Correlation analysis among incidence and mortality of cancers

Transcription

1 Original Paper Correlation analysis among incidence and mortality of cancers and online public attention in China Chenjie Xu 1 *, B.Admin.; Yi Wang 2 *, MSc; Hongxi Yang 1 *, B.Admin.; Jie Hou 3, MSc; Li Sun 4, MD; Xinyu Zhang 1, MD, PhD; Xinxi Cao 1, B.Admin.; Yabing Hou 1, B.Admin.; Yaogang Wang 1, MD, PhD 1 School of Public Health, Tianjin Medical University, Tianjin, China 2 Tandon School of Engineering, New York University, New York, United States 3 School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China 4 School of Nursing, Tianjin Medical University, Tianjin, China *theses authors contributed equally Corresponding Author: Yaogang Wang, MD, PhD School of Public Health Tianjin Medical University No 22, Qixiangtai Road Heping District Tianjin, China Phone: wyg@tmu.edu.cn

2 Abstract Background: Cancers pose a serious threat to health of Chinese people, resulting in a immense challenge to public health work. Nowadays, people could get related information not only from medical workers in hospitals, but also retrieve on Internet everywhere in real-time. Online search behaviors can reflect population s awareness on cancers from a completely new perspective, which could be driven by underlying cancer epidemiology. Objective: The objective of our study was mainly to explore whether there was a correlation among incidence and mortaliy of cancers and online search behaviors. A secondary objective was to predict future incidence and mortality of theses cancers if the correlation was existed. Methods: We used standard Boolean operators to choose search terms of each cancer. We used Spearman s rank correlation coefficient to validate the correlation among monthly search index values of each cancer type and their monthly incidence and mortality rates. ARIMA model and linear regression model were simultaneously used to predict incidence and mortality of these cancers in future. Results: Monthly search index values statistically significantly correlated with cancer incidence rates in 26 of 28 cancers in China, ( lung cancer: r=.80, P<.001; liver cancer: r=.28, P=.016; stomach cancer: r=.50, P<.001; esophageal cancer: r=.50, P<.001; colorectal cancer: r=.81, P<.001; pancreatic cancer: r=.86, P<.001; breast cancer: r=.56, P<.001; brain and nervous system cancer: r=.63, P<.001; leukemia: r=.75, P<.001; Non-Hodgkin lymphoma: r=.88, P<.001; Hodgkin lymphoma: r=.91, P<.001; cervical cancer: r=.64, P<.001; prostate cancer: r=.67, P<.001; bladder cancer: r=.62, P<.001; gallbladder and biliary tract cancer: r=.88, P<.001; lip and oral cavity cancer: r=.88, P<.001; ovarian cancer: r=.58, P<.001; larynx cancer: r=.82, P<.001; kidney cancer: r=.73, P<.001; squamous-cell carcinoma: r=.94, P<.001; multiple myeloma: r=.84, P<.001; thyroid cancer: r=.77, P<.001; malignant skin melanoma: r=.55, P<.001; mesothelioma: r=.79, P<.001; testicular cancer: r=.57, P<.001; basal-cell carcinoma: r=.83, P<.001). Meanwhile, monthly Baidu search index values statistically significantly correlated with cancer mortality rates in 24 of 27 cancers but not for stomach cancer, esophageal cancer and testicular cancer. Conclusions: Online search behaviors indeed reflect public awareness from a different angle. Research on Internet search behaviors may be an innovative and advance-time tool to monitor and estimate cancer incidence and mortality rates, especially for cancers not included in national registries. Keywords: Cancer; incidence; mortality; public attention; Internet searching

