[Establishing Prevention Reporting in Bavaria - Concept, Implementation and First Results]. / Aufbau einer Präventionsberichterstattung in Bayern ­ Konzept und erste Umsetzungsschritte.

BACKGROUND: Bavaria is currently embarking on the endeavour of building up systematic prevention reporting. Prevention reporting is meant to support the implementation of the Bavarian Prevention Plan and the German Prevention Law and to further the ongoing development in the field of health promotion and health-related prevention in Bavaria. GOALS: Prevention reporting in Bavaria aims to draw on general health reporting and complement it with specific prevention-related data taking into special consideration the Bavarian Prevention Plan. Particularly, data on prevention-amenable health determinants, prevention interventions, prevention spending and on indicators related to the targets of the Bavarian Prevention Plan are meant to be included. Periodic reporting will allow ongoing monitoring and the observation of trends. The reporting system will encompass shorter publications for the general public as well as more extensive reports for an expert audience. Where possible, data will be reported not only Bavaria wide but also on a smaller regional level to support local action. IMPLEMENTATION AND FIRST RESULTS: An extensive stocktaking of prevention and health promotion activities in Bavaria in 2014/2015 as well as a survey of the partner organisations in the Bavarian Prevention Alliance in 2016 presented an overview of the prevention scene in Bavaria. A core indicator set to support the implementation of the Bavarian Prevention Plan was developed in 2016 and a first short data brochure for the public was published. A comprehensive compilation of prevention-related data and a Bavarian prevention report are planned for 2017 and 2018. CONCLUSIONS: Like health reporting, prevention reporting, as conceptualised in Bavaria, presents overview data and thus allows a monitoring of prevention. For specific planning or evaluation purposes, it needs to be supplemented by more in-depth analyses. Current challenges include the development of valid, prevention-specific indicators, the generation of an empiric database and the attempt of a coordinated approach amongst the federal states within Germany.

[Regional life expectancy rankings : Methodological artefacts in population updates]. / Rangfolgen der Lebenserwartung auf Kreisebene : Methodische Artefakte durch Bevölkerungsfortschreibungen.

For the calculation of life expectancy on a regional level, data from the mortality statistics and population numbers are needed. The latter are derived from population censuses, which have to be undertaken every 10 years according to the EU regulation No. 763/2008. In Germany, the last census took place in 2011 (Census 2011). The current population numbers are calculated on the basis of the most recent population census (population update). Births, deaths, immigration and migration, in addition to other data, are taken into account in this calculation. However, with passing time since the last census, inaccuracies in population updates may increase, which can affect the value of life expectancy calculations.Based on the comparison of life expectancy rankings, the impact and extent of changing over from the 1987 to the more recent 2011 census for regional comparisons were examined in two parts of Germany, Bavaria and North Rhine-Westphalia. As expected, the results show that larger changes in the calculated life expectancy result from larger changes in population statistics. However, noteworthy changes in life expectancy rankings do not necessarily follow larger changes in the population numbers. Regional life expectancy rankings are potentially always influenced by inaccuracies in the underlying population statistics. This should be taken into account when interpreting such small-scale differences.

Improving well-being at work: A randomized controlled intervention based on selection, optimization, and compensation.

This study aimed to develop, implement, and evaluate an occupational health intervention that is based on the theoretical model of selection, optimization, and compensation (SOC). We conducted a stratified randomized controlled intervention with 70 nurses of a community hospital in Germany (94% women; mean age 43.7 years). Altogether, the training consisted of 6 sessions (16.5 hours) over a period of 9 months. The training took place in groups of 6-8 employees. Participants were familiarized with the SOC model and developed and implemented a personal project based on SOC to cope effectively with 1 important job demand or to activate a job resource. Consistent with our hypotheses, we observed a meaningful trend that the proposed SOC training enhanced mental well-being, particularly in employees with a strong commitment to the intervention. While highly committed training participants reported higher levels of job control at follow-up, the effects were not statistical significant. Additional analyses of moderation effects showed that the training is particularly effective to enhance mental well-being when job control is low. Contrary to our assumptions, perceived work ability was not improved by the training. Our study provides first indications that SOC training might be a promising approach to occupational health and stress prevention. Moreover, it identifies critical success factors of occupational interventions based on SOC. However, additional studies are needed to corroborate the effectiveness of SOC trainings in the occupational contexts.