Background Significant uncertainty has existed about the safety of reopening college and university campuses before the COVID-19 pandemic is better controlled. Moreover, little is known about the effects that on-campus students may have on local higher-risk communities. Objective We aimed to estimate the range of potential community and campus COVID-19 exposures, infections, and mortality under various university reopening plans and uncertainties. Methods We developed campus-only, community-only, and campus × community epidemic differential equations and agent-based models, with inputs estimated via published and grey literature, expert opinion, and parameter search algorithms. Campus opening plans (spanning fully open, hybrid, and fully virtual approaches) were identified from websites and publications. Additional student and community exposures, infections, and mortality over 16-week semesters were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outliers. Sensitivity analyses were conducted to inform potential effective interventions. Results Predicted 16-week campus and additional community exposures, infections, and mortality for the base case with no precautions (or negligible compliance) varied significantly from their medians (4- to 10-fold). Over 5% of on-campus students were infected after a mean of 76 (SD 17) days, with the greatest increase (first inflection point) occurring on average on day 84 (SD 10.2 days) of the semester and with total additional community exposures, infections, and mortality ranging from 1-187, 13-820, and 1-21 per 10,000 residents, respectively. Reopening precautions reduced infections by 24%-26% and mortality by 36%-50% in both populations. Beyond campus and community reproductive numbers, sensitivity analysis indicated no dominant factors that interventions could primarily target to reduce the magnitude and variability in outcomes, suggesting the importance of comprehensive public health measures and surveillance. Conclusions Community and campus COVID-19 exposures, infections, and mortality resulting from reopening campuses are highly unpredictable regardless of precautions. Public health implications include the need for effective surveillance and flexible campus operations.

Background: Significant uncertainty exists about the safety of, and best strategies for, reopening colleges and universities while the Covid-19 pandemic is not well-controlled. Little also is known about the effects that on-campus outbreaks may have on local non-student and/or higher-risk communities. Model-based analysis can help inform decision and policy making across a wide range of assumptions and uncertainties. Objective: To evaluate the potential range of campus and community Covid-19 exposures, infections, and mortality due to various university and college reopening plans and precautions. Methods: We developed and calibrated campus-only, community-only, and campus-x-community epidemic models using standard susceptible-exposed-infected-recovered differential equation and agent-based modeling methods. Input parameters for campus and surrounding communities were estimated via published and grey literature, scenario development, expert opinion, Monte Carlo simulation, and accuracy optimization algorithms; models were cross-validated against each other using February-June 2020 county, state, and country data. Campus opening plans (spanning various fully open, hybrid, and fully virtual approaches) were identified from websites, publications, communications, and surveys. All scenarios were simulated assuming 16-week semesters and best/worst case ranges for disease prevalence among community residents and arriving students, precaution compliance, contact frequency, virus attack rates, and tracing and isolation effectiveness. Day-to-day student and community differences in exposures, infections, and mortality were estimated under each scenario as compared to regular and no re-opening; 10% trimmed medians, standard deviations, and probability intervals were computed to omit extreme outlier scenarios. Factorial analyses were conducted to identify inputs with largest and smallest impacts on outcomes. Results: As a base case, predicted 16-week student infections and mortality under normal operations with no precautions (or no compliance) ranged from 472 to 9,484 (4.7% to 94.8%) and 2 to 61 (0.02% to 0.61%) per 10,000 student population, respectively. In terms of contact tracing and isolation resources, as many as 17 to 1,488 total exposures per 10,000 students could occur on a given day throughout the semester needing to be located, tested, and if warranted quarantined. Attributable total additional predicted community exposures, infections, and mortality ranged from 1 to 187, 13 to 820, and 1 to 21, respectively, assuming the university takes no additional precautions to limit exposure risk. The mean (SD) number of days until 1% and 5% of on-campus students are infected was 11 (3) and 76 (17) days, respectively; 34.8% of replications resulted in more than 10% students infected by semester end. The diffusion first inflection point occurred on average on day 84 (+/- 20 days, 95% interval). Common re-opening precaution strategies reduced the above consequences by 24% to 26% fewer infections (now 360 to 6,976 per 10,000 students) and 36% to 50% fewer deaths (now 1 to 39 per 10,000 students). Perfect testing and immediate quarantining of all students on arrival to campus at semester start further reduced infections by 58% to 95% (now 200 to 468 per 10,000 students) and deaths by 95% to 100% (now 0 to 3 per 10,000 students). Uncertainties in many factors, however, produced tremendous variability in all median estimates, ranging by -67% to +370%. Conclusions: Consequences of reopening college and university physical campuses on student and community Covid-19 exposures, infections, and mortality are very highly unpredictable, depending on a combination of random chance, controllable (e.g. physical layouts), and uncontrollable (e.g. human behavior) factors. Important implications at government and academic institution levels include clear needs for specific criteria to adapt campus operations mid-semester, methods to detect when this is necessary, and well-executed contingency plans for doing so.

