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Accurate determination of the length of very young children is important because weight-for-length standards are used to assess both under- and overweight. Clinical measurements of length, which usually involve a paper-and-pencil method, may often be inaccurate in children younger than 2 years.
To compare length measured by the conventional clinical paper-and-pencil method with length measured by the research standard recumbent length-board method in a sample of children under 2 years of age.
Research assistants measured 160 children 0 through 23 months of age using the recumbent length-board method, and clinical staff measured the same children using the paper-and-pencil method. To assess the relationship between the research and clinical length measurements, we used ordinary least squares regression.
We found a strong linear relationship between the 2 measures of length (R2 = 0.98). The paper-and-pencil method systematically overestimated length in children under 2 years of age. A fitted regression equation estimated that the research standard length was 95.3% of the clinical measurement plus 1.88 cm. Over the entire age span, the mean (SD) difference between clinical and research measurements was 1.3 (1.5) cm.
Using the paper-and-pencil method can lead to underestimates of overweight and exaggerated estimates of thinness. To improve the accuracy of length measurement, medical providers should use standardized procedures with a recumbent length board to measure children under 2 years of age, at least for children whose initial paper-and-pencil measurement of length puts them at one extreme or the other.
Accurate measurement of length in young children is important for assessment of both under- and overweight. If a measurement overstates true length, for example, an overweight child may be classified as within normal range, or a child in the normal range may be labeled as thin. Clinical measurements of length, however, particularly for children younger than 24 months of age, are often inaccurate.[1,2] Although the gold-standard technique for measuring length for children younger than 24 months involves a recumbent length measuring board,[3,4] most pediatric practices in the United States use a paper-and-pencil technique.[2,4] If these 2 techniques provide systematically different results, then quantifying the bias could lead to efforts to improve the quality of length measurements in clinical practice.
Previously published studies have examined reliability of repeated measurements of a variety of length measurement techniques.[2,3, 5–9] We are aware of only 2 studies that compared length measured by the clinical paper-and-pencil method with length measured by the recumbent length board. Corkins and colleagues found no overall statistical difference between these 2 length-measurement techniques in children younger than 36 months of age, but the sample was limited to 25 selected hospital inpatients. Johnson and coworkers found that paper-and-pencil measurements were, on average, 2.51 cm larger than length-board measurements, but the study population was limited to 32 full-term newborns. Advantages of our study include a primary-care-based sample, larger sample size, wider age range than that used Johnson and coworkers’ study, and the ability to examine the extent to which covariates such as sex, age, and true length affected the relationship between the 2 measurement techniques. Lipman and associates conducted a multicenter randomized controlled trial of an intervention to improve the accuracy of linear growth measurement, including recumbent measurements. At baseline (n = 307), the mean difference between clinical and expert length/height measurements was 1.3 cm and only 30% of the clinical measurements were accurate (≤ 0.5 cm from expert). But children ranged in age from newborn to 18 years and differences were not reported separately for children younger than 24 months of age.
In this study we compare length measured by the clinical paper-and-pencil method with length measured by the research standard recumbent length board in a sample of children 0 through 23 months of age. We also discuss the extent to which error in length measurement can result in substantial misclassification of under- and overweight in young children.
Eligible patients were children 0 through 23 months of age who had well-child visits during 1-month periods in 1998 and 2000 at a single health center of a large managed-care organization in New England. This center had a broad representation of race/ethnicity. It is also one of 13 centers participating in an ongoing height and weight surveillance project within this managed-care organization.
During the study periods, we regularly received a list of all children with upcoming well-child visits. Trained research assistants were available during 192 of these visits. Of the 192 children approached for participation, we excluded 1 child because her parent refused participation, 1 child because he had a medical diagnosis that prevented recumbent length-board measurement, and measurements on 30 children because they were ascertained by 1 of 5 clinical staff who measured fewer than 10 study participants each, the minimum we chose a priori to achieve stable estimation. Thus we included 160 participants for analysis. The Human Subjects Committee of Harvard Pilgrim Health Care approved the study.
The research assistant greeted the parent or guardian before the child’s appointment to discuss the aims of the study and to obtain informed consent. As a standard part of the well-child visit, the medical assistant measured the child’s length by the paper-and-pencil technique. The research assistants separately measured the child using the recumbent length board, either before (23%) or after (77%) the medical assistant. The research assistants and medical assistants were blinded to each other’s measurements.
