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Information about the direct comparability of big data of epidemiological cohort studies and the general population still is lacking, especially regarding all-cause mortality rates. The aim of this study was to investigate the overall survival and the influence of several diagnoses in the medical history on survival time, adjusted to common risk factors in a populations-based cohort. From 10,000 subjects of the population-based cohort LIFE-Adult-Study (Leipzig Research Centre for Civilization Diseases), the medical history and typical risk factors such as age, smoking status and body-mass-index (BMI) were assessed. The survival status was identified from the saxonian population register. Univariate and multivariate analyses were used to determine the influence of the medical history and risk factors on overall survival. To develope an optimal model, the method by Collet [1] was used. The mortality rate of the participants is approximately half the mortality rate expected for the german population. The selection bias in epidemiological studies needs to be considered whenever interpreting results of epidemiological cohort studies. Nevertheless we have shown that several diagnoses proved to have a negative influence on overall survival time even in this relatively healthy cohort. This study showed the significantly increased mortality risk if the following diseases are reported in medical history of the participants in a large population-based cohort study including adults aged 18 and over: diabetes mellitus (HR 1.533, p = 0.002), hypertension (HR 1.447, p = 0.005), liver cirrhosis (HR 4.251, p < 0.001), osteoporosis (HR 2.165, p = 0.011), chronic bronchitis (HR 2.179, p < 0.001), peptic ulcer disease (HR 1.531, p = 0.024) and cancer (HR 1.797, p < 0.001). Surprisingly, asthma has the opposite effect on survival time (HR 0.574, p = 0.024), but we believe this may be due to an overrepresentation of mild to moderate asthma and its management, which includes educating patients about a healthy lifestyle. In the LIFE-Adult-Study, common risk factors and several diseases had relevant effect on overall survival. However, selection bias in epidemiological studies needs to be considered whenever interpreting results of epidemiological cohort studies. Nevertheless it was shown that the general cause-and-effect principles also apply in this relatively healthy cohort.

Digital health literacy (DHL) has the potential to improve health among older adults by enhancing access to health-related information and health care services. The aim of this study was to analyze the relationship between DHL and technology commitment in adults aged 65 years and older, while also investigating possible gender differences. The analytical sample consisted of 1824 individuals. The analysis included descriptive comparisons in terms of DHL, technology acceptance, competency, support, and internet use. Multivariate regression models (generalized linear models) were applied in order to test the association between DHL and technology commitment, controlling for internet use as well as health-related and sociodemographic characteristics. Male and female participants did not differ in terms of DHL (mean score: 3.5, SD 1.2 [men] and 3.5, SD 1.3 [women]; P=.70); however, male participants reported significantly higher technology acceptance (P<.001) and higher technology competencies (P<.001), but less support with regard to technology use (P<.001). Within regression models, only higher technology acceptance (coefficient=0.023, 95% CI 0.006-0.041; P=.01) and support (coefficient=0.027, 95% CI 0.014-0.040; P<.001) were significantly linked to greater DHL. The subgroup analysis revealed that DHL was significantly associated with technology acceptance among men (coefficient=0.036, 95% CI 0.012-0.060; P=.003) but not women (coefficient=0.024, 95% CI 0.008-0.040; P=.44). According to the current results, DHL is highly related to technology commitment. Gender differences should be taken into account when developing and evaluating appropriate interventions to improve DHL by addressing the acceptance of technologies and optimizing support infrastructures.

