OSCR

Residential early-life air pollution exposure and oculomotor markers of cognitive workload in children: a prospective birth cohort study.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches
  1. [1] § Methods › Statistics ↔ code/03_cognitive_sensitivity_analyses.R, lines 1 to 57 · score 0.85 · cumulative lifetime, sleep duration, exposure outcome associations, linear model, eye tracking outcomes, adjustments · Report a mismatch
  2. [2] § Methods › Statistics ↔ code/00_setup_cognitive_analysis.R, lines 66 to 136 · score 0.83 · cumulative lifetime exposure, pupil diameter, eye tracking outcomes, hours, adjustments, sleep · Report a mismatch
  3. [3] § Methods › Statistics ↔ code/11_BKMR_DLM_First1000Days.R, lines 614 to 672 · score 0.78 · posterior predictions, lag weight, polynomial kernel, BKMR DLMs, Exposure windows, mixtures · Report a mismatch
  4. [4] § Results › Multi-pollutant and multi-window analysis ↔ code/10_BKMR_DLM_lastYear.R, lines 661 to 711 · score 0.61 · joint mixture, single pollutant, exposure response, BKMR DLM, month exposure · Report a mismatch
  5. [5] § Results › Multi-pollutant and multi-window analysis ↔ code/11_BKMR_DLM_First1000Days.R, lines 614 to 672 · score 0.58 · Lag weight functions, BKMR DLM, posterior, exposure windows, mixture, pollutants · Report a mismatch
  6. [6] § Results › Perinatal and early childhood air pollution exposure and cognitive oculomotor indices ↔ code/01_primary_cognitive_analysis.R, lines 1 to 52 · score 0.55 · multiple linear regression, predefined early, cognitive eye tracking, models, exposure · Report a mismatch
  7. [7] § Results › Perinatal and early childhood air pollution exposure and cognitive oculomotor indices ↔ code/00_setup_cognitive_analysis.R, lines 66 to 136 · score 0.54 · cumulative lifetime, saccade velocity, adjustment, sleep, childhood, days · Report a mismatch
  8. [8] § Results › Perinatal and early childhood air pollution exposure and cognitive oculomotor indices ↔ code/03_cognitive_sensitivity_analyses.R, lines 1 to 57 · score 0.52 · cumulative lifetime, sleep duration, adjustment, trimester, days, cognitive · Report a mismatch
  9. [9] § Results › Perinatal and early childhood air pollution exposure and emotional attention bias ↔ code/09_emotional_figures.R, lines 1 to 50 · score 0.50 · bias scores, emotional attention bias, anger, fear, happy, children · Report a mismatch

Paper

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The authors' code

R · 255 lines · 5.7 KB · no license · 2 matches

  1. # ==============================================================================
  2. # 03_cognitive_sensitivity_analyses.R
  3. #
  4. # Sensitivity analyses for cognitive eye-tracking outcomes.
  5. #
  6. # This script reproduces the cognitive section of Supplemental Table 4.
  7. # Sensitivity analyses are restricted to exposure-outcome associations with
  8. # nominal p < 0.05 in the primary cognitive analysis.
  9. #
  10. # Analyses included:
  11. # 1. Primary model
  12. # 2. Additional adjustment for time of day of follow-up assessment
  13. # 3. Additional adjustment for reported average sleep duration
  14. # 4. Additional adjustment of early-life models for cumulative lifetime
  15. # exposure to the corresponding pollutant
  16. # 5. Mutual adjustment of trimester-specific models for the two remaining
  17. # trimester-specific exposure variables, entered as separate covariates
  18. # ==============================================================================
  20. source(file.path("code", "00_setup_cognitive_analysis.R"))
  23. # ==============================================================================
  24. # 1. Identify primary cognitive associations with nominal p < 0.05
  25. # ==============================================================================
  27. primary_results <- purrr::map_dfr(
  28. all_exposures,
  29. function(exposure) {
  30. purrr::map_dfr(
  31. outcomes,
  32. function(outcome) {
  33. fit_linear_model(
  34. df = dat,
  35. outcome = outcome,
  36. exposure = exposure
  37. )
  38. }
  39. )
  40. }
  41. )
  43. selected_pairs <- primary_results |>
  44. dplyr::filter(
  45. !is.na(p_value),
  46. p_value < 0.05
  47. ) |>
  48. dplyr::select(
  49. exposure,
  50. outcome
  51. ) |>
  52. dplyr::distinct()
  55. # ==============================================================================
  56. # 2. Sensitivity-model function
  57. # ==============================================================================
  59. run_sensitivity_model <- function(
  60. exposure,
  61. outcome,
  62. analysis_type
  63. ) {
  65. if (analysis_type == "primary") {
  67. result <- fit_linear_model(
  68. df = dat,
  69. outcome = outcome,
  70. exposure = exposure
  71. )
  73. } else if (analysis_type == "time_of_day") {
  75. result <- fit_linear_model(
  76. df = dat,
  77. outcome = outcome,
  78. exposure = exposure,
  79. extra_factor = time_of_day_covariate
  80. )
  82. } else if (analysis_type == "sleep_duration") {
  84. result <- fit_linear_model(
  85. df = dat,
  86. outcome = outcome,
  87. exposure = exposure,
  88. extra_numeric = sleep_covariate
  89. )
  91. } else if (analysis_type == "lifetime_exposure") {
  93. # Only applicable to early-life exposures.
  94. if (!exposure %in% early_life_exposures) {
  95. return(tibble::tibble())
  96. }
  98. adjuster <- lifetime_adjuster(exposure)
  100. if (length(adjuster) == 0) {
  101. return(tibble::tibble())
  102. }
  104. result <- fit_linear_model(
  105. df = dat,
  106. outcome = outcome,
  107. exposure = exposure,
  108. extra_numeric = adjuster
  109. )
  111. } else if (analysis_type == "trimester_specific") {
  113. # Only applicable to trimester-specific exposure windows.
  114. # The other two trimesters for the same pollutant are entered separately.
  115. adjusters <- trimester_adjusters(exposure)
  117. if (length(adjusters) == 0) {
  118. return(tibble::tibble())
  119. }
  121. result <- fit_linear_model(
  122. df = dat,
  123. outcome = outcome,
  124. exposure = exposure,
  125. extra_numeric = adjusters
  126. )
  128. } else {
  130. stop(
  131. "Unknown sensitivity-analysis type: ",
  132. analysis_type
  133. )
  134. }
  136. result |>
  137. dplyr::mutate(
  138. analysis_type = analysis_type
  139. )
  140. }
  143. # ==============================================================================
  144. # 3. Run sensitivity analyses for selected primary associations
  145. # ==============================================================================
  147. analysis_types <- c(
  148. "primary",
  149. "time_of_day",
  150. "sleep_duration",
  151. "lifetime_exposure",
  152. "trimester_specific"
  153. )
  155. sensitivity_long <- purrr::pmap_dfr(
  156. selected_pairs,
  157. function(exposure, outcome) {
  159. purrr::map_dfr(
  160. analysis_types,
  161. function(analysis_type) {
  163. run_sensitivity_model(
  164. exposure = exposure,
  165. outcome = outcome,
  166. analysis_type = analysis_type
  167. )
  168. }
  169. )
  170. }
  171. ) |>
  172. dplyr::mutate(
  173. hypothesis_family = vapply(
  174. exposure,
  175. exposure_family,
  176. FUN.VALUE = character(1)
  177. )
  178. ) |>
  179. dplyr::arrange(
  180. outcome,
  181. exposure,
  182. factor(
  183. analysis_type,
  184. levels = analysis_types
  185. )
  186. )
  189. # ==============================================================================
  190. # 4. Wide table for comparison with Supplemental Table 4
  191. # ==============================================================================
  193. sensitivity_wide <- sensitivity_long |>
  194. dplyr::select(
  195. outcome,
  196. exposure,
  197. analysis_type,
  198. n,
  199. estimate_per_iqr,
  200. ci_low_per_iqr,
  201. ci_high_per_iqr,
  202. p_value
  203. ) |>
  204. tidyr::pivot_wider(
  205. names_from = analysis_type,
  206. values_from = c(
  207. n,
  208. estimate_per_iqr,
  209. ci_low_per_iqr,
  210. ci_high_per_iqr,
  211. p_value
  212. ),
  213. names_sep = "__"
  214. )
  217. # ==============================================================================
  218. # 5. Export
  219. # ==============================================================================
  221. output_file <- file.path(
  222. results_dir,
  223. "03_cognitive_sensitivity_analyses.xlsx"
  224. )
  226. workbook <- openxlsx::createWorkbook()
  228. openxlsx::addWorksheet(
  229. workbook,
  230. "Sensitivity_long"
  231. )
  232. openxlsx::writeData(
  233. workbook,
  234. "Sensitivity_long",
  235. sensitivity_long
  236. )
  238. openxlsx::addWorksheet(
  239. workbook,
  240. "Sensitivity_wide"
  241. )
  242. openxlsx::writeData(
  243. workbook,
  244. "Sensitivity_wide",
  245. sensitivity_wide
  246. )
  248. openxlsx::saveWorkbook(
  249. workbook,
  250. output_file,
  251. overwrite = TRUE
  252. )
  254. message("Cognitive sensitivity analyses completed.")
  255. message("Results written to: ", output_file)

03_cognitive_sensitivity_analyses.R at commit 9900793, no license · at the source

