Gray L, Gorman E, White IR, Katikireddi SV, McCartney G, Rutherford L, Leyland AH. Correcting for non-participation bias in health surveys using record-linkage, synthetic observations and pattern mixture modelling.
Stat Methods Med Res 2019;
29:1212-1226. [PMID:
31184280 PMCID:
PMC7188518 DOI:
10.1177/0962280219854482]
[Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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.
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