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Free Content Quality assurance of data: ensuring that numbers reflect operational definitions and contain real measurements [State of the art series. Operational research. Number 3 in the series]

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Any analysis is only as convincing as the quality of the underlying data. In this article, the role of data quality is exemplified by its impact on the interpretation of surveillance data, by operations research projects conducted in the training courses of the International Union Against Tuberculosis and Lung Disease, and the lessons learnt through them. It provides information why double-entry and validation of data are part of ‘good clinical practice’. It is suggested how the efficiency of data entry can be maximized to reduce data entry time and data entry errors, so that psychological and physical barriers to double-entry are reduced.

Keywords: data quality; good clinical practice; research

Document Type: Invited Paper

Affiliations: 1: Tuberculosis Department, International Union Against Tuberculosis and Lung Disease, Paris, France; and Institute of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland 2: Institute of Public Health, Biostatistics Unit, University of Southern Denmark, Odense, Denmark; and EpiData Association, Odense, Denmark

Publication date: 2011-03-01

More about this publication?
  • The International Journal of Tuberculosis and Lung Disease publishes articles on all aspects of lung health, including public health-related issues such as training programmes, cost-benefit analysis, legislation, epidemiology, intervention studies and health systems research. The IJTLD is dedicated to the continuing education of physicians and health personnel and the dissemination of information on tuberculosis and lung health world-wide.

    Certain IJTLD articles are selected for translation into French, Spanish, Chinese or Russian. They are available on the Union website

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