Assessing the Quality of Mortality Data in Zunyi, China: A Comparative Study of Garbage Coding Before and After Intervention

Assessing the Quality of Mortality Data in Zunyi, China: A Comparative Study of Garbage Coding Before and After Intervention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessing the Quality of Mortality Data in Zunyi, China: A Comparative Study of Garbage Coding Before and After Intervention Bo Zhang, haibo tang, Deqin Wei, Tao Long, Yuanmou Huang, Jian wang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5377235/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND : Accurate mortality data are crucial for understanding mortality patterns, informing public health strategies, and evaluating national health programs. In 2022 and 2023, the Centers for Disease Control and Prevention in Zunyi, China, provided specialized training to staff responsible for cause-of-death surveillance. METHODS : This study evaluated the quality of cause-of-death data reported by healthcare organizations in Zunyi city before and after the intervention, with a focus on the classification and extent of garbage codes. By comparing the distributions of various causes of death and their changes over the two years, we analyzed the differences and distribution patterns of garbage codes. The study participants were grouped by age and sex. RESULTS : The cause-of-death data from Zunyi demonstrated good completeness over the two-year period. The proportion of definite causes of death increased significantly from 87.5% to 94.8%, whereas the proportion of unusable causes decreased notably, from 7.32% to 2.87%. Similarly, the proportion of garbage codes relative to total deaths decreased from 12.60% to 5.20%, with significant reductions in categories 3 and 5. The major garbage codes in both years exhibited a positively skewed distribution, which was primarily associated with aging and cardiovascular diseases. The proportion of garbage codes decreased across both the male and the female groups over the age of 65. CONCLUSION : This study offers a cost-effective approach to improve the quality of cause-of-death data through a junk code-based assessment method. By implementing these measures, the accuracy and utility of cause-of-death data can be greatly enhanced. Health sciences/Diseases Health sciences/Health care Health sciences/Risk factors cause of death data quality control intervention junk coding Figures Figure 1 Figure 2 Introduction To gain a deeper understanding of mortality patterns and their causes, timely access to representative, large-scale, high-quality data is essential. 1,2 These data not only illuminate current health trends but also provide policymakers with evidence-based insights to guide effective public health decisions. 3,4 Accurate mortality data and cause-of-death analyses are crucial for developing effective national health policies and serve as key indicators of the progress of the "Healthy China 2023" program, which plays an important role in assessing disease burden. 5 Low-quality mortality data can severely impact policy discussions, monitoring health progress, and evaluating interventions. 6-8 Therefore, ensuring the accuracy, completeness, and timeliness of cause-of-death data collection is critical not only as a fundamental requirement for surveillance but also as a prerequisite for improving population health and designing effective intervention strategies. 3,9 Assessing the accuracy of cause-of-death (COD) data is a critical task. 10,11 Common methods include independent reviews of medical records and comparisons with the causes listed on death certificates or comparative analyses between clinically diagnosed causes of death and autopsy findings. 12,13 Although these methods provide precise assessments, they are often complex and costly, limiting their widespread application. Countries such as the Philippines, South Africa, and Thailand use the verbal autopsy (VA) method to increase the quality of COD data for out-of-hospital deaths. 14 In China, VAs still provide detailed information concerning out-of-hospital deaths reported by primary care providers. 15 The ANACONDA software assists those responsible for routine death data collection by providing simple methods to evaluate the quality of death statistics and identify potential weaknesses. 16 This tool has been used for cross-national analyses to explore the relationship between socioeconomic development and the quality of data from death reporting systems in countries such as Australia and Germany. 17 However, its use has primarily been observed in Tanzania, where it was employed to assess changes in cause-of-death data quality before and after interventions. 18 Despite these applications, limitations in the scope and number of interventions, along with insufficient clarity regarding the meaning and impact of junk coding, hinder the development of effective, long-term strategies. There is currently a widespread lack of in-depth understanding among healthcare personnel regarding the meaning and composition of junk codes and their impact on public health data. 19 These codes often fail to provide meaningful information for public health analyses, and the absence of a long-term, feasible method to identify and exclude junk codes with little public health significance remains a pressing issue that needs to be addressed. 20 To address this issue, we used ANACONDA software to conduct a comprehensive analysis of mortality data collected in 2022 and 2023 from Zunyi city and its 15 counties and districts. The purpose of this study was to develop an economical and sustainable strategy to improve cause-of-death data quality across all levels of healthcare and to evaluate its utility and efficiency. Data And Methods 1.1 Data on causes of death in medical institutions The data used in this study were collected from deaths reported between January 1, 2022, and December 31, 2023, across 15 counties and districts under the jurisdiction of Zunyi city via the Guizhou Province Health Cloud Platform. Trained clinicians and public health personnel certify and code deaths according to the WHO's International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10). 1.2 Interventions The Zunyi CDC conducted two training sessions in November 2022 and June 2023 for county-level cause-of-death surveillance staff to enhance quality control methods for cause-of-death data. The core content focused on integrating the use of ANACONDA software and R for data quality control in healthcare facilities. The training specifically emphasized using R to verify junk codes and required participating healthcare facilities to report the results of cause-of-death data monitoring with R verification on a monthly basis, comparing them with data from the surveillance system to track dynamic changes in data quality in real time.This ensured that the surveillance staff were proficient in the necessary skills. County-level surveillance operators were also required to provide monthly feedback on the quality control results, promoting continuous improvement in data quality. To ensure comprehensive and thorough data quality verification, the monthly assessments did not rely solely on software automation. Instead, junk codes identified by the software were meticulously categorized by type and severity, followed by a more in-depth quality assessment. This approach significantly improved the accuracy and reliability of the evaluation process. 1.3 Garbage coding The accuracy of cause-of-death data is critical for public health decision-making and depends on detailed information provided by physicians or family members when death certificates are completed. This information serves as the foundation for cause-of-death coders to accurately select and code the underlying cause of death. However, biased inferences about the underlying cause can result in "junk" codes—categories that, according to the International Classification of Diseases (ICD) criteria, should not be considered underlying causes because they offer little practical value for shaping effective policy decisions. To gain a deeper understanding of the causes of these "junk" codes and their impact on policy development, the ANACONDA software uses two categorization methods to classify these codes into distinct types. The first method subdivides the junk codes into five categories on the basis of ICD classification principles, which helps to identify inconsistencies in the data and provides guidance for enhancing the accuracy of cause-of-death reporting. Category 1: Codes associated with signs, symptoms, and ill-defined conditions (e.g., R54, age). Category 2: Codes that are not valid as the underlying cause of death (e.g., T12, lower extremity fracture). Category 3: Codes representing an intermediate cause of death (e.g., I10, essential hypertension). Category 4: Codes representing the immediate cause of death (e.g., I46.1, sudden cardiac death). Category 5: Codes representing underspecified causes in ICD chapters or larger disease categories (e.g., I64, stroke). ANACONDA also incorporates a second typology, which places greater emphasis on the potential impacts of spam coding on misinformation policies and planning. In this typology, spam coding is classified into four impact levels, ranging from "very high" (level 1) to "low" (level 4). n Very high (level 1): This highest level represents a true underlying cause, which may be a communicable or noncommunicable disease or the result of an injury (e.g., sepsis). n High (level 2): These are causes with significant negative impacts, but the true cause is typically limited to one of three main categories: infectious diseases, chronic noncommunicable diseases, or injuries. For example, primary hypertension may result from various noncommunicable diseases. n Medium (level 3): Causes of death classified as Rank 3 are considered to have a medium negative impact on policy because the underlying cause may fall within the same ICD section (e.g., unspecified cancer). n Low (level 4): Causes classified as having a low negative impact are those where the true underlying cause may be confined to a single disease within the injury group, such as unspecified stroke or unspecified pneumonia. 