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Objective <p>The EQ-DAPHNIE (EuroQol Data for Assessment of Population Health Needs and Instrument Evaluation) project is a large, multi-country survey initiative designed to generate population norms and enable comparative research using self-reported health measures. This paper describes the quality control processes and summarizes data quality metrics from the United Kingdom (UK) pilot and full implementation across 15 countries.</p> Methods <p>Representative samples were recruited via Dynata, an online survey panel provider, using quota sampling by age, sex, income, community setting, and language (where applicable). The UK pilot (n = 3012) informed survey refinements ahead of full rollout (n = 68,411). Quality metrics included completion rates, bot detection, speeding, missing data, outliers, and quota achievement.</p> Results <p>Across countries, response rates ranged from 80.1 to 100%, with completion rates varying widely (22.9% in Brazil to 60.8% in Japan; average 42.4%). Bot exclusions averaged 3.0%, peaking in China (11.7%). Speeding was low (0.3% average), and duplicate records were rare. Completion times ranged from 18.3 (France) to 31.4&#xa0;min (New Zealand). Missing data varied substantially (0.0–48.7%), with Japan and Spain showing the least. Quota fulfillment ranged from 68.7 to 98.6%. Consistency checks showed strong agreement for repeated items—marital status (92.8–98.9%) and age (92.3–98.7%).</p> Conclusions <p>The quality control measures implemented throughout the EQ-DAPHNIE project effectively addressed common issues such as bot responses, speeding, and missing data, resulting in generally high-quality and representative datasets. However, variability across countries underscores the need to account for quality indicators when using the data for norm-setting or cross-country comparisons.</p>

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Design and implementation of data quality controls in the EQ-DAPHNIE study: insights from the pilot phase and 15-country analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Fatima Al Sayah,
  • Hilary Short,
  • Juan M. Ramos-Goñi,
  • Rosalie Viney,
  • Erica I. Lubetkin,
  • Mathieu F. Janssen,
  • Jeffrey A. Johnson,
  • Jeffrey Johnson,
  • Mathieu F. Janssen,
  • Fatima Al Sayah,
  • Henry Bailey,
  • Mihir Ghandi,
  • Dominik Golicki,
  • Nils Gutacker,
  • Brenden Mulhern,
  • Fredrick Purba,
  • Des Scott,
  • Trudy Sullivan,
  • Zhihao Yang,
  • Victor Zarate,
  • Hilary Short

摘要

Objective

The EQ-DAPHNIE (EuroQol Data for Assessment of Population Health Needs and Instrument Evaluation) project is a large, multi-country survey initiative designed to generate population norms and enable comparative research using self-reported health measures. This paper describes the quality control processes and summarizes data quality metrics from the United Kingdom (UK) pilot and full implementation across 15 countries.

Methods

Representative samples were recruited via Dynata, an online survey panel provider, using quota sampling by age, sex, income, community setting, and language (where applicable). The UK pilot (n = 3012) informed survey refinements ahead of full rollout (n = 68,411). Quality metrics included completion rates, bot detection, speeding, missing data, outliers, and quota achievement.

Results

Across countries, response rates ranged from 80.1 to 100%, with completion rates varying widely (22.9% in Brazil to 60.8% in Japan; average 42.4%). Bot exclusions averaged 3.0%, peaking in China (11.7%). Speeding was low (0.3% average), and duplicate records were rare. Completion times ranged from 18.3 (France) to 31.4 min (New Zealand). Missing data varied substantially (0.0–48.7%), with Japan and Spain showing the least. Quota fulfillment ranged from 68.7 to 98.6%. Consistency checks showed strong agreement for repeated items—marital status (92.8–98.9%) and age (92.3–98.7%).

Conclusions

The quality control measures implemented throughout the EQ-DAPHNIE project effectively addressed common issues such as bot responses, speeding, and missing data, resulting in generally high-quality and representative datasets. However, variability across countries underscores the need to account for quality indicators when using the data for norm-setting or cross-country comparisons.