A Systematic Review of Health Economic Evaluation on Therapies for the Treatment of Inherited Retinal Diseases: A Quality Evaluation

OBJECTIVES: Inherited retinal disease (IRD) places a substantial financial burden on healthcare budgets, prompting an increase in health economic evaluations (HEEs) in this field. This article examines the quality of economic evidence for therapies used to treat IRD.

METHODS: A literature search was conducted in PubMed and Embase to identify articles available up to April 16, 2024. The quality of included studies was assessed using the QHES (Quality of Health Economic Studies) and CHEERS (Consolidated Health Economic Evaluation Reporting Standards) checklists. The CHEERS checklist was converted into a quantitative score and compared with QHES results. The paired Wilcoxon rank test was used for comparisons, with a p-value of <0.05 considered statistically significant.

RESULTS: Overall, limited HEEs (N=8) were found discussing IRDs (Full texts=5, Conference abstracts=3). Since IRD is a rare disease, questions pertaining to subgroup analysis were removed from both the checklists. As per CHEERS assessment, factors such as setting, perspective, time horizon, and discount rate were clearly described. However, justification of data sources, description of heterogeneity, and details on analytics and assumptions were often incomplete or missing. Around 60% of the studies met the accepted standard of good quality, with a score of ≥75% based on the CHEERS and QHES assessment. Understandably, limited information was available in the conference abstracts with respect to estimates utilized, explanation of the direction and magnitude of the potential biases as well as disclosures and funding information. There was no significant difference between the CHEERS and QHES scores (p=0.9453).

CONCLUSIONS: The results indicate that current models generally lack transparency in data description and fail to adequately address potential biases. This makes it challenging to generalize the findings to local clinical setting, and guide decision-making. Future models should improve their frameworks by ensuring the validity of the results and enhancing modeling accuracy.