Data analysis
REGRESSION
To examine the annual prevalence of people with purchases of prescribed sedatives and anxiolytics in the 9-year time window, we applied repeated-measures logistic regression using generalized estimating equations (GEE), which takes into account the intra-individual correlation between measurements. The trajectories were based on 9 observations per person and missing information was handled through full information maximum likelihood. In order to determine which correlation structure described the intra-individual correlation the best, Quasi-likelihood under the independence model criterion from analyses with different correlation structures were compared [After examination of the time trends, the 9-year time window was divided into four distinct periods: “early pre-downsizing” (years -4 and -3), “late pre-downsizing” (-3 to -1), “peri-downsizing” (-1 to +1) and “post-downsizing” (+1 to +4). In order to investigate whether the groups changed their probabilities of purchases over time, odds ratios (OR) and their 95% confidence intervals (95% CI) were calculated contrasting prevalence of purchases during the first and the last 12-month period in each of the four time periods for each exposure group. Crude analyses, only adjusted for calendar time, were followed by analyses controlling for sociodemographic factors and previous sickness absence, added to the model as main effects. We further tested for differences in time trends between the exposed groups and the unexposed group within the four periods. See We stratified the results by sex and age groups, respectively, to test for differences between men and women and across the age groups. Finally, in order to account for a health selection effect we stratified our sample by pre-downsizing sickness absence and conducted the analyses separately for employees with and without a previous sickness absence.