Penilaian Kualitas Data Penyebab Kematian di Indonesia Tahun 2014

Sample Registration System (SRS) is a demographic survey for providing data on causes of death (COD) in Indonesia. The quality of COD will be taken into consideration for health policies development. This paper aims to assess the quality of data on the causes of death in Indonesia through the proportion and level of garbage codes on the impact when used in policy making. The 2014 National COD data set were assessed by applying the Analysis of National Causes of Death for Action (ANACONDA) software tool version 3.7.0. Distributions and levels of unusable and insufficiently specified “garbage” codes were analyzed. The Result shows, Diseases of the circulatory system (62.6%) contributed the most to garbage cause of death. The proportion of unusable COD was 31% of total data. 80% of garbage code were unspecified deaths group. Most of the garbage codes has low-level on severity of impact level for policy, while 11% of total codes has medium, high dan very high level of impact. In Conclusion, the 2014 SRS data was not at high quality, but the implications of garbage code in making inappropriate policies are mostly at low level. The use of low-level codes has less important impact on public health policy. The 2014 SRS data could be considered as a scientific basis evidence for public health policy. Quality improvement still needs to be done by conducting training and refreshing to determine the cause of death for doctors and data collection techniques for data collectors Keywords : Cause of Death, quality of data, Sample Registration System, ANACONDA Abstrak Sample Registration System (SRS) merupakan survei demografi untuk menyediakan data penyebab kematian (COD) di Indonesia. Kualitas COD akan menjadi bahan pertimbangan dalam membuat kebijakan kesehatan. Tulisan ini bertujuan untuk menilai kualitas data penyebab kematian di Indonesia melalui besar proporsi dan level kode sampah terhadap dampak yang ditimbulkan ketika digunakan dalam membuat kebijakan. Data penyebab kematian nasional tahun 2014 dinilai dengan menggunakan perangkat lunak Analisis Penyebab Kematian Nasional untuk Tindakan (ANACONDA) versi 3.7.0. Distribusi dan level kode "sampah" yang tidak dapat digunakan dianalisis dengan menggunakan ANACONDA. Hasil analisis menunjukkan, Diseases of the circulatory system (62.6%) berkontribusi terbanyak dalam hal kode sampah. Proporsi kode sampah yang tidak dapat digunakan adalah 31% dari total kode. Kode sampah yang paling umum digunakan adalah kelompok penyebab kematian tidak spesifik dan kelompok penyebab kematian antara. Berdasarkan tingkat keparahan dalam membuat kebijakan, sebagian besar kode sampah termasuk kategori level rendah, hanya 11% dari total kode memiliki tingkat dampak sedang, tinggi dan sangat tinggi. Kesimpulannya, kualitas data SRS 2014 masih kurang baik, namun implikasi yang ditimbulkan kode sampah dalam membuat kebijakan yang salah sebagian besar berada pada level rendah. Penggunaan kode-kode level rendah memiliki dampak yang kurang penting bagi kebijakan kesehatan masyarakat. Data penyebab kematian SRS 2014 layak dipertimbangkan untuk digunakan sebagai dasar kebijakan Kesehatan masyarakat. Pelatihan penentuan penyebab kematian untuk dokter dan juga petugas AV perlu dilakukan agar kualitas data COD selanjutnya dapat lebih baik Kata kunci: penyebab kematian, kualitas data, Sample Registration System, ANACONDA

Identificação e análise de Garbage code ocorridos no preenchimento da Declaração de Óbito no Hospital Geral de Nova Iguaçu - RJ, uma realidade a ser modificada

A declaração de óbito é de suma importância no âmbito de saúde pública, sendo utilizado como ferramenta para traçar indicadores epidemiológicos para futuras políticas de saúde preventivas. Objetivo: identificar os principais erros e/ou inconsistência de informações, Código Garbage (CG), pertencente ao CID-10 nos preenchimentos das declarações de óbito do Hospital Geral de Nova Iguaçu. Método: trata-se de um trabalho científico exploratório, que tem como base a busca de informações através Sistema de Informação sobre Mortalidade (SIM) do Departamento de Informática do Sistema Único de Saúde (DATASUS), revisão bibliográfica e pesquisa de campo. Resultado: o número de casos CG produzidos em Nova Iguaçu foi de 3058 casos, dentre esses, 1963 foram produzidos no Hospital Geral de Nova Iguaçu. Conclusão: Este estudo aponta a importância de implementar medidas, como palestras de educação continuada e valorização da importância da declaração de óbito, a fim de minimizar a quantidade de CG e oportunizar maior confiabilidade ao SIM, sendo esse uma importante ferramenta para estatísticas epidemiológicas e planejamento das políticas públicas de saúde preventiva.

