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Record W2714126880 · doi:10.47339/ephj.2016.95

Blood concentrations of lead and mercury in British Columbians (2009-2010)

2016· article· en· W2714126880 on OpenAlexfundvenueaboutno aff
Gagandeep Dhillon, Environmental Health BCIT School of Health Sciences, Helen Heacock, Reza Afshari

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsnot available
FundersBritish Columbia Institute of Technology
KeywordsMercury (programming language)Environmental healthMedicinePublic healthMERCURY EXPOSUREHarmLead poisoningMicrosoft excelPsychologyPathologyBiomonitoring

Abstract

fetched live from OpenAlex


 Background and Purpose: Adverse effects of lead and mercury on human health due to environmental and occupational exposures require a public health attention. These metals can cause severe harm to vulnerable populations such as children and pregnant women. The probability of chronic and harmful exposure is higher in occupational settings. Monitoring the levels of these two metals in blood is an important tool to identify and quantify exposure to these metals in the environment. Monitoring data provides vital information required for management of health risk posed by these metals. The purpose of this study was to perform a comparative analysis of blood lead levels and blood mercury levels within the province of British Columbia on the health services data obtained from BC Centre of Disease Control. The primary objective was to compare the levels of lead and mercury in blood among different health authorities of British Columbia. The secondary objective was to compare the levels of lead and mercury among different age groups and gender. Methods: The blood lead and mercury concentrations used for the analysis were provided by Environmental Health Services at the British Columbia Centre for Disease Control (BCCDC). The data comprised of blood analyses that were ordered by physicians during the period of 2009-2010 for reasons not disclosed. Access to this data was provided by Dr. Reza Afshari with the permission of Dr. Tom Kosastsky for the completion of this project only. Statistical analysis of data was performed using Microsoft Excel 2013 and SAS University Edition Analytic Software. Various descriptive and inferential statistical tests were performed on the data to determine the differences of blood mercury and lead levels among different genders, Health Authorities and age groups. Results: The levels of blood mercury and lead concentrations were not significantly different in males and females in province (p-value 0.5543 for mercury; p-value 0.5336 lead). However, it was found that blood levels of lead were higher in Interior Health and “Unknown” category (p<0.02), while blood mercury levels were significantly higher in coastal health authorities (highest in Vancouver Coastal Health Authority, followed by Fraser Health Authority and Vancouver Island Health Authority) (p<0.001). For both toxic metals, levels were highest in age group of 50 and above. (p<0.0001 for mercury, p<0.02 for lead). Conclusion: The statistical analysis of lead and mercury data was useful in characterizing the exposure among Health Authorities, age and sex of the people tested in province of British Columbia. Analysis of mercury data has generated clear patterns inferring association between coastal Health Authorities and elevated mercury levels. Vancouver Coastal Health had highest median mercury levels 4.02 μg/L higher than other health authorities (p<0.0001). Analysis of lead data established a pattern among physicians suggesting that they are more likely to order a test if the patient is under 18 years of age. Median levels were found to be highest in Interior Health Authority and “Unknown”

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.232
Teacher spread0.212 · 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

Citations1
Published2016
Admission routes3
Has abstractyes

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