. Health providers have subsequently expresse...
The 2000 CDC growth charts for the United States, a revision of the National Center for Health Statistics/World Health Organization (NCHS/WHO) growth charts, were released in 2002 to replace the NCHS/WHO charts. We evaluated the differences between the CDC growth charts and the Saudi 2005 reference to determine the implications of using the 2000 CD...
To compare estimates of underweight, stunting, wasting, overweight and obesity based on three growth charts.
Cross-sectional study to estimate weight-for-age, length/height-for-age and weight-for-height comparing the 2006 WHO Child Growth Standards ('the WHO standards'), the 1977 National Center for Health Statistics (NCHS) international growth ref...
Citations
... However, for descriptive purposes, BMI-for-age percentiles were also calculated using the Centers for Disease Control and Prevention (CDC) growth charts. 40 ...
Background
Executive control and temperament have been associated with pediatric obesity. However, interactions between these constructs in relation to future weight outcomes have not been investigated.
Objective
This longitudinal study examined early childhood executive control, early temperament (negative affectivity and surgency), and their interactions as predictors of adolescent BMI trajectories.
Methods
At age 5.25, children (N = 229) completed executive control tasks, and parents completed the Child Behavior Questionnaire to assess temperament. BMI was calculated annually between ages 14–17.
Results
Greater early negative affectivity predicted more positive BMI growth. Although early childhood executive control was not associated with BMI growth, greater negative affectivity predicted greater BMI escalation at average and below average executive control abilities.
Conclusions
For children without robust executive control abilities early in development, negative affectivity may be a risk factor for accelerated adolescent BMI growth. Targeted assessment of early risk factors may be useful for childhood obesity prevention efforts.
... Then, the BMIs were calculated by dividing the person's weight in kilograms by their height in meters squared (kg/m 2 ). BMI classificationsunderweight (BMI < 5th percentile), normal weight (5th percentile ≤ BMI < 85th percentile), overweight (85th percentile ≤ BMI < 95th percentile), and obese (95th percentile ≤ BMI)-were determined based on age and gender-specific BMI percentiles according to US Center for Disease Control (CDC) 2000 standards for children and adolescents aged 2 to 20 years [24,25]. Trained rescue professionals conducted all measurements to ensure accuracy and consistency. ...
Purpose
This study aimed to assess the association between nutrition behavior, food intake, being overweight, and obesity among school-aged children and adolescents aged 9 to 17 years. Additionally, it sought to examine how these factors influence being overweight and obese within this population.
Methods
A population-based cross-sectional study was conducted with a representative multistage cluster sample of 4200 Pakistani school-aged children and adolescents aged 9 to 17 years from 62 schools across seven random districts in Punjab province, Pakistan. Underweight (BMI < 5th percentile), overweight (85th ≤ BMI < 95th percentile), and obese (95th percentile ≤ BMI) were defined using the US Center for Disease Control (CDC) 2000 criteria, and a Chi-square test utilized for comparison. The Pearson correlation coefficient (r) assessed any correlations, while a linear regression analysis explored the predictive power of Nutrition Behavior/Food Intake factors (independent variables) on body-weight (dependent variable). A logistic regression analysis estimated the simultaneous influence of multiple factors on the dichotomous outcomes, and the 95% confidence intervals (CI) were calculated. The statistical significance level was set at p < 0.05.
Results
The study was comprised of 4108 Pakistani school children aged 9 to 17 years (mean age = 13.92 years, 59.3% boys) from 62 schools. Among them, the prevalence of being overweight and obese individuals was 19.4% and 10.7%, respectively. Factors such as skipping breakfast (OR 2.45, 95% CI 1.53–3.93, p < 0.001), consuming vegetables less than once a week (OR 4.12, 95% CI 3.06–5.55, p < 0.001), consuming soft drinks three or more times a week (OR 4.74, 95% CI 3.73–6.04, p < 0.001), and consuming fast food three or more times a week (OR 10.56, 95% CI 8.16–13.67, p < 0.001) were associated with a higher risk of obesity.
