Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China.

Objective: Insulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the "common soil" of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings. Methods: We analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models. Results: The LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc. Conclusion: The ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.

Equity of Health Services Utilisation and Expenditure among Urban and Rural Residents under Universal Health Coverage.

Worldwide countries are recognising the need for and significance of universal health coverage (UHC); however, health inequality continues to persist. This study evaluates the status and equity of residents' demand for and utilisation of health services and expenditure by considering the three components of universal health coverage, urban-rural differences, and different income groups. Sample data from China's Fifth Health Service Survey were analysed and the 'five levels of income classification' were used to classify people into income groups. This study used descriptive analysis and concentration index and concentration curve for equity evaluation. Statistically significant differences were found in the demand and utilisation of health services between urban and rural residents. Rural residents' demand and utilisation of health services decreased with an increase in income and their health expenditure was higher than that of urban residents. Compared with middle- and high-income rural residents, middle- and lower-income rural residents faced higher hospitalisation expenses; and, compared with urban residents, equity in rural residents' demand and utilisation of health services, and annual health and hospitalisation expenditures, were poorer. Thus, equity of health service utilisation and expenditure for urban and rural residents with different incomes remain problematic, requiring improved access and health policies.

Medical students' motivation and academic performance: the mediating roles of self-efficacy and learning engagement.

Background: Motivation matters in medical students' academic performance. However, few studies have specifically examined how motivation and external environmental factors (e.g., institutions) affect medical students' performance with large-scale data sets. The roles of self-efficacy and learning engagement in the mechanisms that govern how motivation affects academic performance are still unclear.Objective: This study aims to advance a comprehensive understanding about the relationships between medical students' motivation, self-efficacy, learning engagement, and academic performance in a nationwide survey, taking students' demographic factors and sociocultural environments into consideration.Design: We collected data from 1930 medical students in China. We probed the relations between studying variables. We then performed structural equation model (SEM) analysis to examine the mediating roles of self-efficacy and learning engagement on the relationship between motivation and academic performance. We further carried out multiple-group SEM analyses to compare differences between males and females, and between students in key universities and colleges (KUCs) and non-key universities and colleges (NKUCs).Results: Medical students in KUCs demonstrated significantly higher intrinsic motivation, better academic performance and lower extrinsic motivation than those in NKUCs. Male students reported higher intrinsic motivation but surprisingly lower academic performance than females. The total effect of intrinsic motivation on academic performance was larger than that of extrinsic motivation. There were significant indirect effects of either intrinsic or extrinsic motivation on academic performance through learning engagement. Besides, both intrinsic motivation and extrinsic motivation predicted self-efficacy; however, the direct effect of self-efficacy on academic performance was not significant.Conclusions: This study provided researchers with a holistic picture of students' types of motivation in relation to academic performance. Findings from this study can help in rethinking the role of self-efficacy in medicine, in finding more effective interventions for promoting medical students' levels of motivation, and in developing motivation-related counselling methods for different groups of medical students.

Low-cost Scholl-coupling microporous polymer as an efficient solid-phase microextraction coating for the detection of light aromatic compounds.

A cost-effective microporous polymer was synthesized using cheap monomer and catalyst via one-step Scholl-coupling reaction, and its chemical, morphological characteristics and pore structure were investigated. The as-synthesized polymer with large surface area and narrow pore distribution (centered in 1.2 nm) was prepared as a fiber coating for solid-phase microextraction (SPME). Headspace SPME was used for the extraction of the light aromatic compounds, e.g. benzene, toluene, ethylbenzene, m-xylene, naphthalene and acenaphthene. The parameters influencing the extraction and desorption efficiencies, such as extraction temperature and time, salt concentration, desorption temperature and time were investigated and optimized. The results showed that the home-made fiber had superior extraction efficiencies compared with the commercial PDMS fiber. Under the optimized conditions, low detection limits (0.01-1.3 ng/L), wide linear ranges (from 50 to 20000 ng/L to 1-20000 ng/L), good repeatability (4.2-9.3%, n = 6) and reproducibility (0.30-11%, n = 3) were achieved. Moreover, the practical applicability of the coating and proposed method was evaluated by determining the target light aromatic compounds in environmental water samples with satisfied recoveries (83.2%-116%).

Inequality in the hepatitis B awareness level in rural residents from 7 provinces in China.

The hepatitis B (HB) awareness level is an important factor affecting the rates of HB virus vaccination. To better understand income-related inequalities in the HB awareness level, it is imperative to identify the sources of inequalities and assess the contribution rates of these influential factors. This study analyzed the unequal distribution of the HB awareness level and the contributions of various influential factors. We performed a cross-sectional household survey with questionnaire-based, face-to-face interviews in 7 Chinese provinces. Responses from 7271 respondents were used in this analysis. Multinomial logistic regression was used for the analysis of contributing factors, and the concentration index was used as a measure of HB awareness inequalities. The HB awareness level varied across participants with different characteristics. Multinomial logistic regression of the explanatory factors of the HB awareness level showed that several estimated coefficients and relative risk ratios were statistically significant for middle- and high-level awareness, except for sex, occupation, and household income. The concentration index of the HB knowledge score was 0.140, indicating inequality gradients disadvantageous to the poor. The contribution rate of socioeconomic factors was the largest (60.8%), followed by demographic characteristics (29.0%) and geographic factors (4.3%). Demographic, socioeconomic, and geographic factors are associated with the HB awareness inequality. Therefore, to reduce inequality, HB-related health education targeting individuals with low socioeconomic status should be performed. Less-developed provinces, especially with high proportions of poor residents, warrant particular attention. Our findings may be beneficial to improve the HB virus vaccination rate for individuals with low socioeconomic status.