For most people, the term “Preventive Care” suggests age appropriate cancer screenings, flu shots and childhood vaccinations. What about appropriate regular monitoring of symptoms that prevents the worsening of a chronic illness? Or timely interventions that reduce or prevent complications due to a medication or a stressful life event?

Preventive care is a much broader concept that includes (but not limited to) activities that lead to the overall reduction of adverse events (e.g. fewer life-threatening complications due to a chronic illness) and the promotion of overall health in the entire population.

The “Annual Wellness Exam” (aka Annual Physical, Annual check-up, Health Maintenance Visit, Preventive Care Visit, etc.) is perhaps one of the most underutilized benefits in a health plan even though it is available at no out-of-pocket cost to the covered individual (with most federal, state and commercial plans).

Getting an Annual Wellness Exam regularly offers two main advantages:

• An individual who does not proactively seek care (often referred to as a “non-utilizer”) gets monitored for any new and/or existing physical and emotional problems, assessed for various risks and guided to close relevant care gaps (e.g. BMI, Mammogram, Blood Pressure check)
• Establishes a relationship between the member (including his/ her family) and the Primary Care Provider (PCP)’s office making it more likely for the PCP to be the first point of contact for any health issue - rather than an Urgent Care or ER

From a Payer (Employer, Health Plan, other) and Provider (individual Physician, Group Practice, Health System, other) standpoint, there is also a financial advantage in ensuring all members get a Wellness Exam every year as described below.

What does the data show?

In looking at the Professional component of medical claims data for the last 3 years, HDMS saw an overwhelming trend among our customers.  We classified members into two groups:

• Those that HAD received an Annual Wellness Exam during the reporting year
• Those that had NOT had an Annual Wellness Exam during the reporting year

Adult members in both groups were then compared for ER Utilization, particularly for Avoidable ER usage using the NYU Emergent Status & AHRQ Prevention Quality Indicator (PQI) methodologies.

The results showed:

• Members who have NOT had an Annual Wellness Exam within the last reporting year, consistently incurred higher overall Cost AND higher number of Visits to the ER for complaints (conditions) that are classified as: “Non-Emergent”, “Primary Care Appropriate” and “Preventable/ Avoidable.”
• There were a higher number of members WITHOUT an Annual Wellness Exam within the last reporting year, with one or more visits to the ER for diagnoses, that qualify as “Ambulatory Care Sensitive Conditions”

What does this mean?

These reports show a clear pattern. Members who get an Annual Wellness Exam are less likely to use the ER for conditions that can be treated and/ or managed at a less expensive site of care. Hence, it is in the best interest of the organization to encourage and incent all their members to establish a relationship with a PCP and get regular Wellness exams.

As healthcare costs continue to increase, more employers and health plans are evaluating the impact of their health and wellness benefits – including the effectiveness of preventive screenings.

Three out of five U.S. employers use health screenings and risk assessments to screen for expensive chronic conditions, such as cancer.¹ Yet, 79 percent of large U.S. employers and 44 percent of mid-sized employers do not measure the effectiveness of employee wellness programs, including preventive screenings.²

With the cost of employee health benefits expected to rise 5 percent in 2019, it is critical that employers and health plans develop a data-centric approach to measuring the effectiveness of preventive screenings.³

How Data Insight Strengthens Preventive Cancer Screening Outcomes

Analytics inform a high-value approach for health benefits design by providing employers and health plans insights into opportunities for targeted interventions that reduce costs and improve health. Data analytics also help avoid “one-size-fits-most” solutions that may not be a good fi t given member and provider characteristics.

Increasingly, analytics are used to track outcomes of preventive care. For example, a recent study examined the impact of preventive cervical cancer screenings and showed these eff orts resulted in substantially lower deaths and increased lifespans.⁴

Analytics can also help employers and health plans prioritize preventive cancer screening offerings. Criteria might include:

• Risk factors such as high proportions of members who are overweight, have high cholesterol or high blood sugar levels, or smoke.
• Regional health trends that may point to potential socioeconomic-based risks for members, like higher-than-average prevalence rates of lung cancer or heart disease. For example, 6.2 percent of Ohio’s population has heart disease, even as rates across the nation dropped.⁵
• Evidence of possible “hot spots” within a plan sponsor membership. For instance, analytics show certain locations where employers and health plans should focus eff orts on encouraging preventative cancer screenings (member education, onsite clinic involvement, etc.).

The analysis of claims data – as well as socioeconomic data that might be available from state and regional health organizations – can provide powerful insights in developing a high-value approach to preventative cancer screening health benefits for members that improves outcomes.

Case Study: Measure the Impact of Preventive Cancer Screenings

Employers and health plans can demonstrate success through data analytics by determining the impact of preventative cancer screenings on access to treatment, risk and costs of care.

For example, a state health plan covering around 205,000 employees and dependents set out to identify the rate at which members were diagnosed with cancer after undergoing preventive screenings for breast, colorectal and cervical cancers.

For the overall state population, new cases of colorectal and cervical cancer have been decreasing while new cases of breast cancer are increasing. However, analysis of claims data for the state health plan differs for state employees:

• While the rate of newly diagnosed cases of breast cancer remained steady, it was higher than the state average.
• The number of new cases of colorectal and cervical cancer among state employees increased; however, the rate of occurrences was lower than the state average.

By collaborating with HDMS experts, the state health plan created episode-based analysis groups, or cohorts, to assess compliance with preventive screenings compared to national guidelines and measure the impact of such screenings on early cancer detection and treatment.

Members in the episode-based analysis group included those who were newly diagnosed with breast, colorectal and cervical cancers as well as those who had been identified as having a recurring cancer diagnosis within two years of initial detection of the cancers. The results were enlightening:

Increased early diagnosis. The majority of new cases of breast, colorectal and cervical cancer were initially diagnosed following preventive screenings:

• Preventive screenings were associated with 80% of new cases of breast cancer among plan members.
• Among members who received preventive screenings, 11% received additional treatments – and not just for cancer (e.g., removal of benign tumors or polyps).
• Cervical cancer screenings helped identify women who need additional testing to detect or rule out uterine or ovarian cancer.

Decreased risk. The study showed early diagnosis of cancer through preventive screenings was associated with significantly reduced members’ risk scores. Members who were diagnosed earlier through preventive screening had significantly lower concurrent risk scores compared to other members with the same type of cancer. Higher risk scores are typically associated with members with later stages of cancer that require more complex treatment.

Specifically, members diagnosed with breast cancer through preventive screenings had an average risk score of less than 1.00 while members diagnosed outside of preventive screenings had average risk scores from 5.88 to 6.53. Similarly, members diagnosed with cervical cancer through preventive screenings had average risk scores of 1.00 while those diagnosed later exhibited risk scores of 3.31 to 4.22.

Reduced costs of care. Analysis also revealed the impact of preventive screenings in lower costs of care. The cost of treating breast and cervical cancer for women identified by preventive screening was lower on average.⁶

Optimize Value Through Claims Analysis

The results showcase the power of using data to measure the effectiveness of preventive screenings. When employers and health plans leverage claims and socioeconomic data analysis to refine their approach to benefits design, they are more empowered to reduce costs and improve outcomes.

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1. “Top 10 Health Conditions Costing Employers the Most,” Employee Benefit News, Feb. 9, 2018, https://www.benefitnews.com/slideshow/top-10-healthconditions-costing-employers-the-most
2. Desai, P., “Why Health Screening Programs Fail and What Employers Can Do About It,” Corporate Wellness Magazine, https://www.corporatewellnessmagazine.com/worksite-wellness/health-screening-programs-fail/
3. https://www.shrm.org/resourcesandtools/hr-topics/benefits/pages/employers-adjust-health-benefits-for-2019.aspx
4. Kim, J.J., Burger, E.A., Regan, C., et al., “Screening for Cervical Cancer in Primary Care: A Decision Analysis for the U.S. Preventive Services Task Force,” JAMA, Aug. 21, 2018, https://jamanetwork.com/journals/jama/fullarticle/2697702
5. “Heart Disease Hotspots: 14 States with Highest Rates,” CBS News, https://www.cbsnews.com/pictures/heart-disease-hotspots-14-states-with-highest-rates/
6. HDMS proprietary data

For many employers, the rise in metabolic syndrome among employees and their dependents is making a deep impact on health care costs.

Thirty-five percent of U.S. adults suffer from metabolic syndrome, a cluster of conditions that puts them at greater risk for heart disease, diabetes and stroke.¹ Individuals with metabolic syndrome have three of the following five characteristics:

• Excess body fat around the waist
• Triglyceride levels of 150 or higher
• A level of high-density lipoprotein (HDL), or “good” cholesterol, that is lower than 40 in men and 50 in women
• A blood pressure rate of 130/85 or higher
• A fasting blood sugar level of 110 or more (levels of 110- 125 indicate prediabetes)

When these risk factors increase among employees and their dependents, so do employers’ health care costs. Nationally, obesity alone has more than a $2 trillion impact on health care costs.² Medical costs for obese individuals are at least 36 percent higher than for Americans of healthy weight.³ The more risk factors an employee has, the greater the impact on health care costs. Employees who have metabolic syndrome also typically have lower productivity and higher rates of absenteeism.

For employers, using claims data to gauge employees’ risk of metabolic syndrome can inform approaches that improve employee health, increase productivity, lower absenteeism rates and reduce health care costs.

Why Claims Data Analysis Is Critical to Addressing Metabolic Syndrome

Measuring the rate of metabolic syndrome among employees can help employers develop targeted interventions that improve employee health and reduce costs. Employers also can use claims data to determine the extent to which employees have health conditions that could lead to metabolic syndrome. With this information, employers can proactively halt the spread of metabolic syndrome by investing in programs and incentives that encourage changes toward healthier behaviors.

