» Articles » PMID: 28916566

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

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.

Citing Articles

NUQUEST-NUtrition QUality Evaluation Strengthening Tools: development of tools for the evaluation of risk of bias in nutrition studies.

Kelly S, Greene-Finestone L, Yetley E, Benkhedda K, Brooks S, Wells G Am J Clin Nutr. 2021; 115(1):256-271.

PMID: 34605544 PMC: 8755056. DOI: 10.1093/ajcn/nqab335.


Colonization Ability and Impact on Human Gut Microbiota of Foodborne Microbes From Traditional or Probiotic-Added Fermented Foods: A Systematic Review.

Roselli M, Natella F, Zinno P, Guantario B, Canali R, Schifano E Front Nutr. 2021; 8:689084.

PMID: 34395494 PMC: 8360115. DOI: 10.3389/fnut.2021.689084.


An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content.

Yang C, Ambayo H, De Baets B, Kolsteren P, Thanintorn N, Hawwash D Nutrients. 2019; 11(6).

PMID: 31181762 PMC: 6628051. DOI: 10.3390/nu11061300.


Perspective: Limiting Dependence on Nonrandomized Studies and Improving Randomized Trials in Human Nutrition Research: Why and How.

Trepanowski J, Ioannidis J Adv Nutr. 2018; 9(4):367-377.

PMID: 30032218 PMC: 6054237. DOI: 10.1093/advances/nmy014.


ONS: an ontology for a standardized description of interventions and observational studies in nutrition.

Vitali F, Lombardo R, Rivero D, Mattivi F, Franceschi P, Bordoni A Genes Nutr. 2018; 13:12.

PMID: 29736190 PMC: 5928560. DOI: 10.1186/s12263-018-0601-y.

References
1.
Sansone S, Rocca-Serra P, Field D, Maguire E, Taylor C, Hofmann O . Toward interoperable bioscience data. Nat Genet. 2012; 44(2):121-6. PMC: 3428019. DOI: 10.1038/ng.1054. View

2.
Heller R, Verma A, Gemmell I, Harrison R, Hart J, Edwards R . Critical appraisal for public health: a new checklist. Public Health. 2007; 122(1):92-8. DOI: 10.1016/j.puhe.2007.04.012. View

3.
Genaidy A, LeMasters G, Lockey J, Succop P, Deddens J, Sobeih T . An epidemiological appraisal instrument - a tool for evaluation of epidemiological studies. Ergonomics. 2007; 50(6):920-60. DOI: 10.1080/00140130701237667. View

4.
Al-Jader L, Newcombe R, Hayes S, Murray A, Layzell J, Harper P . Developing a quality scoring system for epidemiological surveys of genetic disorders. Clin Genet. 2002; 62(3):230-4. DOI: 10.1034/j.1399-0004.2002.620308.x. View

5.
Hoy D, Brooks P, Woolf A, Blyth F, March L, Bain C . Assessing risk of bias in prevalence studies: modification of an existing tool and evidence of interrater agreement. J Clin Epidemiol. 2012; 65(9):934-9. DOI: 10.1016/j.jclinepi.2011.11.014. View