TY - JOUR

T1 - Building the foundation for veterinary register-based epidemiology

T2 - A systematic approach to data quality assessment and validation

AU - Birkegård, Anna Camilla

AU - Fertner, Mette Ely

AU - Jensen, Vibeke Frøkjær

AU - Boklund, Anette

AU - Toft, Nils

AU - Halasa, Tariq

AU - Lopes Antunes, Ana Carolina

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - pigs

KW - quality assessment

KW - register data

KW - swine

U2 - 10.1111/zph.12513

DO - 10.1111/zph.12513

M3 - Journal article

C2 - 30105809

AN - SCOPUS:85052458204

JO - Zoonoses and Public Health

JF - Zoonoses and Public Health

SN - 1863-1959

ER -