1.5 Statistical analysis After the cause of death data were exported from the "Guizhou Health Cloud Platform," we followed the requirements of the Population Cause of Death Surveillance Guidance Manual and carefully verified the data to ensure their quality. We classified the causes of death into four main categories on the basis of the underlying cause of death codes: a. Definite causes of death: This category includes cases where the determination of the underlying cause of death was accurate and unambiguous, providing direct and clear guidance for public health decision-making. b. Unavailable causes of death: This category includes cases that provide little to no useful information about the true underlying cause of death (categories 1--4). In the current context, the data are unsuitable for in-depth analysis because they may mislead public policy and increase the risk of premature death. c. Inadequate causes of death (category 5): Data in this category, while offering some information to guide basic public health interventions, are insufficient to support research and technology development. d. Unknown causes of death: Cases in which the cause of death is not clearly documented are categorized as unknown. Junk coding rates across different categories and levels were compared over a 2-year period. This comparison allowed us to identify differences in junk coding rates and shifts in distribution patterns over time, enabling us to assess whether the data quality had improved. In addition, we categorized the study population into two main age groups: under 65 years and 65 years or older. Within these two age groups, we calculated the distribution of categories and levels of junk codes in the death data for different genders over the two-year period. This stratified analysis allowed us to more precisely identify differences in data quality across age and sex groups. For the 2023 mortality data, we further refined our analysis by calculating the top 10 junk codes and their respective shares by gender. This not only provided detailed information about the most common junk codes but also helped us assess their impact on overall data quality. All the statistical analyses were performed via R 4.4.0. This study has passed ethical review and has no conflicts of interest. Results The cause of death surveillance data were analyzed via ANACONDA software to calculate the crude mortality rates for both males and females in Zunyi city. The results indicated that in 2022, the crude death rate for males in Zunyi city was 8.4 per 1,000, whereas that for females was 6.5 per 1,000. By 2023, the crude death rate for males had increased to 8.9 per 1,000, with a corresponding increase in the rate for females, reaching 8.4 per 1,000. Supplementary Figure 1 illustrates the significant improvement in cause-of-death data quality both before and after the implementation of the training. Specifically, the percentage of definitive causes of death increased from 87.5% to 94.8%, whereas the percentage of unclassified causes of death decreased significantly, from 7.32% to 2.87%. Similarly, the percentage of vaguely stated causes of death showed a downward trend, dropping from 5.17% to 2.35%. Additionally, the number of cases with unknown causes of death decreased from five to two. Figure 1 shows that the distribution of spam codes across the five categories differed significantly between the two years (X2 = 69.86, p < 0.05). The overall proportion of the distribution of spam codes in each category remained similar between the two years, with category 3 and category 5 having the highest number of spam codes and category 4 having the smallest share. However, overall trends showed a general decrease in junk codes across all categories in 2023 compared with those in 2022. Specifically, junk codes as a percentage of total deaths decreased significantly from 12.60% in 2022 to 5.20% in 2023, a decrease of more than half. In particular, Category 3 and Category 5, which had the highest shares in both years, decreased from 4.52% and 5.21% to 1.53% and 2.52%, respectively. Additionally, the proportion of spam codes in the remaining three categories also dropped below 1%. Figure 2 compares the distributions of the four different classes of spam codes between the two years, and the results reveal that there is a difference in the distributions of different classes of spam codes between the two years, and the results are statistically significant (X2 = 69.86, p < 0.05). Level 1 had the highest proportion of spam codes, accounting for 4.90% and 2.02% of total deaths, respectively. In 2022, levels 2, 3, and 4 had similar proportions of 2.81%, 2.18%, and 2.71%, respectively. However, all three levels declined in 2023, with Level 2 dropping to 1.06%. Figure 1 Distribution of different categories of garbage codes in Zuni city, 2022--2023 Figure 2 Distribution of different levels of garbage codes in Zuni city, 2022--2023 Table 1 presents the 20 most common unavailable ICD-10 codes and their associated causes over the two-year period (2022--2023), along with an assessment of their impact on public health policy development. These garbage codes accounted for 60.97% of the 6,261 cases in 2022 and 64.98% of the 2,684 cases in 2023, underscoring their substantial presence in the cause-of-death data. The frequency distribution of garbage codes exhibited significant positive skewness, with the top 5 garbage codes comprising half of the total among the top 20 codes. Table 1 Levelling of the top 20 garbage codes in Zuni, 2022--2023 2022 2023 cause of death Garbage coding level ICD Garbage coding constitute(n,%) cause of death Garbage coding level ICD Garbage coding constitute(n,%) 1 senility level 1 R54 611（9.92%） pneumonia level 4 J18.9 322（12.00%） 2 Stroke level 4 I64 393（6.38%） senility level 1 R54 319（11.89%） 3 cor pulmonale level 2 I27.9 302（4.90%） cor pulmonale level 2 I27.9 116（4.32%） 4 Primary blood pressure level 2 I10 288（4.67%） Primary blood pressure level 2 I10 110（4.10%） 5 Other pulmonary heart disease level 2 I27 283（4.59%） Other pulmonary heart disease level 2 I27 96（3.58%） 6 Carcinoma in situ of the mouth, esophagus and stomach level 3 D00 226（3.67%） Pneumonia (pathogen not specified) level 4 J18 90（3.35%） 7 pneumonia level 4 J18.9 204（3.31%） Stroke level 4 I64 81（3.02%） 8 bacterial pneumonia level 4 J15.9 183（2.97%） sudden cardiac death level 1 I46.1 78（2.91%） 9 sudden cardiac death level 1 I46.1 179（2.90%） bacterial pneumonia level 4 J15.9 73（2.72%） 10 cardiac failure level 1 I50 162（2.63%） Carcinoma in situ of the mouth, esophagus and stomach level 3 D00 67（2.50%） 11 Secondary malignant tumor of lung level 3 C78.0 152（2.47%） pulmonary infection level 3 J98.4 63（2.35%） 12 hypertensive encephalopathy level 1 I67.4 143（2.32%） uremia level 1 N19 47（1.75%） 13 uremia level 1 N19 96（1.56%） Secondary malignant tumor of lung level 3 C78.0 45（1.68%） 14 acute respiratory failure level 1 J96.0 95（1.54%） Other specific pulmonary heart disease level 2 I27.8 42（1.56%） 15 Other specific pulmonary heart disease level 2 I27.8 86（1.40%） bronchopneumonia level 4 J18.0 37（1.38%） 16 cerebrovascular disease level 4 I67.9 76（1.23%） Other sudden death level 1 R96 36（1.34%） 17 hypostatic pneumonia level 4 J18.2 73（1.18%） eating disorder level 3 F50 33（1.23%） 18 pulmonary infection level 3 J98.4 70（1.14%） pulmonary embolism level 1 I26 31（1.15%） 19 pulmonary embolism level 1 I26 69（1.12%） hypostatic pneumonia level 4 J18.2 31（1.15%） 20 eating disorder level 3 F50 66（1.07%） hypertensive encephalopathy level 1 I67.4 27（1.01%） A detailed analysis of cause-of-death data across age groups revealed that the proportion of garbage codes in 2023 was lower than that in 2022 for both the under65 and 65-and-over age groups, with a particularly notable reduction in the 65-and-over group (Table 2). Specifically, in males aged 65 and older, the proportion of garbage codes among total deaths significantly decreased from 5.10% in 2022 to 2.15% in 2023. In this age group, category 5 garbage codes predominated, with their proportion also declining from 2.16% in 2022 to 1.04% in 2023. The distribution pattern of garbage codes in females mirrored that in males, exhibiting the same downward trend. Table 2 Distribution and Proportion of Garbage Coding in Mortality Data by Age and Gender in Zunyi, 2022--2023 ＜65 ≥65 2022 2023 2022 2023 man Garbage coding category % of total deaths % of total deaths % of total deaths % of total deaths Category 1 0.10% 0.09% 0.62% 0.31% Category 2 0.26% 0.08% 0.32% 0.10% Category 3 0.44% 