As the workforce changes year over year, the need for
From a financial perspective, people often want to but can't or don't access mental health services, and
The same study found that employers who support mental health see a return of $4 for every dollar invested in mental health treatment. These data points combine results from the 2015-to-2018 National Surveys on Drug Use and Health with the latest research on the costs of mental health problems for employers.
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Now more than ever, employers need to target the available data to answer questions that can help better design benefits for such a diverse workforce. For example, with data from an internal employee survey such as a Health Risk Assessment (HRA), employers can start to layer additional data and benchmarking to identify physical and mental health trends within their population and the members they serve.
With a strategic approach, employers can use relevant data to design their healthcare benefit packages and provide equitable and accessible care for all employees.
1. Observe and track needs within your population
Most employers don't completely grasp their employees' mental and physical health status, which limits their ability to design benefit programs effectively. Remember, those at the table designing the benefit programs are usually the most versed in healthcare, compared to the average employee who may not understand the benefits offered in their health coverage, especially those related to mental health.
Employers need to focus their efforts on the concerns of their population. An internal employee survey is one of the most effective ways to understand employees' health status. "[Employers] must systematically measure well-being; without that data, they can't design programs that best serve the needs of their particular employee population," according to a November 2023
Here is one situation where employee data informed the design of mental health benefits for a diverse workforce.
A large national employer with locations across the United States focused on addressing anxiety and depression among its employees. They wanted to understand better the differences in access to and use of care based on sociodemographic data, such as income brackets and race/ethnicity.
How the company cut the data made a difference. First, they looked at the use of mental health services by income bracket. They found that when looking at anxiety, bipolar disorder, and depression, people from middle- and high-income neighborhoods consistently used services to treat these conditions 8% more often than those living in low-income neighborhoods. The company then layered additional data to discover more about its employee population.
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2. Layer data to identify and address racial and ethnic disparities
When this company next reviewed data by race and ethnicity, it found some remarkable inequalities. Since the demographic data did not capture race and ethnicity data for employees and family members, they mapped publicly available data in the census tract to link up to medical claims utilization. They found higher rates of depression for members living in both predominantly Black and white neighborhoods, while Asian and Hispanic neighborhoods had lower rates. What they discovered, however, was not an absence of depression but a gap in care.
Next, with three distinct employee locations under review, the company reviewed medical and pharmacy cost data and layered it to better understand cases of depression at each location. One of the three locations had higher rates of depression and progressed to a higher stage of illness. The employer now had information to identify and intervene early to improve outcomes for this employee population. This information enabled the employer to target programs to fit the needs of their employees across the country, and in some cases, this may mean different programs based on areas of concern.
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3. Rely on different levels of data for valuable insights
Organizations can design and refine employee mental health programs by reviewing multiple factors, such as access to community poverty levels or housing and food insecurity. With data layered at different levels and from distinct sources, an organization can examine how mental health issues affect their employees and monitor their impact on health risk, utilization, cost, and even job attendance.
Here are various data examples available for these types of measurement.
- Population health data: Sociodemographic data identifies high-priority health issues, provides insights into broad trends, and identifies populations often at greater risk for mental health challenges.
- Organizational health data: Monitoring and analyzing employee health trends can identify groups that may be underserved, providing opportunities for targeted interventions, such as nationalities that tend to avoid mental health interventions.
Centers for Disease Control and Prevention Social Vulnerability Index (SVI) : This CDC data source aims to identify at-risk communities. When layered with socio-economic data, it can help identify health disparity trends, impacts, and opportunities.
United States Department of Agriculture Food Access Research Atlas : An interactive tool from the USDA uses Zip codes to identify food deserts based on location and information employers can access to address this challenge. A task as simple as putting food on a table for a family with low income can cause undue stress and anxiety.
- Benefits programs: It is essential to know how employees engage with the organization's current mental health benefit offerings to improve and raise awareness of those offerings.
Employers ultimately want to maintain the well-being of their workforce, a goal that supports employee satisfaction and productivity. Without access to equitable and quality health benefits, employees lack vital services to maintain their mental and physical health and work effectively on the job and in life.
