Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods

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.