For the research measurement of length, we used the same technique that the Centers for Disease Control and Prevention (CDC) used to create United States reference growth charts. The recumbent length measuring board has a fixed perpendicular headpiece and sliding footpiece that forms a 90° angle with the measurement surface. Two research assistants, with the help of the parent or guardian, measured length with this instrument using a standard technique. The child lay in a supine position on the measuring board. The parent or guardian cupped her hands over the child’s ears while holding the child’s head firmly against the vertical headboard with line of sight perpendicular to the board. One of the research assistants held the child’s knees together and fully extended them. Then she applied firm pressure with the other hand to shift the movable footboard against the child’s heels. The second research assistant ensured that the child’s shoulders and buttocks were flat against the board, with the shoulders and hips aligned at right angles to the long axis of the body. After checking the child’s alignment to ensure that the child did not change positions, the second research assistant recorded length to the nearest 0.1 cm.
To assure reliable measurements, one of the research assistants (SLR) attended a CDC Pregnancy and Pediatric Nutrition Surveillance Systems measurement training session with a professional anthropometrist, which included double measurements to achieve comparable lengths and weights. The trained research assistant (SLR) conducted a similar rigorous training session for the other 2 research assistants.
Clinicians used the paper-and-pencil technique for measuring recumbent length by written protocol of the health center. The child lay supine on a piece of paper atop an examination table with the child’s face looking at the ceiling. The clinician drew a tick mark abutting the top of the child’s head. The clinician straightened the child’s legs, flattened the child’s knees, flexed the child’s foot to 90°, and marked the paper at the bottom of the child’s heels. The clinician measured and recorded the distance between the marks, to the nearest quarter inch, with a flexible tape.
We converted clinical length values from inches to centimeters by multiplying by 2.54. To assess the relationship between the clinical and research measurements, we used ordinary least squares regression. We also assessed the extent to which adding terms for the specific clinician or research assistant, or the age or sex of the child, contributed to the fit of the model. Because the goal here was not parameter estimation, the statistical significance of these predictors was not of central concern. Rather, the R2 assesses the overall fit of the model.
To assess whether individual clinical paper and pencil measurements were “accurate,” we computed the difference between clinical and research values. We considered the clinical measure to be accurate if the difference was no more than 0.5 cm.[2,11] We performed data analyses using Statistical Analysis System, version 8.2 (SAS Institute; Cary, North Carolina).
Of the 160 participants, 47% were girls. The median age was 5.5 months; 32% were 0–2 months, 24% were 3–6 months, 21% were 7–12 months, and 23% were 13–23 months. Six medical assistants measured the 160 participants, and the median number of measurements was 24 (range, 10–54). Three research assistants, in pairs, carried out the 160 measurements.
As shown in the Figure, we found a strong linear relationship between the 2 measures of length. The fitted regression equation showed that length by the research standard was 95.3% (SE 0.9%) of the clinical measurement plus 1.88 cm (SE 0.65 cm). Thus, on average, an average 6-month-old boy clinically measured as 68 cm would have a predicted research standard length of 66.7 cm ([68 * 0.953] + 1.88 = 66.7), or 1.3 cm less than measured by clinician. Similarly, an average 18-month-old girl measured clinically as 81 cm would have a research measurement of 79.1 cm, or 1.9 cm less. Over the entire age span, the mean (SD) difference between clinical and research measurements was 1.3 (1.5) cm, the mean (SD) percent difference was 2.3% (1.7%), and 49 (31%) of the clinical measurements were “accurate” (≤ 0.5 cm from the research measurement).
Regression equation: research measurement = clinical measurement * 0.953 + 1.88 cm. The vast majority of the variability in the research measurement was explained by the clinical measurement, R2 = 0.98. Adding other covariates to the model did not improve the fit meaningfully; inclusion of child age and sex, and terms for the specific clinician and research assistant together increased the R2 by only 0.001.
Our analysis shows that in the practice we studied, clinical staff systematically overestimated length in children 0–23 months of age. We found that the magnitude of this bias increased with the length of the child. Overestimation included a component proportional to the length of the child (95.3%) plus a constant of 1.88 cm. This bias did not depend meaningfully on a particular clinician or research assistant, or on the age or sex of the child. Thus, clinicians may be underdiagnosing overweight in their practices, a potentially important issue in this era of rapidly rising rates of childhood obesity.