Conclusion
Being overweight and obese pose significant concerns among school-aged children and adolescents in Pakistan, showing a troubling upward trend. Poor nutrition behaviors, including frequenting fast-food restaurants and low consumption of fruits and vegetables, contribute to these issues. It is imperative to comprehend these risk factors to formulate impactful policies and dietary interventions that target childhood obesity in Pakistan. Identifying vulnerable populations and implementing tailored intervention strategies are essential for public health efforts. While further interventions may be needed to reduce the body mass index (BMI) and manage being overweight and obese, the findings of this study provide valuable insights into addressing these critical health challenges.
... We know that in these two categories, diagnosing bone age is less important. After boys reach the age of 16 and girls reach the age of 15, 99 percent of their longitudinal bone growth has taken place [27]. At the age of under 12 months old, there is little information about the shape of the bones [12]. ...
Estimating the Bone Age of children is very important for diagnosing growth defects, and related diseases, and estimating the final height that children reach after maturity. For this reason, it is widely used in different countries. Traditional methods for estimating bone age are performed by comparing atlas images and radiographic images of the left hand, which is time-consuming and error-prone. To estimate bone age using deep neural network models, a lot of research has been done, our effort has been to improve the accuracy and speed of this process by using the introduced approach. After creating and analyzing our initial model, we focused on preprocessing and made the inputs smaller, and increased their quality. we selected small regions of hand radiographs and estimated the age of the bone only according to these regions. by doing this we improved bone age estimation accuracy even further than what was achieved in related works, without increasing the required computational resource. We reached a Mean Absolute Error (MAE) of 3.90 months in the range of 0-20 years and an MAE of 3.84 months in the range of 1-18 years on the RSNA test set.
... We excluded SCD patients who were on chronic blood transfusion therapy, had comorbid chronic conditions, or were on medications known to interfere with calcium or vitamin D absorption or metabolism, known hypercalcemia or vitamin D hypersensitivity, vitamin D treatment for rickets, presence of urolithiasis, liver or renal impairment, and malabsorption disorders. We also excluded obese children with body mass index (BMI) > 85th percentile for age and sex [14] as the adipose tissue is the main site for storing vitamin D [15]. ...
... We calculated the BMI from weight (kg/m 2 ) using a digital scale (Scaletronix, White Plains, NY) and height using a stadiometer (Holtain, Crymych, UK). Age-and gender-specific Z scores for weight, height, and BMI were generated based on Centers for Disease Control and Prevention 2000 reference standards [14]. ...
Little is known about the impact of vitamin D supplementation on hand grip strength (HGS) and health-related quality of life (HRQoL) in children and adolescents with sickle cell disease (SCD). We aimed to evaluate the safety and efficacy of monthly high-dose vitamin D3 supplementation and its implications on bone mineral density (BMD), HGS, and HRQoL in patients with SCD and healthy controls. The study included 42 children with SCD and 42 healthy matched controls. The study participants were supplemented with high-dose monthly oral vitamin D3. Changes in the serum level of 25(OH) vitamin D3, maximum HGS, and BMD from baseline to 6 months were assessed, and the HRQoL questionnaire and Childhood Health Assessment Questionnaire (CHAQ) were used to evaluate the functional capacity. At baseline, SCD subjects had poorer growth status indicated by negative Z scores. Suboptimal BMD was detected by significantly lower Z score, and lower HGS and worse HRQL parameters were found compared to the controls (P < 0.001). Median 25(OH) vitamin D3 was significantly lower in SCD patients compared to controls (16.5 vs. 28 ng/mL, respectively (P < 0.001)). After 6 months of vitamin D supplementation, there was significant improvement in the DEXA Z-score (P < 0.001), limitation of physical health (P = 0.02), pain scores (P < 0.001), and CHAQ grades (P = 0.01) in SCD patients. A significant improvement in HGS (P < 0.001 and P = 0.005) as well as the CHAQ score (P < 0.001 and P = 0.003) was detected in the SCD group and controls, respectively. There were no reported clinical adverse events (AEs) or new concomitant medications (CMs) during the study duration, and safe levels of Ca and 25 (OH) D3 were observed at 3 and 6 months for both groups. There was a significant positive correlation between HGS and total physical score (r = 0.831, P < 0.001) and a negative correlation with CHAQ score (r = − 0.685, P < 0.001). We also detected a significant positive correlation between vitamin D levels at 6 months and HGS (r = 0.584, P < 0.001), pain score (r = 0.446, P < 0.001), and a negative correlation with CHAQ score (r = − 0.399, P < 0.001).
