Changes of mortality and causes of death from 1970 to 2005 and decomposition analysis in Shandong province [山东省1970-2005年人群死亡率极死因变化与差别分解分析]

Objective To describe the trend of overall mortality and major causes of death in Shandong population from 1970 to 2005,and to quantitatively estimate the influential factors. Methods Trends of overall mortality and major causes of death were described using indicators such as mortality rates and age-adjusted death rates by comparing three large-scale mortality surveys in Shandong province. Difference decomposing method was applied to estimate the contribution of demographic and non-demographic factors for the change of mortality. Results The total mortality had had a slight change since 1970s,but had increased since 1990s.However,both the mortality rates of age-adjusted and age-specific decreased significantly. The mortality of Group Ⅰ diseases including infectious diseases as well maternal and perinatal diseases decreased drastically. By contrast, the mortality of non-communicable chronic diseases (NCDs)including cardiovascular diseases(CVDs),cancer and injuries increased. The sustentation of recent overall mortality was caused by the interaction of demographic and non-demographic factors which worked oppositely. Non-demographic factors were responsible for the decrease of Group Ⅰ disease and the increase of injuries. With respect to the increase of NCDs as a whole. Demographic factors might take the full responsibility and the non-demographic factors were the opposite force to reduce the mortality. Nevertheless, for the increase of some leading NCD diseases as CVDs and cancer, the increase was mainly due to non-demographic rather than demographic factors. Conclusion Through the interaction of the aggravation of ageing population and the enhancement of non-demographic effect, the overall mortality in Shandong would maintain a balance or slightly rise in the coming years. Group Ⅰ diseases in Shandong had been effectively under control. Strategies focusing on disease control and prevention should be transferred to chronic diseases, especially leading NCDs, such as CVDs and cancer.

Surveillance report on cardiovascular causes of death in Shouguang, 1993-1997 [寿光市疾病监测点1993～1997年心血管疾病死亡监测报告]

This analysis showed that cardiovascular diseases were the number cause of death claiming 34% of all deaths. More than half (52%) of all cardiovascular deaths were due to Cerebrovascular diseases and about one-third (32%) were due to ischaemic heart disease. The mortality of cardiovascular diseases showed an increasing trend during this period (1993-1997). Abstract in Chinese 心血管疾病是威胁人们健康的重要疾病之一，在居民死因中占主要位置。为了解心血管疾病死亡状况，我们对寿光市农村疾病监测点１９９３～１９９７年居民死亡资料进行了统计分析，现报告如下。（标化死亡率采用１９９０年全国标准人口构成计算）。１９９３～１９９７年监测...

Analysis on causes of death of rural residents in Shouguang City [寿光市农村人群慢性病死因分析]

Among people living in rural areas of Shouguang City, Shandong, China, 88% of deaths were caused by chronic non-communicable diseases. Cardiovascular diseases, respiratory diseases and cancers accounted for 97% of all chronic disease causes and 85% of all causes of death. The proportion of chronic causes increased by 27% from 1993 to 2000. However, the mortality of respiratory diseases showed a decreasing trend over time. Abstract in Chinese 篇首： 随着生活水平的提高,慢性非传染性疾病在居民死因谱中所占比重越来越大,为准确反映居民的死因状况,为农村地区慢性病的控制工作提供科学依据,本文对寿光市农村疾病监测点1993～2000年居民的慢性病死因进行了分析.

Global, regional, and national age–sex specifi c all-cause and cause-specifi c mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

