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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 (PM2.5) 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.
The objective of our study was to explore the public’s awareness of the association of PM2.5 with lung cancer risk in China by analyzing the relationship between the daily PM2.5 concentration and searches for the term “lung cancer” on an Internet big data platform, Baidu.
We collected daily PM2.5 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 PM2.5 concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables.
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 PM2.5 concentration were positive and statistically significant (
The daily average PM2.5 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 PM2.5 with lung cancer risk, to lead the public to seek more information about PM2.5 and its hazards, and to cope with their environment and its risks appropriately.
Air pollution has become the most severe and worrisome environmental problem and a major threat to public health in China [
Lung cancer is the most common incident cancer and the leading cause of cancer death in China. New cancer cases and cancer deaths were estimated to be 733,000 and 591,000, respectively, every year [
With the development of a well-established network, the Internet has become a vital channel for the public to access health information. About 63% of cancer patients use the Internet to search for information regarding cancer specifically, and use of the Internet as a source for oncological information is increasing rapidly [
Baidu is one of the most important Internet big data platforms in China. According to the Chinese Internet Users Search Behavior Study, the Baidu search engine is the most popular among Chinese Internet users, with a priority selection incidence of 93.1% [
Using the Baidu Index, we examined Chinese public search interest in lung cancer. The goal of this study was to explore public awareness of the association of PM2.5 with lung cancer risk by analyzing the relationship between daily PM2.5 concentration and daily Baidu Index searches for the term “lung cancer” in China.
We collected air pollution data from the Chinese Air Quality Online Monitoring and Analysis Platform [
The Baidu Index is a useful tool to process and analyze search query data. Its database contains logs of online and mobile phone search query volume submitted from January 2011. The daily Baidu Index is the weighted sum of the search frequency for a keyword based on its daily search volume on the Baidu search engine. The Baidu Index has been proved to be a useful indicator of public interest in and awareness of health-related topics. In this study, we hypothesized that the Baidu Index would offer potential insight into the general population’s awareness of lung cancer. The conceptualized awareness of lung cancer in this study could be considered to be based on the general population’s ability to seek knowledge and information for the disease or pay attention to the disease. We used the Baidu Index to determine the relevance of the search term “lung cancer” as an indicator of the public’s awareness of lung cancer. We collected daily Baidu Index data from the Baidu Index websites [
The Baidu media index is the number of news items containing a specified keyword in their headlines collected by the Baidu news database, sourced from Chinese major websites, including national and local news websites and networks. We collected daily Baidu media index data for the keyword “lung cancer” from the Baidu Index websites [
We calculated descriptive statistics for the 2 variables. These included the mean, standard deviation, median and interquartile range, and the minimum and maximum values of both variables.
We used the Kruskal-Wallis
We used Spearman and Pearson correlation analyses to explore the correlation between the daily Baidu Index for lung cancer searches, daily Baidu media index for lung cancer, and daily average PM2.5 concentration, with the statistical significance level set at .01. We calculated Pearson partial correlation coefficients to assess the intercorrelations between the Baidu Index, Baidu media index, and daily PM2.5 concentration. Multiple linear regression analysis explored the potential influence of the daily Baidu media index for lung cancer and daily average PM2.5 concentration on the daily Baidu Index for lung cancer searches.
Granger causality is a concept of causality derived from the idea that causes cannot occur after effects and that, if one variable is the cause of another, knowing the status of the cause at an earlier point in time can enhance prediction of the effect at a later point in time [
We conducted the descriptive statistics, Kruskal-Wallis H test, and Spearman correlation analysis in IBM SPSS version 19.0 (IBM Corporation), and the Granger causality test using EViews 9 student version (IHS Global Inc).
Summary statistics of daily average fine particulate matter (PM2.5) concentration and daily Baidu Index for the search term “lung cancer” in 31 Chinese capital cities from January 1, 2014 to December 31, 2016.
