OBJECTIVES:High-quality evidence of effectiveness and cost-effectiveness is rarely available and relevant for health policy decisions in low-resource settings. In such situations, innovative approaches are needed to generate locally relevant evidence. This study aims to inform decision-making on antenatal care (ANC) recommendations in Rwanda by estimating the incremental cost-effectiveness of the recent (2016) WHO antenatal care recommendations compared to current practice in Rwanda. METHODS:Two health outcome scenarios (optimistic, pessimistic) in terms of expected maternal and perinatal mortality reduction were constructed using expert elicitation with gynaecologists/obstetricians currently practicing in Rwanda. Three costing scenarios were constructed from the societal perspective over a 1-year period. The two main inputs to the cost analyses were a Monte Carlo simulation of the distribution of ANC attendance for a hypothetical cohort of 373,679 women and unit cost estimation of the new recommendations using data from a recent primary costing study of current ANC practice in Rwanda. Results were reported in 2015 USD and compared with the 2015 Rwandan per-capita gross domestic product (US$ 697). RESULTS:Incremental health gains were estimated as 162,509 life-years saved (LYS) in the optimistic scenario and 65,366 LYS in the pessimistic scenario. Incremental cost ranged between $5.8 and $11 million (an increase of 42% and 79%, respectively, compared to current practice) across the costing scenarios. In the optimistic outcome scenario, incremental cost per LYS ranged between $36 (for low ANC attendance) and $67 (high ANC attendance), while in the pessimistic outcome scenario, it ranged between $90 (low ANC attendance) and $168 (high ANC attendance) per LYS. Incremental cost effectiveness was below the GDP-based thresholds in all six scenarios. DISCUSSION:Implementing the new WHO ANC recommendations in Rwanda would likely be very cost-effective; however, the additional resource requirements are substantial. This study demonstrates how expert elicitation combined with other data can provide an affordable source of locally relevant evidence for health policy decisions in low-resource settings.