Conclusion: Monthly oral high-dose vitamin D supplementation was safe and effective in improving vitamin D levels, HGS, and HRQoL in SCD children and healthy subjects, and BMD scores in SCD patients. Further randomized controlled trials are warranted to assess an optimal dosing strategy and to investigate the impact on clinically significant outcomes in children and adolescents with SCD and their healthy counterparts.
Trial registration: ClinicalTrials.gov, identifier NCT06274203, date of registration: 23/02/2024, retrospectively registered.What is known:
• Several studies have reported a high prevalence of vitamin D deficiency and suboptimal bone mineral density (BMD) in sickle cell disease (SCD) patients.
• Musculoskeletal dysfunction is reported in SCD patients with a negative impact on physical activity and health-related quality of life (HRQL).
• Little is known regarding the impact of vitamin D3 supplementation in children and adolescents with SCD.
What is new:
• We found that monthly oral high-dose vitamin D3 supplementation was safe, tolerated, and effective in improving serum vitamin D levels, HGS, BMD scores, and HRQL in SCD patients.
... The term Childhood obesity is the accumulation of extra body fat and the proliferation of excess adipocytes [1]. Body mass index (BMI), which is determined from the individual's height and weight measurements (BMI = weight/height (kg/m2)), is the recognised clinical reference for assessing overweight and obesity for children aged 2 years and up [9][10][11][12][13]. Generally, BMI provides a decent estimate of adiposity in the healthy paediatric population [14]. ...
... Overweight and Obesity for Child and Adolescents Ages 2 to 20 Years [12]: ...
Overweight and obesity in children and adolescents and its negative effects on health, including increased risks of long-term diseases like type II DM, CVD, dyslipidemia, , stroke, hypertension, respiratory issues, gallbladder disease, sleep apnea, osteoarthritis, along with certain malignancies, which are already identified during the perinatal and prenatal period is one of the most important worldwide health concerns of the twenty-first century. To overcome the current epidemic of overweight and obesity, obstructing their risk factors is important in an effort to prevent the development of obesity and overweight. Multiple epidemiological research studies have shown a connection between BMI acquired later in life and birth weight; however, the results are constrained by the absence of information on gestational age. Majority of studies reported relation of childhood obesity with the preterm born children in study of relation with the gestational age. Although more likely to become obese in later adulthood, preterm and low birth weight born child are small and/or lean at birth, whereas post-term usually not and above all, children born postterm showed signs of a rapid weight gain that led to obesity decades early. Thus, the purpose of this review study is to determine the impact of the gestational age at delivery and to provide an overview of the evidence supporting the link between childhood obesity and post-term birth.. Thorough systemic review conducted on online database Pubmed, Google Scholar and found only few studies on association with the post- term born children. Limited evidence necessitated the studying of additional adult post-term cohorts to accurately determine future risks to health and to investigate these potential metabolic alterations, as well as if the alterations in adiposity continue or get worse throughout adulthood, and how these correlations vary in adult born post-term in terms of pattern and amplitude.
... Age was classified into three groups: early (10-14 years), middle (15-17 years), and late (18)(19) years) adolescence, based on source material published by the WHO Department of Child and Adolescent Health 17 . Sex assigned at birth was classified as either female or male. ...
... Sex assigned at birth was classified as either female or male. BMI was classified according to the Centers for Disease Control (CDC) classification of BMI-for-age (Appendix D) 18 . Due to low participant numbers in the underweight and obese groups, for statistical analyses, participants below the 85 th percentile were deemed healthy and greater than or equal to the 85 th percentile were categorized as overweight. ...
... At subsequent waves, all participants reported height and weight on the surveys, which have shown strong validity against objective measures in this sample (Lipsky et al., 2019). The U.S. Centers for Disease Control and Prevention sex-and age-specific percentile cutoffs (Kuczmarski et al., 2000) were used to classify weight status until participants reached 20 years of age. Weight status for participants 20 years of age and older was classified using adult BMI cut-offs (NHLBI Obesity Education Initiative Expert Panel on the Identification, 2000). ...
