- Email: [email protected]
Provider Preference for Growth Charts in Tracking Children with Obesity
BRIEF REPORTS Provider Preference for Growth Charts in Tracking Children with Obesity Melissa Chambers, DO1, Sanil P. Reddy, BS2, Muideen T. Olaiya, PhD2, Diana L. Dunnigan, MD2, Dorota Wasak, RN2, Mary A. Hoskin, MS, RD2, William C. Knowler, MD, DrPH2, and Madhumita Sinha, MD2 A web-based survey of pediatric care providers revealed differences in their preference for clinical charts that monitor growth in children with obesity. These findings are attributed to pediatric specialty training. Very few providers believe the currently available Centers for Disease Control and Prevention 2000 body mass index-for-age charts adequately track growth in children with obesity. (J Pediatr 2019;-:1-4).
T
he Centers for Disease Control and Prevention (CDC) released clinical growth charts almost 2 decades ago in 2000. These charts are currently used across the US for tracking growth of children and adolescents.1 Over the last 20 years, the prevalence of obesity in children and adolescents (2-19 years), based on age-sex-specific body mass index (BMI) has significantly increased.2,3 The prevalence of childhood obesity, defined by a BMI percentile of ³95 from the 2000 CDC growth charts remains high at 17%, based on data from the National Health and Nutrition Examination Survey (NHANES).4 Skinner et al reported prevalence estimates of the different obesity classes (class I, ³95th to <120% of the 95th percentile; class II, ³120% to <140% of 95th percentile; and class III, ³140% of 95th percentile of age-sex specific CDC 2000 BMI charts) and obesity trends by 2-year NHANES cycles; they found a positive linear trend significant for all classes of obesity in both sexes.5 Severe, or extreme, obesity is defined as a BMI-for-age of ³120% of the 95th percentile (%BMIp95); it is associated with significant cardiometabolic risks, including youth-onset type 2 diabetes.6,7 Equally concerning, 57% of US children and youth are predicted to become obese by the age of 35 years.8 It is, therefore, important for pediatric health care providers to be adequately equipped to assess, track, and manage excessive weight gain in children. The US Preventive Services Task Force recommends that children and adolescents ³6 years of age should be screened for obesity and referred for any appropriately interventions.9 Currently, weight status in children and adolescents is defined by age-sex-specific BMI percentile on the CDC 2000 growth charts, which were created based on crosssectional data from 5 nationally representative surveys conducted between 1963 and 1994.1 Set 1 of the CDC BMI
BMI BMIaz BMIz %BMIp95 CDC NCHS NHANES
Body mass index Age- and sex-specific BMI modified/adjusted compression z-score Unmodified age- and sex-specific BMI z-score BMI-for-age of ³120% of the 95th percentile Centers for Disease Control and Prevention National Center for Health Statistics National Health and Nutrition Examination Survey
clinical growth charts meant for routine clinical use have an upper limit of BMI-for age at the 95th percentile. Set 2 of the BMI-for-age CDC growth charts are mostly used by pediatric endocrinologists and other subspecialists; they have an upper limit of BMI-for-age at the 97th percentile (BMI [maximum] in both charts: 36 kg/m2).10 The surge in prevalence of childhood obesity and extreme obesity creates a challenge for pediatricians and subspecialists using the current clinical growth charts. In the absence of updated growth charts from CDC and the National Center for Health Statistics (NCHS), others have proposed alternative methods and modifications to the CDC 2000 growth charts to accommodate plotting of extreme BMI percentiles. Gulati et al created growth charts using CDC data tables for the 95th percentile expressed as a percent of the 95th percentile.11 The concept was based on the observation that expressing high BMIfor-age values as a percent of the 95th percentile may be more useful than extrapolating beyond the range of the 97th percentile.12 We recently proposed a modified CDC BMI z-score chart for uniform growth tracking that is suitable for use in children whose BMI fall in very low or high ranges.13 In the current study, we investigated the preference among general and subspecialty trained pediatric providers for currently available and the 2 proposed growth charts used to track growth of children who are obese or severely obese.13
Methods A cross-sectional survey was conducted in February 2018, using the electronic discussion emailing lists of 2 groups: (1) the Section on Obesity of the American Academy of Pediatrics, targeting pediatric healthcare providers dedicated to issues of childhood obesity; and (2) the pediatric-endocrinology electronic mailing list, targeting pediatric endocrine
for From the 1Division of Endocrinology and Diabetes, Phoenix Children’s Hospital; and the 2Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Phoenix, AZ Funded in part by the Intramural Research Program of the NIDDK. The authors declare no conflicts of interest. 0022-3476/$ - see front matter. ª 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.jpeds.2019.11.039
