Review papers with raw data transparency

Visual critique — Healthcare access for migrants in Iran (Ghiasi et al., 2025)

Critical appraisal — strengths

• Clear question addressing an urgent public-health issue in a major refugee-hosting LMIC; timely, policy-relevant focus (covers PHC, curative care, CBR) Context & scope
• Triangulation of data sources: stakeholder interviews + document review strengthens credibility of the themes (policy + lived experience) Triangulation in methods

Critical appraisal — limitations & blindspots

1. Sampling and generalisability: 25 stakeholders is appropriate for in-depth qualitative coding but may underrepresent migrant voices (only 3 migrant interviewees reported) — risk of privileging institutional perspectives over lived experiences of undocumented migrants; limits transferability outside Iran Sample limitations (Ghiasi et al.)
2. Missing utilization data: The paper reports access and barriers but lacks quantitative utilization/outcome measures (service uptake, morbidity/mortality differences, cost burdens) that would permit stronger causal inference or policy costing analyses — the claim that undocumented migrants delay care is credible but not quantified No utilization/outcomes (Ghiasi et al.)
3. Potential selection & confirmation bias: purposive + snowball sampling of stakeholders may bias toward those engaged in migrant health policy; without detailing recruitment frame or saturation criteria, thematic completeness is hard to judge Sampling details (Ghiasi et al.)
4. Policy evidence linkage: recommendations (e.g., multinational social insurance) are bold but speculative; feasibility (political, fiscal) and unintended consequences (adverse selection, portability administration) are not modelled or empirically tested in paper Recommendations vs evidence (Ghiasi et al.)

Concrete ways to strengthen the study (actionable)

• Recruit a larger purposive sample of undocumented migrants (stratified by sex, age, urban/rural, employment) and include health service utilization logs or facility records to triangulate self-report.
• Include simple quantitative measures: rates of vaccination completion, insurance enrollment, OOP expenditure estimates — even survey modules embedded in qualitative sampling would enable mixed-methods inference.
• Perform a basic costed policy simulation comparing current UPHI coverage vs. universal UPHI for migrants (budget impact, projected utilization changes) to test feasibility of recommendations.

Confidence statement and what would falsify the main conclusion

Confidence in primary claims (that free PHC exists but undocumented migrants face major legal/financial barriers) is moderate given concordant interview and document evidence in the paper; falsification would require high-quality administrative data showing no systematic difference in healthcare access or insurance coverage between documented and undocumented migrants in Iran (contradicting authors' claims) Main claim & falsifiability (Ghiasi et al.)

Minimal reproducible replication checklist (what a replicator needs)

1. Study protocol: semi-structured interview guide (not provided in paper) — include verbatim guide in supplement.
2. Recruitment frame: stakeholder categories, inclusion/exclusion criteria, recruitment dates and locations.
3. Document search strategy: exact databases, search terms, inclusion/exclusion for the 13 documents reviewed.
4. Codebook and inter-coder agreement statistics (e.g., % agreement or kappa) — authors state independent review but numeric metrics would help reproducibility.

Short, prioritized next-research experiments (feasible & decisive)

1. Cross-sectional facility survey in high-refugee urban areas to measure (a) vaccination completion among migrant vs host children, (b) percent uninsured, (c) mean OOP payments — will quantify the qualitative claims.
2. Budget-impact model comparing current UPHI reach vs UPHI expansion to all migrants including sensitivity to enrollment/adverse selection — will assess fiscal feasibility of policy recommendations.