Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies.
10.4258/hir.2018.24.4.253
- Author:
Hyeoun Ae PARK
1
;
Hyesil JUNG
;
Jeongah ON
;
Seul Ki PARK
;
Hannah KANG
Author Information
1. College of Nursing, Seoul National University, Seoul, Korea. soledad7@snu.ac.kr
- Publication Type:Review
- Keywords:
Public Health Surveillance;
Epidemiology;
Epidemiological Monitoring;
Social Media;
Internet
- MeSH:
Cell Phones;
Communicable Diseases;
Data Collection;
Dataset;
Epidemiologic Studies*;
Epidemiological Monitoring;
Epidemiology*;
Geographic Information Systems;
Humans;
Information Storage and Retrieval;
Internet;
Life Style;
Machine Learning;
Mental Health;
Methods;
Privacy;
Public Health Surveillance;
Social Media
- From:Healthcare Informatics Research
2018;24(4):253-262
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. METHODS: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. RESULTS: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. CONCLUSIONS: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.
