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Open AccessArticle

Causal Effects of Prenatal Exposure to PM2.5 on Child Development and the Role of Unobserved Confounding

1
Department of Statistics, Computer Science, Applications, University of Florence, 59 50134 Florence, Viale Morgagni, Italy, [email protected]
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BIODONOSTIA Health Research Institute, 20014 San Sebastian, Spain
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Biomedical Research Centre Network for Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
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Faculty of Medicine, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
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Sub-Directorate for Public Health of Guipúzcoa, Department of Health, Government of the Basque Country, 20013 San Sebastian, Spain
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Faculty of Psychology, University of the Basque Country (UPV/EHU), 20018 San Sebastian, Spain
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(22), 4381; https://doi.org/10.3390/ijerph16224381
Received: 12 September 2019 / Revised: 3 November 2019 / Accepted: 6 November 2019 / Published: 9 November 2019
(This article belongs to the Special Issue Population-Based Birth Cohort Studies in Epidemiology)
Prenatal exposure to airborne particles is a potential risk factor for infant neuropsychological development. This issue is usually explored by regression analysis under the implicit assumption that all relevant confounders are accounted for. Our aim is to estimate the causal effect of prenatal exposure to high concentrations of airborne particles with a diameter < 2.5 µm (PM2.5) on children’s psychomotor and mental scores in a birth cohort from Gipuzkoa (Spain), and investigate the robustness of the results to possible unobserved confounding. We adopted the propensity score matching approach and performed sensitivity analyses comparing the actual effect estimates with those obtained after adjusting for unobserved confounders simulated to have different strengths. On average, mental and psychomotor scores decreased of −2.47 (90% CI: −7.22; 2.28) and −3.18 (90% CI: −7.61; 1.25) points when the prenatal exposure was ≥17 μg/m3 (median). These estimates were robust to the presence of unmeasured confounders having strength similar to that of the observed ones. The plausibility of having omitted a confounder strong enough to drive the estimates to zero was poor. The sensitivity analyses conferred solidity to our findings, despite the large sampling variability. This kind of sensitivity analysis should be routinely implemented in observational studies, especially in exploring new relationships. View Full-Text
Keywords: child development; airborne particles; propensity score matching; sensitivity analysis; bias analysis; Monte Carlo simulations child development; airborne particles; propensity score matching; sensitivity analysis; bias analysis; Monte Carlo simulations
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Tozzi, V.; Lertxundi, A.; Ibarluzea, J.M.; Baccini, M. Causal Effects of Prenatal Exposure to PM2.5 on Child Development and the Role of Unobserved Confounding. Int. J. Environ. Res. Public Health 2019, 16, 4381.

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