Association Between the Community Prevalence of COVID-19 and Daily Unscheduled Absences of Anesthesiologists, Nurse Anesthetists, and Residents in an Academic Anesthesia Department.

Introduction An "unscheduled absence" refers to an occurrence when an employee does not appear for work and the absence was not approved in advance by an authorized supervisor. Daily unscheduled absences need to be forecasted when doing staff scheduling to maintain an acceptable risk of being unable to run all anesthetizing locations and operating rooms planned. The number of extra personnel to be scheduled needs to be at least twice as large as the mean number absent. In an earlier historical cohort study, we found that our department's modeled risks of being unavailable unexpectedly differed among types of anesthesia practitioners (e.g., anesthesiologists and nurse anesthetists) and among weekdays (i.e., Mondays, Fridays, and workdays adjacent to holidays versus other weekdays). In the current study, with two extra years of data, we examined the effect of the coronavirus COVID-19 pandemic on the frequency of unscheduled absences. Methods There were 50 four-week periods studied at a large teaching hospital in the United States, from August 30, 2018 to June 29, 2022. The sample size of 120,687 person-assignment days (i.e., a person assigned to work on a given day) included 322 anesthesia practitioners (86 anesthesiologists, 88 certified registered nurse anesthetists, 99 resident and fellow physicians, and 49 student nurse anesthetists). The community prevalence of COVID­19 was estimated using the percentage positive among asymptomatic patients tested before surgery and other interventional procedures at the hospital. Results Each 1% increase in the prevalence of COVID-19 among asymptomatic patients was associated with a 1.131 increase in the odds of unscheduled absence (P < 0.0001, 99% confidence interval 1.086 to 1.178). Using an alternative model with prevalence categories, unscheduled absences were substantively more common when the COVID-19 prevalence exceeded 2.50%, P [Formula: see text] 0.0002. For example, there was a 1% unscheduled absence rate among anesthesiologists working Mondays and Fridays early in the pandemic when the prevalence of COVID-19 among asymptomatic patients was 1.3%. At a 1% unscheduled absence rate, 67 would be the minimum scheduled to maintain a <5.0% risk for being unable to run all 65 anesthetizing locations. In contrast, there was a 3% unscheduled absence rate among nurse anesthetists working Mondays and Fridays during the Omicron variant surge when the prevalence was 4.5%. At a 3% unscheduled absence rate, 70 would be the minimum scheduled to maintain the same risk of not being able to run 65 rooms. Conclusions Increases in the prevalence of COVID-19 asymptomatic tests were associated with more unscheduled absences, with no detected threshold. This quantitative understanding of the impact of communicable diseases on the workforce potentially has broad generalizability to other fields and infectious diseases.

Association Between the Community Prevalence of COVID-19 and Daily Unscheduled Absences of Anesthesiologists, Nurse Anesthetists, and Residents in an Academic Anesthesia Department

Introduction An “unscheduled absence” refers to an occurrence when an employee does not appear for work and the absence was not approved in advance by an authorized supervisor. Daily unscheduled absences need to be forecasted when doing staff scheduling to maintain an acceptable risk of being unable to run all anesthetizing locations and operating rooms planned. The number of extra personnel to be scheduled needs to be at least twice as large as the mean number absent. In an earlier historical cohort study, we found that our department’s modeled risks of being unavailable unexpectedly differed among types of anesthesia practitioners (e.g., anesthesiologists and nurse anesthetists) and among weekdays (i.e., Mondays, Fridays, and workdays adjacent to holidays versus other weekdays). In the current study, with two extra years of data, we examined the effect of the coronavirus COVID-19 pandemic on the frequency of unscheduled absences. Methods There were 50 four-week periods studied at a large teaching hospital in the United States, from August 30, 2018 to June 29, 2022. The sample size of 120,687 person-assignment days (i.e., a person assigned to work on a given day) included 322 anesthesia practitioners (86 anesthesiologists, 88 certified registered nurse anesthetists, 99 resident and fellow physicians, and 49 student nurse anesthetists). The community prevalence of COVID‑19 was estimated using the percentage positive among asymptomatic patients tested before surgery and other interventional procedures at the hospital. Results Each 1% increase in the prevalence of COVID-19 among asymptomatic patients was associated with a 1.131 increase in the odds of unscheduled absence (P < 0.0001, 99% confidence interval 1.086 to 1.178). Using an alternative model with prevalence categories, unscheduled absences were substantively more common when the COVID-19 prevalence exceeded 2.50%, P Conclusions Increases in the prevalence of COVID-19 asymptomatic tests were associated with more unscheduled absences, with no detected threshold. This quantitative understanding of the impact of communicable diseases on the workforce potentially has broad generalizability to other fields and infectious diseases.

