Ng, T.-C., Lo, W.-C., Ku, C.-C. orcid.org/0000-0002-4719-117X et al. (2 more authors) (2020) Improving the use of mortality data in public health : a comparison of garbage code redistribution models. American Journal of Public Health, 110 (2). pp. 222-229. ISSN 0090-0036
Abstract
Objectives. To describe and compare 3 garbage code (GC) redistribution models: naïve Bayes classifier (NB), coarsened exact matching (CEM), and multinomial logistic regression (MLR).
Methods. We analyzed Taiwan Vital Registration data (2008–2016) using a 2-step approach. First, we used non-GC death records to evaluate 3 different prediction models (NB, CEM, and MLR), incorporating individual-level information on multiple causes of death (MCDs) and demographic characteristics. Second, we applied the best-performing model to GC death records to predict the underlying causes of death. We conducted additional simulation analyses for evaluating the predictive performance of models.
Results. When we did not account for MCDs, all 3 models presented high average misclassification rates in GC assignment (NB, 81%; CEM, 86%; MLR, 81%). In the presence of MCD information, NB and MLR exhibited significant improvement in assignment accuracy (19% and 17% misclassification rate, respectively). Furthermore, CEM without a variable selection procedure resulted in a substantially higher misclassification rate (40%).
Conclusions. Comparing potential GC redistribution approaches provides guidance for obtaining better estimates of cause-of-death distribution and highlights the significance of MCD information for vital registration system reform.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 American Public Health Association. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Jan 2021 10:44 |
Last Modified: | 20 Jan 2021 10:44 |
Status: | Published |
Publisher: | American Public Health Association |
Refereed: | Yes |
Identification Number: | 10.2105/ajph.2019.305439 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169835 |