Thinness negatively affects lung function among Sri Lankan children.

BACKGROUND: There have been conflicting findings on the effect of body mass index (BMI) on lung functions in children. Therefore, we studied the relationship between spirometry parameters and BMI among healthy Sri Lankan school children aged 5-7 years. METHODS: A cross-sectional study was conducted among 296 school children (5-7-year-old) without apparent lung disease. Recruitment was done with stratified random sampling. Spirometry parameters, FEV1, FVC, PEFR, and FEV1/FVC ratio were determined. The acceptable and reproducible spirometry recordings were included in the analysis. Simple and multivariate linear regression analysis examined possible associations of lung function parameters with BMI, socio-demographic variables and indoor risk factors. Also, the mediator effect of gender on lung function through BMI was explored. RESULTS: The participants' mean age (SD) was 6.4 (0.65) years. One-third were thin/severely thin (37%). A statistically significant difference in FVC (p = 0.001) and FEV1 (p = 0.001) was observed between BMI groups (obesity/overweight, normal, and thinness). Yet, PEFR or FEV1/FVC did not significantly differ among BMI groups (p = 0.23 and p = 0.84). Multivariate regression analysis showed that FEV1 and FVC were significantly associated with BMI, child's age, gender, family income, father's education, having a pet, and exposure to mosquito coil smoke. Interaction between gender and BMI for lung functions was not significant. The thin children had significantly lower FVC (OR: -0.04, 95%CI: -0.077, -0.012, p = 0.008) and FEV1 (OR: -0.04, 95%CI: -0.075, -0.014, p = 0.004) than normal/overweight/obese children. Family income demonstrated the greatest effect on lung functions; FVC and FEV1 were 0.25L and 0.23L smaller in low-income than the high-income families. CONCLUSION: Lower lung function parameters (FVC and FEV1) are associated with thinness than normal/overweight/obese dimensions among children without apparent lung disease. It informs that appropriate nutritional intervention may play a role in improving respiratory health.

Food environment and diabetes mellitus in South Asia: A geospatial analysis of health outcome data.

BACKGROUND: The global epidemic of type 2 diabetes mellitus (T2DM) renders its prevention a major public health priority. A key risk factor of diabetes is obesity and poor diets. Food environments have been found to influence people's diets and obesity, positing they may play a role in the prevalence of diabetes. Yet, there is scant evidence on the role they may play in the context of low- and middle-income countries (LMICs). We examined the associations of food environments on T2DM among adults and its heterogeneity by income and sex. METHODS AND FINDINGS: We linked individual health outcome data of 12,167 individuals from a network of health surveillance sites (the South Asia Biobank) to the density and proximity of food outlets geolocated around their homes from environment mapping survey data collected between 2018 and 2020 in Bangladesh and Sri Lanka. Density was defined as share of food outlets within 300 m from study participant's home, and proximity was defined as having at least 1 outlet within 100 m from home. The outcome variables include fasting blood glucose level, high blood glucose, and self-reported diagnosed diabetes. Control variables included demographics, socioeconomic status (SES), health status, healthcare utilization, and physical activities. Data were analyzed in ArcMap 10.3 and STATA 15.1. A higher share of fast-food restaurants (FFR) was associated with a 9.21 mg/dl blood glucose increase (95% CI: 0.17, 18.24; p < 0.05). Having at least 1 FFR in the proximity was associated with 2.14 mg/dl blood glucose increase (CI: 0.55, 3.72; p < 0.01). A 1% increase in the share of FFR near an individual's home was associated with 8% increase in the probability of being clinically diagnosed as a diabetic (average marginal effects (AMEs): 0.08; CI: 0.02, 0.14; p < 0.05). Having at least 1 FFR near home was associated with 16% (odds ratio [OR]: 1.16; CI: 1.01, 1.33; p < 0.05) and 19% (OR: 1.19; CI: 1.03, 1.38; p < 0.05) increases in the odds of higher blood glucose levels and diagnosed diabetes, respectively. The positive association between FFR density and blood glucose level was stronger among women than men, but the association between FFR proximity and blood glucose level was stronger among men as well as among those with higher incomes. One of the study's key limitations is that we measured exposure to food environments around residency geolocation; however, participants may source their meals elsewhere. CONCLUSIONS: Our results suggest that the exposure to fast-food outlets may have a detrimental impact on the risk of T2DM, especially among females and higher-income earners. Policies should target changes in the food environments to promote better diets and prevent T2DM.

Food environments and obesity: A geospatial analysis of the South Asia Biobank, income and sex inequalities.

INTRODUCTION: In low-middle income countries (LMICs) the role of food environments on obesity has been understudied. We address this gap by 1) examining the effect of food environments on adults' body size (BMI, waist circumference) and obesity; 2) measuring the heterogeneity of such effects by income and sex. METHODS: This cross-sectional study analysed South Asia Biobank surveillance and environment mapping data for 12,167 adults collected between 2018 and 2020 from 33 surveillance sites in Bangladesh and Sri Lanka. Individual-level data (demographic, socio-economic, and health characteristics) were combined with exposure to healthy and unhealthy food environments measured with geolocations of food outlets (obtained through ground-truth surveys) within 300 m buffer zones around participants' homes. Multivariate regression models were used to assess association of exposure to healthy and unhealthy food environments on waist circumference, BMI, and probability of obesity for the total sample and stratified by sex and income. FINDINGS: The presence of a higher share of supermarkets in the neighbourhood was associated with a reduction in body size (BMI, ß = - 3∙23; p < 0∙0001, and waist circumference, ß = -5∙99; p = 0∙0212) and obesity (Average Marginal Effect (AME): -0∙18; p = 0∙0009). High share of fast-food restaurants in the neighbourhood was not significantly associated with body size, but it significantly increased the probability of obesity measured by BMI (AME: 0∙09; p = 0∙0234) and waist circumference (AME: 0∙21; p = 0∙0021). These effects were stronger among females and low-income individuals. INTERPRETATION: The results suggest the availability of fast-food outlets influences obesity, especially among female and lower-income groups. The availability of supermarkets is associated with reduced body size and obesity, but their effects do not outweigh the role of fast-food outlets. Policies should target food environments to promote better diets and reduce obesity.

Real-time laboratory surveillance of Dengue antigen(NS1) - Best proposed early warning and response system for Dengue in Sri Lanka? - Results from a pilot study.

Introduction: During the recent past, dengue fever and associated complications have been the most important concern for health policy makers of Sri Lanka. The current notification system has considerable inevitable delays in preventive measures. Objectives: Implementing a laboratory-based real-time antigen (NS1) surveillance system for notification coupled with a rapid preventive response within the Colombo district as a pilot project and compares the notifications with existing national surveillance systems. Methods: An online notification platform was established with a centralized database. Seven main laboratories of the private sector linked with the notification system, where they can create new notifications at the central database, whenever the NS1 test detected as positive. Relevant Medical Officers of Health should update action implemented to complete the response process. A dashboard was designed to visualize each notification and its status with a predefined colour code. Results: Patients from 14 Medical Officer of Health (MOH) areas out of 15 were captured. The immediate preventive response was recorded from the field preventive staff for 90% of the reporting. All most all attended patients have given health advice on awareness, prevention, and source reduction through premise inspection by trained field staff with 24hrs of notification. Conclusions: Salient features of the novel system are notification of antigen-positive patients, the rapidity of notification (real-time) and response, user-friendliness, access to multiple stakeholders simultaneously without data duplication, early involvement of the field staff, the ability to trace the cases using checklists and a color-coding system from a dashboard.