Conference Abstract | Volume 8, Abstract ELIC2025266 (Poster 038) | Published: 31 Jul 2025
Yahaya Sakwa1,&, Babatunde Olajumoke1, Nasir Ahmed1, Adesuyi Omoare1, Adama Ahmad1, Fatima Bello1
1Nigeria Centre for Disease Control and Prevention (NCDC), Abuja, Nigeria
&Corresponding author: Yahaya Sakwa, Nigeria Centre for Disease Control and Prevention (NCDC), Abuja, Nigeria, Email: yahaya.sakwa@ncdc.gov.ng
Received: 31 May 2025, Accepted: 09 Jun 2025, Published: 31 Jul 2025
Domain: Infectious Disease Epidemiology, Laboratory Quality Control
Keywords: Lassa fever; Sample rejection; Cold chain; Ambient temperature; Nigeria; Laboratory surveillance; Case investigation form
©Yahaya S. Sakwa et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Yahaya S. Sakwa et al., A multi-quarter assessment of sample rejection and cold chain deviations in Lassa fever surveillance in Nigeria, 2024–2025. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc5):00182. https://doi.org/10.37432/JIEPH-CONFPRO5-00182
Lassa fever, a severe viral disease endemic to Nigeria, poses ongoing public health threats, with rapid diagnosis essential for outbreak control. Rising sample rejection rates and unstable logistical temperatures upon sample arrival at the National Reference Laboratory (NRL) compromise diagnostic accuracy. This study examines rejection trends and causes, identifies states with the highest rejection rates, and assesses regional and seasonal temperature deviations from the First Quarter, Second Quarter, Third Quarter, and Fourth Quarter of 2024 to the First Quarter of 2025. Findings aim to guide improvements in sample handling and surveillance logistics.
he overall rejection rate was 0.73%, rising from 0.28% in Q1 2024 to 4.08% in Q4 2024, before declining to 2.28% in Q1 2025. No rejections occurred in Q2 2024. Kogi and Ondo had the highest rejection proportions (17.65% each), while Benue, contributing over half of the total samples, accounted for 11.76% of rejections. Predominant causes of rejection were Case Investigation Form (CIF) without samples and sample spillage (35.29% each), followed by mismatched information and missing CIFs (11.76% each), and improper packaging (5.88%). Mean reception temperature was 17.68°C, above the World Health Organization’s recommended 2–8°C. The Second Quarter had the highest mean (18.74°C), and the Third Quarter the lowest (15.58°C). Zamfara recorded optimal cold chain conditions (3.2°C), while coastal states such as Rivers and Akwa Ibom recorded elevated rainy season temperatures.
The study reveals that although overall rejection rates were low, temperature instability and documentation gaps significantly compromised sample quality. It emphasizes the impact of these factors on diagnostic reliability and recommends addressing data completeness and transport time to improve outbreak response.
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