Under-reporting of inpatient services utilisation in household surveys -- a population-based study in Hong Kong

BMC Health Serv Res. 2005 Apr 28;5(1):31. doi: 10.1186/1472-6963-5-31.


Background: Recognising that household interviews may produce biased estimates of health services utilisation, we examined for under- and over-reporting of hospitalisation episodes in three recent, consecutive population-based household surveys in Hong Kong.

Methods: Territory-wide inpatient service utilisation volumes as estimated from the 1999, 2001 and 2002 Thematic Household Surveys (THS) were benchmarked against corresponding statistics derived from routine administrative databases. Between-year differences on net under-reporting were quantified by Cohen's d effect size. To assess the potential for systematic biases in under-reporting, age- and sex-specific net under-reporting rates within each survey year were computed and the F-test was performed to evaluate differences between demographic subgroups. We modelled the effects of age and sex on the likelihood of ever hospitalisation through logistic regression to compare the odds ratios respectively derived from survey and administrative data.

Results: The extent of net under-reporting was moderately large in all three years amounting to about one-third of all inpatient episodes. However, there did not appear to be significant systematic biases in the degree of under-reporting by age or sex on stratified analyses and logistic regression modelling.

Conclusion: Under-reporting was substantial in Hong Kong's THS. Recall bias was likely most responsible for such reporting inaccuracies. A proper full-design record-check study should be carried out to confirm the present findings.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Age Distribution
  • Aged
  • Bias
  • Child
  • Child, Preschool
  • Databases as Topic
  • Episode of Care
  • Female
  • Health Care Surveys / methods*
  • Hong Kong / epidemiology
  • Hospitalization / statistics & numerical data*
  • Humans
  • Male
  • Mental Recall*
  • Middle Aged
  • Odds Ratio
  • Registries
  • Risk Factors
  • Self Disclosure*
  • Sex Distribution