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Fang X, Huang S, Yin Y, Chen T, Liao Z, Zhong W. Advancing Underlying Cause of Death Inference Through Wide and Deep Model. China CDC Wkly 2024; 6:487-492. [PMID: 38854462 PMCID: PMC11154107 DOI: 10.46234/ccdcw2024.094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/13/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction Accurately filling out death certificates is essential for death surveillance. However, manually determining the underlying cause of death is often imprecise. In this study, we investigate the Wide and Deep framework as a method to improve the accuracy and reliability of inferring the underlying cause of death. Methods Death report data from national-level cause of death surveillance sites in Fujian Province from 2016 to 2022, involving 403,547 deaths, were analyzed. The Wide and Deep embedded with Convolutional Neural Networks (CNN) was developed. Model performance was assessed using weighted accuracy, weighted precision, weighted recall, and weighted area under the curve (AUC). A comparison was made with XGBoost, CNN, Gated Recurrent Unit (GRU), Transformer, and GRU with Attention. Results The Wide and Deep achieved strong performance metrics on the test set: precision of 95.75%, recall of 92.08%, F1 Score of 93.78%, and an AUC of 95.99%. The model also displayed specific F1 Scores for different cause-of-death chain lengths: 97.13% for single causes, 95.08% for double causes, 91.24% for triple causes, and 79.50% for quadruple causes. Conclusions The Wide and Deep significantly enhances the ability to determine the root causes of death, providing a valuable tool for improving cause-of-death surveillance quality. Integrating artificial intelligence (AI) in this field is anticipated to streamline death registration and reporting procedures, thereby boosting the precision of public health data.
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Affiliation(s)
- Xin Fang
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou City, Fujian Province, China
| | - Shaofen Huang
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou City, Fujian Province, China
| | - Yanrong Yin
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou City, Fujian Province, China
| | - Tiehui Chen
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou City, Fujian Province, China
| | - Zhijun Liao
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou City, Fujian Province, China
| | - Wenling Zhong
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou City, Fujian Province, China
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Ahmed SMA, Cresswell JA, Say L. Incompleteness and misclassification of maternal death recording: a systematic review and meta-analysis. BMC Pregnancy Childbirth 2023; 23:794. [PMID: 37968585 PMCID: PMC10647144 DOI: 10.1186/s12884-023-06077-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/18/2023] [Indexed: 11/17/2023] Open
Abstract
OBJECTIVE To quantify the extent of incompleteness and misclassification of maternal and pregnancy related deaths, and to identify general and context-specific factors associated with incompleteness and/or misclassification of maternal death data. METHODS We conducted a systematic review of incompleteness and/or misclassification of maternal and pregnancy-related deaths. We conducted a narrative synthesis to identify methods used to capture and classify maternal deaths, as well as general and context specific factors affecting the completeness and misclassification of maternal death recording. We conducted a meta-analysis of proportions to obtain estimates of incompleteness and misclassification of maternal death recording, overall and disaggregated by income and surveillance system types. FINDINGS Of 2872 title-abstracts identified, 29 were eligible for inclusions in the qualitative synthesis, and 20 in the meta-analysis. Included studies relied principally on record linkage and review for identifying deaths, and on review of medical records and verbal autopsies to correctly classify cause of death. Deaths to women towards the extremes of the reproductive age range, those not classified by a medical examiner or a coroner, and those from minority ethnic groups in their setting were more likely misclassified or unrecorded. In the meta-analysis, we found maternal death recording to be incomplete by 34% (95% CI: 28-48), with 60% sensitivity (95% CI: 31-81.). Overall, we found maternal mortality was under-estimated by 39% (95% CI: 30-48) due to incompleteness and/or misclassification. Reporting of deaths away from the intrapartum, due to indirect causes or occurring at home were less complete than their counterparts. There was substantial between and within group variability across most results. CONCLUSION Maternal deaths were under-estimated in almost all contexts, but the extent varied across settings. Countries should aim towards establishing Civil Registration and Vital Statistics systems where they are not instituted. Efforts to improve the completeness and accuracy of maternal cause of death recording, such as Confidential Enquiries into Maternal Deaths, are needed even where CRVS is considered to be well-functioning.
