Improving COVID-19 Disease Severity Surveillance Measures: Statewide Implementation Experience.

Measurement of the burden of COVID-19 on U.S. hospitals has been an important element of the public health response to the pandemic. However, because of variation in testing density and policies, the metric is not standardized across facilities. Two types of burdens exist, one related to the infection control measures that patients who test positive for SARS-CoV-2 require and one from the care of severely ill patients receiving treatment of COVID-19. With rising population immunity from vaccination and infection, as well as the availability of therapeutics, severity of illness has declined. Prior research showed that dexamethasone administration was highly correlated with other disease severity metrics and sensitive to the changing epidemiology associated with the emergence of immune-evasive variants.On 10 January 2022, the Massachusetts Department of Public Health began requiring hospitals to expand surveillance to include reports of both the total number of "COVID-19 hospitalizations" daily and the number of inpatients who received dexamethasone at any point during their hospital stay. All 68 acute care hospitals in Massachusetts submitted COVID-19 hospitalization and dexamethasone data daily to the Massachusetts Department of Public Health over a 1-year period. A total of 44 196 COVID-19 hospitalizations were recorded during 10 January 2022 to 9 January 2023, of which 34% were associated with dexamethasone administration. The proportion of patients hospitalized with COVID-19 who had received dexamethasone was 49.6% during the first month of surveillance and decreased to a monthly average of approximately 33% by April 2022, where it has remained since (range, 28.7% to 33%).Adding a single data element to mandated reporting to estimate the frequency of severe COVID-19 in hospitalized patients was feasible and provided actionable information for health authorities and policy makers. Updates to surveillance methods are necessary to match data collection with public health response needs.

Condition-Dependent and Dynamic Impacts of Indoor Masking Policies for COVID-19 Mitigation: A Nationwide, Interrupted Time-Series Analysis.

BACKGROUND: The effectiveness and sustainability of masking policies as a pandemic control measure remain uncertain. Our aim was to evaluate different masking policy types on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence and to identify factors and conditions impacting effectiveness. METHODS: Nationwide, retrospective cohort study of US counties from 4/4/2020-28/6/2021. Policy impacts were estimated using interrupted time-series models with the masking policy change date (eg, recommended-to-required, no-recommendation-to-recommended, no-recommendation-to-required) modeled as the interruption. The primary outcome was change in SARS-CoV-2 incidence rate during the 12 weeks after the policy change; results were stratified by coronavirus disease 2019 (COVID-19) risk level. A secondary analysis was completed using adult vaccine availability as the policy change. RESULTS: In total, N = 2954 counties were included (2304 recommended-to-required, 535 no-recommendation-to-recommended, 115 no-recommendation-to-required). Overall, indoor mask mandates were associated with 1.96 fewer cases/100 000/week (cumulative reduction of 23.52/100 000 residents during the 12 weeks after policy change). Reductions were driven by communities with critical and extreme COVID-19 risk, where masking mandated policies were associated with an absolute reduction of 5 to 13.2 cases/100 000 residents/week (cumulative reduction of 60 to 158 cases/100 000 residents over 12 weeks). Impacts in low- and moderate-risk counties were minimal (<1 case/100 000 residents/week). After vaccine availability, mask mandates were not associated with significant reductions at any risk level. CONCLUSIONS: Masking policy had the greatest impact when COVID-19 risk was high and vaccine availability was low. When transmission risk decreases or vaccine availability increases, the impact was not significant regardless of mask policy type. Although often modeled as having a static impact, masking policy effectiveness may be dynamic and condition dependent.

The next 'pandemic playbook' needs to prioritize the needs of children-and a clear roadmap for opening schools.

