Objectives Chinese county hospitals have been excessively enlarging their scale during the healthcare reform since 2009. The purpose of this paper is to examine the technical efficiency and productivity of county hospitals during the reform process, and to determine whether, and how, efficiency is affected by various factors. Setting and participants 114 sample county hospitals were selected from Henan province, China, from 2010 to 2012. Outcome measures Data envelopment analysis was employed to estimate the technical and scale efficiency of sample hospitals. The Malmquist index was used to calculate productivity changes over time. Tobit regression was used to regress against 4 environmental factors and 5 institutional factors that affected the technical efficiency. Results (1) 112 (98.2%), 112 (98.2%) and 104 (91.2%) of the 114 sample hospitals ran inefficiently in 2010, 2011 and 2012, with average technical efficiency of 0.697, 0.748 and 0.790, respectively. (2) On average, during 2010–2012, productivity of sample county hospitals increased by 7.8%, which was produced by the progress in technical efficiency changes and technological changes of 0.9% and 6.8%, respectively. (3) Tobit regression analysis indicated that government subsidy, hospital size with above 618 beds and average length of stay assumed a negative sign with technical efficiency; bed occupancy rate, ratio of beds to nurses and ratio of nurses to physicians assumed a positive sign with technical efficiency. Conclusions There was considerable space for technical efficiency improvement in Henan county hospitals. During 2010–2012, sample hospitals experienced productivity progress; however, the adverse change in pure technical efficiency should be emphasised. Moreover, according to the Tobit results, policy interventions that strictly supervise hospital bed scale, shorten the average length of stay and coordinate the proportion among physicians, nurses and beds, would benefit hospital efficiency.

Objectives. Infectious disease knowledge and behaviors are key elements that ensure student health and safety. This study explores the impact of health education on student knowledge and behaviors toward infectious diseases and determines the factors affecting infectious diseases knowledge and behaviors among students in Gansu, China. Methods. A cross-sectional study and three sampling methods were used in two counties, 12 schools, and 32 classes in Gansu, China, from 2012 to 2013. Collected data included the following: (1) sociodemographic characteristics of 2002 students (1001 participants in the intervention group and 1001 in the control group); (2) accuracy of student knowledge and behaviors toward infectious diseases based on comparison of intervention and control groups through X2 test; and (3) mean scores on knowledge and behavior of students with different characteristics toward infectious diseases, as analyzed through analysis of variance (ANOVA). Multiple linear regression was conducted to analyze factors affecting student knowledge and behaviors toward infectious diseases. Results. Statistically significant differences were observed among eight items of infectious disease transmission and treatment knowledge between intervention and control groups (P<0.001). Average accuracies of knowledge and behaviors toward infectious diseases reached 72.23% and 60.03%. Significant differences were observed in six items on student behavior in rural and urban areas (P<0.001). Health education, household register, and county affected scores of student knowledge and behaviors toward infectious diseases (P<0.05). Gender and education level also affected scores of student behaviors toward infectious diseases (P<0.001). Conclusions. Health education contributes to student knowledge and behaviors toward infectious diseases. Students in the control group need intensified health education on infectious diseases. Health education needs to pay particular attention to rural students, all male students, and students at senior high school level living on campus.

Objective Township hospitals (THs) are important components of the three-tier rural healthcare system of China. However, the efficiency and productivity of THs have been questioned since the healthcare reform was implemented in 2009. The objective of this study is to analyse the efficiency and productivity changes in THs before and after the reform process. Setting and participants A total of 48 sample THs were selected from the Xiaogan Prefecture in Hubei Province from 2008 to 2014. Outcome measures First, bootstrapping data envelopment analysis (DEA) was performed to estimate the technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) of the sample THs during the period. Second, the bootstrapping Malmquist productivity index was used to calculate the productivity changes over time. Results The average TE, PTE and SE of the sample THs over the 7-year period were 0.5147, 0.6373 and 0.7080, respectively. The average TE and PTE increased from 2008 to 2012 but declined considerably after 2012. In general, the sample THs experienced a negative shift in productivity from 2008 to 2014. The negative change was 2.14%, which was attributed to a 23.89% decrease in technological changes (TC). The sample THs experienced a positive productivity shift from 2008 to 2012 but experienced deterioration from 2012 to 2014. Conclusions There was considerable space for TE improvement in the sample THs since the average TE was relatively low. From 2008 to 2014, the sample THs experienced a decrease in productivity, and the adverse alteration in TC should be emphasised. In the context of healthcare reform, the factors that influence TE and productivity of THs are complex. Results suggest that numerous quantitative and qualitative studies are necessary to explore the reasons for the changes in TE and productivity.

