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In a 'blind' trial on 50 male asymptomatic microfilaraemic subjects with Wuchereria bancrofti infection, the safety, tolerability and filaricidal efficacy of a single dose of albendazole (alb) 600 mg alone or in combination with ivermectin (iver) 400 micrograms/kg or diethylcarbamazine citrate (DEC) 6 mg/kg was compared with a single dose of the combination DEC 6 mg/kg and iver 400 micrograms/kg over a period of 15 months after treatment. All but one subject, with 67 microfilariae (mf)/mL, had pre-treatment counts > 100 mf/mL. All 4 treatments significantly reduced mf counts, but alb/iver was the most effective regimen for clearing mf from night blood: 9 of 13 subjects (69%) were amicrofilaraemic by membrane filtration 15 months after treatment compared to one of 12 (8%), 3 of 11 (27%), and 3 of 10 (30%) in the groups treated with alb, alb/DEC, and DEC/iver, respectively. Filarial antigen tests suggested that all 4 treatments had significant activity against adult W. bancrofti; alb/DEC had the greatest activity according to this test, with antigen levels decreasing by 77% 15 months after therapy. All 4 regimens were well tolerated and clinically safe, although mild, self-limited systemic reactions were observed in all treatment groups. These results suggest that alb/iver is a safe and effective single dose regimen for suppression of microfilaraemia in bancroftian filariasis that could be considered for control programmes. Additional benefits of this combination are its potent, broad spectrum activity against intestinal helminths and potential relative safety in areas of Africa where DEC cannot be used for filariasis control because of co-endemicity with onchocerciasis or loiasis.

BackgroundCurrent critical care prognostic models are predominantly developed in high-income countries (HICs) and may not be feasible in intensive care units (ICUs) in lower- and middle-income countries (LMICs). Existing prognostic models cannot be applied without validation in LMICs as the different disease profiles, resource availability, and heterogeneity of the population may limit the transferability of such scores. A major shortcoming in using such models in LMICs is the unavailability of required measurements. This study proposes a simplified critical care prognostic model for use at the time of ICU admission.MethodsThis was a prospective study of 3855 patients admitted to 21 ICUs from Bangladesh, India, Nepal, and Sri Lanka who were aged 16 years and over and followed to ICU discharge. Variables captured included patient age, admission characteristics, clinical assessments, laboratory investigations, and treatment measures.Multivariate logistic regression was used to develop three models for ICU mortality prediction: model 1 with clinical, laboratory, and treatment variables; model 2 with clinical and laboratory variables; and model 3, a purely clinical model.Internal validation based on bootstrapping (1000 samples) was used to calculate discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer-Lemeshow C-Statistic; higher values indicate poorer calibration). Comparison was made with the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II models.ResultsModel 1 recorded the respiratory rate, systolic blood pressure, Glasgow Coma Scale (GCS), blood urea, haemoglobin, mechanical ventilation, and vasopressor use on ICU admission. Model 2, named TropICS (Tropical Intensive Care Score), included emergency surgery, respiratory rate, systolic blood pressure, GCS, blood urea, and haemoglobin. Model 3 included respiratory rate, emergency surgery, and GCS. AUC was 0.818 (95% confidence interval (CI) 0.800–0.835) for model 1, 0.767 (0.741–0.792) for TropICS, and 0.725 (0.688–0.762) for model 3. The Hosmer-Lemeshow C-Statistic p values were less than 0.05 for models 1 and 3 and 0.18 for TropICS. In comparison, when APACHE II and SAPS II were applied to the same dataset, AUC was 0.707 (0.688–0.726) and 0.714 (0.695–0.732) and the C-Statistic was 124.84 (p < 0.001) and 1692.14 (p < 0.001), respectively.ConclusionThis paper proposes TropICS as the first multinational critical care prognostic model developed in a non-HIC setting.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-017-1843-6) contains supplementary material, which is available to authorized users.

IntroductionObesity is an increasing problem in South Asian countries and Sri Lanka is no exception. The socioeconomic determinants of obesity in Sri Lanka, and in neighbouring countries are inadequately described. Aim was to describe social, cultural and economic determinants of obesity in a representative sample from Kalutara District in Sri Lanka.MethodsThis was a cross sectional descriptive study conducted among adults aged 35–64 years. A representative sample was selected using stratified random cluster sampling method from urban, rural and plantation sectors of Kalutara District. Data were collected using a pre-tested questionnaire. A body mass index of 23.01 kg/m2-27.50 kg/m2 was considered as overweight and ≥27.51 kg/m2 as obese. Waist circumference (WC) of ≥ 90 cm and ≥80 cm was regarded as high for men and women respectively. Significance of prevalence of obesity categories across different socio-economic strata was determined by chi square test for trend.ResultsOf 1234 adults who were screened, age and sex adjusted prevalence of overweight, obesity and abdominal obesity (high WC) were 33.2% (male 27.3%/female 38.7%), 14.3% (male 9.2%/female 19.2%) and 33.6% (male 17.7%/female 49.0%) respectively. The Muslims had the highest prevalence of all three obesity categories. Sector, education, social status quintiles and area level deprivation categories show a non linear social gradient while income shows a linear social gradient in all obesity categories, mean BMI and mean WC. The differences observed for mean BMI and mean WC between the lowest and highest socioeconomic groups were statistically significant.ConclusionThere is a social gradient in all three obesity categories with higher prevalence observed in the more educated, urban, high income and high social status segments of society. The higher socioeconomic groups are still at a higher risk of all types of obesity despite other public health indicators such as maternal and infant mortality displaying an established social gradient.Electronic supplementary materialThe online version of this article (doi:10.1186/s12939-015-0140-8) contains supplementary material, which is available to authorized users.

ObjectiveThis study describes the availability of core parameters for Early Warning Scores (EWS), evaluates the ability of selected EWS to identify patients at risk of death or other adverse outcome and describes the burden of triggering that front-line staff would experience if implemented.DesignLongitudinal observational cohort study.SettingDistrict General Hospital Monaragala.ParticipantsAll adult (age >17 years) admitted patients.Main outcome measuresExisting physiological parameters, adverse outcomes and survival status at hospital discharge were extracted daily from existing paper records for all patients over an 8-month period.Statistical analysisDiscrimination for selected aggregate weighted track and trigger systems (AWTTS) was assessed by the area under the receiver operating characteristic (AUROC) curve.Performance of EWS are further evaluated at time points during admission and across diagnostic groups. The burden of trigger to correctly identify patients who died was evaluated using positive predictive value (PPV).ResultsOf the 16 386 patients included, 502 (3.06%) had one or more adverse outcomes (cardiac arrests, unplanned intensive care unit admissions and transfers). Availability of physiological parameters on admission ranged from 90.97% (95% CI 90.52% to 91.40%) for heart rate to 23.94% (95% CI 23.29% to 24.60%) for oxygen saturation. Ability to discriminate death on admission was less than 0.81 (AUROC) for all selected EWS. Performance of the best performing of the EWS varied depending on admission diagnosis, and was diminished at 24 hours prior to event. PPV was low (10.44%).ConclusionThere is limited observation reporting in this setting. Indiscriminate application of EWS to all patients admitted to wards in this setting may result in an unnecessary burden of monitoring and may detract from clinician care of sicker patients. Physiological parameters in combination with diagnosis may have a place when applied on admission to help identify patients for whom increased vital sign monitoring may not be beneficial. Further research is required to understand the priorities and cues that influence monitoring of ward patients.Trial registration numberNCT02523456.