7.2 Data that are “Fit for Use”

A broad definition of high-quality data is data that are fit for use. Once operationalized, this apparently simple definition has profound implications for a surveillance programme.

“Fit for use” speaks to the need to understand in detail the goals (“use”) of the programme and to assess the data and data systems created. The best programmes embed quality by design into the surveillance process. In practice, the programme should have explicit and detailed information about what it is seeking as the end product of surveillance, and then work back to design the data acquisition and processing that can support the programme’s goals.

The first crucial step is ensuring that everyone – from the programme director to the front-line staff – is clear as to what information the programme wants to assess, track, and disseminate – and why. Even if such goals are flexible and might change with time, making them explicit and measurable is critical. As an example, for a programme that includes NTDs as an eligible condition, what type of data are “fit for use”?

Clearly, the data needed to adequately describe a child with spina bifida will vary to some extent depending on whether these data are for the surgeon, for the clinical follow-up programme, or for public health surveillance. For public health surveillance, the collected data will vary depending on the goals of the programme: is it tracking prevalence among live births (or some other birth outcome) or does it also include tracking linkage to services and health outcomes? For example, meaningful tracking of health outcomes to determine severity and potential complications will require details such as size, location, skin covering and associated findings/sequences such as hydrocephalus. Likewise, if a surveillance programme aims to also track risk factors, then details on exposures and supplement uses will be important.

Reflecting on “fit for use” also helps a programme develop not only explicit data variables and related quality parameters, but also to design efficient data structures and databases. Having clear and detailed goals for what a programme wants to track, report and communicate will help optimize the way the data are organized so that the information is not only easy to input but also extracted and analysed efficiently.