Background Chronic Kidney Disease (CKD) of uncertain origin (CKDu) has affected North Central Province (Anuradhapura and Polonnaruwa districts) of Sri Lanka. The cause is still unknown. The objective of this study was to describe the incidence, prevalence and trend of CKD/CKDu in North Central Province of Sri Lanka. Methods A cross sectional survey conducted in North Central Province with GPS mapping in CKDu highly affected areas. The diagnosis of CKD and staging were made according to the Kidney Disease: Improving Global Outcomes paper. Descriptive statistics used with chi-square test for evaluating dichotomous variables. Log rank test was used to compare survival rates. The population data was obtained from the 2011 Census. Results There were 30,566 CKD/CKDu patients in the North Central Province. Incidence of 0.10 in 2009, 0.39 in 2016 in Anuradhapura district, decreased slightly to 0.29 in 2017. Incidence of 0.09 in 2009, 0.46 in 2016 in Polonnaruwa district, decreased slightly to 0.41 in 2017. The point prevalence in high incidence areas ranged from 2.44–4.35. The 5 year survival rate was 71.2 (Anuradhapura 72.4 and Polonnaruwa 68.3, p = 0.0212). More than 70, 40 and 33% of patients were over 50, 60 and 70 years of age respectively. A male preponderance was seen in all the divisional areas (ranging from 1.3:1 to 2.6:1) and in all the age groups. Farmers were the most affected (70.6% Anuradhapura district and 65.1% Polonnaruwa district). Majority in CKD stage I (4943, 69.6%). There were 1685 deaths (17.5% of total CKD/CKDu patients, 67.6% of total deaths in CKD/CKDu patients) occurring within the first 3 years of diagnosis. GPS mapping shows that there is a clustering of households with CKD/CKDu. Conclusions The incidence of CKD/CKDu increased up to 2016 with a slight decrease in 2017. The most vulnerable age group was 40 to 60 years. There is a male preponderance. Farmers at a higher risk. Majority were in CKD stage 1. More than two thirds of the deaths of CKD/CKDu patients occurred within three years of diagnosis with disparities in 5 year survival rate among the two districts. There is clustering of cases.

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.

Lack of investment in low-income and middle-income countries (LMICs) in systems capturing continuous information regarding care of the acutely unwell patient is hindering global efforts to address inequalities, both at facility and national level. Furthermore, this of lack of data is disempowering frontline staff and those seeking to support them, from progressing setting-relevant research and quality improvement. In contrast to high-income country (HIC) settings, where electronic surveillance has boosted the capability of governments, clinicians and researchers to engage in service-wide healthcare evaluation, healthcare information in resource-limited settings remains almost exclusively paper based. In this practice paper, we describe the efforts of a collaboration of clinicians, administrators, researchers and healthcare informaticians working in South Asia, in addressing the inequality in access to patient information in acute care. Harnessing a clinician-led collaborative approach to design and evaluation, we have implemented a national acute care information platform in Sri Lanka that is tailored to priorities of frontline staff. Iterative adaptation has ensured the platform has the flexibility to integrate with legacy paper systems, support junior team members in advocating for acutely unwell patients and has made information captured accessible to diverse stakeholders to improve service delivery. The same platform is now empowering clinicians to participate in international research and drive forwards improvements in care. During this journey, we have also gained insights on how to overcome well-described barriers to implementation of digital information tools in LMIC. We anticipate that this north–south collaborative approach to addressing the challenges of health system implementation in acute care may provide learning and inspiration to other partnerships seeking to engage in similar work.

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.