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Record W2804327486 · doi:10.1093/pch/pxy054.098

THE ROLE OF DEVELOPMENTAL TOXICANTS AND SOCIO-ECONOMIC STATUS ON CONGENITAL HEART DISEASE IN URBAN AND RURAL ALBERTA

2018· article· en· W2804327486 on OpenAlexaffabout
Deliwe P. Ngwezi, Lisa K. Hornberger, Jesús Serrano-Lomelin, Charlene C. Nielsen, Deborah Fruitman, Álvaro Osornio-Vargas

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsAlberta Children's HospitalStollery Children's HospitalUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsDecileEnvironmental healthDemographyMedicinePoisson regressionToxicologyGeographyStatisticsMathematicsBiologyPopulation

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Evidence is emerging suggesting associations between environmental pollutants, socio-economic status (SES) and congenital heart disease (CHD); however, it is still inconclusive. Furthermore, it has been documented in environmental injustice studies that people with low socio-economic status are disproportionately vulnerable to environmental hazards and therefore are victims of a double jeopardy. OBJECTIVES We sought to explore the effect of exposure to groups of developmental toxicants (DTs) and SES on CHD development in urban and rural Alberta. DESIGN/METHODS We identified 2,413 CHD cases and postal codes (PC) from echocardiographic databases (2003–2010). We used previously defined groups of DTs comprised of: 1- organics and gases, 2-organics and 3-heavy metals. Exposure was assigned to each PC as the sum of the product of multiplying amounts of DTs (tonnes) emitted from any industrial facility within 10 km radius during the whole study period, by the inverse distance from the facility to the centroid of the PC. Exposures were categorized into deciles from 1(lowest) to 10 (highest) for group 1 DTs and tertiles (1=lowest to 3 =highest), for groups 2 and 3 DTs and the SES index. Poisson regression models were used to calculate risk ratios and 95% CI, adjusted for SES index or DTs and traffic-related surrogates (NO2, PM2.5). RESULTS Adjusted Effect of DT Exposure: Group 1 DT showed increased risk in urban and rural regions in the 10th decile of exposure, aRR=1.85(1.5, 2.3) and 2.67(1.04, 6.8, respectively). Group 2 DT risk was increased only in urban 3rd tertile, RR=1.45(1.3, 1.6). Group 3 DTs were associated with an increased risk in urban and rural regions in the 3rd tertile of exposure [aRR=1.16(1.04, 1.3), and 2.8(1.14, 7.1, respectively)]. Adjusted Effect of SES: SES was independently associated with an increased risk of CHD in urban lowest tertile, [aRR=1.13(1.0, 1.3)] and rural lowest and middle SES tertile, [aRR=2.9(1.9, 4.8) and 1.6(1.1, 2.6), respectively]. CONCLUSION High exposures to groups of DTs and SES were independently associated with an increased risk of CHD in urban and rural Alberta. This suggests that neighborhood SES in Alberta does not impose a disproportional exposure to DTs. Furthermore, SES had a greater impact in rural compared to urban regions. We would like to explore for interactions between the SES and DT exposures and to determine if there is environmental injustice in Alberta.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.261
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2018
Admission routes2
Has abstractyes

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