title: A new source of data for public health surveillance: Facebook likes.
abstract: BACKGROUND
  Investigation into personal health has become focused on conditions at an increasingly local level, while response rates have declined and complicated the process of collecting data at an individual level. Simultaneously, social media data have exploded in availability and have been shown to correlate with the prevalence of certain health conditions.
  
  
  OBJECTIVE
  Facebook likes may be a source of digital data that can complement traditional public health surveillance systems and provide data at a local level. We explored the use of Facebook likes as potential predictors of health outcomes and their behavioral determinants.
  
  
  METHODS
  We performed principal components and regression analyses to examine the predictive qualities of Facebook likes with regard to mortality, diseases, and lifestyle behaviors in 214 counties across the United States and 61 of 67 counties in Florida. These results were compared with those obtainable from a demographic model. Health data were obtained from both the 2010 and 2011 Behavioral Risk Factor Surveillance System (BRFSS) and mortality data were obtained from the National Vital Statistics System.
  
  
  RESULTS
  Facebook likes added significant value in predicting most examined health outcomes and behaviors even when controlling for age, race, and socioeconomic status, with model fit improvements (adjusted R(2)) of an average of 58% across models for 13 different health-related metrics over basic sociodemographic models. Small area data were not available in sufficient abundance to test the accuracy of the model in estimating health conditions in less populated markets, but initial analysis using data from Florida showed a strong model fit for obesity data (adjusted R(2)=.77).
  
  
  CONCLUSIONS
  Facebook likes provide estimates for examined health outcomes and health behaviors that are comparable to those obtained from the BRFSS. Online sources may provide more reliable, timely, and cost-effective county-level data than that obtainable from traditional public health surveillance systems as well as serve as an adjunct to those systems.
authors.affiliation: Mktg, Inc, East Islip, NY, United States. Steve@Mktginc.com.
authors.forename: Steven
authors.identifier: http://orcid.org/0000-0003-2126-9634
authors.initials: S
authors.lastname: Gittelman
authors.forename: Victor
authors.identifier: http://orcid.org/0000-0002-4437-400X
authors.initials: V
authors.lastname: Lange
authors.forename: Carol A
authors.identifier: http://orcid.org/0000-0002-1111-819X
authors.initials: CA
authors.lastname: Gotway Crawford
authors.forename: Catherine A
authors.identifier: http://orcid.org/0000-0003-2841-5016
authors.initials: CA
authors.lastname: Okoro
authors.forename: Eugene
authors.identifier: http://orcid.org/0000-0002-8792-1624
authors.initials: E
authors.lastname: Lieb
authors.forename: Satvinder S
authors.identifier: http://orcid.org/0000-0003-1222-8240
authors.initials: SS
authors.lastname: Dhingra
authors.forename: Elaine
authors.identifier: http://orcid.org/0000-0003-3479-4278
authors.initials: E
authors.lastname: Trimarchi
mesh_terms: D040343:Behavioral Risk Factor Surveillance System; D003625:Data Collection / Q000639:trends*; D005260:Female; D005431:Florida; D015438:Health Behavior; D006801:Humans; D008019:Life Style; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D062486:Public Health Surveillance / Q000379:methods*; D061108:Social Media; D014481:United States
keywords: big data; chronic illness; social networks; surveillance