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4 Introduction Cancer is affecting people in every country in the world and at all levels of socioeconomic status [1,2]. Population of China accounts for 19.3% of the global population. The incidence of cancer accounts for 22% of the global incidence of cancer, ranking the first in the world in incidence number. Cancer deaths in China account for about 27% of global cancer deaths. Cancer mortality is also higher than the global average of 17% [3,4]. There is a growing demand for knowledge about cancers, but registration of cancer cases in our country requires complicated procedures. Traditional epidemiologic methods usually have a three-year delay until incidence and mortality data are publicly reported [5]. And some cancers are even not covered by the national surveillance, lacking corresponding epidemiological data. Studies have revealed, about 63% of cancer patients choose to use the Internet to retrieve cancer related information when they produced relevant search requirements [6-8]. In addition to patients themselves, friends and family members look for real-time disease information through search engines, APPs and other online routines, which are often designed to give potentially helpful suggestions [9]. Nowadays, the world is entering the Internet era. A large number of studies have improved surveillance of epidemics and examine public interest in multiple health topics by monitoring the online search behavior of millions of users through Google, Facebook, Twitter and so on [10,11]. Studies in the USA previously had also demonstrated a correlation between Google search volume and cancer incidence and mortality [12,13]. In this study, we tracked and monitored search index values and search behavior of Chinese internet users, in order to explore correlation among network attention and incidence and mortality rate of cancers. We hypothesized that internet search volumes can reflect the real cancer incidence and mortality in China and predicted the future rate based on it. Methods Data of cancer incidence and mortality rates National-level incidence and mortality rate of cancers in China are obtained from Global Burden of Disease Database (GBD) between 2011 and 2017, which is online and publicly available [14]. In this study, we have selected 28 types of cancers, including: lung cancer, liver cancer, stomach cancer, esophageal cancer, colon and rectal cancer, pancreatic cancer, breast cancer, brain and nervous

5 system cancer, cervical cancer, prostate cancer, nasopharyngeal cancer, bladder cancer, gallbladder and biliary tract cancer, lip and oral cavity cancer, ovarian cancer, larynx cancer, kidney cancer, testicular cancer, uterine cancer, thyroid cancer, multiple myeloma, leukemia, Non-Hodgkin lymphoma, malignant skin melanoma, Hodgkin lymphoma, mesothelioma, basal-cell carcinoma and squamous-cell carcinoma. On these bases, incidence and mortality rates of cancers divided by gender were also obtained, yet mortality rate of basalcarcinoma was missing. As lacking the monthly data of incidence and mortality rates, we took the annual incidence and annual mortality rate of each cancer instead of their monthly mortality and incidence rates. Data of Public Attention Online This study mainly considered the cancer search index values, which were included in Chinese search engines. In this study we used the Baidu Index as the entry point to launch the corresponding research [15]. Among the users who have used Chinese search engines (people usually retrieve them in Chinese language), the proportion of Baidu search is 92.1%; Haosou is second; Sougou is third. In the market for mobile integrated search engines, the brand performance is also the same: Baidu search ranked first with 93.1% of the use rate. Baidu is a famous Chinese search engine, it has a powerful real-time function [16]. It occupies an absolute strong position in China. Baidu is an extremely big data information resource sharing platform that Chinese netizens depend on. We assume the Baidu Index can better represent the retrieval preferences of Chinese Internet users. Baidu Index contains five dimensions of the search index like the overall search trend index, PC-side search trend index, mobile search index, consultation index and media index from 2011 up to now, yet other Chinese search engines only contain a short term data about one month or one year intervals. Baidu Index stems from search frequencies on Baidu search engine, and it is calculated and displayed on the basis of special keywords by search volumes used by users on it [17]. We entered the search terms of cancers after the setting in Baidu Index to obtain the monthly total search index values of all cancer from January 2011 to December The monthly Baidu search index value is the average of total daily search index values in a month. We also got the sex distribution data of people who retrieved these types of cancer online. Determination of Search Terms Because Baidu is a Chinese search engine, the search terms are expressed in Chinese characters (Multimedia Appendix 1). We also reflected the differences in expression of cancer between English and Chinese. Due to the diversified meanings of Chinese characters, in addition to the professional Chinese names of these cancers, sometimes some of their names have synonyms. Therefore, we selected the Chinese names and common terms of these cancers while searching. Standard Boolean operation was used to combine the terms. For most cancers,

