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Table 3 Quality assurance strategies

From: Latin American Study of Nutrition and Health (ELANS): rationale and study design

Levels of quality control Phases
Design and planning Pilot testing Data collection Data analysis
Coordinating center •Critical review of protocols
•Harmonization of manual of operations for eight study sites
•Coordination of timelines and activities
•Smoothness and feasibility of field operations assessed •Monitoring field activities •Audit and evaluate validity of findings prior to publication
•Internal peer reviews prior to publication
Principal Investigators •Review of design and planning of the study
•Regular meetings with coordinating center
•Audit after completion of the pilot •Supervising and ensuring accuracy of data entry •Validity checks
•Results review
Field Personnel •Extensive training over a period of 7 to 10 days-theory and practical-by the study managers •Evaluated all field and documenting difficulties •Field coordinator will assure that procedures for data collections and quality control are followed
•Additional training when necessary
Survey Questionnaires •Peer-reviewed
•Translated to local languages
•Consistency in small pilot study will be established •Regular checks done to assess completeness •Incomplete questionnaires identified and discarded
Measuring Equipment •Standardization of equipment and measurements
•Acquired by each country
•Development of anthropometric procedures manual
•Evaluation of calibration techniques, acceptability of use in field •Regular calibration of equipment; faulty equipment replaced when required  
Documentation •Assurance of standardized procedures across the sites
•Training in appropriate and legible documentation
•Recording legibility assessed •Audit recordings  
Data Storage & Confidentiality •Data back-up and protection policies established •Accessibility of software assessed •Identify inconsistencies
•Corrective actions
•Locked and password protected data storage
•Active back-up
•Datasets identified
•Access to personal identifiers limited
Data Entry •Training of staff
•Protocols, consistent data cleaning methods and verification systems established
•Variability assessments conducted •Interim analyses to identify duplicate entries •Reporting of outliers
•Validity checks
•Database errors tracked