How Can We Close Treatment Gaps for Depression?

What you need to know

• Many people with major depressive disorder (MDD) go undetected or untreated, creating gaps in care
• Reducing detection and treatment gaps would increase healthcare costs in the short-term but also improve outcomes
• Efforts to close these gaps appear to be cost-effective across different European countries studied
• More research is needed on the best ways to actually reduce detection and treatment gaps in practice

The challenge of undetected and untreated depression

Major depressive disorder (MDD) is a common mental health condition that affects millions of people across Europe. It can have a major impact on a person’s quality of life, relationships, and ability to work or function day-to-day. While effective treatments like medication and psychotherapy exist, many people with depression do not get diagnosed or receive proper care.

Researchers estimate that about 69% of MDD cases go undetected, meaning people are not diagnosed even though they are experiencing depression. Of those who are diagnosed, only about 50% receive treatment. This creates significant gaps in care for people struggling with depression.

A team of researchers wanted to examine the potential economic impact of reducing these treatment gaps across six European countries: Germany, Hungary, Italy, Portugal, Sweden, and the United Kingdom. They used computer modeling to estimate the costs and benefits of improving detection and treatment rates for MDD.

Modeling the impact of closing treatment gaps

The researchers created a model to simulate what happens when someone has MDD over a 27-month period. The model looked at whether the depression was detected or not, and if detected, whether the person received medication, psychotherapy (specifically cognitive behavioral therapy), a combination of medication and therapy, or no treatment.

They used data on current detection and treatment rates, healthcare costs, and quality of life measures from previous studies. This allowed them to estimate the expected costs and health outcomes under current care patterns.

Then, they modeled what would happen if:

1. The detection gap was reduced from 69% to 50% (so more cases of MDD were diagnosed)
2. The treatment gap was reduced from 50% to 25% (so more diagnosed cases received treatment)
3. Both the detection and treatment gaps were reduced

Key findings on costs and benefits

As expected, the model showed that reducing detection and treatment gaps would increase healthcare costs in the short-term. More people getting diagnosed and treated means more spending on doctor visits, medication, therapy sessions, etc.

However, it would also lead to improved health outcomes, measured in quality-adjusted life years (QALYs). QALYs are a way to quantify both quality and length of life.

For example, in the UK:

• Reducing the detection gap to 50% would cost an extra €456 per person over 27 months, but increase QALYs by 0.0598
• Reducing the treatment gap to 25% would cost an extra €482 and increase QALYs by 0.0488
• Reducing both gaps would cost an extra €994 and increase QALYs by 0.1385

The researchers then calculated the incremental cost-effectiveness ratio (ICER) for each scenario. The ICER shows the additional cost per QALY gained. Lower ICERs indicate better value for money.

In the UK, the ICER for reducing both gaps was €7,182 per QALY gained. This is well below the typical threshold of £20,000-30,000 per QALY used to determine if health interventions are cost-effective.

Similar patterns were seen across the other countries studied, though exact costs varied based on differences in healthcare prices. Sweden had the highest costs while Hungary had the lowest.

Interpreting the results

Overall, the analysis suggests that efforts to reduce detection and treatment gaps for depression would likely be cost-effective across different European healthcare systems. While costs would increase in the short-term, the health benefits appear to justify the added expense.

However, there are some important limitations and assumptions to keep in mind:

• The model didn’t account for costs of interventions to actually reduce the gaps (like training programs for doctors)
• It assumed similar care patterns across countries, which may not be accurate
• Only healthcare costs were included, not broader economic impacts like improved work productivity
• Assumptions had to be made about outcomes for undetected/untreated cases

The researchers conducted sensitivity analyses to test how changing various assumptions affected the results. The findings were most sensitive to assumptions about quality of life for undetected/untreated depression. If these cases do better than assumed, the value of closing treatment gaps decreases.

Conclusions

• Reducing detection and treatment gaps for major depressive disorder is likely to increase healthcare costs in the short-term
• However, it also improves health outcomes in a cost-effective way across different European countries studied
• More research is needed on effective, real-world interventions to actually close these gaps
• Future studies should examine broader economic impacts beyond just healthcare costs
• Improving care for depression could potentially pay for itself through benefits like increased work productivity

While challenges remain, this research suggests there is economic justification for greater efforts to ensure people with depression are diagnosed and connected with effective treatments. Doing so could improve quality of life for millions while providing good value for healthcare spending.