Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers

Abstract

Objective Preterm birth is the primary driver of neonatal mortality worldwide, but it is defined by gestational age (GA) which is challenging to accurately assess in low-resource settings. In a commitment to reducing preterm birth while reinforcing and strengthening facility data sources, the East Africa Preterm Birth Initiative (PTBi-EA) chose eligibility criteria that combined GA and birth weight. This analysis evaluated the quality of the GA data as recorded in maternity registers in PTBi-EA study facilities and the strength of the PTBi-EA eligibility criteria. Methods We conducted a retrospective analysis of maternity register data from March–September 2016. GA data from 23 study facilities in Migori, Kenya and the Busoga Region of Uganda were evaluated for completeness (variable present), consistency (recorded versus calculated GA), and plausibility (falling within the 3 rd and 97 th birth weight percentiles for GA of the INTERGROWTH-21 st Newborn Birth Weight Standards). Preterm birth rates were calculated using: 1) recorded GA <37 weeks, 2) recorded GA <37 weeks, excluding implausible GAs, 3) birth weight <2500g, and 4) PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks. Results In both countries, GA was the least recorded variable in the maternity register (77.6%). Recorded and calculated GA (Kenya only) were consistent in 29.5% of births. Implausible GAs accounted for 11.7% of births. The four preterm birth rates were 1) 14.5%, 2) 10.6%, 3) 9.6%, 4) 13.4%. Conclusions Maternity register GA data presented quality concerns in PTBi-EA study sites. The PTBi-EA eligibility criteria of <2500g and between 2500g and 3000g if the recorded GA is <37 weeks accommodated these concerns by using both birth weight and GA, balancing issues of accuracy and completeness with practical applicability.

Quality of routine health facility data used for newborn indicators in low- and middle-income countries: A systematic review

“…KMC – Kangaroo Mother Care. Figure shows results from 17 studies, six from case notes [ 28 , 57 , 60 , 62 , 65 , 66 ] and eleven from registers [ 39 , 40 , 42 - 46 , 52 , 55 , 58 , 61 ].…”

Newborn skin maturity models for gestational age prediction

“…1 Meanwhile, predicting preterm birth based on birth weight when lacking a gold standard is an imperfect solution. 9 Additionally, the LMP reference and the postnatal scores of newborn maturity have demonstrated low accuracy in determining gestational age and identifying prematurity. 35 Later prenatal care and unquali ed date recollection justify efforts to enhance the reliability of pregnancy dating through more accurate and accessible technologies, seeking to improve pregnancy outcomes and neonatal survival.…”

Newborn skin maturity models for gestational age prediction

“…1 Meanwhile, predicting preterm birth based on birth weight when lacking a gold standard is an imperfect solution. 9 Additionally, the LMP reference and the postnatal scores of newborn maturity have demonstrated low accuracy in determining gestational age and identifying prematurity. 35 Later prenatal care and unquali ed date recollection justify efforts to enhance the reliability of pregnancy dating through more accurate and accessible technologies, seeking to improve pregnancy outcomes and neonatal survival.…”

“…Current approaches to calculating gestational age are sensitive to data quality, resulting in misplaced classi cation of prematurity. 9 The present study was committed to representing a real scenario in terms of data quality, as stated in the research protocol, with data collection and curation to assure the best reference and comparators for the analysis. Before opening the blinding of the trial, a consistency process confronted data entries with digital images of clinical documents taken during the enrollment.…”

“…8 However, access to high-cost equipment, poor training, lack of skills of health professionals, or late prenatal care limit pregnancy dating and, consequently, detection of prematurity. 7,9 Improving preterm birth outcomes demands accurate gestational age assessment to direct opportune decisions regarding neonatal care. 10,11 Approaches to enhance the reliability of pregnancy dating through more accurate and accessible technologies can improve pregnancy outcomes and neonatal survival.…”