Literature DB >> 20690788

Using search engine query data to track pharmaceutical utilization: a study of statins.

Nathaniel M Schuster1, Mary A M Rogers, Laurence F McMahon.   

Abstract

OBJECTIVE: To examine temporal and geographic associations between Google queries for health information and healthcare utilization benchmarks. STUDY
DESIGN: Retrospective longitudinal study.
METHODS: Using Google Trends and Google Insights for Search data, the search terms Lipitor (atorvastatin calcium; Pfizer, Ann Arbor, MI) and simvastatin were evaluated for change over time and for association with Lipitor revenues. The relationship between query data and community-based resource use per Medicare beneficiary was assessed for 35 US metropolitan areas.
RESULTS: Google queries for Lipitor significantly decreased from January 2004 through June 2009 and queries for simvastatin significantly increased (P <.001 for both), particularly after Lipitor came off patent (P <.001 for change in slope). The mean number of Google queries for Lipitor correlated (r = 0.98) with the percentage change in Lipitor global revenues from 2004 to 2008 (P <.001). Query preference for Lipitor over simvastatin was positively associated (r = 0.40) with a community's use of Medicare services. For every 1% increase in utilization of Medicare services in a community, there was a 0.2-unit increase in the ratio of Lipitor queries to simvastatin queries in that community (P = .02).
CONCLUSIONS: Specific search engine queries for medical information correlate with pharmaceutical revenue and with overall healthcare utilization in a community. This suggests that search query data can track community-wide characteristics in healthcare utilization and have the potential for informing payers and policy makers regarding trends in utilization.

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Year:  2010        PMID: 20690788      PMCID: PMC4669678     

Source DB:  PubMed          Journal:  Am J Manag Care        ISSN: 1088-0224            Impact factor:   2.229


  2 in total

1.  Regional variations in diagnostic practices.

Authors:  Yunjie Song; Jonathan Skinner; Julie Bynum; Jason Sutherland; John E Wennberg; Elliott S Fisher
Journal:  N Engl J Med       Date:  2010-05-12       Impact factor: 91.245

2.  Detecting influenza epidemics using search engine query data.

Authors:  Jeremy Ginsberg; Matthew H Mohebbi; Rajan S Patel; Lynnette Brammer; Mark S Smolinski; Larry Brilliant
Journal:  Nature       Date:  2009-02-19       Impact factor: 49.962

  2 in total
  6 in total

1.  [On the seasonality of dermatoses: a retrospective analysis of search engine query data depending on the season].

Authors:  M J Köhler; S Springer; M Kaatz
Journal:  Hautarzt       Date:  2014-09       Impact factor: 0.751

2.  Gauging interest of the general public in laser-assisted in situ keratomileusis eye surgery.

Authors:  Joshua D Stein; David M Childers; Bin Nan; Shahzad I Mian
Journal:  Cornea       Date:  2013-07       Impact factor: 2.651

3.  Using internet search queries for infectious disease surveillance: screening diseases for suitability.

Authors:  Gabriel J Milinovich; Simon M R Avril; Archie C A Clements; John S Brownstein; Shilu Tong; Wenbiao Hu
Journal:  BMC Infect Dis       Date:  2014-12-31       Impact factor: 3.090

4.  The use of google trends in health care research: a systematic review.

Authors:  Sudhakar V Nuti; Brian Wayda; Isuru Ranasinghe; Sisi Wang; Rachel P Dreyer; Serene I Chen; Karthik Murugiah
Journal:  PLoS One       Date:  2014-10-22       Impact factor: 3.240

5.  Analyzing Information Seeking and Drug-Safety Alert Response by Health Care Professionals as New Methods for Surveillance.

Authors:  Alison Callahan; Igor Pernek; Gregor Stiglic; Jure Leskovec; Howard R Strasberg; Nigam Haresh Shah
Journal:  J Med Internet Res       Date:  2015-08-20       Impact factor: 5.428

6.  Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.

Authors:  Amaryllis Mavragani; Gabriela Ochoa; Konstantinos P Tsagarakis
Journal:  J Med Internet Res       Date:  2018-11-06       Impact factor: 5.428

  6 in total

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