BackgroundTo assess the nonresponse rates in a questionnaire survey with respect to administrative register data, and to correct the bias statistically.MethodsThe Finnish Regional Health and Well-being Study (ATH) in 2010 was based on a national sample and several regional samples. Missing data analysis was based on socio-demographic register data covering the whole sample. Inverse probability weighting (IPW) and doubly robust (DR) methods were estimated using the logistic regression model, which was selected using the Bayesian information criteria. The crude, weighted and true self-reported turnout in the 2008 municipal election and prevalences of entitlements to specially reimbursed medication, and the crude and weighted body mass index (BMI) means were compared.ResultsThe IPW method appeared to remove a relatively large proportion of the bias compared to the crude prevalence estimates of the turnout and the entitlements to specially reimbursed medication. Several demographic factors were shown to be associated with missing data, but few interactions were found.ConclusionsOur results suggest that the IPW method can improve the accuracy of results of a population survey, and the model selection provides insight into the structure of missing data. However, health-related missing data mechanisms are beyond the scope of statistical methods, which mainly rely on socio-demographic information to correct the results.

Physical working conditions explained a large part and job control, a somewhat smaller part of socio-economic inequalities in SRH. Improving physical working conditions and increasing job control provide potential routes to reduced inequalities in health among employees.

This study highlights the need for prevention of sleep disturbances and promotion of optimal sleep length to prevent sickness absence.

Improvements in working conditions, especially physical working conditions, and reducing smoking are likely to reduce sickness absence particularly in the lower educational groups. The economic relevance of our findings is notable and the method described for estimating the days of sickness absence can be applied to calculate possible savings in other countries as well.