Comparison of Trihalomethane exposure assessment metrics in epidemiologic analyses of reproductive and developmental outcomes

Background Researchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting for spatial and temporal variability, water consumption, and showering and bathing time with an expectation of decreasing exposure misclassifi...

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Published inJournal of exposure science & environmental epidemiology Vol. 34; no. 1; pp. 115 - 125
Main Authors Luben, Thomas J., Shaffer, Rachel M., Kenyon, Elaina, Nembhard, Wendy N., Weber, Kari A., Nuckols, John, Wright, J. Michael
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.01.2024
Nature Publishing Group
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Abstract Background Researchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting for spatial and temporal variability, water consumption, and showering and bathing time with an expectation of decreasing exposure misclassification compared to the use of measured concentrations at public water supply (PWS) monitoring locations alone. Objective We used exposure data collected for a previous study of DBPs to evaluate how different sources of information impact trihalomethane (THM) exposure estimates. Methods We compared gestational exposure estimates to THMs based on water utility monitoring data alone, statistical imputation of daily concentrations to incorporate temporal variability, and personal water consumption and use (bathing and showering). We used Spearman correlation coefficients and ranked kappa statistics to compare exposure classifications. Results Exposure estimates based on measured or imputed daily THM concentrations, self-reported consumption, or bathing and showering differed substantially from estimates based solely on concentrations from PWS quarterly monitoring reports. Ranked exposure classifications, high to low quartiles or deciles, were generally consistent across each exposure metric (i.e., a subject with “high” exposure based on measured or imputed THM concentrations generally remained in the “high” category across exposure metrics.) The measured concentrations and imputed daily (i.e., spline regression) concentrations were highly correlated ( r  = 0.98). The weighted kappa statistics comparing exposure estimates using different exposure metrics ranged from 0.27 to 0.89, with the highest values for the ingestion + bathing/showering metrics compared to metrics for bathing/showering only (0.76 and 0.89). Bathing and showering contributed the most to “total” THM exposure estimates. Impact Statement We compare exposure metrics capturing temporal variability and multiple estimates of personal THM exposure with THM concentrations from PWS monitoring data. Our results show exposure estimates based on imputed daily concentrations accounting for temporal variability were very similar to the measured THM concentrations. We observed low agreement between imputed daily concentrations and ingestion-based estimates. Considering additional routes of exposure (e.g., inhalation and dermal) slightly increased agreement with the measured PWS exposure estimate in this population. Overall, the comparison of exposure assessment metrics allows researchers to understand the added value of additional data collection for future epidemiologic analyses of DBPs.
AbstractList Background Researchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting for spatial and temporal variability, water consumption, and showering and bathing time with an expectation of decreasing exposure misclassification compared to the use of measured concentrations at public water supply (PWS) monitoring locations alone. Objective We used exposure data collected for a previous study of DBPs to evaluate how different sources of information impact trihalomethane (THM) exposure estimates. Methods We compared gestational exposure estimates to THMs based on water utility monitoring data alone, statistical imputation of daily concentrations to incorporate temporal variability, and personal water consumption and use (bathing and showering). We used Spearman correlation coefficients and ranked kappa statistics to compare exposure classifications. Results Exposure estimates based on measured or imputed daily THM concentrations, self-reported consumption, or bathing and showering differed substantially from estimates based solely on concentrations from PWS quarterly monitoring reports. Ranked exposure classifications, high to low quartiles or deciles, were generally consistent across each exposure metric (i.e., a subject with “high” exposure based on measured or imputed THM concentrations generally remained in the “high” category across exposure metrics.) The measured concentrations and imputed daily (i.e., spline regression) concentrations were highly correlated ( r  = 0.98). The weighted kappa statistics comparing exposure estimates using different exposure metrics ranged from 0.27 to 0.89, with the highest values for the ingestion + bathing/showering metrics compared to metrics for bathing/showering only (0.76 and 0.89). Bathing and showering contributed the most to “total” THM exposure estimates. Impact Statement We compare exposure metrics capturing temporal variability and multiple estimates of personal THM exposure with THM concentrations from PWS monitoring data. Our results show exposure estimates based on imputed daily concentrations accounting for temporal variability were very similar to the measured THM concentrations. We observed low agreement between imputed daily concentrations and ingestion-based estimates. Considering additional routes of exposure (e.g., inhalation and dermal) slightly increased agreement with the measured PWS exposure estimate in this population. Overall, the comparison of exposure assessment metrics allows researchers to understand the added value of additional data collection for future epidemiologic analyses of DBPs.
