Immunostaining of skeletal tissues

Peer reviewedPreprin

Migraine, headache, and mortality in women: a cohort study

Background: Migraine carries a high global burden, disproportionately affects women, and has been implicated as a risk factor for cardiovascular disease. Migraine with aura has been consistently associated with increased risk of cardiovascular mortality. However, published evidence on relationships between migraine or non-migraine headache and all-cause mortality is inconclusive. Therefore, we aimed to estimate the effect of non-migraine headache and migraine as well as migraine subtypes on all-cause and cause-specific mortality in women. Methods: In total, 27,844 Women’s Health Study participants, aged 45 years or older at baseline, were followed up for a median of 22.7 years. We included participants who provided information on migraine (past history, migraine without aura, or migraine with aura) or headache status and a blood sample at study start. An endpoints committee of physicians evaluated reports of incident deaths and used medical records to confirm deaths due to cardiovascular, cancer, or female-specific cancer causes. We used multivariable Cox proportional hazards models to estimate the effect of migraine or headache status on both all-cause and cause-specific mortality. Results: Compared to individuals without any headache, no differences in all-cause mortality for individuals suffering from non-migraine headache or any migraine were observed after adjustment for confounding (HR = 1.01, 95%CI, 0.93–1.10 and HR = 0.96, 95% CI: 0.89–1.04). No differences were observed for the migraine subtypes and all-cause death. Women having the migraine with aura subtype had a higher mortality due to cardiovascular disease (adjusted HR = 1.64, 95%CI: 1.06–2.54). As an explanation for the lack of overall association with all-cause mortality, we observed slightly protective signals for any cancer and female-specific cancers in this group. Conclusions: In this large prospective study of women, we found no association between non-migraine headache or migraine and all-cause mortality. Women suffering from migraine with aura had an increased risk of cardiovascular death. Future studies should investigate the reasons for the increased risk of cardiovascular mortality and evaluate whether changes in migraine patterns across the life course have differential effects on mortality

a cross-sectional study

Objective To investigate whether high levels of screen time exposure are associated with self-perceived levels of attention problems and hyperactivity in higher education students. Design Cross-sectional study among participants of the i-Share cohort. Setting French-speaking students of universities and higher education institutions. Participants 4816 graduate students who were at least 18 years old. Exposure Screen time was assessed by self-report of the average time spent on five different screen activities on smartphone, television, computer and tablet and categorised into quartiles. Main outcome measure We used the Attention Deficit Hyperactivity Disorder Self-Report Scale (ASRS-v1.1) concerning students’ behaviour over the past 6 months to measure self-perceived levels of attention problems and hyperactivity. Responses were summarised into a global score as well as scores for attention problems and hyperactivity. Results The 4816 participants of this study had a mean age of 20.8 years and 75.5% were female. Multivariable ordinary regression models showed significant associations of screen time exposure with quintiles of the total score of self-perceived attention problems and hyperactivity levels as well as the individual domains. Compared to the lowest screen time exposure category, the ORs (95% CI) were 1.58 (1.37 to 1.82) for each increasing level of quintiles of the global score, 1.57 (1.36 to 1.81) for increasing quintiles of attention levels and 1.25 (1.09 to 1.44) for increasing quartiles of hyperactivity. Conclusions Results of this large cross-sectional study among French university and higher education students show dose-dependent associations between screen time and self-perceived levels of attention problems and hyperactivity. Further studies are warranted to evaluate whether interventions could positively influence these associations

