ObjectivesWe reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method.MethodsWe 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.ResultsWe 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.ConclusionsDigital 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.

Background Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates. Objective The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data. Methods The domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules. Results Our childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent’s health belief appeared in 53.21% (15,709/29,521) of posts and positive sentiments appeared in 64.08% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents’ health belief , vaccination availability , and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments. Conclusions The childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data.

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ObjectivesThis study presents the current status of nursing informatics education, the content covered in nursing informatics courses, the faculty efficacy, and the barriers to and additional supports for teaching nursing informatics in Korea.MethodsA set of questionnaires consisting of an 18-item questionnaire for nursing informatics education, a 6-item questionnaire for faculty efficacy, and 2 open-ended questions for barriers and additional supports were sent to 204 nursing schools via email and the postal service. Nursing schools offering nursing informatics were further asked to send their syllabuses. The subjects taught were analyzed using nursing informatics competency categories and other responses were tailed using descriptive statistics.ResultsA total of 72 schools (35.3%) responded to the survey, of which 38 reported that they offered nursing informatics courses in their undergraduate nursing programs. Nursing informatics courses at 11 schools were taught by a professor with a degree majoring in nursing informatics. Computer technology was the most frequently taught subject (27 schools), followed by information systems used for practice (25 schools). The faculty efficacy was 3.76 ± 0.86 (out of 5). The most frequently reported barrier to teaching nursing informatics (n = 9) was lack of awareness of the importance of nursing informatics. Training and educational opportunities was the most requested additional support.ConclusionsNursing informatics education has increased during the last decade in Korea. However, the proportions of faculty with degrees in nursing informatics and number of schools offering nursing informatics courses have not increased much. Thus, a greater focus is needed on training faculty and developing the courses.