title: The utility of web mining for epidemiological research: studying the association between parity and cancer risk.
abstract: The World Wide Web has emerged as a powerful data source for epidemiological studies related to infectious disease surveillance. However, its potential for cancer-related epidemiological discoveries is largely unexplored.
  
  
  
  Using advanced web crawling and tailored information extraction procedures, the authors automatically collected and analyzed the text content of 79 394 online obituary articles published between 1998 and 2014. The collected data included 51 911 cancer (27 330 breast; 9470 lung; 6496 pancreatic; 6342 ovarian; 2273 colon) and 27 483 non-cancer cases. With the derived information, the authors replicated a case-control study design to investigate the association between parity (i.e., childbearing) and cancer risk. Age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for each cancer type and compared to those reported in large-scale epidemiological studies.
  
  
  
  Parity was found to be associated with a significantly reduced risk of breast cancer (OR = 0.78, 95% CI, 0.75-0.82), pancreatic cancer (OR = 0.78, 95% CI, 0.72-0.83), colon cancer (OR = 0.67, 95% CI, 0.60-0.74), and ovarian cancer (OR = 0.58, 95% CI, 0.54-0.62). Marginal association was found for lung cancer risk (OR = 0.87, 95% CI, 0.81-0.92). The linear trend between increased parity and reduced cancer risk was dramatically more pronounced for breast and ovarian cancer than the other cancers included in the analysis.
  
  
  
  This large web-mining study on parity and cancer risk produced findings very similar to those reported with traditional observational studies. It may be used as a promising strategy to generate study hypotheses for guiding and prioritizing future epidemiological studies.
authors.affiliation: Health Data Sciences Institute, Biomedical Science and Engineering Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA tourassig@ornl.gov.
authors.forename: Georgia
authors.initials: G
authors.lastname: Tourassi
authors.affiliation: Health Data Sciences Institute, Biomedical Science and Engineering Center, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
authors.forename: Hong-Jun
authors.initials: HJ
authors.lastname: Yoon
authors.affiliation: Information Systems Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors.forename: Songhua
authors.initials: S
authors.lastname: Xu
authors.affiliation: Surveillance and Health Services Research, American Cancer Society, Atlanta, GA 30303, USA.
authors.forename: Xuesong
authors.initials: X
authors.lastname: Han
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000453:epidemiology; D016022:Case-Control Studies; D003110:Colonic Neoplasms / Q000453:epidemiology; D057225:Data Mining / Q000379:methods*; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D020407:Internet; D008175:Lung Neoplasms / Q000453:epidemiology; D008875:Middle Aged; D009369:Neoplasms / Q000453:epidemiology*; D010051:Ovarian Neoplasms / Q000453:epidemiology; D010190:Pancreatic Neoplasms / Q000453:epidemiology; D010298:Parity; D012307:Risk Factors; D055815:Young Adult
keywords: cancer risk; digital epidemiology; parity; web mining