0.21% 1.87% 0.66% Category 4 0.10% 0.08% 0.14% 0.04% Category 5 0.82% 0.33% 2.16% 1.04% total 1.71% 0.79% 5.10% 2.15% Garbage coding level level 1 0.67% 0.37% 1.87% 0.68% level 2 0.39% 0.10% 1.13% 0.42% level 3 0.32% 0.13% 0.95% 0.32% level 4 0.33% 0.19% 1.15% 0.72% total 1.71% 0.79% 5.10% 2.15% woman Garbage coding category Category 1 0.02% 0.03% 0.73% 0.39% Category 2 0.13% 0.07% 0.30% 0.09% Category 3 0.25% 0.06% 1.91% 0.60% Category 4 0.05% 0.02% 0.11% 0.04% Category 5 0.39% 0.16% 1.80% 0.82% total 0.84% 0.34% 4.85% 1.93% Garbage coding level level 1 0.34% 0.16% 1.97% 0.76% level 2 0.15% 0.04% 1.11% 0.37% level 3 0.18% 0.07% 0.72% 0.25% level 4 0.16% 0.08% 1.05% 0.55% total 0.84% 0.34% 4.85% 1.93% Table 3 offers a detailed analysis of the top 10 garbage codes by age group and gender in 2023. Sudden cardiac death (I46.1, level 1) emerged as a significant garbage code in the under65 age group, leading the list with 1.29% of garbage codes among males and ranking second among females at 0.35%. Additionally, tumor-related garbage codes, including C77, C78.0, and D00, were more common among individuals under 65 years of age. In the 65-and-over age group, aging (R54, level 1) had a particularly significant effect on the quality of cause-of-death data, accounting for 4.68% of the cases in men and 6.46% in women. Table 3 Top 10 garbage codes for different age groups, 2023 ＜65 ≥65 cause of death Garbage coding level ICD Garbage coding constitute(n,%) cause of death Garbage coding level ICD Garbage coding constitute(n,%) man 1 sudden cardiac death level 1 I46.1 1.29% pneumonia level 4 J18.9 5.83% 2 pneumonia level 4 J18.9 0.91% senility level 1 R54 4.68% 3 Other sudden death level 1 R96 0.59% Primary blood pressure level 2 I10 1.99% 4 dilated cardiomyopathy level 4 I42.0 0.56% cor pulmonale level 2 I27.9 1.82% 5 uremia level 1 N19 0.56% Other pulmonary heart disease level 2 I27 1.61% 6 Carcinoma in situ of the mouth level 3 D00 0.42% Pneumonia (organism unspecified) level 4 J18 1.54% 7 bacterial pneumonia level 4 J15.9 0.38% stroke level 4 I64 1.36% 8 Primary blood pressure level 2 I10 0.35% bacterial pneumonia level 4 J15.9 1.33% 9 Other deaths of unknown cause level 1 R99 0.31% pulmonary infection level 3 J98.4 1.19% 10 Secondary malignant tumor of lung level 3 C78.0 0.28% Carcinoma in situ of the mouth level 3 D00 1.15% woman 1 pneumonia level 4 J18.9 0.52% senility level 1 R54 6.46% 2 sudden cardiac death level 1 I46.1 0.35% pneumonia level 4 J18.9 3.98% 3 Secondary malignant tumor of lung level 3 C78.0 0.21% cor pulmonale level 2 I27.9 1.89% 4 Pneumonia (organism unspecified) level 4 J18 0.21% Other pulmonary heart disease level 2 I27 1.64% 5 Carcinoma in situ of the mouth level 3 D00 0.17% Primary blood pressure level 2 I10 1.43% 6 Other deaths of unknown cause level 1 R99 0.17% stroke level 4 I64 1.29% 7 Secondary and unspecified malignancies of lymph nodes level 3 C77 0.14% Pneumonia (organism unspecified) level 4 J18 1.19% 8 pulmonary embolism level 1 I26 0.14% bacterial pneumonia level 4 J15.9 0.77% 9 cardiopathy level 3 I51.9 0.14% pulmonary infection level 3 J98.4 0.73% 10 pulmonary infection level 3 J98.4 0.14% Other specific pulmonary heart disease level 2 I27.8 0.66% Discussion When the cause-of-death data from 2022--2023 in Zunyi city were evaluated, the implementation of two targeted trainings significantly enhanced the quality of the cause-of-death data. However, some unexpected deficiencies in the data were identified, possibly due to incomplete knowledge of reporting protocols by healthcare facility staff, resulting in incorrectly filled death certificates for determining the underlying cause of death. 17 Establishing a sustainable, regular system for quality control and improvement of healthcare-reported cause-of-death data is key to the current effort. 21 According to the Guidance Manual for Population Mortality Surveillance 22 criteria, if the crude death rate is less than 6 per 1,000, it is typically regarded as a sign of underreporting. In contrast, the crude death rate for the population of Zunyi city was greater than 6 per 1,000 in both years, suggesting that the completeness of the cause-of-death data in Zunyi city for both years was satisfactory. Compared with those in 2022, the proportion of clear causes of death increased from 87.5% to 94.8% in the 2023 data, along with a decrease in the proportion of unavailable and inadequately described code. Furthermore, improvements in medical records, clinicians' increased ability to make cause-of-death inferences, and broader recognition of the importance of cause-of-death coding may have contributed to this positive change. 23 Globally, nearly half of the registered deaths still lack a clear cause of death, and a nationwide study in Tanzania in 2021 revealed that only 51.9% of the death data reported by healthcare facilities could be reliably used for policy purposes. 18 Therefore, both low- and middle-income countries and high-income countries need to conduct regular quality assessments of cause-of-death data to avoid reducing the value of the information provided by garbage codes. 16 , 24 The core goal of this study's assessment of cause-of-death data quality is to evaluate it accurately by analyzing the categories and levels of garbage codes. 25 The distribution of garbage codes was marked by positive skewness, with the top 5 codes accounting for half of the total number of garbage codes. This pattern suggests that certain codes are used far more frequently than others are, possibly reflecting the preferences or habitual choices of healthcare card reporters when coding the underlying cause of death 26 .This is likely due to the multiple comorbidities common in the elderly population, such as neoplasms, hypertension, diabetes, and cardiovascular diseases, which complicate the determination of the underlying cause of death when a death certificate is completed. 9 Additionally, men generally have more garbage codes than women do, with this gender difference being more pronounced in those under 65 years of age. This may be related to the prevalence of unhealthy habits among men, such as smoking and alcohol consumption 5 , which not only increase their risk for various diseases but also may result in more deaths from accidents, leading to a higher rate of garbage codes. 27 Therefore, preservice and in-service training or retraining of cause-of-death reporting personnel to avoid the use of these specific codes is necessary. The distribution characteristics of garbage codes in 2023 indicate that diagnosing and coding deaths from sudden cardiac death in individuals under 65 years of age demands greater attention and accuracy. Additionally, sex-related differences may be attributed to the distinct epidemiological patterns of heart disease between men and women. 27,28 The high frequency of tumor-related garbage codes in younger age groups may underscore the complexity of cancer diagnosis. This highlights the challenges in accurately classifying and coding cancer types according to this demographic, emphasizing the need for further clinical and pathological review to increase the accuracy of cause-of-death data. 29 This suggests that more meticulous clinical assessments and coding practices are essential in older populations to ensure that cause-of-death data accurately reflect the true underlying causes. Strengths and challenges This study’s detailed analysis of population-wide cause-of-death data in Zunyi city from 2022--2023 reveals the distributional characteristics of garbage codes across specific populations. This study improves the understanding of discrepancies in the accuracy of cause-of-death data across populations and provides data to support targeted public health interventions. However, there are limitations to this study. First, it did not fully investigate the specific factors leading to the creation of garbage codes, limiting our deeper understanding of the root causes of the issue. Second, the study lacked a long-term trend analysis, which reduces our understanding of changes in cause-of-death data quality over time. Findings We compared and analyzed the categories and levels of garbage codes before and after the intervention to increase the accuracy and utility of cause-of-death data, providing reliable data support for public health decision-making and health policy formulation. This study confirms the utility and efficiency of this approach, offering a cost-effective solution for healthcare organizations to ensure continuous improvement in cause-of-death data quality through regular data review and feedback. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants. Declarations Funding: Science and Technology Fund Project of the Health Commission of Guizhou Province (gzwkj2021–434; gzwkj2023–077) Author Contribution Bo Zhang：Conceptualization , Formal analysis ,Data Curation.Haibo Tan: Writing - Original Draft, Data Curation , Software.Deqin We: Conceptualization , Validation, Data Curation.Tao Long: Resources, Validation, Data Curation.Yuanmou Huang: Resources, Validation, Supervision.Jian Wang: Resources, Validation, Data Curation.Yi Zhang: Conceptualization , Validation, Data Curation.Xiuquan Shi: Conceptualization , Validation, Software.Dalin Tian: Validation, Software.Hailei Guo: Conceptualization , Writing - Review & Editing.All authors reviewed the manuscript.Jiayan Cao: Conceptualization , Conceptualization , Writing - Review & Editing. 