Article

Using weight-for-age for predicting wasted children in Cameroon.

Department of Public Health, Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, Cameroon.
The Pan African medical journal 01/2013; 14:96. DOI: 10.11604/pamj.2013.14.96.1914
Source: PubMed

ABSTRACT The equipment for taking body weights (scales) are more frequently used in Cameroon health centres than measuring boards for heights. Even when the later exist there are some difficulties inherent in their qualities; thus the height measurement is not always available or accurate. Our objective for this study was to construct statistical models for predicting wasting from weight-for-age.
3742 children aged 0 to 59 months were enrolled in a cross-sectional household survey (2004 Cameroon Demographic and Health Surveys (DHS)) covering the entire Cameroon national territory.
There were highly significant association between underweight and wasting. For all discriminant statistical methods used, the test error rates (using an independent testing sample) were less than 5%; the Area Under Curve (AUC) using the Receiver Operating Characteristic (ROC) was 0.86.
The study showed that weight-for-age can be used for accurately classifying a child whose wasting status is unknown. The result is useful in Cameroon as too often the height measurements may not be feasible, thus the need for estimating wasted children. This study provides baseline information that will help to design a preliminary pivotal study on an immediate nutrition intervention for acute undernutrition. Its complications that could lead to morbidity and mortality can be greatly reduced or set up a management control strategy that will go a long way in reducing the cost of health care in Cameroon.

Full-text

Available from: Georges Nguefack-Tsague, Oct 07, 2014
0 Followers
 · 
70 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper examines the relationship between measures of income poverty, undernourishment, childhood undernutrition, and child mortality in developing countries. While there is, as expected, a close aggregate correlation between these measures of deprivation, the measures generate some inter-regional paradoxes. Income poverty and child mortality is highest in Africa, but childhood undernutrition is by far the highest in South Asia, while the share of people with insufficient calories (undernourishment) is highest in the Caribbean. The paper finds that standard explanations cannot account for these interregional paradoxes, particularly the ones related to undernourishment and childhood undernutrition. The paper suggests that measurement issues related to the way undernourishment and childhood undernutrition is measured might play a significant role in affecting these inter-regional puzzles and points to implications for research and policy.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Four issues in the use and interpretation of anthropometry are discussed at the level of the population and of the individual. The first issue is the index or indices of choice: weight-for-height versus height-for-age versus weight-for-age. The selection of an index or indices depends upon many factors, and no one index is completely adequate in all situations. Proposed criteria are provided to assess the severity of low anthropometry within populations. The second issue is the scale of the index: z-scores (or standard deviations) versus percentiles versus percent-of-median. z-Scores have several properties that make them superior to the other two scales. A third issue deals with limitations in the current growth reference; one of these is the disjunction between the growth curves at 2 years of age, resulting from the use of two different populations in the reference. It is important that this disjunction be recognized by researchers so that the anthropometric findings are interpreted correctly for this age range. Lastly, some issues to do with the collection of single versus multiple anthropometric measurements on children are discussed.
    Bulletin of the World Health Organisation 02/1994; 72(2):273-83. · 5.11 Impact Factor
  • Economic Development and Cultural Change 02/2002; 51(1):55-76. DOI:10.1086/345313 · 0.98 Impact Factor