This study provides evidence that early deceleration in weight-gain is useful in identifying infants who later become underweight. Infants whose WAZ dropped more than -0.85 standard deviations between either the two-to-four month or four-to-six month intervals were at increased risk of reaching underweight status by 24 months of age compared to those with a more modest change. This study also demonstrated that this drop in weight-for-age (≥ -0.85 sd) is predictive as early as two-to-four months when the 2000 CDC growth charts are the reference, and the sensitivity analyses supported the robust nature of this model. Because of this, a provider may consider probing for additional information from families whose infants are demonstrating early negative shifts in weight-for-age percentiles. In addition, the provider should be mindful of the need for careful assessment of growth measures at subsequent visits and consider an early intervention in order to improve nutrition and growth outcomes. The cut-off level provided in this methodology resulted in a low sensitivity (0.06) and a high specificity (0.97). Providers can feel confident that the extra time spent providing nutritional guidance to the parents of a child whose WAZ shifts to this degree is directed towards those most at-risk of reaching an underweight status. The cut-off level can be set by each provider to best balance the sensitivity and specificity within their own practice. We chose to keep this cut-off given the high specificity of the results, because this was meant to be a screening that assists the provider in identifying those infants for whom it would be helpful to explore nutritional issues even if parental anxiety might be raised. The AUC was greater than 0.5, but it was not very large (0.615 for the infants born <3.0 kg, and 0.619 for infants ≥ 3.0 kilograms at birth). This methodology may be useful in assisting a care provider, when considered along with clinical factors including previous growth history, nutrition, and overall development. Additionally, because change in growth velocity can be automatically calculated in the study system by using the anthropometric indicators already collected as part of routine well-child visits, the resource investment is minimal. Within each population, the provider would need to determine baseline prevalence rates and determine whether a high sensitivity, or high specificity would be preferable.
This study revealed a significantly higher prevalence of underweight than expected (24% of the total cohort.) This was an especially surprising finding given the population studied was adequately insured, one identified barrier to accessing care. There are several possible factors that may have contributed to this high prevalence. We conducted sensitivity analyses to determine the effect of both exclusions and outcome criterion to explore the reasons why the prevalence was elevated. This study used a period prevalence, and allowed a single instance of meeting case criterion to be sufficient. There were repeated opportunities (up to three) to identify underweight.
Another factor that may have contributed to the elevated prevalence rate is the use of a single criterion (a weight-for-length z-score ≤ -1.67) rather than multiple criteria to define underweight. In a recent study by Olsen and colleagues (2007), 27% of their birth cohort (n=6090) met at least one criterion for growth faltering between 2 and 11 months of life, although there was little concurrence among the seven criteria used in their study (18
). The WLZ is recommended by the WHO, the CDC, and the AAP, although the number of children identified as underweight using this criterion may differ from the number that might be identified by a different criterion. Sensitivity analyses revealed that 12.73% of infants had a WAZ ≤ -1.67, which is still significantly elevated compared to expected (P
<.0001). Clinically, more than one measure of poor growth is conventionally used to identify growth faltering. However, the study was designed to provide an early indicator to assist the care provider in identifying at-risk infants rather than to provide a diagnosis. In a busy practice, the assistant who typically plots the weight and length on a growth chart can flag those infants whose growth is slowing, so that the care provider can ask additional questions to determine whether further investigation is warranted.
Measurement error in length could also contribute to the elevated prevalence of underweight, as the collection of length data was not standardized and accuracy of the measurements is unknown. Subjects would generally have been measured in the same clinics over the study period; however, an over-estimation of length would increase the number of infants identified as underweight (19
). Sensitivity analyses revealed the prevalence of underweight remained elevated when length was reduced by 1.3 cm (14.7%), and the model remained robust (p<.0001).
Additional factors influencing the high prevalence outcome include the possible influence of altitude and feeding regimen, and the influence of the media attention on obesity. There are data that suggest lengths of infants at high altitude are shorter than same-age peers (“stunting”) (20
), although there are no data that have directly shown poor weight gain related to altitude. Because this study was conducted with infants born at an altitude > 5000 feet, the bias due to impact of altitude on the primary outcome would have been toward fewer cases. Nevertheless, replication of observations in a similar population born and reared at a lower altitude would be useful.
