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Objective: This study assessed Nipah virus (NiV) encephalitis epidemiology, clinical outcomes, and risk variables to inform treatment and prevention. Methodology: In a PubMed systematic search, 929 citations were found. After screening and eligibility, 22 studies were included. This study obtained age, gender, geographic regions, diagnostic methods, data collection methods, and bias risk. The case fatality rate (CFR) and NiV infection risk variables were evaluated by meta-analysis. Results: Southeast Asia, especially Bangladesh and Malaysia, had the most NiV cases. The major diagnostic method was blood and cerebrospinal fluid IgM and IgG antibody tests, and males predominated. Proxy respondents and matched controls were utilized for risk factor analyses when patients could not answer. The pooled CFR for NiV encephalitis was 61.0%, indicating severity. Risk factors included pigs, nighttime bats near homes, tree climbing, and male gender. Conclusion: Southeast Asian public health is plagued by NiV encephalitis. The high CFR calls for better diagnosis, treatment, and prevention. NiV's multiple risk factors must be understood for targeted therapy. Future research should fill knowledge gaps and improve NiV infection prevention. [ABSTRACT FROM AUTHOR]

Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple big public health data with modern statistical techniques offer greater granularity for describing and understanding data quality, disease distributions, and potential predictive connections between population-level indicators with areal-based health outcomes. This study applied clustering techniques to explore patterns of diabetes burden correlated with local socio-economic inequalities in Malaysia, with a goal of better understanding the factors influencing the collation of these clusters. Through multi-modal secondary data sources, district-wise diabetes crude rates from 271,553 individuals with diabetes sampled from 914 primary care clinics throughout Malaysia were computed. Unsupervised machine learning methods using hierarchical clustering to a set of 144 administrative districts was applied. Differences in characteristics of the areas were evaluated using multivariate non-parametric test statistics. Five statistically significant clusters were identified, each reflecting different levels of diabetes burden at the local level, each with contrasting patterns observed under the influence of population-level characteristics. The hierarchical clustering analysis that grouped local diabetes areas with varying socio-economic, demographic, and geographic characteristics offer opportunities to local public health to implement targeted interventions in an attempt to control the local diabetes burden. [ABSTRACT FROM AUTHOR]