FPwatch: Facility-based Survey Data for Family Planning Market Analysis in Five FP2020-focus Countries.
Studies in Family Planning
|November 20, 2018
Summary
The FPwatch Project provides unique, large-scale family planning datasets from five countries. These standardized data cover public and private facilities, informing global family planning initiatives.
Area of Science:
- Public Health
- Demography
- Health Services Research
Background:
- Facility-based family planning surveys are crucial for understanding service delivery.
- Previous surveys often lacked comprehensive scope or standardized methodology across diverse settings.
- The FPwatch Project aimed to address these gaps by collecting detailed data on family planning services.
Purpose of the Study:
- To describe the datasets generated by the FPwatch Project, a facility-based family planning survey.
- To detail the research design, methodology, sampling, data collection, and quality assurance of the FPwatch surveys.
- To highlight the unique features and suitability of the FPwatch data for informing global family planning efforts.
Main Methods:
- Conducted a comprehensive, facility-based family planning survey across five countries in Africa and Asia (2015-2017).
- Employed a large-scale, standardized methodology with a comprehensive sampling approach.
- Included both public and private healthcare facilities in data collection for most countries.
Main Results:
- Generated datasets from the Democratic Republic of Congo, Ethiopia, India, Myanmar, and Nigeria.
- Ensured data quality through rigorous assurance measures.
- Collected data offering a complete picture of the family planning supply environment by including diverse facility types.
Conclusions:
- The FPwatch datasets are a valuable resource for family planning research and programming.
- The standardized, large-scale, and comprehensive nature of the data enhances its utility for global family planning efforts.
- These datasets provide critical insights into the family planning supply landscape, supporting evidence-based decision-making.
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