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Diabetic retinopathy is one of the leading causes of blindness among the working age population world-wide due to late detection and intervention. This study pilots the application of deep learning models to automatically diagnose the occurrence and severity of diabetic retinopathy. With color fundus photography as input, this study tested the performance of transfer learning based on the most recent architectures of Convolutional Neural Network (CNN) models, the EfficientNets, claimed to be superior than many current well-performing network architectures.

We propose the so-called Bundled Transfer Learning approach with multiple large architectures for automated detection of Coronavirus using X-Ray and CT images Extracted features from each pretrained model in the bundle are gathered for training new deep layers The dataset includes CT scan images of COVID-19 and normal classes and X-Ray images of COVID-19, Pneumonia, and normal classes © 2020 SPIE

By the time of the abstract, the coronavirus COVID-19 is affecting 213 countries with more than 9.4 million cases, and more than 480 thousand deaths. In the US it has been confirmed that there are more than 2.4 million COVID-19 cases and approximately 123 thousand related deaths, according to Worldometer. The Wuhan originated COVID-19 has become a global challenge since late December 2019. As control measures lift, life and businesses start going to open, the coronavirus pandemic continues to grow. In this research, we propose a Deep Learning model that recognizes COVID-19 cases using X-ray images (DeepCOVID-19). The model would help physicians to be more confident by having a second opinion in assessing patients. An implementation of the model would help countries where there is shortage of test kits, while X-ray devices are widely available.

BACKGROUND Hepatitis B virus (HBV) infection is a significant public health issue in Vietnam. Our goal was to understand the determinants of attitudes towards and practices of hepatitis B vaccine birth dose (HepB-BD) in certain regions of Vietnam. METHOD A rapid qualitative assessment was conducted in three geographically diverse provinces that reported low coverage (

Background Hand-foot-and-mouth disease (HFMD) is a significant public health issue in Asia-pacific countries. Numerous studies have examined the relationship between socio-ecological factors and HFMD however the research findings were inconsistent. This study examined the association between socio-ecologic factors and HFMD in multiple provinces across Vietnam. Methods We applied a spatial autoregressive model using a Bayesian framework to examine the relationship between HFMD and socio-demographic factors. We used a Generalized Linear Model (GLD) with Poisson family to examine the province-specific association between monthly HFMD and climatic factors while controlling for spatial lag, seasonality and long-term trend of HFMD. Then, we used a random-effect meta-analysis to generate pooled effect size of climate-HFMD association for regional and country scale. Results One percent increase in newborn breastfed within 1 h of birth, households with permanent houses, and households accessed to safe water resulted in 1.57% (95% CI: − 2.25, − 0.93), 0.96% (− 1.66, − 0.23), and 1.13% (− 2.16, − 0.18) reduction in HFMD incidence, respectively. At the country-level, HFMD increased 7% (RR: 1.07; 95%CI: 1.052–1.088) and 3.1% (RR: 1.031, 95%CI: 1.024–1.039) for 1 °C increase in monthly temperature above 26 °C and 1% increase in monthly humidity above 76%. Whereas, HFMD decreased 3.1% associated with 1 mm increase in monthly cumulative rainfalls. The climate-HFMD relationship was varied by regions and provinces across the country. Conclusions The findings reflect an important implication for the climate change adaptation strategies and public-health decision, of which development of weather-based early warning systems should be considered to strengthen communicable disease prevention system.

The effects of heatwaves on morbidity in developing and tropical countries have not been well explored. The purpose of this study was to examine the relationship between heatwaves and hospitalization and the potential influence of socio-economic factors on this relationship in Vietnam. Generalized Linear Models (GLM) with Poisson family and Distributed Lag Models (DLM) were applied to evaluate the effect of heatwaves for each province (province-level effect). A random-effects meta-analysis was applied to calculate the pooled estimates (country-level effects) for ‘all causes’, infectious, cardiovascular, and respiratory admissions queried by lag days, regions, sex, and ages. We used random-effects meta-regression to explore the potential influence of socio-economic factors on the relationship between heatwaves and hospitalization. The size of province-level effects varied across provinces. The pooled estimates show that heatwaves were significantly associated with a 2.5% (95%CI: 0.8–4.3) and 3.8% (95%CI, 1.5–6.2) increase in all causes and infectious admissions at lag 0. Cardiovascular and respiratory admissions (0.8%, 95%CI: −1.6–3.3; 2.2%, 95%CI: −0.7–5.2) were not significantly increased after a heatwave event. The risk of hospitalization due to heatwaves was higher in the North than in the South for all causes (5.4%, 95%CI: −0.1–11.5 versus 1.3%, 95%CI: 0.1–2.6), infectious (11.2%, 95%CI: 3.1–19.9 versus 3.2%, 95%CI: 0.7–5.7), cardiovascular (7.5%, 95%CI: 1.1–14.4 versus −1.2%, 95%CI: −2.6–2.3), and respiratory diseases (2.7%, 95%CI: −5.4–11.5 versus 2.1%, 95%CI: −0.8–1.2). A non-significant influence of socio-economic factors on the relationship between heatwave and hospitalization was observed. This study provides important evidence and suggests implications for the projected impacts of climate change related extreme weather. Climate change adaptation programs of the health sector should be developed to protect residents from the effects of extreme weather events such as heatwaves in Vietnam.

