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Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform
BACKGROUND: In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM(2.5)) pollution is becoming a serious public health...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5645640/ https://www.ncbi.nlm.nih.gov/pubmed/28974484 http://dx.doi.org/10.2196/publichealth.8078 |
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author | Yang, Hongxi Li, Shu Sun, Li Zhang, Xinyu Hou, Jie Wang, Yaogang |
author_facet | Yang, Hongxi Li, Shu Sun, Li Zhang, Xinyu Hou, Jie Wang, Yaogang |
author_sort | Yang, Hongxi |
collection | PubMed |
description | BACKGROUND: In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM(2.5)) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public’s awareness and knowledge of modifiable risk factors of lung cancer for prevention. OBJECTIVE: The objective of our study was to explore the public’s awareness of the association of PM(2.5) with lung cancer risk in China by analyzing the relationship between the daily PM(2.5) concentration and searches for the term “lung cancer” on an Internet big data platform, Baidu. METHODS: We collected daily PM(2.5) concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM(2.5) concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables. RESULTS: In 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM(2.5) concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM(2.5) concentration was poor (all r(2)(s)<.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM(2.5) concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5% level for each city. CONCLUSIONS: The daily average PM(2.5) concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public’s awareness of the association of PM(2.5) with lung cancer risk, to lead the public to seek more information about PM(2.5) and its hazards, and to cope with their environment and its risks appropriately. |
format | Online Article Text |
id | pubmed-5645640 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-56456402017-10-24 Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform Yang, Hongxi Li, Shu Sun, Li Zhang, Xinyu Hou, Jie Wang, Yaogang JMIR Public Health Surveill Original Paper BACKGROUND: In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM(2.5)) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public’s awareness and knowledge of modifiable risk factors of lung cancer for prevention. OBJECTIVE: The objective of our study was to explore the public’s awareness of the association of PM(2.5) with lung cancer risk in China by analyzing the relationship between the daily PM(2.5) concentration and searches for the term “lung cancer” on an Internet big data platform, Baidu. METHODS: We collected daily PM(2.5) concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM(2.5) concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables. RESULTS: In 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM(2.5) concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM(2.5) concentration was poor (all r(2)(s)<.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM(2.5) concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5% level for each city. CONCLUSIONS: The daily average PM(2.5) concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public’s awareness of the association of PM(2.5) with lung cancer risk, to lead the public to seek more information about PM(2.5) and its hazards, and to cope with their environment and its risks appropriately. JMIR Publications 2017-10-03 /pmc/articles/PMC5645640/ /pubmed/28974484 http://dx.doi.org/10.2196/publichealth.8078 Text en ©Hongxi Yang, Shu Li, Li Sun, Xinyu Zhang, Jie Hou, Yaogang Wang. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 03.10.2017. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Yang, Hongxi Li, Shu Sun, Li Zhang, Xinyu Hou, Jie Wang, Yaogang Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform |
title | Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform |
title_full | Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform |
title_fullStr | Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform |
title_full_unstemmed | Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform |
title_short | Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform |
title_sort | effects of the ambient fine particulate matter on public awareness of lung cancer risk in china: evidence from the internet-based big data platform |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5645640/ https://www.ncbi.nlm.nih.gov/pubmed/28974484 http://dx.doi.org/10.2196/publichealth.8078 |
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