Quality assurance of routine data (5/10)

4. How to measure good quality data and what to recommend for use by NIPN?

a) General remarks

Most routine data quality assessment tools differentiate amongst types of data quality, which are either called domains, dimensions or attributes:

  • The WHO DQR tool uses domains and metrics to assess routine data quality
  • The Measure Evaluation tool uses dimensions for what is called metric in WHO DQR tool
  • Chen et al., 2014 (1) named them attributes.

All these denominations designate more or less the same thing. Hong Chen et al. concluded in their review that completeness, accuracy, and timeliness were the three most-assessed dimensions of data quality, however they have identified more than 30 dimensions (see below list) .

Data quality dimensions
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(1) Chen, Hong & Hailey, David & Wang, Ning & Yu, Ping. (2014). A Review of Data Quality Assessment Methods for Public Health Information Systems. International journal of environmental research and public health. 11. 5170-207. 10.3390/ijerph110505170.