The authors had no information on the composition of the diet for the infants in the cohort which may have a bearing on the prevalence rate. Data have suggested infants who are exclusively fed breast milk gain weight at a different rate than the national references (21
). Colorado has a high breastfeeding initiation rate (83.3%-84.8% in the years 1999-2000) although the rate decreases by 6 months of age (39.2-44.4%) (24
). This study used the 2000 CDC growth charts because clinically they are common in the United States and were in use during the study period; fewer infants were categorized as underweight when the 2006 WHO growth charts designed for breastfed infants were used as the reference. This finding is similar to that reported in other studies comparing prevalence of underweight across the 2000 CDC and the 2006 WHO growth charts(17
). The model including birthweight and change in WAZ between four and six months remained robust using the WHO growth charts (p<.0001).
Finally, while obesity remains a public health concern, thinness may be tolerated at a higher prevalence rate due to heightened concerns about later obesity. Parents may not be concerned about low weight gain because they are inundated with messages about obesity. Studies also have suggested parents demonstrate a poor ability to visually identify either thinness or overweight status in young children (25
An additional finding was that infants with lower birth weight were at increased risk of underweight than were infants with a heavier birth weight. This finding supports previously published data regarding slower weight-gain in low birth weight infants (26
). However, the infants in the current study were > 2.5 kg at birth and were term gestation, and no infant had a weight-for-age ≤ -1.67 between birth and 180 days. Infants who are > 2.5 kg are not considered low birth weight, but this study suggests they are still at increased risk of growth faltering, supporting the need for close monitoring of growth for these marginally average-for-gestational age (AGA) infants.
Strengths of this study include the large sample size as well as the fact that longitudinal measurements of growth were available for the first two consecutive years of life. Additionally, the infants in the cohort were selected based upon inclusion criteria designed to focus on healthy term infants. This is in contrast to many studies of growth in ill or hospitalized infants and/or preterm infants (28
). This population was also born within a three-year period; therefore, temporal changes in clinical and feeding practices were likely minimal. Finally, these infants were all served by a comprehensive, private health care system. While socio-economic information was not available, the cohort represents a population covered under private health care rather than the public health communities often enrolled in studies of infants at-risk for growth faltering (31
). Few data have been published on cohorts of privately insured infants, where the assumption is made that access to care is not a barrier.
Weaknesses of this study include possible under-representation of specific ethnic/racial minorities, although the percentage of Caucasian infants in the cohort was similar to the percentage reported during the 2000 U.S. Census for the state of Colorado (64.8% vs. 63.9%). Racial/ethnic background was not reported for 8.6% of the cohort. An additional concern regarding generalization of findings is the fact that the cohort for analyses (n=1978) was significantly smaller than the original birth cohort (n=12,362.) The majority of infants lost to follow-up were lost due to a change in insured status within this HMO (n=5656) or prematurity/low birth weight (n=1489). There may have been an unmeasured or unidentified bias introduced during the data exclusion process. The infants in the final cohort were similar in demographic characteristics to both those infants excluded during the analysis and to those infants born in the state of Colorado during the year 2000. In addition, the lack of developmental and health outcome data limit these findings to describing underweight status. And finally, the lack of information regarding feeding regimen (breast milk or formula, and complementary and weaning foods) is an additional weakness. However, we re-ran the analyses using the WHO growth charts in an attempt to determine the influence of feeding regimen on the model, and found the model to be robust when using the 4 to 6 month time period as a predictor. In future studies, the authors would like to apply this standard of weight-for-age deceleration while collecting weights and lengths by trained research staff, and where data are collected regarding feeding regimen.
Application of this weight-for-age deceleration standard can assist both the researcher and the clinical provider in identifying infants at-risk for growth faltering as early as the interval between four and six months of age, regardless of the growth chart reference used by the clinician. Identifying at-risk infants is imperative in designing prospective research studies. In the clinical setting, healthcare providers may scrutinize early growth patterns more closely and use this standard to interpret changes in growth percentile channels. Growth monitoring is a screening tool, inexpensive and readily available. In the sites used for this current study, the weights and lengths are plotted automatically using a nutrition program from the CDC, called Epi Info Nutrition. Weight-for-age, length-for-age, and the weight-for-length ratio percentiles and z-scores can be requested easily using this computer program. The change in z-scores can be quickly calculated once the z-scores are obtained using this public domain program. Given the higher than expected prevalence rate of underweight found in this population, the methods described in this study (combined with ROC analyses) should be replicated in additional populations to determine the best cut-off score for identifying early growth faltering in that population.
Early evidence of growth faltering should trigger additional questions and possibly assessment of an infant's health and development, and feeding patterns. With the expense that is incurred by families whose children demonstrate poor growth and feeding habits in their toddler and preschool years, this screening measure is an inexpensive way to begin identifying those infants who may benefit from anticipatory guidance from their primary care provider.