Forecasting essential childhood cancer drug need: An innovative model-based approach

Background Childhood cancer outcomes in low-income and middle-income countries have not kept pace with advances in care and survival in high-income countries. A contributing factor to this survival gap is unreliable access to essential drugs. Methods The authors created a tool (FOR(x)ECAST) capable of predicting drug quantity and cost for 18 pediatric cancers. FOR(x)ECAST enables users to estimate the quantity and cost of each drug based on local incidence, stage breakdown, treatment regimen, and price. Two country-specific examples are used to illustrate the capabilities of FOR(x)ECAST to predict drug quantities. Results On the basis of domestic public-sector price data, the projected annual cost of drugs to treat childhood cancer cases is 0.8 million US dollars in Kenya and 3.0 million US dollars in China, with average median price ratios of 0.9 and 0.1, respectively, compared with costs sourced from the Management Sciences for Health (MSH) International Medical Products Price Guide. According to the cumulative chemotherapy cost, the most expensive disease to treat is acute lymphoblastic lymphoma in Kenya, but a higher relative unit cost of methotrexate makes osteosarcoma the most expensive diagnosis to treat in China. Conclusions FOR(x)ECAST enables needs-based estimates of childhood cancer drug volumes to inform health system planning in a wide range of contexts. It is broadly adaptable, allowing decision makers to generate results specific to their needs. The resultant estimates of drug need can help equip policymakers and health governance institutions with evidence-informed data to advance innovative procurement strategies that drive global improvements in childhood cancer drug access.

Automated summary

The study developed a tool (FOR(x)ECAST) to predict drug quantity and cost for childhood cancer, providing tailored results based on local circumstances. This tool helps to estimate the needed drug volumes for health system planning.