Use of Sentinel Surveillance Platforms for Monitoring SARS-CoV-2 Activity : Evidence From Analysis of Kenya Influenza Sentinel Surveillance Data
©Daniel Owusu, Linus K Ndegwa, Jorim Ayugi, Peter Kinuthia, Rosalia Kalani, Mary Okeyo, Nancy A Otieno, Gilbert Kikwai, Bonventure Juma, Peninah Munyua, Francis Kuria, Emmanuel Okunga, Ann C Moen, Gideon O Emukule. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 25.03.2024..
BACKGROUND: Little is known about the cocirculation of influenza and SARS-CoV-2 viruses during the COVID-19 pandemic and the use of respiratory disease sentinel surveillance platforms for monitoring SARS-CoV-2 activity in sub-Saharan Africa.
OBJECTIVE: We aimed to describe influenza and SARS-CoV-2 cocirculation in Kenya and how the SARS-CoV-2 data from influenza sentinel surveillance correlated with that of universal national surveillance.
METHODS: From April 2020 to March 2022, we enrolled 7349 patients with severe acute respiratory illness or influenza-like illness at 8 sentinel influenza surveillance sites in Kenya and collected demographic, clinical, underlying medical condition, vaccination, and exposure information, as well as respiratory specimens, from them. Respiratory specimens were tested for influenza and SARS-CoV-2 by real-time reverse transcription polymerase chain reaction. The universal national-level SARS-CoV-2 data were also obtained from the Kenya Ministry of Health. The universal national-level SARS-CoV-2 data were collected from all health facilities nationally, border entry points, and contact tracing in Kenya. Epidemic curves and Pearson r were used to describe the correlation between SARS-CoV-2 positivity in data from the 8 influenza sentinel sites in Kenya and that of the universal national SARS-CoV-2 surveillance data. A logistic regression model was used to assess the association between influenza and SARS-CoV-2 coinfection with severe clinical illness. We defined severe clinical illness as any of oxygen saturation <90%, in-hospital death, admission to intensive care unit or high dependence unit, mechanical ventilation, or a report of any danger sign (ie, inability to drink or eat, severe vomiting, grunting, stridor, or unconsciousness in children younger than 5 years) among patients with severe acute respiratory illness.
RESULTS: Of the 7349 patients from the influenza sentinel surveillance sites, 76.3% (n=5606) were younger than 5 years. We detected any influenza (A or B) in 8.7% (629/7224), SARS-CoV-2 in 10.7% (768/7199), and coinfection in 0.9% (63/7165) of samples tested. Although the number of samples tested for SARS-CoV-2 from the sentinel surveillance was only 0.2% (60 per week vs 36,000 per week) of the number tested in the universal national surveillance, SARS-CoV-2 positivity in the sentinel surveillance data significantly correlated with that of the universal national surveillance (Pearson r=0.58; P<.001). The adjusted odds ratios (aOR) of clinical severe illness among participants with coinfection were similar to those of patients with influenza only (aOR 0.91, 95% CI 0.47-1.79) and SARS-CoV-2 only (aOR 0.92, 95% CI 0.47-1.82).
CONCLUSIONS: Influenza substantially cocirculated with SARS-CoV-2 in Kenya. We found a significant correlation of SARS-CoV-2 positivity in the data from 8 influenza sentinel surveillance sites with that of the universal national SARS-CoV-2 surveillance data. Our findings indicate that the influenza sentinel surveillance system can be used as a sustainable platform for monitoring respiratory pathogens of pandemic potential or public health importance.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
JMIR public health and surveillance - 10(2024) vom: 25. März, Seite e50799 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Owusu, Daniel [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 26.03.2024 Date Revised 11.04.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.2196/50799 |
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| Förderinstitution / Projekttitel: |
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NLM370161009 |
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| 100 | 1 | |a Owusu, Daniel |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Use of Sentinel Surveillance Platforms for Monitoring SARS-CoV-2 Activity |b Evidence From Analysis of Kenya Influenza Sentinel Surveillance Data |
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| 500 | |a Date Revised 11.04.2024 | ||
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a ©Daniel Owusu, Linus K Ndegwa, Jorim Ayugi, Peter Kinuthia, Rosalia Kalani, Mary Okeyo, Nancy A Otieno, Gilbert Kikwai, Bonventure Juma, Peninah Munyua, Francis Kuria, Emmanuel Okunga, Ann C Moen, Gideon O Emukule. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 25.03.2024. | ||
| 520 | |a BACKGROUND: Little is known about the cocirculation of influenza and SARS-CoV-2 viruses during the COVID-19 pandemic and the use of respiratory disease sentinel surveillance platforms for monitoring SARS-CoV-2 activity in sub-Saharan Africa | ||
| 520 | |a OBJECTIVE: We aimed to describe influenza and SARS-CoV-2 cocirculation in Kenya and how the SARS-CoV-2 data from influenza sentinel surveillance correlated with that of universal national surveillance | ||
| 520 | |a METHODS: From April 2020 to March 2022, we enrolled 7349 patients with severe acute respiratory illness or influenza-like illness at 8 sentinel influenza surveillance sites in Kenya and collected demographic, clinical, underlying medical condition, vaccination, and exposure information, as well as respiratory specimens, from them. Respiratory specimens were tested for influenza and SARS-CoV-2 by real-time reverse transcription polymerase chain reaction. The universal national-level SARS-CoV-2 data were also obtained from the Kenya Ministry of Health. The universal national-level SARS-CoV-2 data were collected from all health facilities nationally, border entry points, and contact tracing in Kenya. Epidemic curves and Pearson r were used to describe the correlation between SARS-CoV-2 positivity in data from the 8 influenza sentinel sites in Kenya and that of the universal national SARS-CoV-2 surveillance data. A logistic regression model was used to assess the association between influenza and SARS-CoV-2 coinfection with severe clinical illness. We defined severe clinical illness as any of oxygen saturation <90%, in-hospital death, admission to intensive care unit or high dependence unit, mechanical ventilation, or a report of any danger sign (ie, inability to drink or eat, severe vomiting, grunting, stridor, or unconsciousness in children younger than 5 years) among patients with severe acute respiratory illness | ||
| 520 | |a RESULTS: Of the 7349 patients from the influenza sentinel surveillance sites, 76.3% (n=5606) were younger than 5 years. We detected any influenza (A or B) in 8.7% (629/7224), SARS-CoV-2 in 10.7% (768/7199), and coinfection in 0.9% (63/7165) of samples tested. Although the number of samples tested for SARS-CoV-2 from the sentinel surveillance was only 0.2% (60 per week vs 36,000 per week) of the number tested in the universal national surveillance, SARS-CoV-2 positivity in the sentinel surveillance data significantly correlated with that of the universal national surveillance (Pearson r=0.58; P<.001). The adjusted odds ratios (aOR) of clinical severe illness among participants with coinfection were similar to those of patients with influenza only (aOR 0.91, 95% CI 0.47-1.79) and SARS-CoV-2 only (aOR 0.92, 95% CI 0.47-1.82) | ||
| 520 | |a CONCLUSIONS: Influenza substantially cocirculated with SARS-CoV-2 in Kenya. We found a significant correlation of SARS-CoV-2 positivity in the data from 8 influenza sentinel surveillance sites with that of the universal national SARS-CoV-2 surveillance data. Our findings indicate that the influenza sentinel surveillance system can be used as a sustainable platform for monitoring respiratory pathogens of pandemic potential or public health importance | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Kenya | |
| 650 | 4 | |a SARS-CoV-2 | |
| 650 | 4 | |a cocirculation | |
| 650 | 4 | |a epidemic | |
| 650 | 4 | |a influenza | |
| 650 | 4 | |a local outbreak | |
| 650 | 4 | |a monitoring | |
| 650 | 4 | |a respiratory infection | |
| 650 | 4 | |a respiratory pathogen | |
| 650 | 4 | |a sentinel surveillance | |
| 650 | 4 | |a surveillance | |
| 700 | 1 | |a Ndegwa, Linus K |e verfasserin |4 aut | |
| 700 | 1 | |a Ayugi, Jorim |e verfasserin |4 aut | |
| 700 | 1 | |a Kinuthia, Peter |e verfasserin |4 aut | |
| 700 | 1 | |a Kalani, Rosalia |e verfasserin |4 aut | |
| 700 | 1 | |a Okeyo, Mary |e verfasserin |4 aut | |
| 700 | 1 | |a Otieno, Nancy A |e verfasserin |4 aut | |
| 700 | 1 | |a Kikwai, Gilbert |e verfasserin |4 aut | |
| 700 | 1 | |a Juma, Bonventure |e verfasserin |4 aut | |
| 700 | 1 | |a Munyua, Peninah |e verfasserin |4 aut | |
| 700 | 1 | |a Kuria, Francis |e verfasserin |4 aut | |
| 700 | 1 | |a Okunga, Emmanuel |e verfasserin |4 aut | |
| 700 | 1 | |a Moen, Ann C |e verfasserin |4 aut | |
| 700 | 1 | |a Emukule, Gideon O |e verfasserin |4 aut | |
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