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Migori County Referral Hospital_Gestational Age Data_Nov15-April16

Citation

Miller, Lara (2018), Migori County Referral Hospital_Gestational Age Data_Nov15-April16, UC San Francisco Dash, Dataset, https://doi.org/10.7272/Q69S1P7K

Abstract

Background: Preterm birth is the leading cause of neonatal mortality worldwide and specifically in Kenya. Preterm birth is defined by gestational age (GA) less than 37 weeks, but GA estimates are questionable in the absence of the gold standard of early ultrasound. In Migori County, where the majority of women seek care at government facilities and do not receive ultrasound dating, the accuracy of the GA estimates, and therefore the preterm birth rates, is unknown.

Methods: We conducted a retrospective chart review of 455 preterm births from Migori County Referral Hospital, a level-four government hospital in Western Kenya. Preterm birth was defined in this context as all babies less than 2500g and babies greater than 2500g with a last menstrual period (LMP) calculated GA of less than 37 weeks. GA estimates from both the maternity register and the individual inpatient chart were evaluated for data quality, agreement between measurements, and accuracy when compared to the INTERGROWTH-21st International Newborn Birthweight Standards as a benchmark.

Results:  Data completeness ranged from 35.3% for recorded GA in the inpatient chart to 97.8% for birth weight in the maternity register. LMP and recorded GA agreed in 16.8% of cases in the maternity register and 19.2% of cases in the inpatient chart, while symphysis fundal height agreed in 37.1% and 50.5% of cases, respectively. Of the four GA measures evaluated, the maternity register recorded GA was accurate in 69.4% of cases, maternity register LMP-calculated GA in 57.9% of cases, the inpatient recorded GA in 68.8% of cases, and the inpatient LMP-calculated GA in 56.0% of cases. Preterm birth rates calculated from the four GA measures ranged from 7.5% to 18.8%.

Conclusion: Non-ultrasound methods of estimating GA result in a wide range of results, with up to five different estimates per woman. With such conflicting data, clinical decision making is compromised and preterm birth facility estimates are likely inaccurate. Widespread access to early ultrasound, new technologies, and/or new methods of thinking about GA are urgently needed to improve clinical care for the mother-infant dyad, and to better understand the preterm birth burden in low-resource settings.

Methods

The dataset evaluated was compiled from two routine data sources at MCRH. The first was the maternity register, a large book kept in the maternity ward where providers record demographic and clinical data for every birth at the facility. The second was the maternity unit inpatient record, the individual patient medical chart of each woman admitted to MCRH for labor and delivery. Both the maternity register and the inpatient record are standardized forms from the Ministry of Health and completed by the clinical staff. Data from both sources were entered into a Research Electronic Data Capture (REDCap) tool, an online data collection mechanism hosted on a secure server at the University of California, San Francisco (UCSF) and created in advance by the investigators.

During the study period, there were 2070 total births at MCRH of which 455 were identified as cases from the maternity register by either having a birth weight <2500g or having a birth weight >2500g and an LMP-calculated GA of <37 weeks. Inpatient records were found for 289 (63.5%) of the cases with the remaining 166 missing, mislabeled, or otherwise inaccessible. The five GA estimates analyzed were recorded GA and calculated GA from the maternity register and recorded GA, calculated GA, and SFH from the inpatient chart. 

Usage Notes

Anybody using this data must credit the authors and the East Africa Preterm Birth Initiative.

Funding

Bill and Melinda Gates Foundation, Award: OPP1107312