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  1. Spatial Interaction Between Leptospirosis and Enteric Fever in Kelantan, Malaysia: A 2016-2022 Notification Registry Analysis
    Mohd Hatta H, Musa KI, Mohd Fuzi NMH, Moraga P
    Asia Pac J Public Health, 2024 Nov;36(8):738-745.
    PMID: 39344715 DOI: 10.1177/10105395241286118
    Leptospirosis and enteric fever are prevalent tropical acute bacterial febrile illnesses in Kelantan, Malaysia, that exhibit overlapping features and shared transmission dynamics, yet their spatial relationship remains understudied. This study aimed to analyze their spatial distribution, investigating potential interactions and intersecting patterns. A total of 212 laboratory-confirmed cases of enteric fever and 1106 of leptospirosis between 2016 and 2022, were retrieved from the national e-Notifikasi registry. Point pattern analysis revealed clustering of both diseases in the northern region, but leptospirosis was predominant in the south, exhibiting higher spatial risk. Seven co-infection cases were identified in overlapping hotspot areas. Spatial dependence between the diseases was identified within 4 km distance on average, with varying patterns over time and regions. Recognizing spatial dependence has implications for accurate diagnosis, timely intervention, and tailored public health strategies. The findings underscore the need for multi-disease interventions to address shared risk factors and co-infections in similar geographical contexts.
  2. Fulltext Leptospirosis and Workplace Environmental Risk Factors among Cattle Farmers in Northeastern Malaysia
    Daud AB, Mohd Fuzi NMH, Wan Mohammad WMZ, Amran F, Ismail N, Arshad MM, et al.
    Int J Occup Environ Med, 2018 04;9(2):88-96.
    PMID: 29667646 DOI: 10.15171/ijoem.2018.1164
    BACKGROUND: Leptospirosis is an emerging zoonosis and its occurrence has been reported to be rising globally. The environment plays an important role in the survival of Leptospira and determines the risk of infection. Those who were exposed to and had contact with contaminated environment through their occupational, recreational and other activities can be infected with the organism.

    OBJECTIVE: To determine the seroprevalence of leptospirosis among cattle farmers, prevalence of pathogenic Leptospira, and the workplace environmental risk factors for leptospirosis among cattle farmers in northeastern Malaysia.

    METHODS: A cross-sectional study involving 120 cattle farmers was conducted. The participants answered an interviewer-guided questionnaire that consisted of sociodemographic and workplace environment characteristics questionnaire, before having their blood sample taken for microscopic agglutination test (MAT). Seropositivity was determined using a cut-off titer of ≥1:100. 248 environmental samples were also collected from the cattle farms for polymerase chain reaction (PCR).

    RESULTS: The overall seroprevalence of leptospiral antibodies was 72.5% (95% CI 63.5% to 80.1%) and the prevalence of pathogenic Leptospira in the cattle farms environment was 12.1% (95% CI 8.4% to 17.0%). The independent factors associated with seropositivity of leptospirosis among cattle farmers were positive pathogenic Leptospira in the environment (Adj OR 5.90, 95% CI 1.34 to 26.01) and presence of garbage dumping in the farm (Adj OR 2.40, 95% CI 1.02 to 5.65).

    CONCLUSION: Preventing leptospirosis incidence among cattle farmers necessitates changes in work environment. Identifying modifiable factors may also contribute to the reduction of infection.

  3. Leptospirosis modelling using hydrometeorological indices and random forest machine learning
    Jayaramu V, Zulkafli Z, De Stercke S, Buytaert W, Rahmat F, Abdul Rahman RZ, et al.
    Int J Biometeorol, 2023 Mar;67(3):423-437.
    PMID: 36719482 DOI: 10.1007/s00484-022-02422-y
    Leptospirosis is a zoonosis that has been linked to hydrometeorological variability. Hydrometeorological averages and extremes have been used before as drivers in the statistical prediction of disease. However, their importance and predictive capacity are still little known. In this study, the use of a random forest classifier was explored to analyze the relative importance of hydrometeorological indices in developing the leptospirosis model and to evaluate the performance of models based on the type of indices used, using case data from three districts in Kelantan, Malaysia, that experience annual monsoonal rainfall and flooding. First, hydrometeorological data including rainfall, streamflow, water level, relative humidity, and temperature were transformed into 164 weekly average and extreme indices in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). Then, weekly case occurrences were classified into binary classes "high" and "low" based on an average threshold. Seventeen models based on "average," "extreme," and "mixed" indices were trained by optimizing the feature subsets based on the model computed mean decrease Gini (MDG) scores. The variable importance was assessed through cross-correlation analysis and the MDG score. The average and extreme models showed similar prediction accuracy ranges (61.5-76.1% and 72.3-77.0%) while the mixed models showed an improvement (71.7-82.6% prediction accuracy). An extreme model was the most sensitive while an average model was the most specific. The time lag associated with the driving indices agreed with the seasonality of the monsoon. The rainfall variable (extreme) was the most important in classifying the leptospirosis occurrence while streamflow was the least important despite showing higher correlations with leptospirosis.
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