The Role of Digital Health Equity Audits in Preventing Harmful Infodemiology
Bibliographic record
Abstract
Background: Health disparities persist and are influenced by digital transformation. Although digital tools offer opportunities, they can also exacerbate existing inequalities, a problem amplified by the COVID-19 pandemic and the related infodemic. Health equity audit (HEA) tools, such as those developed in the United Kingdom, provide a framework to assess equity but require adaptation for the digital context. Digital determinants of health (DDoH) are increasingly recognized as crucial factors influencing health outcomes in the digital era. Objective: This editorial proposes an approach to extend HEA principles to create a specific framework, the digital health equity audit (DHEA), designed to systematically assess and address health inequities within the design, implementation, and evaluation of digital health technologies, with a focus on DDoH. Methods: We propose a cyclical DHEA model based on existing HEA principles, integrating them with digital health equity frameworks. The DHEA cycle comprises six phases: (1) scoping the audit and mobilizing the team (including community members); (2) developing the digital health equity profile and identifying inequities (assessing DDoH at individual, interpersonal, community, and societal levels); (3) identifying high-impact actions to address DDoH and inequities; (4) prioritizing actions for maximum equity impact; (5) implementing and supporting change; and (6) evaluating progress and impact, and refining. This method emphasizes multilevel interventions and stakeholder engagement. Results: The main result is the articulation of the DHEA framework: a structured, 6-phase cyclical model to guide organizations in the analysis and proactive mitigation of digital health-related disparities. The framework explicitly integrates the assessment of DDoH across multiple levels (individual, interpersonal, community, societal) and promotes the development of targeted interventions to ensure digital solutions promote equity. Conclusions: The DHEA model offers an integrated approach to consider social, epidemiological, health, and technological variables, aiming to reduce health inequities through the conscious use of new technologies. It is emphasized that digital technologies can be the cause or the solution to inequalities; DHEAs are proposed as a tool to foster equity. Its systematic adoption, along with a collaborative approach (co-design) and trust building, can help ensure that the benefits of health digitization are equitably distributed while strengthening trust in institutions. Continued attention is needed to manage emerging challenges such as infodemiology in the era of big data and artificial intelligence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.130 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".