Juho Kopra1, Pia Mäkelä2, Hanna Tolonen2, Pekka Jousilahti2, Juha Karvanen1. 1. Department of Mathematics and Statistics, University of Jyvaskyla, Finland. 2. Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland.
Abstract
AIMS: We aim to adjust for potential non-participation bias in the prevalence of heavy alcohol consumption. METHODS: Population survey data from Finnish health examination surveys conducted in 1987-2007 were linked to the administrative registers for mortality and morbidity follow-up until end of 2014. Utilising these data, available for both participants and non-participants, we model the association between heavy alcohol consumption and alcohol-related disease diagnoses. RESULTS: Our results show that the estimated prevalence of heavy alcohol consumption is on average of 1.5 times higher for men and 1.8 times higher for women than what was obtained from participants only (complete case analysis). The magnitude of the difference in the mean estimates by year varies from 0 to 9 percentage points for men and from 0 to 2 percentage points for women. CONCLUSION: The proposed approach improves the prevalence estimation but requires follow-up data on non-participants and Bayesian modelling.
AIMS: We aim to adjust for potential non-participation bias in the prevalence of heavy alcohol consumption. METHODS: Population survey data from Finnish health examination surveys conducted in 1987-2007 were linked to the administrative registers for mortality and morbidity follow-up until end of 2014. Utilising these data, available for both participants and non-participants, we model the association between heavy alcohol consumption and alcohol-related disease diagnoses. RESULTS: Our results show that the estimated prevalence of heavy alcohol consumption is on average of 1.5 times higher for men and 1.8 times higher for women than what was obtained from participants only (complete case analysis). The magnitude of the difference in the mean estimates by year varies from 0 to 9 percentage points for men and from 0 to 2 percentage points for women. CONCLUSION: The proposed approach improves the prevalence estimation but requires follow-up data on non-participants and Bayesian modelling.
Authors: Linsay Gray; Emma Gorman; Ian R White; S Vittal Katikireddi; Gerry McCartney; Lisa Rutherford; Alastair H Leyland Journal: Stat Methods Med Res Date: 2019-06-11 Impact factor: 2.494
Authors: Megan A McMinn; Linsay Gray; Tommi Härkänen; Hanna Tolonen; Joonas Pitkänen; Oarabile R Molaodi; Alastair H Leyland; Pekka Martikainen Journal: Epidemiology Date: 2020-07 Impact factor: 4.860