• Component Description
• Eligible Sample
• Description of Laboratory Methodology
• Laboratory Method Files
• Laboratory Quality Assurance and Monitoring
• Data Processing and Editing
• Analytic Notes
• References
• Codebook
  • SAMPLEID - Pool identification number
  • RIDRETH3 - Race\Hispanic origin
  • RIDAGGRP - Age group
  • RIAGENDR - Gender
  • RIANSMP - Number of samples in a pool
  • WTSMSMPA - Sum of adjusted subsample weights
  • LBC028 - PCB 28 (pg/g)
  • LBC028LA - PCB 28 Lipid Adj (ng/g)
  • LBD028LC - PCB 28 Comment Code
  • LBC066 - PCB 66 (pg/g)
  • LBC066LA - PCB 66 Lipid Adj (ng/g)
  • LBD066LC - PCB 66 Comment Code
  • LBC074 - PCB 74 (pg/g)
  • LBC074LA - PCB 74 Lipid Adj (ng/g)
  • LBD074LC - PCB 74 Comment Code
  • LBC099 - PCB 99 (pg/g)
  • LBC099LA - PCB 99 Lipid Adj (ng/g)
  • LBD099LC - PCB 99 Comment Code
  • LBC105 - PCB 105 (pg/g)
  • LBC105LA - PCB 105 Lipid Adj (ng/g)
  • LBD105LC - PCB 105 Comment Code
  • LBC114 - PCB 114 (pg/g)
  • LBC114LA - PCB 114 Lipid Adj (ng/g)
  • LBD114LC - PCB 114 Comment Code
  • LBC118 - PCB 118 (pg/g)
  • LBC118LA - PCB 118 Lipid Adj (ng/g)
  • LBD118LC - PCB 118 Comment Code
  • LBC138 - PCB 138 (pg/g)
  • LBC138LA - PCB 138 Lipid Adj (ng/g)
  • LBD138LC - PCB 138 Comment Code
  • LBC146 - PCB 146 (pg/g)
  • LBC146LA - PCB 146 Lipid Adj (ng/g)
  • LBD146LC - PCB 146 Comment Code
  • LBC153 - PCB 153 (pg/g)
  • LBC153LA - PCB 153 Lipid Adj (ng/g)
  • LBD153LC - PCB 153 Comment Code
  • LBC156 - PCB 156 (pg/g)
  • LBC156LA - PCB 156 Lipid Adj (ng/g)
  • LBD156LC - PCB 156 Comment Code
  • LBC157 - PCB 157 (pg/g)
  • LBC157LA - PCB 157 Lipid Adj (ng/g)
  • LBD157LC - PCB 157 Comment Code
  • LBC167 - PCB 167 (pg/g)
  • LBC167LA - PCB 167 Lipid Adj (ng/g)
  • LBD167LC - PCB 167 Comment Code
  • LBC170 - PCB 170 (pg/g)
  • LBC170LA - PCB 170 Lipid Adj (ng/g)
  • LBD170LC - PCB 170 Comment Code
  • LBC178 - PCB 178 (pg/g)
  • LBC178LA - PCB 178 Lipid Adj (ng/g)
  • LBD178LC - PCB 178 Comment Code
  • LBC180 - PCB 180 (pg/g)
  • LBC180LA - PCB 180 Lipid Adj (ng/g)
  • LBD180LC - PCB 180 Comment Code
  • LBC183 - PCB 183 (pg/g)
  • LBC183LA - PCB 183 Lipid Adj (ng/g)
  • LBD183LC - PCB 183 Comment Code
  • LBC187 - PCB 187 (pg/g)
  • LBC187LA - PCB 187 Lipid Adj (ng/g)
  • LBD187LC - PCB 187 Comment Code
  • LBC189 - PCB 189 (pg/g)
  • LBC189LA - PCB 189 Lipid Adj (ng/g)
  • LBD189LC - PCB 189 Comment Code
  • LBC194 - PCB 194 (pg/g)
  • LBC194LA - PCB 194 Lipid Adj (ng/g)
  • LBD194LC - PCB 194 Comment Code
  • LBC196 - PCB 196 (pg/g)
  • LBC196LA - PCB 196 Lipid Adj (ng/g)
  • LBD196LC - PCB 196 Comment Code
  • LBC199 - PCB 199 (pg/g)
  • LBC199LA - PCB 199 Lipid Adj (ng/g)
  • LBD199LC - PCB 199 Comment Code
  • LBC206 - PCB 206 (pg/g)
  • LBC206LA - PCB 206 Lipid Adj (ng/g)
  • LBD206LC - PCB 206 Comment Code
  • LBC209 - PCB 209 (pg/g)
  • LBC209LA - PCB 209 Lipid Adj (ng/g)
  • LBD209LC - PCB 209 Comment Code

