Expansion of Telehealth Availability for Mental Health Care After State-Level Policy Changes From 2019 to 2022.

Importance: Although telehealth services expanded rapidly during the COVID-19 pandemic, the association between state policies and telehealth availability has been insufficiently characterized. Objective: To investigate the associations between 4 state policies and telehealth availability at outpatient mental health treatment facilities throughout the US. Design, Setting, and Participants: This cohort study measured whether mental health treatment facilities offered telehealth services each quarter from April 2019 through September 2022. The sample comprised facilities with outpatient services that were not part of the US Department of Veterans Affairs system. Four state policies were identified from 4 different sources. Data were analyzed in January 2023. Exposures: For each quarter, implementation of the following policies was indexed by state: (1) payment parity for telehealth services among private insurers; (2) authorization of audio-only telehealth services for Medicaid and Children's Health Insurance Program (CHIP) beneficiaries; (3) participation in the Interstate Medical Licensure Compact (IMLC), permitting psychiatrists to provide telehealth services across state lines; and (4) participation in the Psychology Interjurisdictional Compact (PSYPACT), permitting clinical psychologists to provide telehealth services across state lines. Main Outcome and Measures: The primary outcome was the probability of a mental health treatment facility offering telehealth services in each quarter for each study year (2019-2022). Information on the facilities was obtained from the Mental Health and Addiction Treatment Tracking Repository based on the Substance Abuse and Mental Health Services Administration Behavioral Health Treatment Service Locator. Separate multivariable fixed-effects regression models were used to estimate the difference in the probability of offering telehealth services after vs before policy implementation, adjusting for characteristics of the facility and county in which the facility was located. Results: A total of 12 828 mental health treatment facilities were included. Overall, 88.1% of facilities offered telehealth services in September 2022 compared with 39.4% of facilities in April 2019. All 4 policies were associated with increased odds of telehealth availability: payment parity for telehealth services (adjusted odds ratio [AOR], 1.11; 95% CI, 1.03-1.19), reimbursement for audio-only telehealth services (AOR, 1.73; 95% CI, 1.64-1.81), IMLC participation (AOR, 1.40, 95% CI, 1.24-1.59), and PSYPACT participation (AOR, 1.21, 95% CI, 1.12-1.31). Facilities that accepted Medicaid as a form of payment had lower odds of offering telehealth services (AOR, 0.75; 95% CI, 0.65-0.86) over the study period, as did facilities in counties with a higher proportion (>20%) of Black residents (AOR, 0.58; 95% CI, 0.50-0.68). Facilities in rural counties had higher odds of offering telehealth services (AOR, 1.67; 95% CI, 1.48-1.88). Conclusion and Relevance: Results of this study suggest that 4 state policies that were introduced during the COVID-19 pandemic were associated with marked expansion of telehealth availability for mental health care at mental health treatment facilities throughout the US. Despite these policies, telehealth services were less likely to be offered in counties with a greater proportion of Black residents and in facilities that accepted Medicaid and CHIP.

Technology and Data Implications for the Public Health Workforce.

Achieving a modern equity-oriented public health system requires the development of a public health workforce with the skills and competencies needed to generate findings and integrate knowledge using diverse data. Yet current workforce capabilities and infrastructure are misaligned with what is needed to harness both new and older forms of data and to translate them into information that is equity contextualized. As with other articles in this supplement, this article builds from a literature review, environmental scan, and deliberations from the National Commission to Transform Public Health Data Systems. The article summarizes some of the challenges around current workforce capabilities and pipeline. The article identifies where the technology and data sectors can contribute skills, expertise, and assets in support of innovative workforce models and augment the development of public health workforce competencies.

Improvement of the Laparoscopic Cholecystectomy Service at a District General Hospital During the COVID-19 Pandemic

