Journal of Hospital Infection · 2017 · 47 citations · 21 references
Life SciencesInfectious DiseasesMicrobiologyAir PollutionMedical MicrobiologyVentilationAir QualityHealthcare-associated InfectionsMicrobial ContaminationOperating Room Air Quality ControlOperating Room SafetyRespiratory Tract InfectionsAir Contamination Risk AssessmentMicrobial Air SamplingMicrobial Contamination ControlMicrobial Contamination RiskOperating Room Environmental HygieneOperating Room Infection PreventionTraffic Flow Rate
Current literature examining the relationship between door-opening rate, number of people present, and microbial air contamination in the operating room is limited. Studies are especially needed from low- and middle-income countries, where the risk of surgical site infections is high. To assess microbial air contamination in operating rooms at a Ghanaian teaching hospital and the association with door-openings and number of people present. Moreover, we aimed to document reasons for door-opening. We conducted active air-sampling using an MAS 100® portable impactor during 124 clean or clean-contaminated elective surgical procedures. The number of people present, door-opening rate and the reasons for each door-opening were recorded by direct observation using pretested structured observation forms. During surgery, the mean number of colony-forming units (cfu) was 328 cfu/m3 air, and 429 (84%) of 510 samples exceeded a recommended level of 180 cfu/m3. Of 6717 door-openings recorded, 77% were considered unnecessary. Levels of cfu/m3 were strongly correlated with the number of people present (P = 0.001) and with the number of door-openings/h (P = 0.02). In empty operating rooms, the mean cfu count was 39 cfu/m3 after 1 h of uninterrupted ventilation and 52 (51%) of 102 samples exceeded a recommended level of 35 cfu/m3. The study revealed high values of intraoperative airborne cfu exceeding recommended levels. Minimizing the number of door-openings and people present during surgery could be an effective strategy to reduce microbial air contamination in low- and middle-income settings.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text
“Practicing” Medicine without Risk
James A. Gordon, William M. Wilkerson, David Williamson Shaffer et al. · Academic Medicine · 2001 · 346 citations