A systematic review of spatial and temporal epidemiological approaches, focus on lung cancer risk associated with particulate matter

Basanta Kumar Neupane, Bipin Kumar Acharya, Chunxiang Cao, Min Xu*, Hemraj Bhattarai, Yujie Yang, Shaohua Wang

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Background: Particulate matter (PM), including the major risk factor for lung cancer (LC), greatly impacts human health. Although numerous studies have highlighted spatiotemporal patterns and PM-LC associations, these studies have not been well-reviewed. Thus, we examined epidemiological studies linked with PM-LC and provided concise, up-to-date data. Methods: We used certain keywords to review articles published in PubMed, Web of Science, Scopus, and Google Scholar until 30th June 2024 and identified 1474 research articles. We then filtered the research articles based on our criteria and ultimately dropped down to 30 for this review. Results: Out of the thirty reviewed studies on the PM-LC relation, twenty-four focused on PM2.5, four on PM10, and two on both, indicating that approximately 80% of the respondents were inclined toward fine particles and their health impacts. The study revealed that 22 studies used visualization, 12 used exploration, and 15 used modeling methods. A strong positive relationship was reported between LC and PM2.5, ranging from 1.04 to 1.60 (95% CI) for a 10 µg/m3 increase in PM2.5 exposure. However, compared to PM2.5, PM10 was found to have a significantly less positive association. Conclusions: Very few studies have used advanced spatiotemporal methods to examine the association between LC and PM. Advanced spatiotemporal analysis techniques should be employed to explore this association in specific geographical locations. Further research should utilize spatiotemporal epidemiological approaches to study the link between PM and lung cancer.

Original languageEnglish
Article number2945
Number of pages22
JournalBMC Public Health
Volume24
Issue number1
DOIs
Publication statusPublished - 24 Oct 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

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

  • Lung cancer
  • Epidemiology
  • Particulate matter
  • Spatiotemporal method
  • Pollution

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