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Affiliation(s)
- Sahar M A Ahmed
- Department of Sexual and Reproductive Health Research, World Health Organization, Geneva, Switzerland.
| | - Jenny A Cresswell
- Department of Sexual and Reproductive Health Research, World Health Organization, Geneva, Switzerland
| | - Lale Say
- Department of Sexual and Reproductive Health Research, World Health Organization, Geneva, Switzerland
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Wang M, Li XS, Wang Z, de Oliveira Otto MC, Lemaitre RN, Fretts A, Sotoodehnia N, Budoff M, Nemet I, DiDonato JA, Tang WHW, Psaty BM, Siscovick DS, Hazen SL, Mozaffarian D. Trimethylamine N-oxide is associated with long-term mortality risk: the multi-ethnic study of atherosclerosis. Eur Heart J 2023; 44:1608-1618. [PMID: 36883587 PMCID: PMC10411925 DOI: 10.1093/eurheartj/ehad089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 01/06/2023] [Accepted: 02/03/2023] [Indexed: 03/09/2023] Open
Abstract
AIMS Little is known about associations of trimethylamine N-oxide (TMAO), a novel gut microbiota-generated metabolite of dietary phosphatidylcholine and carnitine, and its changes over time with all-cause and cause-specific mortality in the general population or in different race/ethnicity groups. The study aimed to investigate associations of serially measured plasma TMAO levels and changes in TMAO over time with all-cause and cause-specific mortality in a multi-ethnic community-based cohort. METHODS AND RESULTS The study included 6,785 adults from the Multi-Ethnic Study of Atherosclerosis. TMAO was measured at baseline and year 5 using mass spectrometry. Primary outcomes were adjudicated all-cause mortality and cardiovascular disease (CVD) mortality. Secondary outcomes were deaths due to kidney failure, cancer, or dementia obtained from death certificates. Cox proportional hazards models with time-varying TMAO and covariates assessed the associations with adjustment for sociodemographics, lifestyles, diet, metabolic factors, and comorbidities. During a median follow-up of 16.9 years, 1704 participants died and 411 from CVD. Higher TMAO levels associated with higher risk of all-cause mortality [hazard ratio (HR): 1.12, 95% confidence interval (CI): 1.08-1.17], CVD mortality (HR: 1.09, 95% CI: 1.00-1.09), and death due to kidney failure (HR: 1.44, 95% CI: 1.25-1.66) per inter-quintile range, but not deaths due to cancer or dementia. Annualized changes in TMAO levels associated with higher risk of all-cause mortality (HR: 1.10, 95% CI: 1.05-1.14) and death due to kidney failure (HR: 1.54, 95% CI: 1.26-1.89) but not other deaths. CONCLUSION Plasma TMAO levels were positively associated with mortality, especially deaths due to cardiovascular and renal disease, in a multi-ethnic US cohort.
