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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Measuring underreporting and under-ascertainment in infectious disease datasets: A comparison of methods
BMC Public Health, Volume 14, No. 1, Article 147, Year 2014
Notification
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Description
Background: Efficient and reliable surveillance and notification systems are vital for monitoring public health and disease outbreaks. However, most surveillance and notification systems are affected by a degree of underestimation (UE) and therefore uncertainty surrounds the 'true' incidence of disease affecting morbidity and mortality rates. Surveillance systems fail to capture cases at two distinct levels of the surveillance pyramid: from the community since not all cases seek healthcare (under-ascertainment), and at the healthcare-level, representing a failure to adequately report symptomatic cases that have sought medical advice (underreporting). There are several methods to estimate the extent of under-ascertainment and underreporting. Methods. Within the context of the ECDC-funded Burden of Communicable Diseases in Europe (BCoDE)-project, an extensive literature review was conducted to identify studies that estimate ascertainment or reporting rates for salmonellosis and campylobacteriosis in European Union Member States (MS) plus European Free Trade Area (EFTA) countries Iceland, Norway and Switzerland and four other OECD countries (USA, Canada, Australia and Japan). Multiplication factors (MFs), a measure of the magnitude of underestimation, were taken directly from the literature or derived (where the proportion of underestimated, under-ascertained, or underreported cases was known) and compared for the two pathogens. Results: MFs varied between and within diseases and countries, representing a need to carefully select the most appropriate MFs and methods for calculating them. The most appropriate MFs are often disease-, country-, age-, and sex-specific. Conclusions: When routine data are used to make decisions on resource allocation or to estimate epidemiological parameters in populations, it becomes important to understand when, where and to what extent these data represent the true picture of disease, and in some instances (such as priority setting) it is necessary to adjust for underestimation. MFs can be used to adjust notification and surveillance data to provide more realistic estimates of incidence. © 2014 Gibbons et al.; licensee BioMed Central Ltd.
Authors & Co-Authors
Gibbons, Cheryl L.
United Kingdom, Edinburgh
The University of Edinburgh
Mangen, Marie Josee
Netherlands, Utrecht
University Medical Center Utrecht
Plass, Dietrich
Germany, Bielefeld
Universität Bielefeld
Havelaar, Arie H.
Netherlands, Bilthoven
Rijksinstituut Voor Volksgezondheid en Milieu
Netherlands, Utrecht
Institute for Risk Assessment Sciences
Brooke, Russell John
Netherlands, Utrecht
University Medical Center Utrecht
Kramarz, Piotr
Sweden, Solna
European Centre for Disease Prevention and Control
Peterson, Karen L.
United Kingdom, Edinburgh
The University of Edinburgh
Stuurman, Anke L.
Netherlands, Bilthoven
Rijksinstituut Voor Volksgezondheid en Milieu
Netherlands, Rotterdam
Health Research and Consultancy bv
Cassini, Alessandro
Sweden, Solna
European Centre for Disease Prevention and Control
Fèvre, Eric Maurice
Kenya, Nairobi
International Livestock Research Institute Nairobi
United Kingdom, Liverpool
University of Liverpool
Kretzschmar, Mirjam E.
Netherlands, Utrecht
University Medical Center Utrecht
Netherlands, Bilthoven
Rijksinstituut Voor Volksgezondheid en Milieu
Statistics
Citations: 284
Authors: 11
Affiliations: 9
Identifiers
Doi:
10.1186/1471-2458-14-147
e-ISSN:
14712458
Research Areas
Health System And Policy
Study Design
Cohort Study
Study Approach
Systematic review