... De hecho, los gráficos y tablas que disponen el Centro de control de enfermedades y dolencias (CDC) (Kuczmarski et al., 2000) y la Organización mundial de la salud (OMS) (de Onis et al., 2007) son los más utilizados a nivel mundial para valorar el IMC en niños y adolescentes, aunque algunos países en los últimos años en América del sur han propuesto curvas de crecimiento físico para poblaciones de Colombia, Chile, Perú, entre otros países (Cossio-Bolaños et al., 2020;Díaz-Bonilla et al., 2018;Gómez-Campos et al., 2019). ...
El Índice de Masa Corporal (IMC) es un indicador ampliamente utilizado en poblaciones pediátricas. Estudios vinculan un IMC alto con menor coordinación motora y riesgo de sobrepeso en niños. Objetivo: Relacionar el IMC con las habilidades de locomoción (HL) en escolares de ambos sexos de un colegio estatal de Chile. Metodología: Participaron 70 escolares (35 niños y 35 niñas) con una edad promedio de 10.1±0.37 años pertenecientes a colegios municipales. Se evaluó el peso y estatura con el objetivo de calcular el IMC categorizando por medio de las directrices internacionales para niños y adolescentes, por otra parte, se evaluó las HL por medio del test TGMD-2. Se utilizó la prueba T de student para comparación entre sexos y la prueba de correlación de Pearson para la relación entre las variables de estudio. Resultados: Hubo correlación negativa entre el IMC con las HL en ambos sexos. En niños fue de -0,24 (p< 0,05) y en niñas fue de -0,16 (p< 0,05). Los niños de ambos sexos, mostraron mejor rendimiento en las HL cuando fueron categorizado con normopeso en comparación con los de exceso de peso (EP) (p<0,05). Conclusión: Hubo relación negativa entre el IMC con las HL, lo que permite destacar que el EP en niños de ambos sexos puede ser un factor relevante que condiciona el desempeño de las HL en niños de ambos sexos.
... Additionally, multiple prevalence studies in India have also utilized the International Obesity Taskforce (IOTF) data set and the American Centers for Disease control (CDC) age and sex specific data set. [4,5] Prevalence of overweight and obeSity among indian children ...
... The unequal probability of being recruited and for nonresponse is common to surveys with complex designs, including NHANES [14]. To deal with potential selection bias, the US CDC developed weight variables [15], to account for the unequal probability of sampling and non-response. We used interview weight years and weights were adjusted for the inclusion of multiple surveys, by dividing the weight variable by the number of cycles used in our analyses. ...
Among US adults aged 20 + years in the USA with previously diagnosed type 2 diabetes mellitus (T2DM), we aimed to estimate the prevalence of early-onset T2DM (onset at age < 50.5 years) and to test associations between early-onset T2DM and race/ethnicity, and other hypothesized predictors.
We pooled data from the annual National Health and Nutrition Examination Surveys (NHANES) over the years 2001 through 2018. We tested hypotheses of association and identified predictors using stepwise logistic regression analysis, and 11 supervised machine learning classification algorithms.
After appropriate weighting, we estimated that among adults in the USA aged 20 + years with previously diagnosed T2DM, the prevalence of early-onset was 52.9% (95% confidence intervals, 49.6 to 56.2%). Among Non-Hispanic Whites (NHW) the prevalence was 48.6% (95% CI, 44.6 to 52.6%), among Non-Hispanic Blacks: 56.9% (95% CI, 51.8 to 62.0%), among Hispanics: 62.7% (95% CI, 53.2 to 72.3%). In the final multivariable logistic regression model, the top-3 markers predicting early-onset T2DM in males were NHB ethnicity (OR = 2.97; 95% CI: 2.24–3.95) > tobacco smoking (OR = 2.79; 95% CI: 2.18–3.58) > high education level (OR = 1.65; 95% CI: 1.27–2.14) in males. In females, the ranking was tobacco smoking (OR = 2.59; 95% CI: 1.90–3.53) > Hispanic ethnicity (OR = 1.49; 95% CI: 1.08–2.05) > obesity (OR = 1.30; 95% CI: 0.91–1.86) in females. The acculturation score emerged from the machine learning approach as the dominant marker explaining the race disparity in early-onset T2DM.
The prevalence of early-onset T2DM was higher among NHB and Hispanic people, than among NHW people. Independently of race/ethnicity, acculturation, tobacco smoking, education level, marital status, obesity, and hypertension were also predictive.
