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BackgroundAdverse pregnancy outcomes jointly account for a high proportion of mortality and morbidity among pregnant women and their infants. Furthermore, the burden attributed to adverse pregnancy outcomes remains high and inadequately characterised due to the intricate interplay of its etiology and shared set of important risk factors. This study sought to quantify and map the underlying risk of multiple adverse pregnancy outcomes in Kenya at sub-county level using a shared component space-time modelling framework.MethodsReported sub-county level adverse pregnancy outcomes count from January 2016 - December 2019 were obtained from the Kenyan District Health Information System. A Bayesian hierarchical spatio-temporal model was used to estimate the joint burden of adverse pregnancy outcomes in space (sub-county) and time (year). To improve the precision of our estimates over time and space, information across the outcomes were combined via the shared and the outcome-specific components using a shared component model with spatio-temporal interactions.ResultsOverall, the total number of adverse outcomes in pregnancy increased by 14.2% (95% UI: 14.0-14.5) from 88,816 cases in 2016 to 101,455 cases in 2019. Between 2016 and 2019, the estimated low birth weight rate and the pre-term birth rate were 4.5 (95% UI: 4.4-4.7) and 2.3 (95% UI: 2.2-2.5) per 100 live births. The stillbirth and neonatal death rates were estimated to be 18.7 (95% UI: 18.0-19.4) and 6.9 (95% UI: 6.4-7.4) per 1000 live births. The magnitude of the spatio-temporal variation attributed to shared risk was high for pre-term births, low birth weight, neonatal deaths, stillbirths and neonatal deaths, respectively. The shared risk patterns were dominant in sub-counties located along the Indian ocean coastline, central and western Kenya.ConclusionsThis study demonstrates the usefulness of a Bayesian joint spatio-temporal shared component model in exploiting specific and shared risk of adverse pregnancy outcomes sub-nationally. By identifying sub-counties with elevated risks and data gaps, our estimates not only assert the need for bolstering maternal health programs in the identified high-risk sub-counties but also provides a baseline against which to assess the progress towards the attainment of Sustainable Development Goals.

Original publication





BMC public health

Publication Date





Discipline of Public Health Medicine, College of Health Sciences, University of KwaZulu-Natal, 2nd Floor George Campbell Building, Howard College Campus, Durban, 4001, South Africa.


Humans, Pregnancy Outcome, Infant Mortality, Bayes Theorem, Risk Factors, Pregnancy, Infant, Infant, Newborn, Kenya, Female, Stillbirth