title: Methods for assessing the quality of data in public health information systems: a critical review.
abstract: The quality of data in public health information systems can be ensured by effective data quality assessment. In order to conduct effective data quality assessment, measurable data attributes have to be precisely defined. Then reliable and valid measurement methods for data attributes have to be used to measure each attribute. We conducted a systematic review of data quality assessment methods for public health using major databases and well-known institutional websites. 35 studies were eligible for inclusion in the study. A total of 49 attributes of data quality were identified from the literature. Completeness, accuracy and timeliness were the three most frequently assessed attributes of data quality. Most studies directly examined data values. This is complemented by exploring either data users' perception or documentation quality. However, there are limitations of current data quality assessment methods: a lack of consensus on attributes measured; inconsistent definition of the data quality attributes; a lack of mixed methods for assessing data quality; and inadequate attention to reliability and validity. Removal of these limitations is an opportunity for further improvement.
authors.affiliation: School of Information Systems and Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Australia.
authors.forename: Hong
authors.initials: H
authors.lastname: Chen
authors.affiliation: School of Information Systems and Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Australia.
authors.forename: Ping
authors.initials: P
authors.lastname: Yu
authors.affiliation: School of Information Systems and Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Hailey
authors.affiliation: NCAIDS, China CDC, Beijing, China.
authors.forename: Ning
authors.initials: N
authors.lastname: Wang
mesh_terms: D057225:Data Mining / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D016208:Databases, Factual / Q000592:standards*; D019983:Guideline Adherence / Q000706:statistics & numerical data; D017408:Guidelines as Topic; D063005:Health Information Systems / Q000592:standards* / Q000706:statistics & numerical data*; D038622:Internationality; D040442:Public Health Informatics / Q000592:standards; D011785:Quality Assurance, Health Care / Q000379:methods* / Q000592:standards; D012107:Research Design / Q000592:standards* / Q000706:statistics & numerical data*