Socioeconomic and geographic correlates of intimate partner violence in Sri Lanka: Analysis of the 2016 Demographic and Health Survey

Intimate partner violence (IPV) is a serious public health issue and violation of human rights. The prevalence of IPV in South Asia is especially pronounced. We examined the associations between socioeconomic position (SEP), geographical factors and IPV in Sri Lanka using nationally representative data. Data collected from Sri Lanka’s 2016 Demographic and Health Survey were analysed using multilevel logistic regression techniques. A total of 16,390 eligible ever-partnered women aged 15-49 years were included in the analysis. Analyses were also stratified by ethnicity, type of violence, neighbourhood poverty and post-conflict residential status for selected variables. No schooling/primary educational attainment among women (OR 2.46 95% CI 1.83-3.30) and their partners (OR 2.87 95% CI 2.06-4.00), financial insecurity (OR 2.17 95% CI 1.92-2.45) and poor household wealth (OR 2.64 95% CI 2.22-3.13) were the socioeconomic factors that showed the strongest association with any IPV, after adjusting for age and religion. These associations predominately related to physical and/or sexual violence, with weak associations for psychological violence. Women living in a post-conflict environment had a higher risk (OR 2.96 95% CI 2.51-3.49) of IPV compared to other areas. Ethnic minority women (Tamil and Moor) were more likely to reside in post-conflict areas and experience poverty more acutely compared to the majority Sinhala women, which may explain the stronger associations for low SEP, post-conflict residence and IPV found among Tamil and Moor women. Policies and programs to alleviate poverty, as well as community mobilisation and school-based education programs addressing harmful gender norms may be beneficial. Trauma informed approaches are needed in post-conflict settings. Further exploratory studies investigating the complex interplay of individual, household and contextual factors occurring in this setting is required

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