Scheduling staff for ambulatory anaesthesia.

PURPOSE OF REVIEW: In this study, we summarize six articles published from January 2020 through June 2022 covering anaesthesia staff scheduling and consider their relevance to ambulatory surgery. Staff scheduling refers to the planned shift length of each person working on specific dates. RECENT FINDINGS: Increasing shift lengths compensates for COVID-19 pandemic staffing issues by reducing patient queues and mitigating the impact of staff absence from SAR-CoV-2 infection. Reduced labour costs can often be achieved by regularly scheduling more practitioners than expected from intuition. Probabilities of unscheduled absences, estimated using historical data, should be incorporated into staff scheduling calculations. Anesthetizing locations, wherein anaesthesiologists are scheduled, may need to be revised if the practitioner is lactating to facilitate uninterrupted breast milk pumping sessions. If room assignments are based on the educational value for residents, then schedule other practitioners based on residents' expected work hours, not their planned shift lengths. Mixed integer programming can be used effectively to reduce variability among resident physicians in workloads during their rotations. SUMMARY: Readers can reasonably select among these studies and benefit from the one or two applicable to their facilities' characteristics and work hours.

Design of Clinical Trials Evaluating Sedation in Critically Ill Adults Undergoing Mechanical Ventilation: Recommendations From Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research (SCEPTER) Recommendation III.

OBJECTIVES: Clinical trials evaluating the safety and effectiveness of sedative medication use in critically ill adults undergoing mechanical ventilation differ considerably in their methodological approach. This heterogeneity impedes the ability to compare results across studies. The Sedation Consortium on Endpoints and Procedures for Treatment, Education, and Research Recommendations convened a meeting of multidisciplinary experts to develop recommendations for key methodologic elements of sedation trials in the ICU to help guide academic and industry clinical investigators. DESIGN: A 2-day in-person meeting was held in Washington, DC, on March 28-29, 2019, followed by a three-round, online modified Delphi consensus process. PARTICIPANTS: Thirty-six participants from academia, industry, and the Food and Drug Administration with expertise in relevant content areas, including two former ICU patients attended the in-person meeting, and the majority completed an online follow-up survey and participated in the modified Delphi process. MEASUREMENTS AND MAIN RESULTS: The final recommendations were iteratively refined based on the survey results, participants' reactions to those results, summaries written by panel moderators, and a review of the meeting transcripts made from audio recordings. Fifteen recommendations were developed for study design and conduct, subject enrollment, outcomes, and measurement instruments. Consensus recommendations included obtaining input from ICU survivors and/or their families, ensuring adequate training for personnel using validated instruments for assessments of sedation, pain, and delirium in the ICU environment, and the need for methodological standardization. CONCLUSIONS: These recommendations are intended to assist researchers in the design, conduct, selection of endpoints, and reporting of clinical trials involving sedative medications and/or sedation protocols for adult ICU patients who require mechanical ventilation. These recommendations should be viewed as a starting point to improve clinical trials and help reduce methodological heterogeneity in future clinical trials.

Average and longest expected treatment times for ultraviolet light disinfection of rooms.