National Health and Nutrition Examination Survey

2011-2012 Data Documentation, Codebook, and Frequencies

Non-dioxin-like Polychlorinated Biphenyls & Mono-ortho-substituted Polychlorinated Biphenyls - Pooled Samples (PCBPOL_G)

Data File: PCBPOL_G.xpt

First Published: April 2018

Last Revised: November 2020

Component Description

Polychlorinated biphenyls (PCBs) are a class of chlorinated aromatic hydrocarbon chemicals that once were used as heat-exchanger, transformer, and hydraulic fluids, and as additives to paints, oils, joint caulking, and floor tiles. The different types of PCBs are known as congeners, which are compounds that are distinguished by the number of chlorine atoms and their location on the biphenyl structure. Peak production occurred in the early 1970s, and production was banned in the U.S. after 1979. More than 1.5 million pounds of PCBs were manufactured in the U.S. prior to 1977. Concern about these chemicals continues because of their persistence in the environment and accumulation in wildlife and the animal food chain. PCBs enter the food chain by various routes, including migration into food from external sources, contamination of animal feeds, and accumulation in the fatty tissues of animals. PCBs are found in higher concentrations in fatty foods, (e.g., dairy products and fish), and food is the main source of exposure for the general population. PCBs can be transferred from mother to infant via breast milk. Exposure to these chemicals nearly always occurs as mixtures rather than individual PCB congeners.

Eligible Sample

Examined participants aged 12 years and older from a one-third sample were eligible. 

Description of Laboratory Methodology

Serum samples from NHANES 2011-2012 were stored frozen before analysis. Twenty-nine non-dioxin-like polychlorinated biphenyls were measured in serum through the use of automated liquid/liquid extraction and subsequent sample clean-up. Final determination of target analytes was performed by isotope dilution gas chromatography high-resolution mass spectrometry GC/IDHRMS. The analytical method is described in Jones et al, 2012, which is a modification of the method described by Sjodin et al., 2004.

Refer to the Laboratory Method Files for a detailed description of the laboratory methods used.

There were no changes to the lab method, lab equipment, or lab site for this component in the NHANES 2011-2012 cycle.

Laboratory Method Files

Non-dioxin-like Polychlorinated Biphenyls & Mono-ortho-substituted Polychlorinated Biphenyls (April 2018)

Laboratory Quality Assurance and Monitoring

Serum samples were processed, stored, and shipped to the Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA for analysis.

Detailed instructions on specimen collection and processing are discussed in the NHANES Laboratory Procedures Manual (LPM).  Vials are stored under appropriate frozen (-30^oC) conditions until they are shipped to the National Center for Environmental Health for testing. 

The NHANES quality assurance and quality control (QA/QC) protocols meet the 1988 Clinical Laboratory Improvement Act mandates. Detailed QA/QC instructions are discussed in the NHANES LPM.

Mobile Examination Centers (MECs)

Laboratory team performance is monitored using several techniques. NCHS and contract consultants use a structured competency assessment evaluation during visits to evaluate both the quality of the laboratory work and the quality-control procedures. Each laboratory staff member is observed for equipment operation, specimen collection and preparation; testing procedures and constructive feedback are given to each staff member. Formal retraining sessions are conducted annually to ensure that required skill levels were maintained.

Analytical Laboratories

NHANES uses several methods to monitor the quality of the analyses performed by the contract laboratories. In the MEC, these methods include performing blind split samples collected during “dry run” sessions. In addition, contract laboratories randomly perform repeat testing on 2% of all specimens.

NCHS developed and distributed a quality control protocol for all CDC and contract laboratories, which outlined the use of Westgard rules (Westgard, et al. 1981) when running NHANES specimens. Progress reports containing any problems encountered during shipping or receipt of specimens, summary statistics for each control pool, QC graphs, instrument calibration, reagents, and any special considerations are submitted to NCHS quarterly. The reports are reviewed for trends or shifts in the data. The laboratories are required to explain any identified areas of concern.

All QC procedures recommended by the manufacturers were followed. Reported results for all assays meet the Division of Environmental Health Laboratory Sciences’ quality control and quality assurance performance criteria for accuracy and precision, similar to the Westgard rules (Caudill, et al. 2008).

