Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study.

BACKGROUND: The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for improving the detection of new cases of infectious disease; it is deployed here to screen and diagnose SARS-CoV-2. With the advent of treatment for COVID-19, finding individuals infected with SARS-CoV-2 is an urgent clinical and public health priority. While these kinds of Bayesian search algorithms are used widely in other settings (eg, to find downed aircraft, in submarine recovery, and to aid in oil exploration), this is the first time that Bayesian adaptive approaches have been used for active disease surveillance in the field. OBJECTIVE: This study's objective was to evaluate a Bayesian search algorithm to target hotspots of SARS-CoV-2 transmission in the community with the goal of detecting the most cases over time across multiple locations in Columbus, Ohio, from August to October 2021. METHODS: The algorithm used to direct pop-up SARS-CoV-2 testing for this project is based on Thompson sampling, in which the aim is to maximize the average number of new cases of SARS-CoV-2 diagnosed among a set of testing locations based on sampling from prior probability distributions for each testing site. An academic-governmental partnership between Yale University, The Ohio State University, Wake Forest University, the Ohio Department of Health, the Ohio National Guard, and the Columbus Metropolitan Libraries conducted a study of bandit algorithms to maximize the detection of new cases of SARS-CoV-2 in this Ohio city in 2021. The initiative established pop-up COVID-19 testing sites at 13 Columbus locations, including library branches, recreational and community centers, movie theaters, homeless shelters, family services centers, and community event sites. Our team conducted between 0 and 56 tests at the 16 testing events, with an overall average of 25.3 tests conducted per event and a moving average that increased over time. Small incentives-including gift cards and take-home rapid antigen tests-were offered to those who approached the pop-up sites to encourage their participation. RESULTS: Over time, as expected, the Bayesian search algorithm directed testing efforts to locations with higher yields of new diagnoses. Surprisingly, the use of the algorithm also maximized the identification of cases among minority residents of underserved communities, particularly African Americans, with the pool of participants overrepresenting these people relative to the demographic profile of the local zip code in which testing sites were located. CONCLUSIONS: This study demonstrated that a pop-up testing strategy using a bandit algorithm can be feasibly deployed in an urban setting during a pandemic. It is the first real-world use of these kinds of algorithms for disease surveillance and represents a key step in evaluating the effectiveness of their use in maximizing the detection of undiagnosed cases of SARS-CoV-2 and other infections, such as HIV.

Patients are pragmatic and physicians are perfectionists? Comparing success after liver transplantation among patients and physicians.

Although both patients and physicians are key stakeholders in health care outcomes, patients and physicians often define success differently. The purpose of this study was to compare patient and physician perceptions of success 1 year after liver transplantation. This was a single-institution, qualitative study. We conducted in-person, semi-structured interviews with liver transplant recipients 1 year after transplantation and virtual interviews with transplant surgeons and hepatologists. Transcripts were coded and iteratively analyzed for themes using the principles of phenomenology. Twenty patients, 8 caregivers, 5 transplant surgeons, and 4 hepatologists were interviewed. Subject interviews averaged 57 (patient) and 27 (physician) minutes. Overall, patients and physicians had significant agreement in their definitions of success, which included avoidance of death, restoration of physical and mental function, return to society, acquisition of new health care knowledge, and open communication between the patient and the physician. Patients highlighted relief from worry about their future health status, and physicians highlighted decreased health care costs. Patients noted that a liver transplant did not have to be perfect, that is free from complications, to be successful. Physicians had a more stringent view and felt that any deviation from an ideal course reduced the relative success of a transplant. Detailed assessment of patient and physician responses reveals similar overall goals of regaining physical, mental, and emotional function. Complications are perceived differently by patients and physicians. Awareness of this discordance may serve to enhance relationships between transplant patients and their providers.

Textbook Outcome as a Quality Metric in Living and Deceased Donor Kidney Transplantation.

BACKGROUND: Quality in kidney transplantation is measured using 1-year patient and graft survival. Because 1-year patient and graft survival exceed 95%, this metric fails to measure a spectrum of quality. Textbook outcomes (TO) are a composite quality metric offering greater depth and resolution. We studied TO after living donor (LD) and deceased donor (DD) kidney transplantation. STUDY DESIGN: United Network for Organ Sharing data for 69,165 transplant recipients between 2013 and 2017 were analyzed. TO was defined as patient and graft survival of 1 year or greater, 1-year glomerular filtration rate of greater than 40 mL/min, absence of delayed graft function, length of stay of 5 days or less, no readmissions during the first 6 months, and no episodes of rejection during the first year after transplantation. Bivariate analysis identified characteristics associated with TO, and covariates were incorporated into multivariable models. Five-year conditional survival was measured, and center TO rates were corrected for case complexity to allow center-level comparisons. RESULTS: The national average TO rates were 54.1% and 31.7% for LD and DD transplant recipients. The hazard ratio for death at 5 years for recipients who did not experience TO was 1.92 (95% CI 1.68 to 2.18, p ≤ 0.0001) for LD transplant recipients and 2.08 (95% CI 1.93 to 2.24, p ≤ 0.0001) for DD transplant recipients. Center-level comparisons identify 18% and 24% of centers under-performing in LD and DD transplantation. High rates of TO do not correlate with transplantation center volume. CONCLUSION: Kidney transplant recipients who experience TO have superior long-term survival. Textbook outcomes add value to the current standards of 1-year patient and graft survival.

Maximizing the Efficiency of Active Case Finding for SARS-CoV-2 Using Bandit Algorithms.

Even as vaccination for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expands in the United States, cases will linger among unvaccinated individuals for at least the next year, allowing the spread of the coronavirus to continue in communities across the country. Detecting these infections, particularly asymptomatic ones, is critical to stemming further transmission of the virus in the months ahead. This will require active surveillance efforts in which these undetected cases are proactively sought out rather than waiting for individuals to present to testing sites for diagnosis. However, finding these pockets of asymptomatic cases (i.e., hotspots) is akin to searching for needles in a haystack as choosing where and when to test within communities is hampered by a lack of epidemiological information to guide decision makers' allocation of these resources. Making sequential decisions with partial information is a classic problem in decision science, the explore v. exploit dilemma. Using methods-bandit algorithms-similar to those used to search for other kinds of lost or hidden objects, from downed aircraft or underground oil deposits, we can address the explore v. exploit tradeoff facing active surveillance efforts and optimize the deployment of mobile testing resources to maximize the yield of new SARS-CoV-2 diagnoses. These bandit algorithms can be implemented easily as a guide to active case finding for SARS-CoV-2. A simple Thompson sampling algorithm and an extension of it to integrate spatial correlation in the data are now embedded in a fully functional prototype of a web app to allow policymakers to use either of these algorithms to target SARS-CoV-2 testing. In this instance, potential testing locations were identified by using mobility data from UberMedia to target high-frequency venues in Columbus, Ohio, as part of a planned feasibility study of the algorithms in the field. However, it is easily adaptable to other jurisdictions, requiring only a set of candidate test locations with point-to-point distances between all locations, whether or not mobility data are integrated into decision making in choosing places to test.