A proposal for the systematic assessment of data quality indicators in birth defects surveillance. Issue 6 (11th February 2019)

• Record Type: Journal Article
• Authors: Groisman, Boris; Mastroiacovo, Pierpaolo; Barbero, Pablo; Bidondo, María Paz; Liascovich, Rosa; Botto, Lorenzo D.
• Is Part Of: Birth defects research. Volume 111:Issue 6(2019)
• Page Start: 324
• Page End: 332
• Publication Date: 2019-02-11
• Subjects: birth defects -- data quality -- surveillance; Teratology -- Periodicals; Abnormalities, Human -- Periodicals; Congenital Abnormalities; Embryo, Mammalian -- abnormalities; Teratology; Abnormalities, Human; Teratology; Periodicals; Periodicals; 616.043
• DOI: 10.1002/bdr2.1474
• Languages: English
• ISSNs: 2472-1727

Abstract

Abstract : Background: Quality control and improvement are a key part of the implementation of an effective surveillance program. Data quality indicators (DQI) are key tools but have not been widely used, especially in a low‐resource setting. Methods: We developed a list of data quality indicators of birth defects surveillance. These DQI address ascertainment, description, coding, and classification. Results: We developed 40 DQI that can be used widely to assess the quality of data relative to birth defects of major clinical and public health impact. Conclusions: DQI have to be both comprehensive (e.g., assess all main surveillance processes) and practical (not require sophisticated or costly data elements), so that they can be used effectively in many different settings. We propose this list of DQI for use in surveillance program as a way to document the quality of the program; detect variations within and between programs, and support quality improvements.