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STUDY QUESTION: Have mean age at menarche or mean age at natural menopause changed from the 1939 birth cohort to the 1964 birth cohort? SUMMARY ANSWER: We estimated a minor decrease in mean age at menarche and an increase by nearly 3 years in mean age at natural menopause. WHAT IS KNOWN ALREADY: In the Western world, age at menarche decreased across birth cohorts from the early 1800s until the 1950s. Whether mean age at menarche has continued to decrease in birth cohorts after the 1950s remains uncertain. It is also uncertain whether mean age at natural menopause has changed across birth cohorts. STUDY DESIGN, SIZE, DURATION: We performed a retrospective population study of 312 656 women who were born in Norway during the years 1936-1964. PARTICIPANTS/MATERIALS, SETTING, METHODS: The data were obtained by two self-administered questionnaires from women who participated in the Norwegian breast cancer screening program (BreastScreen Norway) during the years 2006-2014. We used flexible parametric survival models with restricted cubic splines to estimate mean age at menarche, mean age at menopause and mean number of years between menarche and menopause according to the women's year of birth. The women who were still having menstrual periods contributed with follow-up time until the time of data collection, and the women who had reported surgical removal of the uterus and/or both ovaries prior to natural menopause contributed with follow-up time until the time of surgery. MAIN RESULTS AND THE ROLE OF CHANCE: The mean age at menarche was 13.42 years (95% CI: 13.40-13.44 years) among women born during 1936-1939, and it was 13.24 years (95% CI: 13.22-13.25 years) among women born during 1960-1964. The mean age at natural menopause increased from 50.31 years (95% CI: 50.25-50.37 years) among women born during 1936-1939 to 52.73 years (95% CI: 52.64-52.82 years) among women born during 1960-1964. The mean number of years between menarche and menopause increased from 36.83 years (95% CI: 36.77-36.89 years) to 40.22 years (95% CI: 40.11-40.34 years). LIMITATIONS, REASONS FOR CAUTION: Information about age at menarche and age at menopause was based on self-reports. WIDER IMPLICATIONS OF THE FINDINGS: Late menopause is associated with increased risk of breast cancer but also with increased life expectancy. Thus, higher mean age at menopause may partly explain the increase in breast cancer incidence after menopause and the increase in life expectancy in recent time. Also, a longer interval between menarche and menopause could suggest that the number of years of female fecundity has increased. STUDY FUNDING/COMPETING INTEREST(S): This work was funded by the South-Eastern Norway Regional Health Authority [grant number 2016112 to M.S.G.] and by the Norwegian Cancer Society [grant number 6863294-2015 to E.K.B.]. The authors declare no conflicts of interest.

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OBJECTIVE: This study examines if YKL-40 is increased in individuals with psychotic disorders and if elevated YKL-40 levels at baseline is associated with subsequent development of type 2 diabetes. METHOD: A total of 1383 patients with a diagnosis of schizophrenia or affective psychosis and 799 healthy controls were recruited in the period 2002-2015. Plasma YKL-40 and metabolic risk factors were measured and medication was recorded. Using national registry data, association between baseline risk factors and later development of type 2 diabetes was assessed using Cox proportional hazards models. RESULTS: Plasma YKL-40 was higher in patients vs. healthy controls also after adjusting for metabolic risk factors, with no difference between the schizophrenia and affective psychosis groups. Patients were diagnosed with type 2 diabetes at a significantly younger age. Multivariate Cox regression analyses showed that elevated YKL-40 (hazard ratio (HR) = 5.6, P = 0.001), elevated glucose (HR = 3.6, P = 0.001), and schizophrenia diagnosis (HR = 3.0, P = 0.014) at baseline were associated with subsequent development of type 2 diabetes. CONCLUSIONS: Patients with psychotic disorders have at baseline increased levels of YKL-40 beyond the effect of comorbid type 2 diabetes and metabolic risk factors. Elevated YKL-40 level at baseline is associated with later development of type 2 diabetes.

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Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause-effect fashion. How does such immediate causation, that is causation occurring over very short time intervals, relate to DAGs constructed from discrete observations? We introduce a time-continuous model and simulate discrete observations in order to judge the relationship between the DAG and the immediate causal model. We find that there is no clear relationship; indeed the Bayesian network described by the DAG may not relate to the causal model. Typically, discrete observations of a process will obscure the conditional dependencies that are represented in the underlying mechanistic model of the process. It is therefore doubtful whether DAGs are always suited to describe causal relationships unless time is explicitly considered in the model. We relate the issues to mechanistic modeling by using the concept of local (in)dependence. An example using data from the Swiss HIV Cohort Study is presented.

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Influenza can be a serious, sometimes deadly, disease, especially for people in high-risk groups such as the elderly and patients with underlying, severe disease. In this paper we estimated the influenza-related excess mortality in Norway for 1975-2004, comparing it with dominant virus types and estimates of the reproduction number. Analysis was done using Poisson regression, explaining the weekly all-cause mortality by rates of reported influenza-like illness, together with markers for seasonal and year-to-year variation. The estimated excess mortality was the difference between the observed and predicted mortality, removing the influenza contribution from the prediction. We estimated the overall influenza-related excess mortality as 910 deaths per season, or 2.08% of the overall deaths. Age-grouped analyses indicated that the major part of the excess mortality occurred in the > or =65 years age group, but that there was also a significant contribution to mortality in the 0-4 years age group. Estimates of the reproduction number R, ranged from about 1 to 1.69.

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