Birkegård Anna Camilla, Fertner Mette Ely, Jensen Vibeke Frøkjaer, Boklund Anette, Toft Nils, Halasa Tariq, Lopes Antunes Ana Carolina
National Veterinary Institute, Technical University of Denmark, Kongens Lyngby, Denmark.
Zoonoses Public Health. 2018 Dec;65(8):936-946. doi: 10.1111/zph.12513. Epub 2018 Aug 13.
Epidemiological studies often use data from registers. Data quality is of vital importance for the quality of the research. The aim of this study was to suggest a structured workflow to assess the quality of veterinary national registers. As an example of how to use the workflow, the quality of the following three registers was assessed: the Central Husbandry Register (CHR), the database for movement of pigs (DMP) and the national Danish register of drugs for veterinary use (VetStat). A systematic quantitative assessment was performed, with calculation the proportion of farms and observations with "poor quality" of data. "Poor" quality was defined for each measure (variable) either as a mismatch between and/or within registers, registrations of numbers outside the expected range, or unbalanced in- and outgoing movements. Interviews were conducted to make a complementary qualitative assessment. The proportion of farms and observations within each quality measure varied. This study highlights the importance of systematic quality assessment of register data and suggests a systematic approach for such assessments and validations without the use of primary data.
流行病学研究经常使用登记册中的数据。数据质量对于研究质量至关重要。本研究的目的是提出一种结构化工作流程，以评估兽医国家登记册的质量。作为使用该工作流程的示例，对以下三个登记册的质量进行了评估：中央畜牧登记册（CHR）、猪移动数据库（DMP）和丹麦国家兽用药品登记册（VetStat）。进行了系统的定量评估，计算了数据“质量差”的农场和观测值的比例。对于每个测量指标（变量），“质量差”被定义为登记册之间和/或内部的不匹配、超出预期范围的数字登记，或进出移动不平衡。进行访谈以进行补充性的定性评估。每个质量指标内农场和观测值的比例各不相同。本研究强调了对登记册数据进行系统质量评估的重要性，并提出了一种无需使用原始数据即可进行此类评估和验证的系统方法。
