Using electronic medical records to understand the impact of Sars-CoV-2 lockdown measures on maternal and neonatal outcomes in Kampala, Uganda

Bibliographic Data

ID19592673
AuthorsJoseph Ouma (0000-0001-5249-7440, MUJHU Research Collaboration), Lauren Hookham (0000-0001-9703-655X, St George's, University of London, corresponding author), Lorna Aol Akera (0000-0001-6796-1904, MUJHU Research Collaboration), Gordon Rukundo (0000-0002-2622-5254, MUJHU Research Collaboration), Mary Kyohere (MUJHU Research Collaboration), Ayoub Kakande (0000-0002-0243-5601, Uganda Virus Research Institute), Racheal Nakyesige (0000-0002-1061-1599, Uganda Virus Research Institute), Philippa Musoke (0000-0002-1875-202X, MUJHU Research Collaboration), Kirsty Le Doare (0000-0002-5104-085X, St George's, University of London)
EditorsTing Shi (0000-0001-5602-8043)
Year2023
Volume3
Issue12
Pagese0002022
Publication date2023-12-08
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePLOS Global Public Health (JOURNAL)
Journal identifiersISSN: 2767-3375 • E-ISSN: 2767-3375
PublisherPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pgph.0002022
PMID38064420
OpenAlexW4389475759
LanguageEN
References cited20

Kawempe National Referral Hospital (KNRH) is a tertiary facility with over 21,000 pregnant or postpartum women admitted annually. The hospital, located in Kampala, Uganda, uses an Electronic Medical Records (EMR) system to capture patient data. Used since 2017, this readily available electronic health record (EHR) has the benefit of informing real-time clinical care, especially during pandemics such as COVID-19. We investigated the use of EHR to assess risk factors for adverse pregnancy and infant outcomes that can be incorporated into a data visualization dashboard for real time decision making during pandemics. This study analysed data from the UgandaEMR collected at pre-, during- and post-lockdown timepoints of the COVID-19 pandemic to determine its use in monitoring risk factors for adverse pregnancy and neonatal outcomes. Logistic regression models were used to identify the risk factors for adverse pregnancy and maternal outcomes including prematurity, obstetric complications, still births and neonatal deaths. Pearson chi-square test was used for pair-wise comparison of the outcomes at the various stages of the pandemic. Data analysis was performed in R, within the International COVID-19 Data Alliance (ICODA) workbench. A visualisation dashboard was developed based on the risk factors, to support decision making and improved healthcare delivery. Comparison of pre-and post-lockdown variables showed an increased risk of pre-term birth (adjusted Odds Ratio (aOR = 1.67, 95% confidence interval (CI) 1.38–2.01)); obstetric complications (aOR = 2.77, 95% CI: 2.53–3.03); immediate neonatal death (aOR = 3.89, 95% CI 2.65–5.72) and Caesarean section (aOR = 1.22, 95% CI 1.11–1.34). The significant risk factors for adverse outcomes were younger maternal age and gestational age

Confidence interval · Dashboard · Disease · Logistic regression · Medical emergency · Medical record · Obstetrics · Odds ratio · Pandemic · Pregnancy · COVID-19 Impact on Reproduction · Global Maternal and Child Health · Maternal Mental Health During Pregnancy and Postpartum · Medicine · Emergency Medicine · Pediatrics

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