The distribution of usual intake of nutrients in a given population is one of the major concerns in public health nutrition and is used to assess and prevent nutritional problems. The distribution of usual intake cannot be measured directly, but can be estimated from a dietary survey that spans multiple days. The prevalence of nutritionally high-risk people, defined as the proportion of a population that does not achieve the dietary reference intake (DRI), can be estimated from the distribution of usual intake in the population. Although several methods have been proposed, there is no universally accepted method for estimating the distribution and prevalence of nutritionally high-risk people. Nusser et al. proposed a semi-parametric model and developed software that is commonly used to accomplish this estimation; their model is known as the Iowa State University (ISU) method. Although Nusser’s method is available to dietitians, one problem still remains. It is often the case that the usual intake distribution needs to be estimated for subgroups such as sex and age groups. Waijers et al. proposed another parametric model based on a mixed-effect model to resolve this issue. Waijers’s model assumes that the mean structure of usual intake varies depending on the subject’s age and this model is useful when the usual intake of a nutrient is justifiably assumed to vary with age. However, Waijers’s model assumes a constant between-subject variance and a constant within-subject variance of nutritional intakes for different ages: this is problematic because this assumption is not always correct. In this study, we built a mixed-effect model with changing variance depending on the subject’s age to enable nutritional intake modeling that fits better with actual data. We used simulation studies to compare the performance of the new method with that of 2 previously proposed methods. Our proposed method outperformed them, particularly in a realistic situation and with a small sample size, providing a more accurate and precise estimate of the prevalence of nutritionally high-risk people. This method will help promote the use of DRIs, help improve our understanding of the nutritional status among populations, and aid in confronting the challenges of public health nutrition.