BackroundInformation about the direct comparability of big data of epidemiological cohort studies and the general population still is lacking, especially regarding all-cause mortality rates. The aim of this study was to investigate the overall survival and the influence of several diagnoses in the medical history on survival time, adjusted to common risk factors in a populations-based cohort.MethodsFrom 10,000 subjects of the population-based cohort LIFE-Adult-Study (Leipzig Research Centre for Civilization Diseases), the medical history and typical risk factors such as age, smoking status and body-mass-index (BMI) were assessed. The survival status was identified from the saxonian population register. Univariate and multivariate analyses were used to determine the influence of the medical history and risk factors on overall survival. To develope an optimal model, the method by Collet [1] was used.ResultsThe mortality rate of the participants is approximately half the mortality rate expected for the german population. The selection bias in epidemiological studies needs to be considered whenever interpreting results of epidemiological cohort studies. Nevertheless we have shown that several diagnoses proved to have a negative influence on overall survival time even in this relatively healthy cohort. This study showed the significantly increased mortality risk if the following diseases are reported in medical history of the participants in a large population-based cohort study including adults aged 18 and over: diabetes mellitus (HR 1.533, p = 0.002), hypertension (HR 1.447, p = 0.005), liver cirrhosis (HR 4.251, p < 0.001), osteoporosis (HR 2.165, p = 0.011), chronic bronchitis (HR 2.179, p < 0.001), peptic ulcer disease (HR 1.531, p = 0.024) and cancer (HR 1.797, p < 0.001). Surprisingly, asthma has the opposite effect on survival time (HR 0.574, p = 0.024), but we believe this may be due to an overrepresentation of mild to moderate asthma and its management, which includes educating patients about a healthy lifestyle.ConclusionIn the LIFE-Adult-Study, common risk factors and several diseases had relevant effect on overall survival. However, selection bias in epidemiological studies needs to be considered whenever interpreting results of epidemiological cohort studies. Nevertheless it was shown that the general cause-and-effect principles also apply in this relatively healthy cohort.

Background So far, digital health literacy (DHL) has been linked to disparities in access to digital tools, differences in usage habits, and varying levels of proficiency in using digital technologies. The aim of the current study was to investigate the link between sociodemographic or health-related factors and digital health literacy among older adults in Germany. Method A total of 3,000 participants from the LIFE Adult Cohort – a population-based cohort study in Germany – aged 65 years and older were re-contacted and surveyed using a paper-and-pencil questionnaire in May 2024. Participation rate was 74%. A sample of n  = 2.227 participants was analysed with regard to digital health literacy (revised eHealth Literacy Scale (GR-eHEALS)), sociodemographic and health-related factors (overall health status, chronic diseases and body mass index). In addition, participants were compared regarding their internet use, as well as application of health-related devices and services. Results According to the multivariate regression analysis, digital health literacy (overall and sub-scales) was positively associated with better health status ( p  < 0.001). Having a chronic disease or long-term health problem, however, as well as BMI-category were not significantly related to DHL. Internet use was the most prominent predictor across all three models ( p  < 0.001). Being 75 years and older was significantly associated with a decrease in digital health literacy compared to being 65 to 69 years. Moreover, a high educational level (CASMIN) compared to a low level was associated with a 9.3% increase in digital health literacy. Whereas a high income (≥ 3.500 €) compared to an income below 1.500€ was associated with a 7.2% increase in digital health literacy. No significant associations were found with regard to sex or marital status. Conclusion The findings of the current study highlight the importance of sociodemographic and health-related factors in relation to digital health literacy among individuals aged 65 years and older. Further research is needed to clarify these associations in terms of causality and underlying mechanisms in order to develop interventions that focus on older adults in order to improve digital health literacy.

Background Self-reporting is a common approach in observational epidemiological studies. However, information can be biased by several causes and can, therefore, affect the outcomes of the investigations. This analysis aimed to evaluate the agreement between self-reported data from a population-based cohort study with data from two large German health insurance companies. Methods Participants with available self-reported diagnoses of a history of stroke, atrial fibrillation (AF), heart failure (HF), and myocardial infarction (MI) from the baseline and the follow-up (after six years) surveys of the prospective population-based LIFE-Adult study were included in this study. Two health insurance companies provided ICD-10-GM codes. The agreement between the self-reports and health insurance data (HID) was examined by calculating sensitivity, specificity, Cohen`s Kappa, positive and negative predictive values. We used multivariable logistic regression models to examine whether odds ratios (OR) for the association between risk factors and the certain disease changed, depending on whether self-reports or HID was used as the dependent variable. Results One thousand seven hundred eighty four individuals with complete data were included in this interim analysis. Mean age was 58 (SD±12) years and 984 (55%) were female. 52 (2.9%) subjects reported a history of stroke, 99 (5.6%) AF, 63 (3.5%) HF, and 46 (2.6%) MI. Compared with the HID, a high specificity was found for all four diagnoses (stroke: 99% [95% CI 99.3-99.9]; AF: 99% [95% CI 98.1-99.2], HF: 98% [95% CI 97.6-98.9], and MI: 99% [95% CI 98.9-99.7]). Sensitivity ranged from 58% (95% CI 47.4-69.5) for stroke over 61% (95% CI 48.8-74.0) for MI, to 65% (95% CI 56.6-73.9) for AF. Sensitivity in HF was the lowest (20% [95% CI 14.4-26.5]). Conclusion The use of German health insurance data is a feasible method for verifying population-based self-reported diagnoses. The sensitivity varied among the self-reported diseases compared with the health insurance data, whereas the specificity was continuously high. The verification of self-reported diagnoses using health insurance data as an additional data source may be considered in future population-based assessments to reduce misclassification error of self-reported data.

