Estimates of Stillbirths, Neonatal Mortality, and Medically Vulnerable Live Births in Amhara, Ethiopia

Chan, Grace J; Goddard, Frederick G. B.; Hunegnaw, Bezawit Mesfin; Mohammed, Yahya; Hunegnaw, Mesfin; Haneuse, Sebastien; Bekele, Chalachew; Bekele, Delayehu

doi:10.1001/jamanetworkopen.2022.18534

Cited by 12 publications

(10 citation statements)

References 31 publications

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“…If ultrasonographic data were only available after 16 weeks’ gestation, self-reported last menstrual period (LMP) was used if there was a discrepancy between the LMP and ultrasonographic estimates. If no ultrasonographic estimates were available, LMP was used, and if neither of those methods were available, then fundal height was used over a maternal estimate (reported in months) …”

Section: Methodsmentioning

confidence: 99%

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Development of Prediction Models for Antenatal Care Attendance in Amhara Region, Ethiopia

et al. 2023

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ImportanceAntenatal care prevents maternal and neonatal deaths and improves birth outcomes. There is a lack of predictive models to identify pregnant women who are at high risk of failing to attend antenatal care in low-resource settings.ObjectiveTo develop a series of predictive models to identify women who are at high risk of failing to attend antenatal care in a rural setting in Ethiopia.Design, Setting, and ParticipantsThis prognostic study used data from the Birhan Health and Demographic Surveillance System and its associated pregnancy and child cohort. The study was conducted at the Birhan field site, North Shewa zone, Ethiopia, a platform for community- and facility-based research and training, with a focus on maternal and child health. Participants included women enrolled during pregnancy in the pregnancy and child cohort between December 2018 and March 2020, who were followed-up in home and facility visits. Data were analyzed from April to December 2022.ExposuresA wide range of sociodemographic, economic, medical, environmental, and pregnancy-related factors were considered as potential predictors. The selection of potential predictors was guided by literature review and expert knowledge.Main Outcomes and MeasuresThe outcome of interest was failing to attend at least 1 antenatal care visit during pregnancy. Prediction models were developed using logistic regression with regularization via the least absolute shrinkage and selection operator and ensemble decision trees and assessed using the area under the receiving operator characteristic curve (AUC).ResultsThe study sample included 2195 participants (mean [SD] age, 26.8 [6.1] years; mean [SD] gestational age at enrolment, 25.5 [8.8] weeks). A total of 582 women (26.5%) failed to attend antenatal care during cohort follow-up. The AUC was 0.61 (95% CI, 0.58-0.64) for the regularized logistic regression model at conception, with higher values for models predicting at weeks 13 (AUC, 0.68; 95% CI, 0.66-0.71) and 24 (AUC, 0.66; 95% CI, 0.64-0.69). AUC values were similar with slightly higher performance for the ensembles of decision trees (conception: AUC, 0.62; 95% CI, 0.59-0.65; 13 weeks: AUC, 0.70; 95% CI, 0.67-0.72; 24 weeks: AUC, 0.67; 95% CI, 0.64-0.69).Conclusions and RelevanceThis prognostic study presents a series of prediction models for antenatal care attendance with modest performance. The developed models may be useful to identify women at high risk of missing their antenatal care visits to target interventions to improve attendance rates. This study opens the possibility to develop and validate easy-to-use tools to project health-related behaviors in settings with scarce resources.

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Section: Methodsmentioning

confidence: 99%

“…If no ultrasonographic estimates were available, LMP was used, and if neither of those methods were available, then fundal height was used over a maternal estimate (reported in months). 14 …”

Section: Methodsmentioning

confidence: 99%

Development of Prediction Models for Antenatal Care Attendance in Amhara Region, Ethiopia

et al. 2023

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“…Detailed information on the hierarchy of how the best available method was chosen can be found elsewhere. 15…”

Section: Methodsmentioning

confidence: 99%

Development of prediction models for antenatal care attendance in Amhara region, Ethiopia

