Variance estimation for population attributable risk in a complex cross-sectional animal health survey.

Population attributable risk estimates offer a method of combining information on population exposure and disease risk factors into a single measure. Univariate and multivariable methods exist for calculating point estimates and variances under the assumption of equal sampling probabilities. National Animal Health Monitoring System national studies typically use a complex survey design (where selection probabilities vary by design strata), which makes use of these methods of calculating variance inappropriate. We suggest the use of a method called "delete-a-group" jackknife to estimate the variance of population attributable risk when a complex survey design has been implemented. We demonstrate the method using an example of Johne's disease. Advantages of the "delete-a-group" jackknife method include simplicity of implementation and flexibility to estimate variance for any point estimate of interest.

Development of an approach for estimating usual nutrient intake distributions at the population level.

Assessment of the dietary intake of a population must consider the large within-person variation in daily intakes. A 1986 report by the National Academy of Sciences (NAS), commissioned by the U.S. Department of Agriculture (USDA), marked an important milestone in the history of this issue. Since that time, USDA has been working cooperatively with statisticians at Iowa State University (ISU), who have further developed the measurement error model approach proposed by NAS. The method developed by the ISU statisticians can be used to estimate usual dietary intake distributions for a population but not for specific individuals. It is based on the assumption that an individual can more accurately recall and describe the foods eaten yesterday than foods eaten at an earlier time. The method requires as few as two independent days of nutrient intake information or three consecutive days for at least a subsample of the individuals. It removes biases of subsequent reporting days compared with the first day, and temporal effects such as day-of-the-week and seasonal effects can be easily removed. The method developed at ISU is described conceptually and applied to data collected in the 1989-91 USDA Continuing Survey of Food intakes by individuals to estimate the proportion of men and women age 20 y and older having "usual" (long-run average) intakes below 30% of energy from fat, below the 1989 Recommended Dietary Allowances for vitamin A and folate, and above 1000 micrograms for folate. These results were compared with the results from the distributions of 1-d intakes and of 3-d mean intakes to demonstrate the effect of within-person variation and asymmetry on usual nutrient intakes in a population.

Mean proportion and population proportion: two answers to the same question?

Two different, but equally correct, answers can be given to a question such as "What proportion of the cholesterol that is consumed comes from eggs?" This is because the question can have two different meanings, depending on whether one is referring to the mean proportion of cholesterol from eggs or the population proportion. The mean proportion of cholesterol from eggs for a group of persons is determined by first calculating the proportion of cholesterol from eggs for each person and then taking an arithmetic mean of all the proportions. The population proportion is calculated by summing the amount of cholesterol from eggs for all persons and then dividing that by the sum of the cholesterol from all foods for all persons. These two different formulas often yield similar results. Sometimes, however, the results can be quite different because of variation in the ratio, variation in the denominator, and/or the correlation between the ratio and the denominator. Each of these formulas is designed to answer a specific question: the mean proportion addresses the question about the average per person and the population proportion addresses the question of population intakes. But because either may be used to answer the same general question, confusion may result. This article discusses the factors influencing differences between the two formulas and the implications of those differences for reporting and interpreting dietary intake data.