Economic burden of COVID-19 for employers and employees in the United States.

OBJECTIVE: Describe the economic burden of COVID-19 on employers and employees in the United States (US). METHODS: A targeted literature review was conducted to evaluate the impact of COVID-19 on US-based employers and employees in terms of healthcare resource utilization (HCRU), medical costs, and costs associated with work-loss. Searches were conducted in MEDLINE, Embase, and EconLit using a combination of disease terms, populations, and outcomes to identify articles published from January 2021 to November 4, 2022. As data from the employer perspective were lacking, additional literature related to influenza were included to contextualize the impact of COVID-19, as it shifts into an endemic state, within the existing respiratory illness landscape. RESULTS: A total of 41 articles were included in the literature review. Employer and employee perspectives were not well represented in the literature, and very few articles overlapped on any given outcome. HCRU, costs, and work impairment vary by community transmission levels, industry type, population demographics, telework ability, mitigation implementation measures, and company policies. Work-loss among COVID-19 cases were higher among the unvaccinated and in the week following diagnosis and for some, these continued for 6 months. HCRU is increased in those with COVID-19 and COVID-19-related HCRU can also continue for 6 months. CONCLUSIONS: COVID-19 continues to be a considerable burden to employers. The majority of COVID-19 cases impact working age adults. HCRU is mainly driven by outpatient visits, while direct costs are driven by hospitalization. Productivity loss is higher for unvaccinated individuals. An increased focus to support mitigation measures may minimize hospitalizations and work-loss. A data-driven approach to implementation of workplace policies, targeted communications, and access to timely and appropriate therapies for prevention and treatment may reduce health-related work-loss and associated cost burden.

In January 2020, the US government declared COVID-19 a public health emergency. This lasted until May 2023. To fight this health emergency, the US government provided free testing, vaccination, and treatment. Although the US government has declared the emergency over, COVID-19 continues to infect people. For people with private health insurance, costs associated with COVID-19 patient healthcare have now been transferred from the government to employers. In this study, we collected information from published scientific articles about the costs of COVID-19 for employers and workers in the US. We found that people who were not vaccinated against COVID-19 required more medical care and cost more than people who were vaccinated. In some cases, this trend lasted for as long as 6 months. This was mostly because of workers missing work, not working effectively while sick, and needing to be hospitalized. People who could work from home, whose companies had policies to prevent infections, and who took steps to avoid getting infected needed less medical care and missed work less often. This information may be used to help develop policies, communications, and guidance to prevent COVID-19 and limit its impact on employers and workers.

Predicting Chronic Opioid Use Among Patients With Osteoarthritis Using Electronic Health Record Data.

OBJECTIVE: To estimate the risk of a patient with osteoarthritis (OA) developing chronic opioid use (COU) within 1 year of a new opioid prescription by using electronic health record (EHR) data and predictive models. METHODS: We used EHR data from 13 health care organizations to identify patients with OA with an opioid prescription between March 1, 2017 and February 28, 2019 and no record of opioid use in the prior 6 months. We evaluated 4 machine learning models to estimate patients' risk of COU (≥3 prescriptions ≥84 days, maximum gap ≤60 days). We also estimated the transportability of models to organizations outside the training set. RESULTS: The cohort consisted of 33,894 patients with OA, of whom 2,925 (8.6%) developed COU within 1 year. All models demonstrated good discrimination, with the best-performing model (random forest) achieving an area under the receiver operating characteristic curve (AUC) of 0.728 (95% CI 0.711-0.745), but the simplest regression model performed nearly as well (AUC 0.717 [95% CI 0.699-0.734]). Predicted risk deciles spanned a range of 2% risk for the 10th percentile to 18% risk for the 90th percentile for well-calibrated models. Models showed highly variable discrimination across organizations (AUC 0.571-0.842). CONCLUSIONS: We found that EHR-based predictive models could estimate the risk of future COU among patients with OA to help inform care decisions. Black-box methods did not have significant advantages over more interpretable models. Care should be taken when extending all models into organizations not included in model training because of a high variability in performance across held-out organizations.

Clinical Integration of a Smartphone App for Patients With Chronic Pain: Retrospective Analysis of Predictors of Benefits and Patient Engagement Between Clinic Visits.

BACKGROUND: Although many pain-related smartphone apps exist, little attention has been given to understanding how these apps are used over time and what factors contribute to greater compliance and patient engagement. OBJECTIVE: This retrospective analysis was designed to help identify factors that predicted the benefits and future use of a smartphone pain app among patients with chronic pain. METHODS: An app designed for both Android and iOS devices was developed by Brigham and Women's Hospital Pain Management Center (BWH-PMC) for users with chronic pain to assess and monitor pain and communicate with their providers. The pain app offered chronic pain assessment, push notification reminders and communication, personalized goal setting, relaxation sound files, topics of interest with psychological and medical pain management strategies, and line graphs from daily assessments. BWH-PMC recruited 253 patients with chronic pain over time to use the pain app. All subjects completed baseline measures and were asked to record their progress every day using push notification daily assessments. After 3 months, participants completed follow-up questionnaires and answered satisfaction questions. We defined the number of completed daily assessments as a measure of patient engagement with the pain app. RESULTS: The average age of participants was 51.5 years (SD 13.7, range 18-92), 72.8% (182/253) were female, and 36.8% (78/212) reported the low back as their primary pain site. The number of daily assessments ranged from 1 to 426 (average 62.0, SD 49.9). The app was easy to introduce among patients, and it was well accepted. Those who completed more daily assessments (greater patient engagement) throughout the study were more likely to report higher pain intensity, more activity interference, and greater disability and were generally overweight compared with others. Patients with higher engagement with the app rated the app as offering greater benefit in coping with their pain and expressed more willingness to use the app in the future (P<.05) compared with patients showing lower engagement. Patients completing a small number of daily assessments reported less pain intensity, less daily activity interference, and less pain-related disability on average and were less likely to use the two-way messaging than those who were more engaged with the pain app (P<.05). CONCLUSIONS: Patients with chronic pain who appeared to manage their pain better were less likely to report benefits of a smartphone pain app designed for chronic pain management. They demonstrated lower patient engagement in reporting their daily progress, in part, owing to the perceived burden of regularly using an app without a perceived benefit. An intrinsically different pain app designed and targeted for individuals based on early identification of user characteristics and adapted for each individual would likely improve compliance and app-related patient engagement.

Opioid dependence treatment and guidelines.

BACKGROUND: In response to the growing incidence of opioid dependence, guidelines have been created, and new treatments are being developed to assist physicians in treating dependence and withdrawal of opioids. OBJECTIVE: To review treatment modalities and guidelines utilized in opioid dependence. SUMMARY: Guidelines for the treatment of opioid dependence have been developed by organizations such as the American Society of Interventional Pain Physicians (ASIPP) and the American Psychiatric Association (APA). Current guidelines recommend comprehensive treatment with pharmacological agents such as methadone, buprenorphine, or buprenorphine combined with naloxone as well as psychosocial therapy. These guidelines stress the need for an integrated approach to treatment. Office-based opioid treatment is currently being utilized to treat opioid dependent patients in a physician's office setting with buprenorphine/naloxone replacement therapy as an alternative to entering patients into a methadone clinic. These office-based programs offer a breakthrough in access to care for dependent patients. CONCLUSION: Physicians need to be aware of and adhere to currently accepted guidelines and recommendations for treating opioid dependent patients, including integrating psychosocial treatments and behavior modification strategies for optimal results. Clinicians must be educated on the new treatment modalities and regulations surrounding the use of these therapies.