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This project will use a de-identified dataset to examine differences in reported last menstrual period (LMP) as a determinant of a pregnant woman’s delivery data and women’s recorded delivery dates in the CHT application.
A patient's last menstrual period (LMP) is used to estimate her delivery date, set an antenatal care (ANC) schedule, and remind health workers to confirm whether a delivery has occurred. In our data, we currently observe a tendency for CHWs to estimate LMP by months, which can lead to errors in estimating a patient’s delivery date and set a schedule for ANC visits. This can mean that antenatal care is delivered at the wrong times and health workers may not reach a patient within 48 hours of delivery.
We propose examining variation across women’s expected delivery dates based on LMP data and reported delivery dates across multiple deployments of the CHT, ideally in both South Asia and sub-Saharan Africa.
We will want to understand how variation in LMP calculation impacts both women’s expected delivery dates and their associated ANC schedules. In addition, it is likely that this initial analysis will yield additional associated projects focused on ANC care schedules, delivery confirmations, and the calculation of expected delivery dates in deployment data.
In addition, there could be an opportunity to help think through how the findings of this analysis can help inform the workflow design for LMP or other forms of gestational age calculation in the CHT in collaboration with members of the product team.
We will use the findings from this analysis to inform better application design, user training, and potentially data science solutions to better estimate gestational age, set antenatal care schedules, and estimate delivery dates.
Erika Salomon, Data Scientist Helen Olsen, Impact Manager Yuvraj Rimal, Impact Tech Lead
Erika Salomon, Data Scientist Helen Olsen, Impact Manager Yuvraj Rimal, Impact Tech Lead