The national influenza pandemic response plan includes short-term school closures as an infection mitigation measure, based on modeling data regarding the role of pediatric populations and schools as drivers of disease spread. Modeled estimates regarding the role of children and their in-school contacts as drivers of community transmission of endemic respiratory viruses were used in part to justify prolonged school closures throughout the United States. However, disease transmission models extrapolated from endemic pathogens to novel ones may underestimate the degree to which spread is driven by population immunity and overestimate the impact of school closures as a means of reducing child contacts, particularly in the longer-term. These errors, in turn, may have caused incorrect estimations about the potential benefits of closing schools on a society level while simultaneously failing to account for the significant harms of long-term educational disruption. Pandemic response plans need to be updated to include nuances regarding drivers of transmission such as pathogen type, population immunity, and contact patterns, and disease severity in different groups. Expected duration of impact also needs to be considered, recognizing that effectiveness of different interventions, particularly those focused on limiting social interactions, are short-lived. Additionally, future iterations should include risk-benefit assessments. Interventions that are particularly harmful to certain groups, such as school closures are on children, should be de-emphasized and time limited. Finally, pandemic responses should include ongoing and continuous policy re-evaluation and should include a clear plan for de-implementation and de-escalation.

Automating surveillance for healthcare-associated infections: Rationale and current realities (Part I/III).

Infection surveillance is one of the cornerstones of infection prevention and control. Measurement of process metrics and clinical outcomes, such as detection of healthcare-associated infections (HAIs), can be used to support continuous quality improvement. HAI metrics are reported as part of the CMS Hospital-Acquired Conditions Program, and they influence facility reputation and financial outcomes.

The future of automated infection detection: Innovation to transform practice (Part III/III).

Current methods of emergency-room-based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true "Learning Healthcare System" that will support near-real-time quality improvement efforts and advance the scientific basis for the practice of infection control.

Patient- reported reasons for non-participation in a COVID-19 therapeutics clinical trial: Findings from a multi-center investigation.

BACKGROUND: Early in the pandemic, there were no evidence-based treatments for SARS-CoV-2, creating an urgent need to identify effective therapeutics. However, public participation in medical research is low; trial enrollment in the US is typically 10-20%. Thus, the aim of this study was to identify common themes underpinning patient reasons to decline participation and evaluate the impact of specific contextual factors. METHODS: This sub-study was conducted in five VISN-1 Clinical Trials Network participating facilities from 4/10/2020-2/3/2021. The trial evaluated the addition of the IL-6-inhibitor, Sarilumab, to the current standard of care for inpatients with moderate-to-severe SARS-CoV-2. Consent procedures varied by site and included fully in-person and fully remote processes. Reasons for declining enrollment were collected among eligible patients who declined to participate but agreed to answer a short follow-up question. Qualitative data were analyzed using directed content analysis. Enrollment rates were assessed using simple, descriptive statistics. RESULTS: N = 417 COVID-19 positive inpatients were screened and 53/162 eligible patients enrolled. Enrollment varied across study sites and by study period. Prior to identification of effective treatment, the enrollment rate was 10/11 (91%) versus 43/144 (30%) during the later period of the study. N = 85/102 patients who did not enroll answered the follow-up question. The most commonly reported responses were: concerns about the study drug and participation in clinical research in general, comorbidity concerns, competing priorities, external factors, and external advice and influence from family members and clinicians. CONCLUSIONS: Identifying reasons behind declining to enroll may help investigators develop strategies to increase research participation.

Factors associated with the speed and scope of diffusion of COVID-19 therapeutics in a nationwide healthcare setting: a mixed-methods investigation.

BACKGROUND: The global COVID-19 pandemic is an opportunity to evaluate factors associated with high levels of adoption of different therapeutics in a real-world setting. The aim of this nationwide, retrospective cohort study was to evaluate the diffusion and adoption of novel therapeutics with an emerging evidence basis and to identify factors that influenced physicians' treatment decisions. METHODS: Cohort creation: A cohort of Veteran patients with a microbiologically confirmed diagnosis of SARS-CoV2 were identified, and cases were classified by disease severity (outpatient, inpatient with mild and severe disease, intensive care unit ICU]). After classification of disease severity, the proportion of cases (outpatients) and admissions (inpatients) in each category receiving each type of medication were plotted as a function of time. Identification of milestones and guidance changes: Key medications used for the management of COVID-19 milestones in the release of primary research results in various forms (e.g. via press release, preprint or publication in a traditional medical journal), policy events and dates of key guidelines were identified and plotted as a timeline. After a timeline was created, time points were compared to changes in medication use, and factors potentially impacting the magnitude (i.e. proportion of patients who received the treatment) and the speed (i.e. the slope of the change in use) of practice changes were evaluated. RESULTS: Dexamethasone and remdesivir, the first two medications with clinical trial data to support their use, underwent the most rapid, complete and sustained diffusion and adoption; the majority of practice changes occurred after press releases and preprints were available and prior to guideline changes, although some additional uptake occurred following guideline updates. Medications that were not "first in class", that were identified later in the pandemic, and that had higher perceived risk had slower and less complete uptake regardless of the strength and quality of the evidence supporting the intervention. CONCLUSIONS: Our findings suggest that traditional and social media platforms and preprint releases were major catalysts of practice change, particularly prior to the identification of effective treatments. The "first available treatment in class" impact appeared to be the single most important factor determining the speed and scope of diffusion.

