‘Silent losses–silent data’ : reviewing stillbirth data quality in low- and middle-income countries using data quality dimensions

Abstract

Precise data is crucial for policy decision-making, especially in sensitive outcomes like stillbirth, where each data element have significant effects. Following years of advancement in the healthcare domain, there is a pressing need to improve data-based policymaking by addressing both the social context and emotional dimensions. This holds true for any healthcare condition including stillbirth, which demands the attention of healthcare managers, researchers and policymakers. Conditions such as stillbirth signify more than a birth devoid of vital signs. A mother endures months of discomfort and excruciating labour pain and faces the devastating reality that her baby is no longer alive. The absence of her child's initial cry disrupts her life, causing her to struggle with confusion and sadness on the factors that may have led to this catastrophe. In spite of this significant loss, we typically perceive it as merely one death, often neglecting to acknowledge it adequately. Significant advancements in averting stillbirths can be achieved by viewing it as a loss of life, rather than only perceiving it as the birth of a lifeless infant. Examining stillbirth data and comprehending its causes can aid in formulating strategies to avert future incidents. This publication seeks to compile information on the principal issues associated with the reporting and recording of stillbirths in low- and middle-income countries (LMICs) from the perspective of data quality aspects. Furthermore, it also proposes strategies to enhance each aspect of data quality like harmonising stillbirth definitions, linking routine data systems with surveys, facility audits for better data capture, and increasing funding for stillbirth-related research etc.

HIGHLIGHTS • Progress in stillbirth prevention needs robust data for informed decision making. • Concerns in LMIC stillbirth data: no targets, misclassification, fear, stigma etc. • Study recommends better data capture via audits, system links and funding.