Understanding The Mimicker: Epidemiological Pattern and Determinant of Melioidosis Mortality in Negeri Sembilan, Malaysia.

BACKGROUND: Melioidosis, a tropical infectious disease caused by Burkholderia pseudomallei, is epidemic in most region in Southeast Asia with high case fatality. However, there is scanty information regarding the disease's epidemiological pattern, demographics, and underlying risk factors. METHOD: This 5-year retrospective study of 185 confirmed cases which were taken from the Negeri Sembilan Melioidosis Registry between 2018 and 2022. We aim to describe the incidence, mortality rate, case fatality, relationship with meteorology, and factors that influence mortality in this central region of Peninsular Malaysia. RESULTS: Incidence rate (IR) of melioidosis in Negeri Sembilan is varied at 1.9 to 5.1 with mean of 3.1 in 100,000 population per year. IR varied between districts in the state from zero to 22.01 in 100,000 population per year. Mortality rate were ranged from 0.17 to 0.74 cases with mean of 0.44 cases in 100,000 population per year. The case fatality rate of this state scattered from 8.70% to 16.67%. There were no significant linear associations between cases and deaths with monthly rainfall and humidity. The mean age of patients was 52.8 years, predominated with age around 41-60 years old. Males (77.8%) predominated, and the majority of cases were Malays (88.9%) and had exposed to soil related activities (74.6%). Mortality from melioidosis was more likely in Bumiputera and non-Malaysians (p<0.05). Patients who had at least one comorbidity were at a higher risk of death from melioidosis (p<0.05). Diabetes mellitus was found in 41.1% of all identified cases, making it a major underlying risk factor for both developing and dying from melioidosis (aOR:19.32, 95%CI:1.91-195.59, p<0.05). Hypertension and mortality status in melioidosis are also significantly correlated (aOR: 7.75, 95% CI: 2.26-26.61, p<0.05). CONCLUSION: The epidemiological patterns of cases reported from Negeri Sembilan are consistent for the most part from previous studies in other states in Malaysia and global with regard to its incidence, case fatality, demographic and predisposing chronic diseases. Diabetes mellitus and hypertension were significantly linked to increased mortality among all determinants.

Systematic Review of Brain-Eating Amoeba: A Decade Update.

INTRODUCTION: Primary amoebic meningoencephalitis (PAM) is a rare but lethal infection of the brain caused by a eukaryote called Naegleria fowleri (N. fowleri). The aim of this review is to consolidate the recently published case reports of N. fowleri infection by describing its epidemiology and clinical features with the goal of ultimately disseminating this information to healthcare personnel. METHODS: A comprehensive literature search was carried out using PubMed, Web of Science, Scopus, and OVID databases until 31 December 2022 by two independent reviewers. All studies from the year 2013 were extracted, and quality assessments were carried out meticulously prior to their inclusion in the final analysis. RESULTS: A total of 21 studies were selected for qualitative analyses out of the 461 studies extracted. The cases were distributed globally, and 72.7% of the cases succumbed to mortality. The youngest case was an 11-day-old boy, while the eldest was a 75-year-old. Significant exposure to freshwater either from recreational activities or from a habit of irrigating the nostrils preceded onset. The symptoms at early presentation included fever, headache, and vomiting, while late sequalae showed neurological manifestation. An accurate diagnosis remains a challenge, as the symptoms mimic bacterial meningitis. Confirmatory tests include the direct visualisation of the amoeba or the use of the polymerase chain reaction method. CONCLUSIONS: N. fowleri infection is rare but leads to PAM. Its occurrence is worldwide with a significant risk of fatality. The suggested probable case definition based on the findings is the acute onset of fever, headache, and vomiting with meningeal symptoms following exposure to freshwater within the previous 14 days. Continuous health promotion and health education activities for the public can help to improve knowledge and awareness prior to engagement in freshwater activities.

Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance.

Forecasting the severity of occupational injuries shall be all industries' top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database of 66,405 occupational injury records from OSHA is analyzed using five sets of machine learning models: Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. For model comparison, Random Forest outperformed other models with higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more accurate prediction model in the field of occupational injury. In constructing the model, this study also proposed the feature optimization technique that revealed the three most important features; 'nature of injury', 'type of event', and 'affected body part' in developing model. The accuracy of the Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization and amputation, respectively by redeveloping and optimizing the model with hyperparameter tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health Practitioners for future injury corrective and preventive strategies. This study has shown promising potential for smart workplace surveillance.