SARS-CoV-2 lineage assignments using phylogenetic placement/UShER are superior to pangoLEARN machine learning method (preprint)

With the rapid spread and evolution of SARS-CoV-2, the ability to monitor its transmission and distinguish among viral lineages is critical for pandemic response efforts. The most commonly used software for the lineage assignment of newly isolated SARS-CoV-2 genomes is pangolin, which offers two methods of assignment, pangoLEARN and pUShER. PangoLEARN rapidly assigns lineages using a machine learning algorithm, while pUShER performs a phylogenetic placement to identify the lineage corresponding to a newly sequenced genome. In a preliminary study, we observed that pangoLEARN (decision tree model), while substantially faster than pUShER, offered less consistency across different versions of pangolin v3. Here, we expand upon this analysis to include v3 and v4 of pangolin, which moved the default algorithm for lineage assignment from pangoLEARN in v3 to pUShER in v4, and perform a thorough analysis confirming that pUShER is not only more stable across versions but also more accurate. Our findings suggest that future lineage assignment algorithms for various pathogens should consider the value of phylogenetic placement.

Hepatobiliary long-term consequences of COVID-19: dramatically increased rate of secondary sclerosing cholangitis in critically ill COVID-19 patients.

BACKGROUND: Increasing evidence suggests that secondary sclerosing cholangitis (SSC), which can lead to cirrhosis or liver failure, may be a hepatobiliary long-term complication of COVID-19. The aim of this study was to estimate the frequency and outcome of this COVID-19 sequela and to identify possible risk factors. METHODS: This observational study, conducted at University Hospital Charité Berlin and Unfallkrankenhaus Berlin, Germany, involved hospitalized patients with COVID-19 pneumonia, including 1082 ventilated COVID-19 patients. We compared COVID-19 patients who developed SSC with a COVID-19 control group by univariate and multivariate analyses. RESULTS: SSC occurrence after COVID-19 was observed exclusively in critically ill patients with invasive ventilation, albeit with extreme clustering among them. One in every 43 invasively ventilated COVID-19 patients developed this complication. Risk factors preceding the development of secondary sclerosing cholangitis in critically ill COVID-19 patients (SSC-CIP) were signs of systemic reduced blood oxygen supply (e.g., low PaO2/FiO2, ischemic organ infarctions), multi-organ failure (high SOFA score) at admission, high fibrinogen levels and intravenous ketamine use. Multivariate analysis confirmed fibrinogen and increased plasma lactate dehydrogenase as independent risk factors associated with cholangiopathy onset. The 1-year transplant-free survival rate of COVID-19-associated SSC-CIP was 40%. CONCLUSIONS: COVID-19 causes SSC-CIP in a substantial proportion of critically ill patients. SSC-CIP most likely develops due to severe tissue hypoxia and fibrinogen-associated circulatory disturbances. A significant increase of patients with SSC-CIP is to be expected in the post-COVID era.

Economic and Psychosocial Impact of COVID-19 in the Hispanic Community Health Study/Study of Latinos.

Objectives: To examine the prevalence and correlates of economic hardship and psychosocial distress experienced during the initial phase of the coronavirus disease 2019 (COVID-19) pandemic in a large cohort of Hispanic/Latino adults. Methods: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an ongoing multicenter study of Hispanic/Latino adults, collected information about COVID-19 illness and psychosocial and economic distress that occurred during the pandemic (N=11,283). We estimated the prevalence of these experiences during the initial phase of the pandemic (May 2020 to May 2021) and examined the prepandemic factors associated with pandemic-related economic hardship and emotional distress using multivariable log linear models with binomial distributions to estimate prevalence ratios. Results: Almost half of the households reported job losses and a third reported economic hardship during the first year of the pandemic. Pandemic-related household job losses and economic hardship were more pronounced among noncitizens who are likely to be undocumented. Pandemic-related economic hardship and psychosocial distress varied by age group and sex. Contrary to the economic hardship findings, noncitizens were less likely to report pandemic-related psychosocial distress. Prepandemic social resources were inversely related to psychosocial distress. Conclusions: The study findings underscore the economic vulnerability that the pandemic has brought to ethnic minoritized and immigrant populations in the United States, in particular noncitizens. The study also highlights the need to incorporate documentation status as a social determinant of health. Characterizing the initial economic and mental health impact of the pandemic is important for understanding the pandemic consequences on future health. Clinical Trial Registration Number: NCT02060344.

