International Postgraduate Research Conference (IPRC)
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Item 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 LankaItem Cost-Effectiveness of Three Approaches to Hysterectomy: A Randomized Controlled Trial.(In: Proceedings of the International Postgraduate Research Conference 2017 (IPRC – 2017), Faculty of Graduate Studies, University of Kelaniya, Sri Lanka., 2017) Ekanayake, C.D.; Pathmeswaran, A.; Pieris, R.; Wijesinghe, P.S.Hysterectomy is the commonest major gynaecological surgical procedure. There are many approaches in performing a hysterectomy which depend on clinical criteria. However certain patients are suitable to be operated through any approach. The aim of this study was to provide evidence on the optimal approach in terms of cost-effectiveness between non-descent vaginal hysterectomy (NDVH), total laparoscopic hysterectomy (TLH) and total abdominal hysterectomy (TAH).Item 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.Item Individual risk factors of snakebites in Sri Lankan community(Faculty of Graduate Studies, University of Kelaniya, 2015) Ediriweera, E.P.D.S.; Pathmeswaran, A.; Kasturiratne, A.; Gunawardana, N.K.; Jayamanne, S.F.; de Silva, H.J.; Diggle, P.J.Sri Lanka has 92 identified snake species, and one of the highest snakebite incidence (SBI) rates in the world. According to hospital statistics about 37,000 patients are admitted to government hospitals annually as a result of snakebite. The aim of the present study is to identify individual risk factors for snakebite in Sri Lanka. Methodology A community-based island-wide study (―National Snakebite Study‖) was conducted in all nine provinces of Sri Lanka, with 5,000 households sampled in each province. All the residents of the selected households were included. One-year recall data for all permanent residents of that particular household was obtained regarding the experience of snakebite. Generalized linear models were used to model SBI. Individual-level gender, age, ethnicity, religion, income, education and employment were included as explanatory variables. Statistical analysis used the R programming language. Statistical significance was assessed at the 0.01 level. Results and conclusions Out of 125,391 participants, 63,604 (50.7%) were males. There was no SBI difference amongst 10-year interval age groups from 30 to 59, hence these three age groups were collapsed. High SBI was observed in the age 30-59 year group compared to age less than 20 (P<0.001), 20 to 29 (P<0.001), 60 to 69 (P<0.003) and over 70 (P<0.007), with lower SBI in the two extreme age groups. Males had higher SBI compared to females (P<0.001). Field workers had higher SBI compared to non-field workers (P<0.001). Ethnicity and income showed a significant interaction. Low income non-Sinhalese had high SBI compared to middle income (Rs. 5000-19,999, P<0.001) and high income (Rs.>20,000, P=0.001) non- Sinhalese, whereas Sinhalese had high SBI irrespective of their income status. In summary, the high risk categories for snakebites are males, field workers, 30 – 60 year individuals, Sinhalese and low income non-Sinhalese.