estimates and those around requirement estimates—into a single value that reflects the uncertainty around the prevalence estimate.
Other characteristics of dietary studies complicate the matter even further. Dietary intake data suffer from inaccuracies due to underreporting of food, incorrect specification of portion sizes, incomplete or imprecise food composition tables, etc. These factors may have a compound effect on prevalence estimates. In addition, systematic errors in measurement (such as energy underreporting) may increase the bias of the prevalence estimate. All of these factors have an effect on how precisely (or imprecisely) the prevalence of nutrient adequacy in a group can be estimated, and it is difficult to quantify their effect with confidence.
The software developed at Iowa State University (called SIDE) (Dodd, 1996) to estimate usual intake distributions also produces prevalence estimates using the cut-point method and provides an estimate of the standard deviation associated with the prevalence estimate. However, it is important to remember that the standard deviations produced by the program are almost certainly an underestimate of the true standard deviations because they do not consider variability associated with the EAR or with the collection of intake data.
Why should standard deviations be a concern?
Standard deviations of prevalence estimates are needed to determine, for example, whether a prevalence estimate differs from zero or any other target value or to compare two prevalence estimates.
The evaluation of differences in intakes requires the estimation of standard deviations of quantities such as prevalence of nutrient inadequacy or excess (e.g., Application 3 in Chapter 7). As another example, suppose that prevalence of inadequate intake of a nutrient in a group was measured at one point in time as 45 percent. An intervention is applied to the group and then a new estimate of the prevalence of inadequate intake of the nutrient is found to be 38 percent, a decrease of 7 percent. However, to accurately assess the effectiveness of the intervention, the standard deviations around the 45 and 38 percent prevalence estimates are also needed. If the standard deviations are small (e.g., 1 percent), then one could con-