Being affiliated with the occupational healthcare (OHC) qualitynetwork (OQN) – a voluntary collaborative forum in Finland – canreduce the need for disability pensions among employees in the careof the OHC unit. When the OHC unit participates in common qualityimprovement (QI) activities (quality measurements of intervening inexcess use of alcohol, quality facilitator training, and the focus of workability measurements) it may decrease the risk of work disabilitypensions.

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.

Supplementary notes can be added here, including code and math.

Aims: A common objective of epidemiological surveys is to provide population-level estimates of health indicators. Survey results tend to be biased under selective non-participation. One approach to bias reduction is to collect information about non-participants by contacting them again and asking them to fill in a questionnaire. This information is called re-contact data, and it allows to adjust the estimates for non-participation. Methods: We analyse data from the FINRISK 2012 survey, where re-contact data were collected. We assume that the respondents of the re-contact survey are similar to the remaining non-participants with respect to the health given their available background information. Validity of this assumption is evaluated based on the hospitalisation data obtained through record linkage of survey data to the administrative registers. Using this assumption and multiple imputation, we estimate the prevalences of daily smoking and heavy alcohol consumption and compare them to estimates obtained with a commonly used assumption that the participants represent the entire target group. Results: When adjusting for non-participation using re-contact data, higher prevalence estimates were observed compared to prevalence estimates based on participants only. Among men, the smoking prevalence estimate was 28.5% (23.2% for participants) and heavy alcohol consumption prevalence was 9.4% (6.8% for participants). Among women, smoking prevalence was 19% (16.5% for participants) and heavy alcohol consumption was 4.8% (3% for participants). Conclusions: The utilisation of re-contact data is a useful method to adjust for non-participation bias on population estimates in epidemiological surveys.

In epidemiological surveys, data missing not at random (MNAR) due to survey nonresponse may potentially lead to a bias in the risk factor estimates. We propose an approach based on Bayesian data augmentation and survival modelling to reduce the nonresponse bias. The approach requires additional information based on follow-up data. We present a case study of smoking prevalence using FINRISK data collected between 1972 and 2007 with a follow-up to the end of 2012 and compare it to other commonly applied missing at random (MAR) imputation approaches. A simulation experiment is carried out to study the validity of the approaches. Our approach appears to reduce the nonresponse bias substantially, whereas MAR imputation was not successful in bias reduction.

Knowledge of current fishing mortality rates is an important prerequisite for formulating management plans for the recovery of threatened stocks. We present a method for estimating migration and fishing mortality rates for anadromous fishes that combines tag return data from commercial and recreational fisheries with expert opinion in a Bayesian framework. By integrating diverse sources of information and allowing for missing data, this approach may be particularly applicable in data-limited situations.
Wild populations of anadromous sea trout (Salmo trutta) in the northern Baltic Sea have undergone severe declines, with the loss of many populations. The contribution of fisheries to this decline has not been quantified, but is thought to be significant. We apply the Bayesian mark-recapture model to two reared sea trout stocks from the Finnish Isojoki and Lestijoki Rivers. Over the study period (1987–2012), the total harvest rate was estimated to average 0.82 y^{–1} for the Isojoki River stock and 0.74 y^{–1} for the Lestijoki River stock. Recreational gillnet fishing at sea was estimated to be the most important source of fishing mortality for both stocks, particularly during the 1980s and 1990s. Our results indicate a high probability of unsustainable levels of fishing mortality for both stocks, and illustrate the importance of considering the effect of recreational fisheries on fish population dynamics.

This manual represents a review of the potential sources and methods to be applied when providing prior information to Bayesian stock assessments and marine risk analysis. The manual is compiled as a product of the EC Framework 7 ECOKNOWS project (www.ecoknows.eu). The manual begins by introducing the basic concepts of Bayesian inference and the role of prior information in the inference. Bayesian analysis is a mathematical formalization of a sequential learning process in a probabilistic rationale. Prior information (also called ”prior knowledge”, ”prior belief”, or simply a ”prior”) refers to any existing relevant knowledge available before the analysis of the newest observations (data) and the information included in them. Prior information is input to a Bayesian statistical analysis in the form of a probability distribution (a prior distribution) that summarizes beliefs about the parameter concerned in terms of relative support for different values. Apart from specifying probable parameter values, prior information also defines how the data are related to the phenomenon being studied, i.e. the model structure. Prior information should reflect the different degrees of knowledge about different parameters and the interrelationships among them. Different sources of prior information are described as well as the particularities important for their successful utilization. The sources of prior information are classified into four main categories: (i) primary data, (ii) literature, (iii) online databases, and (iv) experts. This categorization is somewhat synthetic, but is useful for structuring the process of deriving a prior and for acknowledging different aspects of it. A …

We propose an approach based on registry data to deal with non‐ignorable missingness in health examination surveys. The approach relies on follow‐up data available from administrative registers several years after the survey. The results indicate that the estimated smoking prevalence rates in Finland may be significantly affected by missing data.

In this work, the aim was to produce a realistic assessment of yearly mortality of Archipelago Sea pike perch during the period 1997-2012. The utilized data origins from the mark-recapture experiment carried out by the Finnish Game and Fisheries Research Institute (FGFRI). In this mark-recapture experiment, returnings of the marks were based on voluntary tag reporting by the fishermen gaining small monetary rewards. In this study design, the count of returned tags is affected by the size of the release cohort, efficiency of the fishing method used by a fisherman and the fisherman’s willingness to return the tag. In addition, each year a proportion of the tags become detached from fish, which means that those tags cannot be returned. All these factors were taken into account in a hierarchical model, which was developed in the same fashion as the well-known Cormack-Jolly-Seber model. Data from the yearly total catch were not used in this work because those data will be used in the subsequent research utilizing results of this work. The objective of this work was to estimate fishing gear specific catchability coefficients and mortality rates, including natural mortality rate. The amount of data and number of parameters to be estimated set their own limitations, so it was decided to estimate parameters of interest by splitting the data into only three fishing fleets: professional fishermen, recreational net fishermen and recreational line fishermen. The estimability of the hierarchical model developed for mark-recapture data was studied using simulation experiments. One was able to find such a model configuration, where the parameters concerning mortality …