Background Up-to-date evidence on levels and trends for age-sex-specific all-cause and cause-specific mortality is essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess whether there is epidemiological convergence across countries. Methods We estimated age-sex-specific all-cause mortality using the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries, an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK, Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six different modelling strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with sufficient information. Trends for Alzheimer's disease and other dementias were informed by meta-regression of prevalence studies. For pathogen-specific causes of diarrhoea and lower respiratory infections we used a counterfactual approach. We computed two measures of convergence (inequality) across countries: the average relative difference across all pairs of countries (Gini coefficient) and the average absolute difference across countries. To summarise broad findings, we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age 15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specific fractions within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions. Findings Global life expectancy for both sexes increased from 65·3 years (UI 65·0–65·6) in 1990, to 71·5 years (UI 71·0–71·9) in 2013, while the number of deaths increased from 47·5 million (UI 46·8–48·2) to 54·9 million (UI 53·6–56·3) over the same interval. Global progress masked variation by age and sex: for children, average absolute differences between countries decreased but relative differences increased. For women aged 25–39 years and older than 75 years and for men aged 20–49 years and 65 years and older, both absolute and relative differences increased. Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea, lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-standardised death rates. Global deaths from injury increased by 10·7%, from 4·3 million deaths in 1990 to 4·8 million in 2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial fibrillation and flutter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases, lower respiratory infections, neonatal causes, and malaria are still in the top five causes of death in children younger than 5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections. Country-specific probabilities of death over three phases of life were substantially varied between and within regions. Interpretation For most countries, the general pattern of reductions in age-sex specific mortality has been associated with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates for some countries; for example, no national data for India are available for the past decade.

Cause of Death, Trend and Disease Burden in Shandong Province, China : the Third Retrospective Sampling Survey of Cause of Death Report

This book describes the mortality for all causes of death and the trend in major causes of death since 1970s in Shandong Province, China.

Cuases of death among rural residents in Shouguang City, 1993-1997 [寿光市1993～1997年农村居民死因分析]

In this rural population, cardiovascular diseases, respiratory diseases, cancers and injuries were identified as the major causes of death, responsible for 34%, 30%, 20% and 9%, respectively. Abstract in Chinese 寿光市地处山东半岛中部,渤海莱洲湾南畔,是全国有名的“蔬菜之乡”。为全面了解我市农村居民的死因分布状况,我们对寿光市部分农村居民1993~1997年的死亡资料进行了统计分析。对象与方法自1993年始,从我市选取有代表性的两个乡镇49个自然村约5000..

Failure of amino acid homeostasis causes cell death following proteasome inhibition

The ubiquitin-proteasome system targets many cellular proteins for degradation and thereby controls most cellular processes. Although it is well established that proteasome inhibition is lethal, the underlying mechanism is unknown. Here, we show that proteasome inhibition results in a lethal amino acid shortage. In yeast, mammalian cells, and flies, the deleterious consequences of proteasome inhibition are rescued by amino acid supplementation. In all three systems, this rescuing effect occurs without noticeable changes in the levels of proteasome substrates. In mammalian cells, the amino acid scarcity resulting from proteasome inhibition is the signal that causes induction of both the integrated stress response and autophagy, in an unsuccessful attempt to replenish the pool of intracellular amino acids. These results reveal that cells can tolerate protein waste, but not the amino acid scarcity resulting from proteasome inhibition.

Automatic ICD-10 classification of cancers from free-text death certificates

Objective Death certificates provide an invaluable source for cancer mortality statistics; however, this value can only be realised if accurate, quantitative data can be extracted from certificates – an aim hampered by both the volume and variable nature of certificates written in natural language. This paper proposes an automatic classification system for identifying cancer related causes of death from death certificates. Methods Detailed features, including terms, n-grams and SNOMED CT concepts were extracted from a collection of 447,336 death certificates. These features were used to train Support Vector Machine classifiers (one classifier for each cancer type). The classifiers were deployed in a cascaded architecture: the first level identified the presence of cancer (i.e., binary cancer/nocancer) and the second level identified the type of cancer (according to the ICD-10 classification system). A held-out test set was used to evaluate the effectiveness of the classifiers according to precision, recall and F-measure. In addition, detailed feature analysis was performed to reveal the characteristics of a successful cancer classification model. Results The system was highly effective at identifying cancer as the underlying cause of death (F-measure 0.94). The system was also effective at determining the type of cancer for common cancers (F-measure 0.7). Rare cancers, for which there was little training data, were difficult to classify accurately (F-measure 0.12). Factors influencing performance were the amount of training data and certain ambiguous cancers (e.g., those in the stomach region). The feature analysis revealed a combination of features were important for cancer type classification, with SNOMED CT concept and oncology specific morphology features proving the most valuable. Conclusion The system proposed in this study provides automatic identification and characterisation of cancers from large collections of free-text death certificates. This allows organisations such as Cancer Registries to monitor and report on cancer mortality in a timely and accurate manner. In addition, the methods and findings are generally applicable beyond cancer classification and to other sources of medical text besides death certificates.

