title: Assessing the prevalence of nutrient inadequacy.
abstract: OBJECTIVE
  To describe an approach for assessing the prevalence of nutrient inadequacy in a group, using daily intake data and the new Estimated Average Requirement (EAR).
  
  
  METHODS
  Observing the proportion of individuals in a group whose usual intake of a nutrient is below their requirement for the nutrient is not possible in general. We argue that this proportion can be well approximated in many cases by counting, instead, the number of individuals in the group whose intakes are below the EAR for the nutrient.
  
  
  METHODS
  This is a methodological paper, and thus emphasis is not on analysing specific data sets. For illustration of one of the statistical methods presented herein, we have used the 1989-91 Continuing Survey on Food Intakes by Individuals.
  
  
  RESULTS
  We show that the EAR and a reliable estimate of the usual intake distribution in the group of interest can be used to assess the proportion of individuals in the group whose usual intakes are not meeting their requirements. This approach, while simple, does not perform well in every case. For example, it cannot be used on energy, since intakes and requirements for energy are highly correlated. Similarly, iron in menstruating women presents some difficulties, due to the fact that the distribution of iron requirements in this group is known to be skewed.
  
  
  CONCLUSIONS
  The apparently intractable problem of assessing the proportion of individuals in a group whose usual intakes of a nutrient are not meeting their requirements can be solved by comparing usual intakes to the EAR for the nutrient, as long as some conditions are met. These are: (1) intakes and requirements for the nutrient must be independent, (2) the distribution of requirements must be approximately symmetric around its mean, the EAR, and (3) the variance of the distribution of requirements should be smaller than the variance of the usual intake distribution.
authors.affiliation: Department of Statistics, Iowa State University, Ames 50011-1210, USA. alicia@iastate.edu
authors.forename: A L
authors.initials: AL
authors.lastname: Carriquiry
mesh_terms: D016001:Confidence Intervals; D002149:Energy Intake; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D009748:Nutrition Disorders / Q000453:epidemiology* / Q000517:prevention & control; D009751:Nutritional Requirements; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D012306:Risk