Trends in coronary calcium score and coronary CT angiography imaging volume during the COVID-19 pandemic.

OBJECTIVES: The COVID-19 pandemic disrupted the delivery of preventative care and management of acute diseases. This study assesses the effect of the COVID-19 pandemic on coronary calcium score and coronary CT angiography imaging volume. MATERIALS AND METHODS: A single institution retrospective review of consecutive patients presenting for coronary calcium score or coronary CT angiography examinations between January 1, 2020 to January 4, 2022 was performed. The weekly volume of calcium score and coronary CT angiogram exams were compared. RESULTS: In total, 1,817 coronary calcium score CT and 5,895 coronary CT angiogram examinations were performed. The average weekly volume of coronary CTA and coronary calcium score CT exams decreased by up to 83% and 100%, respectively, during the COVID-19 peak period compared to baseline (P < 0.0001). The post-COVID recovery through 2020 saw weekly coronary CTA volumes rebound to 86% of baseline (P = 0.024), while coronary calcium score CT volumes remained muted at only a 53% recovery (P < 0.001). In 2021, coronary CTA imaging eclipsed pre-COVID rates (P = 0.012), however coronary calcium score CT volume only reached 67% of baseline (P < 0.001). CONCLUSIONS: A significant decrease in both coronary CTA and coronary calcium score CT volume occurred during the peak-COVID-19 period. In 2020 and 2021, coronary CTA imaging eventually superseded baseline rates, while coronary calcium score CT volumes only reached two thirds of baseline. These findings highlight the importance of resumption of screening exams and should prompt clinicians to be aware of potential undertreatment of patients with coronary artery disease.

Trial of Hybrid Closed-Loop Control in Young Children with Type 1 Diabetes.

BACKGROUND: Closed-loop control systems of insulin delivery may improve glycemic outcomes in young children with type 1 diabetes. The efficacy and safety of initiating a closed-loop system virtually are unclear. METHODS: In this 13-week, multicenter trial, we randomly assigned, in a 2:1 ratio, children who were at least 2 years of age but younger than 6 years of age who had type 1 diabetes to receive treatment with a closed-loop system of insulin delivery or standard care that included either an insulin pump or multiple daily injections of insulin plus a continuous glucose monitor. The primary outcome was the percentage of time that the glucose level was in the target range of 70 to 180 mg per deciliter, as measured by continuous glucose monitoring. Secondary outcomes included the percentage of time that the glucose level was above 250 mg per deciliter or below 70 mg per deciliter, the mean glucose level, the glycated hemoglobin level, and safety outcomes. RESULTS: A total of 102 children underwent randomization (68 to the closed-loop group and 34 to the standard-care group); the glycated hemoglobin levels at baseline ranged from 5.2 to 11.5%. Initiation of the closed-loop system was virtual in 55 patients (81%). The mean (±SD) percentage of time that the glucose level was within the target range increased from 56.7±18.0% at baseline to 69.3±11.1% during the 13-week follow-up period in the closed-loop group and from 54.9±14.7% to 55.9±12.6% in the standard-care group (mean adjusted difference, 12.4 percentage points [equivalent to approximately 3 hours per day]; 95% confidence interval, 9.5 to 15.3; P<0.001). We observed similar treatment effects (favoring the closed-loop system) on the percentage of time that the glucose level was above 250 mg per deciliter, on the mean glucose level, and on the glycated hemoglobin level, with no significant between-group difference in the percentage of time that the glucose level was below 70 mg per deciliter. There were two cases of severe hypoglycemia in the closed-loop group and one case in the standard-care group. One case of diabetic ketoacidosis occurred in the closed-loop group. CONCLUSIONS: In this trial involving young children with type 1 diabetes, the glucose level was in the target range for a greater percentage of time with a closed-loop system than with standard care. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases; PEDAP ClinicalTrials.gov number, NCT04796779.).

Trends in oncological imaging during the COVID-19 pandemic through the vaccination era.

