A Novel Imputation Approach for Sharing Protected Public Health Data Article Swipe
Elizabeth A. Erdman
,
Leonard D. Young
,
Dana Bernson
,
Cici Bauer
,
Kenneth Chui
,
Thomas J. Stopka
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.2105/ajph.2021.306432
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.2105/ajph.2021.306432
Objectives. To develop an imputation method to produce estimates for suppressed values within a shared government administrative data set to facilitate accurate data sharing and statistical and spatial analyses. Methods. We developed an imputation approach that incorporated known features of suppressed Massachusetts surveillance data from 2011 to 2017 to predict missing values more precisely. Our methods for 35 de-identified opioid prescription data sets combined modified previous or next substitution followed by mean imputation and a count adjustment to estimate suppressed values before sharing. We modeled 4 methods and compared the results to baseline mean imputation. Results. We assessed performance by comparing root mean squared error (RMSE), mean absolute error (MAE), and proportional variance between imputed and suppressed values. Our method outperformed mean imputation; we retained 46% of the suppressed value’s proportional variance with better precision (22% lower RMSE and 26% lower MAE) than simple mean imputation. Conclusions. Our easy-to-implement imputation technique largely overcomes the adverse effects of low count value suppression with superior results to simple mean imputation. This novel method is generalizable to researchers sharing protected public health surveillance data. (Am J Public Health. 2021; 111(10):1830–1838. https://doi.org/10.2105/AJPH.2021.306432 )
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2105/ajph.2021.306432
- OA Status
- green
- Cited By
- 9
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3200283789
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3200283789Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2105/ajph.2021.306432Digital Object Identifier
- Title
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A Novel Imputation Approach for Sharing Protected Public Health DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-16Full publication date if available
- Authors
-
Elizabeth A. Erdman, Leonard D. Young, Dana Bernson, Cici Bauer, Kenneth Chui, Thomas J. StopkaList of authors in order
- Landing page
-
https://doi.org/10.2105/ajph.2021.306432Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/8561211Direct OA link when available
- Concepts
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Imputation (statistics), Mean squared error, Statistics, Missing data, Standard error, Mean square, Mean absolute error, Computer science, Econometrics, Mathematics, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2023: 5, 2022: 1Per-year citation counts (last 5 years)
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13Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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