Data Processing and Editing

The data were reviewed. Incomplete data or improbable values were sent to the performing laboratory for confirmation.

Analytic Notes

Refer to the 2011-2012 Laboratory Data Overview for general information on NHANES laboratory data.

Subsample Weights

Non-dioxin-like Polychlorinated Biphenyls & Mono-ortho-substituted Polychlorinated Biphenyls were measured in a one-third subsample of participants 12 years and older. Special sample weights are required to analyze these data properly. Specific sample weights for this subsample are included in this data file and should be used when analyzing these data.

Samples were pooled in groups of 8 samples per pool within 48 demographic groups. The analysis of NHANES 2011-2012 pooled-sample data must be conducted with the basic demographic variables provided in this data file. This pooled-sample data file cannot be linked to other NHANES 2011-2012 data. Because each sample person does not have an equal probability of selection, sample weighting is needed to produce correct population estimates of means, percentiles, and other descriptive statistics.

The pooled-sample weights required to produce estimates from these data are included in this data file. The analysis of pooled-samples from survey data is a relatively new field of study, and there is currently no established method to produce design effect adjusted variance estimates for these pooled results. These data cannot be analyzed using software designed for complex surveys because design features are not available. 

Rationale and Methods Used to Create Pooled Results

The 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 allows for larger sample volumes, which can result in lower limits of detection and reduces the number of measurements and costs. In NHANES 2011-2012, a weighted pooled-sample design was implemented to facilitate pooling samples before making analytical measurements. Table 1 lists the International Union of Pure and Applied Chemistry (IUPAC) names, common abbreviation, and NHANES variable name for the analytes included in this data file.

Pools were prepared from serum collected from a random one-third subset of the NHANES 2011-2012 participants aged 12 years and older. Samples were pooled based on gender, race and Hispanic origin, and age. To implement the pooled-sample design, each participant sample was identified as belonging to one of 48 demographic groups based on race and Hispanic origin (Mexican American: MA, Other Hispanic: OH, non-Hispanic white: NHW, non-Hispanic black: NHB, non-Hispanic Asian: NHA, Other race including multiracial: OTHER), gender (Male, Female), and age group (12-19, 20-39, 40-59, and 60+ years of age and older). Eight (8) samples were included in each pool. The number of pools created for each of the 48 demographic groups varied depending on the total number of individual samples available in a demographic group. The one-third subset of NHANES 2011-2012 represents 2,144 individual samples, but because the pooled-sample design requires that all samples be of sufficient volume and that there be the same number of samples in each pool, 1,940 samples were available to create 251 pools with 8 samples per pool. For NHANES 2011-2012, due to limited sample size, no pooled sample was formed for females 60 years and older in the “OTHER” race/Hispanic origin group. Data user needs to be cautious when making national inference including this population subgroup. See more details on pool sample formation and exceptions in Table 2. The variable SAMPLEID denotes the identification number for each pool and ranges from 1 through 251. Please refer to the Pooled-Sample Technical Support file (POOLTF_G) for detailed information on individual participants included in each pool.In order to incorporate sample weighting into the pooled-sample design it was necessary to use a different volume of material from each sample contributing to a pool. The volume chosen for each sample in a pool was based on the ratio of its sampling weight to the sum of the sampling weights of all samples in the pool. To physically accomplish the pooling in the laboratory required that the ratio of the largest to the smallest sampling weight of samples in the same pool be no larger than about 4 or 5. The individual samples were sorted/stratified by sampling weight within each of the 48 demographic groups and pools were formed using samples with sampling weights adjacent to one another in the sorted list. The number of samples in the one-third subset, number of samples available, the number of these samples that were usable, and the number of pools formed in each demographic group are presented in Table 2. Once the pools were created, summed sampling weights were further adjusted to account for the unused samples. These adjusted summed sampling weights are represented by the variable named WTSMSMPA.

Detection Limits

In the dataset, the whole-weight detection limit (pg/g serum) is a variable and dependent on the available sample size; however, the variation in sample size was low. Lipid adjusted detection limits are variable (ng/g lipid) due to differences of the lipid levels measured in individual pools/specimens. In cases where the result was below the limit of detection, the value for that variable is the detection limit divided by the square root of 2.

Table 1. NHANES Variable name, common abbreviation and International Union of Pure and Applied Chemistry (IUPAC) name for each analyte reported and maximum limit of detection (MLOD). 