1
THE JOURNAL OF PEDIATRICS
www.jpeds.com
subspecialists and allied professionals interested in pediatric endocrinology. Although this sample may not be representative of all pediatricians, it entailed an appropriate blend of pediatric care providers, primary care providers, and subspecialists who are likely to engage in the evaluation and clinical management of children with obesity and severe obesity. This survey was exempted from ethical review by the institutional review board. Survey Development and Data Collection A 10-question electronic survey was developed to assess the preference of respondents for 3 growth charts: (1) the conventional CDC 2000 age- and sex-specific BMI chart, (2) the proposed %BMIp95 chart, and (3) the modified BMI zscore charts. The introductory message was followed by the purpose of the survey, and finally the estimated time for its completion. The questionnaire comprised 1 demographic question and 9 practice-related questions. These included questions related to the volume of pediatric patients with obesity, subspecialty training, and the provider’s ability to plot growth adequately in these children. This was followed by sequential screens with a brief description and unmarked sample of each of the 3 growth charts. The survey ended with presentation of a hypothetical case of 2 adolescent female subjects with serial BMIs plotted using the conventional CDC 2000 age- and sex-specific BMI chart, and the proposed %BMIp95, and the modified BMI z-score charts. Respondents were asked to select the growth chart they found to be most useful for growth tracking. After reviewing the final paper version and verifying all logical options after each response, the survey was transferred to the web-based SurveyMonkey hosting site (SurveyMonkey Inc, San Mateo, California; www.surveymonkey.com). It was then pilot tested remotely among 23 pediatric practitioners for usability. There were no technical problems reported, although the wording on some questions and general formatting were modified for clarity and usability, respectively. The final web-based survey was deployed in February 2018 simultaneously to members of both electronic mailing lists. The survey could be accessed via a URL sent by an email to each member of the mailing list. Each participant remained anonymous and was assigned a unique personal identification number. A reminder email was sent to all members approximately 4 weeks after the initial response. The survey was closed 2 weeks afterward. Statistical Analyses Sex- and practice-related characteristics of respondents were summarized as frequency counts and percentages and compared according to growth chart preference using the c2 test. Factors associated with the growth chart preference of respondents were determined using backward stepwise multivariable linear regression models. Only variables with a P value of < .05 were retained in the final multivariable model. Potential interactions between combinations of predictor variables were investigated by inserting crossproduct terms into multivariable models. All analyses were 2
Volume -
- 2019
performed using SAS (version 9.4, SAS Institute, Cary, North Carolina). A 2-sided P value of < .05 was considered statistically significant.
Results A total of 187 unique responses were received, of which 4 respondents (2%) preferred the CDC 2000 BMI-for-age chart, 109 (60%) the CDC 2000 BMI-for-age chart with additional %BMIp95 lines, and 74 (40%) the modified BMI z-score chart. Given the small number of respondents who preferred the CDC 2000 BMI-for-age chart, analyses were restricted to respondents who preferred either the CDC 2000 BMI-for-age chart with additional %BMIp95 lines or the modified BMI z-score chart only. Moreover, given that this survey was intended for pediatric clinical care providers, data from 2 respondents who identified as dietician and/or nutritionist were excluded. Overall, the present analyses comprised data from 181 responses, with 108 respondents (60%) preferring the CDC 2000 BMI-for-age chart with additional %BMIp95 lines and 73 respondents (40%) preferring the modified BMI z-score chart. One hundred thirty-one respondents (72%) were female, 96 (53%) worked in a children’s hospital, and 102 (56%) identified as pediatric endocrinologists (Table). In univariate analyses, respondents who identified as pediatric endocrinologists more often preferred the modified BMI z-score chart to the %BMIp95 chart (53.9%) than those who were not pediatric endocrinologists (22.8%; P < .001; Table). However, those who preferred the modified BMI zscore chart compared with the %BMIp95 chart less often treated pediatric patients who require growth charting daily (P = .03). In stepwise multivariable linear regression models, training in pediatric endocrinology was associated with preference for growth charts. Indeed, the odds of using the modified BMI z-score chart rather than the % BMIp95 chart to track pediatric growth was greater among pediatric endocrinologists than other pediatric care providers (OR, 3.97; 95% CI, 2.06-7.62; P < .001).
