These measurements were conducted in infant welfare clinics, by trained health care workers who used standardized protocols and regularly validated equipment. Carers were notified in advance of their appointment, and transport was provided by the MRC to bring mothers and their infants to the clinic.
These measurements therefore represent an unselected record of growth because children are measured regardless of their health status at the time of their clinic call. In addition to anthropometric measurements, these visits also included health assessments, advice on feeding practices and diet, and the administration of vaccines according to the Gambian Government schedule at that time.
In the analysis presented here, we tested 3 broad research questions: 1 Is wasting a risk factor for stunting, and vice versa?
Statistical analysis was carried out in R version 3. Anthropometric indices were calculated according to the WHO growth standards The statistical methods used are described separately for each research question below.
Flow diagram of sample selection from data collected at scheduled infant welfare visits at the MRC Keneba clinic. To describe age-related differences in the prevalence of stunting, wasting, or concurrence i. Within a season, each participant's median WLZ was used to classify them as wasted or not wasted. The relationship between wasting during those 3 seasons was estimated through the use of logistic regression, with being wasted in the second wet season as the outcome, and being wasted in the first wet season and the intermediate dry season as binary predictors.
Wasting in the dry season was included as a predictor to distinguish subjects who were wasted continuously year-round from those who experienced seasonal episodes of wasting. To investigate the impact of early patterns of growth on subsequent risk of stunting, growth curves were developed and fitted separately for subjects who were stunted between the ages of 20 and 24 mo and those who were not.
Age- and season-related z score trajectories were modeled as smooth curves: natural cubic splines for the age-related change and second-order Fourier terms for the seasonal changes. An interaction was included to allow for age-related differences in the magnitude of the seasonal variation.
Random intercepts and slopes were included to allow for individual-level variation around the mean growth curves representing individual differences in size and velocity. In addition to random effects for the intercept and slope, random effects were included for the first-order Fourier terms, allowing the amplitude of seasonal variation to vary between individuals. Thus, individual intercepts, slopes, and seasonal terms could be extracted from the models. Each subject's seasonal random effects were summarized through the use of the coefficient of cyclic variation CCV This describes the magnitude of seasonal variation in each growth outcome, above or below the seasonal variation of the mean trajectory.
Logistic regression was used to predict the risk of being stunted between the ages of 20 and 24 mo as a binary outcome, based on previous episodes of being stunted or wasted and the extracted random effects as predictors.
Finally, instead of predicting stunting at 20—24 mo of age as an individual-level, cross-sectional outcome, the risk of becoming stunted was also predicted longitudinally based on the use of time-lagged observations of prior episodes of stunting or wasting.
This provides an age-dependent prediction. Each observation was matched 1-to-1 with a prior observation on the same individual, chosen to be as close as possible to 3 mo earlier. If that individual was not measured between 2. This left a total of 28, observations on subjects Figure 1 ; median 7 observations per individual.
Multilevel logistic regression was used to describe the relations between being currently stunted binary outcome and the time-lagged predictors: age, stunted, and wasted. Stunting was included as a time-lagged predictor to estimate whether being wasted has any predictive power beyond that of knowing whether the individual was already stunted 3 mo previously.
Age and sex were included in each model to account for differences in the risk of stunting. The inclusion of 2-way interaction terms did not improve the model fit, and therefore these are not reported. The cross-sectional prevalence of wasting, stunting, or concurrence by sex in each 1-mo age group is shown in Table 1. The proportions are also plotted in Figure 2 , smoothed across the age range by local regression to illustrate the age-related differences more clearly.
Narrow CIs are obscured by the mean line. The top panel shows overall proportions as in Table 1. The middle and lower panels group the observations by current stunting and wasting, respectively, illustrating that wasting is more common among those who are also stunted than among those who are not, and vice versa.
Participants stunted, wasted, and concurrently wasted and stunted, percentage by age 1. Where a subject contributed multiple measurements in the same age group, their mean z score was used to classify them as stunted, wasted, or both stunted and wasted, and n was adjusted accordingly.
The relationship between wasting and stunting is illustrated in the middle and lower panels of Figure 2 , based on the same cross-sectional data shown in Table 1.
In the middle panel, the proportion of wasted children at each month of age was plotted for children who were either stunted or not by age 20—24 mo. Generally, compared with nonstunted children, a larger proportion of children who were stunted at age 20—24 mo had experienced prior episodes of wasting.
The lower panel shows the same relationship in reverse: the proportion of stunted children by age and sex separated by whether they were wasted or not. Infants who were wasted in the first wet season of their life were more likely to be wasted in their second wet season, even after controlling for whether they were wasted during the intervening dry season OR: 3. This means that infants who were wasted in their first wet season were more likely to be wasted again in their second season, even if they had temporarily recovered in between.
