Use of pooled samples from the national health and nutrition examination survey

Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control & Prevention, Public Health Service, US Department of Health and Human Services, Atlanta, GA 30333, USA. .
Statistics in Medicine (Impact Factor: 1.83). 11/2012; 31(27). DOI: 10.1002/sim.5341
Source: PubMed


The National Centers for Disease Control and Prevention (CDC) provides an ongoing assessment of the US population's exposure to environmental chemicals by using biomonitoring in conjunction with CDC's National Health and Nutrition Examination Survey (NHANES). Characterizing the distributions of concentrations of environmental compounds or their metabolites in the US population is a primary objective of CDC's biomonitoring program. Historically, this characterization has been based on individual measurements of these compounds in body fluid or tissue from representative samples of the population. Pooling samples before making analytical measurements can reduce the costs of biomonitoring by reducing the number of analyses. For the first time in NHANES 2005-2006, a weighted pooled-sample design was implemented to facilitate pooling samples before making analytical measurements. This paper describes this design and the estimation method being developed in the National Center for Environmental Health, Division of Laboratory Sciences (NCEH/DLS) to characterize concentrations of polychlorinated and polybrominated compounds. We present percentile estimates for 2,2 ( ' ) ,4,4 ( ' ) ,5,5 ( ' ) -hexachlorobiphenyl (PCB153) in specific subpopulations of the US based on the NHANES 2005-2006 pooled-sample design. We also compare estimates based on individual samples from NHANES 2003-2004 with estimates based on artificially created pools from NHANES 2003-2004 using a pooled-sample design similar to the one used for NHANES 2005-2006. For NHANES 2005-2006 the number of analyses required to characterize the levels of 61 polychlorinated and 13 polybrominated compounds in the US population was reduced from 2201 to 228. At a cost of $1400 per analytical measurement, this represents a savings of approximately $2.78 million. Copyright © 2012 John Wiley & Sons, Ltd.

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Available from: Samuel P Caudill
    • "Because individual samples were pooled across the sampling design cells of the original NHANES sampling design to accommodate physical limitations associated with weighted pooling (Caudill, 2012), it is not possible to directly estimate the design effects associated with pooledsample estimates. So to create data for which design effects could be estimated, I used the pooled-sample estimates from the various demographic groups to impute individual sample measurements for every subject in the original one-third subset of NHANES 2005–2006. "
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    ABSTRACT: The National Centers for Disease Control and Prevention (CDC) is using a weighted pooled-sample design to characterize concentrations of persistent organic pollutants (POPs) in the U.S. Historically, this characterization has been based on individual measurements of these compounds in body fluid or tissue from representative samples of the population using stratified multistage selection. Pooling samples before making analytical measurements reduces the costs of biomonitoring by reducing the number of analyses. Pooling samples also allows for larger sample volumes which can result in fewer left censored results. But because samples are pooled across the sampling design cells of the original survey, direct calculation of the design effects needed for accurate standard error and confidence interval (CI) estimation is not possible. So in this paper I describe a multiple imputation (MI) method for calculating design effects associated with pooled-sample estimates. I also evaluate the method presented, by simulating NHANES individual sample data from which artificial pools are created for use in a comparison of pooled-sample estimates with estimates based on individual samples. To further illustrate and evaluate the method proposed in this paper I present geometric mean and various percentile estimates along with their 95% CIs for two chemical compounds from NHANES 2005-2006 pooled samples and compare them to individual-sample based estimates from NHANES 1999-2004. Published by Elsevier Ltd.
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    ABSTRACT: Summary Choosing an appropriate sampling strategy for environmental monitoring or storing samples in Environmental Specimen Banks for future analyses includes the important decision of using individual or pooled samples. A number of advantages using individual samples for temporal trend studies can be identified e.g. that information about sample variance is important in it self and changes in variance is often the first sign of a change in contaminant burden, freedom of choosing an appropriate central measure (for right skewed distributions i.e. geometric mean values or medians) whereas pooled samples will represent arithmetic means. Furthermore, individual sampling enables adjustments for confounding factors (e.g. fat content, age, size) and detection of extreme values. However, resources may be saved by using pooled samples, if the sample variance is dominated by small-scale differences in time or space or by genetic and/or physiological differences among individual biological samples rather than of instrumental errors at the chemical analyses. The statistical power at temporal or spatial studies is determined by the random/unexplained sample variation. The relation between the instrumental error and other sources of variation as well as the relation between the cost for chemical analysis and collection and preparation of samples will determine the number of individual samples in each pool and the number of pools that should be analysed to achieve high cost efficiency. Various scenarios of different number of individual samples, different number of pooled samples containing various number of individual specimens, different relation between instrumental error and other sources of sample variance have been compared by simulating random sampling from computer generated populations using realistic measures of variation from ongoing monitoring activities. The results may give guidance to the selection of a cost efficient sampling strategy
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