Discussion The CDC 2000 growth charts have not been updated since they were first published, despite a growing need to accommodate tracking of growth in children with (severe) obesity.1 In the absence of specific guidance from CDC and NCHS, alternate methods of growth tracking have been suggested and adopted by pediatricians. Only 4 of the surveyed providers (2%) preferred the CDC 2000 charts, and most preferred the %BMIp95 charts. Proposed in 2012, these charts use 2 metrics: percentile <95th and percent of the 95th percentile thereafter, within the same chart.11 The subspecialist pediatric endocrinologists, however, who are more likely to receive referrals for management of children with severe obesity, preferred our recently proposed modified z-score charts.13 The modified CDC BMI z-score growth charts are not limited by compression of z-scores into a narrow range, Chambers et al
- 2019
BRIEF REPORTS
Table. Demographic and specialty-related characteristics of respondents according to growth chart preference Characteristics
%BMIp95 Chart
BMI z-score chart
P value
108 (59.7)
73 (40.3)
–
28 (56.0) 80 (61.1)
22 (44.0) 51 (38.9)
.612
102 (61.1) 6 (42.9)
65 (38.9) 8 (57.1)
.257
58 (60.4) 50 (58.8)
38 (39.6) 35 (41.2)
.880
47 (46.1) 61 (77.2)
55 (53.9) 18 (22.8)
<.001
29 (50.0) 23 (65.7) 38 (71.7)
29 (50.0) 12 (34.3) 15 (28.3)
.052
27 (50.9) 60 (60.6) 21 (72.4)
26 (49.1) 39 (39.4) 8 (27.6)
.160
24 (45.3) 37 (61.7) 47 (69.1)
29 (54.7) 23 (38.3) 21 (30.9)
.029
23 (56.1) 40 (52.0) 45 (71.4)
18 (43.9) 37 (48.0) 18 (28.6)
.057
Total Sex Male Female Country of practice US Outside the US Practice setting Children’s hospital Other settings Provider training Pediatric endocrinology Other pediatric clinical care providers Years in pediatric practice* <10 10-20 >20 No. of pediatric patients per day requiring growth charting <10 10-20 >20 Pediatric patients with obesity treated daily requiring growth charting <20% of patients with obesity 20%-30% of patients with obesity >30% of patients with obesity Unable to assess growth of obese pediatric patients whose BMI is off chart limit Never or rarely Sometimes Often Data are number (%). *Data are available for 146 pediatric care providers (80.7%).
