Browsing by Author "Iwamura, T."

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Adjusting for spatial variation when assessing individual-level risk: A case-study in the epidemiology of snake-bite in Sri Lanka
(Public Library of Science, 2019) Ediriweera, D.S.; Kasturiratne, A.; Pathmeswaran, A.; Gunawardena, N.K.; Jayamanne, S.F.; Murray, K.; Iwamura, T.; Lalloo, D.G.; de Silva, H.J.; Diggle, P.J.
BACKGROUND:Health outcomes and causality are usually assessed with individual level sociodemographic variables. Studies that consider only individual-level variables can suffer from residual confounding. This can result in individual variables that are unrelated to risk behaving as proxies for uncaptured information. There is a scarcity of literature on risk factors for snakebite. In this study, we evaluate the individual-level risk factors of snakebite in Sri Lanka and highlight the impact of spatial confounding on determining the individual-level risk effects.METHODS:Data was obtained from the National Snakebite Survey of Sri Lanka. This was an Island-wide community-based survey. The survey sampled 165,665 individuals from all 25 districts of the country. We used generalized linear models to identify individual-level factors that contribute to an individual's risk of experiencing a snakebite event. We fitted separate models to assess risk factors with and without considering spatial variation in snakebite incidence in the country.RESULTS:Both spatially adjusted and non-adjusted models revealed that middle-aged people, males, field workers and individuals with low level of education have high risk of snakebites. The model without spatial adjustment showed an interaction between ethnicity and income levels. When the model included a spatial adjustment for the overall snakebite incidence, this interaction disappeared and income level appeared as an independent risk factor. Both models showed similar effect sizes for gender and age. HEmployment and education showed lower effect sizes in the spatially adjusted model.CONCLUSIONS:Both individual-level characteristics and local snakebite incidence are important to determine snakebite risk at a given location. Individual level variables could act as proxies for underling residual spatial variation when environmental information is not considered. This can lead to misinterpretation of risk factors and biased estimates of effect sizes. Both individual-level and environmental variables are important in assessing causality in epidemiological studies.
Climate change maladaptation for health: Agricultural practice against shifting seasonal rainfall affects snakebite risk for farmers in the tropics
(Cell Press, 2023) Goldstein, E.; Erinjery, J.J.; Martin, G.; Kasturiratne, A.; Ediriweera, D.S.; Somaweera, R.; de Silva, H.J.; Diggle, P.; Lalloo, D.G.; Murray, K.A.; Iwamura, T.
Snakebite affects more than 1.8 million people annually. Factors explaining snakebite variability include farmers' behaviors, snake ecology and climate. One unstudied issue is how farmers' adaptation to novel climates affect their health. Here we examined potential impacts of adaptation on snakebite using individual-based simulations, focusing on strategies meant to counteract major crop yield decline because of changing rainfall in Sri Lanka. For rubber cropping, adaptation led to a 33% increase in snakebite incidence per farmer work hour because of work during risky months, but a 17% decrease in total annual snakebites because of decreased labor in plantations overall. Rice farming adaptation decreased snakebites by 16%, because of shifting labor towards safer months, whereas tea adaptation led to a general increase. These results indicate that adaptation could have both a positive and negative effect, potentially intensified by ENSO. Our research highlights the need for assessing adaptation strategies for potential health maladaptations.
Evaluating spatiotemporal dynamics of snakebite in Sri Lanka: Monthly incidence mapping from a national representative survey sample
(Public Library of Science, 2021) Ediriweera, D.S.; Kasturiratne, A.; Pathmeswaran, A.; Gunawardena, N.K.; Jayamanne, S.F.; Murray, K.; Iwamura, T.; Isbister, G.; Dawson, A.; Lalloo, D.G.; de Silva, H.J.; Diggle, P.J.
BACKGROUND: Snakebite incidence shows both spatial and temporal variation. However, no study has evaluated spatiotemporal patterns of snakebites across a country or region in detail. We used a nationally representative population sample to evaluate spatiotemporal patterns of snakebite in Sri Lanka. METHODOLOGY: We conducted a community-based cross-sectional survey representing all nine provinces of Sri Lanka. We interviewed 165 665 people (0.8% of the national population), and snakebite events reported by the respondents were recorded. Sri Lanka is an agricultural country; its central, southern and western parts receive rain mainly from Southwest monsoon (May to September) and northern and eastern parts receive rain mainly from Northeast monsoon (November to February). We developed spatiotemporal models using multivariate Poisson process modelling to explain monthly snakebite and envenoming incidences in the country. These models were developed at the provincial level to explain local spatiotemporal patterns. PRINCIPAL FINDINGS: Snakebites and envenomings showed clear spatiotemporal patterns. Snakebite hotspots were found in North-Central, North-West, South-West and Eastern Sri Lanka. They exhibited biannual seasonal patterns except in South-Western inlands, which showed triannual seasonality. Envenoming hotspots were confined to North-Central, East and South-West parts of the country. Hotspots in North-Central regions showed triannual seasonal patterns and South-West regions had annual patterns. Hotspots remained persistent throughout the year in Eastern regions. The overall monthly snakebite and envenoming incidences in Sri Lanka were 39 (95%CI: 38-40) and 19 (95%CI: 13-30) per 100 000, respectively, translating into 110 000 (95%CI: 107 500-112 500) snakebites and 45 000 (95%CI: 32 000-73 000) envenomings in a calendar year. CONCLUSIONS/SIGNIFICANCE: This study provides information on community-based monthly incidence of snakebites and envenomings over the whole country. Thus, it provides useful insights into healthcare decision-making, such as, prioritizing locations to establish specialized centres for snakebite management and allocating resources based on risk assessments which take into account both location and season.
Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes
(Public Library of Science, 2021) Goldstein, E.; Erinjery, J.J.; Martin, G.; Kasturiratne, A.; Ediriweera, D.S.; de Silva, H.J.; Diggle, P.; Lalloo, D.G.; Murray, K.A.; Iwamura, T.
ABSTRACT: Snakebite causes more than 1.8 million envenoming cases annually and is a major cause of death in the tropics especially for poor farmers. While both social and ecological factors influence the chance encounter between snakes and people, the spatio-temporal processes underlying snakebites remain poorly explored. Previous research has heavily focused on statistical correlates between snakebites and ecological, sociological, or environmental factors, but the human and snake behavioral patterns that drive the spatio-temporal process have not yet been integrated into a single model. Here we use a bottom-up simulation approach using agent-based modelling (ABM) parameterized with datasets from Sri Lanka, a snakebite hotspot, to characterise the mechanisms of snakebite and identify risk factors. Spatio-temporal dynamics of snakebite risks are examined through the model incorporating six snake species and three farmer types (rice, tea, and rubber). We find that snakebites are mainly climatically driven, but the risks also depend on farmer types due to working schedules as well as species present in landscapes. Snake species are differentiated by both distribution and by habitat preference, and farmers are differentiated by working patterns that are climatically driven, and the combination of these factors leads to unique encounter rates for different landcover types as well as locations. Validation using epidemiological studies demonstrated that our model can explain observed patterns, including temporal patterns, and relative contribution of bites by each snake specie. Our predictions can be used to generate hypotheses and inform future studies and decision makers. Additionally, our model is transferable to other locations with high snakebite burden as well.
Integrating snake distribution, abundance and expert-derived behavioural traits predicts snakebite risk
(Wiley-Blackwell, 2022) Martín, G.; Erinjery, J.; Gumbs, R.; Somaweera, R.; Ediriweera, D.; Diggle, P.J.; Kasturiratne, A.; de Silva, H.J.; Lalloo, D.G.; Iwamura, T.; Murray, K. A.
Despite important implications for human health, distribution, abundance and behaviour of most medically-relevant snakes remain poorly understood. Such data deficiencies hamper efforts to characterise the causal pathways of snakebite envenoming and to prioritise management options in the areas at greatest risk. We estimated the spatial patterns of abundance of seven medically-relevant snake species from Sri Lanka, a snakebite hotspot, and combined them with indices of species’ relative abundance, aggressiveness and envenoming severity obtained from an expert opinion survey to test whether these fundamental ecological traits could explain spatial patterns of snakebite and envenoming incidence. The spatial intensity of snake occurrence records in relation to independent environmental factors (fundamental niches and land cover) was analysed with point process models. Then, with the estimated patterns of abundance, we tested which species’ abundances added together, with and without weightings for aggressiveness, envenoming severity and relative abundance, best correlate with per-capita geographic incidence patterns of snakebite and envenoming. We found that weighting abundance patterns by species’ traits increased correlation with incidence. The best performing combination had three species weighted by aggressiveness and abundance, with a correlation of r = 0.47 (P < 0.01) with snakebite incidence. An envenoming severity and relative abundance-weighted combination of two species was the most strongly associated with envenoming incidence (r = 0.46, P = 0). SYNTHESIS AND APPLICATIONS. We show that snakebite risk is explained by abundance, aggressiveness and envenoming severity of the snake species most frequently involved in envenoming cases. Incorporating causality via ecological information of key snake species is critical for snakebite risk mapping, help to tailor preventive measures for dominant snake species and deploy the necessary antivenom therapies.
A Mechanistic model of snakebite as a zoonosis: Envenoming incidence is driven by snake ecology, socioeconomics and its impacts on snakes
(Public Library of Science,San Francisco, 2022) Martín, G.; Erinjery, J.J.; Ediriweera, D.; de Silva, H.J.; Lalloo, D.G.; Iwamura, T.; Murray, K.A.
Snakebite is the only WHO-listed, not infectious neglected tropical disease (NTD), although its eco-epidemiology is similar to that of zoonotic infections: envenoming occurs after a vertebrate host contacts a human. Accordingly, snakebite risk represents the interaction between snake and human factors, but their quantification has been limited by data availability. Models of infectious disease transmission are instrumental for the mitigation of NTDs and zoonoses. Here, we represented snake-human interactions with disease transmission models to approximate geospatial estimates of snakebite incidence in Sri Lanka, a global hotspot. Snakebites and envenomings are described by the product of snake and human abundance, mirroring directly transmitted zoonoses. We found that human-snake contact rates vary according to land cover (surrogate of occupation and socioeconomic status), the impacts of humans and climate on snake abundance, and by snake species. Our findings show that modelling snakebite as zoonosis provides a mechanistic eco-epidemiological basis to understand snakebites, and the possible implications of global environmental and demographic change for the burden of snakebite.