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Medical certificate cause of death (MCCD) data is an important source of information for public health monitoring, priority setting, and planning. The completeness and accuracy of data on the cause of death affect the value of the policies adopted. Poor-quality data can lead to missed opportunities to improve the health of the population. This study aims to obtain a strategy to improve the quality of data on MCCD. We used DKI Jakarta province mortality surveillance data, which was collected from health facilities. The death cases from 2016 to 2019 and the deceased who lived in the DKI Jakarta area were selected. The Analysis of National Causes of Death for Action (ANACONDA) software 5.0 was used to determine the quality of the MCCD data. Results: The number of Health Facilities sending data from 2016 to 2019 has increased. Annual percentages Garbage Code (GC) tend not to change (more than 50%). GC in categories 3, 5, and 1 tend to be stable with an annual average of 34%, 26%, and 21%. Meanwhile, Category 4 tends to increase though it is small (from 5.7 to 14.4%). Based on the potential impact that GC causes in guiding or misguiding public policy to prevent, more than 60% is identified as being in the very high-level category. Conclusion: Identifying the accuracy of cause of death data was needed to make the right strategy for improving the quality of data. [ABSTRACT FROM AUTHOR]

Medical certificate cause of death (MCCD) data is an important source of information for public health monitoring, priority setting, and planning. The completeness and accuracy of data on the cause of death affect the value of the policies adopted. Poor-quality data can lead to missed opportunities to improve the health of the population. This study aims to obtain a strategy to improve the quality of data on MCCD. We used DKI Jakarta province mortality surveillance data, which was collected from health facilities. The death cases from 2016 to 2019 and the deceased who lived in the DKI Jakarta area were selected. The Analysis of National Causes of Death for Action (ANACONDA) software 5.0 was used to determine the quality of the MCCD data Results: The number of Health Facilities sending data from 2016 to 2019 has increased. Annual percentages Garbage Code (GC) tend not to change (more than 50%). GC in categories 3, 5, and 1 tend to be stable with an annual average of 34%, 26%, and 21%. Meanwhile, Category 4 tends to increase though it is small (from 5.7 to 14.4%). Based on the potential impact that GC causes in guiding or misguiding public policy to prevent, more than 60% is identified as being in the very high-level category. Conclusion: Identifying the accuracy of cause of death data was needed to make the right strategy for improving the quality of data. [ABSTRACT FROM AUTHOR]

Differential diagnosis of intracerebral hemorrhage due to traumatic and non-traumatic origin is a challenging issue, especially in the absence of visible body injuries and other signs of violence. For a forensic expert-practitioner, the main thing is objectivity, accuracy, and speed of obtaining the result, which could fully satisfy the methods of laser polarimetry in the case of differential diagnosis of hemorrhages of traumatic and non-traumatic origin in the human brain matter. The purpose: To develop forensic criteria for the differential diagnosis of intracerebral hemorrhage of traumatic and nontraumatic origin using Mueller-matrix microscopy of linear dichroism. Materials and methods: The object of the study was brain matter samples, collected in 115 corpses of both sexes aged 22 to 86 years with accurately known causes of death. The causes of death included coronary artery disease, traumatic brain hemorrhage, ischemic brain infarction, and non-traumatic brain hemorrhages. Brain matter samples have been frozen rapidly, and histological sections have been made using a freezing microtome. The slices have been evaluated using the method of azimuthal-invariant Mueller-matrix microscopy, followed by mathematical and statistical processing of the results. Results: significant topographic heterogeneity of Mueller-matrix invariants of brain matter samples linear dichroism maps among all research groups have been found. Individual and significant variations of dispersion, asymmetry, and kurtosis magnitude have confirmed the heterogeneity of Mueller-matrix invariants. The difference of images and histograms among groups is explained by the presence of necrotic changes in the case of ischemic brain necrosis and blood cells in the brain matter samples. For the set of central statistical moments of the 1st, 3rd, and 4th orders, which characterize the distributions of circular dichroism, the method of MM-microscopy in the differentiation of samples of non-hemorrhage and hemorrhage groups reaches a satisfactory level - 78% - 84%. Conclusions: Brain matter samples Muller-matrix microscopy of linear dichroism is effective for solving diagnostic problems of forensic medicine related to evaluating the cause of death from intracerebral hemorrhage of various origins. Diagnostic efficiency of Mueller-matrix mapping of polarization manifestations of linear dichroism method for intergroup differentiation of samples of deaths from traumatic hemorrhage (Group 2) and ischemic cerebral infarction (Group 3) reaches a satisfactory level of 79 - 84%. [ABSTRACT FROM AUTHOR]

Timely mortality surveillance in France is based on the monitoring of electronic death certificates to provide information to health authorities. This study aims to analyze the performance of a rule-based and a supervised machine learning method to classify medical causes of death into 60 mortality syndromic groups (MSGs). Performance was first measured on a test set. Then we compared the trends of the monthly numbers of deaths classified into MSGs from 2012 to 2016 using both methods. Among the 60 MSGs, 31 achieved recall and precision over 0.95 for either one or the other method on the test set. On the whole dataset, the correlation coefficient of the monthly numbers of deaths obtained by the two methods were close to 1 for 21 of the 31 MSGs. This approach is useful for analyzing a large number of categories or when annotated resources are limited. [ABSTRACT FROM AUTHOR]

IRIS is an automated coding software for the causes of death. It is used in many European countries for the production of death statistics. The purpose of our work was to study the usability of this software in Africa where the quality of statistics is insufficient. For this, we have developed a device consisting of two software: "collector" and "encoder" cooperating via the same database. [ABSTRACT FROM AUTHOR]