which has a maximum value that varies by age and sex.12,14 Although the World Health Organization has defined cutoffs for normal weight, overweight, and obese in their BMI-for-age z-score charts, the CDC has not published zscore charts.15 Weight categories are defined based exclusively on BMI-for-age percentile ranges.9 The ideal growth chart for a child would provide a measure of changes in adiposity over time compared with other children of the same age and sex. Freedman et al used data from a longitudinal cohort of the Bogalusa Heart Study to examine correlations between examinations of 3 specific metrices: (1) age- and sex-specific BMI modified/adjusted for compression (BMIaz), (2) unmodified (BMIz), and (3) %BMIp95; BMIz correlations were weaker than %BMIp95 and BMIaz.16 The authors noted similar findings among severely obese 2to 4-year-old children and concluded that it is more favorable to express BMI relative to %BMIp95 or modified z-scores in these children.17 Based on these observations, we proposed the modified BMI-for-age-sex growth chart and provided both the %BMIp95 and modified BMI-z score growth charts as promising alternatives in our survey. Although the survey did not have a free text response section, we received emails in response. An important suggestion was that charts for children with obesity should be separate from clinical growth charts that are used to track normal and overweight children. Such specialized charts would be more useful in explaining children’s growth curves and the significance of their growth trajectory to their parents
and/or guardians. However, it seems there are no interactive training modules for their appropriate use. An initial approach can be to provide multiple examples of illustrating patients’ growth on all 3 charts to determine the most appropriate method of tracking patients’ growth based on their respective histories. Additional recommendations entailed color coding percentile and z-score lines, or their enclosed regions, for easier interpretation. Some limitations of the study include a relatively low response rate at approximately 10%. The respondents may not have been representative of all pediatricians and subspecialty practitioners, such as gastroenterologists and obesity medicine physicians, who likely treat children with unhealthy weight gain. Furthermore, providers may or may not have used the 2 newly proposed growth charts in practice; the presented cases were hypothetical, although realistic. Some respondents could have preferred a more detailed, theoretical understanding of each growth chart to determine the most appropriate chart for each hypothetical case. Nevertheless, our study highlights an impending public health concern. The optimal method to accurately assess change in adiposity over time is a basic requirement in pediatric practice for clinical decision making, and thereby appropriate care. n Submitted for publication May 17, 2019; last revision received Oct 31, 2019; accepted Nov 22, 2019. Reprint requests: Melissa Chambers, DO, Division of Endocrinology and Diabetes, Phoenix Children’s Hospital, 1919 E Thomas Rd, Phoenix, AZ 85016. E-mail: [email protected]
Provider Preference for Growth Charts in Tracking Children with Obesity
3
THE JOURNAL OF PEDIATRICS
www.jpeds.com
Data Statement Data sharing statement available at www.jpeds.com.
References 1. Kuczmarski RJ, Ogden CL, Guo SS, Grummer-Strawn LM, Flegal KM, Mei Z, et al. 2000 CDC growth charts for the United States: methods and development. National Center for Health Statistics. Vital Health Stat 2002;11:1-190. 2. Ogden CL, Carroll MD, Fryer CD, Flegal KM. Prevalence of obesity among adults and youth: United States, 2011-2014. NCHS Data Brief 2015;219:1-8. 3. Ogden CL, Flegal KM. Changes in terminology for childhood overweight and obesity. Natl Health Stat Report 2010:1-5. 4. Ogden CL, Carroll MD, Lawman HG, Fryar CD, Kruszon-Moran D, Kit BK, et al. Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 through 2013-2014. JAMA 2016;315:2292-9. 5. Skinner AC, Ravanbakht SN, Skelton JA, Perrin EM, Armstrong SC. Prevalence of obesity and severe obesity in US children, 1999-2016. Pediatrics 2018;141:e20173459. 6. Skinner AC, Perrin EM, Moss LA, Skelton JA. Cardiometabolic risks and severity of obesity in children and young adults. N Engl J Med 2015;373: 1307-17. 7. Tanamas SK, Reddy SP, Chambers MA, Clark EJ, Dunnigan DL, Hanson RL, et al. Effect of severe obesity in childhood and adolescence on risk of type 2 diabetes in youth and early adulthood in an American Indian population. Pediatr Diabetes 2018;19:622-9.
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8. Ward ZJ, Long MW, Resch SC, Giles CM, Cradock AL, Gortmaker SL. Simulation of growth trajectories of childhood obesity into adulthood. N Engl J Med 2017;377:2145-53. 9. Barlow SE, The Expert Committee. Expert Committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics 2007;120(Suppl 4):S164-92. 10. Centers for Disease Control and Prevention (CDC). Clinical growth charts. www.cdc.gov/growthcharts/clinical_charts.htm. Accessed June 15, 2018. 11. Gulati AK, Kaplan DW, Daniels SR. Clinical tracking of severely obese children: a new growth chart. Pediatrics 2012;130: 1136-40. 12. Flegal KM, Wei R, Ogden CL, Freedman DS, Johnson CL, Curtin LR. Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts. Am J Clin Nutr 2009;90:1314-20. 13. Chambers M, Tanamas SK, Clark EJ, Dunnigan DL, Kapadia CR, Hanson RL, et al. Growth tracking in severely obese or underweight children. Pediatrics 2017;140:e20172248. 14. Centers for Disease Control and Prevention (CDC). Modified z-scores in the CDC growth charts. www.cdc.gov/nccdphp/dnpao/growthcharts/ resources/biv-cutoffs.pdf. Accessed June 28, 2018. 15. World Health Organization. Training course on child growth assessment. Geneva: WHO; 2008. 16. Freedman DS, Berenson GS. Tracking of BMI z scores for severe obesity. Pediatrics 2017;140:1-4. 17. Freedman D, Butte NF, Taveras EM, Goodman A, Blanck HM. Longitudinal changes in BMI z-scores among 45 414 2–4-year olds with severe obesity. Ann Human Biol 2017;44: 1-6.
Chambers et al
T
he Centers for Disease Control and Prevention (CDC) released clinical growth charts almost 2 decades ago in 2000. These charts are currently used across the US for tracking growth of children and adolescents.1 Over the last 20 years, the prevalence of obesity in children and adolescents (2-19 years), based on age-sex-specific body mass index (BMI) has significantly increased.2,3 The prevalence of childhood obesity, defined by a BMI percentile of ³95 from the 2000 CDC growth charts remains high at 17%, based on data from the National Health and Nutrition Examination Survey (NHANES).4 Skinner et al reported prevalence estimates of the different obesity classes (class I, ³95th to <120% of the 95th percentile; class II, ³120% to <140% of 95th percentile; and class III, ³140% of 95th percentile of age-sex specific CDC 2000 BMI charts) and obesity trends by 2-year NHANES cycles; they found a positive linear trend significant for all classes of obesity in both sexes.5 Severe, or extreme, obesity is defined as a BMI-for-age of ³120% of the 95th percentile (%BMIp95); it is associated with significant cardiometabolic risks, including youth-onset type 2 diabetes.6,7 Equally concerning, 57% of US children and youth are predicted to become obese by the age of 35 years.8 It is, therefore, important for pediatric health care providers to be adequately equipped to assess, track, and manage excessive weight gain in children. The US Preventive Services Task Force recommends that children and adolescents ³6 years of age should be screened for obesity and referred for any appropriately interventions.9 Currently, weight status in children and adolescents is defined by age-sex-specific BMI percentile on the CDC 2000 growth charts, which were created based on crosssectional data from 5 nationally representative surveys conducted between 1963 and 1994.1 Set 1 of the CDC BMI
BMI BMIaz BMIz %BMIp95 CDC NCHS NHANES
Body mass index Age- and sex-specific BMI modified/adjusted compression z-score Unmodified age- and sex-specific BMI z-score BMI-for-age of ³120% of the 95th percentile Centers for Disease Control and Prevention National Center for Health Statistics National Health and Nutrition Examination Survey
clinical growth charts meant for routine clinical use have an upper limit of BMI-for age at the 95th percentile. Set 2 of the BMI-for-age CDC growth charts are mostly used by pediatric endocrinologists and other subspecialists; they have an upper limit of BMI-for-age at the 97th percentile (BMI [maximum] in both charts: 36 kg/m2).10 The surge in prevalence of childhood obesity and extreme obesity creates a challenge for pediatricians and subspecialists using the current clinical growth charts. In the absence of updated growth charts from CDC and the National Center for Health Statistics (NCHS), others have proposed alternative methods and modifications to the CDC 2000 growth charts to accommodate plotting of extreme BMI percentiles. Gulati et al created growth charts using CDC data tables for the 95th percentile expressed as a percent of the 95th percentile.11 The concept was based on the observation that expressing high BMIfor-age values as a percent of the 95th percentile may be more useful than extrapolating beyond the range of the 97th percentile.12 We recently proposed a modified CDC BMI z-score chart for uniform growth tracking that is suitable for use in children whose BMI fall in very low or high ranges.13 In the current study, we investigated the preference among general and subspecialty trained pediatric providers for currently available and the 2 proposed growth charts used to track growth of children who are obese or severely obese.13