City | Daily Baidu Index for lung cancer searches | Daily average PM2.5 concentration (μg/m3) | ||
Mean (SD) | Median (25th, 75th percentile) | Mean (SD) | Median (25th, 75th percentile) | |
Beijing | 408 (69.53) | 91 (366, 441) | 79.23 (68.25) | 60.85 (29.9, 106.43) |
Changchun | 146 (28.52) | 74 (139, 162) | 58.68 (50.44) | 43.25 (28.43, 72.38) |
Changsha | 195 (31.42) | 75.5 (178, 216) | 62.8 (39.91) | 52.15 (36, 79.2) |
Chengdu | 277 (58.8) | 80 (236,308) | 65.9 (44.22) | 53.35 (35.53, 82.98) |
Chongqing | 208 (24.63) | 71 (194, 222) | 57.24 (34.19) | 47.3 (33.9, 68.98) |
Fuzhou | 165 (28.19) | 52 (151, 180) | 29.17 (16.14) | 26 (18.5, 36.1) |
Guangzhou | 285 (40.96) | 55.5 (258.25, 306.75) | 40.78 (21.95) | 35.8 (24.4, 51.78) |
Guiyang | 122 (29.93) | 56.5 (87, 143) | 40.13 (21.75) | 35.9 (24.73, 49.28) |
Harbin | 168 (24.36) | 67 (159, 182) | 64.39 (65.96) | 40.95 (24.13, 83.48) |
Haikou | 111 (30.37) | 35 (76, 135) | 21.64 (15.2) | 16.3 (12.3, 26.5) |
Hangzhou | 250 (42.41) | 72 (219.25, 278.75) | 55.23 (30.74) | 48.9 (33, 70.28) |
Hefei | 188 (31.58) | 85 (171, 209) | 67.45 (41.39) | 58.2 (41.03, 83.35) |
Hohhot | 124 (30.98) | 74 (91, 146) | 42.24 (33.96) | 32.05 (19.2, 55.3) |
Jinan | 196 (29.76) | 111 (177, 214) | 85.06 (51.35) | 73.3 (51.83, 103.28) |
Kunming | 157 (27.95) | 51 (148, 173) | 29.79 (13.25) | 27.25 (19.9, 37.15) |
Lanzhou | 117 (32.65) | 83 (83, 146) | 54.06 (28.25) | 46.2 (35.73, 65) |
Lhasa | 26 (31.4) | 57 (0, 57) | 25.17 (12.11) | 22 (16.5, 30.3) |
Nanchang | 149 (32.31) | 63 (137, 167) | 45.21 (30.93) | 37.1 (23.5, 57.68) |
Nanjing | 189 (25.97) | 78 (173, 205) | 58.49 (38.18) | 49.65 (31.03, 76.25) |
Nanning | 143 (29.23) | 57 (133, 161) | 41.91 (28.42) | 34.15 (22, 53.5) |
Shanghai | 335 (52.13) | 64 (303, 358) | 50.17 (32.09) | 42.15 (27.2, 65) |
Shenyang | 171 (25.25) | 82 (156, 188) | 65.68 (54.92) | 49.6 (33.6, 82.43) |
Shijiazhuang | 176 (32.92) | 115 (158, 200) | 104.01 (88.55) | 80.25 (43.53, 131.1) |
Taiyuan | 150 (32.5) | 88 (137, 171) | 64.81 (45.83) | 53.15 (32.8, 83.33) |
Tianjin | 204 (24.9) | 90 (186, 220) | 74.63 (53.61) | 60.6 (37.33, 95.08) |
Urumqi | 116 (29.92) | 87 (82, 139) | 66.97 (61.11) | 41.65 (27.6, 82.28) |
Wuhan | 227 (26.54) | 87 (212.25, 240) | 69.48 (46.95) | 58.9 (38.13, 86.28) |
Xian | 207 (29.64) | 87 (192, 223) | 68.3 (54.44) | 51.35 (35.2, 79.1) |
Xining | 73 (29.5) | 80 (59, 74) | 52.63 (24.34) | 47.35 (35.73, 65.05) |
Yinchuan | 82 (30.54) | 76 (61, 118) | 50.04 (31.12) | 40.9 (30.13, 58.38) |
Zhengzhou | 223 (29.74) | 107 (203, 242) | 87.02 (61.2) | 71.4 (46.8, 108.78) |
All cities | 180 (83.21) | 73 (136, 218) | 57.37 (47.54) | 44 (27.6, 71.3) |
Distribution of (A) mean daily average fine particulate matter (PM2.5) concentration and (B) mean daily Baidu Index for the search term “lung cancer” in 31 Chinese capital cities, January 1, 2014 to December 31, 2016.
Compared with 2014, the Baidu Index for lung cancer searches across all cities for 2015 and 2016 decreased by 2% and 5%, respectively. The annual mean daily average PM2.5 concentration had decreased slightly from 2014 to 2016. The Baidu media index for lung cancer ranged from 0 to 6523, with a median of 9 (25th, 75th percentile 4, 14) in 2016. The Baidu media index for lung cancer peaked on September 17, 2015.
