A comparison of changes in venous lactate and haematocrit during fluid resuscitation of dengue haemorrhagic fever.

BACKGROUND: Judicious fluid resuscitation and stringent monitoring of clinical parameters improve the outcome of dengue haemorrhagic fever (DHF). The usefulness of serum lactate to monitor adequate fluid therapy has not been adequately explored. METHODS: An observational study was conducted in Sri Lanka, recruiting 162 DHF patients within 12 h of diagnosis of the critical phase. Venous lactate level was measured at each time of performing haematocrit (HCT), using a prevalidated handheld lactate analyser. RESULTS: The median venous lactate level was 1.3 (range 0.3-6) mmol/L in the study population and 154 (95.2%) patients had median lactate levels of <2 mmol/L. The HCT values in the study participants ranged from 28 to 62, with a median value of 43. There was no statistically significant correlation between the lactate and HCT values obtained at the same time. A significant reduction in venous lactate was not observed following the administration of fluid boluses. The expected reduction in HCT was seen following the administration of dextran and crystalloid/dextran combination. The maximum recorded lactate level positively correlated with the duration of hospital stay. CONCLUSIONS: This study concludes that venous lactate is not an appropriate parameter with which to monitor the response to fluid therapy in uncomplicated DHF.

Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS).

Dengue is one of the most important vector-borne infection in Sri Lanka currently leading to vast economic and social burden. Neither a vaccine nor drug is still not being practiced, vector controlling is the best approach to control disease transmission in the country. Therefore, early warning systems are imminent requirement. The aim of the study was to develop Geographic Information System (GIS)-based multivariate analysis model to detect risk hotspots of dengue in the Gampaha District, Sri Lanka to control diseases transmission. A risk model and spatial Poisson point process model were developed using separate layers for patient incidence locations, positive breeding containers, roads, total buildings, public places, land use maps and elevation in four high risk areas in the district. Spatial correlations of each study layer with patient incidences was identified using Kernel density and Euclidean distance functions with minimum allowed distance parameter. Output files of risk model indicate that high risk localities are in close proximity to roads and coincide with vegetation coverage while the Poisson model highlighted the proximity of high intensity localities to public places and possibility of artificial reservoirs of dengue. The latter model further indicate that clustering of dengue cases in a radius of approximately 150 m in high risk areas indicating areas need intensive attention in future vector surveillances.

Entomological surveillance with viral tracking demonstrates a migrated viral strain caused dengue epidemic in July, 2017 in Sri Lanka.

Dengue is the most important mosquito-borne viral infection disease in Sri Lanka triggering extensive economic and social burden in the country. Even after numerous source reduction programmes, more than 30,000 incidences are reporting in the country every year. The last and greatest dengue epidemic in the country was reported in July, 2017 with more than 300 dengue related deaths and the highest number of dengue incidences were reported from the District of Gampaha. There is no Dengue Virus (DENV) detection system in field specimens in the district yet and therefore the aim of the study is development of entomological surveillance approach through vector survey programmes together with molecular and phylogenetic methods to identify detection of DENV serotypes circulation in order to minimize adverse effects of imminent dengue outbreaks. Entomological surveys were conducted in five study areas in the district for 36 months and altogether, 10,616 potential breeding places were investigated and 423 were positive for immature stages of dengue vector mosquitoes. During adult collections, 2,718 dengue vector mosquitoes were collected and 4.6% (n = 124) were Aedes aegypti. While entomological indices demonstrate various correlations with meteorological variables and reported dengue incidences, the mosquito pools collected during the epidemic in 2017 were positive for DENV. The results of the phylogenetic analysis illustrated that Envelope (E) gene sequences derived from the isolated DENV belongs to the Clade Ib of Cosmopolitan genotype of the DENV serotype 2 which has been the dominant stain in South-East Asian evidencing that a recent migration of DENV strain to Sri Lanka.

Use of Novaluron-Based Autocidal Gravid Ovitraps to Control Aedes Dengue Vector Mosquitoes in the District of Gampaha, Sri Lanka.

Dengue is the most important mosquito-borne viral infection in Sri Lanka causing an enormous social and economic burden in the country. In the absence of therapeutic drugs and the developed vaccines are under investigation, vector control is the best strategy to reduce the disease transmission. Therefore, the development of novel tools to control dengue vector mosquitoes has become the need of the hour. Novaluron is a recently developed Insect Growth Regulator (IGR) which inhibits chitin synthesis in immature stages of insects. The aim of the study was to identify the efficacy of a simple and cost-effective Autocidal Gravid Ovitrap (AGO) developed using Novaluron to control dengue outbreaks in the District of Gampaha, Sri Lanka. Laboratory and semifield experiments were performed to identify the activity range, optimum field dosage, and residual effects of Novaluron following the World Health Organization guidelines, and field experiments were performed in the Ragama Medical Officer of Health (MOH) area. Two study areas 800 m apart were selected and assigned as treated and control areas randomly. In each study area, 30 households were selected randomly. Each household was given two ovitraps, one placed indoors and the other placed outdoors. Mortality and survival counts were recorded separately for one-year time period and data were analyzed using a two-way repeated measures analysis of variance model. During the laboratory experiments, the adult emerging inhibition was 100% in all tested concentrations. The optimum field dosage was 2 ppm and the residual effect was 28 days. In the field experiments, significantly higher mortality counts were recorded in treated areas both indoor- and outdoor-placed AGOs. Two-factor repeated measures ANOVA followed by Tukey's test confirmed that the mean mortality count is high for the developed AGOs both indoor and outdoor settings. The developed AGO can be deployed to control both indoor and outdoor dengue vector mosquito populations, and in dengue-risk areas, the ovitrap will be supportive to local health authorities to enhance the efficiency of future vector control programs.

A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka.

BACKGROUND: Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike's information criterion, Bayesian information criterion and residual analysis. RESULTS: The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity. CONCLUSIONS: The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month's dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.

A pilot model for predicting the success of prehospital endotracheal intubation.

OBJECTIVES: We sought to evaluate the success of prehospital, non-drug-assisted endotracheal intubation (ETI) performed by Virginia prehospital care providers and to develop a model designed to predict the probability of success of ETI. METHODS: We conducted a retrospective observational study on prehospital, non-drug-assisted ETI (N=4002) performed by Virginia prehospital care providers, from January 1, 2012, to December 31, 2012. Using descriptive statistics, we quantified patient, provider, and system characteristics. Success rates were calculated by provider certification level and number of ETI attempts. Procedure complications were evaluated for the entire cohort. Variables were recoded for modeling purposes. Univariate analyses using χ2 tests were performed to identify candidate parameters to be included in the model. We performed a backward stepwise logistic regression to predict ETI success. RESULTS: An overall success rate of 69.9% was found. Binary logistic regression revealed the following covariates associated with ETI success: community type, provider certification level, gender, age group, myocardial infarction, and ethnicity which were all significant (P<0.05) with a -2 log-likelihood value of 3705.574. This was the most parsimonious model evaluated and demonstrated good fit (Hosmer-Lemeshow test P=.646) but poor discrimination (area under the receiver operating characteristic curve=0.595). CONCLUSION: This study characterized prehospital ETI success using retrospective state data and found a low overall success rate. Binary logistic regression was performed to create a model and equation identifying a set of factors associated with ETI success.