A precise estimate of the target population is inherently important in population statistics. However, factors such as increased immigration, and few incentives for deregistration after emigration mean that population registers may not always accurately reflect the target population. This study aims to identify unregistered emigration using “signs of life”. That is, detecting historical inactivity of individuals who have emigrated, but are still listed as residents in the population register. Unregistered emigration contributes to over-coverage, as the number of actual emigrations exceeds the number of registered emigrants. This estimation error affects not only size and composition of the population, but also impacts demographic indicators, such as death and fertility rates. Statistics on households and families may also become skewed due to these discrepancies. There is still no consensus on how to identify or deal with unregistered emigration. Addressing this, we first provide a comparison of methods adapted from the literature for estimating the number of unregistered emigrations. The Zero-Income Approach provides a method with minimal computational and data quality requirements, which serves as a foundation for the estimation. The Household-Income Approach builds upon this by correcting for household income factors. Finally, the Register Trace Approach provides the most comprehensive and detailed picture of unregistered emigration. Our estimates suggest that unregistered emigrants account for approximately 0.44 percent of the adult population in Norway. Second, we analyse the demographic characteristics of the non-deregistration group. We find that the problem of unregistered emigration is not equally distributed across the population, indicating that some subgroups are more prone to discrepancies than the rest of the population. Among immigrants, the over-coverage due to unregistered emigration is substantially higher, accounting for 2.29 percent of the population.

The German cause-of-death statistics are often used to draw conclusions about the health status of the population and the significance of certain diseases. Unfortunately, cause-of-death statistics - not only in Germany - often show a relatively high proportion of ill-defined deaths. Ill-defined deaths have an invalid or unspecific ICD code as underlying cause of death. This may be the case when the indicated ICD code is intermediate (e.g. heart failure) or non-specific (e.g. unspecified cancer). These ICD codes are not informative for public health planning and for example in the context of burden of disease calculations. They do not adequately reflect the underlying cause of death.

The Global Burden of Disease Study (GBD) of the Institute for Health Metrics and Evaluation (IHME) has a specific list of ICD codes that shall be considered invalid resulting in an ill-defined death. In 2015 the proportion of invalid codes in Germany was 26,6%, in 2017 it was 26.0%.; with quite substantial regional variation (Wengler et al. 2019). Following the GBD classification we want to up-date this analysis, looking at the time from 2012 to 2021 and the share of ill-defined deaths in the German federal states.

In general, a further decrease in the share of ill-defined deaths is expected. Especially since more federal states use automatic (re-)coding offered in the Iris/MUSE-system, which incorporates the WHO rules for coding of causes of death. Having less ill-defined deaths and hence better quality of causes of death data is of high importance for public health planning and efficient measurements.

Background: In Germany, the majority of long-lived individuals (LLI) aged 85+ suffer from dementia at the time of their death. Additional medical costs of people with dementia (PwD) are mainly caused by differences in hospital care. The aim of this study is how the risk of hospitalization of LLI changes with age and age at death .

Methods: We drew a random sample of quarterly data from all AOK insured persons aged 50+ (N=250,000) in 2004 with follow-up to 2019 and followed the 1918 to 1923 birth cohort (n=4,067 males and 13,303 females), who reached age 85 years between 2004 and 2009, to the end of the study period or to death. We estimated a multivariate logistic regression model, clustering variances by person ID, to examine the simultaneous effects of age, age at death and last year of life on the risk of hospitalization, stratified by PwD and non-PwD.

Results: Overall, 41.23% of men and 48.37% of women had received a dementia diagnosis, and more than half had been hospitalized (men: 56.41%, women 57.19%). PwD were more likely to be in hospital (men: 68%, women: 68%) than non-PwD (men: 46%, women: 53%). In the multivariate analysis (Table 1), the risk of hospitalization increased significantly with each year of age, by 7.6% for PwD and by 9.3% for non-PwD. With increasing age at death, the risk decreased significantly by 11% for both. These trends levelled off at the highest ages. In the last year of life, the risk of hospitalization increased more for non-PwD (OR=7.36) than for PwD (OR=4.79); women had a significantly lower risk.

Conclusion: LLI have lower hospitalization risks the later they die, both in people with and without dementia.

Governments are grappling with demographic shifts, such as an aging population and rising old-age dependency, prompting discussions on delaying retirement age. To safeguard vulnerable groups from prolonged unemployment, policies promoting longer careers need careful planning. Existing research has centred on predicting work-life expectancy, serving as a foundation for policy development. This study extends this work, adopting a life course perspective, by recognizing that retirement is a gradual process characterized by complex and multiple transitions. Using comprehensive data from the German Pension Insurance, we employ advanced machine learning techniques like sequence-to-sequence Transformers and LSTM models, along with sequence analysis, to predict and analyse work-life trajectories from ages 50 to 65. These analyses provide valuable insights for understanding complex retirement transitions and show accurate predictions at the aggregate level.