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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 strengthened maternity registers at 23 facilities over 18 months. Data strengthening efforts included provision of supplies, training on standard indicator definitions, and monthly feedback on completeness. 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.
This study is a descriptive, cross-sectional analysis of labor ward maternity registers in Kenya and Uganda between October 1st, 2016 and March 31st, 2018. Data were collected as part of the East Africa Preterm Birth Initiative (PTBi) [13]. 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, {"type":"clinical-trial","attrs":{"text":"NCT03112018","term_id":"NCT03112018"}}NCT03112018). The full study protocol is available elsewhere [14]. This cross-sectional analysis includes both control and intervention sites and is not an evaluation of the impact of the trial. Maternity register data were gathered from 23 health facilities including 17 in Migori county in western Kenya and six in Busoga region in eastern Uganda. In Migori county, facility births represent 53% of all births [15] and in Busoga approximately 77% of deliveries occur in facilities [16]. The facilities included in this analysis were the largest facilities in each location and based on population and reported births, it is estimated that included facilities covered approximately 20–30% of all births in the two regions [17–19]. Within each country the level of care of included facilities varied. However, across both countries facilities ranging from level III through VI were represented [for facility level definitions see references 20, 21]. In Kenya, the 17 facilities included nine level III health centers and eight level IV district referral hospitals. Cesarean sections were performed in level IV facilities only. Two of the 17 Kenyan facilities had newborn special care units. However, only one had a pediatrician on staff. Six facilities had a general doctor, six had a clinical officer, and the remaining five employed nurse midwives only [22]. In Uganda by contrast, all facilities were hospitals including five level V and one level VI facility. All Ugandan facilities were capable of performing cesarean sections and all had newborn special care units. Two hospitals had staff pediatricians and the remainder employed a general doctor [22]. 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. The impact of data strengthening on register completeness has been evaluated and full results are available elsewhere [23]. In brief, in Kenya average completion rates increased from 93 to 97% for gestational age, 87 to 98% for birthweight, 97 to 99% for 1-minute APGAR, and 74 to 88% for infant status at discharge from the preliminary assessment to 6 months post data strengthening [23]. In Uganda, average completion rates increased from 52 to 87% for gestational age, 89 to 94% for birthweight, 93 to 96% for 1-minute APGAR, and 86 to 88% for infant status at discharge [23]. Infant, maternal and facility characteristics abstracted from registers and their completeness in this study are as follows: infant sex 91%, multiple gestation 97%, gestational age 86%, birth weight 92%, maternal age 99%, incoming maternal referral status 59% (only available in Uganda), delivery mode 93%, and facility level 100%. 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. This differs from the WHO definition of live birth, which includes any infant born with signs of life regardless of gestational age or birth weight [12]. The definition was chosen in part to permit classification of spontaneous abortions, which were defined as any fetus born weighing <500 grams or, if no birthweight was recorded, <24 weeks gestational age. 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. Training was provided on visual differentiation of fresh versus macerated stillbirths but fetal heart tone monitoring was not routinely available in study facilities. Discharge outcomes were examined for all live born infants. In Kenya, registers included a field for discharge outcome distinct from birth outcome. In Uganda, there was only one field for infant status. In both countries, when delivery and discharge status could not be distinguished or there was conflicting information (i.e. non-zero 1-minute Apgar categorized as a stillbirth), Apgar scores were used to differentiate stillbirths from live births experiencing an immediate neonatal or pre-discharge death. Pre-discharge maternal mortality was also examined and was a unique field in registers in both countries. 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 are summarized using descriptive frequencies. Pearson chi square test was used to compare pregnancy outcomes by country as well as by maternal, infant, and facility-based co-variates. The Fisher’s exact test was substituted for cases of small sample size (n<5) in instances where models converged. Early stillbirths and spontaneous abortions were excluded from the analyses by birth weight and gestational age as these outcomes were pre-defined by a narrow range of birth weights and gestational ages. A sub analysis was performed to compare fresh versus macerated late stillbirths. Other analyses available upon request include: country specific analyses and a sub analysis of multiple gestation vs singletons. All analyses except Fisher’s exact tests were performed using SPSS 23 [24]. Fisher’s exact tests were performed in STATA 14 [25]. This study was approved by Institutional Review Board at the University of California San Francisco (Study no: 16–19162), the Kenyan Medical Institute Scientific and Ethics Review Unit (SERU protocol no: KEMRI/SERU/CCR/0034/3251), the Makerere University Higher Degrees, Research, and Ethics Committee (Protocol ID: IRB00011353), and the Uganda National Council of Science and Technology. There was a waiver of consent to obtain line-item level data from maternity registers. De-identified data were collected from maternity registers, so there was no direct patient contact or time spent for this analysis. Results will be disseminated to health workers and health authorities from research areas.
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