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Febrile patients admitted to remote hospitals in Northeastern Kenya: seroprevalence, risk factors and a clinical prediction tool for Q-Fever
BACKGROUND: Q fever in Kenya is poorly reported and its surveillance is highly neglected. Standard empiric treatment for febrile patients admitted to hospitals is antimalarials or penicillin-based antibiotics, which have no activity against Coxiella burnetii. This study aimed to assess the seropreva...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4891891/ https://www.ncbi.nlm.nih.gov/pubmed/27260261 http://dx.doi.org/10.1186/s12879-016-1569-0 |
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author | Njeru, J. Henning, K. Pletz, M. W. Heller, R. Forstner, C. Kariuki, S. Fèvre, E. M. Neubauer, H. |
author_facet | Njeru, J. Henning, K. Pletz, M. W. Heller, R. Forstner, C. Kariuki, S. Fèvre, E. M. Neubauer, H. |
author_sort | Njeru, J. |
collection | PubMed |
description | BACKGROUND: Q fever in Kenya is poorly reported and its surveillance is highly neglected. Standard empiric treatment for febrile patients admitted to hospitals is antimalarials or penicillin-based antibiotics, which have no activity against Coxiella burnetii. This study aimed to assess the seroprevalence and the predisposing risk factors for Q fever infection in febrile patients from a pastoralist population, and derive a model for clinical prediction of febrile patients with acute Q fever. METHODS: Epidemiological and clinical data were obtained from 1067 patients from Northeastern Kenya and their sera tested for IgG antibodies against Coxiella burnetii antigens by enzyme-linked-immunosorbent assay (ELISA), indirect immunofluorescence assay (IFA) and quantitative real-time PCR (qPCR). Logit models were built for risk factor analysis, and diagnostic prediction score generated and validated in two separate cohorts of patients. RESULTS: Overall 204 (19.1 %, 95 % CI: 16.8–21.6) sera were positive for IgG antibodies against phase I and/or phase II antigens or Coxiella burnetii IS1111 by qPCR. Acute Q fever was established in 173 (16.2 %, 95 % CI: 14.1–18.7) patients. Q fever was not suspected by the treating clinicians in any of those patients, instead working diagnosis was fever of unknown origin or common tropical fevers. Exposure to cattle (adjusted odds ratio [aOR]: 2.09, 95 % CI: 1.73–5.98), goats (aOR: 3.74, 95 % CI: 2.52–9.40), and animal slaughter (aOR: 1.78, 95 % CI: 1.09–2.91) were significant risk factors. Consumption of unpasteurized cattle milk (aOR: 2.49, 95 % CI: 1.48–4.21) and locally fermented milk products (aOR: 1.66, 95 % CI: 1.19–4.37) were dietary factors associated with seropositivity. Based on regression coefficients, we calculated a diagnostic score with a sensitivity 93.1 % and specificity 76.1 % at cut off value of 2.90: fever >14 days (+3.6), abdominal pain (+0.8), respiratory tract infection (+1.0) and diarrhoea (−1.1). CONCLUSION: Q fever is common in febrile Kenyan patients but underappreciated as a cause of community-acquired febrile illness. The utility of Q fever score and screening patients for the risky social-economic and dietary practices can provide a valuable tool to clinicians in identifying patients to strongly consider for detailed Q fever investigation and follow up on admission, and making therapeutic decisions. |
format | Online Article Text |
id | pubmed-4891891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48918912016-06-10 Febrile patients admitted to remote hospitals in Northeastern Kenya: seroprevalence, risk factors and a clinical prediction tool for Q-Fever Njeru, J. Henning, K. Pletz, M. W. Heller, R. Forstner, C. Kariuki, S. Fèvre, E. M. Neubauer, H. BMC Infect Dis Research Article BACKGROUND: Q fever in Kenya is poorly reported and its surveillance is highly neglected. Standard empiric treatment for febrile patients admitted to hospitals is antimalarials or penicillin-based antibiotics, which have no activity against Coxiella burnetii. This study aimed to assess the seroprevalence and the predisposing risk factors for Q fever infection in febrile patients from a pastoralist population, and derive a model for clinical prediction of febrile patients with acute Q fever. METHODS: Epidemiological and clinical data were obtained from 1067 patients from Northeastern Kenya and their sera tested for IgG antibodies against Coxiella burnetii antigens by enzyme-linked-immunosorbent assay (ELISA), indirect immunofluorescence assay (IFA) and quantitative real-time PCR (qPCR). Logit models were built for risk factor analysis, and diagnostic prediction score generated and validated in two separate cohorts of patients. RESULTS: Overall 204 (19.1 %, 95 % CI: 16.8–21.6) sera were positive for IgG antibodies against phase I and/or phase II antigens or Coxiella burnetii IS1111 by qPCR. Acute Q fever was established in 173 (16.2 %, 95 % CI: 14.1–18.7) patients. Q fever was not suspected by the treating clinicians in any of those patients, instead working diagnosis was fever of unknown origin or common tropical fevers. Exposure to cattle (adjusted odds ratio [aOR]: 2.09, 95 % CI: 1.73–5.98), goats (aOR: 3.74, 95 % CI: 2.52–9.40), and animal slaughter (aOR: 1.78, 95 % CI: 1.09–2.91) were significant risk factors. Consumption of unpasteurized cattle milk (aOR: 2.49, 95 % CI: 1.48–4.21) and locally fermented milk products (aOR: 1.66, 95 % CI: 1.19–4.37) were dietary factors associated with seropositivity. Based on regression coefficients, we calculated a diagnostic score with a sensitivity 93.1 % and specificity 76.1 % at cut off value of 2.90: fever >14 days (+3.6), abdominal pain (+0.8), respiratory tract infection (+1.0) and diarrhoea (−1.1). CONCLUSION: Q fever is common in febrile Kenyan patients but underappreciated as a cause of community-acquired febrile illness. The utility of Q fever score and screening patients for the risky social-economic and dietary practices can provide a valuable tool to clinicians in identifying patients to strongly consider for detailed Q fever investigation and follow up on admission, and making therapeutic decisions. BioMed Central 2016-06-03 /pmc/articles/PMC4891891/ /pubmed/27260261 http://dx.doi.org/10.1186/s12879-016-1569-0 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Njeru, J. Henning, K. Pletz, M. W. Heller, R. Forstner, C. Kariuki, S. Fèvre, E. M. Neubauer, H. Febrile patients admitted to remote hospitals in Northeastern Kenya: seroprevalence, risk factors and a clinical prediction tool for Q-Fever |
title | Febrile patients admitted to remote hospitals in Northeastern Kenya: seroprevalence, risk factors and a clinical prediction tool for Q-Fever |
title_full | Febrile patients admitted to remote hospitals in Northeastern Kenya: seroprevalence, risk factors and a clinical prediction tool for Q-Fever |
title_fullStr | Febrile patients admitted to remote hospitals in Northeastern Kenya: seroprevalence, risk factors and a clinical prediction tool for Q-Fever |
title_full_unstemmed | Febrile patients admitted to remote hospitals in Northeastern Kenya: seroprevalence, risk factors and a clinical prediction tool for Q-Fever |
title_short | Febrile patients admitted to remote hospitals in Northeastern Kenya: seroprevalence, risk factors and a clinical prediction tool for Q-Fever |
title_sort | febrile patients admitted to remote hospitals in northeastern kenya: seroprevalence, risk factors and a clinical prediction tool for q-fever |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4891891/ https://www.ncbi.nlm.nih.gov/pubmed/27260261 http://dx.doi.org/10.1186/s12879-016-1569-0 |
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