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Misclassification of child body mass index from cut-points defined by rounded percentiles instead of Z-scores.

ABSTRACT: To evaluate the misclassification resulting from the use of body mass index (BMI) cut-points defined by rounded percentiles instead of Z-scores in early childhood. Using data from the TARGet Kids primary care network we conducted a cross-sectional study among 5836 children < 6 years of age. The World Health Organization growth standards were used to calculate BMI-for-age Z-scores. BMI Z-score cut-points of < - 2.0, > 1.0, > 2.0, > 3.0 are recommended to define wasted, at risk of overweight, overweight and obese. However, rounded percentiles of the 3rd, 85th, 97th, and 99.9th are commonly used. Misclassification was calculated comparing the frequency distributions for BMI categories defined by rounded percentiles and Z-score cut-points.Using rounded percentiles, the proportion of children who were wasted, at risk of overweight, overweight, and obese was 4.2, 12.5, 4.3 and 0.8%, whereas the distribution using Z-scores was: 3.6, 13.8, 3.4 and 1.0%, respectively. Overall, 117 (2%) children were misclassified when using percentiles instead of Z-scores; however, 13% (33/245) of children who were wasted and 14% (8/57) of children who were obese were misclassified. Misclassification of child growth results from the use of cut-points defined by rounded percentiles instead of Z-scores and limits comparability between studies. Trial registration Clinicaltrials.gov NCT01869530 June 5, 2013.

SUBMITTER: Anderson LN

PROVIDER: S-EPMC5706297 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Misclassification of child body mass index from cut-points defined by rounded percentiles instead of Z-scores.

Anderson Laura N LN Carsley Sarah S Lebovic Gerald G Borkhoff Cornelia M CM Maguire Jonathon L JL Parkin Patricia C PC Birken Catherine S CS

BMC research notes 20171128 1

<h4>Objective</h4>To evaluate the misclassification resulting from the use of body mass index (BMI) cut-points defined by rounded percentiles instead of Z-scores in early childhood. Using data from the TARGet Kids primary care network we conducted a cross-sectional study among 5836 children < 6 years of age. The World Health Organization growth standards were used to calculate BMI-for-age Z-scores. BMI Z-score cut-points of < - 2.0, > 1.0, > 2.0, > 3.0 are recommended to define wasted, at risk o ...[more]

PMID: 29183360

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Project description:ObjectivesTo estimate the prevalence and factors associated with smoking and misclassification in pregnant women from INMA (INfancia y Medio Ambiente, Environment and Childhood) project, Spain, and to assess the optimal cut-offs for urinary cotinine (UC) that best distinguish daily and occasional smokers with varying levels of second-hand smoke (SHS) exposure.DesignWe used logistic regression models to study the relationship between sociodemographic variables and self-reported smoking and misclassification (self-reported non-smokers with UC >50 ng/ml). Receiver operating characteristic (ROC) curves were used to calculate the optimal cut-off point for discriminating smokers. The cut-offs were also calculated after stratification among non-smokers by the number of sources of SHS exposure. The cut-off points used to discriminate smoking status were the level of UC given by Youden's index and for 50 and 100 ng/ml for daily smokers, or 25 and 50 ng/ml for occasional smokers.ParticipantsAt the third trimester of pregnancy, 2263 pregnant women of the INMA Project were interviewed between 2004 and 2008 and a urine sample was collected.ResultsPrevalence of self-reported smokers at the third trimester of pregnancy was 18.5%, and another 3.9% misreported their smoking status. Variables associated with self-reported smoking and misreporting were similar, including born in Europe, educational level and exposure to SHS. The optimal cut-off was 82 ng/ml (95% CI 42 to 133), sensitivity 95.2% and specificity 96.6%. The area under the ROC curve was 0.986 (95% CI 0.982 to 0.990). The cut-offs varied according to the SHS exposure level being 42 (95% CI 27 to 57), 82 (95% CI 46 to 136) and 106 ng/ml (95% CI 58 to 227) for not being SHS exposed, exposed to one, and to two or more sources of SHS, respectively. The optimal cut-off for discriminating occasional smokers from non-smokers was 27 ng/ml (95% CI 11 to 43).ConclusionsPrevalence of smoking during pregnancy in Spain remains high. UC is a reliable biomarker for classifying pregnant women according to their smoking status. However, cut-offs would differ based on baseline exposure to SHS.

Project description:This study aimed to examine the validity and accuracy of wrist accelerometers for classifying sedentary behavior (SB) in children.Fifty-seven children (5-8 and 9-12 yr) completed an ~170-min protocol, including 15 semistructured activities and transitions. Nine ActiGraph (GT3X+) and two GENEActiv wrist cut points were evaluated. Direct observation was the criterion measure. The accuracy of wrist cut points was compared with that achieved by the ActiGraph hip cut point (?25 counts per 15 s) and the thigh-mounted activPAL3. Analyses included equivalence testing, Bland-Altman procedures, and area under the receiver operating curve (ROC-AUC).The most accurate ActiGraph wrist cut points (Kim; vector magnitude, ?3958 counts per 60 s; vertical axis, ?1756 counts per 60 s) demonstrated good classification accuracy (ROC-AUC = 0.85-0.86) and accurately estimated SB time in 5-8 yr (equivalence P = 0.02; mean bias = 4.1%, limits of agreement = -20.1% to 28.4%) and 9-12 yr (equivalence P < 0.01; -2.5%, -27.9% to 22.9%). The mean bias of SB time estimates from Kim were smaller than ActiGraph hip (5-8 yr: 15.8%, -5.7% to 37.2%; 9-12 yr: 17.8%, -3.9% to 39.5%) and similar to or smaller than activPAL3 (5-8 yr: 12.6%, -39.8% to 14.7%; 9-12 yr: -1.4%, -13.9% to 11.0%), although classification accuracy was similar to ActiGraph hip (ROC-AUC = 0.85) but lower than activPAL3 (ROC-AUC = 0.92-0.97). Mean bias (5-8 yr: 6.5%, -16.1% to 29.1%; 9-12 yr: 10.5%, -13.6% to 34.6%) for the most accurate GENEActiv wrist cut point (Schaefer: ?0.19 g) was smaller than ActiGraph hip, and activPAL3 in 5-8 yr, but larger than activPAL3 in 9-12 yr. However, SB time estimates from Schaefer were not equivalent to direct observation (equivalence P > 0.05) and classification accuracy (ROC-AUC = 0.79-0.80) was lower than for ActiGraph hip and activPAL3.The most accurate SB ActiGraph (Kim) and GENEActiv (Schaefer) wrist cut points can be applied in children with similar confidence as the ActiGraph hip cut point (?25 counts per 15 s), although activPAL3 was generally more accurate.

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Misclassification of child body mass index from cut-points defined by rounded percentiles instead of Z-scores.

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Misclassification of child body mass index from cut-points defined by rounded percentiles instead of Z-scores.

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