Evidence map›Paper›PMID 29202718›Full record

ArticleBMC cancer2017

Google as a cancer control tool in Queensland.

Xiaodong Huang, Peter Baade, Danny R Youlden, Philippa H Youl, Wenbiao Hu, Michael G Kimlin

Abstract read
In one paragraph

Article in BMC cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
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

6 authors.

Xiaodong HuangFaculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Sippy Downs Dr, Sippy Downs, QLD, 4556, Australia.
Peter BaadeCancer Council Queensland, Brisbane, QLD, Australia.
Danny R YouldenCancer Council Queensland, Brisbane, QLD, Australia.
Philippa H YoulFaculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Sippy Downs Dr, Sippy Downs, QLD, 4556, Australia.
Wenbiao HuSchool of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia.
Michael G KimlinFaculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Sippy Downs Dr, Sippy Downs, QLD, 4556, Australia. mkimlin@usc.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecent advances in methodologies utilizing "big data" have allowed researchers to investigate the use of common internet search engines as a real time tool to track disease. Little is known about its utility with tracking cancer incidence. This study aims to investigate the potential correlates of monthly internet search volume indexes (SVIs) and observed monthly age standardised incidence rates (ASRs) for breast cancer, colorectal cancer, melanoma and prostate cancer.

methodsThe monthly ASRs for the four cancers in Queensland were calculated using data from the Queensland Cancer Registry between January 2006 and December 2012. The monthly SVIs of the respective cancer search terms in Queensland were accessed from Google Trends for the same period. A time series seasonal decomposition method was performed to detect the seasonal patterns of SVIs and ASRs. Pearson's correlation coefficient and time series cross-correlation analysis were used to assess the associations between SVIs and ASRs. Linear regression models were used to examine the power of SVIs to predict monthly in ASRs.

resultsIncreases in the monthly ASRs of the four cancers were significantly correlated with increases in the monthly SVIs of the respective cancers except for colorectal cancer. The predictive power of the SVIs to explain variances in the corresponding ASRs varied by cancer type, with the percent explained ranging from 5.6% for breast cancer to 17.9% for skin cancer (SVI) with melanoma (ASR). Some improvement in the variation explained was obtained by including more search terms or lagged SVIs for the respective cancers in the linear regression models. The seasonal analysis indicated that the SVIs peaked periodically at around their respective cancer awareness months.

conclusionsUsing SVIs from a popular internet search engine was only able to explain a small portion of changes in the respective ASRs. While an expanded regression model explained a higher proportion of variability, the interpretation of this was difficult. Further development and refinement of this approach will be needed before search-based cancer surveillance can provide useful information regarding resource deployment to guide cancer control and track the impact of cancer awareness and education programmes.

Indexed as

Epidemiological MonitoringBreast NeoplasmsColorectal NeoplasmsFemaleHumansIncidenceInternetMaleMelanomaProstatic NeoplasmsQueenslandRegistriesSearch EngineSeasonsSkin NeoplasmsAge standardised ratesCancer incidenceCross-correlationGoogle TrendsSearch volume indexes

Identifiers

PMID29202718
PMCPMC5715989

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.