Social, cultural and economical determinants of diabetes mellitus in Kalutara district, Sri Lanka: a cross sectional descriptive study

<p>Abstract</p> <p>Introduction</p> <p>Sri Lanka is a country that is expected to face a high burden of diabetes mellitus (DM). There is a paucity of data on social and demographic determinants of DM, especially in the plantation sector.</p> <p>Aims</p> <p>To describe social and economic correlates and inequalities of DM in Kalutara District.</p> <p>Methods</p> <p>A cross sectional descriptive study was carried out among adults over the age of 35 years. A sample of 1300 individuals was selected using stratified random cluster sampling method from 65 Grama Niladari Divisions (GND), which were representative of urban, rural and plantation sectors. Twenty households were randomly selected from each division and one adult was randomly selected from each household. Data were collected using a pre-tested questionnaire. Fasting plasma blood sugar of ≥126mg/dl was used to define DM. Significance of prevalence of diseases and risk factors across different socio-economic strata were determined by chi square test for trend.</p> <p>Results</p> <p>Of 1234 adults who were screened (628 males), 202 (14.7%) had DM. Higher DM proportions (16.1%) were seen in the highest income quintile and in those educated up to Advanced Levels (AL) and above (17.3%). Prevalence in the urban, rural and plantation sectors were 23.6%, 15.5% and 8.5% respectively. Prevalence among Sinhalese, Tamils and Muslims were 14.4%, 29.0% and 20.0% respectively. There was a gradient in prevalence according to the unsatisfactory basic needs index of the GND with the highest proportion (20.7%) observed in the richest GND. The highest social status quintile demonstrated the highest proportion (17.4%) with diabetes mellitus.</p> <p>Conclusion</p> <p>There is a higher prevalence of diabetes mellitus in the more affluent and educated segments of society. There is also a higher prevalence among urban compared to rural and estates. Sri Lanka is in an early stage of the epidemic where the wealthy people are at a higher risk of DM.</p

Simplified prognostic model for critically ill patients in resource limited settings in South Asia

Abstract Background Current 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. Methods This 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. Results Model 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. Conclusion This paper proposes TropICS as the first multinational critical care prognostic model developed in a non-HIC setting

Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country

Abstract Background Explorations into quantifying the inequalities for diabetes mellitus (DM) and its risk factors are scarce in low and lower middle income countries (LICs/LMICs). The aims of this study were to assess the inequalities of DM and its risk factors in a suburban district of Sri Lanka. Methods A sample of 1300 participants, (aged 35–64 years) randomly selected using a stratified multi-stage cluster sampling method, were studied employing a cross sectional descriptive design. The socioeconomic indicators (SEIs) of the individual were education level and occupational category, and at the household level, the household income, social status level and area deprivation level. DM was diagnosed if the fasting plasma glucose was ≥126 and a body mass index (BMI) of > 27.5 kg/m2 was considered high. Asian cut-off values were used for high waist circumference (WC). Validated tools were used to assess the diet and level of physical activity. The slope index of inequality (SII), relative index of inequality (RII) and concentration index (CI) were used to assess inequalities. Results The prevalence of DM and its risk factors (at individual or household level) showed no consistent relationship with the three measures of inequality (SII, RII and CI) of the different indices of socio economic status (education, occupation, household income, social status index or area unsatisfactory basic needs index). The prevalence of diabetes showed a more consistent pro-rich distribution in females compared to males. Of the risk factors in males and females, the most consistent and significant pro-rich relationship was for high BMI and WC. In males, the significant positive relationship with high BMI for SII ranged from 0.18 to 0.35, and RII from 1.56 to 2.25. For high WC, the values were: SII from 0.13 to 0.27 and RII from 1.9 to 3.97. In females the significant positive relationship with high BMI in SII ranged from 0.13 to 0.29, and RII from 2.3 to 4.98. For high WC the values were: SII from 028 to 0.4 and RII 1.99 to 2.39. Of the other risk factors, inadequate fruit intake showed a consistent significant pro-poor distribution only in males using SII (− 0.25 to − 0.36) and in both sexes using CI. Smoking also showed a pro-poor distribution in males especially using individual measures of socio-economic status (i.e. education and occupation). Conclusions The results show a variable relationship between socioeconomic status and prevalence of diabetes and its risk factors. The inequalities in the prevalence of diabetes and risk factors vary depending on gender and the measures used. The study suggests that measures to prevent diabetes should focus on targeting specific factors based on sex and socioeconomic status. The priority target areas for interventions should include prevention of obesity (BMI and central obesity) specifically in more affluent females. Males who have a low level of education and in non-skilled occupations should be especially targeted to reduce smoking and increase fruit intake