BACKGROUND: Planning Ultraviolet-C (UV-C) disinfection of operating rooms (ORs) is equivalent to scheduling brief OR cases. The study purpose was evaluation of methods for predicting surgical case duration applied to treatment times for ORs and hospital rooms. METHODS: Data used were disinfection times with a 3-tower UV-C disinfection system in N=700 rooms each with ≥100 completed treatments. RESULTS: The coefficient of variation of mean treatment duration among rooms was 19.6% (99% confidence interval [CI] 18.2%-21.0%); pooled mean 18.3 minutes among the 133,927 treatments. The 50th percentile of coefficients of variation among treatments of the same room was 27.3% (CI 26.3%-28.4%), comparable to variabilities in durations of surgical procedures. The ratios of the 90th percentile to mean differed among rooms. Log-normal distributions had poor fits for 33% of rooms. Combining results, we calculated 90% upper prediction limits for treatment times by room using a distribution-free method (e.g., third longest of preceding 29 durations). This approach was suitable because, once UV-C disinfection started, the median difference between the duration estimated by the system and actual time was 1 second. CONCLUSIONS: Times for disinfection should be listed as treatment of a specific room (e.g., "UV-C main OR16"), not generically (e.g., "UV-C"). For estimating disinfection time after single surgical cases, use distribution-free upper prediction limits, because of considerable proportional variabilities in duration.

Proportions of Surgical Patients Discharged Home the Same or the Next Day Are Sufficient Data to Assess Cases' Contributions to Hospital Occupancy.

Introduction When the hospital census is high, perioperative medical directors or operating room (OR) managers may need to consider postponing some surgical cases scheduled to be performed within the next three workdays. This scenario has arisen at hospitals in regions with large increases in admissions due to coronavirus disease 2019 (COVID-19). We compare summary measures for hospital length of stay (LOS) to guide the OR manager having to decide which cases may need to be postponed to ensure a sufficient reserve of available inpatient beds. Methods We studied the 1,201,815 ambulatory and 649,962 inpatient elective cases with a major therapeutic procedure performed during 2018 at all 412 non-federal hospitals in Florida. The data were sorted by the hospital, and then by procedure category. Statistical comparisons of LOS were made pairwise among all procedure categories with at least 100 cases at (the) each hospital, using the chi-square test (LOS ≤ 1 day versus LOS > 1 day), Student's t-test with unequal variances, and the Wilcoxon-Mann-Whitney test. The comparisons among the three tests then were repeated having sorted the data by procedure category and making statistical comparisons among all hospitals with at least 100 cases for the procedure category. Results Whether using a criterion for statistical significance of P < 0.05 or P < 0.01, and whether compared with Student's t-test with unequal variances or Wilcoxon-Mann-Whitney test, the chi-square test had greater odds (i.e., greater statistical power) to detect differences in LOS (all four with P < 0.0001 and all 95% lower confidence limits for odds ratios ≥ 3.00). The findings were consistent when the data, first sorted by procedure category and then by probability distributions of LOS, were compared between hospitals (all P < 0.0001 and the 95% lower confidence limits for odds ratio ≥ 3.72). Conclusions For purposes of comparing procedure categories pairwise at the same hospital, there was no loss of information by summarizing the probability distributions using single numbers, the percentages of cases among patients staying longer than overnight. This finding substantially simplifies the mathematics for constructing dashboards or summaries of OR information system data to help the perioperative OR manager or medical director decide which cases may need to be postponed, when the hospital census is high, to provide a sufficient reserve of inpatient hospital beds.

Sample times for surveillance of S. aureus transmission to monitor effectiveness and provide feedback on intraoperative infection control.

