Cost-effectiveness of running a paediatric oncology unit in Ethiopia

Abstract

Objective: To estimate the cost-effectiveness of running a paediatric oncology unit in Ethiopia to inform the revision of the Ethiopia Essential Health Service Package (EEHSP), which ranks the treatment of childhood cancers at a low and medium priority.

Methods: We built a decision analytical model-a decision tree-to estimate the cost-effectiveness of running a paediatric oncology unit compared with a do-nothing scenario (no paediatric oncology care) from a healthcare provider perspective. We used the recently (2018-2019) conducted costing estimate for running the paediatric oncology unit at Tikur Anbessa Specialized Hospital (TASH) and employed a mixed costing approach (top-down and bottom-up). We used data on health outcomes from other studies in similar settings to estimate the disability-adjusted life years (DALYs) averted of running a paediatric oncology unit compared with a do-nothing scenario over a lifetime horizon. Both costs and effects were discounted (3%) to the present value. The primary outcome was incremental cost in US dollars (USDs) per DALY averted, and we used a willingness-to-pay (WTP) threshold of 50% of the Ethiopian gross domestic product per capita (USD 477 in 2019). Uncertainty was tested using one-way and probabilistic sensitivity analyses.

Results: The incremental cost and DALYs averted per child treated in the paediatric oncology unit at TASH were USD 876 and 2.4, respectively, compared with no paediatric oncology care. The incremental cost-effectiveness ratio of running a paediatric oncology unit was USD 361 per DALY averted, and it was cost-effective in 90% of 100 000 Monte Carlo iterations at a USD 477 WTP threshold.

Conclusions: The provision of paediatric cancer services using a specialised oncology unit is most likely cost-effective in Ethiopia, at least for easily treatable cancer types in centres with minimal to moderate capability. We recommend reassessing the priority-level decision of childhood cancer treatment in the current EEHSP.

Keywords: Health economics; Health policy; Paediatric oncology.

Figure 1

A decision-analytic model structure (decision tree) with an average 2-year childhood cancer treatment duration divided into 8-month treatment intervals. The model compares a simulated child with cancer (without a specific diagnosis) who receives services from the paediatric oncology unit to a do-nothing scenario (defined as no paediatric oncology care). The p_survival_rate_8 represents the probability of survival in the first 8 months of treatment. Similarly, p_survival_rate_16 is the probability of survival in 9–16 months of treatment, and p_survival_rate_24 is the probability of survival in 17–24 months of treatment. DALYs, disability-adjusted life years.

Figure 2

Tornado diagram of the results of the one-way sensitivity analysis of the cost-effectiveness analysis of running a paediatric oncology unit in Ethiopia, summarising the key variables tested for one-way sensitivity analysis, the ranges of values tested and their impacts on the ICER estimate. The longer the horizontal bar, the greater the impact in the direction of the bar (to the left or right). ICER, incremental cost-effectiveness ratio.

Figure 3

Probabilistic sensitivity analysis for the cost-effectiveness of running a paediatric oncology in Ethiopia. The figure depicts the range of WTP thresholds in which the no paediatric oncology care scenario will have higher probability of being cost-effective compared with paediatric oncology care (WTPUSD 361). WTP, willingness-to-pay.