• Component Description
• Eligible Sample
• Description of Laboratory Methodology
• Laboratory Quality Assurance and Monitoring
• Data Processing and Editing
• Analytic Notes
• References
• Codebook
  • SEQN - Respondent sequence number
  • WTSSBJ2Y - Surplus specimen B 17-18 2 yr weights
  • SSOHIM - 5-Hydroxyimidacloprid (ug/L)
  • SSOHIMLC - 5-Hydroxyimidacloprid (ug/L) Cmt code
  • SSAND - N-Desmethylacetamiprid (ug/L)
  • SSANDLC - N-Desmethylacetamiprid (ug/L) Cmt code
  • SSFLUP - Flupyradifurone (ug/L)
  • SSFLUPLC - Flupyradifurone (ug/L) Cmt code
  • SSSLF1 - Sulfoxaflor 1 (ug/L)
  • SSSLF1LC - Sulfoxaflor 1 (ug/L) Cmt code
  • SSSLF2 - Sulfoxaflor 2 (ug/L)
  • SSSLF2LC - Sulfoxaflor 2 (ug/L) Cmt code

National Health and Nutrition Examination Survey

2017-2018 Data Documentation, Codebook, and Frequencies

Neonicotinoids - Urine (Surplus) (SSNEON_J)

Data File: SSNEON_J.xpt

First Published: July 2025

Last Revised: NA

Component Description

Neonicotinoids are a class of insecticides used in growing genetically-modified corn, soybeans, cotton, sunflowers, and canola, as well as various other genetically-modified and non-genetically-modified vegetables and fruits (Cimino et. al., 2017). Approximately 90% of the corn and 50% of the soybeans planted in the USA have been treated with neonicotinoids (Douglas, 2015). Neonicotinoid insecticides are also used in lawn treatment and in pet collars (Tomizawa, 2005). The neonicotinoid family includes acetamiprid, clothianidin, imidacloprid, nitenpyram, nithiazine, thiacloprid, and thiamethoxam; imidacloprid is the world’s single most widely applied insecticide. Neonicotinoids have preferred neurotoxic action on the insects` nicotinic acetylcholine receptor. Flupyradifurone and sulfoxaflor are two neonicotinoid-like pesticides which share the same mode of action as neonicotinoids. 

Neonicotinoids have received increased scrutiny because they may adversely affect pollinators and are linked to the bee colony collapse disorder (Hladik et. al., 2014) and the decline of insectivorous birds (Hallmann et. al., 2014). In addition, neonicotinoids are persistent in the environment and have been detected in streams and food, among other environmental matrices (Chen et. al., 2014, Hladik et. al. 2014). Multiple neonicotinoids were detected in 72% of fruits and 45% of vegetables procured in 2012 from Boston neighborhood grocery stores, with imidacloprid present in about 70% of fruits and vegetables (Chen et. al., 2014). Previous data from NHANES 2015-2016 indicated that about half of the U.S. general population 3 years of age and older was recently exposed to neonicotinoids and that, compared to other age ranges and ethnicities, young children and Asians may experience higher exposures (Ospina et. al., 2019). To better understand exposure to some of the most commonly used neonicotinoids, measurements were made for 5-Hydroxy imidacloprid and N-Desmethyl Acetamiprid, and three neonicotinoid-like biomarkers: Flupyradifurone, Sulfoxaflor isomer 1 (SULF1) and Sulfoxaflor isomer 2 (SULF2).

Eligible Sample

All examined participants aged 3 to 5 years and a one-third subsample of examined participants aged 6 years and older were eligible.

Description of Laboratory Methodology

The analysis requires 0.2 mL urine and the procedure is a modified version of a previously published method (Baker et. al., 2019), which consists of enzymatic hydrolysis of urinary conjugates of the target analytes, clean up and concentration by off-line solid phase extraction, separation of analytes by reversed phase high-performance liquid chromatography, and quantification by isotope dilution-electrospray ionization tandem mass spectrometry.

Laboratory Quality Assurance and Monitoring

The analytical measurements were conducted following strict quality control/quality assurance CLIA guidelines. Along with the study samples, each analytical run included high- and low-concentration quality control materials (QCs) and reagent blanks to assure the accuracy and reliability of the data. The concentrations of the high-concentration QCs and the low-concentration QCs, averaged to obtain one measurement of high-concentration QC and low-concentration QC for each run, were evaluated using standard statistical probability rules (Caudill et. al. 2008).

Data Processing and Editing

Data were received after all analyses were complete. The data were not edited.

Analytic Notes

Refer to the 2017-2018 Laboratory Data Overview for general information on NHANES laboratory data.

