Underascertainment

Underascertainment is the shortfall between the true number of cases, events, or conditions and the number detected or recorded by surveillance systems, surveys, or reporting mechanisms. It occurs when a portion of cases remains unobserved due to limitations in testing, reporting, or data collection. Because surveillance data reflect only a fraction of true burden, underascertainment can skew estimates of incidence and prevalence and influence public health decisions. The complement of underascertainment is the ascertainment or detection rate, which varies by setting and over time.

Common causes include restricted testing capacity and access, mild or asymptomatic infections that do not prompt care, evolving or narrow case definitions, reporting delays, and fragmented data sources. Social and geographic disparities, stigma, and language barriers can further reduce ascertainment, as can changes in surveillance practices or seasonality in data collection. Underascertainment is therefore often heterogeneous across populations and over time.

Measurement and estimation approaches aim to correct observed data to reflect the true burden. Seroprevalence surveys can reveal cumulative infections beyond reported cases. Capture-recapture methods use overlaps among multiple data sources to estimate hidden cases. Multiplier methods, back-calculation from downstream outcomes such as hospitalizations, and statistical or Bayesian models incorporate an ascertainment parameter. Data linkage and sentinel surveillance can improve detection and support more robust corrections.

Implications include biased estimates of incidence and prevalence, distorted understanding of transmission dynamics, and potential misallocation of resources or misjudgment of intervention effects. Underascertainment is particularly pronounced during emerging infections or in settings with weak health systems, where true case counts substantially exceed reported figures.

Mitigation involves expanding testing and care access, integrating diverse data sources, rapid reporting, and periodic serosurveys. Clear reporting of ascertainment assumptions and uncertainty, along with cross-method validation, enhances the reliability of burden estimates.