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This figure shows the estimated number of pregnant women by ANC visit count in 2 scenarios: without aerial logistics (gray) and with aerial logistics (red), based on a probabilistic <t>microsimulation.</t> The simulation draws from the 2021-2022 DHS birth cohorts in Ghana and uses the19.9% increase in ANC visits due to Zipline’s aerial logistics from Kremer et al (2025). Total ANC visits were first adjusted to remove the observed effect of aerial logistics and then redistributed across visit categories using national DHS patterns. The red bars reflect 1000 stochastic simulations reallocating visits within the same target population, based on conditional probabilities of visit increases. Confidence intervals (black lines) are shown for the aerial logistics scenario.
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This figure shows the estimated number of pregnant women by ANC visit count in 2 scenarios: without aerial logistics (gray) and with aerial logistics (red), based on a probabilistic <t>microsimulation.</t> The simulation draws from the 2021-2022 DHS birth cohorts in Ghana and uses the19.9% increase in ANC visits due to Zipline’s aerial logistics from Kremer et al (2025). Total ANC visits were first adjusted to remove the observed effect of aerial logistics and then redistributed across visit categories using national DHS patterns. The red bars reflect 1000 stochastic simulations reallocating visits within the same target population, based on conditional probabilities of visit increases. Confidence intervals (black lines) are shown for the aerial logistics scenario.
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This figure shows the estimated number of pregnant women by ANC visit count in 2 scenarios: without aerial logistics (gray) and with aerial logistics (red), based on a probabilistic <t>microsimulation.</t> The simulation draws from the 2021-2022 DHS birth cohorts in Ghana and uses the19.9% increase in ANC visits due to Zipline’s aerial logistics from Kremer et al (2025). Total ANC visits were first adjusted to remove the observed effect of aerial logistics and then redistributed across visit categories using national DHS patterns. The red bars reflect 1000 stochastic simulations reallocating visits within the same target population, based on conditional probabilities of visit increases. Confidence intervals (black lines) are shown for the aerial logistics scenario.
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This figure shows the estimated number of pregnant women by ANC visit count in 2 scenarios: without aerial logistics (gray) and with aerial logistics (red), based on a probabilistic <t>microsimulation.</t> The simulation draws from the 2021-2022 DHS birth cohorts in Ghana and uses the19.9% increase in ANC visits due to Zipline’s aerial logistics from Kremer et al (2025). Total ANC visits were first adjusted to remove the observed effect of aerial logistics and then redistributed across visit categories using national DHS patterns. The red bars reflect 1000 stochastic simulations reallocating visits within the same target population, based on conditional probabilities of visit increases. Confidence intervals (black lines) are shown for the aerial logistics scenario.
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This figure shows the estimated number of pregnant women by ANC visit count in 2 scenarios: without aerial logistics (gray) and with aerial logistics (red), based on a probabilistic microsimulation. The simulation draws from the 2021-2022 DHS birth cohorts in Ghana and uses the19.9% increase in ANC visits due to Zipline’s aerial logistics from Kremer et al (2025). Total ANC visits were first adjusted to remove the observed effect of aerial logistics and then redistributed across visit categories using national DHS patterns. The red bars reflect 1000 stochastic simulations reallocating visits within the same target population, based on conditional probabilities of visit increases. Confidence intervals (black lines) are shown for the aerial logistics scenario.

Journal: Journal of Health Economics and Outcomes Research

Article Title: Cost-Effectiveness of Aerial Logistics for Maternal and Newborn Health: A Simulation-Based Analysis Grounded in Real-World Evidence from the Ashanti Region in Ghana

doi: 10.36469/001c.143065

Figure Lengend Snippet: This figure shows the estimated number of pregnant women by ANC visit count in 2 scenarios: without aerial logistics (gray) and with aerial logistics (red), based on a probabilistic microsimulation. The simulation draws from the 2021-2022 DHS birth cohorts in Ghana and uses the19.9% increase in ANC visits due to Zipline’s aerial logistics from Kremer et al (2025). Total ANC visits were first adjusted to remove the observed effect of aerial logistics and then redistributed across visit categories using national DHS patterns. The red bars reflect 1000 stochastic simulations reallocating visits within the same target population, based on conditional probabilities of visit increases. Confidence intervals (black lines) are shown for the aerial logistics scenario.

Article Snippet: To estimate changes in ANC visit distribution and the number of additional women crossing these thresholds, we used a probabilistic microsimulation approach that applies stochastic resampling to model shifts in ANC visit patterns following the introduction of Zipline’s aerial logistics.

Techniques: