A measurement error approach to assess the association between dietary diversity, nutrient intake, and mean probability of adequacy.
Collection of dietary intake information requires time-consuming and expensive methods, making it inaccessible to many resource-poor countries. Quantifying the association between simple measures of usual dietary diversity and usual nutrient intake/adequacy would allow inferences to be made about the adequacy of micronutrient intake at the population level for a fraction of the cost. In this study, we used secondary data from a dietary intake study carried out in Bangladesh to assess the association between 3 food group diversity indicators (FGI) and calcium intake; and the association between these same 3 FGI and a composite measure of nutrient adequacy, mean probability of adequacy (MPA). By implementing Fuller's error-in-the-equation measurement error model (EEM) and simple linear regression (SLR) models, we assessed these associations while accounting for the error in the observed quantities. Significant associations were detected between usual FGI and usual calcium intakes, when the more complex EEM was used. The SLR model detected significant associations between FGI and MPA as well as for variations of these measures, including the best linear unbiased predictor. Through simulation, we support the use of the EEM. In contrast to the EEM, the SLR model does not account for the possible correlation between the measurement errors in the response and predictor. The EEM performs best when the model variables are not complex functions of other variables observed with error (e.g. MPA). When observation days are limited and poor estimates of the within-person variances are obtained, the SLR model tends to be more appropriate.
Joseph
Maria L
ML
Department of Statistics, Iowa State University, Ames, IA 50011, USA. josepall@iastate.edu
Carriquiry
Alicia
A
D000293:Adolescent; D000328:Adult; D001459:Bangladesh / Q000453:epidemiology; D002136:Calcium, Dietary / Q000008:administration & dosage; D003198:Computer Simulation; D016208:Databases, Factual; D003677:Deficiency Diseases / Q000453:epidemiology* / Q000517:prevention & control; D004032:Diet; D005260:Female; D005502:Food / Q000145:classification; D006801:Humans; D018977:Micronutrients / Q000008:administration & dosage*; D008875:Middle Aged; D015233:Models, Statistical; D009749:Nutrition Surveys; D009751:Nutritional Requirements; D011204:Poverty Areas; D015203:Reproducibility of Results; D012307:Risk Factors; D055815:Young Adult
D002136:Calcium, Dietary; D018977:Micronutrients