Systematic Literature Review of Health Utility Values in Adult Patients with Post-Traumatic Stress Disorder

OBJECTIVES: Post-traumatic stress disorder (PTSD) is a debilitating mental health disorder that develops after a traumatic event. Health utility values (HUVs) are vital parameters in model-based economic and health technology assessment (HTA) evaluations. This systematic literature review (SLR) identifies HUVs in PTSD.

METHODS: An SLR was conducted using key databases (EMBASE^®, PubMed) to identify English language publications, from database inception to January 2024. Search terms included different combinations of PTSD along with utility values. Each publication was reviewed by two independent reviewers; disagreements were resolved by a third reviewer.

RESULTS: Seven of 2,380 screened publications (Europe=4, USA=2, USA/UK=1) were included. The sample size ranged from 73 to 324 patients. Six studies used the EQ-5D instrument to assess utility values, while one study used a time trade-off technique (TTO), standard gamble (SG), and visual analog scale (VAS). The mean HUV of PTSD patients was higher in the USA compared to the UK (0.627 vs. 0.482). Compared to TTO (0.66) and VAS (0.64), the utility values derived using SG (0.87) were higher due to risk aversion. Significantly lower HUVs were reported for psychotherapy vs. sertraline and treatment response vs. non-response (p<0.001 for both comparisons). Across Europe, PTSD patients exhibited worse outcomes compared to non-PTSD patients at different follow-up time points, with significant findings reported at 24-months (mean EQ-5D scores: 0.56 vs. 0.87, p<0.001; mean health utilities index mark 3 scores (HUI3): 0.51 vs. 0.83, p<0.001). Moreover, patients with an intensive care unit (ICU) stay ≥7 days developed PTSD symptoms more often and reported significantly lower median utility scores compared to patients with an ICU stay< 7 days (0.78 vs. 0.81, p<0.001).

CONCLUSIONS: The findings of this SLR highlighted varied HUVs in PTSD. Geographic, treatment, and ICU differences offer insights for clinicians and policymakers, stressing the need for nuanced approaches to assess quality of life.