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Health . 48 , 801–808. 10.1177/1403494819890990 (2020). Hart, J. D. et al. Improving medical certification of cause of death: effective strategies and approaches based on experiences from the Data for Health Initiative. BMC Med. 18 10.1186/s12916-020-01519-8 (2020). Naghavi, M. et al. Improving the quality of cause of death data for public health policy: are all 'garbage' codes equally problematic? BMC Med. 18 10.1186/s12916-020-01525-w (2020). Drozd, M. et al. Causes of Death in People With Cardiovascular Disease: A UK Biobank Cohort Study. J. Am. Heart Association . 10 , e023188. 10.1161/jaha.121.023188 (2021). Madrid, L. et al. Causes of stillbirth and death among children younger than 5 years in eastern Hararghe, Ethiopia: a population-based post-mortem study. Lancet Global health . 11 , e1032–e1040. 10.1016/s2214-109x(23)00211-5 (2023). Wang, L. DONG Jing-wu & Song Gui-xiang. Manual of guidance for population Cause-of-death surveillance. (2017). Gamage, U. S. H. et al. Effectiveness of training interventions to improve quality of medical certification of cause of death: systematic review and meta-analysis. BMC Med. 18 10.1186/s12916-020-01840-2 (2020). Lloyd-Sherlock, P., Sempe, L., McKee, M. & Guntupalli, A. Problems of Data Availability and Quality for COVID-19 and Older People in Low- and Middle-Income Countries. Gerontologist 61 , 141–144. 10.1093/geront/gnaa153 (2021). Mikkelsen, L., Moesgaard, K., Hegnauer, M. & Lopez, A. D. ANACONDA: a new tool to improve mortality and cause of death data. BMC Med. 18 10.1186/s12916-020-01521-0 (2020). Xu, Z., Hockey, R., McElwee, P., Waller, M. & Dobson, A. Accuracy of death certifications of diabetes, dementia and cancer in Australia: a population-based cohort study. BMC public. health . 22 , 902. 10.1186/s12889-022-13304-8 (2022). Khaw, W. F. et al. Malaysian burden of disease: years of life lost due to premature deaths. BMC public. health . 23 , 1383. 10.1186/s12889-023-16309-z (2023). Wong, N. D. & Sattar, N. Cardiovascular risk in diabetes mellitus: epidemiology, assessment and prevention. Nat. Rev. Cardiol. 20 , 685–695. 10.1038/s41569-023-00877-z (2023). Schwartz, S. M. Epidemiology of Cancer. Clin. Chem. 70 , 140–149. 10.1093/clinchem/hvad202 (2024). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5377235","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":388587780,"identity":"84f6b818-9d55-40aa-97f3-db4109f69964","order_by":0,"name":"Bo Zhang","email":"","orcid":"","institution":"Zunyi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zhang","suffix":""},{"id":388587781,"identity":"39d03c17-8358-40c5-8aeb-f04138e06c22","order_by":1,"name":"haibo tang","email":"","orcid":"","institution":"Zunyi Medical 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08:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5377235/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5377235/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71757428,"identity":"4b04614a-a28c-405c-9d0d-38961c31ce18","added_by":"auto","created_at":"2024-12-18 10:20:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186477,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of different categories of garbage codes in Zuni city, 2022--2023\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5377235/v1/bb5bf462b48ea10ea2c8cec4.jpeg"},{"id":71756607,"identity":"8d1108f7-ab43-430a-ac1b-d4ebf6807409","added_by":"auto","created_at":"2024-12-18 10:12:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161828,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of different levels of garbage codes in Zuni city, 2022--2023\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5377235/v1/5bf7f9656aa4c8c8663eaab9.jpeg"},{"id":84855509,"identity":"bd26a767-5e09-4096-b860-d6ce1c3b8042","added_by":"auto","created_at":"2025-06-18 06:03:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1171277,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5377235/v1/7bd6b140-42c8-4201-a55a-b15607637c31.pdf"},{"id":71756601,"identity":"6f135c53-f08a-42db-b337-d28620fe198b","added_by":"auto","created_at":"2024-12-18 10:12:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":100588,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5377235/v1/2c6495b71c5df8c493aba365.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Quality of Mortality Data in Zunyi, China: A Comparative Study of Garbage Coding Before and After Intervention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTo gain a deeper understanding of mortality patterns and their causes, timely access to representative, large-scale, high-quality data is essential.\u003csup\u003e1,2\u003c/sup\u003eThese data not only illuminate current health trends but also provide policymakers with evidence-based insights to guide effective public health decisions.\u003csup\u003e3,4\u003c/sup\u003e Accurate mortality data and cause-of-death analyses are crucial for developing effective national health policies and serve as key indicators of the progress of the \u0026quot;Healthy China 2023\u0026quot; program, which plays an important role in assessing disease burden.\u003csup\u003e5\u003c/sup\u003e Low-quality mortality data can severely impact policy discussions, monitoring health progress, and evaluating interventions.\u003csup\u003e6-8\u003c/sup\u003eTherefore, ensuring the accuracy, completeness, and timeliness of cause-of-death data collection is critical not only as a fundamental requirement for surveillance but also as a prerequisite for improving population health and designing effective intervention strategies.\u003csup\u003e3,9\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAssessing the accuracy of cause-of-death (COD) data is a critical task.\u0026nbsp;\u003csup\u003e10,11\u003c/sup\u003eCommon methods include independent reviews of medical records and comparisons with the\u0026nbsp;causes listed on death certificates or comparative analyses between clinically diagnosed causes of death and autopsy findings.\u003csup\u003e12,13\u003c/sup\u003eAlthough these methods provide precise assessments, they are often complex and costly, limiting their widespread application. Countries such as the Philippines, South Africa, and Thailand use the verbal autopsy (VA) method to increase the quality of COD data for out-of-hospital deaths.\u0026nbsp;\u003csup\u003e14\u003c/sup\u003eIn China, VAs still provide detailed information concerning out-of-hospital deaths reported by primary care providers.\u0026nbsp;\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe ANACONDA software assists those responsible for routine death data collection by providing simple methods to evaluate the quality of death statistics and identify potential weaknesses.\u0026nbsp;\u003csup\u003e16\u003c/sup\u003eThis tool has been used for cross-national analyses to explore the relationship between socioeconomic development and the quality of data from death reporting systems in countries such as Australia and Germany.\u003csup\u003e17\u003c/sup\u003eHowever, its use has primarily been observed in Tanzania, where it was employed to assess changes in cause-of-death data quality before and after interventions.\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e Despite these applications, limitations in the scope and number of interventions, along with insufficient clarity regarding the meaning and impact of junk coding, hinder the development of effective, long-term strategies.\u003c/p\u003e\n\u003cp\u003eThere is currently a widespread lack of in-depth understanding among healthcare personnel regarding the meaning and composition of junk codes and their impact on public health data.\u0026nbsp;\u003csup\u003e19\u003c/sup\u003e These codes often fail to provide meaningful information for public health analyses, and the absence of a long-term, feasible method to identify and exclude junk codes with little public health significance remains a pressing issue that needs to be addressed.\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eTo address this issue, we used ANACONDA software to conduct a comprehensive analysis of mortality data collected in 2022 and 2023 from Zunyi city and its 15 counties and districts. The purpose of this study was to develop an economical and sustainable strategy to improve cause-of-death data quality across all levels of healthcare and to evaluate its utility and efficiency.\u003c/p\u003e"},{"header":"Data And Methods","content":"\u003cp\u003e1.1\u0026nbsp;Data on causes of death in medical institutions\u003c/p\u003e\n\u003cp\u003eThe data used in this study were collected from deaths reported between January 1, 2022, and December 31, 2023, across 15 counties and districts under the jurisdiction of Zunyi city via the Guizhou Province Health Cloud Platform. Trained clinicians and public health personnel certify and code deaths according to the WHO\u0026apos;s International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10).