Nurses play a pivotal role in the delivery of medical services. Professional commitment is crucial for nursing professionals’ long-term, healthy, and sustainable development. However, nursing students’ professional commitment levels are currently unsatisfactory in China, especially given that the COVID-19 pandemic has posed unprecedented challenges to the profession. Therefore, studies investigating the professional commitment levels of nursing students and the underlying influencing factors are urgently required. This study explored how nursing students’ risk perceptions, negative emotions, and psychological capital affected their professional commitment during the COVID-19 pandemic. A cross-sectional study was conducted among nursing students using risk perception, professional commitment, negative emotions, and psychological capital scales. An analysis of 1142 Chinese nursing students suggested that nursing students’ risk perception positively impacted professional commitment and that negative emotions mediated this association. Importantly, psychological capital moderates the mediating effect of negative emotions and can buffer the negative emotions caused by risk perception. This study demonstrated that effective intervention strategies should be implemented in multiple dimensions such as education, individual, public and society to improve the professional commitment of nursing students.

Background and Objectives To characterize the association of ambient particulate matter (PM) pollution of different sizes (PM ≤1 µm in aerodynamic diameter [PM₁], PM_2.5, and PM₁₀) with in-hospital case fatality among patients with stroke in China. Methods We collected hospitalizations due to stroke in 4 provinces in China from 2013 to 2019. Seven-day and annual averages of PM prior to hospitalization were estimated using bilinear interpolation and residential addresses. Associations with in-hospital case fatality were estimated using random-effects logistic regression models. Potential reducible fraction and the number of fatalities attributed to PM were estimated using a counterfactual approach. Results Among 3,109,634 stroke hospitalizations (mean age 67.23 years [SD 12.22]; 1,765,644 [56.78%] male), we identified 32,140 in-hospital stroke fatalities (case fatality rate 1.03%). Each 10 µg/m³ increase in 7-day average (short-term) exposure to PM was associated with increased in-hospital case fatality: odds ratios (ORs) were 1.058 (95% CI 1.047–1.068) for PM₁, 1.037 (95% CI 1.031–1.043) for PM_2.5, and 1.025 (95% CI 1.021–1.029) for PM₁₀. Similar but larger ORs were observed for annual averages (long-term): 1.240 (95% CI 1.217–1.265) for PM₁, 1.105 (95% CI 1.094–1.116) for PM_2.5, and 1.090 (95% CI 1.082–1.099) for PM₁₀. In counterfactual analyses, PM₁₀ was associated with the largest potential reducible fraction in in-hospital case fatality (10% [95% CI 8.3–11.7] for short-term exposure and 21.1% [19.1%–23%] for long-term exposure), followed by PM₁ and PM_2.5. Discussion PM pollution is a risk factor for in-hospital stroke-related deaths. Strategies that target reducing PM pollution may improve the health outcomes of patients with stroke.

There is little evidence on the association between PM_2.5 chemical components and fatality among hospitalized stroke patients. This study used an inpatient discharge database from 2013 to 2019 in four provinces (Sichuan, Shanxi, Guangxi, and Guangdong) in China. Annual average exposure to PM_2.5 and its five chemical components [black carbon (BC), organic matter (OM), sulphate ( S O 4 2 - ), nitrate ( N O 3 - ), and ammonium ( N H 4 + )] were estimated using bilinear interpolation at patient's residential address. Mixed-effects logistic regression models were conducted to estimate the odds ratios (ORs). Counterfactual analyses were used to estimate the population attributable burden (PAF). Among 3,069,093 hospitalized patients with stroke, each interquartile (IQR) increment in PM_2.5 and its chemical components was significantly associated with stroke fatality: the ORs were 1.137 [95% confidence interval (CI): 1.118-1.157; IQR: 15.14 μg/m³] for PM_2.5, 1.108 (95% CI: 1.091-1.126; IQR: 0.71 μg/m³) for BC, 1.086 (95% CI: 1.069-1.104; IQR: 3.47 μg/m³) for OM, and 1.065 (95% CI: 1.048-1.083; IQR: 2.81 μg/m³) for S O 4 2 - . We did not find significant associations for N O 3 - (OR: 0.991, 95% CI: 0.975-1.008; IQR: 3.30 μg/m³). The associations were larger among patients with ischemic stroke than those with hemorrhagic stroke. The PAFs were 10.6% (95% CI: 9.1-12.2%) for BC, 9.9% (95% CI: 8.2-11.7%) for OM, and 6.6% (4.9-8.3%) for S O 4 2 - . Ambient BC, OM, and S O 4 2 - might be important risk factors for stroke fatality. The findings advocate the need to develop tailored guidelines for PM chemical components and curb the emissions of the most hazardous chemical components. Bill & Melinda Gates Foundation (INV-016826).