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Record W2924184739 · doi:10.1016/j.envres.2019.03.047

Factors affecting interpretation of national biomonitoring data from multiple countries: BPA as a case study

2019· article· en· W2924184739 on OpenAlexaffabout
Judy S. LaKind, Tyler Pollock, Daniel Q. Naiman, Sungroul Kim, Audra Nagasawa, Janine Clarke

Bibliographic record

VenueEnvironmental Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsStatistics Canada
FundersAmerican Chemistry Council
KeywordsBiomonitoringNational Health and Nutrition Examination SurveyEnvironmental healthMedicineUrinary systemDemographyGeographyChemistryPopulationEnvironmental chemistryEndocrinology

Abstract

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INTRODUCTION: The use of biomonitoring data as an indicator of national levels of human exposure to environmental chemicals has grown in importance and prevalence. Nationally representative urinary bisphenol A (BPA) data are now available for Canada, the United States and Korea. Here we address the following questions: Are urinary BPA data from these countries comparable? What can be discerned regarding geographic and/or temporal similarities or differences? Are there generalizable lessons to be learned regarding comparison of biomonitoring results from different countries? METHODS: We examined underlying methods and resultant urinary BPA data from national surveys of three countries: Canada (Canadian Health Measures Survey, CHMS, 2009-2015); United States (National Health and Nutrition Examination Survey, NHANES, 2009-2014); and Korea (Korean National Environmental Health Survey, KoNEHS, 2009-2014). We estimated BPA daily intakes on both a volume- and creatinine-adjusted basis. RESULTS: The three countries use similar methods for analyzing urine samples for BPA and participate in external proficiency testing with acceptable results. Field blanks are only used in the CHMS program. There were program-specific differences in fasting times of participants. Median urinary BPA levels in Canada remained relatively constant over the three cycles (1.1-1.2 ng/ml), while US levels decreased (from 1.9 to 1.3 ng/ml) and Korean levels increased (from 0.7 to 1.1 ng/ml) over similar time periods. The most recent survey year data indicate that levels do not differ substantially across countries. Canadian urinary BPA levels have been stable; the subtle, non-significant decrease in intakes may be due to higher body weight in the more recent Canadian surveys. In contrast, the decrease in intakes in the US appears to be due to decreases in urinary BPA as body weights in the US have been stable. Estimated 95th percentile intakes are over an order of magnitude below current health-based guidance values. DISCUSSION: Our assessment of urinary BPA data from Canada, the US and Korea indicates that methodological differences, methods for dilution adjustment, and population characteristics should be carefully considered when interpreting biomonitoring data. Despite the plethora of publications describing issues with use of creatinine levels for urinary dilution adjustment, there have been no major methodological advances that would assist in interpreting urinary chemical data. A combination of biomonitoring and traditional exposure assessment approaches may be needed to fully assess human exposures to BPA and other chemicals. CONCLUSIONS: National biomonitoring surveys provide important information on population levels of chemicals such as BPA and can assist in understanding temporal and geographic similarities, differences, and trends. However, caution must be exercised when using these data to draw anything but broad conclusions, due to both intercountry methodological differences and factors affecting urinary chemical levels that are still poorly understood. While the issues raised in this paper do not appear to be a major concern specifically for the national-scale monitoring of BPA described here, they must be considered when comparing data for other chemicals measured as part of both national and smaller-scale biomonitoring-based research as well as for BPA data from other studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.079
GPT teacher head0.459
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations51
Published2019
Admission routes2
Has abstractyes

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