\u003c/p\u003e\n\u003cp\u003e1.2\u0026nbsp;Interventions\u003c/p\u003e\n\u003cp\u003eThe Zunyi CDC conducted two training sessions in November 2022 and June 2023 for county-level cause-of-death surveillance staff to enhance quality control methods for cause-of-death data. The core content focused on integrating the use of ANACONDA software and R for data quality control in healthcare facilities. The training specifically emphasized using R to verify junk codes and required participating healthcare facilities to report the results of cause-of-death data monitoring with R verification on a monthly basis, comparing them with data from the surveillance system to track dynamic changes in data quality in real time.This ensured that the surveillance staff were proficient in the necessary skills. County-level surveillance operators were also required to provide monthly feedback on the quality control results, promoting continuous improvement in data quality.\u003c/p\u003e\n\u003cp\u003eTo ensure comprehensive and thorough data quality verification, the monthly assessments did not rely solely on software automation. Instead, junk codes identified by the software were meticulously categorized by type and severity, followed by a more in-depth quality assessment. This approach significantly improved the accuracy and reliability of the evaluation process.\u003c/p\u003e\n\u003cp\u003e1.3\u0026nbsp;Garbage coding\u003c/p\u003e\n\u003cp\u003eThe accuracy of cause-of-death data is critical for public health decision-making and depends on detailed information provided by physicians or family members when death certificates are completed. This information serves as the foundation for cause-of-death coders to accurately select and code the underlying cause of death. However, biased inferences about the underlying cause can result in \u0026quot;junk\u0026quot; codes\u0026mdash;categories that, according to the International Classification of Diseases (ICD) criteria, should not be considered underlying causes because they offer little practical value for shaping effective policy decisions.\u003c/p\u003e\n\u003cp\u003eTo gain a deeper understanding of the causes of these \u0026quot;junk\u0026quot; codes and their impact on policy development, the ANACONDA software uses two categorization methods to classify these codes into distinct types. The first method subdivides the junk codes into five categories on the basis of ICD classification principles, which helps to identify inconsistencies in the data and provides guidance for enhancing the accuracy of cause-of-death reporting.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCategory 1: Codes associated with signs, symptoms, and ill-defined conditions (e.g., R54, age).\u003c/li\u003e\n \u003cli\u003eCategory 2: Codes that are not valid as the underlying cause of death (e.g., T12, lower extremity fracture).\u003c/li\u003e\n \u003cli\u003eCategory 3: Codes representing an intermediate cause of death (e.g., I10, essential hypertension).\u003c/li\u003e\n \u003cli\u003eCategory 4: Codes representing the immediate cause of death (e.g., I46.1, sudden cardiac death).\u003c/li\u003e\n \u003cli\u003eCategory 5: Codes representing underspecified causes in ICD chapters or larger disease categories (e.g., I64, stroke).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eANACONDA also incorporates a second typology, which places greater emphasis on the potential impacts of spam coding on misinformation policies and planning. In this typology, spam coding is classified into four impact levels, ranging from \u0026quot;very high\u0026quot; (level 1) to \u0026quot;low\u0026quot; (level 4).\u003c/p\u003e\n\u003cp\u003en Very high (level 1): This highest level represents a true underlying cause, which may be a communicable or noncommunicable disease or the result of an injury (e.g., sepsis).\u003c/p\u003e\n\u003cp\u003en High (level 2): These are causes with significant negative impacts, but the true cause is typically limited to one of three main categories: infectious diseases, chronic noncommunicable diseases, or injuries. For example, primary hypertension may result from various noncommunicable diseases.\u003c/p\u003e\n\u003cp\u003en Medium (level 3): Causes of death classified as Rank 3 are considered to have a medium negative impact on policy because the underlying cause may fall within the same ICD section (e.g., unspecified cancer).\u003c/p\u003e\n\u003cp\u003en Low (level 4): Causes classified as having a low negative impact are those where the true underlying cause may be confined to a single disease within the injury group, such as unspecified stroke or unspecified pneumonia.\u003c/p\u003e\n\u003cp\u003e1.5 Statistical analysis\u003c/p\u003e\n\u003cp\u003eAfter the cause of death data were exported from the \u0026quot;Guizhou Health Cloud Platform,\u0026quot; we followed the requirements of the Population Cause of Death Surveillance Guidance Manual and carefully verified the data to ensure their quality. We classified the causes of death into four main categories on the basis of the underlying cause of death codes:\u003c/p\u003e\n\u003cp\u003ea. Definite causes of death: This category includes cases where the determination of the underlying cause of death was accurate and unambiguous, providing direct and clear guidance for public health decision-making.\u003c/p\u003e\n\u003cp\u003eb. Unavailable causes of death: This category includes cases that provide little to no useful information about the true underlying cause of death (categories 1--4). In the current context, the data are unsuitable for in-depth analysis because they may mislead public policy and increase the risk of premature death.\u003c/p\u003e\n\u003cp\u003ec. Inadequate causes of death (category 5): Data in this category, while offering some information to guide basic public health interventions, are insufficient to support research and technology development.\u003c/p\u003e\n\u003cp\u003ed. Unknown causes of death: Cases in which the cause of death is not clearly documented are categorized as unknown.\u003c/p\u003e\n\u003cp\u003eJunk coding rates across different categories and levels were compared over a 2-year period. This comparison allowed us to identify differences in junk coding rates and shifts in distribution patterns over time, enabling us to assess whether the data quality had improved.\u003c/p\u003e\n\u003cp\u003eIn addition, we categorized the study population into two main age groups: under 65 years and 65 years or older. Within these two age groups, we calculated the distribution of categories and levels of junk codes in the death data for different genders over the two-year period. This stratified analysis allowed us to more precisely identify differences in data quality across age and sex groups. For the 2023 mortality data, we further refined our analysis by calculating the top 10 junk codes and their respective shares by gender. This not only provided detailed information about the most common junk codes but also helped us assess their impact on overall data quality. All the statistical analyses were performed via R 4.4.0. This study has passed ethical review and has no conflicts of interest.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe cause of death surveillance data were analyzed via ANACONDA software to calculate the crude mortality rates for both males and females in Zunyi city. The results indicated that in 2022, the crude death rate for males in Zunyi city was 8.4 per 1,000, whereas that for females was 6.5 per 1,000. By 2023, the crude death rate for males had increased to 8.9 per 1,000, with a corresponding increase in the rate for females, reaching 8.4 per 1,000.\u003c/p\u003e\n\u003cp\u003eSupplementary Figure 1 illustrates the significant improvement in cause-of-death data quality both before and after the implementation of the training. Specifically, the percentage of definitive causes of death increased from 87.5% to 94.8%, whereas the percentage of unclassified causes of death decreased significantly, from 7.32% to 2.87%. Similarly, the percentage of vaguely stated causes of death showed a downward trend, dropping from 5.17% to 2.35%. Additionally, the number of cases with unknown causes of death decreased from five to two.\u003c/p\u003e\n\u003cp\u003eFigure 1 shows that the distribution of spam codes across the five categories differed significantly between the two years (X2 = 69.86, p \u0026lt; 0.05). The overall proportion of the distribution of spam codes in each category remained similar between the two years, with category 3 and category 5 having the highest number of spam codes and category 4 having the smallest share. However, overall trends showed a general decrease in junk codes across all categories in 2023 compared with those in 2022. Specifically, junk codes as a percentage of total deaths decreased significantly from 12.60% in 2022 to 5.20% in 2023, a decrease of more than half. In particular, Category 3 and Category 5, which had the highest shares in both years, decreased from 4.52% and 5.21% to 1.53% and 2.52%, respectively. Additionally, the proportion of spam codes in the remaining three categories also dropped below 1%.\u003c/p\u003e\n\u003cp\u003eFigure 2 compares the distributions of the four different classes of spam codes between the two years, and the results reveal that there is a difference in the distributions of different classes of spam codes between the two years, and the results are statistically significant (X2 = 69.86, p \u0026lt; 0.05). Level 1 had the highest proportion of spam codes, accounting for 4.90% and 2.02% of total deaths, respectively. In 2022, levels 2, 3, and 4 had similar proportions of 2.81%, 2.18%, and 2.71%, respectively. However, all three levels declined in 2023, with Level 2 dropping to 1.06%.