BACKGROUND: Reductions in perioperative surgical site infections are obtained by a multifaceted approach including patient decolonization, vascular care, hand hygiene, and environmental cleaning. Associated surveillance of S. aureus transmission quantifies the effectiveness of these basic measures to prevent transmission of pathogenic bacteria and viruses to patients and clinicians, including Coronavirus Disease 2019 (COVID-19). To measure transmission, the observational units are pairs of successive surgical cases in the same operating room on the same day. In this prospective cohort study, we measured sampling times for inexperienced and experienced personnel. METHODS: OR PathTrac kits included 6 samples collected before the start of surgery and 7 after surgery. The time for consent also was recorded. We obtained 1677 measurements of time among 132 cases. RESULTS: Sampling times were not significantly affected by technician's experience, type of anesthetic, or patient's American Society of Anesthesiologists' Physical Status. Sampling times before the start of surgery averaged less than 5 min (3.39 min [SE 0.23], P < 0.0001). Sampling times after surgery took approximately 5 min (4.39 [SE 0.25], P = 0.015). Total sampling times averaged less than 10 min without consent (7.79 [SE 0.50], P < 0.0001), and approximately 10 min with consent (10.22 [0.56], P = 0.70). CONCLUSIONS: For routine use of monitoring S. aureus transmission, when done by personnel already present in the operating rooms of the cases, the personnel time budget can be 10 min per case.

Operating room air delivery design to protect patient and surgical site results in particles released at surgical table having greater concentration along walls of the room than at the instrument tray.

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, recommendations have included that personnel not involved in procedures releasing airborne contaminants reduce their exposure by moving >2 m away. We tested whether air particle concentrations in operating rooms (ORs) are greater in the periphery, downstream from the supply airflow. METHODS: We analyzed data from 15 mock surgical procedures performed in 3 ORs. Two ORs were modern, one with a single large diffuser system above the surgical table, and the other using a multiple diffuser array design. An air particle counting unit was located on the instrument table, another adjacent to an air return grille. RESULTS: Concentrations of air particles were greater at return grille than instrument table for the single large diffuser at 26 air exchanges per hour, and the multiple diffuser array at both 26 and 20 air exchanges per hour (all P ≤ .0044), including during electrocautery (all P ≤ .0072). The ratios of concentrations, return grille versus instrument table, were greater during electrocautery for 0.5 to 1.0-micron particles and 1.0 to 5.0-micron particles (both P < .0001). CONCLUSIONS: Modern OR airflow systems are so effective at protecting the surgical field and team from airborne particles emitted during surgery that concentrations of particles released at the OR table are greater at the OR walls than near the center of the room.

Comparison of Percentage Prolonged Times to Tracheal Extubation Between a Japanese Teaching Hospital and One in the United States, Without and With a Phase I Postanesthesia Care Unit.

BACKGROUND: Prolonged times to tracheal extubation are those from end of surgery (dressing on the patient) to extubation 15 minutes or longer. They are so long that others in the operating room (OR) generally have exhausted whatever activities can be done. They cause delays in the starts of surgeons' to-follow cases and are associated with longer duration workdays. Anesthesiologists rate them as being inferior quality. We compare prolonged times to extubation between a teaching hospital in the United States with a phase I postanesthesia care unit (PACU) and a teaching hospital in Japan without a PACU. Our report is especially important during the coronavirus disease 2019 (COVID-19) pandemic. Anesthesiologists with some patients undergoing general anesthetics and having initial PACU recovery in the ORs where they had surgery can learn from the Japanese anesthesiologists with all patients recovering in ORs. METHODS: The historical cohort study included all patients undergoing gynecological surgery at a US hospital (N = 785) or Japanese hospital (N = 699), with the time from OR entrance to end of surgery of at least 4 hours. RESULTS: The mean times from end of surgery to OR exit were slightly longer at the US hospital than at the Japanese hospital (mean difference 1.9 minutes, P < .0001). The mean from end of surgery to discharge to surgical ward at the US hospital also was longer (P < .0001), mean difference 2.2 hours. The sample standard deviations of times from end of surgery until tracheal extubation was 40 minutes for the US hospital versus 4 minutes at the Japanese hospital (P < .0001). Prolonged times to tracheal extubation were 39% of cases at the US hospital versus 6% at the Japanese hospital; relative risk 6.40, 99% confidence interval (CI), 4.28-9.56. Neither patient demographics, case characteristics, surgeon, anesthesiologist, nor anesthesia provider significantly revised the risk ratio. There were 39% of times to extubation that were prolonged among the patients receiving neither remifentanil nor desflurane (all such patients at the US hospital) versus 6% among the patients receiving both remifentanil and desflurane (all at the Japanese hospital). The relative risk 7.12 (99% CI, 4.59-11.05) was similar to that for the hospital groups. CONCLUSIONS: Differences in anesthetic practice can facilitate major differences in patient recovery soon after anesthesia, useful when the patient will recover initially in the OR or if the phase I PACU is expected to be unable to admit the patient.

