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Data in the MMWR

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Many thanks for the gracious reactions to my recent post about panic disorder.
Posted to LinkedIn

2025-12-02

NotePreviously published

Posted to LinkedIn on 2025-12-02. The text below is the post as published.

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Many thanks for the gracious reactions to my recent post about panic disorder. I hope to interleave more work-life reflections in the future. Now I’m returning to the series on data-related resources for learners, on reading public health reports. The first few of these are fairly low-key, but they get deeper as we go.

Data in the MMWR (§4.1) On this site (opens in a new tab)
Describe data sources in anticipation of analysis and interpretation

Principle: Given the public health issue that the report addresses, clearly describe data sources, sizes, and structures, including information about choices and constraints. Include information that will influence analysis and interpretation, such as selection criteria and biases, quality, completeness, missingness, inclusions, exclusions, and losses.

Observations: The 56 selected reports consistently describe data in sufficient detail, including emerging sources such as web-based surveys and electronic health records.

Recommendations: Current practice is strong. Authors might benefit from guidance on how to relate characteristics of data to analysis and interpretation, especially for emerging and nontraditional data sources and types, such as web-based surveys and other convenience samples; administrative, claims, and electronic health records; fusion of multiple data sources; and wastewater and other ecological measures.
Reports do not typically need to indicate data management technology, such as REDCap, dplyr (R package), or pandas (Python library), unless data management practice or technology substantively influences analysis, interpretation, or potential reproducibility.

Next topic: Describe methods, including software (§5.1).

Data in the MMWR