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Integrating Facility-Based Surveillance With Healthcare Utilization Surveys to Estimate Enteric Fever Incidence: Methods and Challenges
Cohort studies and facility-based sentinel surveillance are common approaches to characterizing infectious disease burden, but present trade-offs; cohort studies are resource-intensive and may alter disease natural history, while sentinel surveillance underestimates incidence in the population. Hybr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6226762/ https://www.ncbi.nlm.nih.gov/pubmed/30184162 http://dx.doi.org/10.1093/infdis/jiy494 |
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author | Andrews, Jason R Barkume, Caitlin Yu, Alexander T Saha, Samir K Qamar, Farah N Garrett, Denise Luby, Stephen P |
author_facet | Andrews, Jason R Barkume, Caitlin Yu, Alexander T Saha, Samir K Qamar, Farah N Garrett, Denise Luby, Stephen P |
author_sort | Andrews, Jason R |
collection | PubMed |
description | Cohort studies and facility-based sentinel surveillance are common approaches to characterizing infectious disease burden, but present trade-offs; cohort studies are resource-intensive and may alter disease natural history, while sentinel surveillance underestimates incidence in the population. Hybrid surveillance, whereby facility-based surveillance is paired with a community-based healthcare utilization assessment, represents an alternative approach to generating population-based disease incidence estimates with moderate resource investments. Here, we discuss this method in the context of the Surveillance for Enteric Fever in Asia Project (SEAP) study. We describe how data are collected and utilized to adjust enteric fever incidence for blood culture sensitivity, facility-based enrollment, and healthcare seeking, incorporating uncertainty in these parameters in the uncertainty around incidence estimates. We illustrate how selection of surveillance sites and their coverage may influence precision and bias, and we identify approaches in the study design and analysis to minimize and control for these biases. Rigorously designed hybrid surveillance systems can be an efficient approach to generating population-based incidence estimates for infectious diseases. |
format | Online Article Text |
id | pubmed-6226762 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62267622018-11-15 Integrating Facility-Based Surveillance With Healthcare Utilization Surveys to Estimate Enteric Fever Incidence: Methods and Challenges Andrews, Jason R Barkume, Caitlin Yu, Alexander T Saha, Samir K Qamar, Farah N Garrett, Denise Luby, Stephen P J Infect Dis Supplement Articles Cohort studies and facility-based sentinel surveillance are common approaches to characterizing infectious disease burden, but present trade-offs; cohort studies are resource-intensive and may alter disease natural history, while sentinel surveillance underestimates incidence in the population. Hybrid surveillance, whereby facility-based surveillance is paired with a community-based healthcare utilization assessment, represents an alternative approach to generating population-based disease incidence estimates with moderate resource investments. Here, we discuss this method in the context of the Surveillance for Enteric Fever in Asia Project (SEAP) study. We describe how data are collected and utilized to adjust enteric fever incidence for blood culture sensitivity, facility-based enrollment, and healthcare seeking, incorporating uncertainty in these parameters in the uncertainty around incidence estimates. We illustrate how selection of surveillance sites and their coverage may influence precision and bias, and we identify approaches in the study design and analysis to minimize and control for these biases. Rigorously designed hybrid surveillance systems can be an efficient approach to generating population-based incidence estimates for infectious diseases. Oxford University Press 2018-12-01 2018-09-03 /pmc/articles/PMC6226762/ /pubmed/30184162 http://dx.doi.org/10.1093/infdis/jiy494 Text en © The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. http://creativecommons.org/licenses/by/4.0/ 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Supplement Articles Andrews, Jason R Barkume, Caitlin Yu, Alexander T Saha, Samir K Qamar, Farah N Garrett, Denise Luby, Stephen P Integrating Facility-Based Surveillance With Healthcare Utilization Surveys to Estimate Enteric Fever Incidence: Methods and Challenges |
title | Integrating Facility-Based Surveillance With Healthcare Utilization Surveys to Estimate Enteric Fever Incidence: Methods and Challenges |
title_full | Integrating Facility-Based Surveillance With Healthcare Utilization Surveys to Estimate Enteric Fever Incidence: Methods and Challenges |
title_fullStr | Integrating Facility-Based Surveillance With Healthcare Utilization Surveys to Estimate Enteric Fever Incidence: Methods and Challenges |
title_full_unstemmed | Integrating Facility-Based Surveillance With Healthcare Utilization Surveys to Estimate Enteric Fever Incidence: Methods and Challenges |
title_short | Integrating Facility-Based Surveillance With Healthcare Utilization Surveys to Estimate Enteric Fever Incidence: Methods and Challenges |
title_sort | integrating facility-based surveillance with healthcare utilization surveys to estimate enteric fever incidence: methods and challenges |
topic | Supplement Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6226762/ https://www.ncbi.nlm.nih.gov/pubmed/30184162 http://dx.doi.org/10.1093/infdis/jiy494 |
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