Carl Gibbons|Marie?Josée J. Mangen|Dietrich Plaß|Arie H. Havelaar|Russell J. Brooke|Piotr Kramarz|Karen L. Peterson|Anke L. Stuurman|Alessandro Cassini|Eric M. Fèvre|Mirjam Kretzschmar
Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods
Centre for Immunity, Infection and Evolution|University of Edinburgh|University Medical Center Utrecht|Bielefeld University|National Institute for Public Health and the Environment|University Medical Center Utrecht|European Centre for Disease Prevention and Control|Centre for Immunity, Infection and Evolution|University of Edinburgh|National Institute for Public Health and the Environment|European Centre for Disease Prevention and Control|International Livestock Research Institute|National Institute for Public Health and the Environment
efficient reliable surveillance notification systems vital monitoring public health disease outbreaks however surveillance notification systems affected degree underestimation ue therefore uncertainty surrounds true incidence disease affecting morbidity mortality rates surveillance systems fail capture cases two distinct levels surveillance pyramid community since cases seek healthcare underascertainment healthcarelevel representing failure adequately report symptomatic cases sought medical advice underreporting several methods estimate extent underascertainment underreporting within context ecdcfunded burden communicable diseases europe bcodeproject extensive literature review conducted identify studies estimate ascertainment reporting rates salmonellosis campylobacteriosis european union member states ms plus european free trade area efta countries iceland norway switzerland four oecd countries usa canada australia japan multiplication factors mfs measure magnitude underestimation taken directly literature derived proportion underestimated underascertained underreported cases known compared two pathogens mfs varied within diseases countries representing need carefully select appropriate mfs methods calculating appropriate mfs often disease country age sexspecific routine data used make decisions resource allocation estimate epidemiological parameters populations becomes important understand extent data represent true picture disease instances priority setting necessary adjust underestimation mfs used adjust notification surveillance data provide realistic estimates incidence