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BackgroundClosing loops to complete diagnostic referrals remains a significant patient safety problem in most health systems, with 65%–73% failure rates and significant delays common despite years of improvement efforts, suggesting new approaches may be useful. Systems engineering (SE) methods increasingly are advocated in healthcare for their value in studying and redesigning complex processes.ObjectiveConduct a formative SE analysis of process logic, variation, reliability and failures for completing diagnostic referrals originating in two primary care practices serving different demographics, using dermatology as an illustrating use case.MethodsAn interdisciplinary team of clinicians, systems engineers, quality improvement specialists, and patient representatives collaborated to understand processes of initiating and completing diagnostic referrals. Cross-functional process maps were developed through iterative group interviews with an urban community-based health centre and a teaching practice within a large academic medical centre. Results were used to conduct an engineering process analysis, assess variation within and between practices, and identify common failure modes and potential solutions.ResultsProcesses to complete diagnostic referrals involve many sub-standard design constructs, with significant workflow variation between and within practices, statistical instability and special cause variation in completion rates and timeliness, and only 21% of all process activities estimated as value-add. Failure modes were similar between the two practices, with most process activities relying on low-reliability concepts (eg, reminders, workarounds, education and verification/inspection). Several opportunities were identified to incorporate higher reliability process constructs (eg, simplification, consolidation, standardisation, forcing functions, automation and opt-outs).ConclusionFrom a systems science perspective, diagnostic referral processes perform poorly in part because their fundamental designs are fraught with low-reliability characteristics and mental models, including formalised workaround and rework activities, suggesting a need for different approaches versus incremental improvement of existing processes. SE perspectives and methods offer new ways of thinking about patient safety problems, failures and potential solutions.

Background Surgical site infections (SSIs) cause significant patient suffering. Surveillance and feedback of SSI rates is an evidence-based strategy to reduce SSIs, but traditional surveillance methods are slow and prone to bias. The objective of this cluster randomized controlled trial (RCT) is to determine if using optimized statistical process control (SPC) charts for SSI surveillance and feedback lead to a reduction in SSI rates compared to traditional surveillance. Methods The Early 2RIS Trial is a prospective, multicenter cluster RCT using a stepped wedge design. The trial will be performed in 29 hospitals in the Duke Infection Control Outreach Network (DICON) and 105 clusters over 4 years, from March 2016 through February 2020; year one represents a baseline period; thereafter, 8–9 clusters will be randomized to intervention every 3 months over a 3-year period using a stepped wedge randomization design. All patients who undergo one of 13 targeted procedures at study hospitals will be included in the analysis; these procedures will be included in one of six clusters: cardiac, orthopedic, gastrointestinal, OB-GYN, vascular, and spinal. All clusters will undergo traditional surveillance for SSIs; once randomized to intervention, clusters will also undergo surveillance and feedback using optimized SPC charts. Feedback on surveillance data will be provided to all clusters, regardless of allocation or type of surveillance. The primary endpoint is the difference in rates of SSI between the SPC intervention compared to traditional surveillance and feedback alone. Discussion The traditional approach for SSI surveillance and feedback has several major deficiencies because SSIs are rare events. First, traditional statistical methods require aggregation of measurements over time, which delays analysis until enough data accumulate. Second, traditional statistical tests and resulting p values are difficult to interpret. Third, analyses based on average SSI rates during predefined time periods have limited ability to rapidly identify important, real-time trends. Thus, standard analytic methods that compare average SSI rates between arbitrarily designated time intervals may not identify an important SSI rate increase on time unless the “signal” is very strong. Therefore, novel strategies for early identification and investigation of SSI rate increases are needed to decrease SSI rates. While SPC charts are used throughout industry and healthcare to improve and optimize processes, including other types of healthcare-associated infections, they have not been evaluated as a tool for SSI surveillance and feedback in a randomized trial. Trial registration ClinicalTrials.govNCT03075813, Registered March 9, 2017.