Background British Association of Day Surgery and Royal College of Anaesthetists guidelines specify that 75% of elective surgery should be done as a day-case. Our Trust reported a laparoscopic cholecystectomy day-case rate of 25% pre-pandemic. Following the first wave of the pandemic our waiting list increased significantly. Therefore, to address this, we aimed to improve the day-case rate by developing the booking pathway, such as introducing the Cholecystectomy As A Day-case (CAAD) score. Methods Retrospective data for laparoscopic cholecystectomy were reviewed between 19th March and 9th July 2021. Specific documents reviewed were the operation booking forms, hospital-specific ‘boarding cards’ for booking and CAAD score completion, and a day-case rate was calculated. Results A total of 86 procedures were performed. There was an overall day-case rate of 54.7%. Of those booked to be day-cases (n=39), 61.5% remained day-case post-operatively and 28.2% were discharged the next day. Of the patients that were not discharged the same day (n=39), 18 cases had no documented reason for the additional stay. Incomplete booking forms (n=42) demonstrated a day-case rate of 50% versus 60.5% with complete forms (n=38). Conclusions Overall, the day-case rate has improved. We believe this is from adhering to the boarding card and introduction of the CAAD score to guide appropriate booking. However, for further improvement we are going to revise the booking form and create a Standard Operating Procedure (SOP) for the booking of these operations. Together with CAAD scoring, this should improve day-case rates further to reach the nationally accepted standard.

Protecting the mental and physical well-being of frontline health care workers during COVID-19: Study protocol of a cluster randomized controlled trial.

INTRODUCTION: The COVID-19 pandemic has placed health care workers at unprecedented risk of stress, burnout, and moral injury. This paper describes the design of an ongoing cluster randomized controlled trial to compare the effectiveness of Stress First Aid (SFA) to Usual Care (UC) in protecting the well-being of frontline health care workers. METHODS: We plan to recruit a diverse set of hospitals and health centers (eight matched pairs of hospitals and six pairs of centers), with a goal of approximately 50 HCW per health center and 170 per hospital. Participating sites in each pair are randomly assigned to SFA or UC (i.e., whatever psychosocial support is currently being received by HCW). Each site identified a leader to provide organizational support of the study; SFA sites also identified at least one champion to be trained in the intervention. Using a "train the trainer" model, champions in turn trained their peers in selected HCW teams or units to implement SFA over an eight-week period. We surveyed HCW before and after the implementation period. The primary outcomes are posttraumatic stress disorder and general psychological distress; secondary outcomes include depression and anxiety symptoms, sleep problems, social functioning problems, burnout, moral distress, and resilience. In addition, through in-depth qualitative interviews with leaders, champions, and HCW, we assessed the implementation of SFA, including acceptability, feasibility, and uptake. DISCUSSION: Results from this study will provide initial evidence for the application of SFA to support HCW well-being during a pandemic. TRIAL REGISTRATION: (Clinicaltrials.govNCT04723576).

Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset.

OBJECTIVE: The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. MATERIALS AND METHODS: The National COVID Cohort Collaborative (N3C) table of laboratory measurement data-over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. RESULTS: Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units). DISCUSSION: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. CONCLUSION: The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.

Worker and employer experiences with COVID-19 and the California Workers' Compensation System: A review of the literature.

BACKGROUND: Given workplace risks from COVID-19, California policymakers passed Senate Bill (SB) 1159 to facilitate access to workers' compensation (WC) benefits for frontline workers. However there has been no review of the available evidence needed to inform policy decisions about COVID-19 and WC. METHODS: We conducted a literature review on worker and employer experiences surrounding COVID-19 and WC, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Forty articles were included (16 about worker experiences and 24 about employer practices). Most were not about experiences and practices related to COVID-19 and WC. Worker studies indicated that paid sick leave reduced new COVID-19 cases and COVID-19 activity. Studies also found that rural agricultural and food processing workers lacked sick leave protection and faced severe housing and food insecurity. Studies on workplace health and safety indicated that healthcare workers with access to personal protective equipment had lower stress levels. Studies about employer practices found that unrestricted work in high-contact industries was associated with increased risks to at-risk workers, and with health disparities. No studies examined worker COVID-19 experiences and WC claims or benefits, job loss, retaliation, workers' medical care experiences, and return-to-work or leave practices. CONCLUSIONS: Our review identified experiences and practice related to COVID-19 and the WC system, but not specifically about WC and COVID-19 WC claims or benefits. Further research is needed to document and understand evidence underpinning the need for WC coverage for COVID-19 and to evaluate the impact of the current SB 1159 bill on WC in California.

Association Between COVID-19 Relief Funds and Hospital Characteristics in the US.