Methods A cross-sectional survey was conducted in February 2018, using the electronic discussion emailing lists of 2 groups: (1) the Section on Obesity of the American Academy of Pediatrics, targeting pediatric healthcare providers dedicated to issues of childhood obesity; and (2) the pediatric-endocrinology electronic mailing list, targeting pediatric endocrine
for From the 1Division of Endocrinology and Diabetes, Phoenix Children’s Hospital; and the 2Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Phoenix, AZ Funded in part by the Intramural Research Program of the NIDDK. The authors declare no conflicts of interest. 0022-3476/$ - see front matter. ª 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.jpeds.2019.11.039
1
THE JOURNAL OF PEDIATRICS
www.jpeds.com
subspecialists and allied professionals interested in pediatric endocrinology. Although this sample may not be representative of all pediatricians, it entailed an appropriate blend of pediatric care providers, primary care providers, and subspecialists who are likely to engage in the evaluation and clinical management of children with obesity and severe obesity. This survey was exempted from ethical review by the institutional review board. Survey Development and Data Collection A 10-question electronic survey was developed to assess the preference of respondents for 3 growth charts: (1) the conventional CDC 2000 age- and sex-specific BMI chart, (2) the proposed %BMIp95 chart, and (3) the modified BMI zscore charts. The introductory message was followed by the purpose of the survey, and finally the estimated time for its completion. The questionnaire comprised 1 demographic question and 9 practice-related questions. These included questions related to the volume of pediatric patients with obesity, subspecialty training, and the provider’s ability to plot growth adequately in these children. This was followed by sequential screens with a brief description and unmarked sample of each of the 3 growth charts. The survey ended with presentation of a hypothetical case of 2 adolescent female subjects with serial BMIs plotted using the conventional CDC 2000 age- and sex-specific BMI chart, and the proposed %BMIp95, and the modified BMI z-score charts. Respondents were asked to select the growth chart they found to be most useful for growth tracking. After reviewing the final paper version and verifying all logical options after each response, the survey was transferred to the web-based SurveyMonkey hosting site (SurveyMonkey Inc, San Mateo, California; www.surveymonkey.com). It was then pilot tested remotely among 23 pediatric practitioners for usability. There were no technical problems reported, although the wording on some questions and general formatting were modified for clarity and usability, respectively. The final web-based survey was deployed in February 2018 simultaneously to members of both electronic mailing lists. The survey could be accessed via a URL sent by an email to each member of the mailing list. Each participant remained anonymous and was assigned a unique personal identification number. A reminder email was sent to all members approximately 4 weeks after the initial response. The survey was closed 2 weeks afterward. Statistical Analyses Sex- and practice-related characteristics of respondents were summarized as frequency counts and percentages and compared according to growth chart preference using the c2 test. Factors associated with the growth chart preference of respondents were determined using backward stepwise multivariable linear regression models. Only variables with a P value of < .05 were retained in the final multivariable model. Potential interactions between combinations of predictor variables were investigated by inserting crossproduct terms into multivariable models. All analyses were 2
Volume -
- 2019
performed using SAS (version 9.4, SAS Institute, Cary, North Carolina). A 2-sided P value of < .05 was considered statistically significant.
