Outborn Neonates in Nairobi’s Public Hospitals: Mortality Burden and Associated Risk Factors in a Retrospective Cohort Study

<ns3:p>Background Neonatal mortality remains a significant challenge in Sub-Saharan Africa. Outborn neonates - those born outside the admitting hospital - are particularly vulnerable, yet their outcomes remain under-characterized in many low-resource urban settings. This study examines the burden, characteristics, and mortality risk among outborn neonates admitted to public neonatal units in Nairobi, Kenya. Methods We conducted a retrospective cohort study across four high-volume public hospitals in Nairobi County, Kenya. A stratified random sample of 400 outborn neonates admitted between January and December 2023 was reviewed using medical records. To estimate outborn prevalence and the Population Attributable Fraction (PAF) for mortality, we analyzed registry data from 9,021 neonatal admissions with complete outcome and place-of-birth information. Referral distances were calculated using geocoded coordinates of referring and receiving facilities. Multivariable logistic regression identified factors independently associated with inpatient neonatal mortality. Results Outborn neonates accounted for 17% of neonatal admissions, with a mortality rate of 25.6%, double that of inborn (12.5%; RR=2.05, p&lt;0.001). The PAF was 15.0% (95% CI: 12.3-17.7%), indicating that one in seven neonatal deaths could be attributed to outborn status. Key factors were significantly associated with increased odds of mortality including; extremely low birth weight (&lt;1000g; AOR=7.8, 95% CI: 2.6, 23), very low birth weight (1000-1499g; AOR= 4.7 95% CI:2.3, 9.7), hypothermia (AOR=2.2, 95% CI: 1.1, 4.5), respiratory distress (AOR=2.1, 95% CI: 1.2, 3.8), and referral distance &gt;12.1 km (AOR=2.6, 95% CI: 1.1, 6.6). Most deaths (68%) occurred within three days of admission. Conclusion Outborn neonates in Nairobi’s public hospitals face substantially higher early mortality, primarily driven by low birth weight, hypothermia, respiratory distress, and longer referral distances. Interventions targeting improved stabilization, timely referral, and early neonatal care are urgently needed. These findings provide actionable evidence to inform policies aimed at reducing neonatal mortality and advancing progress toward SDG 3.2.</ns3:p>

Authors

Institutions

Publication Details

Journal
Wellcome Open Research
Published
2026-04-13
DOI
https://doi.org/10.12688/wellcomeopenres.25241.1
Primary Topic
Global Maternal and Child Health
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Outborn Neonates in Nairobi’s Public Hospitals: Mortality Burden and Associated Risk Factors in a Retrospective Cohort Study

Jalemba Aluvaala, John Wainaina, John Gachohi
Wellcome Open Research
Global Maternal and Child Health
article

Outborn Neonates in Nairobi’s Public Hospitals: Mortality Burden and Associated Risk Factors in a Retrospective Cohort Study

Jalemba Aluvaala, John Wainaina, John Gachohi
article en

Abstract

<ns3:p>Background Neonatal mortality remains a significant challenge in Sub-Saharan Africa. Outborn neonates - those born outside the admitting hospital - are particularly vulnerable, yet their outcomes remain under-characterized in many low-resource urban settings. This study examines the burden, characteristics, and mortality risk among outborn neonates admitted to public neonatal units in Nairobi, Kenya. Methods We conducted a retrospective cohort study across four high-volume public hospitals in Nairobi County, Kenya. A stratified random sample of 400 outborn neonates admitted between January and December 2023 was reviewed using medical records. To estimate outborn prevalence and the Population Attributable Fraction (PAF) for mortality, we analyzed registry data from 9,021 neonatal admissions with complete outcome and place-of-birth information. Referral distances were calculated using geocoded coordinates of referring and receiving facilities. Multivariable logistic regression identified factors independently associated with inpatient neonatal mortality. Results Outborn neonates accounted for 17% of neonatal admissions, with a mortality rate of 25.6%, double that of inborn (12.5%; RR=2.05, p&lt;0.001). The PAF was 15.0% (95% CI: 12.3-17.7%), indicating that one in seven neonatal deaths could be attributed to outborn status. Key factors were significantly associated with increased odds of mortality including; extremely low birth weight (&lt;1000g; AOR=7.8, 95% CI: 2.6, 23), very low birth weight (1000-1499g; AOR= 4.7 95% CI:2.3, 9.7), hypothermia (AOR=2.2, 95% CI: 1.1, 4.5), respiratory distress (AOR=2.1, 95% CI: 1.2, 3.8), and referral distance &gt;12.1 km (AOR=2.6, 95% CI: 1.1, 6.6). Most deaths (68%) occurred within three days of admission. Conclusion Outborn neonates in Nairobi’s public hospitals face substantially higher early mortality, primarily driven by low birth weight, hypothermia, respiratory distress, and longer referral distances. Interventions targeting improved stabilization, timely referral, and early neonatal care are urgently needed. These findings provide actionable evidence to inform policies aimed at reducing neonatal mortality and advancing progress toward SDG 3.2.</ns3:p>

Wellcome Open ResearchVol. 11
Washington State Department of Health (US), University of Nairobi (KE), Jomo Kenyatta University of Agriculture and Technology (KE), Kenya Medical Research Institute (KE), Washington State University (US)
Wellcome Trust
Good health and well-being
Openalex Percentile: Top 12%
Global Maternal and Child Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.