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Data in the MMWR: A purposive review with recommendations

1 Summary

The purpose of this project was to develop and propose guidance for MMWR authors, reviewers of MMWR reports, and MMWR scientific editors. We reviewed 56 full reports published between January 2019 and May 2022, to identify specific practices regarding presentations of data, analytic methods and results, tables and graphics, and interpretation of results. We also reviewed guidance from journals, the American Statistical Association, the International Committee of Medical Journal Editors, and related sources.

For this assessment, we adapted general principles regarding style, accuracy, and significance from existing norms for presenting and reviewing statistics in peer-reviewed articles. We focused on clarity, precision, and accuracy in presenting data, analysis, results, and interpretation. We consulted current practices for presenting data-oriented elements in narrative text, tables, and figures and considered how to balance important details with concise presentation formats. Finally, we recommended changes geared toward style, accuracy, and significance, mindful of MMWR’s purpose, orientation, and house style.

This assessment led with issues of clarity and style in text, tables, and figures as seen in 5 example reports, followed by issues of accuracy and significance seen in 3 published reports. The broader assessment of 56 reports was then organized around 4 themes:

  • Design choices and constraints: What data were used?
  • Analysis and formal results: What do the data show?
  • Results in context and beyond: What do the data signify?
  • Additional practices: How else might MMWR modernize?

MMWR could clarify matters of style and precision, such as conditions for naming software and analytic methods in text, tables, and figures; practices for comparing values directly, usually as differences or ratios; improvements in use of statistical inference and shedding outmoded practices; and modernizing tables and figures. In addition, MMWR could clarify matters of accuracy and significance. Authors should emphasize public health significance, including how calculated values are interpreted in context, and they should deemphasize statistical significance, especially binary inferences. MMWR could also refine how reports place findings and context and link reports to recommendations.