Developing the Global Health Cost Consortium Unit Cost Study Repository for HIV and TB: methodology and lessons learned. Issue 4 (6th December 2019) - DeCormier Plosky, Willyanne; Bollinger, Lori A; Alexander, Lily; Cameron, Drew B; Carroll, Lauren N; Cunnama, Lucy; Gomez, Gabriela B; Levin, Carol; Marseille, Elliot; Mustafa Diab, Mohamed; Siapka, Mariana; Sinanovic, Edina; et al.
Abstract : Consistently defined, accurate, and easily accessible cost data are a valuable resource to inform efficiency analyses, budget preparation, and sustainability planning in global health. The Global Health Cost Consortium (GHCC) designed the Unit Cost Study Repository (UCSR) to be a resource for standardised HIV and TB intervention cost data displayed by key characteristics such as intervention type, country, and target population. To develop the UCSR, the GHCC defined a typology of interventions for each disease; aligned interventions according to the standardised principles, methods, and cost and activity categories from the GHCC Reference Case for Estimating the Costs of Global Health Services and Interventions; completed a systematic literature review; conducted extensive data extraction; performed quality assurance; grappled with complex methodological issues such as the proper approach to the inflation and conversion of costs; developed and implemented a study quality rating system; and designed a web-based user interface that flexibly displays large amounts of data in a user-friendly way. Key lessons learned from the extraction process include the importance of assessing the multiple uses of extracted data; the critical role of standardising definitions (particularly units of measurement); using appropriate classifications of interventions and components of costs; the efficiency derived from programming data checks; and the necessity of extraction quality monitoring by senior analysts. For the web interface, lessons were: understanding the target audiences, including consulting them regarding critical characteristics; designing the display of data in "levels"; and incorporating alert and unique trait descriptions to further clarify differences in the data.