Using Passive Surveillance to Maintain Elimination as a Public Health Problem for Neglected Tropical Diseases: A Model-Based Exploration.


Journal

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
ISSN: 1537-6591
Titre abrégé: Clin Infect Dis
Pays: United States
ID NLM: 9203213

Informations de publication

Date de publication:
25 Apr 2024
Historique:
medline: 25 4 2024
pubmed: 25 4 2024
entrez: 25 4 2024
Statut: ppublish

Résumé

Great progress is being made toward the goal of elimination as a public health problem for neglected tropical diseases such as leprosy, human African trypanosomiasis, Buruli ulcer, and visceral leishmaniasis, which relies on intensified disease management and case finding. However, strategies for maintaining this goal are still under discussion. Passive surveillance is a core pillar of a long-term, sustainable surveillance program. We use a generic model of disease transmission with slow epidemic growth rates and cases detected through severe symptoms and passive detection to evaluate under what circumstances passive detection alone can keep transmission under control. Reducing the period of infectiousness due to decreasing time to treatment has a small effect on reducing transmission. Therefore, to prevent resurgence, passive surveillance needs to be very efficient. For some diseases, the treatment time and level of passive detection needed to prevent resurgence is unlikely to be obtainable. The success of a passive surveillance program crucially depends on what proportion of cases are detected, how much of their infectious period is reduced, and the underlying reproduction number of the disease. Modeling suggests that relying on passive detection alone is unlikely to be enough to maintain elimination goals.

Sections du résumé

BACKGROUND BACKGROUND
Great progress is being made toward the goal of elimination as a public health problem for neglected tropical diseases such as leprosy, human African trypanosomiasis, Buruli ulcer, and visceral leishmaniasis, which relies on intensified disease management and case finding. However, strategies for maintaining this goal are still under discussion. Passive surveillance is a core pillar of a long-term, sustainable surveillance program.
METHODS METHODS
We use a generic model of disease transmission with slow epidemic growth rates and cases detected through severe symptoms and passive detection to evaluate under what circumstances passive detection alone can keep transmission under control.
RESULTS RESULTS
Reducing the period of infectiousness due to decreasing time to treatment has a small effect on reducing transmission. Therefore, to prevent resurgence, passive surveillance needs to be very efficient. For some diseases, the treatment time and level of passive detection needed to prevent resurgence is unlikely to be obtainable.
CONCLUSIONS CONCLUSIONS
The success of a passive surveillance program crucially depends on what proportion of cases are detected, how much of their infectious period is reduced, and the underlying reproduction number of the disease. Modeling suggests that relying on passive detection alone is unlikely to be enough to maintain elimination goals.

Identifiants

pubmed: 38662695
pii: 7657833
doi: 10.1093/cid/ciae097
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

S169-S174

Subventions

Organisme : Bill & Melinda Gates Foundation
Pays : United States
Organisme : Bill & Melinda Gates Foundation
ID : INV-030046
Pays : United States

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.

Déclaration de conflit d'intérêts

Potential conflicts of interest. A. M. reports consulting fees from the University of Oxford to their business, Equations of Disease C.I.C., and support for travel from the University of Oxford as part of their consultancy contract. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Auteurs

Amanda Minter (A)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford.

Graham F Medley (GF)

Department of Global Health and Development, London School of Hygiene and Tropical Medicine, United Kingdom.

T Déirdre Hollingsworth (TD)

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford.

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