• 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
  • WTBSMSMA - Sum of adjusted subsample B weights
  • LBCBHC - Beta-hexachlorocyclohexane (pg/g)
  • LBCBHCLA - B-hexachlorocyclohexane Lipid Adj (ng/g)
  • LBDBHCLC - Beta-hexachlorocyclohexane comment code
  • LBCHCB - Hexachlorobenzene (pg/g)
  • LBCHCBLA - Hexachlorobenzene Lipid Adj (ng/g)
  • LBDHCBLC - Hexachlorobenzene comment code
  • LBCMIR - Mirex (pg/g)
  • LBCMIRLA - Mirex Lipid Adj (ng/g)
  • LBDMIRLC - Mirex comment code
  • LBCOXY - Oxychlordane (pg/g)
  • LBCOXYLA - Oxychlordane Lipid Adj (ng/g)
  • LBDOXYLC - Oxychlordane comment code
  • LBCPDE - p,p'-DDE (pg/g)
  • LBCPDELA - p,p'-DDE Lipid Adj (ng/g)
  • LBDPDELC - p,p'-DDE comment code
  • LBCPDT - p,p'-DDT (pg/g)
  • LBCPDTLA - p,p'-DDT Lipid Adj (ng/g)
  • LBDPDTLC - p,p'-DDT comment code
  • LBCTNA - trans-Nonachlor (pg/g)
  • LBCTNALA - trans-Nonachlor Lipid Adj (ng/g)
  • LBDTNALC - trans-Nonachlor comment code

National Health and Nutrition Examination Survey

2015-2016 Data Documentation, Codebook, and Frequencies

Pesticides - Organochlorine Pesticides - Pooled Samples (PSTPOL_I)

Data File: PSTPOL_I.xpt

First Published: August 2019

Last Revised: November 2020

Component Description

Organochlorine pesticides are a diverse group of synthetic chemicals that persist in the environment and tend to bioaccumulate. Most of these chemicals have been banned from use in the U.S. Assessment of exposure to persistent organochlorine pesticides in a representative sample of the U.S. population is needed to determine current prevalence and level of exposure, and the potential for human health effects from exposure to these chemicals.

Eligible Sample

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

Description of Laboratory Methodology

Serum samples from NHANES 2015-2016 were stored frozen before analysis. Eight organochlorine pesticides and metabolites (Table 1) were measured in serum using 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 Methods Files section 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 2015-2016 cycle.

Laboratory Method Files

Polybrominated diphenyl ethers (PBDEs), Polybrominated Biphenyls (PBBs), Polychlorinated biphenyls (PCBs) and Persistent Pesticides (PPs) – Serum (August 2019)

Laboratory Quality Assurance and Monitoring

Serum specimens 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 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 2015-2016 Laboratory Data Overview for general information on NHANES laboratory data.

Please refer to the NHANES Analytic Guidelines and the on-line NHANES Tutorial for details on the use of sample weights and analytic issues.

Subsample Weights

Organochlorine pesticides 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 2015-2016 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 2015-2016 data. Because each sample participant 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 U.S. 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 U.S. 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 (LLODs) and reduces the number of measurements and costs. In NHANES 2015-2016, 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 subsample of the NHANES 2015-2016 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 subsample of NHANES 2015-2016 represents 2,133 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, 1,989 samples were available to create 265 pools with 8 samples per pool. 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 265. Please refer to the Pooled-Sample Technical Support file (POOLTF_I) 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 10. 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 subsample, 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 WTBSMSMA.

Demographic and Other Related Variables

The analysis of NHANES laboratory data must be conducted using the appropriate survey design and demographic variables. The NHANES 2015-2016 Demographics File contains demographic data, health indicators, and other related information collected during household interviews as well as the sample design variables. The recommended procedure for variance estimation requires use of stratum and PSU variables (SDMVSTRA and SDMVPSU, respectively) in the demographic data file.

The Fasting Questionnaire File includes auxiliary information, such as fasting status, length of fast, and the time of venipuncture.

This laboratory data file can be linked to the other NHANES data files using the unique survey participant identifier (i.e., SEQN).

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).