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

Objectives. To describe and compare 3 garbage code (GC) redistribution models: naïve Bayes classifier (NB), coarsened exact matching (CEM), and multinomial logistic regression (MLR). Methods. We analyzed Taiwan Vital Registration data (2008–2016) using a 2-step approach. First, we used non-GC death records to evaluate 3 different prediction models (NB, CEM, and MLR), incorporating individual-level information on multiple causes of death (MCDs) and demographic characteristics. Second, we applied the best-performing model to GC death records to predict the underlying causes of death. We conducted additional simulation analyses for evaluating the predictive performance of models. Results. When we did not account for MCDs, all 3 models presented high average misclassification rates in GC assignment (NB, 81%; CEM, 86%; MLR, 81%). In the presence of MCD information, NB and MLR exhibited significant improvement in assignment accuracy (19% and 17% misclassification rate, respectively). Furthermore, CEM without a variable selection procedure resulted in a substantially higher misclassification rate (40%). Conclusions. Comparing potential GC redistribution approaches provides guidance for obtaining better estimates of cause-of-death distribution and highlights the significance of MCD information for vital registration system reform.

Coronary heart disease mortality is decreasing in Argentina, and Colombia, but keeps increasing in Mexico: a time trend study

Background: Mortality rates due to coronary heart disease (CHD) have decreased in most countries, but increased in low and middle-income countries. Few studies have analyzed the trends of coronary heart disease mortality in Latin America, specifically the trends in young-adults and the effect of correcting these comparisons for nonspecific causes of death (garbage codes). The objective of this study was to describe and compare standardized, age-specific, and garbage-code corrected mortality trends for coronary heart disease from 1985 to 2015 in Argentina, Colombia, and Mexico. Methods: Deaths from coronary heart disease were grouped by country, year of registration, sex, and 10-year age bands to calculate age-adjusted and age and sex-specific rates for adults aged ≥25. We corrected for garbage-codes using the methodology proposed by the Global Burden of Disease. Finally, we fitted Joinpoint regression models.Results: In 1985, age-standardized mortality rates per 100,000 population were 136.6 in Argentina, 160.6 in Colombia, and 87.51 in Mexico; by 2015 rates decreased 51% in Argentina and 6.5% in Colombia, yet increased by 61% in Mexico, where an upward trend in mortality was observed in young adults. Garbage-code corrections produced increases in mortality rates, particularly in Argentina with approximately 80 additional deaths per 100,000, 14 in Colombia and 13 in Mexico.Conclusions: Latin American countries are at different stages of the cardiovascular disease epidemic. Garbage code correction produce large changes in the mortality rates in Argentina, yet smaller in Mexico and Colombia, suggesting garbage code corrections may be needed for specific countries. While coronary heart disease (CHD) mortality is falling in Argentina, modest falls in Colombia and substantial increases in Mexico highlight the need for the region to propose and implement population-wide prevention policies.

Coronary heart disease mortality is decreasing in Argentina and Colombia, but keeps increasing in Mexico: a time trend study

Background: Mortality rates due to coronary heart disease (CHD) have decreased in most countries, but increased in low and middle-income countries. Few studies have analyzed the trends of coronary heart disease mortality in Latin America, specifically the trends in young-adults and the effect of correcting these comparisons for nonspecific causes of death (garbage codes). Objective: To describe and compare standardized, age-specific, and garbage-code corrected mortality trends for coronary heart disease from 1985 to 2015 in Argentina, Colombia, and Mexico. Methods: Deaths from coronary heart disease were grouped by country, year of registration, sex, and 10-year age bands to calculate age-adjusted and age and sex-specific rates for adults aged ≥25. We corrected for garbage-codes using the methodology proposed by the Global Burden of Disease. Finally, we fitted Joinpoint regression models. Results: In 1985, age-standardized mortality rates per 100,000 population were 136.6 in Argentina, 160.6 in Colombia, and 87.51 in Mexico; by 2015 rates decreased 51% in Argentina and 6.5% in Colombia, yet increased by 61% in Mexico, where an upward trend in mortality was observed in young adults. Garbage-code corrections produced increases in mortality rates, particularly in Argentina with approximately 80 additional deaths per 100,000, 14 in Colombia and 13 in Mexico. Conclusions: Latin American countries are at different stages of the cardiovascular disease epidemic. Garbage code correction produce large changes in the mortality rates in Argentina, yet smaller in Mexico and Colombia, suggesting garbage code corrections may be needed for specific countries. While coronary heart disease (CHD) mortality is falling in Argentina, modest falls in Colombia and substantial increases in Mexico highlight the need for the region to propose and implement population-wide prevention policies.