Policy Implications for the COVID-19 Pandemic in Light of Most Patients (≥72%) Spending at Most One Night at the Hospital After Elective, Major Therapeutic Procedures.

A large number of inpatients with Coronavirus disease 2019 (COVID-19) in some regions of the United States may interfere with the ability of hospitals to take care of patients requiring treatment for other conditions. Nonetheless, many patients need surgery to improve their quality of life and to prevent deterioration in health. Curtailment of services also negatively affects the financial health of hospitals and health systems. Broad policies to prohibit all "elective" surgical procedures to ensure that there is sufficient hospital capacity for pandemic patients may be unnecessarily restrictive because, for many such procedures, patients are rarely admitted following surgery or only stay overnight. We studied all elective inpatient and ambulatory cases involving major therapeutic procedures performed in the state of Florida in 2018. We mapped the primary procedure to the corresponding Clinical Classification Software (CCS) category. We determined the distributions of lengths of stay overall and as stratified by CCS category, then calculated the percentage of cases that had a hospital length of stay of ≤1 night (i.e., 0 or 1 day). A threshold of one night was selected because patients discharged home on the day of surgery have no effect on the inpatient census, and those staying overnight would either have a transient effect or no effect if observed overnight in the postoperative care unit. Among the 1,852,391 elective cases with one or more major therapeutic procedures, 65.2% (95% lower confidence limit [LCL] = 65.1%) of cases had a length of stay of 0 days and 72.9% (95% LCL = 72.8%) had stay ≤1 day. There were 38 different CCS categories for which at least 95% of patients had a length of stay of ≤1 day. There were 28 CCS codes that identified 80% of the patients who were discharged with a length of stay ≤1 day, showing representation of multiple surgical specialties. Our results show that even in the face of constraints imposed by a high hospital census, many categories of major therapeutic elective procedures could be performed without necessarily compromising hospital capacity. Most patients will be discharged on the day of surgery. If overnight admission is required, there would be an option to care for them in the postanesthesia care unit, thus not affecting the census. Thus, policies can reasonably be based on allowing cases with a substantial probability of at most an overnight stay rather than a blanket ban on "elective" surgery or creating a carve-out for specified surgical subspecialties. Such policies would apply to at least 72% of elective, major therapeutic surgical procedures.

Caseload is increased by resequencing cases before and on the day of surgery at ambulatory surgery centers where initial patient recovery is in operating rooms and cleanup times are longer than typical.