Odds ratios of wasting in the second wet season of life, based on wasting in the first wet season and in the intervening dry season 1. Multilevel models were used to describe longitudinal trajectories of WLZ according to whether the children were stunted or not between the ages of 20 and 24 mo, for the sexes separately.
These models were used to predict the growth trajectories for children born on 1 July start of the wet season , 30 October end of the wet season , and 1 March middle of the dry season , which were then plotted to illustrate the relationships between seasonality on growth.
Figure 3 shows the mean WLZ as purple lines for children grouped by whether they were stunted between the ages of 20 and 24 mo dashed line , or not solid line. Also shown in this figure are the 3 predicted WLZ trajectories for children born at different times of the year, separately for those children who were or were not stunted at 20—24 mo of age. This illustrates the seasonal pattern of weight gain and loss in this population. In both groups stunted and not stunted , WLZ increases towards the WHO standard during the first 3 mo as reflected in the decline in wasting prevalence in Table 1 and Figure 2.
A distinct separation in WLZ between children stunted and those not stunted at 20—24 mo of age is observed at 5—10 mo of age, although these 2 groups show the same seasonal fluctuations. The predicted WLZ trajectories illustrate how season of birth influences the pattern of subsequent growth in this population.
In particular, the downward seasonal effect for subjects born at the start of the wet season orange lines partly counteracts the early catch-up of WLZ that was observed among participants born at other times of the year.
The mean trajectory purple is plotted together with predicted WLZ trajectories for children born at different times of the year, separately among those children who were or were not stunted at 20—24 mo of age.
The horizontal colored bars represent ages that coincide with the wet season July—October , for children born at different times of year.
DoB, date of birth; WLZ, weight-for-length z score. Table 3 shows ORs from logistic regression models with being stunted at 20—24 mo of age as the outcome.
The random effects represent sources of between-individual variation in the trajectories of WLZ. Odds ratios of stunting at 20—24 mo, based on features of growth before 20 mo 1. Random effects estimates extracted from WLZ trajectories. The random effects represent individual variation around the mean trajectory of WLZ with mean zero , e. This is an important measure of model performance, because it represents the proportion of stunted individuals who were not correctly identified.
Adding a prior episode of being wasted model II did not improve the predictions as individuals identified as at risk of stunting were already identified with the use of model I.
However, the ORs in Table 3 show that subjects who had been wasted before the age of 20 mo had 1. A similar analysis was attempted based on a previous period of having a small MUAC instead of a period of being wasted, and applying random effects taken from MUAC trajectories instead of WLZ trajectories.
The results of this analysis did not differ meaningfully from the results for WLZ and so are not reported. Using time-lagged observations, being wasted increases the odds of becoming stunted 3 mo later by a factor of 3. Also, this suggests that treatment interventions should focus on children who are both wasted and stunted and therefore have the greatest deficits in muscle mass, instead of focusing on one or the other form of malnutrition.
Interventions should also focus on young infants and children, who have a low muscle mass in relation to body weight to start with. Using mid-upper-arm circumference MUAC to select children in need of treatment may represent a simple way to target young wasted and stunted children efficiently in situations where these two conditions are present. Wasting is also associated with decreased fat mass. A decreased fat mass is frequent but inconsistent in stunting. Your email address will not be published.
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Stunting Stunting is a medical condition in which a child has impaired growth and development. Wasting This occurs when a child has a low weight with respect to his height. Footnote These conditions are mostly triggered due to poverty. Share On. Tags: Body kids Underweight. About Author. Read other articles here Nmami Life Editorial. Leave a Reply Cancel reply Your email address will not be published. Recommended For You. Creamy Broccoli and Cauliflowe Recipes Beat away all the blues with this healthy, nutritious, and delicious creamy broccoli cauliflower blend Rising Air Pollution: Try thes Awareness After the auspicious festival of Diwali in India, we noticed a surge in the pollution But not always!
Abstract The study describes the patterns of concurrent wasting and stunting WaSt among children age months living in the s in Niakhar, a rural area of Senegal Abstract The study describes the patterns of concurrent wasting and stunting Buenas noches. View this article as a pdf Summary of research1 Location: Gambia What we know: There are gaps in understanding the relationship between wasting and stunting that often Is it contraindicated to give Viatamin A for severe wasting during admission given the child didn't received in the last six months?
She is In Zinder the rate was Research Summary 1 Poor nutrition in early life threatens the growth and development of children, which has a knock-on effect on the sustainable development of nations. Wasting and stunting—similarities and differences: Policy and programmatic implications. The below files can be imported into your preferred reference management tool, most tools will allow you to manually import the RIS file. Endnote may required a specific filter file to be used.
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