Dynamics and an efficient malware detection system using opcode sequence graph generation and ml algorithm

IoT(Internet of things), for the most part, comprises of the various scope of Internet-associated gadgets and hubs. In the context of military and defence systems (called as IoBT) these gadgets could be personnel wearable battle outfits, tracking devices, cameras, clinical gadgets etc., The integrity and safety of these devices are critical in mission success and it is of utmost importance to keep them secure. One of the typical ways of the attack on these gadgets is through the use of malware, whose aim could be to compromise the device and or breach the communications. Generally, these IoBT gadgets and hubs are a much more significant target for cyber criminals due to the value they pose, more so than IoT devices. In this paper we attempt at creating a significant learning based procedure to distinguish, classify and tracksuch malware in IoBT(Internet of battlefield things) through operational codes progression. This is achieved by transforming the aforementioned OpCodes into a vector space, upon which a Deep Eigen space learning technique is applied to differentiate between harmful and safe applications. For robust classification, Support vector machine and n gram Sequencing algorithms are proposed in this paper. Moreover, we evaluate the quality of our proposed approach in malware recognition and also its maintainability against garbage code injection assault. These results are presented on a web page which has separate components and levels of accessibility for user and admin credentials. For the purpose of tracking the prevalence of various malwares on the network, counts and against garbage code injection assault. These results are presented on a web page which has separate components and levels of accessibility for user and admin credentials. For the purpose of tracking the prevalence of various malwares on the network, counts and trends of different malicious opcodes are displayed for both user and admin. Thereby our proposed approach will be beneficial for the users, especially for those who want to communicate confidential information within the network. It is also beneficial if a user wants to know whether a message is secure or not. This has also been made malware test accessible, which ideally will profit future research endeavors.

Coronary heart disease mortality is decreasing in Argentina and Colombia, but keeps increasing in Mexico: a time trend study

Background: Coronary heart disease (CHD) mortality rates have decreased in most countries, but increased in low and middle-income countries. Few studies have analyzed CHD mortality trends in Latin America, specifically the trends in young-adults and the effect of correcting these comparisons for nonspecific causes of death (garbage codes). Objective: To describe and compare standardized, age-specific, and garbage-code corrected mortality trends for CHD from 1985 to 2015 in Argentina, Colombia, and Mexico. Methods: CHD deaths were grouped by country, year of registration, sex, and 10-year age bands to calculate age-adjusted and age and sex specific rates for adults aged ≥25. We corrected for garbage-codes using the Global Burden of Disease methodology. Finally, we fitted Joinpoint regression models. Results: In 1985, age-standardized mortality rates per 100,000 were 136.6 in Argentina, 160.6 in Colombia and 87.51 in Mexico. Compared to 2015, mortality fell in Argentina and Colombia (51% and 6.5% respectively) and increased by 61% in Mexico. The steepest decline was observed in Argentinian women and the sharpest increment in Mexican men. There has been an upward trend in young Mexicans since 1985. Garbage-code corrections produced increases in mortality rates, particularly in Argentina: approximately 80 additional deaths per 100,000 (14 in Colombia and 13 in Mexico). Conclusions: Latin American countries are at different stages of the epidemic. The disease burdens are bigger after correcting for misclassification. Although CHD mortality is falling in Argentina, the modest falls in Colombia and substantial rises in Mexico highlight the region’s need for effective, population-wide prevention policies.

