Gittelman Steven, Lange Victor, Gotway Crawford Carol A, Okoro Catherine A, Lieb Eugene, Dhingra Satvinder S, Trimarchi Elaine
Mktg, Inc, East Islip, NY, United States.
J Med Internet Res. 2015 Apr 20;17(4):e98. doi: 10.2196/jmir.3970.
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.
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.
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.
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).
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.
对个人健康的调查已越来越聚焦于地方层面的状况，然而回应率却有所下降，这使得个体层面的数据收集过程变得复杂。与此同时，社交媒体数据的可得性呈爆炸式增长，并且已被证明与某些健康状况的患病率相关。
脸书点赞可能是一种数字数据来源，可补充传统公共卫生监测系统，并在地方层面提供数据。我们探讨了将脸书点赞用作健康结果及其行为决定因素的潜在预测指标。
我们进行了主成分分析和回归分析，以检验脸书点赞在美国214个县以及佛罗里达州67个县中的61个县的死亡率、疾病和生活方式行为方面的预测质量。将这些结果与从人口统计学模型中获得的结果进行比较。健康数据来自2010年和2011年行为风险因素监测系统（BRFSS），死亡率数据来自国家生命统计系统。
即使在控制了年龄、种族和社会经济地位的情况下，脸书点赞在预测大多数所研究的健康结果和行为方面仍具有显著价值，在13个不同的健康相关指标模型中，相较于基本社会人口统计学模型，模型拟合优度（调整R²）平均提高了58%。小区域数据的数量不足，无法测试该模型在估计人口较少市场的健康状况时的准确性，但使用佛罗里达州数据进行的初步分析显示，该模型对肥胖数据的拟合效果很强（调整R² = 0.77）。
脸书点赞为所研究的健康结果和健康行为提供的估计与从BRFSS获得的估计相当。在线来源可能比传统公共卫生监测系统提供更可靠、及时且具成本效益的县级数据，并且可作为这些系统的补充。
