Skip to main navigation Skip to search Skip to main content

Estimating the causal impact of proximity to gold and copper mines on respiratory diseases in Chilean children: An application of targeted maximum likelihood estimation

  • Ronald Herrera
  • , Ursula Berger
  • , Ondine S. Von Ehrenstein
  • , Iván Díaz
  • , Stella Huber
  • , Daniel Moraga Muñoz
  • , Katja Radon
  • Ludwig Maximilian University of Munich
  • University of California at Los Angeles
  • Johns Hopkins University

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

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.

Original languageEnglish
Article number39
JournalInternational Journal of Environmental Research and Public Health
Volume15
Issue number1
DOIs
StatePublished - Jan 2018

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Causal inference
  • Children
  • Chile
  • Environmental public health
  • Machine learning
  • Respiratory health
  • TMLE

Fingerprint

Dive into the research topics of 'Estimating the causal impact of proximity to gold and copper mines on respiratory diseases in Chilean children: An application of targeted maximum likelihood estimation'. Together they form a unique fingerprint.

Cite this