Coronary heart disease mortality is decreasing in Argentina and Colombia, but keeps increasing in Mexico: a time trend study

Background : Coronary heart disease (CHD) mortality rates have decreased in most countries but increased in low and middle-income countries. Few studies have analyzed CHD mortality trends in Latin America, specifically trends in young-adults and the effect of correcting these comparisons for nonspecific causes of death (garbage codes).Objective: To describe and compare standardized, age-specific, and garbage-code corrected mortality trends for CHD from 1985 to 2015 in Argentina, Colombia and Mexico. Methods: CHD deaths were grouped by country, year of registration, sex and 10-year age bands to calculate age-adjusted and age and sex specific rates for adults aged ≥25. We corrected for garbage-codes using the Global Burden of Disease methodology. Finally, we fitted Joinpoint regression models.Results: In 1985, age-standardized mortality rates per 100,000 were 136.6 in Argentina, 160.6 in Colombia and 87.51 in Mexico. Compared to 2015, mortality fell in Argentina and Colombia (51% and 6.5% respectively) and increased by 61% in Mexico. The steepest decline was observed in Argentinian women, and the sharpest increment in Mexican men. There has been an upward trend in young Mexicans since 1985. Garbage-code corrections produced increases in mortality rates, particularly in Argentina: approximately 80 additional deaths per 100,000 (14 in Colombia and 13 in Mexico). Conclusions: Latin American countries are at different stages of the epidemic. The disease burdens are bigger after correcting for misclassification. Although CHD mortality is falling in Argentina, the modest falls in Colombia and substantial rises in Mexico highlight the region’s need for effective, population-wide prevention policies.

Redistribution of heart failure deaths using two methods: linkage of hospital records with death certificate data and multiple causes of death data

Heart failure is considered a garbage code when assigned as the underlying cause of death. Reassigning garbage codes to plausible causes reduces bias and increases comparability of mortality data. Two redistribution methods were applied to Brazilian data, from 2008 to 2012, for decedents aged 55 years and older. In the multiple causes of death method, heart failure deaths were redistributed based on the proportion of underlying causes found in matched deaths that had heart failure listed as an intermediate cause. In the hospitalization data method, heart failure deaths were redistributed based on data from the decedents’ corresponding hospitalization record. There were 123,269 (3.7%) heart failure deaths. The method with multiple causes of death redistributed 25.3% to hypertensive heart and kidney diseases, 22.6% to coronary heart diseases and 9.6% to diabetes. The total of 41,324 heart failure deaths were linked to hospitalization records. Heart failure was listed as the principal diagnosis in 45.8% of the corresponding hospitalization records. For those, no redistribution occurred. For the remaining ones, the hospitalization data method redistributed 21.2% to a group with other (non-cardiac) diseases, 6.5% to lower respiratory infections and 9.3% to other garbage codes. Heart failure is a frequently used garbage code in Brazil. We used two redistribution methods, which were straightforwardly applied but led to different results. These methods need to be validated, which can be done in the wake of a recent national study that will investigate a big sample of hospital deaths with garbage codes listed as underlying causes.

Investigation of garbage code deaths to improve the quality of cause-of-death in Brazil: results from a pilot study

ABSTRACT Introduction: Reliable cause-of-death statistics are an important source of information on trends and differentials in population health. In Brazil, the Mortality Information System is responsible for compiling cause of death (CoD) data. Despite the success in reducing R-codes ill-defined causes of death, other garbage codes (GC), classified as causes that cannot be the underlying CoD, according to the Global Burden of Disease study, remain a challenge. The Ministry of Health (MoH) aims to decrease the proportion of all GCs, and a pilot study tested a comprehensive strategy to investigate GC deaths that occurred in 2015. Methods: The research was conducted in seven Brazilian cities during five months in 2016: two rural cities, one metropolitan area, and four capitals. For all GCs selected, municipal healthcare workers collected information about the terminal disease from hospital records, autopsies, family health teams, and home investigation. The fieldwork was coordinated at Federal level in partnership with State and municipal teams. Results: Out of 1,242 deaths selected, physicians analyzed the information collected and certified the CoD in 1,055 deaths, resulting in 92.6% of cases having their underlying cause changed to a usable ICD-10 code. Discussion: It is noteworthy the capacity the health teams in the seven cities showed during the implementation of the pilot. Conclusion: After results analysis, the GC investigation protocol was modified, and the implementation scaled up to 60 cities in 2017.