There are over 800 laboratory tests performed on NHANES participants. However, not all participants provided biospecimens or enough volume for all the tests to be performed. Additionally, availability of specimens for surplus projects is lower than for other laboratory tests performed on NHANES participants. The specimen availability can also vary by age or other population characteristics. Analysts should evaluate the extent of missing data in the dataset related to the outcome of interest as well as any predictor variables used in the analyses to determine whether additional re-weighting for item non-response is necessary.

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

Subsample Weights

Special sample weights are required to analyze these data properly. Specific sample weights for this subsample, WTSSBJ2Y, are included in this data file and should be used when analyzing these data. The sample weights created for this file used the examination sample weight, i.e., WTMEC2YR, as the base weight. The base weight was adjusted for additional nonresponse to these lab tests and re-poststratified to the population total using sex, age, and race/Hispanic origin. Participants who were part of the eligible population but who did not provide a urine specimen, or did not have sufficient volume of biospecimens, or who did not give consent for their specimens to be used for future research are included in the file, but they have a sample weight assigned “0” in their records.

Demographic and Other Related Variables

The analysis of NHANES laboratory data must be conducted using the appropriate survey design and demographic variables. The NHANES 2017-2018 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 file. 

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

The variable URXUCR (urine creatinine) will not be reported in this file. URXUCR can be found in the data file titled “Albumin & Creatinine – Urine”.

Detection Limits

The detection limits were constant for all of the analytes in the data set. Two variables are provided for each of these analytes. The variable name ending in “LC” (ex., SSOHIMLC) indicates whether the result was below the limit of detection: the value “0” means that the result was at or above the limit of detection, “1” indicates that the result was below the limit of detection. The other variable (ex., SSOHIM) provides the analytic result for that analyte. For analytes with analytic results below the lower limit of detection (ex., SSOHIMLC=1), an imputed fill value was placed in the analyte results field. This value is the lower limit of detection divided by square root of 2 (LLOD/sqrt[2]).

The lower limit of detection (LLOD) for SSOHIM, SSAND, SSFLUP, SSSLF1, and SSSLF2:

Variable Name	SAS Label	LLOD
SSOHIM	5-Hydroxyimidacloprid (µg/L)	0.1
SSAND	N-Desmethylacetamiprid (µg/L)	0.05
SSFLUP	Flupyradifurone (µg/L)	0.05
SSSLF1	Sulfoxaflor 1 (µg/L)	0.05
SSSLF2	Sulfoxaflor 2 (µg/L)	0.05

References

• Baker S.E., Serafim A.B., Morales-Agudelo P., Vidal M., Calafat A.M., & Ospina M. Quantification of DEET and neonicotinoid pesticide biomarkers in human urine by online solid-phase extraction high-performance liquid chromatography-tandem mass spectrometry. Anal Bioanal Chem. (2019) 411(3): 669–678.
• Caudill S.P., Schleicher R.L., Pirkle J.L. Multi-rule quality control for the age-related eye disease study. Statist Med. (2008) 27:4094-4106.
• Chen M., Tao L., McLean J., Lu C. Quantitative analysis of neonicotinoid insecticide residues in foods: implication for dietary exposures J Agric Food Chem. (2014) 62:6082−6090.
• Cimino A.M., Boyles A.L., Thayer K.A., Perry M.J. Effects of Neonicotinoid Pesticide Exposure on Human Health: A Systematic Review. Environ Health Perspect. (2017) 125:155-162.
• Douglas M.R., Tooker J.F. Large-Scale Deployment of Seed Treatments Has driven rapid increase in use of neonicotinoid insecticides and preemptive pest management in U.S. field crops. Environ Sci Technol. (2015) 49:5088−5097.
• Hallmann C.A., Foppen R.P., van Turnhout C.A., de Kroon H., Jongejans E. Declines in insectivorous birds are associated with high neonicotinoid concentrations. Nature. (2014) 511(7509):341-343.
• Hladik M.L., Kolpin D.W., Kuivila K.M. Widespread occurrence of neonicotinoid insecticides in streams in a high corn and soybean producing region, USA. Environ Pollut. (2014) 193:189-196.
• Ospina M., Wong L.Y., Baker S.E., Serafim A.B., Morales-Agudelo P., Calafat A.M. Exposure to neonicotinoid insecticides in the U.S. general population: Data from the 2015-2016 National Health and Nutrition Examination Survey. Environ Res. (2019) 176:108555.
• Tomizawa M., Casida J.E. Neonicotinoid insecticide toxicology: mechanisms of selective action. Annu Rev Pharmacol Toxicol. (2005) 45:247–268.

Codebook and Frequencies