METHODS: We conducted a systematic search in MEDLINE (Ovid), PubMed, EconLit, Embase (Ovid), the Cochrane Library, and the gray literature. Using a predefined checklist, we extracted the key features of economic evaluation and the general characteristics of EEGs. We conducted a comparative analysis, including a summary of similarities and differences across EEGs.

RESULTS: Thirteen EEGs were identified, three pertaining to lower-middle-income countries (Bhutan, Egypt, and Indonesia), nine to upper-middle-income countries (Brazil, China, Colombia, Cuba, Malaysia, Mexico, Russian Federation, South Africa, and Thailand), in addition to Mercosur, and none to low-income countries. The majority (n = 12) considered cost-utility analysis and health-related quality-of-life outcome. Half of the EEGs recommended the societal perspective, whereas the other half recommended the healthcare perspective. Equity considerations were required in ten EEGs. Most EEGs (n = 11) required the incremental cost-effectiveness ratio and recommended sensitivity analysis, as well as the presentation of a budget impact analysis (n = 10). Seven of the identified EEGs were mandatory for pharmacoeconomics submission. Methodological gaps, contradictions, and heterogeneity in terminologies used were identified within the guidelines.

RESULTS: We developed a stand-alone software that implements the FineMAV statistic. To graphically visualise the FineMAV scores, it outputs the statistics as bigWig files, which is a common file format supported by many genome browsers. It is available as a command-line and graphical user interface. The software was tested by replicating the FineMAV scores obtained using 1000 Genomes Project African, European, East and South Asian populations and subsequently applied to whole-genome sequencing datasets from Singapore and China to highlight population specific variants that can be subsequently modelled. The software tool is publicly available at https://github.com/fadilla-wahyudi/finemav .