Except for Chengdu and Hohhot, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and daily average PM2.5 concentration were positive. Most of the Spearman rank correlation coefficients were statistically significant (
We used the augmented Dickey-Fuller unit root test to test the stationarity of the 2 time series. The lag length was determined automatically using the Schwarz information criterion. The series for all cities except Chengdu were stationary at the statistical significance level set at .01, and the series for Chengdu were also stationary at the first difference (
Spearman correlation between daily Baidu Index for the search term “lung cancer,” daily Baidu media index, and daily fine particulate matter (PM2.5) concentration.
City | Correlation | |||||
Baidu Index & PM2.5 | Baidu Index & Baidu media index | Baidu media index & PM2.5 | ||||
Beijing | .093a | .002 | .359a | <.001 | –.013 | .68 |
Changchun | .060b | .048 | .167a | <.001 | .122a | <.001 |
Changsha | .184a | <.001 | .196a | <.001 | .047 | .12 |
Chengdu | –.041 | .17 | .006 | .84 | .026 | .40 |
Chongqing | .139a | <.001 | .189a | <.001 | –.015 | .62 |
Fuzhou | .125a | <.001 | .191a | <.001 | .099a | .001 |
Guangzhou | .167a | <.001 | .303a | <.001 | .003 | .93 |
Guiyang | .062b | .04 | .096a | .001 | .029 | .34 |
Harbin | .219a | <.001 | .269a | <.001 | .145a | <.001 |
Haikou | .091a | .002 | .164a | <.001 | –.018 | .55 |
Hangzhou | .240a | <.001 | .253a | <.001 | .133a | <.001 |
Hefei | .231a | <.001 | .225a | <.001 | .098a | .001 |
Hohhot | .036 | .23 | .188a | <.001 | .028 | .36 |
Jinan | .149a | <.001 | .164a | <.001 | .052 | .09 |
Kunming | .024 | .42 | .081a | .007 | .031 | .30 |
Lanzhou | .057 | .06 | .149a | <.001 | .046 | .13 |
Lhasa | .001 | .98 | .039 | .19 | .123a | <.001 |
Nanchang | .118a | <.001 | .228a | <.001 | .047 | .12 |
Nanjing | .220a | <.001 | .232a | <.001 | .125a | <.001 |
Nanning | .039 | .19 | .165a | <.001 | –.005 | .87 |
Shanghai | .050 | .10 | .269a | <.001 | .115a | <.001 |
Shenyang | .100a | .001 | .276a | <.001 | .103a | .001 |
Shijiazhuang | .204a | <.001 | .179a | <.001 | .030 | .33 |
Taiyuan | .088a | .003 | .218a | <.001 | .027 | .38 |
Tianjin | .085a | .005 | .290a | <.001 | .037 | .22 |
Urumqi | .153a | <.001 | .115a | <.001 | .058 | .06 |
Wuhan | .154a | <.001 | .201a | <.001 | .055 | .07 |
Xian | .111a | <.001 | .253a | <.001 | .035 | .25 |
Xining | .081a | .007 | .105a | .001 | .109a | <.001 |
Yinchuan | –.012 | .68 | .102a | .001 | .012 | .69 |
Zhengzhou | .238a | <.001 | .277a | <.001 | .085a | .005 |
aCorrelation is significant at the .01 level (2-tailed).
bCorrelation is significant at the .05 level (2-tailed).
Results of unit root tests for the time series of daily average fine particulate matter (PM2.5) concentration and daily Baidu Index for the search term “lung cancer.”
