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Record W3107972991 · doi:10.1016/j.envint.2020.106252

Cohort profile: China National Human Biomonitoring (CNHBM)—A nationally representative, prospective cohort in Chinese population

2020· article· en· W3107972991 on OpenAlexaboutno aff
Zhaojin Cao, Shaobin Lin, Feng Zhao, Yuebin Lv, Yingli Qu, Xiaojian Hu, Shicheng Yu, Shixun Song, Yifu Lu, Huifang Yan, Yingchun Liu, Liang Ding, Ying Zhu, Ling Liu, Miao Zhang, Tong Wang, Wenli Zhang, Hui Fu, Jin Yong-jin, Jiayi Cai, Xu Zhang, Chonghuai Yan, Saisai Ji, Zhuona Zhang, Jiayin Dai, Huijuan Zhu, Lixue Gao, Yanwei Yang, Chengcheng Li, Jinhui Zhou, Bo Ying, Lei Zheng, Qi Kang, Junming Hu, Weixia Zhao, Mingyuan Zhang, Xiaoyi Yu, Bing Wu, Tongzhang Zheng, Yang Liu, P. Barry Ryan, Dana Boyd Barr, Weidong Qu, Yuxin Zheng, Xiaoming Shi

Bibliographic record

VenueEnvironment International · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Health Commission of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsBiomonitoringBiorepositoryEnvironmental healthCohortPopulationMedicineCohort studyChinaDemographyEpidemiologyNational Health and Nutrition Examination SurveyGeographyEnvironmental protectionGerontologyBiobankPathologyBiologyEcology

Abstract

fetched live from OpenAlex

OBJECTIVE: Globally, developed countries such as the United States, Canada, Germany, Korea, have carried out long-term and systematic biomonitoring programs for environmental chemicals in their populations. The China National Human Biomonitoring (CNHBM) was to document the extent of human exposure to a wide array of environmental chemicals, to understand exposure profiles, magnitude and ongoing trends in exposure in the general Chinese population, and to establish a national biorepository. METHODS: CNHBM adopted three-stage sampling method to obtain a nationally representative sample of the population. A total of 21,888 participants who were permanent residents in 31 provinces were designed to interviewed in this national biomonitoring (152 monitoring sites × 3 survey units × 2 sexes × 6 age groups × 4 persons = 21,888 persons) in 2017-2018. Unlike the US National Health and Nutrition Examination Survey, the CNHBM will follow the same participants in subsequent cycles allowing for dynamic, longitudinal data sets for epidemiologic follow-up. Each survey cycle of CNHBM will last 2 years and each subsequent cycle will occur 3 years after the prior cycle's completion. RESULTS: In 2017-2018, the CNHBM created a large cohort of Chinese citizens that included districts/counties questionnaire, community questionnaire collecting information on villages/communities, individual questionnaire, household questionnaire, comprehensive medical examination, and collection of blood and urine samples for measurement of clinical and exposure biomarkers. A total of 21,746 participants were finally included in CNHBM, accounting for 99.4% of the designed sample size; and 152 PSUs questionnaires, 454 community questionnaires, 21,619 family questionnaires, 21,712 cases of medical examinations, 21,700 individual questionnaires, 21,701 blood samples and 21,704 urine samples were collected, respectively. Planned analyses of blood and urine samples were to measure both inorganic and organic chemicals, including 13 heavy metals and metalloids, 18 poly- and per-fluorinated alkyl substances, 12 phthalate metabolites, 9 polycyclic aromatic hydrocarbons metabolites, 4 environmental alkylated phenols, and 2 benzene metabolites. CONCLUSIONS: CNHBM established the first nationally representative, prospective cohort in the Chinese population to understand the baseline and trend of internal exposure of environmental chemicals in general population, and to understand environmental toxicity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.338
Teacher spread0.329 · 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; a candidate call from one teacher head, not a consensus.

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

Citations78
Published2020
Admission routes1
Has abstractyes

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