STUDY OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic impacts operating room (OR) management in regions with high prevalence (e.g., >1.0% of asymptomatic patients testing positive). Cases with aerosol producing procedures are isolated to a few ORs, initial phase I recovery of those patients is in the ORs, and multimodal environmental decontamination applied. We quantified the potential increase in productivity from also resequencing these cases among those 2 or 3 ORs. DESIGN: Computer simulation provided sample sizes requiring >100 years experimentally. Resequencing was limited to changes in the start times of surgeons' lists of cases. SETTING: Ambulatory surgery center or hospital outpatient department. MAIN RESULTS: With case resequencing applied before and on the day of surgery, there were 5.6% and 5.5% more cases per OR per day for the 2 ORs and 3 ORs, respectively, both standard errors (SE) < 0.1%. Resequencing cases among ORs to start cases earlier permitted increases in the hours into which cases could be scheduled from 10.5 to 11.0 h, while assuring >90% probability of each OR finishing within the prespecified 12-h shift. Thus, the additional cases were all scheduled before the day of surgery. The greater allocated time also resulted in less overutilized time, a mean of 4.2 min per OR per day for 2 ORs (SE 0.5) and 6.3 min per OR per day for 3 ORs (SE 0.4). The benefit could be achieved while limiting application of resequencing to days when the OR with the fewest estimated hours of cases has ≤8 h. CONCLUSIONS: Some ambulatory surgery ORs have unusually long OR times and/or room cleanup times (e.g., infection control efforts because of the pandemic). Resequencing cases before and on the day of surgery should be considered, because moving 1 or 2 cases occasionally has little to no cost with substantive benefit.

A Predictive Model for Patient Census and Ventilator Requirements at Individual Hospitals During the Coronavirus Disease 2019 (COVID-19) Pandemic: A Preliminary Technical Report.

During the initial wave of the coronavirus disease 2019 (COVID-19) pandemic, many hospitals struggled to forecast bed capacity and the number of mechanical ventilators they needed to have available. Numerous epidemiological models forecast regional or national peak bed and ventilator needs, but these are not suitable for predictions at the hospital level. We developed an analytical model to assist hospitals in determining their census and ventilator requirements for COVID-19 patients during future periods of the pandemic, by using their data. This model is based on (1) projection of future daily admissions using counts from the previous seven days, (2) lengths of stay and duration of mechanical ventilation, and (3) the percentage of inpatients requiring mechanical ventilation. The implementation is done within an Excel (Microsoft, Redmond, WA) workbook without the use of add-ins or macro programming. The model inputs for each currently hospitalized patient with COVID-19 are the duration of hospitalization, whether the patient is currently receiving or has previously received mechanical ventilation, and the duration of the current ventilation episode, if applicable. Data validity and internal consistency are checked within the workbook, and errors are identified. Durations of care (length of hospital stay and duration of mechanical ventilation) are generated by fitting a two-parameter Weibull distribution to the hospital's historical data from the initial phase of the pandemic (incorporating censoring due to ongoing care), for which we provide source code in the R programming language (R Foundation for Statistical Computing, Vienna, Austria). Conditional distributions are then calculated using the hospital's current data. The output of the model is nearly instantaneous, producing an estimate of the census and the number of ventilators required in one, three, and seven days following the date on which the simulation is run. Given that the pandemic is ongoing, and a second surge of cases is expected with the reopening of the economy, having such a tool to predict resource needs for hospital planning purposes has been useful. A major benefit to individual hospitals from such modeling has been to provide reassurance to state and local governments that the hospitals have sufficient resources available to meet anticipated needs for new COVID-19 patients without having to set aside substantially greater numbers of beds or ventilators for such care. Such ongoing activity is important for the economic recovery of hospitals that have been hard-hit economically by the shutdown in elective surgery and other patient care activities. The modeling software is freely available at https://FDshort.com/COVID19, and its parameters can easily be modified by end-users.

Perioperative COVID-19 Defense: An Evidence-Based Approach for Optimization of Infection Control and Operating Room Management.