City | Unit root test for time series of daily Baidu Index |
Unit root test for time series of daily PM2.5 |
Resulta | ||||
ADFb | 1% Level | ADF | 1% Level | ||||
Beijing | –5.23 | –3.44 | <.001 | –18.44 | –3.44 | <.001 | Stationarity |
Changchun | –7.83 | –3.44 | <.001 | –7.32 | –3.44 | <.001 | Stationarity |
Chengdu | –23.41 | –3.44 | <.001 | –17.18 | –3.44 | <.001 | Stationarity |
Chongqing | –5.70 | –3.44 | <.001 | –10.18 | –3.44 | <.001 | Stationarity |
Changsha | –5.25 | –3.44 | <.001 | –9.76 | –3.44 | <.001 | Stationarity |
Fuzhou | –5.00 | –3.44 | <.001 | –8.88 | –3.44 | <.001 | Stationarity |
Guiyang | –29.62 | –3.44 | <.001 | –8.78 | –3.44 | <.001 | Stationarity |
Guizhou | –6.01 | –3.44 | <.001 | –13.39 | –3.44 | <.001 | Stationarity |
Harbin | –7.21 | –3.44 | <.001 | –6.49 | –3.44 | <.001 | Stationarity |
Hefei | –4.50 | –3.44 | <.001 | –7.13 | –3.44 | <.001 | Stationarity |
Hohhot | –27.24 | –3.44 | <.001 | –4.58 | –3.44 | <.001 | Stationarity |
Haikou | –30.13 | –3.44 | <.001 | –10.92 | –3.44 | <.001 | Stationarity |
Hangzhou | –3.79 | –3.44 | <.001 | –4.53 | –3.44 | <.001 | Stationarity |
Jinan | –5.10 | –3.44 | <.001 | –15.65 | –3.44 | <.001 | Stationarity |
Kunming | –7.64 | –3.44 | <.001 | –9.28 | –3.44 | <.001 | Stationarity |
Lhasa | –4.33 | –2.57 | <.001 | –4.24 | –3.44 | <.001 | Stationarity |
Lanzhou | –28.38 | –3.44 | <.001 | –5.47 | –3.44 | <.001 | Stationarity |
Nanchang | –7.17 | –3.44 | <.001 | –10.42 | –3.44 | <.001 | Stationarity |
Nanjing | –5.60 | –3.44 | <.001 | –6.29 | –3.44 | <.001 | Stationarity |
Nanning | –7.48 | –3.44 | <.001 | –7.62 | –3.44 | <.001 | Stationarity |
Shanghai | –5.68 | –3.44 | <.001 | –18.64 | –3.44 | <.001 | Stationarity |
Shijiazhuang | –4.58 | –3.44 | <.001 | –4.71 | –3.44 | <.001 | Stationarity |
Shenyang | –5.23 | –3.44 | <.001 | –12.08 | –3.44 | <.001 | Stationarity |
Tianjin | –5.84 | –3.44 | <.001 | –6.99 | –3.44 | <.001 | Stationarity |
Taiyuan | –5.80 | –3.44 | <.001 | –4.69 | –3.44 | <.001 | Stationarity |
Wuhan | –5.47 | –3.44 | <.001 | –4.38 | –3.44 | <.001 | Stationarity |
Xian | –5.20 | –3.44 | <.001 | –4.31 | –3.44 | <.001 | Stationarity |
Xining | –30.74 | –3.44 | <.001 | –5.49 | –3.44 | <.001 | Stationarity |
Yinchuan | –19.20 | –3.44 | <.001 | –10.21 | –3.44 | <.001 | Stationarity |
Zhengzhou | –5.16 | –3.44 | <.001 | –5.44 | –3.44 | <.001 | Stationarity |
aTime series of daily average PM2.5 concentration and of daily Baidu Index for lung cancer were stationary at the same level.
bADF: augmented Dickey-Fuller unit root test.
Estimate of regression coefficient (β) with 95% CI.
For 17 of the 31 Chinese capital cities, regression analysis revealed that the positive effects of daily average PM2.5 concentration on the daily Baidu Index for lung cancer searches were statistically significant (
The result of the panel co-integration (Engle-Granger) test indicated the existence of co-integration between variables for each city at the 1% significance level (
Results of co-integration test of the 2 time series of daily average fine particulate matter (PM2.5) concentration and daily Baidu Index for the search term “lung cancer.”
City | Unit root test for the residual series | Resulta | ||||
ADFb | 1% Level | 5% Level | 10% Level | |||
Beijing | –5.26 | –2.57 | –1.94 | –1.62 | <.001 | Co-integration |
Changchun | –7.84 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Chengdu | –23.32 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Chongqing | –5.89 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Changsha | –5.52 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Fuzhou | –5.08 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Guiyang | –29.63 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Guizhou | –6.18 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Harbin | –7.53 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Hefei | –4.74 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Hohhot | –27.24 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Haikou | –30.22 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Hangzhou | –4.06 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Jinan | –5.24 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Kunming | –7.65 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Lhasa | –20.05 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Lanzhou | –28.46 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Nanchang | –7.22 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Nanjing | –5.78 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Nanning | –7.48 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Shanghai | –5.71 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Shijiazhuang | –4.85 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Shenyang | –5.35 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Tianjin | –5.95 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Taiyuan | –5.83 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Wuhan | –5.51 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Xian | –5.21 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Xining | –30.80 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Yinchuan | –19.20 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
Zhengzhou | –5.44 | –3.44 | –2.86 | –2.57 | <.001 | Co-integration |
aTime series of daily average PM2.5 concentration and of daily Baidu Index for lung cancer were co-integrated.
bADF: augmented Dickey-Fuller unit root test.
