Background: Artificial intelligence (AI)-based computational models that analyze breast cancer have been developed for decades. The present study was implemented to investigate the accuracy and efficiency of combined mammography images and clinical records for breast cancer detection using machine learning and deep learning classifiers. Methods: This study was verified using 731 images from 357 women who underwent at least one mammogram and had clinical records for at least six months before mammography. The model was trained on mammograms and clinical variables to discriminate benign and malignant lesions. Multiple pre-trained deep CNN models to detect cancer in mammograms, including X-ception, VGG16, ResNet-v2, ResNet50, and CNN3 were employed. Machine learning models were constructed using k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), Artificial Neural Network (ANN), and gradient boosting machine (GBM) in the clinical dataset. Results: The detection performance obtained an accuracy of 84.5% with a specificity of 78.1% at a sensitivity of 89.7% and an AUC of 0.88. When trained on mammography image data alone, the result achieved a slightly lower score than the combined model (accuracy, 72.5% vs. 84.5%, respectively). Conclusions: A breast cancer-detection model combining machine learning and deep learning models was performed in this study with a satisfactory result, and this model has potential clinical applications.

Adolescence is a vulnerable period for many lifestyle risk behaviors. In this study, we aimed to 1) examine a clustering pattern of lifestyle risk behaviors; 2) investigate roles of the school health promotion programs on this pattern among adolescents in Vietnam. We analyzed data of 7,541 adolescents aged 13–17 years from the 2019 nationally representative Global School-based Student Health Survey, conducted in 20 provinces and cities in Vietnam. We applied the latent class analysis to identify groups of clustering and used Bayesian 2-level logistic regressions to evaluate the correlation of school health promotion programs on these clusters. We reassessed the school effect size by incorporating different informative priors to the Bayesian models. The most frequent lifestyle risk behavior among Vietnamese adolescents was physical inactivity, followed by unhealthy diet, and sedentary behavior. Most of students had a cluster of at least two risk factors and nearly a half with at least three risk factors. Latent class analysis detected 23% males and 18% females being at higher risk of lifestyle behaviors. Consistent through different priors, high quality of health promotion programs associated with lower the odds of lifestyle risk behaviors (highest quality schools vs. lowest quality schools; males: Odds ratio (OR) = 0·67, 95% Highest Density Interval (HDI): 0·46 – 0·93; females: OR = 0·69, 95% HDI: 0·47 – 0·98). Our findings demonstrated the clustering of specific lifestyle risk behaviors among Vietnamese in-school adolescents. School-based interventions separated for males and females might reduce multiple health risk behaviors in adolescence. The 2019 Global School-based Student Health Survey was conducted with financial support from the World Health Organization. The authors received no funding for the data analysis, data interpretation, manuscript writing, authorship, and/or publication of this article.

Social support plays a crucial role in the treatment and recovery process of patients engaging in methadone maintenance treatment (MMT). However, there is a paucity of research about social support among MMT patients, possibly due to a lack of appropriate measuring tools. This study aimed to evaluate the psychometric properties of the Vietnamese version of the Medical Outcomes Study: Social Support Survey (MOS-SSS) among MMT patients. A cross-sectional survey of 300 patients was conducted in a methadone clinic in Ho Chi Minh City, Vietnam. MMT patients who agreed to participate in the study completed a face-to-face interview in a private room. The MOS-SSS was translated into Vietnamese using standard forward-backward process. Internal consistency was measured by Cronbach’s alpha. The intra-class correlation coefficient was used to determine the test-retest reliability of the MOS-SSS in 75 participants two weeks after the first survey. Concurrent validity of the MOS-SSS was evaluated by correlations with the Multidimensional Scale of Perceived Social Support (MSPSS) and the Perceived Stigma of Addiction Scale (PSAS). Construct validity was investigated by confirmatory factor analysis. The MOS-SSS had good internal consistency with Cronbach’s alpha from 0.95 to 0.97 for the four subscales and 0.97 for the overall scale. The two-week test-retest reliability was at moderate level with intra-class correlation coefficients of 0.61–0.73 for the four subscales and 0.76 for the overall scale. Strong significant correlations between the MOS-SSS and the MSPSS (r = 0.77; p < 0.001) and the PSAS (r = − 0.76; p < 0.001) indicated good concurrent validity. Construct validity of the MOS-SSS was established since a final four-factor model fitted the data well with Comparative Fit Index (0.97), Tucker-Lewis Index (0.97), Standardized Root Mean Square Residual (0.03) and Root Mean Square Error of Approximation (0.068; 90% CI = 0.059–0.077). The MOS-SSS is a reliable and valid tool for measuring social support in Vietnamese MMT patients. Further studies among methadone patients at different stages of their treatment and among those from different areas of Vietnam are needed.

Given the rapid spread of the COVID-19 pandemic and the huge negative impacts it is causing, researching on COVID-19-related issues is very important for designing proactive and comprehensive public health interventions to fight against the pandemic. We describe the characteristics of COVID-19 patients detected in the two phases of the epidemic in Vietnam. Data used in this paper were mainly obtained from the official database of the Ministry of Health of Vietnam. Descriptive statistics were carried out using Stata 16 software. As of 18 May 2020, the cumulative number of COVID-19 cases detected in Vietnam was 324, 16 cases from 4 cities and provinces in the first phase (during 20 days, 0.8 cases detected per day) and 308 cases from 35 cities, provinces in the second phase (during 76 days, 4.1 cases detected per day). Vietnam has mobilized its entire political system to fight the COVID-19 and achieved some initial successes. We found both similarities and differences between the two phases of the COVID-19 epidemic in Vietnam. We demonstrated that the situation of the COVID-19 epidemic in Vietnam is getting more complicated and unpredictable.

To assess the reproductive, maternal, newborn and child health (RMNCH) service coverage in Vietnam with trends in 2000−2014, projections and probability of achieving targets in 2030 at national and sub-national levels; and to analyze the socioeconomic, regional and urban-rural inequalities in RMNCH service indicators. We used national population-based datasets of 44,624 households in Vietnam from 2000 to 2014. We applied Bayesian regression models to estimate the trends in and projections of RMNCH indicators and the probabilities of achieving the 2030 targets. Using the relative index, slope index, and concentration index of inequality, we examined the patterns and trends in RMNCH coverage inequality. We projected that 9 out of 17 health service indicators (53%) would likely achieve the 2030 targets at the national level, including at least one and four ANC visits, BCG immunization, access to improved water and adequate sanitation, institutional delivery, skilled birth attendance, care-seeking for pneumonia, and ARI treatment. We observed very low coverages and zero chance of achieving the 2030 targets at national and sub-national levels in early initiation and exclusive breastfeeding, family planning needs satisfied, and oral rehydration therapy. The most deprived households living in rural areas and the Northwest, Northeast, North Central, Central Highlands, and Mekong River Delta regions would not reach the 80% immunization coverage of DPT3, Polio3, Measles and full immunization. We found socioeconomic, regional, and urban-rural inequalities in all RMNCH indicators in 2014 and no change in inequalities over 15 years in the lowest-coverage indicators. Vietnam has made substantial progress toward UHC. By improving the government's health system reform efforts, re-allocating resources focusing on people in the most impoverished rural regions, and restructuring and enhancing current health programs, Vietnam can achieve the UHC targets and other health-related SDGs. The authors did not receive any funds for conducting this study.