Results A total of 187 unique responses were received, of which 4 respondents (2%) preferred the CDC 2000 BMI-for-age chart, 109 (60%) the CDC 2000 BMI-for-age chart with additional %BMIp95 lines, and 74 (40%) the modified BMI z-score chart. Given the small number of respondents who preferred the CDC 2000 BMI-for-age chart, analyses were restricted to respondents who preferred either the CDC 2000 BMI-for-age chart with additional %BMIp95 lines or the modified BMI z-score chart only. Moreover, given that this survey was intended for pediatric clinical care providers, data from 2 respondents who identified as dietician and/or nutritionist were excluded. Overall, the present analyses comprised data from 181 responses, with 108 respondents (60%) preferring the CDC 2000 BMI-for-age chart with additional %BMIp95 lines and 73 respondents (40%) preferring the modified BMI z-score chart. One hundred thirty-one respondents (72%) were female, 96 (53%) worked in a children’s hospital, and 102 (56%) identified as pediatric endocrinologists (Table). In univariate analyses, respondents who identified as pediatric endocrinologists more often preferred the modified BMI z-score chart to the %BMIp95 chart (53.9%) than those who were not pediatric endocrinologists (22.8%; P < .001; Table). However, those who preferred the modified BMI zscore chart compared with the %BMIp95 chart less often treated pediatric patients who require growth charting daily (P = .03). In stepwise multivariable linear regression models, training in pediatric endocrinology was associated with preference for growth charts. Indeed, the odds of using the modified BMI z-score chart rather than the % BMIp95 chart to track pediatric growth was greater among pediatric endocrinologists than other pediatric care providers (OR, 3.97; 95% CI, 2.06-7.62; P < .001).
Discussion The CDC 2000 growth charts have not been updated since they were first published, despite a growing need to accommodate tracking of growth in children with (severe) obesity.1 In the absence of specific guidance from CDC and NCHS, alternate methods of growth tracking have been suggested and adopted by pediatricians. Only 4 of the surveyed providers (2%) preferred the CDC 2000 charts, and most preferred the %BMIp95 charts. Proposed in 2012, these charts use 2 metrics: percentile <95th and percent of the 95th percentile thereafter, within the same chart.11 The subspecialist pediatric endocrinologists, however, who are more likely to receive referrals for management of children with severe obesity, preferred our recently proposed modified z-score charts.13 The modified CDC BMI z-score growth charts are not limited by compression of z-scores into a narrow range, Chambers et al
- 2019
BRIEF REPORTS
Table. Demographic and specialty-related characteristics of respondents according to growth chart preference Characteristics
%BMIp95 Chart
BMI z-score chart
P value
108 (59.7)
73 (40.3)
–
28 (56.0) 80 (61.1)
22 (44.0) 51 (38.9)
.612
102 (61.1) 6 (42.9)
65 (38.9) 8 (57.1)
.257
58 (60.4) 50 (58.8)
38 (39.6) 35 (41.2)
.880
47 (46.1) 61 (77.2)
55 (53.9) 18 (22.8)
<.001
29 (50.0) 23 (65.7) 38 (71.7)
29 (50.0) 12 (34.3) 15 (28.3)
.052
27 (50.9) 60 (60.6) 21 (72.4)
26 (49.1) 39 (39.4) 8 (27.6)
.160
24 (45.3) 37 (61.7) 47 (69.1)
29 (54.7) 23 (38.3) 21 (30.9)
.029
23 (56.1) 40 (52.0) 45 (71.4)
18 (43.9) 37 (48.0) 18 (28.6)
.057
Total Sex Male Female Country of practice US Outside the US Practice setting Children’s hospital Other settings Provider training Pediatric endocrinology Other pediatric clinical care providers Years in pediatric practice* <10 10-20 >20 No. of pediatric patients per day requiring growth charting <10 10-20 >20 Pediatric patients with obesity treated daily requiring growth charting <20% of patients with obesity 20%-30% of patients with obesity >30% of patients with obesity Unable to assess growth of obese pediatric patients whose BMI is off chart limit Never or rarely Sometimes Often Data are number (%). *Data are available for 146 pediatric care providers (80.7%).
