(Subar et al., 1994). Seasonally available local cultural food may affect seasonal and yearly average nutrient intakes (Kuhnlein et al., 1996; Receveur et al., 1997). The effects of seasonality on estimated nutrient intakes can be alleviated by a well-designed data collection plan.

Within-person variability also may include other nonrandom components (Tarasuk and Beaton, 1992), some of which may be related to sociocultural factors (e.g., intakes may differ between weekdays and weekend days) (Beaton et al., 1979; Van Staveren et al., 1982) and some of which is physiological (e.g., women's energy intakes vary across the menstrual cycle) (Barr et al., 1995; Tarasuk and Beaton, 1991a).

Chronic illness affecting intakes of a part of the population is reflected in group dietary intakes and may bias the prevalence of inadequate intakes in what is assumed to be a normal, healthy population (Kohlmeier et al., 1995; McDowell, 1994; Van Staveren et al., 1994). Parasitism, eating disorders, and dieting—which may be prevalent in segments of a population—may affect food intake. Unlike dieting, illness presents a problem not only with regard to intake data but also in the assumptions underpinning the assessment of adequacy because the DRIs were established for normal, healthy populations.

Data may be biased by individuals whose dietary intakes are affected by rapidly changing life circumstances (such as migration or refugee status) or by successfully implemented nutrition intervention programs. Thus, it is important to consider how many affected individuals are included in the data sample (Crane and Green, 1980; Immink et al., 1983; Kristal et al., 1990, 1997; Yang and Read, 1996).

Data on nutrient intakes are sometimes collected for households rather than for individuals. When this is the case, the level of aggregation of the dietary data must be matched with an appropriate level of aggregation for the requirements. Appendix E discusses how requirement data may be aggregated at the household level. It