Sources of variability in dietary intake in two distinct regions of rural India: implications for nutrition study design and interpretation

Abstract

Objective: Limited inter-person variability in nutrient intake hampers epidemiologic studies of diet ± disease relationships. Despite conjecture about non-traditional study bases providing large inter-person differences, virtually nothing is known about variations in nutrient intake outside of Europe and North America. The current study was conducted in India to determine sources of variability in the intakes of nutrients thought to be of public health importance. Design: Adult subjects in Gujarat (North India; n 60) and in Kerala (South India, n 60) were administered 24 h diet recall interviews six and eight times, respectively, over a 1 y period. To assess subject (inter-person) and residual (intra-person) contributions to variance, regression models were ®t to the data. From this, the variance ratio (VR, total within or intra-person: total between or inter-person) was computed. Setting: Rural communities in North and South India. Results: In both regions, inter-person variability was larger than that observed in the West. This was most pronounced in Gujarat, for which the VR was`1.0 for seven of the 12 energy-adjusted nutrient intakes in both men and women. In analyses of the combined data, region accounted for b 20% of variance for fat, iron, copper, zinc, b-carotene and ribo¯avin in both men and women. With the region term removed from this model, virtually all of the variability ascribed to region contributed to inter-person variability. Conclusions:The relatively large inter-person variability observed here could contribute to improved`resolution' of diet ± outcome relationships in epidemiologic studies. While this applies to data from each region, it was especially evident in analyses of the combined data.

Variance sources and ratios to estimate energy and nutrient intakes in a sample of adolescents from public schools, Natal, Brazil

“…The interpersonal variance found by the present study was the greatest source of nutrient intake variance, resulting in a VR of less than 1 and corroborating the findings of Laningan et al 1 in children and Herbert et al 24 in adults. The lack of variation in habitual dietary intake can be attributed to the low purchasing power and low education levels of the study population.…”

“…Studies on food intake and dietary patterns done in other regions of Brazil found a VR greater than 1, stemming from higher intrapersonal variance 13,14 . This demonstrates that the daily food intake of an individual is a random event, even though a particular group displays a stable food intake pattern 24 .…”

Seasonal variation in food intake, physical activity, and body weight in a predominantly overweight population

“…There is evidence suggesting that the intake of total protein does not change by season (Hackett et al, 1985;Krauchi and Wirz-Justice, 1988;Hebert et al, 2000;Cai et al, 2004), as well as contradictory evidence of increased protein intake during the autumn and winter (Doyle et al, 1999). A separate study reported that the intake of protein showed suggestive, although not significant, seasonal changes (De Castro, 1991).…”

The number of 24 h dietary recalls using the US Department of Agriculture's automated multiple-pass method required to estimate nutrient intake in overweight and obese adults

“…Others have suggested that three 24 h dietary recalls were best for estimating energy intake in normalweight women (9) . The number of days that dietary intake should be collected can be determined by estimating within-subject variance, which is the day-to-day variation in each individual's nutrient intake, and between-subject variance, which is the extent that individuals' nutrient intake varies from one another (10)(11)(12)(13)(14)(15)(16) . Several factors such as sex, day of the week, season, culture and environment influence nutrient variation (17)(18)(19) .…”