Skip to main content
Dryad logo

Pregnancy outcomes in facility deliveries in Kenya and Uganda: A large cross-sectional analysis of maternity registers illuminating opportunities for mortality prevention

Citation

Waiswa, Peter et al. (2020), Pregnancy outcomes in facility deliveries in Kenya and Uganda: A large cross-sectional analysis of maternity registers illuminating opportunities for mortality prevention, Dryad, Dataset, https://doi.org/10.7272/Q6ZG6QFC

Abstract

Introduction

As facility-based deliveries increase globally, maternity registers offer a promising way of documenting pregnancy outcomes and understanding opportunities for perinatal mortality prevention. This study aims to contribute to global quality improvement efforts by characterizing facility-based pregnancy outcomes in Kenya and Uganda including maternal, neonatal, and fetal outcomes at the time of delivery and neonatal discharge outcomes using strengthened maternity registers.

Methods

Cross sectional data were collected from previously strengthened maternity registers at 23 facilities over 18 months. Pregnancy outcomes were classified as live births, early stillbirths, late stillbirths, or spontaneous abortions according to birth weight or gestational age. Discharge outcomes were assessed for all live births. Outcomes were assessed by country and by infant, maternal, and facility characteristics. Maternal mortality was also examined.

Results

Among 50,981 deliveries, 91.3% were live born and, of those, 1.6% died before discharge. An additional 0.5% of deliveries were early stillbirths, 3.6% late stillbirths, and 4.7% spontaneous abortions. There were 64 documented maternal deaths (0.1%). Preterm and low birthweight infants represented a disproportionate number of stillbirths and pre-discharge deaths, yet very few were born at ≤1500g or <28w. More pre-discharge deaths and stillbirths occurred after maternal referral and with cesarean section. Half of maternal deaths occurred in women who had undergone cesarean section.

Conclusion

Maternity registers are a valuable data source for understanding pregnancy outcomes including those mothers and infants at highest risk of perinatal mortality. Strengthened register data in Kenya and Uganda highlight the need for renewed focus on improving care of preterm and low birthweight infants and expanding access to emergency obstetric care. Registers also permit enumeration of pregnancy loss <28 weeks. Documenting these earlier losses is an important step towards further mortality reduction for the most vulnerable infants.

Methods

Data were collected as part of the East Africa Preterm Birth Initiative (PTBi). This initiative is a partnership between the University of California San Francisco, Kenya Medical Research Institute, University of Rwanda, Rwanda Biomedical Center, and Makerere University in Uganda. In Kenya and Uganda specifically, PTBi is conducting a randomized cluster trial to evaluate the impact of an intrapartum quality improvement package on neonatal survival in preterm and low birthweight infants (clinicaltrials.gov, NCT03112018). The full study protocol is available elsewhere (Otieno et al 2018). This cross-sectional analysis includes both control and intervention sites and is not an evaluation of the impact of the trial.

Maternity register data was gathered from 23 health facilities including 17 in Migori county in western Kenya and six in Busoga region in eastern Uganda. Anonymized patient level delivery data were extracted monthly from maternity registers. Pre-existing national maternity registers were used for this study. However, prior to the study period, data strengthening efforts were completed as part of the PTBi trial to improve the accuracy and completeness of these maternity registers. These efforts included provision of supplies (pregnancy wheels, tape measures, digital scales) with skill building sessions, monthly training and mentoring of labor and delivery staff on standard indicator definitions, and monthly feedback on the completeness of registers. Particular emphasis was placed on the accuracy of gestational age assessments, which were estimated by labor and delivery providers based on reported last menstrual period, fundal height, or antenatal records carried by the mother. Ultrasound was not universally available during antenatal care or at the time of delivery.

Register entries were identified as deliveries if at least one of the following indices was documented: 1-minute Apgar score, birth weight, infant sex, birth outcome, or discharge status. Pregnancy outcomes were then classified as 1) live birth, 2) early stillbirth, 3) late stillbirth, or 4) spontaneous abortion.

Live births were defined in this study as infants born with signs of life (as noted by the health care provider at the time of birth and validated by non-zero 1-minute Apgar score) weighing ≥500 grams or, if no birth weight was recorded, ≥24 weeks completed gestation.

Stillbirths were classified as early or late. The WHO definition of stillbirth was used to define late stillbirths in this analysis-- infants born without signs of life weighing ≥1000 grams or, if no birth weight was recorded, ≥28 weeks completed gestation (2). Early stillbirths were defined as infants born without signs of life weighing between 500 and 999 grams or, if no birth weight was recorded, between 24 and 27 weeks completed gestation. Some stillbirths were further identified as fresh (i.e. intrapartum) or macerated based on infant appearance to the provider at the time of delivery, although not a required field in registers.

Entries excluded from this analysis included 1) births before arrival (n=606), as the aim was to characterize facility-based outcomes and 2) births with no documented birth weight or gestational age (n=36), as this prohibited outcome classification. Mothers were excluded if 1) they delivered before arrival (n=562) or 2) were discharged pregnant (n=9202). A unique maternal identification code was used to link maternal and neonatal data.

Data were collected in ODK, preliminary cleaning and matching was done in SQL, and the dataset was exported to Stata for analysis.

Funding

Bill and Melinda Gates Foundation, Award: OPP1107312