We describe an evidence-based approach for optimization of infection control and operating room management during the coronavirus disease 2019 (COVID-19) pandemic. Confirmed modes of viral transmission are primarily, but not exclusively, contact with contaminated environmental surfaces and aerosolization. Evidence-based improvement strategies for attenuation of residual environmental contamination involve a combination of deep cleaning with surface disinfectants and ultraviolet light (UV-C). (1) Place alcohol-based hand rubs on the intravenous (IV) pole to the left of the provider. Double glove during induction. (2) Place a wire basket lined with a zip closure plastic bag on the IV pole to the right of the provider. Place all contaminated instruments in the bag (eg, laryngoscope blades and handles) and close. Designate and maintain clean and dirty areas. After induction of anesthesia, wipe down all equipment and surfaces with disinfection wipes that contain a quaternary ammonium compound and alcohol. Use a top-down cleaning sequence adequate to reduce bioburden. Treat operating rooms using UV-C. (3) Decolonize patients using preprocedural chlorhexidine wipes, 2 doses of nasal povidone-iodine within 1 hour of incision, and chlorhexidine mouth rinse. (4) Create a closed lumen IV system and use hub disinfection. (5) Provide data feedback by surveillance of Enterococcus, Staphylococcus aureus, Klebsiella, Acinetobacter, Pseudomonas, and Enterobacter spp. (ESKAPE) transmission. (6) To reduce the use of surgical masks and to reduce potential COVID-19 exposure, use relatively long (eg, 12 hours) staff shifts. If there are 8 essential cases to be done (each lasting 1-2 hours), the ideal solution is to have 2 teams complete the 8 cases, not 8 first case starts. (7) Do 1 case in each operating room daily, with terminal cleaning after each case including UV-C or equivalent. (8) Do not have patients go into a large, pooled phase I postanesthesia care unit because of the risk of contaminating facility at large along with many staff. Instead, have most patients recover in the room where they had surgery as is done routinely in Japan. These 8 programmatic recommendations stand on a substantial body of empirical evidence characterizing the epidemiology of perioperative transmission and infection development made possible by support from the Anesthesia Patient Safety Foundation (APSF).

Sample sizes for surveillance of S. aureus transmission to monitor effectiveness and provide feedback on intraoperative infection control including for COVID-19.

Reductions in perioperative surgical site infections are obtained by a multifaceted approach including patient decolonization, hand hygiene, and hub disinfection, and environmental cleaning. Associated surveillance of S. aureus transmission quantifies the effectiveness of the basic measures to prevent the transmission to patients and clinicians of pathogenic bacteria and viruses, including Coronavirus Disease 2019 (COVID-19). To measure transmission, the observational units are pairs of successive surgical cases in the same operating room on the same day. We evaluated appropriate sample sizes and strategies for measuring transmission. There was absence of serial correlation among observed counts of transmitted isolates within each of several periods (all P ≥.18). Similarly, observing transmission within or between cases of a pair did not increase the probability that the next sampled pair of cases also had observed transmission (all P ≥.23). Most pairs of cases had no detected transmitted isolates. Also, although transmission (yes/no) was associated with surgical site infection (P =.004), among cases with transmission, there was no detected dose response between counts of transmitted isolates and probability of infection (P =.25). The first of a fixed series of tests is to use the binomial test to compare the proportion of pairs of cases with S. aureus transmission to an acceptable threshold. An appropriate sample size for this screening is N =25 pairs. If significant, more samples are obtained while additional measures are implemented to reduce transmission and infections. Subsequent sampling is done to evaluate effectiveness. The two independent binomial proportions are compared using Boschloo's exact test. The total sample size for the 1st and 2nd stage is N =100 pairs. Because S. aureus transmission is invisible without testing, when choosing what population(s) to screen for surveillance, another endpoint needs to be used (e.g., infections). Only 10/298 combinations of specialty and operating room were relatively common (≥1.0% of cases) and had expected incidence ≥0.20 infections per 8 hours of sampled cases. The 10 combinations encompassed ≅17% of cases, showing the value of targeting surveillance of transmission to a few combinations of specialties and rooms. In conclusion, we created a sampling protocol and appropriate sample sizes for using S. aureus transmission within and between pairs of successive cases in the same operating room, the purpose being to monitor the quality of prevention of intraoperative spread of pathogenic bacteria and viruses.