which has a maximum value that varies by age and sex.12,14 Although the World Health Organization has defined cutoffs for normal weight, overweight, and obese in their BMI-for-age z-score charts, the CDC has not published zscore charts.15 Weight categories are defined based exclusively on BMI-for-age percentile ranges.9 The ideal growth chart for a child would provide a measure of changes in adiposity over time compared with other children of the same age and sex. Freedman et al used data from a longitudinal cohort of the Bogalusa Heart Study to examine correlations between examinations of 3 specific metrices: (1) age- and sex-specific BMI modified/adjusted for compression (BMIaz), (2) unmodified (BMIz), and (3) %BMIp95; BMIz correlations were weaker than %BMIp95 and BMIaz.16 The authors noted similar findings among severely obese 2to 4-year-old children and concluded that it is more favorable to express BMI relative to %BMIp95 or modified z-scores in these children.17 Based on these observations, we proposed the modified BMI-for-age-sex growth chart and provided both the %BMIp95 and modified BMI-z score growth charts as promising alternatives in our survey. Although the survey did not have a free text response section, we received emails in response. An important suggestion was that charts for children with obesity should be separate from clinical growth charts that are used to track normal and overweight children. Such specialized charts would be more useful in explaining children’s growth curves and the significance of their growth trajectory to their parents
and/or guardians. However, it seems there are no interactive training modules for their appropriate use. An initial approach can be to provide multiple examples of illustrating patients’ growth on all 3 charts to determine the most appropriate method of tracking patients’ growth based on their respective histories. Additional recommendations entailed color coding percentile and z-score lines, or their enclosed regions, for easier interpretation. Some limitations of the study include a relatively low response rate at approximately 10%. The respondents may not have been representative of all pediatricians and subspecialty practitioners, such as gastroenterologists and obesity medicine physicians, who likely treat children with unhealthy weight gain. Furthermore, providers may or may not have used the 2 newly proposed growth charts in practice; the presented cases were hypothetical, although realistic. Some respondents could have preferred a more detailed, theoretical understanding of each growth chart to determine the most appropriate chart for each hypothetical case. Nevertheless, our study highlights an impending public health concern. The optimal method to accurately assess change in adiposity over time is a basic requirement in pediatric practice for clinical decision making, and thereby appropriate care. n Submitted for publication May 17, 2019; last revision received Oct 31, 2019; accepted Nov 22, 2019. Reprint requests: Melissa Chambers, DO, Division of Endocrinology and Diabetes, Phoenix Children’s Hospital, 1919 E Thomas Rd, Phoenix, AZ 85016. E-mail: [email protected]
Provider Preference for Growth Charts in Tracking Children with Obesity
3
THE JOURNAL OF PEDIATRICS
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Data Statement Data sharing statement available at www.jpeds.com.
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8. Ward ZJ, Long MW, Resch SC, Giles CM, Cradock AL, Gortmaker SL. Simulation of growth trajectories of childhood obesity into adulthood. N Engl J Med 2017;377:2145-53. 9. Barlow SE, The Expert Committee. Expert Committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics 2007;120(Suppl 4):S164-92. 10. Centers for Disease Control and Prevention (CDC). Clinical growth charts. www.cdc.gov/growthcharts/clinical_charts.htm. Accessed June 15, 2018. 11. Gulati AK, Kaplan DW, Daniels SR. Clinical tracking of severely obese children: a new growth chart. Pediatrics 2012;130: 1136-40. 12. Flegal KM, Wei R, Ogden CL, Freedman DS, Johnson CL, Curtin LR. Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts. Am J Clin Nutr 2009;90:1314-20. 13. Chambers M, Tanamas SK, Clark EJ, Dunnigan DL, Kapadia CR, Hanson RL, et al. Growth tracking in severely obese or underweight children. Pediatrics 2017;140:e20172248. 14. Centers for Disease Control and Prevention (CDC). Modified z-scores in the CDC growth charts. www.cdc.gov/nccdphp/dnpao/growthcharts/ resources/biv-cutoffs.pdf. Accessed June 28, 2018. 15. World Health Organization. Training course on child growth assessment. Geneva: WHO; 2008. 16. Freedman DS, Berenson GS. Tracking of BMI z scores for severe obesity. Pediatrics 2017;140:1-4. 17. Freedman D, Butte NF, Taveras EM, Goodman A, Blanck HM. Longitudinal changes in BMI z-scores among 45 414 2–4-year olds with severe obesity. Ann Human Biol 2017;44: 1-6.
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