Protocol for PMA-Ethiopia: A new data source for cross-sectional and longitudinal data of reproductive, maternal, and newborn health.
Linnea A ZimmermanSelam DestaMahari YihdegoAnn RogersAyanaw AmogneCelia KarpShannon N WoodAndreea CreangaSaifuddin AhmedAssefa SemeSolomon ShiferawPublished in: Gates open research (2020)
Background: Performance Monitoring for Action Ethiopia (PMA-Ethiopia) is a survey project that builds on the PMA2020 and PMA Maternal and Newborn Health projects to generate timely and actionable data on a range of reproductive, maternal, and newborn health (RMNH) indicators using a combination of cross-sectional and longitudinal data collection. Objectives: This manuscript 1) describes the protocol for PMA- Ethiopia, and 2) describes the measures included in PMA Ethiopia and research areas that may be of interest to RMNH stakeholders. Methods: Annual data on family planning are gathered from a nationally representative, cross-sectional survey of women age 15-49. Data on maternal and newborn health are gathered from a cohort of women who were pregnant or recently postpartum at the time of enrollment. Women are followed at 6-weeks, 6-months, and 1-year to understand health seeking behavior, utilization, and quality. Data from service delivery points (SDPs) are gathered annually to assess service quality and availability. Households and SDPs can be linked at the enumeration area level to improve estimates of effective coverage. Discussion: Data from PMA-Ethiopia will be available at www.pmadata.org. PMA-Ethiopia is a unique data source that includes multiple, simultaneously fielded data collection activities. Data are available partner dynamics, experience with contraceptive use, unintended pregnancy, empowerment, and detailed information on components of services that are not available from other large-scale surveys. Additionally, we highlight the unique contribution of PMA Ethiopia data in assessing the impact of coronavirus disease 2019 (COVID-19) on RMNH.
Keyphrases
- electronic health record
- healthcare
- mental health
- big data
- coronavirus disease
- cross sectional
- public health
- randomized controlled trial
- pregnancy outcomes
- sars cov
- data analysis
- type diabetes
- machine learning
- pregnant women
- quality improvement
- climate change
- polycystic ovary syndrome
- adipose tissue
- human immunodeficiency virus
- health promotion
- health insurance