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Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation

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  • Ronald Herrera

    (Occupational and Environmental Epidemiology and NetTeaching Unit, Institute for Occupational, Social and Environmental Medicine, University Hospital Munich (Ludwig Maximilians University), 80336 Munich, Germany
    Institute for Medical Informatics, Biometry and Epidemiology-IBE, Ludwig Maximilians University, 81377 Munich, Germany)

  • Ursula Berger

    (Institute for Medical Informatics, Biometry and Epidemiology-IBE, Ludwig Maximilians University, 81377 Munich, Germany)

  • Ondine S. Von Ehrenstein

    (Departments of Community Health Sciences and Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90025, USA)

  • Iván Díaz

    (Department of Biostatistics Bloomberg, School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA)

  • Stella Huber

    (Occupational and Environmental Epidemiology and NetTeaching Unit, Institute for Occupational, Social and Environmental Medicine, University Hospital Munich (Ludwig Maximilians University), 80336 Munich, Germany)

  • Daniel Moraga Muñoz

    (Medicine School, Science Faculty, Tarapaca University, Past Staff Catholic University of the North, Coquimbo 1781421, Chile)

  • Katja Radon

    (Occupational and Environmental Epidemiology and NetTeaching Unit, Institute for Occupational, Social and Environmental Medicine, University Hospital Munich (Ludwig Maximilians University), 80336 Munich, Germany)

Abstract

In a town located in a desert area of Northern Chile, gold and copper open-pit mining is carried out involving explosive processes. These processes are associated with increased dust exposure, which might affect children’s respiratory health. Therefore, we aimed to quantify the causal attributable risk of living close to the mines on asthma or allergic rhinoconjunctivitis risk burden in children. Data on the prevalence of respiratory diseases and potential confounders were available from a cross-sectional survey carried out in 2009 among 288 (response: 69 % ) children living in the community. The proximity of the children’s home addresses to the local gold and copper mine was calculated using geographical positioning systems. We applied targeted maximum likelihood estimation to obtain the causal attributable risk (CAR) for asthma, rhinoconjunctivitis and both outcomes combined. Children living more than the first quartile away from the mines were used as the unexposed group. Based on the estimated CAR, a hypothetical intervention in which all children lived at least one quartile away from the copper mine would decrease the risk of rhinoconjunctivitis by 4.7 percentage points (CAR: − 4.7 ; 95 % confidence interval ( 95 % CI): − 8.4 ; − 0.11 ); and 4.2 percentage points (CAR: − 4.2 ; 95 % CI: − 7.9 ; − 0.05 ) for both outcomes combined. Overall, our results suggest that a hypothetical intervention intended to increase the distance between the place of residence of the highest exposed children would reduce the prevalence of respiratory disease in the community by around four percentage points. This approach could help local policymakers in the development of efficient public health strategies.

Suggested Citation

  • Ronald Herrera & Ursula Berger & Ondine S. Von Ehrenstein & Iván Díaz & Stella Huber & Daniel Moraga Muñoz & Katja Radon, 2017. "Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation," IJERPH, MDPI, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:gam:jijerp:v:15:y:2017:i:1:p:39-:d:124551
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Alan E. Hubbard & Mark J. van der Laan, 2008. "Population intervention models in causal inference," Biometrika, Biometrika Trust, vol. 95(1), pages 35-47.
    3. Porter Kristin E. & Gruber Susan & van der Laan Mark J. & Sekhon Jasjeet S., 2011. "The Relative Performance of Targeted Maximum Likelihood Estimators," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-34, August.
    4. Gruber, Susan & Laan, Mark van der, 2012. "tmle: An R Package for Targeted Maximum Likelihood Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i13).
    5. Westreich, D. & Edwards, J.K. & Rogawski, E.T. & Hudgens, M.G. & Stuart, E.A. & Cole, S.R., 2016. "Causal impact: Epidemiological approaches for a public health of consequence," American Journal of Public Health, American Public Health Association, vol. 106(6), pages 1011-1012.
    6. Ahern, J. & Margerison-Zilko, C. & Hubbard, A. & Galea, S., 2013. "Alcohol outlets and binge drinking in urban neighborhoods: The implications of nonlinearity for intervention and policy," American Journal of Public Health, American Public Health Association, vol. 103(4), pages 81-87.
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