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A New Source of Data for Public Health Surveillance: Facebook Likes
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 show...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4419195/ https://www.ncbi.nlm.nih.gov/pubmed/25895907 http://dx.doi.org/10.2196/jmir.3970 |
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author | Gittelman, Steven Lange, Victor Gotway Crawford, Carol A Okoro, Catherine A Lieb, Eugene Dhingra, Satvinder S Trimarchi, Elaine |
author_facet | Gittelman, Steven Lange, Victor Gotway Crawford, Carol A Okoro, Catherine A Lieb, Eugene Dhingra, Satvinder S Trimarchi, Elaine |
author_sort | Gittelman, Steven |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4419195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | JMIR Publications Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-44191952015-05-13 A New Source of Data for Public Health Surveillance: Facebook Likes Gittelman, Steven Lange, Victor Gotway Crawford, Carol A Okoro, Catherine A Lieb, Eugene Dhingra, Satvinder S Trimarchi, Elaine J Med Internet Res Original Paper 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. JMIR Publications Inc. 2015-04-20 /pmc/articles/PMC4419195/ /pubmed/25895907 http://dx.doi.org/10.2196/jmir.3970 Text en ©Steven Gittelman, Victor Lange, Carol A Gotway Crawford, Catherine A Okoro, Eugene Lieb, Satvinder S Dhingra, Elaine Trimarchi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.04.2015. http://creativecommons.org/licenses/by/2.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Gittelman, Steven Lange, Victor Gotway Crawford, Carol A Okoro, Catherine A Lieb, Eugene Dhingra, Satvinder S Trimarchi, Elaine A New Source of Data for Public Health Surveillance: Facebook Likes |
title | A New Source of Data for Public Health Surveillance: Facebook Likes |
title_full | A New Source of Data for Public Health Surveillance: Facebook Likes |
title_fullStr | A New Source of Data for Public Health Surveillance: Facebook Likes |
title_full_unstemmed | A New Source of Data for Public Health Surveillance: Facebook Likes |
title_short | A New Source of Data for Public Health Surveillance: Facebook Likes |
title_sort | new source of data for public health surveillance: facebook likes |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4419195/ https://www.ncbi.nlm.nih.gov/pubmed/25895907 http://dx.doi.org/10.2196/jmir.3970 |
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