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 Drug related problems among patients with diabetes; a descriptive analysis of data from an urban hospital in Sri Lanka(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Mamunuwa, N.; Jayamanne, S.; Coombes, J.; de Silva, A.; Lynch, C.; Wickramasinghe, D.Drug related problems (DRPs) result in reduced quality of care and even morbidity and mortality. The aim of this study is to assess the frequency and nature of DRPs and their causes among patients with diabetes attending an outpatient clinic. The prospective study was conducted in medical clinics of Colombo North teaching hospital and included 400 outpatients with diabetes. The identified DRPs were classified according to Pharmaceutical Care Network Europe tool (PCNE V6.2). A total of 151 DRPs were detected. The highest number of DRPs (61.58%) related to treatment effectiveness while 21.19% related to treatment costs, 9.93% related to adverse effects and 7.28% related to other non-classified problems. The most common DRP identified was ‘effect of drug treatment not optimal’ (39.73%) followed by ‘unnecessary drug treatment’ (16.55%) and ‘untreated indication’ (12.58%). Half (50.33%) of the DRPs detected were caused by the way patients use the medicines, in spite of proper prescribing and instructions. This included ‘deliberate under-use of the drug’ (61.84%), ‘drug not taken at all’ (15.78%), ‘inability to use the drug as directed’ (9.21%) and ‘drug overuse’ (6.57%). 31.12% of the DRPs were related to selection of drugs including ‘inappropriate drug’ (40.42%), ‘drug required not given’ (23.4%) and ‘duplications’ (21.27%). DRPs are frequent among diabetes outpatients. Early detection and addressing the causes of the actual and potential DRPs may improve the quality use of medicines and ensure safe, appropriate and cost-effective out-patient care.Item A clinic-based pharmacy counselling service to improve medication adherence among diabetes out-patients(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Mamunuwa, N.; Jayamanne, S.; Coombes, J.; de Silva, A.; Lynch, C.; Wickramasinghe, D.The burden of diabetes is increasing with the rising prevalence of the disease and its complications. Medication adherence is a significant factor in the management of diabetes. Pharmacists’ role in the improvement of medication adherence is well-studied in the world. Despite the high and rising prevalence of diabetes in Sri Lanka, this is the first study to evaluate a pharmacy counselling service in a Sri Lankan diabetes population. To assess how a clinic-based pharmacy counselling service may affect patient medication adherence. 400 consecutive patients with diabetes mellitus attending outpatient medical clinics at Colombo North Teaching Hospital were randomized into either intervention group (IG) or control group (CG). Patients in the IG received pharmacist counselling (verbal and written) for four consecutive monthly visits in addition to standard care at the clinic, while patients in the CG received standard care only. Adherence for both groups was measured at baseline and post intervention using ©Morisky Medication Adherence Scale (8-Items). Mean age of the participants was 61.79 ± 9.06 and 67% were female. The IG had a median score of 4 out of 8 (IQR 5-3) at baseline which increased to 7 (IQR 8-6) after intervention. The median score of the CG was not significantly changed; 5 (IQR 7-4) at baseline and 5 (IQR 7- 6.5) after intervention period. Patients in the IG had a statistically significant improvement in adherence compared to the CG, using the Mann-Whitney U test (P<0.005). The IG had a 74.15% improvement in adherence whereas the CG had an improvement of 1.78%. Pharmacist counselling in outpatient clinics can improve medication adherence of the patients with diabetes.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.