We compare exposure metrics capturing temporal variability and multiple estimates of personal THM exposure with THM concentrations from PWS monitoring data. Our results show exposure estimates based on imputed daily concentrations accounting for temporal variability were very similar to the measured THM concentrations. We observed low agreement between imputed daily concentrations and ingestion-based estimates. Considering additional routes of exposure (e.g., inhalation and dermal) slightly increased agreement with the measured PWS exposure estimate in this population. Overall, the comparison of exposure assessment metrics allows researchers to understand the added value of additional data collection for future epidemiologic analyses of DBPs.
Researchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting for spatial and temporal variability, water consumption, and showering and bathing time with an expectation of decreasing exposure misclassification compared to the use of measured concentrations at public water supply (PWS) monitoring locations alone.BACKGROUNDResearchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting for spatial and temporal variability, water consumption, and showering and bathing time with an expectation of decreasing exposure misclassification compared to the use of measured concentrations at public water supply (PWS) monitoring locations alone.We used exposure data collected for a previous study of DBPs to evaluate how different sources of information impact trihalomethane (THM) exposure estimates.OBJECTIVEWe used exposure data collected for a previous study of DBPs to evaluate how different sources of information impact trihalomethane (THM) exposure estimates.We compared gestational exposure estimates to THMs based on water utility monitoring data alone, statistical imputation of daily concentrations to incorporate temporal variability, and personal water consumption and use (bathing and showering). We used Spearman correlation coefficients and ranked kappa statistics to compare exposure classifications.METHODSWe compared gestational exposure estimates to THMs based on water utility monitoring data alone, statistical imputation of daily concentrations to incorporate temporal variability, and personal water consumption and use (bathing and showering). We used Spearman correlation coefficients and ranked kappa statistics to compare exposure classifications.Exposure estimates based on measured or imputed daily THM concentrations, self-reported consumption, or bathing and showering differed substantially from estimates based solely on concentrations from PWS quarterly monitoring reports. Ranked exposure classifications, high to low quartiles or deciles, were generally consistent across each exposure metric (i.e., a subject with "high" exposure based on measured or imputed THM concentrations generally remained in the "high" category across exposure metrics.) The measured concentrations and imputed daily (i.e., spline regression) concentrations were highly correlated (r = 0.98). The weighted kappa statistics comparing exposure estimates using different exposure metrics ranged from 0.27 to 0.89, with the highest values for the ingestion + bathing/showering metrics compared to metrics for bathing/showering only (0.76 and 0.89). Bathing and showering contributed the most to "total" THM exposure estimates.RESULTSExposure estimates based on measured or imputed daily THM concentrations, self-reported consumption, or bathing and showering differed substantially from estimates based solely on concentrations from PWS quarterly monitoring reports. Ranked exposure classifications, high to low quartiles or deciles, were generally consistent across each exposure metric (i.e., a subject with "high" exposure based on measured or imputed THM concentrations generally remained in the "high" category across exposure metrics.) The measured concentrations and imputed daily (i.e., spline regression) concentrations were highly correlated (r = 0.98). The weighted kappa statistics comparing exposure estimates using different exposure metrics ranged from 0.27 to 0.89, with the highest values for the ingestion + bathing/showering metrics compared to metrics for bathing/showering only (0.76 and 0.89). Bathing and showering contributed the most to "total" THM exposure estimates.We compare exposure metrics capturing temporal variability and multiple estimates of personal THM exposure with THM concentrations from PWS monitoring data. Our results show exposure estimates based on imputed daily concentrations accounting for temporal variability were very similar to the measured THM concentrations. We observed low agreement between imputed daily concentrations and ingestion-based estimates. Considering additional routes of exposure (e.g., inhalation and dermal) slightly increased agreement with the measured PWS exposure estimate in this population. Overall, the comparison of exposure assessment metrics allows researchers to understand the added value of additional data collection for future epidemiologic analyses of DBPs.IMPACT STATEMENTWe compare exposure metrics capturing temporal variability and multiple estimates of personal THM exposure with THM concentrations from PWS monitoring data. Our results show exposure estimates based on imputed daily concentrations accounting for temporal variability were very similar to the measured THM concentrations. We observed low agreement between imputed daily concentrations and ingestion-based estimates. Considering additional routes of exposure (e.g., inhalation and dermal) slightly increased agreement with the measured PWS exposure estimate in this population. Overall, the comparison of exposure assessment metrics allows researchers to understand the added value of additional data collection for future epidemiologic analyses of DBPs.
BackgroundResearchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting for spatial and temporal variability, water consumption, and showering and bathing time with an expectation of decreasing exposure misclassification compared to the use of measured concentrations at public water supply (PWS) monitoring locations alone.ObjectiveWe used exposure data collected for a previous study of DBPs to evaluate how different sources of information impact trihalomethane (THM) exposure estimates.MethodsWe compared gestational exposure estimates to THMs based on water utility monitoring data alone, statistical imputation of daily concentrations to incorporate temporal variability, and personal water consumption and use (bathing and showering). We used Spearman correlation coefficients and ranked kappa statistics to compare exposure classifications.ResultsExposure estimates based on measured or imputed daily THM concentrations, self-reported consumption, or bathing and showering differed substantially from estimates based solely on concentrations from PWS quarterly monitoring reports. Ranked exposure classifications, high to low quartiles or deciles, were generally consistent across each exposure metric (i.e., a subject with “high” exposure based on measured or imputed THM concentrations generally remained in the “high” category across exposure metrics.) The measured concentrations and imputed daily (i.e., spline regression) concentrations were highly correlated (r = 0.98). The weighted kappa statistics comparing exposure estimates using different exposure metrics ranged from 0.27 to 0.89, with the highest values for the ingestion + bathing/showering metrics compared to metrics for bathing/showering only (0.76 and 0.89). Bathing and showering contributed the most to “total” THM exposure estimates.Impact StatementWe compare exposure metrics capturing temporal variability and multiple estimates of personal THM exposure with THM concentrations from PWS monitoring data. Our results show exposure estimates based on imputed daily concentrations accounting for temporal variability were very similar to the measured THM concentrations. We observed low agreement between imputed daily concentrations and ingestion-based estimates. Considering additional routes of exposure (e.g., inhalation and dermal) slightly increased agreement with the measured PWS exposure estimate in this population. Overall, the comparison of exposure assessment metrics allows researchers to understand the added value of additional data collection for future epidemiologic analyses of DBPs.
Researchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting for spatial and temporal variability, water consumption, and showering and bathing time with an expectation of decreasing exposure misclassification compared to the use of measured concentrations at public water supply (PWS) monitoring locations alone. We used exposure data collected for a previous study of DBPs to evaluate how different sources of information impact trihalomethane (THM) exposure estimates. We compared gestational exposure estimates to THMs based on water utility monitoring data alone, statistical imputation of daily concentrations to incorporate temporal variability, and personal water consumption and use (bathing and showering). We used Spearman correlation coefficients and ranked kappa statistics to compare exposure classifications. Exposure estimates based on measured or imputed daily THM concentrations, self-reported consumption, or bathing and showering differed substantially from estimates based solely on concentrations from PWS quarterly monitoring reports. Ranked exposure classifications, high to low quartiles or deciles, were generally consistent across each exposure metric (i.e., a subject with "high" exposure based on measured or imputed THM concentrations generally remained in the "high" category across exposure metrics.) The measured concentrations and imputed daily (i.e., spline regression) concentrations were highly correlated (r = 0.98). The weighted kappa statistics comparing exposure estimates using different exposure metrics ranged from 0.27 to 0.89, with the highest values for the ingestion + bathing/showering metrics compared to metrics for bathing/showering only (0.76 and 0.89). Bathing and showering contributed the most to "total" THM exposure estimates. We compare exposure metrics capturing temporal variability and multiple estimates of personal THM exposure with THM concentrations from PWS monitoring data. Our results show exposure estimates based on imputed daily concentrations accounting for temporal variability were very similar to the measured THM concentrations. We observed low agreement between imputed daily concentrations and ingestion-based estimates. Considering additional routes of exposure (e.g., inhalation and dermal) slightly increased agreement with the measured PWS exposure estimate in this population. Overall, the comparison of exposure assessment metrics allows researchers to understand the added value of additional data collection for future epidemiologic analyses of DBPs.