Causes of injuries resulting in hospitalisation in Australia : assessing coder agreement on external causes

Objective: To assess extent of coder agreement for external causes of injury using ICD-10-AM for injury-related hospitalisations in Australian public hospitals. Methods: A random sample of 4850 discharges from 2002 to 2004 was obtained from a stratified random sample of 50 hospitals across four states in Australia. On-site medical record reviews were conducted and external cause codes were assigned blinded to the original coded data. Code agreement levels were grouped into the following agreement categories: block level, 3-character level, 4-character level, 5th-character level, and complete code level. Results: At a broad block level, code agreement was found in over 90% of cases for most mechanisms (eg, transport, fall). Percentage disagreement was 26.0% at the 3-character level; agreement for the complete external cause code was 67.6%. For activity codes, the percentage of disagreement at the 3-character level was 7.3% and agreement for the complete activity code was 68.0%. For place of occurrence codes, the percentage of disagreement at the 4-character level was 22.0%; agreement for the complete place code was 75.4%. Conclusions: With 68% agreement for complete codes and 74% agreement for 3-character codes, as well as variability in agreement levels across different code blocks, place and activity codes, researchers need to be aware of the reliability of their specific data of interest when they wish to undertake trend analyses or case selection for specific causes of interest.

Teaching cause-of-death certification : lessons from international experience

The accuracy of cause-of-death statistics substantially depends on the quality of cause-of-death information in death certificates, primarily completed by medical doctors. Deficiencies in cause-of-death certification have been observed across the world, and over time. Despite educational interventions targeting to improve the quality of death certification, their intended impacts are rarely evaluated. This review aims to provide empirical evidence that could guide the modification of existing educational programs, or the development of new interventions, which are necessary to improve the capacity of certifiers as well as the quality of cause-of-death certification, and thereby, the quality of mortality statistics.

Quality of cause-of-death reporting using ICD-10 drowning codes : a descriptive study of 69 countries

Background: The systematic collection of high-quality mortality data is a prerequisite in designing relevant drowning prevention programmes. This descriptive study aimed to assess the quality (i.e., level of specificity) of cause-of-death reporting using ICD-10 drowning codes across 69 countries.---------- Methods: World Health Organization (WHO) mortality data were extracted for analysis. The proportion of unintentional drowning deaths coded as unspecified at the 3-character level (ICD-10 code W74) and for which the place of occurrence was unspecified at the 4th character (.9) were calculated for each country as indicators of the quality of cause-of-death reporting.---------- Results: In 32 of the 69 countries studied, the percentage of cases of unintentional drowning coded as unspecified at the 3-character level exceeded 50%, and in 19 countries, this percentage exceeded 80%; in contrast, the percentage was lower than 10% in only 10 countries. In 21 of the 56 countries that report 4-character codes, the percentage of unintentional drowning deaths for which the place of occurrence was unspecified at the 4th character exceeded 50%, and in 15 countries, exceeded 90%; in only 14 countries was this percentage lower than 10%.---------- Conclusion: Despite the introduction of more specific subcategories for drowning in the ICD-10, many countries were found to be failing to report sufficiently specific codes in drowning mortality data submitted to the WHO.

The impact of globalisation on health

After reading this chapter, you should be able to: • understand the concept of globalisation and appreciate its complexity • identify the significant impacts of globalisation on population health, particularly the incidence of communicable and non-communicable diseases • understand the distribution of the global burden of disease in high-, middle- and low-income countries • critically evaluate the factors contributing to the major causes of death in low-income countries • understand some of the achievements of the global public health community and appreciate the challenges it faces.