Browsing by Author "Diggle, P."

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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.
Envenoming Snakebite Risk Map for Sri Lanka
(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Ediriweera, D.; Kasturiratne, A.; Pathmeswaran, A.; Pathmeswaran, A.; Gunawardena, N.; Jayamanne, S.; Wijayawickrama, B.; Isbister, G.; Giorgi, A.D.E.; Diggle, P.; Lalloo, D.; de Silva, J.
Snakebite is a neglected tropical disease. Hospital based statistics often underestimate snakebite incidence because a significant proportion of victims seek traditional treatments. Since geospatial risk assessments of snakebite envenoming are rare, health care resources are distributed based on administrative boundaries rather than on a need analysis. The aim of the study was to develop a snakebite envenoming risk map for Sri Lanka. Epidemiological data was obtained from a community-based island-wide survey. The sample was distributed equally among the nine provinces. 165,665 participants living in 1118 Grama Niladhari divisions were surveyed. Model-based geostatistics was used to determine the geographical distribution of envenoming bite incidence. The Monte Carlo maximum likelihood method was used to obtain parameter estimates and plug-in spatial predictions of risk. A predictive model was developed with natural and social environmental variables to construct an estimated envenoming bite incidence map and a probability contour map (PCM) to demonstrate the spatial variation in the predictive probability that local incidence does or does not exceed national envenoming snakebite incidence (i.e. 151 per 100,000). Envenoming bite incidence had a positive association with elevation up to 195 meters above sea level, with incidence dropping at higher elevations. The incidence of envenoming was higher in the dry zone compared to intermediate and wet climatic zones and decreased with increasing population density. Developed risk maps showed substantial within-country spatial variation in envenoming bites. Conclusion: The risk maps provide useful information for healthcare decision makers to allocate resources to manage snakebite envenoming in Sri Lanka. We used replicable methods which can be adapted to other geographic regions after re-estimating spatial covariance parameters for each region of interest.
Evaluating Spatiotemporal Dynamics of Snakebite in Sri Lanka
(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Ediriweera, D.; Diggle, P.; Kasturiratne, A.; Pathmeswaran, A.; Gunawardena, N.; Jayamanne, S.; Lalloo, D.; de Silva, J.
Snakebite data has shown spatial and temporal variations in many countries and regions. Yet, no study has evaluated spatiotemporal patterns of snakebites across a country in detail. We used data from the National Snakebite Survey (NSS), which sampled 0.8% of the national population (165665 people) living in 1118 clusters representing all the provinces. Explanatory variables of previously published spatial and temporal models for the NSS data were considered as candidate explanatory variables for our spatiotemporal models. Spatial prediction models for snakebite incidence was a geostatistical binomial logistic model and the temporal prediction model was a Poisson log-linear model, which predicted snakebite incidence at the national level. These spatial and temporal models could not explain locally varying temporal patterns in the country. Therefore, we constructed spatiotemporal models at the provincial levels. The NSS was conducted for 11 consecutive months, and different clusters were surveyed in each month. Therefore, the NSS can be considered as a set of 11 repeated cross-sectional surveys at different locations. NSS captured bite events that occurred in the survey month and in the 12 preceding months. Hence, each individual provided information regarding the number of bites experienced in each of 13 months. In the NSS data, the location of each sampled individual was fixed at the cluster centroid and the data contain the month of each recorded bite, if any, over a 13 month period covering the survey month and each of the preceding 12 months. We modelled the data from each cluster as an inhomogenous Poisson process with cluster-level explanatory variables and estimated the model parameters by maximising the pooled log-likelihood over all. The fitted cluster-level spatiotemporal models were aggregated so as to predict the province-level monthly bite incidence rates in Sri Lanka. Snakebite incidence showed complex spatiotemporal patterns in Sri Lanka. Models fitted for Southern, North Central, Uva and Sabaragamuwa provinces showed both spatial and temporal variation in snakebites. The geographical extent of the high-risk areas (i.e. hotspots) in these provinces dynamically changed over a period of a year. The remaining five models (i.e. Western, Central, North Western, Northern and Eastern) did not show any spatio-temporal interaction, in risk, i.e. the geographical extent of the hotspots persisted throughout the year. Southern, Sabaragamuwa and North Central provinces showed triannual seasonal trends. High snakebite incidences in Southern and Sabaragamuwa provinces were noticed in April followed by December and August to September. Peak incidences in North Central province were seen in November and another two smaller peaks were observed in April and July. Uva province showed a biannual trend with highest incidences in June followed by December. These findings can inform healthcare decision-making at local level, taking account of the seasonal variations in order to prevent and manage snakebites in Sri Lanka
Geographically regulated designs of incidence surveys can match the precision of classical survey designs whilst requiring smaller sample sizes: the case of snakebite envenoming in Sri Lanka
(BMJ Publishing Group Ltd, 2022) Ediriweera, D.S.; de Silva, T.; Kasturiratne, A.; de Silva, H.J.; Diggle, P.
BACKGROUND: Snakebite envenoming is a neglected tropical disease. Data from the worst affected countries are limited because conducting epidemiological surveys is challenging. We assessed the utility of inhibitory geostatistical design with close pairs (ICP) to estimate snakebite envenoming incidence. METHODS: The National Snakebite Survey (NSS) in Sri Lanka adopted a multistage cluster sampling design, based on population distribution, targeting 1% of the country's population. Using a simulation-based study, we assessed predictive efficiency of ICP against a classical survey design at different fractions of the original sample size of the NSS. We also assessed travel distance, time taken to complete the survey, and sensitivity and specificity for detecting high-risk areas for snake envenoming, when using these methods. RESULTS: A classical survey design with 33% of the original NSS sample size was able to yield a similar predictive efficiency. ICP yielded the same at 25% of the NSS sample size, a 25% reduction in sample size compared with a classical survey design. ICP showed >80% sensitivity and specificity for detecting high-risk areas of envenoming when the sampling fraction was >20%. When ICP was adopted with 25% of the original NSS sample size, travel distance was reduced by >40% and time to conduct the survey was reduced by >75%. CONCLUSIONS: This study showed that snakebite envenoming incidence can be estimated by adopting an ICP design with similar precision at a lower sample size than a classical design. This would substantially save resources and time taken to conduct epidemiological surveys and may be suited for low-resource settings.
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.