Alicia L Carriquiry1, Judith H Spungen2, Suzanne P Murphy3, Pamela R Pehrsson4, Johanna T Dwyer5, WenYen Juan2, Mark S Wirtz2. 1. Department of Statistics, Iowa State University, Ames, IA; alicia@iastate.edu. 2. US Food and Drug Administration, College Park, MD; 3. University of Hawaii Cancer Center, Honolulu, HI; 4. Nutrient Data Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, USDA, Beltsville, MD; and. 5. Office of Dietary Supplements, NIH, Bethesda, MD.
Abstract
BACKGROUND: Food-composition tables typically give measured nutrient concentrations in foods as a single summary value, often the mean, without providing information as to the shape of the distribution. OBJECTIVE: Our objective was to explore how the statistical approach chosen to describe the iodine concentrations of foods affects the proportion of the population identified as having either insufficient or excessive iodine intakes. DESIGN: We used food intake data reported by the 2009-2010 NHANES and measured iodine concentrations of Total Diet Study (TDS) foods from 4 US regions sampled in 2004-2011. We created 4 data sets, each by using a different summary statistic (median, mean, and 10th and 90th percentiles), to represent the iodine concentration distribution of each TDS food. We estimated the iodine concentration distribution of each food consumed by NHANES participants as the 4 iodine concentration summary statistics of a similar TDS food and used these, along with NHANES food intake data, to develop 4 estimates of each participant's iodine intake on each survey day. Using the 4 estimates in turn, we calculated 4 usual iodine intakes for each sex- and age-specific subgroup. We then compared these to guideline values and developed 4 estimates of the proportions of each subgroup with deficient and excessive usual iodine intakes. RESULTS: In general, the distribution of iodine intakes was poorly characterized when food iodine concentrations were expressed as mean values. In addition, mean values predicted lower prevalences of iodine deficiency than did median values. For example, in women aged 19-50 y, the estimated prevalence of iodine deficiency was 25% when based on median food iodine concentrations but only 5.8% when based on mean values. CONCLUSION: For nutrients such as iodine with highly variable concentrations in important food sources, we recommend that food-composition tables provide useful variability information, including the mean, SD, and median.
BACKGROUND: Food-composition tables typically give measured nutrient concentrations in foods as a single summary value, often the mean, without providing information as to the shape of the distribution. OBJECTIVE: Our objective was to explore how the statistical approach chosen to describe the iodine concentrations of foods affects the proportion of the population identified as having either insufficient or excessive iodine intakes. DESIGN: We used food intake data reported by the 2009-2010 NHANES and measured iodine concentrations of Total Diet Study (TDS) foods from 4 US regions sampled in 2004-2011. We created 4 data sets, each by using a different summary statistic (median, mean, and 10th and 90th percentiles), to represent the iodine concentration distribution of each TDS food. We estimated the iodine concentration distribution of each food consumed by NHANES participants as the 4 iodine concentration summary statistics of a similar TDS food and used these, along with NHANES food intake data, to develop 4 estimates of each participant's iodine intake on each survey day. Using the 4 estimates in turn, we calculated 4 usual iodine intakes for each sex- and age-specific subgroup. We then compared these to guideline values and developed 4 estimates of the proportions of each subgroup with deficient and excessive usual iodine intakes. RESULTS: In general, the distribution of iodine intakes was poorly characterized when food iodine concentrations were expressed as mean values. In addition, mean values predicted lower prevalences of iodine deficiency than did median values. For example, in women aged 19-50 y, the estimated prevalence of iodine deficiency was 25% when based on median food iodine concentrations but only 5.8% when based on mean values. CONCLUSION: For nutrients such as iodine with highly variable concentrations in important food sources, we recommend that food-composition tables provide useful variability information, including the mean, SD, and median.
Authors: Sara Kathleen Egan; Philip Michael Bolger; Clark Dewitt Carrington Journal: J Expo Sci Environ Epidemiol Date: 2007-04-04 Impact factor: 5.563
Authors: Clarence William Murray; Sara Kathleen Egan; Henry Kim; Nega Beru; Philip Michael Bolger Journal: J Expo Sci Environ Epidemiol Date: 2008-01-02 Impact factor: 5.563
Authors: Pamela R Pehrsson; Kristine Y Patterson; Judith H Spungen; Mark S Wirtz; Karen W Andrews; Johanna T Dwyer; Christine A Swanson Journal: Am J Clin Nutr Date: 2016-08-17 Impact factor: 7.045
Authors: WenYen Juan; Paula R Trumbo; Judith H Spungen; Johanna T Dwyer; Alicia L Carriquiry; Thea P Zimmerman; Christine A Swanson; Suzanne P Murphy Journal: Am J Clin Nutr Date: 2016-08-17 Impact factor: 7.045
Authors: S Maria O'Kane; L Kirsty Pourshahidi; Maria S Mulhern; Ruth R Weir; Sarah Hill; Jennifer O'Reilly; Diana Kmiotek; Christian Deitrich; Emer M Mackle; Edel Fitzgerald; Carole Lowis; Mike Johnston; J J Strain; Alison J Yeates Journal: Nutrients Date: 2018-03-01 Impact factor: 5.717