Call for Papers

Advancing statistical methods for better health outcomes.

International Journal of Health Statistics invites submissions that advance statistical methodology, epidemiologic analysis, and health data science. We welcome original research, reviews, and applied studies that improve decision making in clinical and public health settings.

Priority is given to rigorous, reproducible analyses with clear explanations of methods and practical implications for health policy or care delivery.

We welcome interdisciplinary submissions that connect statistical methods with clinical, public health, or health services applications.

We welcome contributions that advance causal inference, Bayesian modeling, and real world evidence methods in health research.

Submissions addressing data quality, bias assessment, or measurement error in health datasets are encouraged.

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

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

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

We welcome contributions across biostatistics, health analytics, and statistical modeling, including:

• Causal inference, propensity modeling, and observational study design
• Bayesian statistics and probabilistic modeling for health data
• Survival analysis, competing risks, and longitudinal modeling
• Health equity metrics and disparity measurement
• Real world evidence, registries, and surveillance analytics

Submissions that integrate health economics, policy evaluation, or implementation analytics are especially valued.

We value studies that communicate implications for health systems performance and resource allocation.

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

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

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

Authors should clearly describe data sources, linkage methods, and validation strategies for complex datasets.

Submissions that bridge statistical innovation with clinical impact are welcomed.

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

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

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

Our process is designed for clarity and consistent communication.

We encourage method comparisons that clarify practical tradeoffs for clinical trials, registries, and surveillance systems.

Interdisciplinary teams are encouraged to clarify roles and analytic responsibilities.

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

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

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

Submissions should describe data sources, assumptions, model diagnostics, and limitations transparently. Provide sufficient detail for replication and peer review scrutiny.

When possible, include code or supplemental materials to support reproducibility.

• Clear model assumptions and diagnostics
• Sensitivity analyses for key assumptions
• Transparent data preprocessing steps

Please include reproducibility notes and code availability statements whenever feasible.

We encourage clear statements of statistical contribution and practical relevance in the abstract.

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

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

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

Studies that translate statistical methods into actionable clinical guidance or public health decisions are prioritized.

Highlight how findings inform health policy, service delivery, or population outcomes.

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

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

Both submission routes receive the same editorial review and production standards.

• ManuscriptZone submission: https://oap.manuscriptzone.net/
• Simple submission form: https://openaccesspub.org/manuscript-submission-form

Methodological papers should include worked examples and guidance for practitioners applying the approach.

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

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

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