The Relationship between Hospital Infection Surveillance and Control Activities and Antibiotic Resistant Pathogen Rates

Objective: The purpose of the study was to examine the relationship of surveillance and control activities in Canadian hospitals with rates of nosocomial methicillin-resistant S. aureus (MRSA), C. difficile associated diarrhea (CDAD), and vancomycin-resistant Enterococcus (VRE). Methods: Surveys were sent to Infection Control programs in hospitals that participated in an earlier survey of infection control practices in Canadian acute care hospitals. Results: One hundred and twenty of 145 (82.8%) hospitals responded to the survey. The mean MRSA rate was 2.0 (SD 2.9) per 1,000 admissions, the mean CDAD rate was 3.8 (SD 4.3), and the mean VRE rate was 0.4 (SD 1.5). Multiple stepwise regression analysis found hospitals that reported infection rates by specific risk groups (r = - 0.27, p < 0.01) and that kept attendance records of infection control teaching activities (r = - 0.23, p < 0.01) were associated with lower MRSA rates. Multiple stepwise regression analysis found larger hospitals (r = 0.25, p < 0.01) and hospitals where infection control committees or staff had the direct authority to close a ward or unit to further admissions due to outbreaks (r = 0.22, p < 0.05) were associated with higher CDAD rates. Multiple logistic regression analysis found larger hospitals (OR = 1.6, CI 1.2 - 2.0, p = 0.003) and teaching hospitals (OR = 3.7, CI 1.2 - 11.8, p = 0.02) were associated with the presence of VRE. Hospitals were less likely to have VRE when infection control staff frequently contacted physicians and nurses for reports of new infections (OR = 0.5, CI 0.3 - 0.7, p = 0.02) and there were in-service programs for updating nursing and ancillary staff on current infection control practices (OR = 0.2, CI 0.1 - 0.7, p = 0.01). Conclusions: Surveillance and control activities were associated with MRSA and CDAD rates and the presence of VRE. Surveillance and control activities might be especially beneficial in large and teaching hospitals.

Estimating the Heading of a Husky Mobile Robot with a LiDAR Compass based on Direction Maps

Without an absolute position sensor (e.g., GPS), an accurate heading estimate is necessary for proper localization of an autonomous unmanned vehicle or robot. This paper introduces direction maps (DMs), which represent the directions of only dominant surfaces of the vehicle’s environment and can be created with negligible effort. Given an environment with reoccurring surface directions (e.g., walls, buildings, parked cars), lines extracted from laser scans can be matched with a DM to provide an extremely lightweight heading estimate that is shown, through experimentation, to drastically reduce the growth of heading errors. The algorithm was tested using a Husky A200 mobile robot in a warehouse environment over traverses hundreds of metres in length. When a simple a priori DM was provided, the resulting heading estimation showed virtually no error growth.