Physicians have a key role to play in combating tobacco use and reducing the tobacco induced harm to health. However, there is a paucity of information about tobacco-use and cessation among physicians in developing countries. To assess the need for and nature of smoking cessation services among physicians in developing countries, a detailed literature review of studies published in English, between 1987 and 2010 was carried out. The electronic databases Medline and Pub Med were searched for published studies. The findings show that there are regional variations in the current smoking prevalence, quitting intentions, and cessation services among physicians. Smoking prevalence (median) was highest in Central/Eastern Europe (37%), followed by Africa (29%), Central and South America (25%) and Asia (17.5%). There were significant gender differences in smoking prevalence across studies, with higher prevalence among males than females. Smoking at work or in front of patients was commonly practiced by physicians in some countries. Asking about smoking status or advising patients to quit smoking was not common practice among the physicians, especially among smoker physicians. Organized smoking cessation programs for physicians did not exist in all of these regions. This review suggests that while smoking of physicians varies across different developing regions; prevalence rates tend to be higher than among physicians in developed countries. Quitting rates were low among the physicians, and the delivery of advice on quitting smoking was not common across the studies. To promote tobacco control and increase cessation in populations, there is a need to build physicians’ capacity so that they can engage in tobacco use prevention and cessation activities.

The age of the population is shifting toward the elderly range, which may lead to an increased risk of mild cognitive impairment (MCI). The aims of this study are to evaluate the cognitive function in elderly people using the Montreal Cognitive Assessment (MoCA), to identify the relationship between cognitive function and different characteristics, and to evaluate the efficacy of the intervention after six months of cognitive training. In this study, we included 2886 subjects aged ≧60 years in the baseline survey, and 140 subjects with MCI who participated in the baseline survey were randomly divided into an intervention group (N = 70) and a control group (N = 70). The control group was not provided any intervention measures, and the intervention group was administered cognitive training. The education level, monthly income, sleep time, exercise time, reading times, and time spent engaging in community activities and performing housework were positively correlated with MoCA scores, but age was negatively correlated with MoCA scores. The total MoCA score of the intervention group increased from 19.77 ± 2.24 points to 21.09 ± 2.20 points after six months of cognitive training, but the score of the control group decreased from 20.41 ± 2.10 points to 19.17 ± 2.57 points. The two-way repeated-measures ANOVA revealed a very significant effect of the interaction between time and cognitive training on the total MoCA score. Seventeen participants in the intervention group improved to normal levels, and no participants progressed to dementia after six months of cognitive training. Thus, the efficacy of the intervention was statistically significant. Our study concludes that older age is associated with a cognitive decline. Factors that are more likely to protect against cognitive decline included a higher education level and monthly income, sufficient sleep time, regular physical exercise and reading, frequently engaging in community activities, and continuing to perform housework. Moreover, the cognitive training intervention is effective and may help to decrease the deterioration of cognitive function in patients with MCI, and the interaction between intervention time and cognitive training significantly improves cognitive function.

The problem of overweight and obesity in children and adolescents is considered an epidemic in both developed and developing world by the WHO. There has been little study on the relationship between health literacy and body weight among adolescents.

Objectives To determine the incidence and intensity of household impoverishment induced by cancer treatment in China. Design Average income and daily consumption per capita of the households and out-of-pocket payments for cancer care were estimated. Household impoverishment was determined by comparing per capita daily consumption against the Chinese poverty line (CPL, US$1.2) and the World Bank poverty line (WBPL, US$1.9) for 2015. Both pre-treatment and post-treatment consumptions were calculated assuming that the households would divert daily consumption money to pay for cancer treatment. Participants Cancer patients diagnosed initially from 1 January 2015 to 31 December 2016 who had received cancer treatment subsequently. Those with multiple cancer diagnoses were excluded. Data sources A household questionnaire survey was conducted on 2534 cancer patients selected from nine hospitals in seven provinces through two-stage cluster/convenience sampling. Findings 5.89% (CPL) to 12.94% (WBPL) households were impoverished after paying for cancer treatment. The adjusted OR (AOR) of post-treatment impoverishment was higher for older patients (AOR=2.666–4.187 for ≥50 years vs <50 years, p<0.001), those resided in central region (AOR=2.619 vs eastern, p<0.01) and those with lower income (AOR=0.024–0.187 in higher income households vs the lowest 20%, p<0.001). The patients without coverage from social health insurance had higher OR (AOR=1.880, p=0.040) of experiencing post-treatment household impoverishment than those enrolled with the insurance for urban employees. Cancer treatment is associated with an increase of 5.79% (CPL) and 12.45% (WBPL) in incidence of household impoverishment. The median annual consumption gap per capita underneath the poverty line accumulated by the impoverished households reached US$128 (CPL) or US$212 (WBPL). US$31 170 395 (CPL) or US$115 238 459 (WBPL) were needed to avoid household impoverishment induced by cancer treatment in China. Conclusions The financial burden of cancer treatment imposes a significant risk of household impoverishment despite wide coverage of social health insurance in China.

The aim of this study was to develop and internally validate an interpretable machine learning (ML) model for predicting the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) infection. We retrospectively collected clinical data from patients with HCC and CHB treated at the Fourth Affiliated Hospital of Guangxi Medical University from January 2022 to December 2022, including demographics, comorbidities, and laboratory parameters. The datasets were randomly divided into a training set (361 cases) and a validation set (155 cases) in a 7:3 ratio. Variables were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and multifactor logistic regression. The prediction model of HCC risk in CHB patients was constructed based on five machine learning models, including logistic regression (LR), K-nearest neighbour (KNN), support vector machine (SVM), random forest (RF) and artificial neural network (ANN). Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the model in terms of identification, calibration and clinical application. The SHapley Additive exPlanation (SHAP) method was used to rank the importance of the features and explain the final model. Among the five ML models constructed, the RF model has the best performance, and the RF model predicts the risk of HCC in patients with CHB in the training set [AUC: 0.996, 95% confidence interval (CI) (0.991-0.999)] and internal validation set [AUC: 0.993, 95% CI (0.986-1.000)]. It has high AUC, specificity, sensitivity, F1 score and low Brier score. Calibration showed good agreement between observed and predicted risks. The model yielded higher positive net benefits in DCA than when all participants were considered to be at high or low risk, indicating good clinical utility. In addition, the SHAP plot of the RF showed that age, basophil/lymphocyte ratio (BLR), D-Dimer, aspartate aminotransferase/alanine aminotransferase (AST/ALT), γ-glutamyltransferase (GGT) and alpha-fetoprotein (AFP) can help identify patients with CHB who are at high or low risk of developing HCC. ML models can be used as a tool to predict the risk of HCC in patients with CHB. The RF model has the best predictive performance and helps clinicians to identify high-risk patients and intervene early to reduce or delay the occurrence of HCC. However, the model needs to be further improved through large sample studies.