Epidemiological study of snakebite cases in Sikkim: Risk modeling with regard to the habitat suitability of common venomous snakes

Abstract

Background Snakebite envenoming is listed as category ‘A’ Neglected Tropical Disease. To achieve the target of WHO (World Health Organization) 2019, it becomes necessary to understand various attributes associated with snakebite including community awareness, improvisation of medical facilities and to map the potential distribution of venomous snakes responsible for the bite. Hence this study is conducted in Sikkim, India to understand the epidemiology of snakebite in Sikkim. The potential distribution and risk mapping of five common venomous snakes are done for effective management of snakebite cases. Methods and findings The snakebite cases registered in six district hospitals and four PHCs (Primary Health Centers) of Sikkim were collected from the year 2011 to 2018. Community survey was also conducted to supplement the data. Ecological Niche Modeling (ENM) was performed to predict the potential habitat of five common venomous snakes of Sikkim. The risk modeling of snakebite cases was done at the level of Gram Panchayat Unit (GPU) using Geographically Weighted Regression (GWR) and Ordinary Linear Square (OLS) model. We found higher number of male victims inflicted with snakebite envenomation. The potential distribution of the five venomous snakes showed satisfactory mean AUC (Area under Curve) value. Both the models showed significant positive association of snakebite cases with habitat suitability of the venomous snakes. Hospital data revealed no death cases whereas community data reported 24 deaths. Conclusions Death from snakebite reflected in community data but not in hospital data strongly indicates the people’s belief in traditional medicine. Though people of Sikkim have rich traditional knowledge, in case of snakebite traditional practices may be ineffective leading to loss of life. Sensitizing people and improving medical facilities along with proper transport facilities in rural areas might significantly reduce the snakebite casualties in the state.

Snakebite epidemiology, outcomes and multi-cluster risk modelling in Eswatini

“…The seasonal distribution of snakebites followed weather patterns in Eswatini, with October to March being the 'wet' season characterised by rainfall and warmer temperatures, similar to other countries [6,7,22,26,[81][82][83][84]. As shown in other studies [82], patients were mostly bitten at low geographical elevations where, relative to the rest of the country, temperatures are warmer.…”

“…Patients aged 10-19 years, and males had a greater relative risk of being bitten than other patients, supporting snakebite data from other countries [7,9,22,26,[81][82][83][84]87,88]. Targeted public health prevention interventions to these populations through schools, community structures, peer educators should be prioritised, using traditional and social media [31,89].…”

“…In addition, susceptibility to snakebite is increased in situations where individuals live in houses whose construction does not prevent snake ingress and that have limited lighting and sanitation facilities [25] or where people need to collect firewood [8], factors that are underpinned by poverty. Understanding and modelling risk of snakebite may be one strategy to reduce the incidence and improve treatment outcomes of snakebite [26][27][28][29]. These studies have used snakebite incidence data, habitat suitability of common venomous snakes, land use land cover, geographic and socioeconomic status of snakebite patients, and/or venomous species distribution and occurrence data to geo-spatially model the risk of snakebite within a particular region of a country.…”

Conducting epidemiological studies on snakebite in nomadic populations: A methodological paper

“…Our search words for surveys on snakebite included "snakebite*" or "snake envenoming*" or "snake bites", with alternating combinations of MeSH and free-text words, without any geographic or language restrictions, and we considered surveys or cross-sectional studies published between 2012 and 2022. The search returned 184 articles, of which 18 [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] were community-based cross-sectional surveys and were included in our review. Detailed search strategies and the results of both reviews are provided as supplementary material (S1 and S2 Tables , and S1 and S2 Figs).…”

Tree stand assessment before and after windthrow based on open-access biodiversity data and aerial photography

“…The Global Biodiversity Information Facility (GBIF) is the most extensive international open data infrastructure with free and open access to biodiversity data (Heberling et al, 2021), aggregated more than 2 billion species occurrence records worldwide. GBIF open-access data are widely used for the assessment of the effect of global climate changes on species distribution (Dyderski et al, 2018;Ribeiro et al, 2018;Gallagher et al, 2019), for modelling the factors that contribute to the dispersal of invasive species (Jarnevich et al, 2018;Mally et al, 2021), and the risks of distribution of dangerous diseases (Cardador & Blackburn, 2019;Rai et al, 2021) and other macroregional studies. For local studies, GBIF data is rarely used but could be a crucial source for several tasks.…”