FP2020 is a global initiative funded by the Bill and Melinda Gates Foundation. It aims to bring on an additional 120 million additional users of modern contraception in 69 of the world poorest countries by the year 2020. This year, this goal has been extended to 2030 in line with the United Nations Sustainable Development goals.

The initiative uses a data driven approach to monitor the progression of its goals. One of the key indicators used to monitor this progress is estimating a country's modern contraceptive use. Presently, the Family Planning Estimation Model (FPEM) calculates estimates of modern contraceptive use (EMUs) using a Bayesian hierarchical model and Demographic Health Survey (DHS) data. The DHS are a highly reliable data source but expensive to collect. As such, the dataset used to predict modern contraceptive use is very scare with some countries having no data whatsoever.

Hannah’s talk will look at the work she has been doing to use an alternative grassroots data source called Service Statistics for producing EMUs. Niamh will highlight the existing methodology associated with producing EMUs from Service Statistics and the modelling she has been doing to quantify the uncertainty associated with these estimates. Lastly, Niamh will show an R package she have been working on called “ss2emu” which takes raw service statistics and produces EMUs using the existing methodologies as well as incorporating some of her own modelling strategies.

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