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Prev Med. 2017 Aug;101:18-22. doi: 10.1016/j.ypmed.2017.05.009. Epub 2017 May 17.

Disparities in digital reporting of illness: A demographic and socioeconomic assessment.

Author information

1
Department of Economics, University of Washington, Seattle, WA, United States.
2
Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, United States.
3
Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, United States; Department of Pediatrics, Harvard Medical School, Boston, MA, United States.
4
Department of Health, Kinesiology, and Recreation, University of Utah College of Health, Salt Lake City, UT, United States.
5
Epidemico, Booz Allen Hamilton, Boston, MA, United States.
6
Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.
7
Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States. Electronic address: onelaine@vt.edu.

Abstract

Although digital reports of disease are currently used by public health officials for disease surveillance and decision making, little is known about environmental factors and compositional characteristics that may influence reporting patterns. The objective of this study is to quantify the association between climate, demographic and socio-economic factors on digital reporting of disease at the US county level. We reference approximately 1.5 million foodservice business reviews between 2004 and 2014, and use census data, machine learning methods and regression models to assess whether digital reporting of disease is associated with climate, socio-economic and demographic factors. The results show that reviews of foodservice businesses and digital reports of foodborne illness follow a clear seasonal pattern with higher reporting observed in the summer, when most foodborne outbreaks are reported and to a lesser extent in the winter months. Additionally, factors typically associated with affluence (such as, higher median income and fraction of the population with a bachelor's degrees) were positively correlated with foodborne illness reports. However, restaurants per capita and education were the most significant predictors of illness reporting at the US county level. These results suggest that well-known health disparities might also be reflected in the online environment. Although this is an observational study, it is an important step in understanding disparities in the online public health environment.

KEYWORDS:

Digital disease surveillance; Foodborne diseases; Foodborne illness surveillance; Socioeconomic disparities

PMID:
28528170
PMCID:
PMC5553633
DOI:
10.1016/j.ypmed.2017.05.009
[Indexed for MEDLINE]
Free PMC Article

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