Improving the Use of Mortality Data in Public Health

A Comparison of Garbage Code Redistribution Models

Bibliographic Data

ID11026773
AuthorsTa-Chou Ng (Ta-Chou Ng, Wei-Cheng Lo, and Hsien-Ho Lin are with the Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan. Wei-Cheng Lo is also with the Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. Chu-Chang Ku is with the School of Health and Related Research, University of Sheffield, Sheffield, UK. Tsung-Hsueh Lu is with the National Cheng Kung University Research Center for Health Data and Department of Public Health,...), Ta-Chou Vincent Ng (0000-0003-1446-5864, National Chengchi University), Wei-Cheng Lo (Ta-Chou Ng, Wei-Cheng Lo, and Hsien-Ho Lin are with the Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan. Wei-Cheng Lo is also with the Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. Chu-Chang Ku is with the School of Health and Related Research, University of Sheffield, Sheffield, UK. Tsung-Hsueh Lu is with the National Cheng Kung University Research Center for Health Data and Department of Public Health,...), Chu-Chang Ku (Ta-Chou Ng, Wei-Cheng Lo, and Hsien-Ho Lin are with the Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan. Wei-Cheng Lo is also with the Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. Chu-Chang Ku is with the School of Health and Related Research, University of Sheffield, Sheffield, UK. Tsung-Hsueh Lu is with the National Cheng Kung University Research Center for Health Data and Department of Public Health,...), Tsung-Hsueh Lu (0000-0003-4715-3787, Ta-Chou Ng, Wei-Cheng Lo, and Hsien-Ho Lin are with the Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan. Wei-Cheng Lo is also with the Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. Chu-Chang Ku is with the School of Health and Related Research, University of Sheffield, Sheffield, UK. Tsung-Hsueh Lu is with the National Cheng Kung University Research Center for Health Data and Department of Public Health,...), Hsien-Ho Lin (0000-0002-7481-6016, Ta-Chou Ng, Wei-Cheng Lo, and Hsien-Ho Lin are with the Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan. Wei-Cheng Lo is also with the Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. Chu-Chang Ku is with the School of Health and Related Research, University of Sheffield, Sheffield, UK. Tsung-Hsueh Lu is with the National Cheng Kung University Research Center for Health Data and Department of Public Health,...)
Year2020
Volume110
Issue2
Pages222-229
Publication date2020-02-01
Open AccessNo
TypeARTICLE
VenueAmerican Journal of Public Health (JOURNAL)
Journal identifiersISSN: 0090-0036 • E-ISSN: 1541-0048
PublisherAmerican Public Health Association (PUBLISHER • US)
DOI10.2105/ajph.2019.305439
PMID31855478
OpenAlexW2995940367
LanguageEN
Citations received3
References cited11

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

Bayes' theorem · Bayesian probability · Logistic regression · Machine learning · Multinomial logistic regression · Statistics · Artificial Intelligence · Autopsy Techniques and Outcomes · Computer Science · Health disparities and outcomes · Insurance, Mortality, Demography, Risk Management · Mathematics

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