The Effect of Governance on Industrial Wastewater Pollution in China.

Water pollution not only aggravates the deterioration of the ecological environment and endanger human health, but also has a significantly negative impact on economic growth and social development. It is crucial to investigate the relationship between industrial wastewater governance and industrial wastewater pollution on the path to reduce water pollution. In this paper, we studied whether industrial wastewater governance affected industrial wastewater pollution using the panel fixed effect model and system generalized moment estimation model (SYS-GMM) with the panel data of 30 provinces from 2005 to 2020 in China. This is the only empirical analysis of the relationship between industrial wastewater governance and industrial wastewater pollution. We proxied industrial wastewater pollution by organic pollutants and inorganic pollutants and measured the per capita investment in industrial wastewater governance. The results shed light on the positive correlation between the per capita investment in industrial wastewater governance and industrial wastewater pollution. The increase in per capita investment in industrial wastewater governance promoted the increase of pollutant emissions from industrial wastewater. The estimation also indicated that there was an inverted U-shaped relationship between per capita GDP and inorganic /organic pollutants in industrial wastewater. Our empirical research shows that it is necessary to increase investment in industrial wastewater treatment and optimize the investment structure of environmental treatment, so as to pave the way for the comprehensive utilization of a variety of environmental treatment solutions.

Machine learning-based long-term outcome prediction in patients undergoing percutaneous coronary intervention.

BACKGROUND: Traditional prognostic risk assessment in patients with coronary artery disease undergoing percutaneous coronary intervention (PCI) is based on a limited selection of clinical and imaging findings. Machine learning (ML) can consider a higher number and complexity of variables and may be useful for characterising cardiovascular risk, predicting outcomes, and identifying biomarkers in large population studies. METHODS: We prospectively enrolled 9,680 consecutive patients with coronary artery disease who underwent PCI at our institution between January 2013 and December 2013. Clinical features were selected and used to train 6 different ML models (support vector machine, decision tree, random forest, gradient boosting decision tree, neural network, and logistic regression) to predict cardiovascular outcomes, 10-fold cross-validation to evaluate the performance of models. RESULTS: During the 5-year follow-up, 467 (4.82%) patients died. Eighty-seven risk baseline measurements were used to train ML models. Compared with the other models, the random forest model (RF-PCI) exhibited the best performance on predicting all-cause mortality (area under the receiver operating characteristic curve: 0.71±0.04). Calibration plots demonstrated a slight overprediction for patients using the RF-PCI model (Hosmer-Lemeshow test: P>0.05). The top 15 features related to PCI candidates' long-term prognosis, among which 11 were laboratory measures. CONCLUSIONS: ML models improved the prediction of long-term all-cause mortality in patients with coronary artery disease before PCI. The performance of the RF model was better than that of the other models, providing a meaningful stratification.

A novel risk model for mortality and hospitalization following cardiac resynchronization therapy in patients with non-ischemic cardiomyopathy: the alpha-score.

BACKGROUND: Non-ischemic cardiomyopathy (NICM) has been associated with a better left ventricle reverse remodeling response and improved clinical outcomes after cardiac resynchronization therapy (CRT). The aims of our study were to identify the predictors of mortality and heart failure hospitalization in patients treated with CRT and design a risk score for prognosis. METHODS: A cohort of 422 consecutive NICM patients with CRT was retrospectively enrolled between January 2010 and December 2017. The primary endpoint was all-cause mortality and heart transplantation. RESULTS: In a multivariate analysis, the predictors of all-cause death were left atrial diameter [Hazard ratio (HR): 1.056, 95% confidence interval (CI): 1.020-1.093, P = 0.002]; non-left bundle branch block [HR: 1.793, 95% CI: 1.131-2.844, P = 0.013]; high sensitivity C-reactive protein [HR: 1.081, 95% CI: 1.029-1.134 P = 0.002]; and N-terminal pro-B-type natriuretic peptide [HR: 1.018, 95% CI: 1.007-1.030, P = 0.002]; and New York Heart Association class IV [HR: 1.018, 95% CI: 1.007-1.030, P = 0.002]. The Alpha-score (Atrial diameter, non-LBBB, Pro-BNP, Hs-CRP, NYHA class IV) was derived from each independent risk factor. The novel score had good calibration (Hosmer-Lemeshow test, P > 0.05) and discrimination for both primary endpoints [c-statistics: 0.749 (95% CI: 0.694-0.804), P < 0.001] or heart failure hospitalization [c-statistics: 0.692 (95% CI: 0.639-0.745), P < 0.001]. CONCLUSION: The Alpha-score may enable improved discrimination and accurate prediction of long-term outcomes among NICM patients with CRT.