Factors Associated With Severe COVID-19 Among Vaccinated Adults Treated in US Veterans Affairs Hospitals.

Importance: With a large proportion of the US adult population vaccinated against SARS-CoV-2, it is important to identify who remains at risk of severe infection despite vaccination. Objective: To characterize risk factors for severe COVID-19 disease in a vaccinated population. Design, Setting, and Participants: This nationwide, retrospective cohort study included US veterans who received a SARS-CoV-2 vaccination series and later developed laboratory-confirmed SARS-CoV-2 infection and were treated at US Department of Veterans Affairs (VA) hospitals. Data were collected from December 15, 2020, through February 28, 2022. Exposures: Demographic characteristics, comorbidities, immunocompromised status, and vaccination-related variables. Main Outcomes and Measures: Development of severe vs nonsevere SARS-CoV-2 infection. Severe disease was defined as hospitalization within 14 days of a positive SARS-CoV-2 diagnostic test and either blood oxygen level of less than 94%, receipt of supplemental oxygen or dexamethasone, mechanical ventilation, or death within 28 days. Association between severe disease and exposures was estimated using logistic regression models. Results: Among 110 760 patients with infections following vaccination (97 614 [88.1%] men, mean [SD] age at vaccination, 60.8 [15.3] years; 26 953 [24.3%] Black, 11 259 [10.2%] Hispanic, and 71 665 [64.7%] White), 10 612 (9.6%) had severe COVID-19. The strongest association with risk of severe disease after vaccination was age, which increased among patients aged 50 years or older with an adjusted odds ratio (aOR) of 1.42 (CI, 1.40-1.44) per 5-year increase in age, such that patients aged 80 years or older had an aOR of 16.58 (CI, 13.49-20.37) relative to patients aged 45 to 50 years. Immunocompromising conditions, including receipt of different classes of immunosuppressive medications (eg, leukocyte inhibitor: aOR, 2.80; 95% CI, 2.39-3.28) or cytotoxic chemotherapy (aOR, 2.71; CI, 2.27-3.24) prior to breakthrough infection, or leukemias or lymphomas (aOR, 1.87; CI, 1.61-2.17) and chronic conditions associated with end-organ disease, such as heart failure (aOR, 1.74; CI, 1.61-1.88), dementia (aOR, 2.01; CI, 1.83-2.20), and chronic kidney disease (aOR, 1.59; CI, 1.49-1.69), were also associated with increased risk. Receipt of an additional (ie, booster) dose of vaccine was associated with reduced odds of severe disease (aOR, 0.50; CI, 0.44-0.57). Conclusions and Relevance: In this nationwide, retrospective cohort of predominantly male US Veterans, we identified risk factors associated with severe disease despite vaccination. Findings could be used to inform outreach efforts for booster vaccinations and to inform clinical decision-making about patients most likely to benefit from preexposure prophylaxis and antiviral therapy.

Associations of Depression, Anxiety, Worry, Perceived Stress, and Loneliness Prior to Infection With Risk of Post-COVID-19 Conditions.

