Village-level surveillance of neonatal disease with integrated real-time dashboards and quality-control in Uganda
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Introduction
Neonatal mortality remains disproportionately high in sub-Saharan Africa, where an estimated 27 neonatal deaths per 1,000 live births occur annually. Infections, including sepsis and meningitis, account for a substantial proportion of these deaths, while neural tube defects (NTDs) contribute significantly to both neonatal mortality and long-term disability. Existing surveillance systems in the region are predominantly facility-based, missing the substantial proportion of births and deaths that occur in the community. Population-based surveillance platforms that capture community-level data are urgently needed to generate accurate incidence estimates, identify modifiable risk factors, and guide evidence-based interventions.
Cohort Description
The Consortium to Reduce Infant Mortality (CONRIM) is a multi-institutional partnership among Ugandan physicians and scientists, Yale University, Penn State University, Boston Children’s Hospital/Harvard Medical School, and Uganda’s National Planning Authority. CONRIM conducts prospective, community-based neonatal surveillance within the Busoga Kingdom in eastern Uganda. A network of 813 trained Village Health Team members conducts household-level visits using a structured Open Data Kit (ODK)-based mobile questionnaire to capture every birth, assess for danger signs of possible serious bacterial infection (pSBI), screen for NTDs, and record maternal nutrition and folic acid use, water, sanitation and hygiene (WASH) conditions, and health care utilization.
Findings to Date
Since surveillance began in June 2025, the platform has registered approximately 22,200 household submissions and over 5,700 newborn encounters across the Jinja District (population 660,000). Early data have identified higher than expected rates of infants with NTDs including encephalocele and spina bifida; documented folic acid non-use in before and during most pregnancies; characterized WASH conditions in birthplaces; and mapped geospatial hotspots of neonatal infection risk in northeastern rural subcounties. Prospective 28-day follow-up of all live births has demonstrated a neonatal mortality rate of 21.5 per 1000 live births. A real-time data quality monitoring system with 21 automated quality control flags maintains a 99% clean-record rate.
Future Plans
Ongoing and planned activities include laboratory-based confirmation of neonatal sepsis via blood culture and cerebrospinal fluid analysis with polymerase chain reaction capacity, portable neuroimaging for NTDs, environmental sampling, genomic studies of folate metabolism pathway genes, linkage with facility-based records at Jinja Regional Referral Hospital and Mulago National Referral Hospital, and community-level interventions informed by surveillance findings.
Key Messages
What is already known on this topic
Neonatal mortality remains disproportionately high in sub-Saharan Africa, with sepsis and neural tube defects (NTDs) among the leading preventable causes. Existing surveillance systems are predominantly facility-based and fail to capture births, deaths, and environmental exposures occurring at the community level. Emerging approaches in digital health, geospatial analytics, and pathogen genomics have demonstrated potential to enhance infectious disease surveillance, but these have rarely been integrated into population-based neonatal monitoring systems in low-resource settings.
What this study adds
The Consortium to Reduce Infant Mortality (CONRIM) is a multidisciplinary initiative designed to develop scalable, population-based systems for understanding and reducing neonatal mortality through integrated epidemiologic, environmental, and biologic data.
This paper describes one implementation of the CONRIM framework in the Busoga Kingdom of eastern Uganda, where a network of 813 trained Village Health Team members conducts longitudinal, community-based surveillance of births, neonatal outcomes, NTDs, maternal nutrition (including folic acid use), water, sanitation and hygiene (WASH) conditions, and care-seeking behaviour.
This implementation integrates:
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real-time digital data capture with automated quality control,
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geospatial information systems (GIS) and remote sensing to characterize environmental risk factors,
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population-level genomic and metagenomic sampling to investigate host and pathogen factors, and
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a One Health framework linking human, animal, and environmental exposures.
Early findings highlight high data completeness, geospatial clustering of neonatal infection risk, low preconception folic acid use, and identification of NTD cases not captured by facility-based systems.
How this study might affect research, practice, or policy
This study demonstrates the feasibility of implementing a community-based, real-time neonatal surveillance system within an existing community health worker network in a low-resource setting. By integrating geospatial, genomic, and environmental data within a unified platform, the CONRIM framework enables more precise identification of drivers of neonatal morbidity and mortality.
The approach supports targeted public health interventions, including geographically informed infection control strategies, improved referral pathways, and evidence generation for folic acid fortification policies. More broadly, CONRIM provides a scalable infrastructure for future interventional studies and precision public health strategies aimed at reducing neonatal mortality.
