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Adv Nutr. 2017 Sep 15;8(5):639-651. doi: 10.3945/an.117.015651. Print 2017 Sep.

Perspective: Essential Study Quality Descriptors for Data from Nutritional Epidemiologic Research.

Author information

  • 1Departments of Food Safety and Food Quality.
  • 2Molecular Epidemiology Research Group, Max Delbrück Centre for Molecular Medicine, Berlin, Germany.
  • 3Charité - Berlin University of Medicine, Berlin, Germany.
  • 4Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany.
  • 5German Centre for Cardiovascular Research, partner site, Berlin, Germany.
  • 6Vitamin Research Group, Trinity College Dublin, Dublin, Ireland.
  • 7Food and Nutrition Research Centre, Rome, Italy.
  • 8Department of Public Health, University of Liège, Liège, Belgium.
  • 9Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
  • 10Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznan, Poland.
  • 11Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg, Denmark.
  • 12Netherlands Organisation for Applied Scientific Research, Zeist, Netherlands.
  • 13Vrije Universiteit Brussels, Brussels, Belgium.
  • 14Department of Soil, Plant and Food Science, University of Bari Aldo Moro, Bari, Italy.
  • 15Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy.
  • 16Department of Public Health and Surveillance, Scientific Institute of Public Health, Brussels, Belgium.
  • 17Department of Computer Languages and Systems, University Jaume I, Castellón, Spain.
  • 18Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain.
  • 19Biomedical Research Centre in Physiopathology of Obesity and Nutrition, Institute of Health Carlos III, Madrid, Spain.
  • 20Flanders research institute for agriculture, fisheries and food, Technology and Food Science Unit, Food Safety and Product Innovation, Melle, Belgium.
  • 21School of Food and Nutritional Sciences, University College Cork, Cork, Ireland.
  • 22KU Leuven, Clinical and Experimental Endocrinology and University Hospitals Leuven/KU Leuven, Department of Endocrinology, Campus Gasthuisberg, Leuven, Belgium.
  • 23Mathematical Modelling, Statistics and Bioinformatic.
  • 24Telecommunications and Information Processing, and.
  • 25Department of Clinical Medicine and Surgery, School of Medicine, University Federico II, Naples, Italy.
  • 26Institute of Food Sciences of National Research Council, Avellino, Italy.
  • 27The Microsoft Research, University of Trento Centre for Computational and Systems Biology, Trento, Italy.
  • 28Department of Biochemistry, Ghent University, Faculty of Medicine and Health Sciences, Ghent, Belgium; and.
  • 29Biochemistry, Ghent University, Ghent, Belgium.
  • 30VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.
  • 31Departments of Food Safety and Food Quality, carl.lachat@ugent.be.

Abstract

Pooled analysis of secondary data increases the power of research and enables scientific discovery in nutritional epidemiology. Information on study characteristics that determine data quality is needed to enable correct reuse and interpretation of data. This study aims to define essential quality characteristics for data from observational studies in nutrition. First, a literature review was performed to get an insight on existing instruments that assess the quality of cohort, case-control, and cross-sectional studies and dietary measurement. Second, 2 face-to-face workshops were organized to determine the study characteristics that affect data quality. Third, consensus on the data descriptors and controlled vocabulary was obtained. From 4884 papers retrieved, 26 relevant instruments, containing 164 characteristics for study design and 93 characteristics for measurements, were selected. The workshop and consensus process resulted in 10 descriptors allocated to "study design" and 22 to "measurement" domains. Data descriptors were organized as an ordinal scale of items to facilitate the identification, storage, and querying of nutrition data. Further integration of an Ontology for Nutrition Studies will facilitate interoperability of data repositories.

KEYWORDS:

data interoperability; data quality; dietary assessment; nutritional epidemiology; observational study

PMID:
28916566
PMCID:
PMC5593109
[Available on 2018-09-01]
DOI:
10.3945/an.117.015651
[PubMed - in process]
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