Importance: In response to financial stress created by the reduction in care during the COVID-19 pandemic, hospitals received financial assistance through the Coronavirus Aid, Relief, and Economic Security (CARES) Act program. To date, the allocation of CARES Act funding is not well understood. Objective: To examine the disbursement of the High-Impact Distribution CARES Act funds and the association between financial assistance and hospital-level financial resources prior to the COVID-19 pandemic. Design Setting and Participants: This cross-sectional analysis of US-based hospitals and health systems assesses the hospital characteristics associated with CARES Act funding with linear regression models using linked hospital and health system-level information on CARES Act funding with hospital characteristics from Hospital Cost Report data. Exposures: Hospital and health system CARES Act financial assistance. Main Outcomes and Measures: Hospital and health system affiliation, status, and financial health prior to the COVID-19 pandemic. Data analysis took place from December 2020 through June 2021. Results: The analysis included 952 hospital-level entities with an average payment of $33.6 million, most of which was received during the first payment round. Wide ranges existed in CARES Act funding, with 24% of matched hospitals receiving less than $5 million in funding and 8% receiving more than $50 million. Academic-affiliated hospitals, hospitals with higher pre-COVID-19 assets and hospitals with higher COVID-19 cases received higher levels of funding, while critical access hospitals received lower levels of financial assistance. A 10% increase in hospital assets, endowment size, and COVID-19 cases was associated with 1.4% (95% CI, 0.8% to 2.0%; P = .003), 0.2% (95% CI, 0.1% to 0.3%; P < .001), and 3.5% (95% CI, 2.8% to 4.2%; P < .001) increases in CARES Act funding, respectively. Conclusions and Relevance: In this cross-sectional study of US hospitals and health systems, findings suggest that High-Impact Distribution CARES Act funds may have disproportionately gone to hospitals that were in a stronger financial situation prior to the pandemic compared with those that were not, but funds also went disproportionately to those that eventually had the most cases.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.

OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative.

Importance: The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. Objectives: To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and Participants: In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Main Outcomes and Measures: Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. Results: The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance: This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.

Trust in experts, not trust in national leadership, leads to greater uptake of recommended actions during the COVID-19 pandemic.

Evidence suggests that people vary in their desire to undertake protective actions during a health emergency, and that trust in authorities may influence decision making. We sought to examine how the trust in health experts and trust in White House leadership during the COVID-19 pandemic impacts individuals' decisions to adopt recommended protective actions such as mask-wearing. A mediation analysis was conducted using cross-sectional U.S. survey data collected between March 27 and 30, 2020, to elucidate how individuals' trust in health experts and White House leadership, their perceptions of susceptibility and severity to COVID-19, and perceived benefits of protecting against COVID-19, influenced their uptake of recommended protective actions. Trust in health experts was associated with greater perceived severity of COVID-19 and benefits of taking action, which led to greater uptake of recommended actions. Trust in White House leadership was associated with lower perceived susceptibility to COVID-19 and was not associated with taking recommended actions. Having trust in health experts is a greater predictor of individuals' uptake of protective actions than having trust in White House leadership. Public health messaging should emphasize the severity of COVID-19 and the benefits of protecting oneself while ensuring consistency and transparency to regain trust in health experts.

La evidencia sugiere que las personas varían en su deseo de emprender acciones de protección durante una emergencia de salud y que la confianza en las autoridades puede influir en la toma de decisiones. Buscamos examinar cómo la confianza en los expertos en salud y la confianza en el liderazgo de la Casa Blanca durante la pandemia de COVID­19 impactan las decisiones de las personas para adoptar las acciones de protección recomendadas, como el uso de máscaras. Se realizó un análisis de mediación utilizando datos de encuestas transversales de EE. UU. Recopilados entre el 27 y el 30 de marzo de 2020 para dilucidar cómo la confianza de las personas en los expertos en salud y el liderazgo de la Casa Blanca, sus percepciones de susceptibilidad y gravedad al COVID­19, y los beneficios percibidos de protegerse contra COVID­19, influyó en su adopción de las acciones de protección recomendadas. La confianza en los expertos en salud se asoció con una mayor gravedad percibida de COVID­19 y los beneficios de tomar medidas, lo que llevó a una mayor aceptación de las acciones recomendadas. La confianza en el liderazgo de la Casa Blanca se asoció con una menor susceptibilidad percibida al COVID­19 y no con la adopción de las acciones recomendadas. Tener confianza en los expertos en salud es un factor de predicción mayor de la adopción de acciones de protección por parte de los individuos que tener confianza en el liderazgo de la Casa Blanca. Los mensajes de salud pública deben enfatizar la gravedad de COVID­19 y los beneficios de protegerse a sí mismo, al tiempo que se garantiza la coherencia y la transparencia para recuperar la confianza en los expertos en salud.

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.