Assessing the quality of risk factor survey data: lessons from the WHO MONICA Project

Tolonen, Hanna, Dobson, Annette and Kulathinal, Sangita (2006) Assessing the quality of risk factor survey data: lessons from the WHO MONICA Project. European Journal of Cardiovascular Prevention and Rehabilitation, 13 1: 104-114. doi:10.1097/00149831-200602000-00017


Author Tolonen, Hanna
Dobson, Annette
Kulathinal, Sangita
Title Assessing the quality of risk factor survey data: lessons from the WHO MONICA Project
Journal name European Journal of Cardiovascular Prevention and Rehabilitation   Check publisher's open access policy
ISSN 1741-8267
Publication date 2006-02
Sub-type Article (original research)
DOI 10.1097/00149831-200602000-00017
Volume 13
Issue 1
Start page 104
End page 114
Total pages 11
Place of publication London
Publisher Lippincott Williams & Wilkins
Collection year 2006
Language eng
Subject C1
321203 Health Information Systems (incl. Surveillance)
730217 Health status (e.g. indicators of well-being)
1117 Public Health and Health Services
Abstract Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.
Keyword Cardiac & Cardiovascular Systems
Data Quality
Epidemiology
Reporting
Response Rate
Event Rates
Population
Respondents
Nonrespondents
Registers
Accuracy
Trends
Lists
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 10:04:35 EST