Author Weber, Kari A.
Luben, Thomas J.
Nuckols, John
Kenyon, Elaina
Wright, J. Michael
Shaffer, Rachel M.
Nembhard, Wendy N.
AuthorAffiliation 3 Arkansas Center for Birth Defects Research and Prevention and the Department of Epidemiology, Fay. W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA
4 Colorado State University, Fort Collins, CO, USA; JRN Environmental Health Sciences, LTD, North Bethesda, MD, USA
1 Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency. RTP, NC, Washington, DC; Cincinnati, OH, USA
2 Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, RTP, NC, USA
AuthorAffiliation_xml – name: 2 Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, RTP, NC, USA
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– name: 4 Colorado State University, Fort Collins, CO, USA; JRN Environmental Health Sciences, LTD, North Bethesda, MD, USA
– name: 3 Arkansas Center for Birth Defects Research and Prevention and the Department of Epidemiology, Fay. W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37316533$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1038_s41370_024_00639_0
crossref_primary_10_1016_j_ecoenv_2024_117421
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ContentType Journal Article
Copyright This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023
2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023.
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IngestDate Thu Aug 21 18:34:38 EDT 2025
Fri Jul 11 00:52:01 EDT 2025
Sat Aug 23 14:09:23 EDT 2025
Mon Jul 21 06:07:06 EDT 2025
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Issue 1
Keywords Trihalomethanes
Exposure Assessment
Temporal Variation
Disinfection By-Products
Water Utility Monitoring Data
Language English
License 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c475t-b50ca9f4622edb14c06c26743ac8b763b2c09a3f47c36e1002605306d3a1962a3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
TJL, RMS, and JMW conceived and designed the study, WNN and KW provided the analytic sample and specified relevant exposure variables, EK developed PBPK-based exposure metrics, TJL conducted the analyses. TJL, RMS, JMW, and JN interpreted the results. TJL, RMS EK and JMW prepared the draft manuscript. WNN, KW and JN reviewed the manuscript and provided substantial feedback. All authors reviewed the results and approved the final version of the manuscript.
Author Contribution Statement
OpenAccessLink https://www.ncbi.nlm.nih.gov/pmc/articles/11898099
PMID 37316533
PQID 2933662047
PQPubID 29347
PageCount 11
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_11898099
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crossref_citationtrail_10_1038_s41370_023_00559_5
crossref_primary_10_1038_s41370_023_00559_5
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PublicationDateYYYYMMDD 2024-01-01
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  day: 01
PublicationDecade 2020
PublicationPlace New York
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PublicationSubtitle Official journal of the International Society of Exposure Science
PublicationTitle Journal of exposure science & environmental epidemiology
PublicationTitleAbbrev J Expo Sci Environ Epidemiol
PublicationTitleAlternate J Expo Sci Environ Epidemiol
PublicationYear 2024
Publisher Nature Publishing Group US
Nature Publishing Group
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Snippet Background Researchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting...
Researchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting for spatial...
BackgroundResearchers have developed exposure assessment metrics for disinfection by-products (DBPs) utilizing drinking water monitoring data and accounting...
We compare exposure metrics capturing temporal variability and multiple estimates of personal THM exposure with THM concentrations from PWS monitoring data....
SourceID pubmedcentral
proquest
pubmed
crossref
springer
SourceType Open Access Repository
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Index Database
Enrichment Source
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StartPage 115
SubjectTerms Bathing
Classification
Correlation coefficient
Correlation coefficients
Data Collection
Disinfection
Drinking water
Epidemiology
Estimates
Exposure
Household Products
Humans
Ingestion
Inhalation
Medicine
Medicine & Public Health
Public waters
Respiration
Statistical analysis
Statistics
Trihalomethanes
Water consumption
Water monitoring
Water supply
Water utilities
Title Comparison of Trihalomethane exposure assessment metrics in epidemiologic analyses of reproductive and developmental outcomes
URI https://link.springer.com/article/10.1038/s41370-023-00559-5
https://www.ncbi.nlm.nih.gov/pubmed/37316533
https://www.proquest.com/docview/2933662047
https://www.proquest.com/docview/2826218660
https://pubmed.ncbi.nlm.nih.gov/PMC11898099
Volume 34
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