Traumatic brain injury (TBI) outcomes in an LMIC tertiary care centre and performance of trauma scores

Abstract Background This study evaluates post-ICU outcomes of patients admitted with moderate and severe Traumatic Brain Injury (TBI) in a tertiary neurocritical care unit in an low middle income country and the performance of trauma scores: A Severity Characterization of Trauma, Trauma and Injury Severity Score, Injury Severity Score and Revised Trauma Score in this setting. Methods Adult patients directly admitted to the neurosurgical intensive care units of the National Hospital of Sri Lanka between 21st July 2014 and 1st October 2014 with moderate or severe TBI were recruited. A telephone administered questionnaire based on the Glasgow Outcome Scale Extended (GOSE) was used to assess functional outcome of patients at 3 and 6 months after injury. The economic impact of the injury was assessed before injury, and at 3 and 6 months after injury. Results One hundred and one patients were included in the study. Survival at ICU discharge, 3 and 6 months after injury was 68.3%, 49.5% and 45.5% respectively. Of the survivors at 3 months after injury, 43 (86%) were living at home. Only 19 (38%) patients had a good recovery (as defined by GOSE 7 and 8). Three months and six months after injury, respectively 25 (50%) and 14 (30.4%) patients had become “economically dependent”. Selected trauma scores had poor discriminatory ability in predicting mortality. Conclusions This observational study of patients sustaining moderate or severe TBI in Sri Lanka (a LMIC) reveals only 46% of patients were alive at 6 months after ICU discharge and only 20% overall attained a good (GOSE 7 or 8) recovery. The social and economic consequences of TBI were long lasting in this setting. Injury Severity Score, Revised Trauma Score, A Severity Characterization of Trauma and Trauma and Injury Severity Score, all performed poorly in predicting mortality in this setting and illustrate the need for setting adapted tools

Experiences of ICU survivors in a low middle income country- a multicenter study

Abstract Background Stressful patient experiences during the intensive care unit (ICU) stay is associated with reduced satisfaction in High Income Countries (HICs) but has not been explored in Lower and Middle Income Countries (LMICs). This study describes the recalled experiences, stress and satisfaction as perceived by survivors of ICUs in a LMIC. Methods This follow-up study was carried out in 32 state ICUs in Sri Lanka between July and December 2015.ICU survivors’ experiences, stress factors encountered and level of satisfaction were collected 30 days after ICU discharge by a telephone questionnaire adapted from Granja and Wright. Results Of 1665 eligible ICU survivors, 23.3% died after ICU discharge, 49.1% were uncontactable and 438 (26.3%) patients were included in the study. Whilst 78.1% (n = 349) of patients remembered their admission to the hospital, only 42.3% (n = 189) could recall their admission to the ICU. The most frequently reported stressful experiences were: being bedridden (34.2%), pain (34.0%), general discomfort (31.7%), daily needle punctures (32.9%), family worries (33.6%), fear of dying and uncertainty in the future (25.8%). The majority of patients (376, 84.12%) found the atmosphere of the ICU to be friendly and calm. Overall, the patients found the level of health care received in the ICU to be “very satisfactory” (93.8%, n = 411) with none of the survivors stating they were either “dissatisfied” or “very dissatisfied”. Conclusion In common with HIC, survivors were very satisfied with their ICU care. In contrast to HIC settings, specific ICU experiences were frequently not recalled, but those remembered were reported as relatively stress-free. Stressful experiences, in common with HIC, were most frequently related to uncertainty about the future, dependency, family, and economic concerns