7.5 Key Characteristics of Data Quality in Public Health Surveillance

Once a programme has defined its SMART goals, developed the associated data variables so that these are adequately structured and relevant to its goals, and planned its processes and documentation (e.g. a SOP manual), how does such a programme begin to assess data quality? Three basic characteristics of high-quality data in public health surveillance are completeness, accuracy, and timeliness – summarized as the acronym CAT (see Fig. 7.3).

Data are complete when all cases are included (no cases are missed), and all data variables for cases are entered. Assessing the first criterion – whether all cases are included – can be challenging and might require additional information, resources and investigation. However, it is a crucial issue, as it speaks directly to the sensitivity of the surveillance programme to detect true cases. The use of selected data quality indicators for ascertainment, for example, can be helpful to assess this component and will be discussed further below.

Data are accurate when the information entered reflects the truth (e.g. a case of spina bifida is indeed a case of spina bifida as defined by a programme’s operational procedure manual, or when coding and classification are correct). Training and evaluation, including the use of data quality indicators for description, coding and classification, can help assess and improve this component of data quality.

Data are timely when they are available and disseminated at the time the programme needs them. Timeliness is particularly important in public health surveillance because of the focus on ongoing tracking of health events. Timeliness might be defined differently by programmes and even within a programme. For example, a programme might designate spina bifida for rapid ascertainment at the time of folic acid fortification, or microcephaly at the time of a potential Zika epidemic. Regardless, timeliness must be defined at the outset so it can be assessed and tracked as a key quality indicator. In the context of quality assessment and improvement, timeliness is best assessed not only for the overall surveillance system – for example, time from case detection to data analysis and reporting – but also for the individual processes that make up the system. This approach helps identify areas of “waste” (e.g. data sitting for long time waiting for the next phase) and improvement.

Having qualitatively described some elements of data quality in surveillance, the next step is developing a quantitative system of measurement to track them. This means assessing the current status of data quality and whether it is improving, stable, or getting worse. Objective data are easier to measure and track, and often less prone to noise (because definitions are more reproducible) than subjective or difficult-to-measure data. For this reason, data quality indicators are a powerful tool for surveillance programmes. However, to better understand the function and role of data quality indicators, it is crucial to understand the processes of the specific surveillance system. The reason is that the quality of data and information is a direct consequence of the quality of the processes that generate them.