Importance: Few risk factors for long-lasting (≥4 weeks) COVID-19 symptoms have been identified. Objective: To determine whether high levels of psychological distress before SARS-CoV-2 infection, characterized by depression, anxiety, worry, perceived stress, and loneliness, are prospectively associated with increased risk of developing post-COVID-19 conditions (sometimes called long COVID). Design, Setting, and Participants: This prospective cohort study used data from 3 large ongoing, predominantly female cohorts: Nurses' Health Study II, Nurses' Health Study 3, and the Growing Up Today Study. Between April 2020 and November 2021, participants were followed up with periodic surveys. Participants were included if they reported no current or prior SARS-CoV-2 infection at the April 2020 baseline survey when distress was assessed and returned 1 or more follow-up questionnaires. Exposures: Depression, anxiety, worry about COVID-19, perceived stress, and loneliness were measured at study baseline early in the pandemic, before SARS-CoV-2 infection, using validated questionnaires. Main Outcomes and Measures: SARS-CoV-2 infection was self-reported during each of 6 monthly and then quarterly follow-up questionnaires. COVID-19-related symptoms lasting 4 weeks or longer and daily life impairment due to these symptoms were self-reported on the final questionnaire, 1 year after baseline. Results: Of 54 960 participants, 38.0% (n = 20 902) were active health care workers, and 96.6% (n = 53 107) were female; the mean (SD) age was 57.5 (13.8) years. Six percent (3193 participants) reported a positive SARS-CoV-2 test result during follow-up (1-47 weeks after baseline). Among these, probable depression (risk ratio [RR], 1.32; 95% CI = 1.12-1.55), probable anxiety (RR = 1.42; 95% CI, 1.23-1.65), worry about COVID-19 (RR, 1.37; 95% CI, 1.17-1.61), perceived stress (highest vs lowest quartile: RR, 1.46; 95% CI, 1.18-1.81), and loneliness (RR, 1.32; 95% CI, 1.08-1.61) were each associated with post-COVID-19 conditions (1403 cases) in generalized estimating equation models adjusted for sociodemographic factors, health behaviors, and comorbidities. Participants with 2 or more types of distress prior to infection were at nearly 50% increased risk for post-COVID-19 conditions (RR, 1.49; 95% CI, 1.23-1.80). All types of distress were associated with increased risk of daily life impairment (783 cases) among individuals with post-COVID-19 conditions (RR range, 1.15-1.51). Conclusions and Relevance: The findings of this study suggest that preinfection psychological distress may be a risk factor for post-COVID-19 conditions in individuals with SARS-CoV-2 infection. Future work should examine the biobehavioral mechanism linking psychological distress with persistent postinfection symptoms.

Lack of Association between Pandemic School Mode Policy and Pediatric Body Mass Index Percentile over Time.

During the 2020-2021 academic year, schools across the country were closed for prolonged periods. Prior research suggests that children tend to gain more weight during times of extended school closures, such as summer vacation; however, little is known about the impact of school learning mode on changes. Thus, the aim of this study was to measure the association between school mode (in-person, hybrid, remote) and pediatric body mass index (BMI) percentile increases over time. In this longitudinal, statewide retrospective cohort study in Massachusetts, we found that BMI percentile increased in elementary and middle school students in all learning modes, and that increases slowed but did not reverse following the statewide reopening. Body mass percentile increases were highest in elementary school aged children. Hispanic ethnicity and receipt of Medicaid insurance were also associated with increases. Additional research is needed to identify strategies to combat pediatric body mass percentile increases and to address disparities.

Bronchopleural fistula due to cavitary pneumonia after SARS-CoV-2 infection treated with open thoracostomy.

Severe coronavirus disease of 2019 (COVID-19) disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes substantial parenchymal damage in some patients. There is a paucity of literature describing the surgical management COVID-19 associated bronchopleural fistula after failure of medical therapy. We present the case of a 59-year-old woman with SARS-CoV-2 pneumonia, secondary bacterial pneumonia with bronchopleural fistula and radiographic and clinical evidence of disease refractory to medical therapy. After a course of culture-driven antimicrobial therapy and failure to improve following drainage with tube thoracostomy, she was treated successfully with Clagett open thoracostomy. After resolution of the bronchopleural fistula, the thoracostomy was closed and she was discharged home. In cases of severe COVID-19 complicated by bronchopleural fistula with parenchymal destruction, a tailored approach involving surgical management when indicated can lead to acceptable outcomes without significant morbidity.