\u003c/p\u003e\n\u003cp\u003eFigure 1 Distribution of different categories of garbage codes in Zuni city, 2022--2023\u003c/p\u003e\n\u003cp\u003eFigure 2 Distribution of different levels of garbage codes in Zuni city, 2022--2023\u003c/p\u003e\n\u003cp\u003eTable 1 presents the 20 most common unavailable ICD-10 codes and their associated causes over the two-year period (2022--2023), along with an assessment of their impact on public health policy development. These garbage codes accounted for 60.97% of the 6,261 cases in 2022 and 64.98% of the 2,684 cases in 2023, underscoring their substantial presence in the cause-of-death data. The frequency distribution of garbage codes exhibited significant positive skewness, with the top 5 garbage codes comprising half of the total among the top 20 codes.\u003c/p\u003e\n\u003cp\u003eTable 1 Levelling of the top 20 garbage codes in Zuni, 2022--2023\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"575\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 259px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 275px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ecause of death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eGarbage coding level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eICD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eGarbage coding constitute(n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ecause of death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eGarbage coding level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eICD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eGarbage coding constitute(n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003esenility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eR54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e611（9.92%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003epneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eJ18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e322（12.00%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e393（6.38%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003esenility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eR54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e319（11.89%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ecor pulmonale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e302（4.90%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ecor pulmonale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eI27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e116（4.32%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003ePrimary blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e288（4.67%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ePrimary blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eI10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e110（4.10%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eOther pulmonary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e283（4.59%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eOther pulmonary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eI27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e96（3.58%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eCarcinoma in situ of the mouth, esophagus and stomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eD00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e226（3.67%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ePneumonia (pathogen not specified)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eJ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e90（3.35%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003epneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eJ18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e204（3.31%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eI64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e81（3.02%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003ebacterial pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eJ15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e183（2.97%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003esudden cardiac death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eI46.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e78（2.91%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003esudden cardiac death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI46.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e179（2.90%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ebacterial pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eJ15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e73（2.72%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003ecardiac failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e162（2.63%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eCarcinoma in situ of the mouth, esophagus and stomach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eD00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e67（2.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eSecondary malignant tumor of lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eC78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e152（2.47%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003epulmonary infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eJ98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e63（2.35%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003ehypertensive encephalopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI67.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e143（2.32%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003euremia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eN19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e47（1.75%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003euremia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eN19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e96（1.56%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eSecondary malignant tumor of lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eC78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e45（1.68%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eacute respiratory failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eJ96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e95（1.54%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eOther specific pulmonary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eI27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e42（1.56%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eOther specific pulmonary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e86（1.40%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ebronchopneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eJ18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e37（1.38%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ecerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e76（1.23%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eOther sudden death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eR96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e36（1.34%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ehypostatic pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eJ18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e73（1.18%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eeating disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eF50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e33（1.23%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003epulmonary infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eJ98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e70（1.14%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003epulmonary embolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eI26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e31（1.15%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003epulmonary embolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eI26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e69（1.12%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ehypostatic pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eJ18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e31（1.15%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eeating disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003eF50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e66（1.07%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ehypertensive encephalopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eI67.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e27（1.01%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA detailed analysis of cause-of-death data across age groups revealed that the proportion of garbage codes in 2023 was lower than that in 2022 for both the under65 and 65-and-over age groups, with a particularly notable reduction in the 65-and-over group (Table 2). Specifically, in males aged 65 and older, the proportion of garbage codes among total deaths significantly decreased from 5.10% in 2022 to 2.15% in 2023. In this age group, category 5 garbage codes predominated, with their proportion also declining from 2.16% in 2022 to 1.04% in 2023. The distribution pattern of garbage codes in females mirrored that in males, exhibiting the same downward trend.