Decoding Maternal Health Data: Are Birth Certificates and PRAMS Reliable?
"A deep dive into the accuracy of birth certificates and PRAMS in capturing maternal height and weight, and why it matters for public health."
Monitoring trends in pre-pregnancy obesity and gestational weight gain is a national priority, vital for understanding and addressing adverse health outcomes for both mothers and infants. Accurate data is the backbone of effective public health strategies, influencing everything from resource allocation to targeted interventions. Two key sources of this data in the United States are the revised birth certificate and the Pregnancy Risk Assessment Monitoring System (PRAMS).
The 2003 revision of the U.S. Standard Certificate of Live Birth included maternal height, pre-pregnancy weight, and delivery weight, enabling the calculation of pre-pregnancy BMI and gestational weight gain relative to Institute of Medicine (IOM) recommendations. PRAMS, a population-based surveillance system, gathers state-specific information on pregnancy-related characteristics, including self-reported height and pre-pregnancy weight.
But how reliable are these sources? A recent study dug deep into the quality of data collected from birth certificates and PRAMS questionnaires, comparing them against the gold standard of medical records. This article breaks down the study's findings, revealing the strengths and weaknesses of each data source, and highlighting the implications for maternal health research and surveillance.
Standardizing Maternal Health Data
Expert-endorsed common data element recommendations cover maternal health conditions and outcomes, medical care, health history, behavioral health, social factors, and demographics, with guidance on data sources, measurement methods, and ethical considerations. A separate article calls for linking publicly available resources—including deidentified health-system utilization data, geocoded locations of hospitals offering different levels of maternal care, and social and demographic data—to support county-level investigations of maternal health equity. A scoping review examined provider- and payer-used maternal health quality measures alongside nationally representative federal survey datasets. A health-center implementation guide identifies gaps in prenatal and postpartum follow-up coordination and recommends stronger data systems and partnerships to address local care gaps.
Tracking Maternal and Child Health Over Time
CDC’s Division of Reproductive Health monitors maternal and infant mortality and gathers data on morbidity, behaviors, and conditions that can affect reproductive health. HRSA’s Maternal and Child Health Timeline traces the history of maternal and child health in the United States and serves as an orientation resource for people entering the field, grantees, trainees, and students. NIH’s database portal lists resources including the HRSA Maternal and Infant Health Mapping Tool and the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). A 2026 historical chapter examines global maternal and child health milestones, policy frameworks, and international collaborations over the past century.
Birth Certificates vs. PRAMS: Unpacking the Data Quality
The study, conducted in New York City and Vermont, compared data from birth certificates and PRAMS to medical records, looking at pre-pregnancy BMI, gestational weight gain, and component variables like height and weight. Researchers calculated mean differences and agreement rates, providing a clear picture of how well each source aligns with the gold standard.
- Weight Variables: The mean differences for most maternal weight variables between the birth certificate or PRAMS and the medical record were less than 1 kg.
- Pre-pregnancy BMI: The birth certificate classified similar proportions into pre-pregnancy BMI categories (agreement=89%, к=0.83) compared to the medical record. PRAMS slightly underestimated overweight and obesity (agreement=84%, к=0.73).
- Gestational Weight Gain: The birth certificate overestimated gestational weight gain below recommendations and underestimated weight gain within recommendations (agreement=81%, к=0.69), compared to the medical record.
- InconsistenciesAgreement varied by maternal and pregnancy-related characteristics.
Recent Studies Using Maternal Health Data
A 2022 South Carolina study on gestational weight gain records compares electronic medical record data with birth-certificate and PRAMS data; its snippet reports differing directions of comparison across these sources. A JAMA study of postpartum cardiovascular health counseling included individuals seen 4 to 6 weeks after delivery with available counseling and risk-factor data: 167,705 people, representing a weighted 8,714,459. A 2022 JAMA Network Open study categorized prepregnancy BMI as underweight, healthy weight, overweight, or class 1, 2, or 3 obesity, using specified BMI ranges from below 18.5 to 40 or higher. Nature’s maternal and child health research page highlights recent research and methods, but the provided material gives no individual study details.
Why Data Accuracy Matters for Maternal Health
The quality of maternal height and weight data directly impacts our ability to monitor trends in pre-pregnancy obesity and gestational weight gain. These trends are critical indicators of maternal and infant health, influencing the development of targeted interventions and resource allocation.
Building Reliable Data for Prediction
A review describes predictive analytics as a promising approach to reproductive health care and patient outcomes, noting that accurate prediction of pregnancy and birth risks could enable earlier interventions. In a 2022 study, maternal-health experts said predictive analytics could help care and potentially limit pregnancy-related cardiovascular disease and depression. Those experts emphasized that predictive tools must be grounded in quality data and integrated with existing health-management systems. Separately, a 2025 Delphi process produced recommendations for standardizing a minimum set of priority constructs in maternal-health research.
Expanding Maternal and Infant Health Data
In 2023, CDC made maternal- and infant-health data, visualizations, and automated tools available to public health jurisdictions through the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET). A 2026 study aims to assess maternal mortality levels and trends globally and across regions, countries, and territories, evaluate progress toward Sustainable Development Goal 3.1, and examine pandemic-year trends. A 2025 report describes the U.S. maternal and infant health situation as precarious and says as many as 80% of maternal deaths may be preventable, with many occurring within the first 42 days after birth. A 2026 report preview also points to disparities, policy changes, reproductive-health restrictions, access challenges, and maternity deserts as issues shaping maternal care.
Disparities and the Need for Better Data
A 2026 article describes the U.S. as facing a maternal-health crisis, with maternal mortality rates significantly higher than those of other high-income countries and stark disparities across racial and ethnic groups. A 2025 KFF brief presents pregnancy and birth outcomes by race and ethnicity, discusses factors driving disparities, and reviews policies affecting maternal and infant health. A 2024 policy brief calls for improving maternal-health data collection and quality measures, expanding understanding of social determinants, and investing in and diversifying the perinatal workforce through community-based organizations. These sources frame data quality and collection as part of a wider effort to understand and address inequities.
When using birth certificate or PRAMS data, be aware of potential misclassifications, particularly in pre-pregnancy BMI and gestational weight gain categories. Further research is needed to fully understand the impact of these misclassifications on epidemiological associations.
Improving the accuracy of delivery weight data on birth certificates could significantly enhance the reliability of gestational weight gain classifications. As electronic medical record systems become more integrated with birth registration systems, the potential for more accurate and standardized data collection increases. Continued evaluation and refinement of data collection methods are essential to ensure the best possible outcomes for mothers and their children.