Developing the Global Health Cost Consortium Unit Cost Study Repository for HIV and TB: methodology and lessons learned.

ECONOMIC aspects of diseases; HIV infections; INFORMATION storage & retrieval systems; MEDICAL databases; MEDICAL information storage & retrieval systems; MEDLINE; ONLINE information services; TUBERCULOSIS; WORLD health; SYSTEMATIC reviews; COST analysis

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. [ABSTRACT FROM AUTHOR]

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