\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp;Distribution and Proportion of Garbage Coding in Mortality Data by Age and Gender\u0026nbsp;in Zunyi, 2022--2023\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e＜65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGarbage coding category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e% of total deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e% of total deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e% of total deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e% of total deaths\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e5.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGarbage coding level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e5.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ewoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGarbage coding category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCategory 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e4.85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGarbage coding level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.05%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e4.85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 offers a detailed analysis of the top 10 garbage codes by age group and gender in 2023. Sudden cardiac death (I46.1, level 1) emerged as a significant garbage code in the under65 age group, leading the list with 1.29% of garbage codes among males and ranking second among females at 0.35%. Additionally, tumor-related garbage codes, including C77, C78.0, and D00, were more common among individuals under 65 years of age. In the 65-and-over age group, aging (R54, level 1) had a particularly significant effect on the quality of cause-of-death data, accounting for 4.68% of the cases in men and 6.46% in women.\u003c/p\u003e\n\u003cp\u003eTable 3 Top 10 garbage codes for different age groups, 2023\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 298px;\"\u003e\n \u003cp\u003e＜65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 285px;\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ecause of death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eGarbage coding level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eICD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eGarbage coding constitute(n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ecause of death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eGarbage coding level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eICD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eGarbage coding constitute(n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003eman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003esudden cardiac death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eI46.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003epneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJ18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003epneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eJ18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003esenility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eR54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eOther sudden death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eR96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePrimary blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eI10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003edilated cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eI42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ecor pulmonale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eI27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003euremia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eN19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eOther pulmonary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eI27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eCarcinoma in situ of the mouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eD00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePneumonia (organism unspecified)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ebacterial pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eJ15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003estroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eI64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ePrimary blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eI10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ebacterial pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJ15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eOther deaths of unknown cause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eR99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003epulmonary infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJ98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eSecondary malignant tumor of lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eC78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCarcinoma in situ of the mouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eD00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003ewoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003epneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eJ18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003esenility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eR54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e6.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003esudden cardiac death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eI46.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003epneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJ18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3.98%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eSecondary malignant tumor of lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eC78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ecor pulmonale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eI27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ePneumonia (organism unspecified)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eJ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eOther pulmonary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eI27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eCarcinoma in situ of the mouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eD00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePrimary blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eI10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eOther deaths of unknown cause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eR99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003estroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eI64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eSecondary and unspecified malignancies of lymph nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eC77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ePneumonia (organism unspecified)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003epulmonary embolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eI26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003ebacterial pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJ15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003ecardiopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eI51.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003epulmonary infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eJ98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003epulmonary infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eJ98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eOther specific pulmonary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003elevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eI27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhen the cause-of-death data from 2022--2023 in Zunyi city were evaluated, the implementation of two targeted trainings significantly enhanced the quality of the cause-of-death data. However, some unexpected deficiencies in the data were identified, possibly due to incomplete knowledge of reporting protocols by healthcare facility staff, resulting in incorrectly filled death certificates for determining the underlying cause of death.\u0026nbsp;\u003csup\u003e17\u003c/sup\u003eEstablishing a sustainable, regular system for quality control and improvement of healthcare-reported cause-of-death data is key to the current effort.\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the \u003cem\u003eGuidance Manual for Population Mortality Surveillance\u003c/em\u003e\u003csup\u003e22\u003c/sup\u003ecriteria, if the crude death rate is less than 6 per 1,000, it is typically regarded as a sign of underreporting. In contrast, the crude death rate for the population of Zunyi city was greater than 6 per 1,000 in both years, suggesting that the completeness of the cause-of-death data in Zunyi city for both years was satisfactory.