Market size estimations and demand forecasts use a variety of methodological approaches to inform decision-making around new (and lesser-used) contraceptive methods. For contraceptive products already available at scale in a market, historical procurement and consumption data can help to inform these forecasts. However, little published guidance is available on appropriate approaches to estimating contraceptive demand in the absence of historical data.
This landscape review aimed to describe the variety of approaches for modeling demand for new contraceptive methods, highlight opportunities for alignment around forecasting practices, and make recommendations to support more accurate forecasting and sound decision-making based on forecasts. We used the published scientific and gray literature to inform the development of a semistructured guide for key informant interviews. We conducted interviews with 29 experts representing a spectrum of interests in market size estimation and demand forecasting for new contraceptive methods (e.g., ministries of health, donors, manufacturers, technical assistance providers, and demand forecasting specialists). We coded notes from the interviews using thematic content analysis.
The purposes of market size estimation and demand forecasting for new contraceptive methods vary widely, as do associated model inputs and outputs. Key informants revealed a need for more standardized language around market size estimation and demand forecasting and highlighted key recommendations: select models that are fit-for-purpose, clearly articulate assumptions and uncertainty in model outputs, consider a broad range of contraceptive options in a forecast to capture the complete contraceptive supply environment, and perform a reality check of results and refresh assumptions.
We recommend following a simple decision pathway to ensure that forecasts are fit-for-purpose, with appropriate inputs, outputs, and assumptions clearly articulated. Common pitfalls around overestimating demand should be avoided. Incorporating best practices into forecasting exercises will ensure that models are useful for the stakeholders.
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