Wilder

Pons‐Duran

Goddard

et al. 2022

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Background. In low-resource settings, coverage of at least four antenatal care (ANC) visits remains low. As a first step towards enhancing ANC attendance, this study aims to develop a series of predictive models to identify women who are at high risk of failing to attend ANC in a rural setting in Ethiopia. Methods. This is a cohort study conducted in the Birhan field site, North Shewa Zone. Using data of a surveillance system and a pregnancy cohort, we developed and internally validated a series of logistic regressions with regularization (LASSO), and ensembles of decision trees. Discrimination was estimated using the area under the receiving operator characteristic curve (AUC). Three prediction time points were considered: conception, and gestational weeks 13 and 24. Results. The study sample size was 2195. Mean age of participants was 26.8 years (Standard Deviation (SD) 6.1) and mean gestational age at enrolment was 25.5 weeks (SD 8.8). A total of 582 women (26.5%) failed to attend ANC during cohort follow-up. We observed AUC in the range of 0.61-0.70, with higher values for models predicting at weeks 13 and 24. All AUC values were similar with slightly higher performance for the ensembles of decision trees. Conclusions. The developed models may be useful to identify women at high risk of missing their ANC visits to target interventions to improve attendance rates. This study calls for new efforts to generate data that enables accurate prediction of ANC access, and it opens the possibility to develop and validate easy-to-use tools to predict health-related behaviours in settings with scarce resources.

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“…Detailed information on the hierarchy of these estimations can be found elsewhere. 24 The selection of potential predictors was guided by literature review and expert knowledge from study obstetrician and pediatricians. Predictors that were not available in the HDSS and MCH cohort datasets were not considered.…”

Section: Study Variables and Definitionsmentioning

confidence: 99%

Development of risk prediction models for preterm delivery in a rural setting in Ethiopia

Pons‐Duran

Wilder

Hunegnaw

et al. 2022

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Importance: There is a lack of accurate predictive models to identify pregnancies that are at high risk of preterm delivery, especially in resource-limited settings. Objective: To assess whether it is possible to develop highly accurate prognostic predictive models for preterm delivery using data from a resource-limited setting in Ethiopia. Design: Nested cohort study using data from the Birhan Health and Demographic Surveillance System and its associated pregnancy and child cohort. Setting: The study was conducted at the Birhan field site, North Shewa zone, Ethiopia, a platform for community and facility-based research and training, with a focus on maternal and child health. Participants: Women enrolled during pregnancy in the pregnancy and child cohort between December 2018 and March 2020, who are followed-up in home and facility visits beyond 28 weeks of gestation and through delivery. Exposures: A wide range of socio-demographic, economic, clinical, environmental, and pregnancy-related factors were considered as potential predictors for preterm delivery. The selection of predictors was guided by literature review and expert knowledge. Predictors that were not available in the surveillance system and cohort datasets were not considered. Main Outcome Measure: Preterm delivery, a composite indicator defined as any delivery occurring before 37 completed weeks of gestation, regardless of vital status of the fetus or neonate. For time-to-event models, time to delivery in weeks was the main outcome measure. Results: A total of 2493 pregnancies were included. Of those, 138 women were censored due to loss-to-follow-up before delivery. Overall, predictive performance was poor. The area under the receiving operator characteristic curve was highest for the tree ensemble classifier (0.60, 95%CI[0.57, 0.63]). When models were calibrated so that 90% of women who experienced a preterm delivery were classified as high risk, at least 75% of those classified as high risk did not experience the outcome. Conclusions and Relevance: Prediction of preterm delivery remains a major challenge. In resource-limited settings, predicting high-risk deliveries would not only save lives, but also inform resource allocation. It may not be possible to accurately predict risk of preterm delivery without investing in novel technologies to identify genetic factors, immunological biomarkers or the expression of specific proteins.

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Estimates of Stillbirths, Neonatal Mortality, and Medically Vulnerable Live Births in Amhara, Ethiopia

Cited by 12 publications

References 31 publications

Development of Prediction Models for Antenatal Care Attendance in Amhara Region, Ethiopia

Development of Prediction Models for Antenatal Care Attendance in Amhara Region, Ethiopia

Development of prediction models for antenatal care attendance in Amhara region, Ethiopia

Development of risk prediction models for preterm delivery in a rural setting in Ethiopia

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