Sociodemographic Correlates of Medical Mistrust among African American Men Living in the East Bay.

This study examined correlates of medical mistrust among African American men living in the East Bay. We conducted a cross-sectional analysis using survey data from 207 adult African American males, recruited from barbershops. We used linear regression to assess associations between socioeconomic status (SES) and two medical mistrust outcomes (mistrust of health care organizations (HCOs) and physicians). There was a strong relationship between health insurance, income, education, and mistrust. Insured subjects were 8.5% (95% CI -0.154 to -0.016) less likely to mistrust HCOs and 8.5% less likely (95% CI -0.145 to -0.025) to mistrust physicians. Those in the highest levels of income (>$60,000 annual income) or education (bachelor's degree or higher) were 5.4% (95% CI -0.115 to -0.007) and 5.7% (95% CI -0.104 to -0.011) less likely to mistrust HCO and physicians, respectively, than others. We conclude that sociodemographic factors are correlated with medical mistrust and discuss options for reducing medical mistrust.

Poverty and Community-Acquired Antimicrobial Resistance with Extended-Spectrum ß-Lactamase-Producing Organisms, Hyderabad, India.

The decreasing effectiveness of antimicrobial agents is a global public health threat, yet risk factors for community-acquired antimicrobial resistance (CA-AMR) in low-income settings have not been clearly elucidated. Our aim was to identify risk factors for CA-AMR with extended-spectrum ß-lactamase (ESBL)-producing organisms among urban-dwelling women in India. We collected microbiological and survey data in an observational study of primigravidae women in a public hospital in Hyderabad, India. We analyzed the data using multivariate logistic and linear regression and found that 7% of 1,836 women had bacteriuria; 48% of isolates were ESBL-producing organisms. Women in the bottom 50th percentile of income distribution were more likely to have bacteriuria (adjusted odds ratio 1.44, 95% CI 0.99-2.10) and significantly more likely to have bacteriuria with ESBL-producing organisms (adjusted odds ratio 2.04, 95% CI 1.17-3.54). Nonparametric analyses demonstrated a negative relationship between the prevalence of ESBL and income.

Childhood Illness and the Gender Gap in Adolescent Education in Low- and Middle-Income Countries.

BACKGROUND: Achieving gender equality in education is an important development goal. We tested the hypothesis that the gender gap in adolescent education is accentuated by illnesses among young children in the household. METHODS: Using Demographic and Health Surveys on 41 821 households in 38 low- and middle-income countries, we used linear regression to estimate the difference in the probability adolescent girls and boys were in school, and how this gap responded to illness episodes among children <5 years old. To test the hypothesis that investments in child health are related to the gender gap in education, we assessed the relationship between the gender gap and national immunization coverage. RESULTS: In our sample of 120 708 adolescent boys and girls residing in 38 countries, girls were 5.08% less likely to attend school than boys in the absence of a recent illness among young children within the same household (95% confidence interval [CI], 5.50%-4.65%). This gap increased to 7.77% (95% CI, 8.24%-7.30%) and 8.53% (95% CI, 9.32%-7.74%) if the household reported 1 and 2 or more illness episodes, respectively. The gender gap in schooling in response to illness was larger in households with a working mother. Increases in child vaccination rates were associated with a closing of the gender gap in schooling (correlation coefficient = 0.34, P = .02). CONCLUSIONS: Illnesses among children strongly predict a widening of the gender gap in education. Investments in early childhood health may have important effects on schooling attainment for adolescent girls.