^aThe values given in the tables are the maximum limit of detection (MLOD) divided by the square root of 2 and are expressed as pg/g serum and ng/g lipid.

 

Table 2.  Number of subjects in the one-third subsample, number of individual serum samples available, number of usable samples, and number of pools formed from NHANES 2011-2012 participants per demographic group

¹ Value of this categorical variable in the data set; ² 38 Usable samples: four 8 sample and one 6 sample pool were created;
³ 28 Usable samples: three 8 sample and one 4 sample pool; ⁴ 15 Usable samples: one 8 sample and one 7 sample pool;
⁵ 30 Usable samples: three 8 sample and two 6 sample pools; ⁶ 20 Usable samples: two 8 sample and one 4 sample pool;
⁷ 19 Usable samples: two 8 sample and one 3 sample pool; ⁸ 11 Usable samples: one 8 sample and one 3 sample pool;
⁹ 21 Usable samples: two 8 sample and one 5 sample pool; ¹⁰ 27 Usable samples: three 8 sample and one 3 sample pool;
¹¹ 44Usable samples: five 8 sample and one 4 sample pool; ¹² 94 Usable samples: eleven 8 sample and one 6 sample pool;
¹³ 101 Usable samples: twelve 8 sample and one 5 sample pool; ¹⁴ 107 Usable samples: thirteen 8 sample and one 3 sample pool; ¹⁵ 55 Usable samples: six 8 sample and one 7 sample pool; ¹⁶ 42 Usable samples: five 8 sample and one 2 sample pool;
¹⁷ 6 Usable samples: one 6 sample pool; ¹⁸ 3 Usable samples: one 3 sample pool; ¹⁹ 13 Usable samples: one 8 sample and one 5 sample pool; ²⁰ 4 Usable samples: one 4 sample pool; ²¹ 0 Usable samples: no pool formed.

 

Methodological approaches for analysis

There are a few pooled-sample methodological publications, which address approaches related to the following topics:

• Variance estimation and bias correction when pooling samples from log-normally distributed populations (Caudill, 2010a; Caudill, 2010b; Caudill et al., 2007; Li et al., 2014; Mary-Huard et al., 2007);
• Models that incorporate measurement error when analyzing pooled-samples from normally or log-normally distributed populations (Caudill, 2010a; Caudill, 2015);
• Incorporation of sample weighting into a pooled-sample design (Caudill, 2012; Caudill, 2010a); and
• Estimation of standard errors and confidence limits for point estimates from pooled-samples (Caudill, 2012; Caudill, 2010a). 

References

• Caudill SP, Confidence interval estimation for pooled-sample biomonitoring from a complex survey design. Environ Int 2015;85:40-45.
  
• Caudill SP, Schleicher RL, Pirkle JL, Multi-rule quality control for the age-related eye disease study. Stat Med 2008;27:4094-4106.
  
• Caudill SP, Turner WE, and Patterson Jr. DG. Geometric mean estimation from pooled samples. Chemosphere 2007;69:371-80.
  
• Caudill SP, Use of pooled samples from the National Health and Nutrition Examination Survey. Stat Med 2012;31(27):3269-77.
  
• Caudill SP, Characterizing populations of individuals using pooled samples. J Expo Sci Environ Epidemiol 2010a;20(1):29-37.
  
• Caudill SP, Important issues related to using pooled samples for environmental chemical biomonitoring. Stat Med 2010b;30(5):515-21.
  
• Jones R, Edenfield E, Anderson S, Zhang Y, Sjödin A. Semi-automated extraction and cleanup method for measuring persistent organic pollutants in human serum. Organohalogen Compounds 2012;A3:103.
  
• Mary-Huard T, Daudin J, Baccini M, Biggeri A, Bar-Hen A. Biases induced by pooling samples in microarray experiments. Bioinformatics 2007; 23(13):i313-8.
  
• Sjodin A, Jones RS, Lapeza CR, Focant J-F, McGahee EE III, Patterson DG Jr. Semiautomated high-throughput extraction and cleanup method for the measurement of polybrominated diphenyl ethers, polybrominated biphenyls, and polychlorinated biphenyls in human serum. Anal. Chem. 2004; 76:1921-7.
  
• Westgard JO, Barry PL, Hunt MR, Groth T. A multi-rule Shewhart chart for quality control in clinical chemistry. Clin Chem 1981;27(3):493-501.
  

Codebook and Frequencies