Leveraging the FCSM Data Quality Framework to Better Communicate 

Data Quality

Kate Lamoreaux (ORISE Fellow, National Center for Health Statistics, Centers for Disease Control and 

Prevention) – Presenting Author

Jennifer Parker (Director, Division of Research and Methodology, National Center for Health Statistics , 

Centers for Disease Control and Prevention)

Session H-4: Data Quality: Addressing Disclosure, Deidentification, and the Utility of 

Publicly Available Data (Thursday, October 26, 2023; 8:30-10:15 AM)

National Center for Health Statistics

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Why Study Data Quality Communication?

1. NCHS Goal: expand relevance and 

external engagement

2. Adherence to Federal Plain 

Language Guidelines (Plain Writing 

Act of 2010)

3. The Federal Committee on 

Statistical Methodology (FCSM) 

launched the Framework for Data 

Quality in 2020

2

An estimated 54% 

of U.S. adults lack 

literacy 

proficiency.^

^Microsoft Word - BBFoundation_GainsFromEradicatingIlliteracy_9_8.docx (barbarabush.org)
FCSM Framework for Data Quality

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Federal Committee on Statistical Methodology. 2020. A Framework for Data Quality. FCSM 20-04, September 2020.
Input

• NCHS Data 

Releases

Interpretation

• News outlets

• Academic 

Publications

Output: Public 

Discussion and 

Opinions

• Tweets, Reddit 

posts

Hypothesis: Data Quality Communication
Input

• NCHS Data 

Releases

Interpretation

• Media News 

Articles

Output: Public 

Discussion and 

Opinions

• Tweets, Reddit 

posts

Anticipating Noise
Research Questions and Exploratory Study Methods

• This research aims to understand:

• How is data quality communicated to NCHS data users, specifically public 

audiences? 

• How does the FCSM data quality framework fit into data quality 

conversations?

• By:

• Identifying methods and practices used for communication of NCHS data 

quality

• Reviewing language across public-facing sources 

• Compiling case studies, or “conversations,” about NCHS data 

• Evaluating message clarity using FCSM data quality framework

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Selected conversation
Case study: 2021 Release of Maternal Mortality Data 

and Reactions

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Evaluating Data Quality Communication: Input

• Utility

• Objectivity

• Integrity

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Interpretations: 

News Organizations

• Clarity • FCSM Framework Domains of 

Data Quality communicated: • Utility • Objectivity • Integrity

• Findings and arguments contextualized

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Interpretations: 

News Organizations

• Introducing noise

• Headline tells a different 

narrative than NCHS 

report alone

• Later, not contextualizing 

or citing new information 

when bringing it in 

dialogue with prior 

information

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Text sourced from NCHS input source 

in blue, from external sources in yellow
Output: 

Twitter Reactions • Maternal Mortality was a top trending topic on Twitter • Tweets’ short format: • Outside reports and other topics fused together without 

context

• Domains of data quality communicated to a lesser 

extent with different 

language: “study finds”, “new 

report”, “during pandemic”

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Output: 

Reddit Reactions • High visibility: many posts, 

thousands of upvotes, and 

hundreds of comments

• Like Twitter: posts as direct 

reactions to news interpretations, 

fuses topics together without 

context

• Unlike Twitter: Comment threads 

enabled the domains of data 

quality and context to be 

discussed in depth

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Summary

• FCSM Framework domains and dimensions of data quality are evident across 

different stages of communication but can become distorted as data quality 

conversations go on.

• While the source of communication was an ideal communication input through 

the lens of the FCSM data quality framework, many reactions ignored 

statements of nuance and failed to contextualize findings.

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Findings and Future Directions
Preliminary Findings

• The domains and dimensions of the FCSM Data Quality framework can be used 

evaluate data quality communication.

• Clear communication of data quality cannot prevent all noise introduced through 

interpretations and reactions

• Data quality communication can become noisy when: 

• Information isn’t contextualized

• Language differences exist between the input and output levels

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Future Directions

• This selected example is a case study. Further research would need to be 

conducted to gauge whether this case study represents a systemic problem.

• Scaling up quantitatively using Natural Language Processing (NLP) text annotation 

machine learning methods to measure:

• Communication of data quality domains and dimensions 

• Contextualization of information

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Thank you!

• Contact: clamor@umd.edu

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For more information, contact CDC

1-800-CDC-INFO (232-4636)

TTY: 1-888-232-6348 www.cdc.gov

The findings and conclusions in this report are those of the authors and do not necessarily represent the 

official position of the Centers for Disease Control and Prevention.

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Referenced Links 1/2

1. FCSM-20-04 A Framework for Data Quality

2. Microsoft Word - BBFoundation_GainsFromEradicatingIlliteracy_9_8.docx (barbarabush.org)

3. PIAAC - What PIAAC Measures (ed.gov)

4. Maternal Mortality Rates in the United States, 2021 (cdc.gov)

5. Covid Worsened a Maternal Mortality Crisis in the U.S. - The New York Times 

(nytimes.com)

6. Maternal mortality rose during the pandemic, hitting Black women especially 

hard : Shots - Health News : NPR

7. GAO-23-105871, Accessible Version, MATERNAL HEALTH: Outcomes Worsened 

and Disparities Persisted During the Pandemic

8. US maternal death rate rose for another year, CDC data shows | CNN
Referenced Links 2/2

9. The Washington Post on Twitter: "The rate of Americans dying while 

giving birth — or in the weeks afterward — increased by more than 

one-third in 2021 compared with a year earlier, with the burden of 

death disproportionately borne by communities of color, according to a 

new report. https://t.co/t86Xli5yUJ" / Twitter

10. US maternal death rate rose sharply in 2021, CDC data shows, and 

experts worry the problem is getting worse : r/news (reddit.com)