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

Gray, L. , Gorman, E., White, I. R., Katikireddi, S. V. , McCartney, G., Rutherford, L. and Leyland, A. H. (2019) Correcting for non-participation bias in health surveys using record-linkage, synthetic observations and pattern mixture modelling. Statistical Methods in Medical Research, (doi:10.1177/0962280219854482) (PMID:31184280) (Early Online Publication)

[img]
Preview
Text
186220.pdf - Published Version
Available under License Creative Commons Attribution.

704kB

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.

Item Type:Articles
Additional Information:This work is supported by the Medical Research Council Methodology Research Panel under the Population and Patient Data Sharing Initiative for Research into Mental Health grant number [MC_EX_MR/J013498/1]. Ian R White acknowledges support from the Medical Research Council [Unit Programme MC_UU_12023/21].
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Katikireddi, Dr Vittal and McCartney, Gerard and Gray, Dr Linsay and Gorman, Ms Emma and Leyland, Professor Alastair
Authors: Gray, L., Gorman, E., White, I. R., Katikireddi, S. V., McCartney, G., Rutherford, L., and Leyland, A. H.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > MRC/CSO SPHSU
Journal Name:Statistical Methods in Medical Research
Publisher:SAGE Publications
ISSN:0962-2802
ISSN (Online):1477-0334
Published Online:11 June 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Statistical Methods in Medical Research 2019
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
727651SPHSU Core Renewal: Measuring and Analysing Socioeconomic Inequalities in Health Research ProgrammeAlastair LeylandMedical Research Council (MRC)MC_UU_12017/13IHW - MRC/CSO SPHU
SPHSU13
727671SPHSU Core Renewal: Informing Healthy Public Policy Research ProgrammePeter CraigMedical Research Council (MRC)MC_UU_12017/15IHW - MRC/CSO SPHU
SPHSU15
699162Understanding the impacts of welfare policy on health: A novel data linkage studySrinivasa KatikireddiChief Scientist office (CSO)SCAF/15/02IHW - MRC/CSO SPHU

Downloads per month over past year

View more statistics