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Affiliation(s)
- Meng Wang
- Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Ave, Boston, MA 02111, USA
| | - Xinmin S Li
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
- Center for Microbiome and Human Health, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
| | - Zeneng Wang
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
- Center for Microbiome and Human Health, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
| | - Marcia C de Oliveira Otto
- Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, 1200 Pressler Street, Houston, TX 77030, USA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 1730 Minor Ave, Suite 1360, Seattle, WA 98101, USA
| | - Amanda Fretts
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 1730 Minor Ave, Suite 1360, Seattle, WA 98101, USA
- Department of Epidemiology, University of Washington, 3980 15th Ave NE, Seattle, WA 98195, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 1730 Minor Ave, Suite 1360, Seattle, WA 98101, USA
| | - Matthew Budoff
- Department of Medicine, Lundquist Institute at Harbor-UCLA Medical Center, 124 West Carson Street, Torrance, CA 90502, USA
| | - Ina Nemet
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
- Center for Microbiome and Human Health, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
| | - Joseph A DiDonato
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
- Center for Microbiome and Human Health, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
| | - Wai Hong Wilson Tang
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
- Center for Microbiome and Human Health, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 1730 Minor Ave, Suite 1360, Seattle, WA 98101, USA
- Department of Epidemiology, University of Washington, 3980 15th Ave NE, Seattle, WA 98195, USA
- Department of Health Systems and Population Health, University of Washington, 3980 15th Ave NE, Seattle, WA 98195, USA
| | - David S Siscovick
- The New York Academy of Medicine, 1216 5th Ave, New York City, NY 10029, USA
| | - Stanley L Hazen
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
- Center for Microbiome and Human Health, Lerner Research Institute, 9500 Euclid Ave, Cleveland, OH 44195, USA
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195, USA
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Ave, Boston, MA 02111, USA
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Isgut M, Gloster L, Choi K, Venugopalan J, Wang MD. Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era. IEEE Rev Biomed Eng 2023; 16:53-69. [PMID: 36269930 DOI: 10.1109/rbme.2022.3216531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.
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An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens. LIFE (BASEL, SWITZERLAND) 2022; 12:life12081134. [PMID: 36013313 PMCID: PMC9410465 DOI: 10.3390/life12081134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/22/2022] [Accepted: 07/23/2022] [Indexed: 11/17/2022]
Abstract
It is of great significance to correctly infer the underlying cause of death for citizens, especially under the current worldwide situation. The medical resources of all countries are overwhelmed under the impact of coronavirus disease 2019 (COVID-19) and countries need to allocate limited resources to the most suitable place. Traditionally, the cause-of-death inference relies on manual methods, which require a large resource cost and are not so efficient. To address the challenges, in this work, we present a mixed inference method named Sink-CF. The Sink-CF algorithm is based on confidence measurement and is used to automatically infer the underlying cause of death of citizens. The method proposed in this paper combines a mathematical statistics method and a collaborative filtering and analysis algorithm in machine learning. Thus, our method can not only effectively achieve a certain accuracy, but also does not rely on a large quantity of manually labeled data to continuously optimize the model, which can save computer computing power and time, and has the characteristics of being simple, easy and efficient. The experimental results show that our method generates a reasonable precision (93.82%) and recall (90.11%) and outperforms other state-of-the-art machine learning algorithms.
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Zhu Y, Sha Y, Wu H, Li M, Hoffman RA, Wang MD. Proposing Causal Sequence of Death by Neural Machine Translation in Public Health Informatics. IEEE J Biomed Health Inform 2022; 26:1422-1431. [PMID: 35349461 PMCID: PMC9452006 DOI: 10.1109/jbhi.2022.3163013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Each year there are nearly 57 million deaths worldwide, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical for public health, especially during the COVID-19 pandemic, as institutions and government agencies rely on death reports to formulate responses to communicable diseases. Unfortunately, determining the causes of death is challenging even for experienced physicians. The novel coronavirus and its variants may further complicate the task, as physicians and experts are still investigating COVID-related complications. To assist physicians in accurately reporting causes of death, an advanced Artificial Intelligence (AI) approach is presented to determine a chronically ordered sequence of conditions that lead to death (named as the causal sequence of death), based on decedent's last hospital discharge record. The key design is to learn the causal relationship among clinical codes and to identify death-related conditions. There exist three challenges: different clinical coding systems, medical domain knowledge constraint, and data interoperability. First, we apply neural machine translation models with various attention mechanisms to generate sequences of causes of death. We use the BLEU (BiLingual Evaluation Understudy) score with three accuracy metrics to evaluate the quality of generated sequences. Second, we incorporate expert-verified medical domain knowledge as constraints when generating the causal sequences of death. Lastly, we develop a Fast Healthcare Interoperability Resources (FHIR) interface that demonstrates the usability of this work in clinical practice. Our results match the state-of-art reporting and can assist physicians and experts in public health crisis such as the COVID-19 pandemic.
