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Design, Setting, and Participants This cohort study included participants in a US workplace mental health program implemented by 66 employers across 40 states from January 1, , to January 1, Participants were employees who enrolled in the mental health benefit program and had at least moderate anxiety or depression, at least 1 appointment, and at least 2 outcome assessments. Main Outcomes and Measures Primary outcomes were the Patient Health Questionnaire-9 for depression range, score and the Generalized Anxiety Disorder 7-item scale range, score.

The ROI was calculated by comparing the cost of treatment to salary costs for time out of the workplace due to mental health symptoms, measured with the Sheehan Disability Scale. Data were collected through 6 months of follow-up and analyzed using mixed-effects regression. Results A total of participants of who reported gender [ Conclusions and Relevance Results of this cohort study suggest that an employer-sponsored workplace mental health program was associated with large clinical effect sizes for employees and positive financial ROI for employers.

Introduction In the past few decades, the number of employers offering wellness benefits has increased considerably. However, workplace wellness programs vary in design, scope, and intensity. Researchers have identified some components commonly present in successful programs, including educational interventions, supportive company culture and policies, reduced barriers to access, and systematic health risk assessments with tailored follow-up.

At present, whether workplace wellness programs focused on physical health have delivered benefits to both employers and employees is unclear. Workplace mental health programs that provide treatment for employee mental health difficulties may help close this gap.

Mental illness is the leading cause of disability worldwide. To the extent that workplace mental health programs reduce the burden of mental illness, they may generate an ROI that provides for their widespread and sustained implementation. To date, few workplace mental health programs have reported clinical improvement, 7 , 8 and only 1 intervention has demonstrated both clinical and financial ROI.

The sustainability of workplace mental health programs may be enhanced by identifying when and for whom mental health programming is most effective. Program effectiveness may vary depending on individual characteristics such as age and gender participant self-reported identity , 10 disease characteristics such as symptom severity or case complexity, 11 , 12 and environmental characteristics associated with or that sustain mental health difficulties.

Recently, the COVID pandemic has led to increases in depression and anxiety due to a range of chronic and relatively immutable environmental factors routine disruption, social isolation, and financial difficulties , resulting in a surge in demand for mental health care. In this study, we evaluated an employer-sponsored mental health program incorporating several evidence-based components, including a digital mental health screening platform, telephone and video appointments with care navigators who helped members find care matched to their needs, internet-based cognitive behavioral therapy resources, free or low-cost access to video or in-person psychotherapy and medication management, and a symptom tracking framework to support measurement-based care.

We investigated the primary outcomes of clinical improvement in depression and anxiety symptoms, tested dose-response associations for each treatment component, and examined factors associated with clinical improvement including the COVID pandemic. We also examined the secondary outcomes of increased time in the workplace, employee retention, and financial ROI. Methods This cohort study was categorized by the Yale Institutional Review Board as not human participant research and was therefore exempt from approval and the need for informed patient consent.

The program was implemented by 66 employers across 40 states in the US and was available to eligible participants from January 1, , through January 1, Employees were notified of their eligibility by their employer via email and on-site events and enrolled online for free. Program Design The platform first invited participants to identify common mental health difficulties that have been bothering them eg, stress, anxiety, sleeping, eating, or relationship difficulties from a predetermined list.

Depending on the issues that the participant identified, additional self-report questionnaires were triggered eMethods 1 in the Supplement. Follow-up assessments were completed at regular intervals chosen by participants with the default being every 2 weeks. Data were collected through 6 months of follow-up. After taking an assessment, participants could schedule appointments with members of their care team, consisting of a care navigator, a therapist, and a medication manager.

Participants who were deemed by their care team to need more intensive services were referred outside the online mental health benefit program by their clinicians. All care navigators were licensed masters degree—level mental health clinicians.

All therapists had masters-level or doctoral-level licenses. All medication managers were medical doctors or doctors of osteopathic medicine. All participating health care clinicians had at least 3 years of experience postsupervision before becoming a part of the network. In March , due to the COVID pandemic, in-person treatment was paused and care appointments were delivered via video, except when medically necessary.

Cost Sharing To reduce financial barriers to accessing care, participants were able to book an unlimited number of free appointments with their care navigator. An episode of care was defined as the time from the first assessment to the last appointment that happened within 6 months of the first assessment. Of people eligible for the benefit, 20 enrolled in the program, resulting in a 7. Of enrolled participants, met criteria for having at least 1 therapy session, 2 assessments, and a score above the cutpoint on the PHQ-9 or GAD This scale is a self-report measure of family, work, and social functional impairment due to emotional symptoms.

Time-invariant covariates were age, gender, number of positive screenings, and participant demographic characteristics. The association between the pandemic and participant trajectories was modeled with a piecewise log-linear model coded as 0 prepandemic and the log-days of treatment that occurred postlockdowns and was interpreted as the deviation from the main association of log-time that occurred during COVID lockdowns. Additionally, an exploratory model was developed using a machine learning approach, elastic net regression.

Employer cost of care for all participants was calculated based on outpatient fee-for-service rates eMethods 3 in the Supplement. Employer break-even cost was calculated as the dollar amount per employee per month that equals the dollar amount generated from increased productivity and decreased absenteeism.

The enrollment rate the number of employees who enrolled in the benefit, whether or not they engaged in care and rate of engagement in care number of employees who completed at least 1 episode of care are also reported. Employee Retention To test whether platform use was associated with differential retention, group differences in retention rates were calculated between employees who enrolled in the program and those who did not eMethods 4 in the Supplement.

This analysis was limited to 3 employers whose retention data were provided in an adequate form for this analysis. Statistical Analysis Mixed-effects models were used to accommodate the multilevel data structure. Repeated observations level 1 were nested within participants level 2 , and random intercepts and time effects were included at level 2.

An identity link was used for continuous outcomes depression, anxiety , a Poisson model with a log link for count data productivity , and a logit link for categorical outcomes reliable change, reliable recovery. Connors is widely regarded as one of the leading educators in the financial markets industry. Connors has also been a featured speaker at a number of major investment conferences over the past two decades. Abstract Fundamental and Technical analysis are often thought of as separate, competing ideologies.

We regard this world view as short-sighted. Our research shows that combining fundamental and technical analysis in a quantitative, rules-based framework leads to greatly improved performance. Introduction Factor investing has taken the investment management industry by storm.

In this paper, we show that traditional factor investing can be greatly enhanced by combining factors in an intelligent way. More specifically, combining fundamental factors with technical factors leads to large increases in performance. These different disciplines, fundamental and technical analysis, have rarely been combined throughout history.

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