@article {Jones-Hepler571, author = {Bonnie Jones-Hepler and Katelin Moran and Jennifer Griffin and Elizabeth M McClure and Doris Rouse and Carolina Barbosa and Emily MacGuire and Elizabeth Robbins and Robert L Goldenberg}, title = {Maternal and Neonatal Directed Assessment of Technologies (MANDATE): Methods and Assumptions for a Predictive Model for Maternal, Fetal, and Neonatal Mortality Interventions}, volume = {5}, number = {4}, pages = {571--580}, year = {2017}, doi = {10.9745/GHSP-D-16-00174}, publisher = {Global Health: Science and Practice}, abstract = {MANDATE is a mathematical model designed to estimate the relative impact of different interventions on maternal, fetal, and neonatal lives saved in sub-Saharan Africa and India. A key advantage is that it allows users to explore the contribution of preventive interventions, diagnostics, treatments, and transfers to higher levels of care to mortality reductions, and at different levels of penetration, utilization, and efficacy.Maternal, fetal, and neonatal mortality disproportionately impact low- and middle-income countries, and many current interventions that can save lives are often not available nor appropriate for these settings. Maternal and Neonatal Directed Assessment of Technologies (MANDATE) is a mathematical model designed to evaluate which interventions have the greatest potential to save maternal, fetal, and neonatal lives saved in sub-Saharan Africa and India. The MANDATE decision-support model includes interventions such as preventive interventions, diagnostics, treatments, and transfers to different care settings to compare the relative impact of different interventions on mortality outcomes. The model is calibrated and validated based on historical and current rates of disease in sub-Saharan Africa and India. In addition, each maternal, fetal, or newborn condition included in MANDATE considers disease rates specific to sub-Saharan Africa and India projected to intervention rates similar to those seen in high-income countries. Limitations include variance in quality of data to inform the estimates and generalizability of findings of the effectiveness of the interventions. The model serves as a valuable resource to compare the potential impact of multiple interventions, which could help reduce maternal, fetal, and neonatal mortality in low-resource settings. The user should be aware of assumptions in evaluating the model and interpret results accordingly.}, URL = {https://www.ghspjournal.org/content/5/4/571}, eprint = {https://www.ghspjournal.org/content/5/4/571.full.pdf}, journal = {Global Health: Science and Practice} }