Instructions for Author

Prepare clear, transparent, and reproducible health statistics manuscripts.

International Journal of Health Statistics publishes methodological and applied research in health statistics, biostatistics, and epidemiology. Manuscripts should focus on quantitative approaches that improve health decision making.

Submissions may include original research, applied analyses, methodological innovations, or evidence syntheses that advance statistical practice.

Provide a clear statistical analysis plan, including model assumptions, diagnostics, and missing data handling procedures.

When using Bayesian approaches, describe prior selection and sensitivity to prior assumptions.

Include a brief limitations section that addresses data constraints and modeling assumptions explicitly.

If using geospatial data, describe spatial resolution and any aggregation applied.

Report preregistration links or protocols when applicable.

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

Prepare manuscripts with structured abstract, introduction, methods, results, and discussion. Clearly link statistical methods to health outcomes.

Provide concise titles and keywords that reflect the statistical focus and clinical or public health application.

• Structured abstract with objectives and conclusions
• Clear methods with model specifications
• Results with effect sizes and uncertainty
• Discussion of implications and limitations

For predictive modeling, report validation strategy, calibration metrics, and clinical utility interpretation.

For longitudinal analyses, specify follow up intervals, censoring rules, and handling of time varying covariates.

Document any software packages or libraries used for specialized methods or visualization.

When reporting risk scores, clarify thresholds and clinical decision contexts.

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

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

Describe sampling design, weighting procedures, and population representativeness for survey based studies.

Explain how external validation datasets were selected and describe differences from the primary cohort.

For health economics components, specify cost sources, discount rates, and sensitivity analyses.

Provide a concise summary of clinical relevance for statistical audiences.

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

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

Report assumptions, diagnostics, and sensitivity analyses for all models. Provide enough detail to enable replication and peer review.

Include code availability statements when feasible and specify software versions used in analysis.

• Model diagnostics and goodness of fit
• Handling of missing data
• Sensitivity analyses for key assumptions
• Clear notation and variable definitions

When reporting health equity analyses, define subgroup criteria and specify interaction testing approaches.

Provide a rationale for subgroup analyses and report multiplicity adjustments when applicable.

When using registry or administrative data, describe coding systems and validation procedures.

Describe quality control steps applied to raw datasets before analysis.

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

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

Studies involving human data must include ethics approvals and consent documentation. Describe privacy safeguards and data governance for sensitive datasets.

Provide data availability statements that explain how readers can access underlying data or code.

Include data availability statements and repository links for reproducibility where possible.

For causal inference methods, include clear definitions of treatment, outcome, and confounder sets.

Provide a glossary of key statistical terms if the manuscript targets a multidisciplinary readership.

If models are updated over time, explain monitoring and recalibration plans.

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

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

We encourage sharing analytic code and documentation to support reproducibility and secondary analysis.

If data are restricted, describe the access request process and expected timelines.

For randomized trials, report allocation concealment, interim analyses, and stopping rules if applicable.

Describe any imputation procedures and include diagnostics for imputed datasets.

If using adaptive designs, explain decision rules and timing of adaptations clearly.

Include a brief statement on limitations related to data coverage or representativeness.

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

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

Use tables and figures to summarize model outputs, uncertainty, and sensitivity analyses. Provide high resolution visuals with descriptive captions.

Supplementary files may include code, extended results, or additional validation data.

If using machine learning, discuss bias mitigation and explainability approaches for clinical audiences.

When reporting simulation studies, specify parameter settings, number of iterations, and evaluation metrics.

For meta analyses, describe heterogeneity measures and model selection criteria.

Describe how you verified key model assumptions and diagnostics.

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

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

Use clear labeling for equations, tables, and figures to support reviewer navigation.

References should include DOIs where available and follow consistent citation formatting.

• Define abbreviations on first use
• Include units and scales for all measures
• Ensure tables are interpretable without the main text

Provide code, scripts, or algorithm descriptions so statistical methods can be replicated.

For clustered data, report intra class correlation estimates and variance decomposition where appropriate.

Report transparency on data exclusions and justify any major exclusions from analysis.

Provide a summary of the software environment used for analysis.

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

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

Submit through ManuscriptZone or the Simple Submission Form. Both routes follow the same peer review workflow.

Include a cover letter summarizing scope alignment, data sources, and statistical contribution.

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

Use reporting guidelines such as CONSORT, STROBE, or PRISMA when relevant.

Ensure figures include confidence intervals or uncertainty bands to support interpretation.

Discuss how results might inform guidelines or policy decisions beyond statistical significance.

Clarify statistical significance thresholds or decision rules where applicable.

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

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

Include a brief statement on how results inform health decision making or policy.

When reporting prediction models, describe how cut points or thresholds were chosen for clinical use.

For data linkage studies, report linkage rates and error checks to support validity.

State how statistical packages were configured for reproducibility.

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

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

Accepted manuscripts undergo copyediting, layout, and proof review. Authors confirm accuracy before publication.

APC invoices are issued after acceptance. Publication proceeds after payment confirmation or approved waivers.

Document variable definitions, data cleaning decisions, and transformation steps that influence model estimates.

For algorithm comparisons, report computational resources and runtime considerations for reproducibility.

Include ethics approvals and data sharing permissions in a dedicated section for clarity.

Include a brief note on data limitations and generalizability.

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