Overview

Authors: Kenneth Vanbrabant1, Eleni Renaers1, Liesa Engelen1, Brigitte Reimann1, Hanne Croons1, Thessa Van Pee1, Maartje Vangeneugden1, Nick Giesberts1, Marcel Ameloot2, Tim S. Nawrot1,3, Michelle Plusquin1
ORCID iDs: Kenneth Vanbrabant, Brigitte Reimann, Michelle Plusquin
  1. Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium
  2. Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
  3. Department of Public Health and Primary Care, Environment and Health Unit, Leuven University, Leuven, Belgium
Institutions: Hasselt University (Belgium); KU Leuven (Belgium)
Journal: EBioMedicine, volume 132, article 106469
Dates: received 16 December 2025; accepted 21 August 2026; published online 5 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.ebiom.2026.106469 · PMID 42700721 · PMCID PMC13572176 · OpenAlex W7208807174
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: behavior only (modality), human (organism)
Methods: Statistics, Machine learning, Physiology & signal measures
Keywords: Air pollution, Neurobehavioral development, Eye-tracking, Cognitive workload, Emotional attention bias
Topic: Air Quality and Health Impacts (Health, Toxicology and Mutagenesis, Environmental Science), according to OpenAlex
Funding: ENVIRONAGE; HORIZON EUROPE Framework Programme; MISTRAL (101095119); Flemish Scientific Research Foundation (G059219); Special Research; Bijzonder Onderzoeksfonds UGent
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

Limburgsgeboortecohort/Residential-air-pollution-oculomotor-markers

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 9900793e18b7571dce7eb7263f449e5e8c19ad45, 18 August 2026
Languages: R (12)
Size: 14 files, 12 scripts
Software Heritage: not archived
Found in: “Data sharing statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (12 files), ggplot2 (6 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
13 files
At the source: github.com/Limburgsgeboortecohort/Residential-air-pollution-oculomotor-markers

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1016/j.ebiom.2026.106469.

Versions

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Version 3, 28 September 2026

  • Authors: added Michelle Plusquin (0000-0002-7271-9722); removed Michelle Plusquin

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 11 authors, 5 keywords, 6 funders, 52 references.

Cite

This paper

Vanbrabant, K., Renaers, E., Engelen, L., Reimann, B., Croons, H., Van Pee, T., Vangeneugden, M., Giesberts, N., Ameloot, M., Nawrot, T. S., & Plusquin, M. (2026). Residential early-life air pollution exposure and oculomotor markers of cognitive workload in children: a prospective birth cohort study. EBioMedicine, 132, 106469. https://doi.org/10.1016/j.ebiom.2026.106469

BibTeX

@article{vanbrabant2026residential,
author = {Vanbrabant, Kenneth and Renaers, Eleni and Engelen, Liesa and Reimann, Brigitte and Croons, Hanne and Van Pee, Thessa and Vangeneugden, Maartje and Giesberts, Nick and Ameloot, Marcel and Nawrot, Tim S. and Plusquin, Michelle},
title = {{Residential early-life air pollution exposure and oculomotor markers of cognitive workload in children: a prospective birth cohort study}},
journal = {EBioMedicine},
year = {2026},
month = sep,
volume = {132},
pages = {106469},
publisher = {Elsevier},
issn = {2352-3964},
doi = {10.1016/j.ebiom.2026.106469},
url = {https://doi.org/10.1016/j.ebiom.2026.106469},
pmid = {42700721},
pmcid = {PMC13572176}
}

RIS

TY - JOUR
AU - Vanbrabant, Kenneth
AU - Renaers, Eleni
AU - Engelen, Liesa
AU - Reimann, Brigitte
AU - Croons, Hanne
AU - Van Pee, Thessa
AU - Vangeneugden, Maartje
AU - Giesberts, Nick
AU - Ameloot, Marcel
AU - Nawrot, Tim S.
AU - Plusquin, Michelle
TI - Residential early-life air pollution exposure and oculomotor markers of cognitive workload in children: a prospective birth cohort study
T2 - EBioMedicine
J2 - EBioMedicine
PY - 2026
DA - 2026/09/05
VL - 132
SP - 106469
SN - 2352-3964
PB - Elsevier
DO - 10.1016/j.ebiom.2026.106469
UR - https://doi.org/10.1016/j.ebiom.2026.106469
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.ebiom.2026.106469",
"type": "article-journal",
"title": "Residential early-life air pollution exposure and oculomotor markers of cognitive workload in children: a prospective birth cohort study",
"container-title": "EBioMedicine",
"author": [
{
"family": "Vanbrabant",
"given": "Kenneth"
},
{
"family": "Renaers",
"given": "Eleni"
},
{
"family": "Engelen",
"given": "Liesa"
},
{
"family": "Reimann",
"given": "Brigitte"
},
{
"family": "Croons",
"given": "Hanne"
},
{
"family": "Van Pee",
"given": "Thessa"
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{
"family": "Vangeneugden",
"given": "Maartje"
},
{
"family": "Giesberts",
"given": "Nick"
},
{
"family": "Ameloot",
"given": "Marcel"
},
{
"family": "Nawrot",
"given": "Tim S."
},
{
"family": "Plusquin",
"given": "Michelle"
}
],
"container-title-short": "EBioMedicine",
"volume": "132",
"page": "106469",
"DOI": "10.1016/j.ebiom.2026.106469",
"PMID": "42700721",
"PMCID": "PMC13572176",
"ISSN": "2352-3964",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.ebiom.2026.106469",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
5
]
]
}
}

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