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Monasta L, Alicandro G, Pasovic M, Cunningham M, Armocida B, Ronfani L, Naghavi M, Monasta L, Alicandro G, Pasovic M, Cunningham M, Armocida B, Albano L, Beghi E, Beghi M, Bosetti C, Bragazzi NL, Carreras G, Castelpietra G, Catapano AL, Cattaruzza MS, Collatuzzo G, Conti S, Damiani G, Ferrara P, Fornari C, Gallus S, Giampaoli S, Golinelli D, Isola G, Lauriola P, La Vecchia C, Leonardi M, Magnani FG, Minelli G, Moccia M, Pedersini P, Perico N, Raggi A, Remuzzi G, Sanmarchi F, Sattin D, Unim B, Villafañe JH, Violante FS, Murray CJL, Ronfani L, Naghavi M. Redistribution of garbage codes to underlying causes of death: a systematic analysis on Italy and a comparison with most populous Western European countries based on the Global Burden of Disease Study 2019. Eur J Public Health 2022; 32:456-462. [PMID: 35061890 PMCID: PMC9159332 DOI: 10.1093/eurpub/ckab194] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background The proportion of reported causes of death (CoDs) that are not underlying causes can be relevant even in high-income countries and seriously affect health planning. The Global Burden of Disease (GBD) study identifies these ‘garbage codes’ (GCs) and redistributes them to underlying causes using evidence-based algorithms. Planners relying on vital registration data will find discrepancies with GBD estimates. We analyse these discrepancies, through the analysis of GCs and their redistribution. Methods We explored the case of Italy, at national and regional level, and compared it to nine other Western European countries with similar population sizes. We analysed differences between official data and GBD 2019 estimates, for the period 1990–2017 for which we had vital registration data for most select countries. Results In Italy, in 2017, 33 000 deaths were attributed to unspecified type of stroke and 15 000 to unspecified type of diabetes, these making a fourth of the overall garbage. Significant heterogeneity exists on the overall proportion of GCs, type (unspecified or impossible underlying causes), and size of specific GCs among regions in Italy, and among the select countries. We found no pattern between level of garbage and relevance of specific GCs. Even locations performing below average show interesting lower levels for certain GCs if compared to better performing countries. Conclusions This systematic analysis suggests the heterogeneity in GC levels and causes, paired with a more detailed analysis of local practices, strengths and weaknesses, could be a positive element in a strategy for the reduction of GCs in Italy.
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Affiliation(s)
- Lorenzo Monasta
- Institute for Maternal and Child Health–IRCCS “Burlo Garofolo”, Trieste, Italy
| | - Gianfranco Alicandro
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
- Cystic Fibrosis Center, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Maja Pasovic
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Matthew Cunningham
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Benedetta Armocida
- Institute for Maternal and Child Health–IRCCS “Burlo Garofolo”, Trieste, Italy
| | - Luca Ronfani
- Institute for Maternal and Child Health–IRCCS “Burlo Garofolo”, Trieste, Italy
| | - Mohsen Naghavi
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
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Mezuk B, Kalesnikava VA, Kim J, Ko TM, Collins C. Not discussed: Inequalities in narrative text data for suicide deaths in the National Violent Death Reporting System. PLoS One 2021; 16:e0254417. [PMID: 34270588 PMCID: PMC8284808 DOI: 10.1371/journal.pone.0254417] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 06/25/2021] [Indexed: 11/27/2022] Open
Abstract
Background The rate of suicide in the US has increased substantially in the past two decades, and new insights are needed to support prevention efforts. The National Violent Death Reporting System (NVDRS), the nation’s most comprehensive registry of suicide mortality, has qualitative text narratives that describe salient circumstances of these deaths. These texts have great potential for providing novel insights about suicide risk but may be subject to information bias. Objective To examine the relationship between decedent characteristics and the presence and length of NVDRS text narratives (separately for coroner/medical examiner (C/ME) and law enforcement (LE) reports) among 233,108 suicide and undetermined deaths from 2003–2017. Methods Generalized estimating equations (GEE) logistic and quasi-Poisson modeling was used to examine variation in the narratives (proportion of missing texts and character length of the non-missing texts, respectively) as a function of decedent age, sex, race/ethnicity, education, marital status, military history, and homeless status. Models adjusted for site, year, location of death, and autopsy status. Results The frequency of missing narratives was higher for LE vs. C/ME texts (19.8% vs. 5.2%). Decedent characteristics were not consistently associated with missing text across the two types of narratives (i.e., Black decedents were more likely to be missing the LE narrative but less likely to be missing the C/ME narrative relative to non-Hispanic whites). Conditional on having a narrative, C/ME were significantly longer than LE (822.44 vs. 780.68 characters). Decedents who were older, male, had less education and some racial/ethnic minority groups had shorter narratives (both C/ME and LE) than younger, female, more educated, and non-Hispanic white decedents. Conclusion Decedent characteristics are significantly related to the presence and length of narrative texts for suicide and undetermined deaths in the NVDRS. Findings can inform future research using these data to identify novel determinants of suicide mortality.
