Background: This study aims to explore the impacts of knowledge and attitude on the behavior of antibiotic use during the treatment of the common cold based on the expanding KAP model, and then identify the critical behavioral stage. Methods: A cross-sectional study was conducted on 815 public from 21 community health centers (CHCs) in Chongqing, China. Based on the expanding KAP model, a self-administered questionnaire was designed to measure knowledge, attitude, multi-stage behavior, and perceived threat, in which multi-stage behavior was divided into pre-use antibiotic behavior, during-use antibiotic behavior, and post-use antibiotic behavior. A structural equation model was used to examine the model fit and the direct, indirect, mediating effects, and moderating effect of the variables. Results: The expanding KAP showed good model fit indices with χ²/df = 0.537, RMSEA = 0.033, CFI = 0.973, GFI = 0.971, NFI = 0.934, TLI = 0.979. Knowledge had a positive effect on attitude (β = 0.503, p < 0.05), pre-use antibiotic behavior (β = 0.348, p < 0.05), during-use antibiotic behavior (β = 0.461, p < 0.001), and post-use antibiotic behavior (β = 0.547, p < 0.001). Attitude had a positive effect on during-use antibiotic behavior (β = 0.296, p < 0.001), and post-use antibiotic behavior (β = 0.747, p < 0.001). The mediating effect of attitude was positive among knowledge, during-use antibiotic behavior (β = 0.149, p < 0.05), and post-use antibiotic behavior (β = 0.376, p < 0.001). Perceived threat also had a positive moderating effect between knowledge and post-use antibiotic behavior (β = 0.021, p < 0.001). Conclusions: Knowledge, attitude and perceived threat had different effects on different stages of antibiotic behavior. The critical behavioral stage prioritized the post-use antibiotic behavior and during-use antibiotic behavior over pre-use antibiotic behavior.

Background: The current evidence of extra length of stay (LOS) attributable to healthcare-associated infection (HCAI) scarcely takes time-dependent bias into consideration. Plus, limited evidences were from developing countries. We aim to estimate the extra LOS and risk factors of mortality attributable to HCAI for inpatients. Methods: Multi-state model (MSM) was adopted to estimate the extra LOS attributable to HCAI of each type and subgroup. COX regression model was used to examine the risk of mortality. Results: A total of 51,691 inpatients were included and 1709 (3.31%) among them developed HCAI. Lower respiratory tract infection and Acinetobacter baumannii were the most prevalent HCAI and causative pathogen in surveyed institute. Generally, the expected extra LOS attributable to HCAI was 2.56 days (95% confidence interval: 2.54–2.61). Patients below 65 had extra LOS attributable to HCAI longer about 2 days than those above. The extra LOS attributable to HCAI of male patients was 1.33 days longer than female. Meanwhile, age above 65 years old and HCAI were the risk factors of mortality for inpatients. Conclusions: HCAI contributes to an increase in extra LOS of inpatients in China. The effect of HCAI on extra LOS is different among subgroups, with the age below 65, male and medicine department more sensitive.

Background: Infection prevention and control (IPC) measures are crucial to combat the COVID-19 pandemic. This study aimed to explore the levels and determinants of HCWs’ IPC behaviors based on the theoretical domains framework (TDF), which has been shown to be effective in guiding behavior change. Methods: A cross-sectional survey was conducted in Wuhan, China in January 2020. Self-reported hand hygiene and droplet isolation behaviors (including the use of masks, gloves, goggles and gowns) were set as dependent variables. TDF domains and HCWs’ characteristics were independent variables. Negative binomial regression analyses were performed to explore their relationships. Results: HCWs reported good IPC behaviors, while the compliance with goggle and gown use was relatively low (below 85%). Environmental context and resources domain was significantly related to hand hygiene (β = 0.018, p = 0.026), overall droplet isolation behaviors (β = 0.056, p = 0.001), goggle (β = 0.098, p = 0.001) and gown use (β = 0.101. p < 0.001). Knowledge domain was significantly related to goggle (β = 0.081, p = 0.005) and gown use (β = 0.053, p = 0.013). Emotion domain was a predictor of overall droplet isolation behaviors (β = 0.043, p = 0.016), goggle (β = 0.074, p = 0.026) and gown use (β = 0.106, p < 0.001). Social influences domain was a predictor of overall droplet isolation behaviors (β = 0.031, p = 0.029) and gown use (β = 0.039, p = 0.035). HCWs in high-risk departments had better behaviors of gown use (β = 0.158, p = 0.032). HCWs who had encountered confirmed or suspected patients reported worse behaviors of goggle (β = − 0.127, p = 0.050) and gown use (β = − 0.153, p = 0.003). Conclusions: Adequate personal protective materials and human resources, education and training, as well as supervision and role model setting are necessary to improve IPC behaviors regarding the COVID-19 pandemic.

Introduction: The outbreak of COVID-19 has spread worldwide. The evidence about risk factors of healthcare workers who infected COVID-19 is limited. This study aims to describe characteristics and influencing factors of the COVID-19 infection in healthcare workers. Methodology: The study was performed among COVID-19 infected and uninfected healthcare workers in three hospitals in Wuhan. A total of 325 healthcare workers participated; among them 151 COVID-19-infected healthcare workers were included. Characteristics of infected healthcare workers, and influencing factors including exposure histories, the use of protective equipment in different risk conditions and areas, perceptions, emotions, satisfactions and educations were described and analyzed. Results: Healthcare workers got infected clustered mostly in the physical examination center. When performing general operations on confirmed or suspected patients, the use of protective equipment including the effectiveness of masks (p < 0.001), gloves (p < 0.001); and the use of gloves (p < 0.001), suits (p < 0.001), gowns (p < 0.001), shoe covers (p < 0.001), and hats (p < 0.001) were protective factors. The use of protective equipment was a protective factor in most cases. Negative emotions and dissatisfaction to the hospital response were associated with the increased risk of infection. Conclusions: The use of protective equipment, emotions and satisfactions to hospital responses are key COVID-19-infected factors. The awareness, the supply and the use of protective equipment, the layout of departments and other environmental and management factors should be strictly equipped. In addition, hospitals should also pay attention to emotions and satisfaction of healthcare workers.