Making stillbirths count, making numbers talk - issues in data collection for stillbirths

Area of Science:

• Perinatal Public Health
• Maternal and Child Health Surveillance
• Global Health Data Systems

Background:

• Stillbirths represent the majority of perinatal deaths but remain largely uncounted and invisible.
• Accurate data on timing, circumstances, causes, and care quality are vital for stillbirth prevention.
• Current stillbirth data collection faces challenges in definitions, registration, and analysis.

Purpose of the Study:

• To assess the impact of definitions and registration limits on stillbirth data capture.
• To identify essential data elements for stillbirth registration and analysis.
• To propose improvements for stillbirth classification and reporting systems.

Main Methods:

• Analysis of stillbirth registration challenges and data capture limitations.
• Review of existing stillbirth registration practices across different cultural settings.
• Evaluation of alternative data collection and classification systems for low- and middle-income countries.

Main Results:

• Different definitions and registration limits significantly affect stillbirth data capture.
• A core dataset for stillbirth registration and analysis, including causes and quality indicators, is proposed.
• Experiences from various cultural settings highlight implementation challenges and successes.

Conclusions:

• High-quality stillbirth data require consistent definitions, systematic registration, and improved tools.
• Capacity building, training in cause-of-death identification, and locally adapted quality indicators are necessary.
• Enhancements to classification systems and reporting are critical for effective stillbirth prevention efforts.