Ensuring that patients have continuous access to tuberculosis (TB) treatment requires complex projections and calculations by program staff. These projections are becoming more challenging as new diagnostic tools rapidly increase the number of individuals diagnosed, and thus the quantities of medicines needed. If treatment regimens change, national programs must plan carefully for phasing in and out various medicines in order to manage the risks of stock out. QuanTB improves this process by combining two different methods of quantification, morbidity and consumption, to produce a more accurate estimate.
What is QuanTB?
QuanTB is an electronic forecasting, quantification, and early warning tool designed to improve procurement processes, ordering, and planning for TB treatment. Created by the USAID-funded Systems for Improved Access to Pharmaceutical and Services (SIAPS) Program, QuanTB is a downloadable desktop tool that transforms complicated calculations into a user-friendly dashboard displaying key information for managing medicines. When used on a regular basis (e.g., monthly, quarterly), QuanTB serves as an early warning mechanism, providing information on actual versus planned consumption, potential expiries, and stock outs of medicines.
- Accurate quantification projections: combines morbidity and consumption methods of quantification
- Adaptable and customizable: users can modify indicators such as medicines and treatment regimens, buffer stock, and minimum and maximum months of stock so that quantification reflects local procurement, distribution, and funding
- Early warning mechanism: alerts to risk of expiring medicines, stock out, and overstock; when used regularly, allows staff sufficient time to head off supply problems
- Build models for different scenarios: compares planned versus actual consumption rates and costs
- Full cost of order: incorporates cost of medicine, insurance, and customs clearance, among other expenses
QuanTB is available in English, Russian, French, Spanish, Portuguese, and Chinese