underrecording

Underrecording is the systematic failure to record or document all relevant data elements or events in a dataset, registry, or information system. It leads to incomplete data that can bias analyses, obscure true patterns, and reduce the usefulness of the information collected. Some sources distinguish underrecording from underreporting by focusing on data capture within existing systems rather than on whether an event was reported at all, but both concepts contribute to data quality problems.

Common domains where underrecording is discussed include health care and epidemiology (such as incomplete medical histories or vaccination data), crime and public safety (undercounted incidents), environmental monitoring (sensor gaps or missing measurements), and administrative data (inaccurate or incomplete civil records). Causes are varied and can include limited resources, insufficient or incompatible data standards, human error in data entry, failures in automated systems, privacy constraints, and fragmented data integration across sources.

Mitigation strategies emphasize improving data quality and completeness. These include standardized data collection protocols, mandatory fields, automated validation checks, regular audits and reconciliations, and efforts to link data across sources. Training for staff and transparent reporting of data completeness are also important. In research settings, analytical approaches such as data imputation and sensitivity analyses can help assess the potential impact of missing data, though they do not replace the need for better data capture.