\u003c/p\u003e\n\u003cp\u003eCompared with those in 2022, the proportion of clear causes of death increased from 87.5% to 94.8% in the 2023 data, along with a decrease in the proportion of unavailable and inadequately described code. Furthermore, improvements in medical records, clinicians\u0026apos; increased ability to make cause-of-death inferences, and broader recognition of the importance of cause-of-death coding may have contributed to this positive change.\u003csup\u003e23\u003c/sup\u003eGlobally, nearly half of the registered deaths still lack a clear cause of death, and a nationwide study in Tanzania in 2021 revealed that only 51.9% of the death data reported by healthcare facilities could be reliably used for policy purposes.\u003csup\u003e18\u003c/sup\u003e Therefore, both low- and middle-income countries and high-income countries need to conduct regular quality assessments of cause-of-death data to avoid reducing the value of the information provided by garbage codes.\u0026nbsp;\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe core goal of this study\u0026apos;s assessment of cause-of-death data quality is to evaluate it accurately by analyzing the categories and levels of garbage codes.\u003csup\u003e25\u003c/sup\u003eThe distribution of garbage codes was marked by positive skewness, with the top 5 codes accounting for half of the total number of garbage codes. This pattern suggests that certain codes are used far more frequently than others are, possibly reflecting the preferences or habitual choices of healthcare card reporters when coding the underlying cause of death\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e.This is likely due to the multiple comorbidities common in the elderly population, such as neoplasms, hypertension, diabetes, and cardiovascular diseases, which complicate the determination of the underlying cause of death when a death certificate is completed.\u0026nbsp;\u003csup\u003e9\u003c/sup\u003eAdditionally, men generally have more garbage codes than women do, with this gender difference being more pronounced in those under 65 years of age. This may be related to the prevalence of unhealthy habits among men, such as smoking and alcohol consumption\u003csup\u003e5\u003c/sup\u003e, which not only increase\u0026nbsp;their risk for various diseases but also may result in more deaths from accidents, leading to a higher rate of garbage codes.\u003csup\u003e27\u003c/sup\u003eTherefore, preservice and in-service training or retraining of cause-of-death reporting personnel to avoid the use of these specific codes is necessary.\u003c/p\u003e\n\u003cp\u003eThe distribution characteristics of garbage codes in 2023 indicate that diagnosing and coding deaths from sudden cardiac death in individuals under 65 years of age demands greater attention and accuracy. Additionally, sex-related differences may be attributed to the distinct epidemiological patterns of heart disease between men and women.\u0026nbsp;\u003csup\u003e27,28\u003c/sup\u003eThe high frequency of tumor-related garbage codes in younger age groups may underscore the complexity of cancer diagnosis. This highlights the challenges in accurately classifying and coding cancer types according to this demographic, emphasizing the need for further clinical and pathological review to increase the accuracy of cause-of-death data.\u0026nbsp;\u003csup\u003e29\u003c/sup\u003eThis suggests that more meticulous clinical assessments and coding practices are essential in older populations to ensure that cause-of-death data accurately reflect the true underlying causes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and challenges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study\u0026rsquo;s detailed analysis of population-wide cause-of-death data in Zunyi city from 2022--2023 reveals the distributional characteristics of garbage codes across specific populations. This study improves the understanding of discrepancies in the accuracy of cause-of-death data across populations and provides data to support targeted public health interventions. However, there are limitations to this study. First, it did not fully investigate the specific factors leading to the creation of garbage codes, limiting our deeper understanding of the root causes of the issue. Second, the study lacked a long-term trend analysis, which reduces our understanding of changes in cause-of-death data quality over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe compared and analyzed the categories and levels of garbage codes before and after the intervention to increase the accuracy and utility of cause-of-death data, providing reliable data support for public health decision-making and health policy formulation. This study confirms the utility and efficiency of this approach, offering a cost-effective solution for healthcare organizations to ensure continuous improvement in cause-of-death data quality through regular data review and feedback.\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eScience and Technology Fund Project of the Health Commission of Guizhou Province (gzwkj2021\u0026ndash;434; gzwkj2023\u0026ndash;077)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBo Zhang：Conceptualization , Formal analysis\u0026nbsp;,Data Curation.Haibo Tan: Writing - Original Draft, Data Curation , Software.Deqin We: Conceptualization , Validation, Data Curation.Tao Long: Resources, Validation, Data Curation.Yuanmou Huang: Resources, Validation, Supervision.Jian Wang: Resources, Validation, Data Curation.Yi Zhang: Conceptualization , Validation, Data Curation.Xiuquan Shi: Conceptualization , Validation, Software.Dalin Tian: Validation, Software.Hailei Guo: Conceptualization , Writing - Review \u0026amp; Editing.All authors reviewed the manuscript.Jiayan Cao: Conceptualization , Conceptualization , Writing - Review \u0026amp; Editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohnson, S. C. et al. Public health utility of cause of death data: applying empirical algorithms to improve data quality. \u003cem\u003eBMC Med. Inf. Decis. Mak.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12911-021-01501-1\u003c/span\u003e\u003cspan address=\"10.1186/s12911-021-01501-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou, H., Li, Z., Tian, X. \u0026amp; Ren, Y. The top 5 causes of death in China from 2000 to 2017. \u003cem\u003eSci. 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Chem.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e, 140\u0026ndash;149. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/clinchem/hvad202\u003c/span\u003e\u003cspan address=\"10.1093/clinchem/hvad202\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cause of death data, quality control, intervention, junk coding","lastPublishedDoi":"10.21203/rs.3.rs-5377235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5377235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND\u003c/strong\u003e: Accurate mortality data are crucial for understanding mortality patterns, informing public health strategies, and evaluating national health programs. In 2022 and 2023, the Centers for Disease Control and Prevention in Zunyi, China, provided specialized training to staff responsible for cause-of-death surveillance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS\u003c/strong\u003e: This study evaluated the quality of cause-of-death data reported by healthcare organizations in Zunyi city before and after the intervention, with a focus on the classification and extent of garbage codes. By comparing the distributions of various causes of death and their changes over the two years, we analyzed the differences and distribution patterns of garbage codes. The study participants were grouped by age and sex.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e: The cause-of-death data from Zunyi demonstrated good completeness over the two-year period. The proportion of definite causes of death increased significantly from 87.5% to 94.8%, whereas the proportion of unusable causes decreased notably, from 7.32% to 2.87%. Similarly, the proportion of garbage codes relative to total deaths decreased from 12.60% to 5.20%, with significant reductions in categories 3 and 5. The major garbage codes in both years exhibited a positively skewed distribution, which was primarily associated with aging and cardiovascular diseases. The proportion of garbage codes decreased across both the male and the female groups over the age of 65.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION\u003c/strong\u003e: This study offers a cost-effective approach to improve the quality of cause-of-death data through a junk code-based assessment method. By implementing these measures, the accuracy and utility of cause-of-death data can be greatly enhanced.\u003c/p\u003e","manuscriptTitle":"Assessing the Quality of Mortality Data in Zunyi, China: A Comparative Study of Garbage Coding Before and After Intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 10:12:46","doi":"10.21203/rs.3.rs-5377235/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a87e3105-26cb-4dd0-8444-adb1374679f3","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41412786,"name":"Health sciences/Diseases"},{"id":41412787,"name":"Health sciences/Health care"},{"id":41412788,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-06-18T05:38:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 10:12:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5377235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5377235","identity":"rs-5377235","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}