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

Ouma, J; Hookham, L; Akera, LA; Rukundo, G; Kyohere, M; Kakande, A; Nakyesige, R; Musoke, P; Le Doare, K (2023) Using electronic medical records to understand the impact of SARS-CoV-2 lockdown measures on maternal and neonatal outcomes in Kampala, Uganda. PLOS Glob Public Health, 3 (12). e0002022. ISSN 2767-3375 https://doi.org/10.1371/journal.pgph.0002022
SGUL Authors: Le Doare, Kirsty

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Abstract

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 <32weeks at labour. This study demonstrates the feasibility of using EHR to identify and monitor at-risk subpopulation groups accessing health services in real time. This information is critical for the development of timely and appropriate interventions in outbreaks and pandemic situations.

Item Type: Article
Additional Information: Copyright: © 2023 Ouma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
SGUL Research Institute / Research Centre: Academic Structure > Infection and Immunity Research Institute (INII)
Journal or Publication Title: PLOS Glob Public Health
ISSN: 2767-3375
Language: eng
Dates:
DateEvent
8 December 2023Published
9 October 2023Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
D43 TW012275FIC NIH HHSUNSPECIFIED
PubMed ID: 38064420
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/115958
Publisher's version: https://doi.org/10.1371/journal.pgph.0002022

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