6 we used two or more search terms in Chinese to cover as many synonyms as possible. However, for prostate cancer, larynx cancer, Hodgkin lymphoma and mesothelioma, we only used one search term because their synonyms were not included in Baidu Index. All the search terms and numbers of them are displayed in the appendix. Statistical Analysis To examine correlation among monthly search index values, incidence rate and mortality rate, we used the Spearman s rank correlation coefficient to examine the association between Baidu search index values and cancer incidence per 100,000 people for each year during the 6-year study period. We also used the Spearman s rank correlation coefficient to examine the association between Baidu search index values and cancer mortality per 100,000 people from 2011 to The prediction of cancer incidence and mortality was mainly based on the correlation. Because of seasonal fluctuations in Baidu search index data, we need to denoise the original data to predict the incidence and mortality of cancer. The specific steps are as follows: 1) change baidu index data type to time series; 2) use the log version of the data; 3) use stl function to decompose baidu index data; 4) use ARIMA model to predict the future baidu search index values; 5) use linear regression model with baidu search index values and the previous year rate to predict the future incidence and mortality rates. All the statistical significance was defined as two-side P<.05. In this study, IBM SPSS20.0 Version (IBM Corporation) and R project(v3.4.1) were used to conduct statistical analysis. Results Disease Burden of Cancer in China We examined cancer incidence and mortality rate using GBD data. We illustrated the differences of the incidence and mortality rate of 28 cancers between male and female in China in 2016 (Figure 1).

7 Time Series Trend Analysis of Caner Incidence, Mortality and Online Public Attention to Cancer Search index of these cancers is relatively flat at first, and finally shows the trend of fluctuating with time (Figure 2, Figure 3, Figure 4, Figure 5 and Figure6). For multiple myeloma, there is a part of figure showing the shape of "W", on its behalf during this time, the search index values have a significant fluctuation. For mesothelioma, there is a part of figure showing the shape of V, representing one search valley. For malignant skin melanoma, its monthly Baidu search index values showed a downward trend at first. In addition to January 15 to April 16, the two search trends of Non-Hodgkin lymphoma and Hodgkin lymphoma showed a trend of consistent changes in volatility. In general, search trends of cancer terms have shown an upward trend in total. At the same time, there is one peak or multiple peaks emerging in the fluctuation. However, the time-series trend analysis of breast cancer can be seen to have reached a peak in 2015, the search index value reached a maximum of 32284, and the search frequency suddenly increased dramatically. This situation can also be seen in testicular cancer in November 2016, even though the average search index is high. The trend figures of uncommon cancers are relatively volatile.

8 Figure 2. Time series of search index values, incidence rate and mortality rate. Monthly search index values of cancers from were obtained from Incidence and mortality rates of cancers from were obtained from Global Burden of Disease Database(GBD). Mortality rate of basal-cell carcinoma couldn t be obtained from GBD.

9 Figure 3. Time series of search index values, incidence rate and mortality rate. Monthly search index values of cancers from were obtained from Incidence and mortality rates of cancers from were obtained from Global Burden of Disease Database(GBD). Mortality rate of basal-cell carcinoma couldn t be obtained from GBD.

10 Figure 4. Time series of search index values, incidence rate and mortality rate. Monthly search index values of cancers from were obtained from Incidence and mortality rates of cancers from were obtained from Global Burden of Disease Database(GBD). Mortality rate of basal-cell carcinoma couldn t be obtained from GBD. Figure 1. Incidence and mortality rate of cancers divided by gender in China, Data was obtained from Global Burden of Disease. The upper part of the figure represents male, the lower part of the figure represents female. The blue part represents incidence rate and the red part represents mortality rate.

11 Figure 5. Time series of search index values, incidence rate and mortality rate. Monthly search index values of cancers from were obtained from Incidence and mortality rates of cancers from were obtained from Global Burden of Disease Database(GBD). Mortality rate of basal-cell carcinoma couldn t be obtained from GBD.

12 Figure 6. Time series of search index values, incidence rate and mortality rate. Monthly search index values of cancers from were obtained from Incidence and mortality rates of cancers from were obtained from Global Burden of Disease Database(GBD). Mortality rate of basal-cell carcinoma couldn t be obtained from GBD.

13 Analysis of Cancer Incidence, Mortality and Public Attention to Cancer Online by Gender For Hodgkin lymphoma, gender percentage data was missing before May We found that, in addition to breast, cervical, uterine cancers( Female specific illnesses), prostate and testicular cancers (Male specific illnesses), incidence and mortality of brain and nervous system and thyroid cancers for women are higher than those of men as well as for gallbladder and biliary tract cancers. In addition, other cancers for men have higher incidence and mortality than women. For lung cancer, liver cancer, colorectal cancer, pancreatic cancer, brain and nervous system cancer, Non-Hodgkin lymphoma, prostate cancer, bladder cancer, gallbladder and biliary tract cancer, lip and oral cavity cancer, ovarian cancer, kidney cancer, multiple myeloma and malignant skin melanoma, incidence and mortality of men and women both have increased year by year. Relatively speaking, more men have paid attention to these cancer related search terms than women. In terms of the whole population, the number of females who searched for cancer related information has slowly risen with time since 2015 while the percentage of males showed a downward trend. This trend is even more obvious in female specific cancers, such as breast, cervical, ovarian and uterine cancers. At first, more men would search related terms of breast cancer. As time goes on, more and more women searched about them. For prostate cancer and testicular cancer, the number of men in attention has been far more than that of women (Figure 7, Figure 8 and Figure9).

14 Figure 7. Incidence, mortality, search sex distribution of each cancer divided by gender. The percentile chart represents the change in the sex distribution of the search population from September 2013 to September Data of Hodgkin lymphoma was missing before April The blue line represents incidence rate of each cancer. The red line represents mortality rate of each cancer.

15 Figure 8. Incidence, mortality, search sex distribution of each cancer divided by gender. The percentile chart represents the change in the sex distribution of the search population from September 2013 to September Data of Hodgkin lymphoma was missing before April The blue line represents incidence rate of each cancer. The red line represents mortality rate of each cancer.

16 Figure 9. Incidence, mortality, search sex distribution of each cancer divided by gender. The percentile chart represents the change in the sex distribution of the search population from September 2013 to September Data of Hodgkin lymphoma was missing before April The blue line represents incidence rate of each cancer. The red line represents mortality rate of each cancer.

17 Correlation Analysis Among Cancer Incidence, Mortality and Online Public Attention We found statistically significant correlations between the incidence rates and the search index values for lung cancer (r=.80, P<.001), liver cancer (r=.28, P=.016), stomach cancer (r=.50, P<.001), esophageal cancer (r=.50, P<.001), colorectal cancer (r=.81, P<.001), pancreatic cancer (r=.86, P<.001), breast cancer (r=.56, P<.001), brain and nervous system cancer (r=.63, P<.001), leukemia (r=.75, P<.001), non-hodgkin lymphoma (r=.88, P<.001), Hodgkin lymphoma(r=.91, P<.001), cervical cancer(r=.64, P<.001), prostate cancer (r=.67, P<.001), bladder cancer (r=.62, P<.001), gallbladder and biliary tract cancer (r=0.88, P<0.001), lip and oral cavity cancer(r=.88, P<.001), ovarian cancer(r=.58, P<.001), larynx cancer (r=.82, P<.001), kidney cancer (r=.73, P<.001), squamous-cell carcinoma (r=.94, P<.001), multiple myeloma (r=.84, P<.001), thyroid cancer (r=.77, P<.001), malignant skin melanoma (r=.55, P<.001), mesothelioma (r=.79, P<.001), testicular cancer (r=.57, P<.001), basalcell carcinoma (r=.83, P<.001) (Table 1). Meanwhile, we found statistically significant correlations between the mortality rates and the search index values for lung cancer (r=.80, P<.001), liver cancer (r=.28, P=.016), colorectal cancer (r=.81, P<.001), pancreatic cancer (r=.86, P<.001), breast cancer (r=.76, P<.001), brain and nervous system cancer (r=.63, P<.001), Non-Hodgkin lymphoma (r=.88, P<.001), cervical cancer (r=.65, P<.001), prostate cancer (r=.67, P<.001), nasopharyngeal cancer (r=.44, P<.001), bladder cancer (r=.62, P<.001), gallbladder and biliary tract cancer (r=.88, P<.001), lip and oral cavity cancer (r=.88, P<.001), ovarian cancer (r=.58, P<.001), larynx cancer (r=.74, P<.001), kidney cancer (r=.73, P<.001), squamous-cell carcinoma (r=.87, P<.001), multiple myeloma (r=.84, P<.001), thyroid cancer (r=.77, P<.001), malignant skin melanoma (r=.55, P<.001), mesothelioma (r=.79, P<.001), leukemia (r=-.70, P<.001), Hodgkin lymphoma (r=-.91, P<.001) and uterine cancer (r=-.42, P<.001) (Table 1).

18 Table1. Correlation coefficients between search index values and incidence and mortality rate of cancers. Cancer Term Correlation between incidence and search index values Correlation between mortality and search index values R(Correlation Coefficient) P Value R(Correlation Coefficient) P Value Lung cancer.80 < <.001 Liver cancer Stomach cancer.50 < Esophageal cancer.50 < Colorectal cancer.81 < <.001 Pancreatic cancer.86 < <.001 Breast cancer.56 < <.001 Leukemia.75 < <.001 Brain and nervous system.63 < <.001 cancer Cervical cancer.64 < <.001 Non-Hodgkin lymphoma.88 < <.001 Prostate cancer.67 < <.001 Nasopharyngeal cancer <.001 Bladder cancer.62 < <.001 Gallbladder and biliary tract.88 < <.001 cancer Lip and oral cavity cancer.88 < <.001 Ovarian cancer.58 < <.001 Larynx cancer.82 < <.001 Kidney cancer.73 < <.001 Squamous-cell carcinoma.94 < <.001 Uterine cancer <.001 Multiple myeloma.84 < <.001 Thyroid cancer.77 < <.001 Malignant skin melanoma.55 < <.001 Hodgkin lymphoma.91 < <.001 Mesothelioma.79 < <.001 Testicular cancer.57 < Basal-cell carcinoma.83 <.001 NA a NA a NA: not available.

19 Prediction of incidence and mortality rates of cancers The above correlation shows that there is a good correlation among the Internet search index and cancer incidence and mortality. The following prediction was based on this foundation (Figure2, Figure3). We obtained the Baidu search index values of each cancer every month, but only obtained their annual incidence and mortality data. Because the original Baidu search index values are too volatile, mortality rate of cancers are affected by multiple factors (eg. eating habits, environmental changes, changes in medical conditions, drug changes, etc.), to make the prediction more accurate, we had denoised the Baidu search index values to eliminate the seasonality of them, and only retained trend of them.

20 Table 2. Prediction of incidence rate of 28 types of cancer from 2017 to Cancer term Incidence (95% CI ) Incidence(95% CI ) Incidence (95% CI ) Incidence (95% CI ) Lung cancer ( to ) ( to ) ( to ) ( to ) Liver cancer ( to ( to ) ( to ) ( to ) ) Stomach cancer ( to ) ( to ) ( to ) ( to ) Colorectal cancer ( to ) ( to ) ( to ) ( to ) Breast cancer ( to ) ( to ) ( to ) ( to ) Esophageal cancer ( to ) ( to ) ( to ) ( to ) Leukemia (8.198 to 8.358) (8.494 to 8.665) (8.809 to 8.996) (9.146 to 9.351) Prostate cancer (7.790 to ) (8.297 to 8.300) (8.855 to 8.859) (9.472 to 9.476) Brain and (7.733 to 7.846) (8.173 to 8.294) (8.668 to 8.800) (9.214 to 9.360) nervous system cancer Cervical cancer (6.546 to 6.846) (6.541 to 6.838) (6.522 to 6.818) (6.511 to 6.805) Basal-cell (5.434 to 6.981) (5.606 to 7.210) (5.806 to 7.494) (5.991 to 7.762) carcinoma Pancreatic cancer (4.997 to 5.530) (5.081 to 5.628) (5.157 to 5.719) (5.229 to 5.807) Non-Hodgkin (4.991 to 5.035) (5.251 to 5.299) (5.504 to 5.556) (5.753 to 5.810) lymphoma Uterine cancer (4.781 to 5.125) (4.724 to 5.069) (4.761 to 5.101) (4.730 to 5.074) Bladder cancer (4.452 to 4.456) (4.657 to 4.661) (4.880 to 4.885) (5.124 to 5.129) Nasopharyngeal (3.265 to 3.348) (3.273 to 3.359) (3.282 to 3.373) (3.294 to 3.389) cancer Lip and oral (3.183 to 3.501) (3.095 to 3.398) (3.095 to 3.398) (3.100 to 3.398) cavity cancer Kidney cancer (3.225 to 3.251) (3.299 to 3.326) (3.370 to 3.397) (3.437 to 3.465) Thyroid cancer (3.13 to 3.17) (3.284 to 3.328) (3.434 to 3.480) (3.587 to 3.636) Squamous-cell (2.431 to 2.926) (2.491 to 2.999) (2.553 to 3.076) (2.611 to 3.149) carcinoma Larynx cancer (2.395 to 2.401) (2.494 to 2.500) (2.600 to 2.608) (2.715 to Ovarian caner (2.236 to 2.266) (2.299 to 2.330) (2.366 to 2.398) (2.437 to 2.471) Gallbladder and (2.047 to 2.159) (2.002 to 2.110) (2.002 to 2.110) (2.002 to 2.110) biliary cancer Multiple (1.201 to 1.230) (1.256 to 1.286) (1.309 to 1.340) (1.360 to 1.393) melanoma Malignant skin myeloma.908 (.901 to.914).946 (.939 to.953).982 (.975 to.989) (1.009 to 1.024)

21 Hodgkin.498 (.471 to.526).489 (.46 to.52).489 (.462 to.516).489 (.462 to.516) lymphoma Testicular cancer.3709 (.367 to.375).395 (.391 to.399).420 (.416 to.425).448 (.443 to.453) Mesothelioma.247 (.245 to.249).249 (.247 to.251).250 (.249,.253).252 (.250 to.254)

22 Table 3. Prediction of mortality rate of 28 types of cancer from 2017 to Cancer term Mortality (95% CI ) Mortality (95% CI ) Mortality (95% CI ) Mortality (95% CI ) Lung cancer ( to ) ( to ) ( to ) ( to ) Liver cancer ( to ( to ( to ) ( to ) ) Stomach cancer ( to ) ( to ) ( to ) ( to ) Esophageal cancer ( to ) ( to ) ( to ) ( to ) Colorectal cancer ( to ( to ( to ( to ) ) ) ) Pancreatic cancer (4.977 to 5.445) (5.051 to 5.531) (5.117 to 5.609) (5.180 to 5.685) Breast cancer (5.118 to 5.256) (5.118 to 5.256) (5.122 to 5.260) (5.125 to 5.263) Leukemia (4.400 to 4.444) (4.403 to 4.447) (4.403 to 4.447) (4.402 to 4.446) Brain and (4.298 to 4.380) (4.359 to 4.442) (4.431 to 4.518) (4.504 to 4.596) nervous system cancer Cervical cancer (2.768 to 2.911) (2.763 to 2.903) (2.754 to 2.894) (2.749 to 2.889) Non-Hodgkin (2.742 to 2.782) (2.779 to 2.820) (2.806 to 2.848) (2.829 to 2.872) lymphoma Prostate cancer (2.474 to 2.501) (2.567 to 2.596) (2.672 to 2.703) (2.790 to 2.825) Nasopharyngeal (2.035 to 2.084) (2.043 to 2.094) (2.051 to 2.103) (2.059 to 2.112) cancer Bladder caner (1.886 to 1.927) (1.940 to 1.983) (1.995 to 2.041) (2.054 to 2.104) Gallbladder and (1.798 to 1.867) (1.772 to 1.838) (1.772 to 1.838) (1.772 to 1.838) biliary cancer Lip and oral (1.403 to 1.525) (1.370 to 3.398) (1.370 to 1.486) (1.370 to 1.486) cavity cancer Ovarian cancer (1.353 to 1.376) (1.383 to 1.407) (1.414 to 1.434) (1.445 to 1.471) Kidney cancer (1.161 to 1.176) (1.184 to 1.200) (1.205 to 1.221) (1.225 to 1.242) Larynx cancer (1.262 to 1.312) (1.266 to 1.316) (1.268 to 1.319) (1.271 to 1.321) Squamous-cell (0.986 to 1.046) (0.991 to 1.050) (0.995 to 1.055) (0.999 to 1.060) carcinoma Uterine cancer (0.926 to 0.977) (0.915 to 0.966) (0.902 to 0.953) (0.891 to 0.942) Multiple.762 (.751 to.773).776 (.765 to.787).789 (.777 to.800).800 (.788 to.811) melanoma Thyroid cancer (0.432 to 0.439) (0.436 to 0.440) (0.435 to 0.442) (0.437 to 0.444) Malignant skin.370 (.368 to.372).375 (.373 to.377).379 (.37 to.381).383 (.381 to.385) myeloma Hodgkin.214 (.213 to.215).214 (.213 to.215).214 (.212 to.215).214 (.212 to.215) lymphoma Testicular cancer.0472 (.0468 to.0476).0473 (.0469 to.0477).0473 (.0469 to.0477).0473 (.0469 to.0477) Mesothelioma.200 (.199 to.202).202 (.200 to.203).203 (.201 to.205).204 (.202 to.205)

23 a NA: not available. We didn t get mortality rate of basal-cel carcinoma in GBD database so we couldn t predict its mortality rate.

24 Discussion Principal Findings Previous studies have mainly used Google Trends or Baidu Index to analyze the burden of epidemic diseases and predict their trends. It is the first study to explore association among public attention and cancer incidence and mortality rates in China. Nowadays, people could get related information not only from medical workers in hospitals, but also may retrieve it on Internet everywhere in real-time. Online search behaviors can reflect population s awareness on cancers from a completely new perspective. The above correlation analysis confirmed that there was indeed a positive correlation among internet search index values and incidence and mortality rates of most cancers. It indicates that the data of search engine can reflect the epidemic of real prevalence to some extent. The association of some cancers was not shown, probably due to some public health activities, such as the National Cancer Outreach Week (April each year) or the public figure of concern. For example, the popularity of breast cancer in the month of January 2015 has risen sharply. The well-known domestic singer died of breast cancer that month. So when national surveillance data is missing (e.g, mortality rate of basal-cell carcinoma) or outdated, a web search index can be used to predict cancer incidence and mortality. The increase in the percentage of women searching, especially for breast cancer, ovarian cancer and uterine cancer, reflects the increased awareness of women about their health. For other cancers, the gender structure of Internet users tended to be balanced and basically consistent with the sex ratio of the population. In the process of obtaining search index data, we also found that people over the age of 50 are the most cancerprone age group, yet their proportion among Internet cancer-related information searchers is low, because they are unfamiliar with the operation of mobile devices and do not understand how to retrieve information on the Internet [18]. They do not often utilize the Internet to find relevant information. Therefore, cancer prevention activities should pay more attention to this age group to help them understand relevant information. In China, the diagnosis of cancer is generally late, the cure rate of cancer is not high, in the same time there are non-standard treatments and other issues [5].How to start an effective cancer control work has become an important public health issue in our nation. Relevant literature has shown that the tracking and monitoring of search index values and search behavior of Twitter or Google users provides a new way to study the crowd's concern about the disease and the information seeking behavior [10]. The search terms represent people s immediate needs [19,20]. Chinese cancer registry annual report in 2016 released by National Cancer Registration Center comes from the 2013 tumor

25 registration data reported by 347 cancer registries throughout the country [21]. It can be seen clearly that there is a significant time lag in the registration of cancers. Importantly, a large portion of these cancers are preventable [22]. So in this era of big data, it is far from enough to prevent and control cancer by traditional means. It is necessary to enhance the efficiency of prevention and early diagnosis by drawing more attention to online supportive care for patients with cancer or general population. So in this article, we also tried to predict the incidence and mortality of these cancers in the future five years based maily on search data online. Collecting real-time relevant search data in search engine provides a new way for cancer prevention and control, and can even further predict the prevalence of cancer in all regions and cities by analyzing and retrieving the user's IP address. Our prediction model had considered those factors, which may have a great impact on the outcome of the forecast, such as changes in eating habits, changes in the environment, changes in medical conditions, etc. These factors would affect people's search frequency to a certain extent, and thus affect our prediction. So when we make the prediction, we had denoised the search data online. We were aware of that the model was first derived from the theory structure, and we should choose the suitable models. And the model should be trained to optimize itself by the training of the data [22]. In 2006, Norman and Skinner defined ehealth as "The ability to search, understand, and evaluate health information on electronic resources, and harnessing the information they receive to address and solve health problems." The six core skills of ehealth include information literacy, media literacy, computer literacy and so on [23,24]. Nevertheless, at the moment there are few studies in China on public cancer-related electronic health literacy or health literacy research. However, public retrieval of cancer-related health information is indirectly reflecting the level of individual's electronic health literacy. Researching on the attention to health information on Internet and the characteristics of the population can be helpful to predict some of the indicators of population diseases, which can contribute to improve the allocation of health resources and implement accurate public health measures effectively. Limitations Not all Internet users would choose to use Baidu search engine to search healthrelated information about cancers. Therefore, Baidu Index can t reflect search behaviors of all citizens. Due to China s vast territory and large population base, registry of cancer statistics is usually lagging and not comprehensive. We were unable to obtain timely data on monthly incidence and mortality rates of all cancers in China and we only used the annual incidence and mortality rate to replace the monthly incidence and mortality rates. As some diseases are more affected by other factors, when analyzing the correlation, it is also hard to use

26 this method because some diseases are affected by public health activities [25]. Conclusion With the application and popularization of Internet technology, all walks of life apply and rely on the Internet. In medical field, it is often intended to prompt informed conversations with clinical professionals and to suggest potentially helpful resources to patients or other persons. Our study indicated that there is indeed a correlation among incidence and mortality rates of most types of cancers and search index values. Online searches indeed reflect public awareness from another perspective. Advance understanding of online search patterns could augment traditional epidemiologic surveillance and allow us to better achieve the goal of cancer prevention and control. It is beneficial for us to pay attention to Internet search data, particularly when registry data is insufficient or lagging. Contributors Yaogang Wang developed the original research idea for the study and directed the study. All authours conducted the analysis and interpreted the results. Chenjie Xu, Yi Wang and Hongxi Yang developed the first manuscript draft. All authours critically revised the manuscript. All authors critically reviewed and contributed to the final version, and approved it. All authors had full access to study data. Yaogang Wang has final responsibility for the study. Acknowledgments This research was funded by National Natural Science Foundation of China ( ; ). Conflicts of Interest We declare no competing interests. Abbreviations GBD: Global Durden of Disease Database NA: not available

27 Multimedia Appendix 1 Table 1. Search terms of 28 cancers in Chinese online. Cancer type Key words of each cancer Numbers of the key words Lung cancer 肺癌 + 肺腺癌 + 肺鳞癌 + 肺部肿瘤 4 Liver cancer 肝癌 + 肝肿瘤 + 肝脏肿瘤 3 Stomach cancer 胃癌 + 胃腺癌 2 Esophageal cancer 食管癌 + 食道癌 2 Colon and rectum cancer 结肠癌 + 大肠癌 + 肠癌 6 直肠癌 + 大肠癌 + 肠癌 Pancreatic cancer 胰腺癌 + 胰头癌 + 消化道肿瘤 + 胰腺肿瘤 4 Breast cancer 乳腺癌 + 乳癌 + 乳腺肿瘤 3 Leukemia 白血病 + 血癌 2 Brain and nervous system cancer 脑瘤 + 脑癌 + 颅内肿瘤 + 脑部肿瘤 4 Cervical cancer 宫颈癌 + 子宫颈癌 2 Non-Hodgkin lymphoma 非霍奇金淋巴瘤 + 淋巴瘤 + 淋巴肿瘤 3 Prostate cancer 前列腺癌 1 Nasopharyngeal cancer 鼻咽癌 + 鼻癌 + 咽喉癌 3 Bladder cancer 膀胱癌 + 膀胱肿瘤 2 Gallbladder and biliary tract cancer 胆囊癌 + 胆管癌 + 胆管肿瘤 3 Lip and cavity cancer 口腔癌 + 口底癌 + 牙龈癌 + 舌癌 4 Ovarian cancer 卵巢癌 + 卵巢肿瘤 + 卵巢恶性肿瘤 3 Larynx cancer 喉癌 1 Kidney cancer 肾癌 + 肾细胞癌 + 肾肿瘤 + 肾脏肿瘤 4 Squamous-cell carcinoma 鳞状细胞癌 + 鳞癌 2 Uterine cancer 子宫癌 + 子宫内膜癌 2 Multiple myeloma 多发性骨髓瘤 + 骨髓瘤 2 Thyroid cancer 甲状腺癌 + 甲状腺肿瘤 2 Malignant skin melanoma 恶性黑色素瘤 + 恶性黑素瘤 + 黑色素瘤 3 Hodgkin lymphoma 霍奇金淋巴瘤 1 Mesothelioma 间皮瘤 1 Testicular cancer 睾丸癌 + 睾丸肿瘤 2 Basal-cell carcinoma 基底细胞癌 + 基底细胞瘤 2

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