Evidence map›Paper›PMID 32163035›Full record

ArticleJournal of medical Internet research2020

Detecting Lung Cancer Trends by Leveraging Real-World and Internet-Based Data: Infodemiology Study.

Chenjie Xu, Hongxi Yang, Li Sun, Xinxi Cao, Yabing Hou, Qiliang Cai, Peng Jia, Yaogang Wang

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Article
  8. Article
  9. Modeling COVID-19 incidence with Google Trends.Frontiers in research metrics and analytics · 2022
    Article
  10. Article
  11. Article
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Chenjie Xu *School of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-8997-9299
Hongxi Yang *School of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-9545-5715
Li Sun *School of Nursing, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-8211-6124
Xinxi CaoSchool of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0003-0260-5823
Yabing HouSchool of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0003-1572-5584
Qiliang CaiSchool of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-7147-0953
Peng JiaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China.ORCID 0000-0003-0110-3637
Yaogang WangSchool of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-7325-0663

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInternet search data on health-related terms can reflect people's concerns about their health status in near real time, and hence serve as a supplementary metric of disease characteristics. However, studies using internet search data to monitor and predict chronic diseases at a geographically finer state-level scale are sparse.

objectiveThe aim of this study was to explore the associations of internet search volumes for lung cancer with published cancer incidence and mortality data in the United States.

methodsWe used Google relative search volumes, which represent the search frequency of specific search terms in Google. We performed cross-sectional analyses of the original and disease metrics at both national and state levels. A smoothed time series of relative search volumes was created to eliminate the effects of irregular changes on the search frequencies and obtain the long-term trends of search volumes for lung cancer at both the national and state levels. We also performed analyses of decomposed Google relative search volume data and disease metrics at the national and state levels.

resultsThe monthly trends of lung cancer-related internet hits were consistent with the trends of reported lung cancer rates at the national level. Ohio had the highest frequency for lung cancer-related search terms. At the state level, the relative search volume was significantly correlated with lung cancer incidence rates in 42 states, with correlation coefficients ranging from 0.58 in Virginia to 0.94 in Oregon. Relative search volume was also significantly correlated with mortality in 47 states, with correlation coefficients ranging from 0.58 in Oklahoma to 0.94 in North Carolina. Both the incidence and mortality rates of lung cancer were correlated with decomposed relative search volumes in all states excluding Vermont.

conclusionsInternet search behaviors could reflect public awareness of lung cancer. Research on internet search behaviors could be a novel and timely approach to monitor and estimate the prevalence, incidence, and mortality rates of a broader range of cancers and even more health issues.

Indexed as

Cross-Sectional StudiesHumansIncidenceInternetLung NeoplasmsPrevalenceincidenceinfodemiologyinternet searcheslung cancermortality

Identifiers

PMID32163035
PMCPMC7099398

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.