Prognostic factors and standards of care for astrocytoma, isocitrate dehydrogenase (IDH)-mutant, CNS WHO grade 4, remain poorly defined. Here we sought to explore disease characteristics, prognostic markers, and outcome in patients with this newly defined tumor type. We determined molecular biomarkers and assembled clinical and outcome data in patients with IDH-mutant astrocytomas confirmed by central pathology review. Patients were identified in the German Glioma Network cohort study; additional cohorts of patients with CNS WHO grade 4 tumors were identified retrospectively at two sites. In total, 258 patients with IDH-mutant astrocytomas (114 CNS WHO grade 2, 73 CNS WHO grade 3, 71 CNS WHO grade 4) were studied. The median age at diagnosis was similar for all grades. Karnofsky performance status at diagnosis inversely correlated with CNS WHO grade ( p  < 0.001). Despite more intensive treatment upfront with higher grade, CNS WHO grade was strongly prognostic: median overall survival was not reached for grade 2 (median follow-up 10.4 years), 8.1 years (95% CI 5.4–10.8) for grade 3, and 4.7 years (95% CI 3.4–6.0) for grade 4. Among patients with CNS WHO grade 4 astrocytoma, median overall survival was 5.5 years (95% CI 4.3–6.7) without ( n  = 58) versus 1.8 years (95% CI 0–4.1) with ( n  = 12) homozygous CDKN2A deletion. Lower levels of global DNA methylation as detected by LINE-1 methylation analysis were strongly associated with CNS WHO grade 4 ( p  < 0.001) and poor outcome. MGMT promoter methylation status was not prognostic for overall survival. Histomolecular stratification based on CNS WHO grade, LINE-1 methylation level, and CDKN2A status revealed four subgroups of patients with significantly different outcomes. In conclusion, CNS WHO grade, global DNA methylation status, and CDKN2A homozygous deletion are prognostic in patients with IDH-mutant astrocytoma. Combination of these parameters allows for improved prediction of outcome. These data aid in designing upcoming trials using IDH inhibitors.

Sleep is negatively affected by environmental noise. In the present study, we investigated self-reported high sleep disturbances (being “highly sleep disturbed”—HSD) from road traffic (primary and secondary road networks), rail (train and tram) and air traffic noise in the LIFE-Adult cohort study in Leipzig, Germany. For this, we used exposure data from 2012 and outcome data of Wave 2 (collected during 2018–2021). HSD was determined and defined according to internationally standardized norms. The highest risk for transportation noise-related HSD was found for aircraft noise: the odds ratio (OR) was 19.66, 95% CI 11.47–33.71 per 10 dB increase in Lnight. For road and rail traffic, similar risk estimates were observed (road: OR = 2.86, 95% CI 1.92–4.28; rail: OR = 2.67, 95% CI 2.03–3.50 per 10 dB Lnight increase). Further, we compared our exposure-risk curves with the curves of the WHO environmental noise guidelines for the European region. The proportion of individuals with HSD for a given noise level was lower for rail traffic but higher for aircraft noise in the LIFE study than in the WHO curves. For road traffic, curves are not directly comparable because we also included the secondary road network. The results of our study add to the body of evidence for increased health risks by traffic noise. Moreover, the results indicate that aircraft noise is particularly harmful to health. We recommend reconsidering threshold values for nightly aircraft exposure.