Obesity in childhood and adolescence is associated with both short- and long-term adverse outcomes, including both physical and psychosocial consequences. Children who are overweight tend to become overweight adults, and once present, obesity is notoriously hard to treat. Among school-age children and adolescents, Field and colleagues found that high normal weight status (BMI between the 50th and 84th percentile) in childhood predicted becoming overweight or obese and (among boys) having hypertension as an adult. Therefore, we believe that it is important to assess length accurately in all children, not only those above a set cut point.
Bias in clinical measurement may be due to several factors: The child may move on the paper after the clinician draws the tick mark at the top of the child’s head but before marking the feet; the paper may be wrinkled under the child but smoothed out before the clinician measures the difference between the points; the clinician may draw the tick marks in a diagonal; or the diameter of the pen may increase the length measurement. Also, clinicians measured length to the nearest quarter inch and researchers measured length to the nearest tenth of a centimeter. Such differences in precision might have added to the inaccuracy of clinical values. In this study we were not able to determine the cause of the observed measurement error.
To assess the degree to which clinical length measurement bias affects population estimates of weight-for-length, we applied the regression correction factor developed in this study to 6408 length measurements among children 0–23 months of age in the year 2000 participating in a height and weight surveillance system at 13 centers of a managed-care organization, of which the study center is one. Before application of the regression correction factor, 13.0% (832/6408) of children had weight-for-length less than the 5th percentile, and 4.7% (303/6408) were above the 95th percentile. After correction, 5.7% (368/6408) were below the 5th percentile while 9.3% (597/6408) were above the 95th percentile, proportions similar to recent national data.[14,15]
Examining these data another way, one would detect only about half as many children in this population with weight-for-length exceeding the 95th percentile by using the paper-and-pencil method (303) compared with the gold standard (597, 303/597 = 51%). Conversely, one would “overdiagnose” about 2 times as many children in this population with weight-for-length below the 5th percentile by using the paper-and-pencil method (832) vs the gold standard (368, 832/368 = 2.3). These analyses demonstrate the marked biases in underestimating overweight, and in overestimating underweight, introduced by the paper-and-pencil measurement that is commonly used in pediatric practices.
A possible shortcoming of our study is that we recruited participants from a single health center. Because clinicians in other practices may measure with different protocols, generalizability may be limited. We note, however, that measurement bias did not depend on a particular clinician. Also, although the research assistants and medical assistants were blinded to each other’s measurements, the medical assistants knew that there was a length-measurement study in progress, which might have caused them to measure more accurately than usual. If this were the case, however, our regression equation would conservatively estimate the bias. Also, for the research measurement, 2 research assistants, with the help of the parent, measured length. The paper-and-pencil measurement involved only the medical assistant. It is possible that the paper-and-pencil method would be more accurate if more clinical staff were involved.
A further limitation of this study is that we did not explicitly assess interrater reliability. By adding a term for each research assistant to the model, however, we did assess the possibility that the 3 assistants had different measuring characteristics. This addition did not meaningfully improve the fit of the model, evidence that the observed bias did not depend on which research assistant did the measuring. Also, our data included children with clinical measurements from 47 cm to 91 cm; one should exercise caution in extrapolating the regression equation beyond these lengths.
Medical providers should be aware that using the paper-and-pencil method can lead to underestimates of overweight and exaggerated estimates of thinness. To improve the accuracy of length measurement, clinical practices should use standardized procedures with a recumbent length board to measure children under 2 years of age, at least for children whose initial paper-and-pencil measurement of length puts them at one extreme or the other.
We thank the following persons for their diverse contributions to this project: Cara Singer, Sandy Vickery, and Anita Feins, MD.
Readers are encouraged to respond to George Lundberg, MD, Editor of MedGenMed, for the editor’s eye only or for possible publication via email: ten.epacsdem@grebdnulg
Authors’ contributions: SRS, JRE, KSS, and MWG conceived the study and SRS drafted the paper. SLR and KPK designed the analysis, and SLR did the programming. MWG obtained funding. All authors made substantial contributions to design of the overall study, data collection, and writing of the paper. All authors read and approved the final manuscript.
Sheryl L. Rifas-Shiman, Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts.
Janet W. Rich-Edwards, Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care; Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts.
Kelley S. Scanlon, Division of Nutrition and Physical Activity, Centers for Disease Control and Prevention, Atlanta, Georgia.
Ken P. Kleinman, Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts.
Matthew W. Gillman, Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care; Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts.