Reviewer Benefits

Benefits of reviewing for IJHS.

Reviewers receive acknowledgment letters and professional recognition for service.

Peer review builds critical appraisal skills and professional recognition.

We encourage authors to document assumptions and sensitivity analyses so conclusions remain robust across populations.

Explain how missing data were handled and why chosen strategies were appropriate for the study design.

Report software versions and packages to support reproducibility across analytic environments.

Use tables and figures to communicate effect sizes, uncertainty, and subgroup comparisons clearly.

For time series analyses, describe seasonality handling and any interventions or policy changes considered.

Reviewers receive acknowledgment letters and may be considered for editorial roles.

Transparent reporting of data provenance and governance supports reproducibility and ethical compliance in health statistics.

When presenting predictive models, report calibration, discrimination, and decision curve metrics where relevant.

When combining datasets, document linkage procedures and quality checks for matching accuracy.

If external validation is performed, describe population differences and implications for generalizability.

When reporting health disparities, describe how social determinants and contextual factors are measured.

Peer review builds critical appraisal skills and exposure to emerging methods.

Contributions strengthen the quality of health statistics research.

Well structured manuscripts accelerate peer review and help readers apply statistical insights to real world health decisions.

Define statistical terminology clearly for multidisciplinary readers who apply methods in clinical settings.

Highlight ethical safeguards for patient privacy, especially when working with linked or sensitive datasets.

Describe any model tuning or hyperparameter selection to support reproducibility in machine learning workflows.

Reviewers help maintain quality, transparency, and reproducibility in health statistics publishing.

Clear statistical reporting improves the interpretability of health evidence for clinicians, policymakers, and research funders.

Provide uncertainty measures such as confidence intervals or credible intervals for key estimates and model outputs.

Summaries that connect statistical findings to health outcomes improve translation to policy and practice.

Include brief rationale for study design choices to support reviewer understanding and methodological transparency.

If data access is restricted, describe the approval process for qualified researchers and expected timelines.