Metabolic syndrome is one of the few health conditions that can be reversed with changes in lifestyle or pharmacologic treatment, such as exercise, cholesterol medication and/or a healthier diet. By using claims data to measure the number of employees who have high blood pressure, obesity, high cholesterol and diabetes – as well as the percentage of those who have two or more of these risk factors – employers can better assess risk for metabolic syndrome. A deeper dive into claims data for this population also can reveal the extent to which these risk factors increase employers’ health care costs.

For example, one study found people with high blood pressure who are also obese spend about $1,000 a year more on health care than individuals who don’t have these risk factors.⁴ Meanwhile, those who have high blood pressure, obesity, low HDL cholesterol and triglyceride levels of 150 or higher spend about $1,600 more on care per year, according to the study. In fact, the presence of even one of these health factors increases health care costs, researchers found.

Drilling down into employee claims data gives employers a powerful tool for reducing the proportion of employees who suffer from metabolic syndrome. Employers can use the data to determine:

• Which populations to target.
• The right approaches for intervention (e.g., wellness initiatives designed to help employees maintain a healthier weight, lower their cholesterol or blood pressure, or bring blood sugar levels in line).
• The right incentives to encourage behavior change for improved health.

With actionable insight, the potential to prevent metabolic syndrome for at-risk employees rises. The likelihood of reversing metabolic syndrome among employees who already have been diagnosed with this syndrome also increases.

How Employers Are Using Data to Find Solutions

Employers can learn a great deal about their employees’ risk for metabolic syndrome through claims data analysis. Let’s take a look at how one national employer’s review of medical claims provided the insight needed for action.

A national car retailer partnered with HDMS to evaluate ways to reduce its employee health care costs. Short-term disability claims and employee absences were trending upward, and the company sought to gain a greater understanding of the health conditions and risk factors employees faced.

A claims data analysis showed incidence of metabolic syndrome and risk factors for this syndrome were prevalent among employees:

• 22% of employees were diagnosed with high blood pressure
• 13% were obese
• 10% had high cholesterol
• 8% had diabetes

In addition, a deeper dive into the data showed the greater the risk factors for metabolic syndrome among employees, the more employee health care costs increased. While the average per member per month (PMPM) costs per employee totaled $348, the average PMPM cost per obese employee was $987. Among employees with high cholesterol, average PMPM costs totaled $1,188.

Costs were even higher for employees with two or more conditions:

• Employees with both high blood pressure and cholesterol recorded $1,367 in PMPM costs.
• PMPM costs for those who were diagnosed with all four conditions totaled $2,033.

Medical costs for employees on short-term disability were 9 percent higher when employees exhibited signs of metabolic syndrome, the analysis showed. Incidence of short-term disability and the duration of short-term disability also rose when claimants had metabolic syndrome, according to the analysis.

With this information in hand, the company was better positioned to proactively address these health conditions among its employee population. Based on the analysis, the company revamped its health and wellness program to focus on reducing high cholesterol, high blood pressure, body mass index and more.

Within one year, the number of employees at risk for metabolic syndrome dropped 4 percent. Employee absenteeism decreased, reducing labor expenses (such as the expense of replacement labor to cover absences) by $140,000. Today, the company continues to use claims data analysis to improve employee health and wellness, reduce health care costs and limit the impact of metabolic syndrome on productivity.⁵

Lessons Learned: Improving Health and Wellness Through Data

A data-driven approach to lowering the risk of developing metabolic syndrome can help employers, employees and their dependents find success in better managing their health.

Access to claims data and analysis empowers employers to actively address the health of their employee population. For example, a data-driven approach to improving employee health can be the starting point for creating a healthier companywide culture. It can help establish company-specific health and wellness goals as well as determine incentives that could drive the behavior change needed to lower metabolic risk. It also empowers employers to reduce health care costs by better managing employees’ health conditions before they become high-cost conditions.

One of the lessons learned from a data-driven approach to addressing metabolic syndrome is that managing health conditions while they are at the early stage of the “disease pathway” can have a deeper, longer-term impact on employers’ health care costs. Many times, employers look to the 5 percent of conditions that comprise 20 percent of health care costs nationwide in determining where to focus health and wellness initiatives. However, by using claims data to dig deeper, such as by determining the conditions most prevalent among employees and developing targeted interventions based on population-specific insights, our experience shows employers are better able to make a more meaningful impact on employee health, productivity, absenteeism rates and health care costs.

Using claims data analysis to address potentially costly conditions before they reach the high-cost stage can set the foundation for a healthier workplace for years to come.

Metablic Syndrome Perspective Paper

1. https://www.cdc.gov/pcd/issues/2017/16_0287.html
2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409636/
3. https://www.brookings.edu/research/obesity-prevention-and-health-care-costs/
4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125563/
5. HDMS proprietary data