Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average $F_1$ score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.

Background: Leptospirosis is considered a neglected zoonotic disease in temperate regions but an endemic disease in countries with tropical climates such as South America, Southern Asia, and Southeast Asia. There has been an increase in leptospirosis incidence in Malaysia from 1.45 to 25.94 cases per 100,000 population between 2005 and 2014. With increasing incidence in Selangor, Malaysia, and frequent climate change dynamics, a study on the disease hotspot areas and their association with the hydroclimatic factors would further enhance disease surveillance and public health interventions. Objective: This study aims to examine the association between the spatio-temporal distribution of leptospirosis hotspot areas from 2011 to 2019 with the hydroclimatic factors in Selangor using the geographical information system and remote sensing techniques to develop a leptospirosis hotspot predictive model. Methods: This will be an ecological cross-sectional study with geographical information system and remote sensing mapping and analysis concerning leptospirosis using secondary data. Leptospirosis cases in Selangor from January 2011 to December 2019 shall be obtained from the Selangor State Health Department. Laboratory-confirmed cases with data on the possible source of infection would be identified and georeferenced according to their longitude and latitudes. Topographic data consisting of subdistrict boundaries and the distribution of rivers in Selangor will be obtained from the Department of Survey and Mapping. The ArcGIS Pro software will be used to evaluate the clustering of the cases and mapped using the Getis-Ord Gi* tool. The satellite images for rainfall and land surface temperature will be acquired from the Giovanni National Aeronautics and Space Administration EarthData website and processed to obtain the average monthly values in millimeters and degrees Celsius. Meanwhile, the average monthly river hydrometric levels will be obtained from the Department of Drainage and Irrigation. Data are then inputted as thematic layers and in the ArcGIS software for further analysis. The artificial neural network analysis in artificial intelligence Phyton software will then be used to obtain the leptospirosis hotspot predictive model. Results: This research was funded as of November 2022. Data collection, processing, and analysis commenced in December 2022, and the results of the study are expected to be published by the end of 2024. The leptospirosis distribution and clusters may be significantly associated with the hydroclimatic factors of rainfall, land surface temperature, and the river hydrometric level. Conclusions: This study will explore the associations of leptospirosis hotspot areas with the hydroclimatic factors in Selangor and subsequently the development of a leptospirosis predictive model. The constructed predictive model could potentially be used to design and enhance public health initiatives for disease prevention. International Registered Report Identifier (IRRID): PRR1-10.2196/43712

Plasmodium knowlesi is an emerging species for malaria in Malaysia, particularly in East Malaysia. This infection contributes to almost half of all malaria cases and deaths in Malaysia and poses a challenge in eradicating malaria. The aim of this study was to develop a predictive model for P. knowlesi susceptibility areas in Sabah, Malaysia, using geospatial data and artificial neural networks (ANNs). Weekly malaria cases from 2013 to 2014 were used to identify the malaria hotspot areas. The association of malaria cases with environmental factors (elevation, water bodies, and population density, and satellite images providing rainfall, land surface temperature, and normalized difference vegetation indices) were statistically determined. The significant environmental factors were used as input for the ANN analysis to predict malaria cases. Finally, the malaria susceptibility index and zones were mapped out. The results suggested integrating geospatial data and ANNs to predict malaria cases, with overall correlation coefficient of 0.70 and overall accuracy of 91.04%. From the malaria susceptibility index and zoning analyses, it was found that areas located along the Crocker Range of Sabah and the East part of Sabah were highly susceptible to P. knowlesi infections. Following this analysis, targetted entomological mapping and malaria control programs can be initiated.

Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences. Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences.

Introduction: The World Health Organization (WHO) has targeted eliminating viral hepatitis as a public health problem by 2030. Thus, high-risk groups such as Methadone Maintenance Therapy (MMT) clients should be targeted for hepatitis C health intervention to increase intention for hepatitis C infection treatment. This study aims to evaluate the effectiveness of theory-based hepatitis C health education and learning module (HEAL) in increasing intention to participate in hepatitis C treatment among MMT clients in Malacca. Methods: A single-blinded cluster randomised control trial was conducted among selected MMT clients from government health clinics in Malacca from July 2023 to February 2024. The clinics involved was randomly allocated into intervention and control groups. The intervention group received HEAL module developed based on the Theory of Planned Behavioural while the control group attended clinic session as usual. Generalized Estimating Equation (GEE) analysis was used for statistical analysis. Results: GEE shows that there is significant interaction between time and group for both intention for hepatitis C treatment and knowledge on hepatitis C. Intervention group at immediately post intervention follow up had higher intention for hepatitis C treatment as compared to control group (β = 0.56, 95% CI = 0.12, 1.01, p-value < 0.01). However, there were no significant different in intention for hepatitis C treatment between the two group at 3-month post intervention follow up. Furthermore, intervention group had significant higher knowledge at immediately post intervention (β = 8.85, 95% CI = 7.35, 10.36, p-value < 0.001) and at 3-month post intervention (β = 5.25, 95% CI = 3.76, 6.75, p-value < 0.001) as compared to the control group. However, the knowledge level among the intervention group reduces at 3-month post intervention follow up as compared to knowledge level at immediately post intervention follow up. Conclusion: HEAL module which utilizes motion video and group discussion was effective in increasing MMT client’s intention for hepatitis C treatment and knowledge about hepatitis C. However, future research should focus on finding effective strategies to ensure retention of the outcomes of HEAL module intervention over time.