Results of Granger causality test of the causal relationship between daily average fine particulate matter (PM2.5) concentration and daily Baidu Index for the search term “lung cancer.”
City | Null hypothesisa | |||||
Daily average PM2.5 concentration does not Granger |
Daily Baidu Index for lung cancer searches does not |
|||||
Beijing | 1.25 | 15, 1066 | .23 | 0.66 | 15, 1066 | .81 |
Changchun | 1.62 | 5, 1086 | .15 | 0.30 | 5, 1086 | .91 |
Chengdu | 0.17 | 5, 1086 | .97 | 1.39 | 5, 1086 | .22 |
Chongqing | 1.32 | 6, 1084 | .24 | 1.41 | 6, 1084 | .20 |
Changsha | 1.30 | 6, 1084 | .25 | 1.14 | 6, 1084 | .33 |
Fuzhou | 0.93 | 6, 1079 | .47 | 1.21 | 6, 1079 | .29 |
Guiyang | 0.19 | 1, 1090 | .66 | 0.36 | 1, 1090 | .54 |
Guizhou | 1.54 | 5, 1086 | .17 | 1.84 | 5, 1086 | .10 |
Harbin | 1.14 | 6, 1084 | .33 | 2.78 | 6, 1084 | .01 |
Hefei | 1.94 | 6, 1084 | .07 | 2.62 | 6, 1084 | .01 |
Hohhot | 1.72 | 1, 1090 | .19 | 0.15 | 1, 1090 | .69 |
Haikou | 1.69 | 1,1090 | .19 | 1.44 | 1,1090 | .22 |
Hangzhou | 0.86 | 6, 1084 | .52 | 1.86 | 6, 1084 | .08 |
Jinan | 1.57 | 6, 1084 | .15 | 1.42 | 6, 1084 | .20 |
Kunming | 1.13 | 6, 1084 | .34 | 0.88 | 6, 1084 | .50 |
Lhasa | 0.81 | 1, 1090 | .37 | 0.14 | 1, 1090 | .70 |
Lanzhou | 1.97 | 1, 1090 | .16 | 2.91 | 1, 1090 | .08 |
Nanchang | 1.28 | 6, 1084 | .26 | 1.45 | 6, 1084 | .19 |
Nanjing | 1.49 | 6, 1084 | .18 | 2.05 | 6, 1084 | .05 |
Nanning | 0.62 | 5, 1086 | .68 | 1.82 | 5, 1086 | .10 |
Shanghai | 0.59 | 8, 1080 | .79 | 0.65 | 8, 1080 | .73 |
Shijiazhuang | 0.52 | 6, 1084 | .79 | 1.62 | 6, 1084 | .13 |
Shenyang | 0.39 | 6, 1078 | .88 | 0.88 | 6, 1078 | .50 |
Tianjin | 0.89 | 5, 1086 | .48 | 0.90 | 5, 1086 | .47 |
Taiyuan | 2.42 | 7, 1082 | .02 | 0.66 | 7, 1082 | .7 |
Wuhan | 0.95 | 6, 1084 | .45 | 1.09 | 6, 1084 | .36 |
Xian | 1.35 | 7, 1082 | .22 | 0.72 | 7, 1082 | .64 |
Xining | 3.40 | 1, 1090 | .06 | 0.17 | 1, 1090 | .67 |
Yinchuan | 0.24 | 1, 1090 | .61 | 0.00 | 1, 1090 | .99 |
Zhengzhou | 1.71 | 6, 1084 | .11 | 1.28 | 6, 1084 | .26 |
aNull hypothesis is rejected when
Our analysis showed a slightly positive correlation between daily average PM2.5 concentration and the daily Baidu Index for the search term “lung cancer” in most of the 31 cities. The result of the regression analysis also showed that daily average PM2.5 concentration had a weak impact on the daily Baidu Index for lung cancer searches. The Granger causality test indicated that there was no causal relationship between daily average PM2.5 concentration and the daily Baidu Index for lung cancer searches.
Some studies have assessed the association between PM2.5 and subsequent risks of lung cancer incidence and mortality, suggesting that PM2.5 could be a risk factor for lung cancer. Therefore, the mass media in China often remind people to use the necessary protection at a high concentration of PM2.5. The public’s search interest in lung cancer reflects their concern about this disease. In China, the general population can easily get daily information about the PM2.5 concentration through the government’s official website, the news media, and many weather forecast mobile phone apps. However, little is known about whether the reported daily information about PM2.5 concentration significantly stimulates the public’s interest in lung cancer in China. Google Trends and the Baidu Index have proved to be useful indicators of public interest in and attention to health-related topics [
Wang et al [
In 2013, the European Study of Cohorts for Air Pollution Effects reported that each 5 μg/m3increase of PM2.5 was statistically significantly associated with a hazard ratio for lung cancer of 1.18 (95% CI 0.96-1.46) [
Our result showed that the daily average PM2.5 concentration had a modest impact on the daily Baidu Index for lung cancer searches, but there was still substantial uncertainty about the association. First, the effect of daily average PM2.5 concentration on the public’s awareness of its health hazards might be marginal. People may not be concerned much about lung cancer risks until serious health hazards of PM2.5 emerge. However, online searches for lung cancer may decline when the significance of PM2.5 has become widely recognized. Similarly, the initial panic over lung cancer caused by some events might increase searches for the term “lung cancer” during the first few days, which may drop after the initial panic; such possibilities may have biased our results. Second, lung cancer is a chronic disease with a slow onset, and exposure to PM2.5 is more detrimental to lung cancer risk in the long term. The daily average PM2.5 concentration had a relatively long, slow impact on the search rate for lung cancer, indicating a possible long time lag in the relationship. Third, lack of awareness that PM2.5 can increase the risk of lung cancer might have an important effect on the association between PM2.5 and the Baidu Index for lung cancer searches.
China is a vast and diverse country, with a population of more than 1.3 billion people. The effect of PM2.5 on the Baidu Index for lung cancer searches might also depend on demographic and socioeconomic conditions, and differences in health literacy among residents in different cities. For the city Shijiazhuang, the daily average PM2.5 concentration was highest, but the Baidu Index for lung cancer searches was significantly lower than for some developed cities, such as Beijing, Fuzhou, and Guangzhou. People in the densely populated and economically developed cities in east China have higher health awareness, have better access to the Internet, and more frequently search for health information than do people in sparsely populated and developing cities. The daily average PM2.5 concentration in Lhasa was similar to that in Haikou, but the Baidu Index for lung cancer searches in these 2 cities was notably different. In our data, the mean daily average PM2.5 concentration across all cities was 53.47 (SD 47.54) μg/m3, which is more than the World Health Organization standard of 25 μg/m3[
November is Lung Cancer Awareness Month internationally, and November 18 is Lung Cancer Day, which aim to raise lung cancer awareness among the public. In this study, we found a significant difference in the Baidu media index of lung cancer among different months by Kruskal-Wallis
Contrary to our expectations, the daily average PM2.5 concentration did not notably enhance the public’s awareness of lung cancer. Lung cancer is one of the most prevalent and deadliest cancers. An increase of 10 μg/m3of PM2.5 could result in up to a 22% increase in lung cancer prevalence [
The strength of this study is that it is the first, to our knowledge, to explore the relationship between daily average PM2.5 concentration and the daily Baidu Index for the search term “lung cancer” across 31 cities in China.
There are some limitations to this study. We collected the Internet search data from a single search engine, Baidu. Baidu is the most commonly used search engine in China. The Baidu Index provides absolute search data by cities and can be used to perform a direct comparative analysis among cities. We used only the term “lung cancer,” which might have limited the search data. It was also not possible to identify the type of Internet user or which stakeholders were responsible for the search activity. Search engine search term trends might be affected by factors such as public panic [
Daily average PM2.5 concentration has a weak positive impact on Internet searches on the term “lung cancer.” Well-designed awareness campaigns are needed to improve general public awareness of the association of PM2.5 with lung cancer risk, to lead the public to seek more information about PM2.5 and its hazards, and to cope with their environment and its risks appropriately.
Results of Pearson correlation analysis.
Results of multiple linear regression analysis.
Mean rank of the daily Baidu Index for the term “lung cancer," daily Baidu media index, and daily PM2.5 concentration for different months from 2014 to 2016.
ambient fine particulate matter <5 μm in diameter
This work was supported by the National Natural Science Foundation of China (71473175; 71673199), the Key Scientific Project of Tianjin Science and Technology Commission of China (15ZCZDSY00500), and the Philosophy and Social Sciences projects of Tianjin in China (TJSR13-006).
None declared.