METHODS: This study included all deaths that occurred in Malaysia in 2018. The YLL was derived by adding the number of deaths from 113 specific diseases and multiplying it by the remaining life expectancy for that age and sex group. Data on life expectancy and mortality were collected from the Department of Statistics Malaysia.
RESULTS: In 2018, there were 3.5 million YLL in Malaysia. Group II (NCDs) caused 72.2% of total YLL. Ischaemic heart disease was the leading cause of premature mortality among Malaysians (17.7%), followed by lower respiratory infections (9.7%), road traffic injuries (8.7%), cerebrovascular disease (stroke) (8.0%), and diabetes mellitus (3.9%).
CONCLUSIONS: NCDs are a significant health concern in Malaysia and are the primary contributor to the overall burden of disease. These results are important in guiding the national health systems on how to design and implement effective interventions for NCDs, as well as how to prioritise and allocate healthcare resources. Key strategies to consider include implementing health promotion campaigns, adopting integrated care models, and implementing policy and regulatory measures. These approaches aim to enhance health outcomes and the managements of NCDs in Malaysia.
Methods: Coroners' files for the 25 years between 1993 and 2017 were interrogated. All cases of death on or at the cliffs were examined, and demographic data were extracted, including date of death, gender, age, nationality, whether the victims were alone at the cliffs prior to their death, whether the fall was witnessed, prevailing weather conditions, post-mortem examinations, toxicology reports and inquest verdicts.
Results: Overall, 66 deaths occurred on or at the base of the Cliffs of Moher during the period 1993 through August 2017. In total, 18 (27.3%) of the victims were international visitors to Ireland, including 11 males (61.1%). The mean age of travellers (n = 17) was 34.2 years. Victims were nationals of 12 different countries, with 13 being European nationals. Most deaths occurred in summer (n = 7) or spring (n = 6), with eight deaths (44%) reported at weekends. In total, 15 victims (83.3%) had walked along the cliff path alone. A jump or fall from the cliffs was witnessed in only two cases (11.1%). Post-mortem examinations revealed multiple traumatic injuries consistent with a fall from a height. Four cases had evidence of alcohol intoxication. Suicide or open verdicts were returned in 50% (n = 9) of the cases.
Conclusions: Travelling alone to the site, purchasing one-way tickets, or depositing belongings on the clifftop support the possibility of suicidal intent, while being intoxicated could be a co-factor in suicidal jumps or support the possibility of an accidental fall. This knowledge could help to identify travellers at the greatest risk of death at cliffs.
METHODS: Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system.
RESULTS: Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines.
CONCLUSION: The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.
MATERIAL & METHOD: 61 road traffic accidental death cases underwent both PMCT and conventional autopsy. The imaging findings were compared to the conventional autopsy findings.
RESULT: The sensitivity, specificity, PPV and NPV for liver injuries in PMCT was 71%, 82%, 68% and 85% while that of splenic injuries was 73%, 80%, 55% and 90% respectively. The accuracy of PMCT scan was 79% for both liver and splenic injuries. There is strong association between lower left ribs fracture and splenic injury (p=0.005) and significant association between positive liver and splenic PMCT finding and intraabdominal fatal injury (p=0.037).
CONCLUSION: In conclusion PMCT has high specificity and NPV for liver and splenic injuries; however the sensitivity and PPV are low. The overall accuracy is not high enough to enable PMCT to be used as a replacement for conventional autopsy; however it is a useful complementary examination and has potential to be used as decision making tool for selective internal autopsy.