Ethical, Legal, and Sociocultural Issues in the Use of Mobile Technologies and Call Detail Records Data for Public Health in the East African Region: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The exponential scale and pace of real-time data generated from mobile phones present opportunities for new insights and challenges across multiple sectors, including health care delivery and public health research. However, little attention has been given to the new ethical, social, and legal concerns related to using these mobile technologies and the data they generate in Africa. OBJECTIVE: The objective of this scoping review was to explore the ethical and related concerns that arise from the use of data from call detail records and mobile technology interventions for public health in the context of East Africa. METHODS: We searched the PubMed database for published studies describing ethical challenges while using mobile technologies and related data in public health research between 2000 and 2020. A predefined search strategy was used as inclusion criteria with search terms such as "East Africa," "mHealth," "mobile phone data," "public health," "ethics," or "privacy." We screened studies using prespecified eligibility criteria through a two-stage process by two independent reviewers. Studies were included if they were (1) related to mobile technology use and health, (2) published in English from 2000 to 2020, (3) available in full text, and (4) conducted in the East African region. We excluded articles that (1) were conference proceedings, (2) studies presenting an abstract only, (3) systematic and literature reviews, (4) research protocols, and (5) reports of mobile technology in animal subjects. We followed the five stages of a published framework for scoping reviews recommended by Arksey and O'Malley. Data extracted included title, publication year, target population, geographic region, setting, and relevance to mobile health (mHealth) and ethics. Additionally, we used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Extension for Scoping Reviews checklist to guide the presentation of this scoping review. The rationale for focusing on the five countries in East Africa was their geographic proximity, which lends itself to similarities in technology infrastructure development. RESULTS: Of the 94 studies identified from PubMed, 33 met the review inclusion criteria for the final scoping review. The 33 articles retained in the final scoping review represent studies conducted in three out of five East African countries: 14 (42%) from Uganda, 13 (39%) from Kenya, and 5 (16%) from Tanzania. Three main categories of concerns related to the use of mHealth technologies and mobile phone data can be conceptualized as (1) ethical issues (adequate informed consent, privacy and confidentiality, data security and protection), (2) sociocultural issues, and (3) regulatory/legal issues. CONCLUSIONS: This scoping review identified major cross-cutting ethical, regulatory, and sociocultural concerns related to using data from mobile technologies in the East African region. A comprehensive framework that accounts for the critical concerns raised would be valuable for guiding the safe use of mobile technology data for public health research purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.016 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".