BACKGROUND: This study examines the impact that the COVID-19 pandemic has had on computed tomography (CT)-based oncologic imaging utilization. METHODS: We retrospectively analyzed cancer-related CT scans during four time periods: pre-COVID (1/5/20-3/14/20), COVID peak (3/15/20-5/2/20), post-COVID peak (5/3/20-12/19/20), and vaccination period (12/20/20-10/30/21). We analyzed CTs by imaging indication, setting, and hospital type. Using percentage decrease computation and Student's t-test, we calculated the change in mean number of weekly cancer-related CTs for all periods compared to the baseline pre-COVID period. This study was performed at a single academic medical center and three affiliated hospitals. RESULTS: During the COVID peak, mean CTs decreased (-43.0%, p < 0.001), with CTs for (1) cancer screening, (2) initial workup, (3) cancer follow-up, and (4) scheduled surveillance of previously treated cancer dropping by 81.8%, 56.3%, 31.7%, and 45.8%, respectively (p < 0.001). During the post-COVID peak period, cancer screenings and initial workup CTs did not return to prepandemic imaging volumes (-11.4%, p = 0.028; -20.9%, p = 0.024). The ED saw increases in weekly CTs compared to prepandemic levels (+31.9%, p = 0.008), driven by increases in cancer follow-up CTs (+56.3%, p < 0.001). In the vaccination period, cancer screening CTs did not recover to baseline (-13.5%, p = 0.002) and initial cancer workup CTs doubled (+100.0%, p < 0.001). The ED experienced increased cancer-related CTs (+75.9%, p < 0.001), driven by cancer follow-up CTs (+143.2%, p < 0.001) and initial workups (+46.9%, p = 0.007). CONCLUSIONS AND RELEVANCE: The pandemic continues to impact cancer care. We observed significant declines in cancer screening CTs through the end of 2021. Concurrently, we observed a 2× increase in initial cancer workup CTs and a 2.4× increase in cancer follow-up CTs in the ED during the vaccination period, suggesting a boom of new cancers and more cancer examinations associated with emergency level acute care.

Trends in Academic Productivity Among Radiologists During the COVID-19 Pandemic.

PURPOSE: There is a scarcity of literature examining changes in radiologist research productivity during the COVID-19 pandemic. The current study aimed to investigate changes in academic productivity as measured by publication volume before and during the COVID-19 pandemic. METHODS: This single-center, retrospective cohort study included the publication data of 216 researchers consisting of associate professors, assistant professors, and professors of radiology. Wilcoxon's signed-rank test was used to identify changes in publication volume between the 1-year-long defined prepandemic period (publications between May 1, 2019, and April 30, 2020) and COVID-19 pandemic period (May 1, 2020, to April 30, 2021). RESULTS: There was a significantly increased mean annual volume of publications in the pandemic period (5.98, SD = 7.28) compared with the prepandemic period (4.98, SD = 5.53) (z = -2.819, P = .005). Subset analysis demonstrated a similar (17.4%) increase in publication volume for male researchers when comparing the mean annual prepandemic publications (5.10, SD = 5.79) compared with the pandemic period (5.99, SD = 7.60) (z = -2.369, P = .018). No statistically significant changes were found in similar analyses with the female subset. DISCUSSION: Significant increases in radiologist publication volume were found during the COVID-19 pandemic compared with the year before. Changes may reflect an overall increase in academic productivity in response to clinical and imaging volume ramp down.

Evaluation of the Aggregated Time Savings in Adopting Fast Brain MRI Techniques for Outpatient Brain MRI.

INTRODUCTION: Clinical validation studies have demonstrated the ability of accelerated MRI sequences to decrease acquisition time and motion artifact while preserving image quality. The operational benefits, however, have been less explored. Here, we report our initial clinical experience in implementing fast MRI techniques for outpatient brain imaging during the COVID-19 pandemic. METHODS: Aggregate acquisition times were extracted from the medical record on consecutive imaging examinations performed during matched pre-implementation (7/1/2019-12/31/2019) and post-implementation periods (7/1/2020-12/31/2020). Expected acquisition time reduction for each MRI protocol was calculated through manual collection of acquisition times for the conventional and accelerated sequences performed during the pre- and post-implementation periods. Aggregate and expected acquisition times were compared for the five most frequently performed brain MRI protocols: brain without contrast (BR-), brain with and without contrast (BR+), multiple sclerosis (MS), memory loss (MML), and epilepsy (EPL). RESULTS: The expected time reductions for BR-, BR+, MS, MML, and EPL protocols were 6.6 min, 11.9 min, 14 min, 10.8 min, and 14.1 min, respectively. The overall median aggregate acquisition time was 31 [25, 36] min for the pre-implementation period and 18 [15, 22] min for the post-implementation period, with a difference of 13 min (42%). The median acquisition time was reduced by 4 min (25%) for BR-, 14.0 min (44%) for BR+, 14 min (38%) for MS, 11 min (52%) for MML, and 16 min (35%) for EPL. CONCLUSION: The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.

Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.

Risk prediction requires comprehensive integration of clinical information and concurrent radiological findings. We present an upgraded chest radiograph (CXR) explainable artificial intelligence (xAI) model, which was trained on 241,723 well-annotated CXRs obtained prior to the onset of the COVID-19 pandemic. Mean area under the receiver operating characteristic curve (AUROC) for detection of 20 radiographic features was 0.955 (95% CI 0.938-0.955) on PA view and 0.909 (95% CI 0.890-0.925) on AP view. Coexistent and correlated radiographic findings are displayed in an interpretation table, and calibrated classifier confidence is displayed on an AI scoreboard. Retrieval of similar feature patches and comparable CXRs from a Model-Derived Atlas provides justification for model predictions. To demonstrate the feasibility of a fine-tuning approach for efficient and scalable development of xAI risk prediction models, we applied our CXR xAI model, in combination with clinical information, to predict oxygen requirement in COVID-19 patients. Prediction accuracy for high flow oxygen (HFO) and mechanical ventilation (MV) was 0.953 and 0.934 at 24 h and 0.932 and 0.836 at 72 h from the time of emergency department (ED) admission, respectively. Our CXR xAI model is auditable and captures key pathophysiological manifestations of cardiorespiratory diseases and cardiothoracic comorbidities. This model can be efficiently and broadly applied via a fine-tuning approach to provide fully automated risk and outcome predictions in various clinical scenarios in real-world practice.

Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients.

Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits.

Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.

Objective: To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis. Methods: A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model. Results: 801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes. Conclusion: Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome. Advances in knowledge: Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.

Institutional Surgical Response and Associated Volume Trends Throughout the COVID-19 Pandemic and Postvaccination Recovery Period.

Importance: The COVID-19 pandemic is associated with decreased surgical procedure volumes, but existing studies have not investigated this association beyond the end of 2020, analyzed changes during the post-vaccine release period, or quantified these changes by patient acuity. Objective: To quantify changes in the volume of surgical procedures at a 1017-bed academic quaternary care center from January 6, 2019, to December 31, 2021. Design, Setting, and Participants: In this cohort study, 129 596 surgical procedure volumes were retrospectively analyzed during 4 periods: pre-COVID-19 (January 6, 2019, to January 4, 2020), COVID-19 peak (March 15, 2020, to May 2, 2020), post-COVID-19 peak (May 3, 2020, to January 2, 2021), and post-vaccine release (January 3, 2021, to December 31, 2021). Surgery volumes were analyzed by subspecialty and case class (elective, emergent, nonurgent, urgent). Statistical analysis was by autoregressive integrated moving average modeling. Main Outcomes and Measures: The primary outcome of this study was the change in weekly surgical procedure volume across the 4 COVID-19 periods. Results: A total of 129 596 records of surgical procedures were reviewed. During the COVID-19 peak, overall weekly surgical procedure volumes (mean [SD] procedures per week, 406.00 [171.45]; 95% CI, 234.56-577.46) declined 44.6% from pre-COVID-19 levels (mean [SD] procedures per week, 732.37 [12.70]; 95% CI, 719.67-745.08; P < .001). This weekly volume decrease occurred across all surgical subspecialties. During the post-COVID peak period, overall weekly surgical volumes (mean [SD] procedures per week, 624.31 [142.45]; 95% CI, 481.85-766.76) recovered to only 85.8% of pre-COVID peak volumes (P < .001). This insufficient recovery was inconsistent across subspecialties and case classes. During the post-vaccine release period, although some subspecialties experienced recovery to pre-COVID-19 volumes, others continued to experience declines. Conclusions and Relevance: This quaternary care institution effectively responded to the pressures of the COVID-19 pandemic by substantially decreasing surgical procedure volumes during the peak of the pandemic. However, overall surgical procedure volumes did not fully recover to pre-COVID-19 levels well into 2021, with inconsistent recovery rates across subspecialties and case classes. These declines suggest that delays in surgical procedures may result in potentially higher morbidity rates in the future. The differential recovery rates across subspecialties may inform institutional focus for future operational recovery.

