Back

The risk of Asthma hospitalisation associated with proximity to the liquefied natural gas Ports in Gladstone, Australia

Lam, T. T. K.

2025-10-02 epidemiology
10.1101/2025.10.01.25337041 medRxiv
Show abstract

The mining of natural gases has been ongoing for several decades, along with on-going research effort on the impact it may have on human health. Despite the research effort invested into this issue, a knowledge gap still exist on how extensive extractive industry emissions can impact the health of populations living in proximity to these mining activities. This study aims to investigate the risk of asthma hospitalisation associated with proximity to the liquefied natural gas (LNG) ports in Gladstone, Australia. A total of three LNG terminals were identified on Curtis Island that were set as the exposure. The study area was established using a 100km circular buffer around the exposure, which encapsulated a total of 167 suburbs. The asthma hospitalisation rate was calculated for each of the 167 suburbs and sociodemographic data was collated using the Socio-Economic Indexes for Areas (SEIFA), this includes the Index of Relative Socio-Economic Advantages and Disadvantages (IRSAD), the Index of Education and Occupation (IEO), and the Index of Economic Resources (IER). Data collected for the suburbs in Gladstone were all tested for spatial autocorrelation. Spatial autocorrelation analysis was performed using GeoDa, implementing a Global Bivariate Morans I and Local Moran analysis. The results of this study have identified that a lack of diverse variable incorporation or multivariable analysis can greatly impact the implementation of spatial and temporal analyses. The study conclude that the use of spatial statistics is highly recommended for future studies as incorporating spatial analysis can create a more comprehensive visualisation of high-risk areas. Additionally, the incorporation of a multivariable spatial analysis can potentially benefit prevalence modelling in complex scenarios concerning multiple variables that can impact determinant of disease in the general population.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.