Improving the Use of Mortality Data in Public Health: A Comparison of Garbage Code Redistribution Models

Ng, Ta Chou; Lo, Wei Cheng; Ku, Chu Chang; Lu, Tsung Hsueh; Lin, Hsien Ho

doi:10.2105/ajph.2019.305439

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Our study illustrated that the CEM prefer better than the LR. However, another study (15) conducted that multinomial logistic regression was the suitable model for GC redistribution, compared to naive Bayes classi er and CEM. The reason for the different results might be that Taiwan, which owns 23 million population, has a larger population quantity than that of Weifang or Xuanwei.…”

Section: Difference Between the Two Redistribution Approachesmentioning

confidence: 99%

A study on garbage code redistribution methods for heart failure at city level by two approaches

Liu

Cai

Wang

et al. 2023

Preprint

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Background Garbage code (GC) in death surveillance data may affect the statistics on causes of death. The Global Burden of Disease (GBD) studies have proposed several methods to redistribute GCs to the plausible correct underlying cause of death (UCOD). Take heart failure as an example, this study aimed to explore suitable GC redistribution methods at the city level. Methods The cause of death surveillance data was from Weifang city, from 2010 to 2017, and Xuanwei county, from 2010 to 2016, China. Firstly, the death records of heart failure were corrected to UCOD based on the World Health Organization (WHO) guidelines. Secondly, the records with UCOD remaining to be heart failure were proceeded by coarsened exact matching (CEM) and linear regression (LR), respectively. Finally, the change of cause-specific mortalities before and after redistributed by two methods was calculated, respectively. Results The UCOD stated as heart failure was 1556 (0.33%) in Weifang and 226 (0.41%) in Xuanwei, respectively. After redistribution based on the WHO guideline, around 75% of the UCOD records in both cities remained the same. In Weifang, by CEM, heart failure was mainly redistributed to ischemic heart disease (IHD, 45.31%) and hypertensive heart disease (HHD, 21.56%). By LR, 91.20% of heart failure was redistributed to IHD. In Xuanwei, by CEM, heart failure was mainly redistributed to IHD (24.70%), diabetes mellitus and chronic kidney disease (DMCKD 23.25%). By LR, 94.83% of heart failure was redistributed to chronic obstructive pulmonary disease (COPD). Conclusions During conducting GC redistribution, careful consideration of which method to choose is necessary, especially for the city-level data. In this study, the CEM approach might probably be more suitable for the city level, compared to LR.

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Section: Difference Between the Two Redistribution Approachesmentioning

confidence: 99%

A study on garbage code redistribution methods for heart failure at city level by two approaches

Liu

Cai

Wang

et al. 2023

Preprint

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show abstract

“…To this end, there is a growing body of literature on national BoD assessments. [e.g., 9 , 14 – 16 ] In Belgium, the national institute for health, Sciensano, initiated the Belgian national burden of disease study (BeBOD) in 2016, to generate DALY estimates rooted in local data and evidence. This article presents the four-step method used in BeBOD to redistribute IDDs making use of multiple causes of death data, and provides a quantification of the fatal BoD in Belgium for the period 2004–2019.…”

Section: Introductionmentioning

confidence: 99%

Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach

Devleesschauwer,

Scohy,

De Pauw

et al. 2023

Arch Public Health

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Introduction Information on years of life lost (YLL) due to premature mortality is instrumental to assess the fatal impact of disease and necessary for the calculation of Belgian disability-adjusted life years (DALYs). This study presents a novel method to reallocate causes of death data. Materials and methods Causes of death data are provided by Statistics Belgium (Statbel). First, the specific ICD-10 codes that define the underlying cause of death are mapped to the GBD cause list. Second, ill-defined deaths (IDDs) are redistributed to specific ICD-10 codes. A four-step probabilistic redistribution was developed to fit the Belgian context: redistribution using predefined ICD codes, redistribution using multiple causes of death data, internal redistribution, and redistribution to all causes. Finally, we used the GBD 2019 reference life table to calculate Standard Expected Years of Life Lost (SEYLL). Results In Belgium, between 2004 and 2019, IDDs increased from 31 to 34% of all deaths. The majority was redistributed using predefined ICD codes (14–15%), followed by the redistribution using multiple causes of death data (10–12%). The total number of SEYLL decreased from 1.83 to 1.73 million per year. In 2019, the top cause of SEYLL was lung cancer with a share of 8.5%, followed by ischemic heart disease (8.1%) and Alzheimer’s disease and other dementias (5.7%). All results are available in an online tool https://burden.sciensano.be/shiny/mortality2019/. Conclusion The redistribution process assigned a specific cause of death to all deaths in Belgium, making it possible to investigate the full mortality burden for the first time. A large number of estimates were produced to estimate SEYLL by age, sex, and region for a large number of causes of death and every year between 2004 and 2019. These estimates are important stepping stones for future investigations on Disability-Adjusted Life Years (DALYs) in Belgium.

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Quantifying the potential effects of air pollution reduction on population health and health expenditure in Taiwan

Roger Chen,

Lee,

Liu

et al. 2023

Environmental Pollution

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Improving the Use of Mortality Data in Public Health: A Comparison of Garbage Code Redistribution Models

Cited by 5 publications

References 10 publications

A study on garbage code redistribution methods for heart failure at city level by two approaches

A study on garbage code redistribution methods for heart failure at city level by two approaches

Investigating years of life lost in Belgium, 2004–2019: A comprehensive analysis using a probabilistic redistribution approach

Quantifying the potential effects of air pollution reduction on population health and health expenditure in Taiwan

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