Effectiveness of interventions to reduce indoor air pollution and/or improve health in homes using solid fuel in lower and middle income countries : protocol for a systematic review

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Effectiveness of interventions to reduce household air pollution and/or improve health in homes using solid fuel in low-and-middle income countries : A systematic review and meta-analysis

Peer reviewedPostprin

Social factors and overweight: evidence from nine Asian INDEPTH Network sites

Background: Overweight/obesity increases the risk of morbidity and mortality from a number of chronic conditions, including heart disease, stroke, diabetes and some cancers. This study examined the distribution of body mass index (BMI) in nine Health and Demographic Surveillance System (HDSS) sites in five Asian countries and investigated the association between social factors and overweight. Data and methods: This cross-sectional study was conducted in nine HDSS sites in Bangladesh, India, Indonesia, Thailand and Vietnam. The methodology of the WHO STEPwise approach to Surveillance with core risk factors (Step 1) and physical measurements for weight, height and waist circumference (Step 2) were included. In each site, about 2,000 men and women aged 25Á64 years were selected randomly using the HDSS database. Weight was measured using electronic scales, height was measured by portable stadiometers and waist circumference was measured by measuring tape. Overweight/obesity was assessed by BMI defined as the weight in kilograms divided by the square of the height in metres (kg/

Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice

Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6-91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice

Mortality from external causes in Africa and Asia: evidence from INDEPTH Health and Demographic Surveillance System Sites.

BACKGROUND: Mortality from external causes, of all kinds, is an important component of overall mortality on a global basis. However, these deaths, like others in Africa and Asia, are often not counted or documented on an individual basis. Overviews of the state of external cause mortality in Africa and Asia are therefore based on uncertain information. The INDEPTH Network maintains longitudinal surveillance, including cause of death, at population sites across Africa and Asia, which offers important opportunities to document external cause mortality at the population level across a range of settings. OBJECTIVE: To describe patterns of mortality from external causes at INDEPTH Network sites across Africa and Asia, according to the WHO 2012 verbal autopsy (VA) cause categories. DESIGN: All deaths at INDEPTH sites are routinely registered and followed up with VA interviews. For this study, VA archives were transformed into the WHO 2012 VA standard format and processed using the InterVA-4 model to assign cause of death. Routine surveillance data also provide person-time denominators for mortality rates. RESULTS: A total of 5,884 deaths due to external causes were documented over 11,828,253 person-years. Approximately one-quarter of those deaths were to children younger than 15 years. Causes of death were dominated by childhood drowning in Bangladesh, and by transport-related deaths and intentional injuries elsewhere. Detailed mortality rates are presented by cause of death, age group, and sex. CONCLUSIONS: The patterns of external cause mortality found here generally corresponded with expectations and other sources of information, but they fill some important gaps in population-based mortality data. They provide an important source of information to inform potentially preventive intervention designs