Ongoing special issues highlight urgent topics such as pandemic analytics, health equity metrics, or data quality methods.

Authors should monitor announcements and align manuscripts with current themes and deadlines.

Contact [email protected] for updates on active collections.

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

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

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

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

Describe cohort selection, inclusion criteria, and data exclusions to reduce ambiguity in analytic interpretation.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Include data dictionary summaries or variable definitions for key covariates to improve interpretability.

Manuscripts benefit from concise discussion of clinical relevance and potential implications for health systems.

Provide transparency about funding sources and potential conflicts of interest affecting analytic decisions.

Ensure titles and abstracts reflect the statistical contribution and health domain application accurately.

A clear narrative of methods to results supports readers who are translating findings into practice.

Explain how sampling weights or survey design elements were applied in national or regional datasets.