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Correcting for non-participation bias in health surveys using record-linkage, synthetic observations and pattern mixture modelling

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • L. Gray
  • Emma Gorman
  • I.R. White
  • S.V. Katikireddi
  • G. McCartney
  • L. Rutherford
  • A.H. Leyland
<mark>Journal publication date</mark>1/04/2020
<mark>Journal</mark>Statistical Methods in Medical Research
Issue number4
Number of pages15
Pages (from-to)1212-1226
Publication StatusPublished
Early online date11/06/19
<mark>Original language</mark>English


Surveys are key means of obtaining policy-relevant information not available from routine sources. Bias arising from non-participation is typically handled by applying weights derived from limited socio-demographic characteristics. This approach neither captures nor adjusts for differences in health and related behaviours between participants and non-participants within categories. We addressed non-participation bias in alcohol consumption estimates using novel methodology applied to 2003 Scottish Health Survey responses record-linked to prospective administrative data. Differences were identified in socio-demographic characteristics, alcohol-related harm (hospitalisation or mortality) and all-cause mortality between survey participants and, from unlinked administrative sources, the contemporaneous general population of Scotland. These were used to infer the number of non-participants within each subgroup defined by socio-demographics and health outcomes. Synthetic observations for non-participants were then generated, missing only alcohol consumption. Weekly alcohol consumption values among synthetic non-participants were multiply imputed under missing at random and missing not at random assumptions. Relative to estimates adjusted using previously derived weights, the obtained mean weekly alcohol intake estimates were up to 59% higher among men and 16% higher among women, depending on the assumptions imposed. This work demonstrates the universal value of multiple imputation-based methodological advancement incorporating administrative health data over routine weighting procedures.