This study examined the short-term effects of temperature on cardiovascular hospital admissions (CHA) in the largest tropical city in Southern Vietnam. We applied Poisson time-series regression models with Distributed Lag Non-Linear Model (DLNM) to examine the temperature-CHA association while adjusting for seasonal and long-term trends, day of the week, holidays, and humidity. The threshold temperature and added effects of heat waves were also evaluated. The exposure-response curve of temperature-CHA reveals a J-shape relationship with a threshold temperature of 29.6 °C. The delayed effects temperature-CHA lasted for a week (0-5 days). The overall risk of CHA increased 12.9% (RR, 1.129; 95%CI, 0.972-1.311) during heatwave events, which were defined as temperature ≥ the 99th percentile for ≥2 consecutive days. The modification roles of gender and age were inconsistent and non-significant in this study. An additional prevention program that reduces the risk of cardiovascular disease in relation to high temperatures should be developed.

This study examined the temporal and spatial patterns of hand, foot, and mouth disease (HFMD) in the Mekong Delta region in Vietnam. A time-series analysis was used to examine the temporal patterns of HFMD in relation to climate factors while a retrospective space-time scan was used to detect the high-risk space-time clusters of this disease. A 1°C increase in average temperature was associated with 5.6% increase in HFMD rate at lag 5days (95% CI 0.3-10.9). A 1% increase in humidity had equal influence of 1.7% increases on HFMD rate at both lag 3days and 6days (95% CI 0.7-2.7 and 95% CI 0.8-2.6, respectively). An increase in 1 unit of rainfall was associated with a 0.5% increase of HFMD rate on the lag 1 and 6days (95% CI 0.2-0.9 and 95% CI 0.1-0.8, respectively). The predictive model indicated that the peak of HFMD was from October to December - the rainy season in the Mekong Delta region. Most high-risk clusters were located in areas with high population density and close to transport routes. The findings suggest that HFMD is influenced by climate factors and is likely to increase in the future due to climate change related weather events.

ObjectiveTo examine the effects of socioecological factors on multiple communicable diseases across Vietnam.MethodsWe used the Moran's I tests to evaluate spatial clusters of diseases and applied multilevel negative binomial regression models using the Bayesian framework to analyse the association between socioecological factors and the diseases queried by oral, airborne, vector-borne, and animal transmission diseases.Results and significanceThe study found that oral-transmission diseases were spatially distributed across the country; whereas, the airborne-transmission diseases were more clustered in the Northwest and vector-borne transmission diseases were more clustered in the South. Most of diseases were sensitive with climatic factors. For instance, a 1 degrees C increase in average temperature is significantly associated with 0.4% ( 95CI, 0.3 +/- 0.5), 2.5% ( 95% CI, 1.4 +/- 3.6), 0.9% ( 95% CI, 0.6 +/- 1.4), 1.1% ( 95% CI), 5% ( 95% CI, 3-.7.4), 0.4% ( 95% CI, 0.2 +/- 0.7), and 2% ( 95% CI, 1.5 +/- 2.8) increase in risk of diarrhoea, shigellosis, mumps, influenza, dengue, malaria, and rabies respectively. The influences of socio-economic factors on risk of communicable diseases are varied by factors with the biggest influence of population density. The research findings reflect an important implication for the climate change adaptation strategies of health sectors. A development of weather-based early warning systems should be considered to strengthen communicable disease prevention in Vietnam.

Flooding causes significant public health issues. The Mekong Delta has been considered the region to be the most vulnerable to flooding in Vietnam. This study assessed the spatial vulnerability of the health impacts of flooding in the Mekong Delta region, Vietnam. This study applied a vulnerability assessment framework which was computed as the function of three dimensions: exposure, sensitivity, and adaptive capacity. The indicators for each dimension were derived from the relevant literature, consultations with experts, and data availability. An analytic hierarchy process (AHP) and a principal component analysis (PCA) were used to determine the weight of indicators. Vulnerability indexes (VIs) were then computed for each province. A total of 29 indicators (sensitivity index, 14; adaptive capacity index, 13; and exposure index, 2) were employed to evaluate the vulnerability to the health impacts of flooding at a provincial level. The results of AHP revealed that the highest VIs were found in the Dong Thap and An Giang provinces (VI, 1.948 and 1.574, respectively). VIs were distributed with higher indexes in upstream provinces close to a river than in coastal provinces. PCA generated three components from the 29 indicators, and the VIs computed from the PCA method are in substantial agreement with the AHP method (ICC = 0.71, p

Background The Mekong Delta is the most vulnerable region to climate change in South-East Asia; however, the association between climate and children9s health has rarely been studied in this region. Objective We examined the short-term association between daily temperature and hospital admissions for all causes, gastrointestinal and respiratory infection, among young children in the Mekong Delta area in Vietnam. Methods Daily paediatric hospital admissions and meteorological data were obtained from January 2008 to December 2012. A time-series approach was used with a combination of a Poisson regression and constrained distributed lag models to analyse the data. The long-term and seasonal trends, as well as other time-varying covariates, were adjusted using spline functions. Temperature—pediatric admission relationship was evaluated by age-specific (0–2 and 3–5-year-olds) and cause of admission groupings. Results A 1°C increase in the 2-day moving average temperature was significantly associated with a 3.4% (95% CI 1.2% to 5.5%), 4.6% (95% CI 2.2% to 7.3%), 2.6% (95% CI 0.6% to 4.6%), 4.4% (95% CI 0.6% to 8.2%) and 3.8%(95% CI 0.4% to 7.2%) increase in hospital admissions with 0–2-year-old children, 3–5-year-old children, all causes, gastrointestinal infection and respiratory infection, respectively. The cumulative effects from 1-day to 6-day moving average temperature on hospital admissions were greater for 3–5-year-old children and gastrointestinal infection than for 0–2-year-old children and other causes. Conclusions Temperature was found to be significantly associated with hospital admissions in young children with the highest association between temperature and gastrointestinal infection. The government agencies of Mekong Delta should implement measures to protect children from the changing temperature conditions related to climate change.