The Pregnancy Risk Assessment Monitoring System (PRAMS) dataset contains maternal and child health survey data from a sample of mothers from Massachusetts with a recent live birth. Mothers were surveyed 2-6 months after giving birth on their maternal attitudes and experiences before, during, and shortly after pregnancy.
Guide PHIT Data: Pregnancy Risk Assessment Monitoring System (PRAMS)
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What you need to know
Massachusetts PRAMS oversamples by race and Hispanic ethnicity to ensure adequate representation of racial and ethnic minority mothers. PRAMS data are linked to selected birth certificate demographic variables and provide state-level estimates of maternal and child health indicators.
Overall estimates as well as estimates by maternal race/Hispanic ethnicity, age group, educational attainment, household poverty level, nativity, and marital status are available. The data were weighted using selected maternal demographics to account for non-response and adjusted for sampling probabilities and coverage to represent the Massachusetts birth population in a given year.
PRAMS data are generalizable only to pregnancies resulting in a live birth of singletons or multiples of fewer than four, to Massachusetts residents who gave birth in the state.
Explore PRAMS data
PRAMS data can be used to identify groups of women and infants at high risk for health problems, to monitor changes in health status, and to measure progress towards goals in improving the health of mothers and infants. PRAMS data are used to investigate emerging issues in the field of reproductive health and to plan and review programs and policies aimed at reducing health problems among mothers and infants.
Information about these data
The PRAMS survey is currently only administered in English and Spanish. This might present a limitation in collecting data from mothers who speak neither survey language. Since PRAMS is based on self-reported information, there is the potential for misclassification error. Bias may occur if some groups of mothers recall experiences more or less accurately than others.