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Affiliation(s)
- Briana Mezuk
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Research Center for Group Dynamics, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
| | - Viktoryia A. Kalesnikava
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Jenni Kim
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Tomohiro M. Ko
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Robert Wood Johnson Medical School, Rutgers University – New Brunswick, New Brunswick, New Jersey, United States of America
| | - Cassady Collins
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
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Ge H, Gao K, Li S, Wang W, Chen Q, Lin X, Huan Z, Su X, Yang X. An Automatic Approach Designed for Inference of the Underlying Cause-of-Death of Citizens. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18052414. [PMID: 33801219 PMCID: PMC7967784 DOI: 10.3390/ijerph18052414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/25/2021] [Accepted: 02/26/2021] [Indexed: 11/16/2022]
Abstract
It is very important to have a comprehensive understanding of the health status of a country’s population, which helps to develop corresponding public health policies. Correct inference of the underlying cause-of-death for citizens is essential to achieve a comprehensive understanding of the health status of a country’s population. Traditionally, this relies mainly on manual methods based on medical staff’s experiences, which require a lot of resources and is not very efficient. In this work, we present our efforts to construct an automatic method to perform inferences of the underlying causes-of-death for citizens. A sink algorithm is introduced, which could perform automatic inference of the underlying cause-of-death for citizens. The results show that our sink algorithm could generate a reasonable output and outperforms other stat-of-the-art algorithms. We believe it would be very useful to greatly enhance the efficiency of correct inferences of the underlying causes-of-death for citizens.
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Affiliation(s)
- Hui Ge
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (H.G.); (S.L.); (W.W.); (Q.C.)
| | - Keyan Gao
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; (K.G.); (X.L.); (Z.H.)
| | - Shaoqiong Li
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (H.G.); (S.L.); (W.W.); (Q.C.)
| | - Wei Wang
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (H.G.); (S.L.); (W.W.); (Q.C.)
| | - Qiang Chen
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (H.G.); (S.L.); (W.W.); (Q.C.)
| | - Xialv Lin
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; (K.G.); (X.L.); (Z.H.)
| | - Ziyi Huan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; (K.G.); (X.L.); (Z.H.)
| | - Xuemei Su
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (H.G.); (S.L.); (W.W.); (Q.C.)
- Correspondence: (X.S.); (X.Y.)
| | - Xu Yang
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; (K.G.); (X.L.); (Z.H.)
- Correspondence: (X.S.); (X.Y.)
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Ng TC, Lo WC, Ku CC, Lu TH, Lin HH. Improving the Use of Mortality Data in Public Health: A Comparison of Garbage Code Redistribution Models. Am J Public Health 2020; 110:222-229. [PMID: 31855478 PMCID: PMC6951373 DOI: 10.2105/ajph.2019.305439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2019] [Indexed: 11/04/2022]
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.
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Affiliation(s)
- Ta-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, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - 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, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - 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, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Tsung-Hsueh Lu
- 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, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Hsien-Ho Lin
- 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, College of Medicine, National Cheng Kung University, Tainan, Taiwan
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