Evaluation of an ENT hybrid internship by medical students

Introduction With the onset of the Covid 19 pandemic, there was a need to reduce contacts in the clinic in order to minimise the risk of infection for patients, staff and students. Therefore, a hybrid block practical (virtual with short face-to-face phase) was created to maintain teaching for medical students. The aim of this study was to evaluate student evaluation results for this new form of teaching. Method Student evaluation results for the ENT internship of 2018-2020 (faceto- face teaching) were compared with those from 2020-2021 (hybrid internship). In addition, the evaluation and acceptance of the hybrid internship was statistically compared over the course of the pandemic. The survey of the students was conducted in anonymised form (evasys analysis) following the internship. Results The hybrid internships showed constant, very good evaluation results, which were significantly better in several categories (punctuality of lecturers, structure, knowledge gain and learning atmosphere) than in the previously conducted face-to-face internship. However, over the course of the pandemic, student comments in the evaluation indicated a decrease in acceptance of virtual teaching and an increasing desire for face-to-face hours at the bedside, in the outpatient clinic and in the operating rooms. Conclusion The conversion of the internship to virtual teaching did not mean a reduction in teaching quality for the students. A permanent virtualisation of teaching is not to be aimed at, since manual-practical skills as well as doctor-patient interactions can only be learned and trained in the clinic. However, the establishment of an internship with virtual parts seems to be useful even after the pandemic.

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Upper Midwest tribal natural resource managers' perspectives on chronic wasting disease outreach, surveillance, and management

Management strategies for chronic wasting disease (CWD) across tribal lands have varied in response to changing dynamics of CWD risk. As CWD continues to spread across the United States, concerns associated with the disease are increasing. We interviewed 19 natural resource managers representing Anishinaabe and Dakota tribes in Minnesota, Michigan, and Wisconsin with goals of understanding needs and opportunities for CWD engagement, surveillance, and outreach on tribal lands;the implementation of natural resources policy and management across tribal nations;and opportunities for tribal partnership‐development to control CWD. Qualitative data analyses of interview responses revealed substantial variation in the number of tribal hunters, hunter regulation, and huntable tribal lands across our study area. Proximity of tribal lands in relation to CWD detections impacted tribal agency management strategies for CWD. Our results indicate a desire for CWD outreach and surveillance, mutually beneficial collaborations, and a need for incorporating cultural knowledge into CWD management strategies. We conclude that tribal CWD management and surveillance plans will be enhanced through strategic and thoughtful CWD outreach methods. Moreover, partnerships must recognize tribal sovereignty and respectfully integrate tribal values, knowledge, and worldview.

Munchausen by Internet and false perinatal crises

Background: In representative cases of Munchausen by internet (MBI), an individual (or "poser") goes online to falsely report or exaggerate illnesses or life crises. The principal goal, as in factitious disorder imposed on self or another, is to garner emotional satisfaction. We provide the first evidence that MBI can target a specific type of health care provider-in this case, birthing doulas. Methods: We describe 5 cases in which individuals have utilized social media platforms to report factitious perinatal illnesses and crises, including neonatal death, in real time. Current health headlines, such as those involving the COVID-19 pandemic, can be relevant to the ruses. Posers can engage in deceptions with several health care professionals concurrently or serially, and may portray multiple people ("sock puppets") at the same time. Results: MBI has consequences that can be highly disruptive. In the cases highlighted in this report, many hours of support were given to individuals who had fabricated their pregnancies, infants, and perinatal complications. The doulas experienced feelings ranging from resignation to anger and betrayal. Conclusions: Health care professionals of all types who offer services online should be vigilant to the risks of potential MBI. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

COVID-19 in Prisons: State Health Care Contracting and the Pandemic Behind Bars.

The COVID-19 pandemic has had far-reaching consequences, resulting in millions of infections and deaths worldwide. Given the vast impact of the virus and how it has highlighted health care access/treatment concerns within the general public, this study examined how these issues specifically impacted COVID-19 cases and deaths among incarcerated individuals. In particular, we examined how the method of medical care delivery (i.e., provided by correctional versus contracted practitioners) impacted the reported rates of COVID-19 cases/deaths within correctional institutions. Findings indicated that COVID-19 diagnosis and mortality rates were significantly lower in states where at least some health care for incarcerated individuals was directly provided by correctional employees. We offer various explanations for these findings while also highlighting potential key reporting differences between these two forms of correctional health care delivery.