Variable	Common	IUPAC Name	MLODa
Name	Abbrev.		(pg/g serum)	(ng/g lipid)
LBCHCB	HCB	Hexachlorobenzene	3.5	0.92
LBCBHC	B-HCCH	b-Hexachlorocyclohexane	3.5	0.92
LBCOXY	OXYCHLOR	Oxychlordane	3.5	0.92
LBCTNA	T-NONA	Trans-Nonachlor	3.5	0.92
LBCPDE	PP-DDE	2,2-Bis(4-chlorophenyl)-1,1-dichloroethene	3.5	0.92
LBCPDT	PP-DDT	2,2-Bis(4-chlorophenyl-1,1,1-trichloroethan	3.5	0.92
LBCMIR	MIREX	Mirex	3.5	0.92

^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 2015-2016 participants per demographic group

^1: 47 usable samples, one 6 sample pool, and one 8 sample pool; ^2: 31 usable samples, one 7 sample pool, and three 8 sample pool ; ^3: 37 usable samples, one 5 sample pool, and four 8 sample pool; ^4: 44 usable samples, one 4 sample pool, and five 8 sample pool ; ^5: 60 usable samples, one 4 sample pool, and seven 8 sample pool ; ^6: 53 usable samples, one 5 sample pool, and six 8 sample pool; ^7: 40 usable samples, one 6 sample pool, and two 8 sample pool ; ^8: 34 usable samples, one 2 sample pool, and four 8 sample pool; ^9: 29 usable samples, one 5 sample pool, and three 8 sample pool; ^10: 53 usable samples, one 5 sample pool, and four 8 sample pool; ^11: 35 usable samples, one 3 sample pool, and four 8 sample pool; ^12: 47 usable samples, one 7 sample pool, and five 8 sample pool; ^13: 77 usable samples, one 5 sample pool, and nine 8 sample pool; ^14: 79 usable samples, one 7 sample pool, and nine 8 sample pool; ¹⁵: 108 usable samples, one 4 sample pool, and thirteen 8 sample pool; ^16: 84 usable samples, one 4 sample pool, and ten 8 sample pool; ^17: 101 usable samples, one 5 sample pool, and twelve 8 sample pool; ^18: 39 usable samples, one 7 sample pool, and four 8 sample pool; ^19: 50 usable samples, one 2 sample pool, and six sample pool; ^20: 55 usable samples, one 7 sample, and six 8 sample pool; ^21: 51 usable samples, one 3 sample pool, and six 8 sample pool; ^22: 29 usable samples, one 5 sample pool, and three 8 sample pool; ^23: 92 usable samples, one 4 sample pool, and eleven 8 sample pool; ^24: 51 usable samples, one 3 sample pool, and six 8 sample pool; ^25: 20 usable samples, one 4 sample pool, and two 8 sample pool; ^26: 38 usable samples, one 6 sample pool, and four 8 sample pool; ^27: 20 usable samples, one 4 sample pool, and two 8 sample pool; ^28: 29 usable samples, one 3 sample pool, and two 8 sample pool; ^29: 37 usable samples, one 5 sample pool, and four 8 sample pool; ^30: 21 usable samples, one 5 sample pool, and two 8 sample pool; ^31: 11 usable samples, one 3 sample pool, and one 8 sample pool; ^32: 16 usable samples, one 2 sample pool, one 6 sample pool, and one 8 sample pool; ^33: 14 usable samples, one 6 sample pool, and one 8 sample pool; ^34: 5 usable samples, one 5 sample pool; ^35: 11 usable samples, one 3 sample pool, and one 8 sample pool; ^36: 11 usable samples, one 3 sample pool, and one 8 sample pool; ^37: 10 usable samples, one 2 sample pool, and one 8 sample pool.

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, Characterizing populations of individuals using pooled samples. J Expo Sci Environ Epidemiol 2010a;20(1):29-37.
• Caudill SP, Confidence interval estimation for pooled-sample biomonitoring from a complex survey design. Environ Int 2015;85:40-45.
• Caudill SP, Important issues related to using pooled samples for environmental chemical biomonitoring. Stat Med 2010b;30(5):515-21.
• 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.
• 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.
• Li X, Kuk AYC, Xu J, Empirical Bayes Gaussian likelihood estimation of exposure distributions from pooled samples in human biomonitoring. Stat Med 2014;33(28):4999-5014.
• 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