| Title: |
A Nationwide Evaluation of the Prevalence of Human Papillomavirus in Brazil (POP-Brazil Study): Protocol for Data Quality Assurance and Control. |
| Authors: |
Correia Horvath, Jaqueline Driemeyer, Bessel, Marina, Kops, Natália Luiza, Alves Souza, Flávia Moreno, Pereira, Gerson Mendes, Wendland, Eliana Marcia |
| Source: |
JMIR Research Protocols; 2022, Vol. 11 Issue 1, p1-9, 9p |
| Subject Terms: |
PAPILLOMAVIRUSES, DATA quality, QUALITY assurance, QUALITY control |
| Geographic Terms: |
BRAZIL |
| Abstract: |
Background: The credibility of a study and its internal and external validity depend crucially on the quality of the data produced. An in-depth knowledge of quality control processes is essential as large and integrative epidemiological studies are increasingly prioritized. Objective: This study aimed to describe the stages of quality control in the POP-Brazil study and to present an analysis of the quality indicators. Methods: Quality assurance and control were initiated with the planning of this nationwide, multicentric study and continued through the development of the project. All quality control protocol strategies, such as training, protocol implementation, audits, and inspection, were discussed one by one. We highlight the importance of conducting a pilot study that provides the researcher the opportunity to refine or modify the research methodology and validating the results through double data entry, test-retest, and analysis of nonresponse rates. Results: This cross-sectional, nationwide, multicentric study recruited 8628 sexually active young adults (16-25 years old) in 119 public health units between September 2016 and November 2017. The Human Research Ethics Committee of the Moinhos de Vento Hospital approved this project. Conclusions: Quality control processes are a continuum, not restricted to a single event, and are fundamental to the success of data integrity and the minimization of bias in epidemiological studies. The quality control steps described can be used as a guide to implement evidence-based, valid, reliable, and useful procedures in most observational studies to ensure data integrity. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
