Response rates in postal surveys of healthcare professionals between 1996 and 2005: an observational study

BMC Health Serv Res. 2009 Sep 14:9:160. doi: 10.1186/1472-6963-9-160.


Background: Postal surveys are a frequently used method of data collection in health services research. Low response rates increase the potential for bias and threaten study validity. The objectives of this study were to estimate current response rates, to assess whether response rates are falling, to explore factors that might enhance response rates and to examine the potential for non-response bias in surveys mailed to healthcare professionals.

Methods: A random sample of postal or electronic surveys of healthcare workers (1996-2005) was identified from Medline, Embase or Psycinfo databases or Biomed Central. Outcome measures were survey response rate and non response analysis. Multilevel, multivariable logistic regression examined the relationship between response rate and publication type, healthcare profession, country and number of survey participants, questionnaire length and use of reminders.

Results: The analysis included 350 studies. Average response rate in doctors was 57.5% (95%CI: 55.2% to 59.8%) and significantly lower than the estimate for the prior 10 year period. Response rates were higher when reminders were sent (adjusted OR 1.3; 95%CI 1.1-1.6) but only half the studies did this. Response rates were also higher in studies with fewer than 1000 participants and in countries other than US, Canada, Australia and New Zealand. They were not significantly affected by publication type or healthcare profession (p > 0.05). Only 17% of studies attempted assessment of possible non-response bias.

Conclusion: Response rates to postal surveys of healthcare professionals are low and probably declining, almost certainly leading to unknown levels of bias. To improve the informativeness of postal survey findings, researchers should routinely consider the use of reminders and assess potential for non-response bias.

Publication types

  • Comparative Study

MeSH terms

  • Bias
  • Data Collection / methods*
  • Health Personnel / statistics & numerical data*
  • Health Services Research / methods*
  • Humans
  • Logistic Models
  • Observation
  • Postal Service*
  • Reminder Systems
  • Surveys and Questionnaires