Spatio‐temporal trends in caries: A study on children in Berlin‐Mitte

Background: Significant inequalities in caries distribution among children in Germany have been reported, but small-scale areas remain understudied. Aim: To examine spatio-temporal trends in children's dental caries at the small-area level in Berlin-Mitte. Design: Routinely collected data from Berlin's annual Health Examination Surveys were used, which also include information on age, sex, country of origin, and residential area. The study population consists of 14,866 children aged 5 to 7 between 2006 and 2014 in the district of Berlin-Mitte. Outcome variables are the dmft (decayed, missing, and filled teeth), the presence of any caries experience, untreated caries, and caries risk. The outcomes are summarized descriptively and graphically presented for 10 quarters and 41 communities within Berlin-Mitte. Results: Relevant gaps in children's dental caries were discovered between the quarters of Mitte. Three quarters in the northeast part of Mitte have consistently indicated the lowest oral health status in all four outcomes, and children having high caries risk have been increasingly concentrating in this area over time. Despite the continuous improvements in the southern part, the averages in total of Mitte for all outcomes have risen. Conclusion: Our findings confirm the spatiotemporally mounting disparities in children's oral health between the quarters in Berlin-Mitte and that particular quarters need urgent attention. The small-area approach made it easier and more effective to reveal the spatial distribution of children's dental caries at the local level. The small-area analysis should be strongly encouraged in future caries research to narrow the inequalities in children's oral health

Directed acyclic graphs and causal thinking in clinical risk prediction modeling

Background: In epidemiology, causal inference and prediction modeling methodologies have been historically distinct. Directed Acyclic Graphs (DAGs) are used to model a priori causal assumptions and inform variable selection strategies for causal questions. Although tools originally designed for prediction are finding applications in causal inference, the counterpart has remained largely unexplored. The aim of this theoretical and simulation-based study is to assess the potential benefit of using DAGs in clinical risk prediction modeling. Methods: We explore how incorporating knowledge about the underlying causal structure can provide insights about the transportability of diagnostic clinical risk prediction models to different settings. We further probe whether causal knowledge can be used to improve predictor selection in clinical risk prediction models. Results: A single-predictor model in the causal direction is likely to have better transportability than one in the anticausal direction in some scenarios. We empirically show that the Markov Blanket, the set of variables including the parents, children, and parents of the children of the outcome node in a DAG, is the optimal set of predictors for that outcome. Conclusions: Our findings provide a theoretical basis for the intuition that a diagnostic clinical risk prediction model including causes as predictors is likely to be more transportable. Furthermore, using DAGs to identify Markov Blanket variables may be a useful, efficient strategy to select predictors in clinical risk prediction models if strong knowledge of the underlying causal structure exists or can be learned

Validation of an algorithm for automated classification of migraine and tension-type headache attacks in an electronic headache diary

Background: This study evaluates the accuracy of an automated classification tool of single attacks of the two major primary headache disorders migraine and tension-type headache used in an electronic headache diary. Methods: One hundred two randomly selected reported headache attacks from an electronic headache-diary of patients using the medical app M-sense were classified by both a neurologist with specialisation in headache medicine and an algorithm, constructed based on the ICHD-3 criteria for migraine and tension-type headache. The level of agreement between the headache specialist and the algorithm was compared by using a kappa statistic. Cases of disagreement were analysed in a disagreement validity assessment. Result: The neurologist and the algorithm classified migraines with aura (MA), migraines without aura (MO), tension-type headaches (TTH) and non-migraine or non-TTH events. Of the 102 headache reports, 86 cases were fully agreed on, and 16 cases not, making the level of agreement unweighted kappa 0.74 and representing a substantial level of agreement. Most cases of disagreement (12 out of 16) were due to inadvertent mistakes of the neurologist identified in the disagreement validity assessment. The second most common reason (3 out of 16) was insufficient information for classification by the neurologist. Conclusions: The substantial level of agreement indicates that the classification tool is a valuable instrument for automated evaluation of electronic headache diaries, which can thereby support the diagnostic and therapeutic clinical processes. Based on this study’s results, additional diagnostic functionalities of primary headache management apps can be implemented. Finally, future research can use this classification algorithm for large scale database analysis for epidemiological studies

Illness-Death Model as a Framework for Chronic Disease Burden Projection: Application to Mental Health Epidemiology

Introduction: Estimates of future disease burden supports public health decision-making. Multistate modeling of chronic diseases is still limited despite a long history of mathematicalmodeling of diseases.We introduce a discrete time approach to the illness- death model and a recursion formula, which can be utilized to project chronic disease burden. We further illustrate an example of the technique applied to anxiety disorders in Germany. Materials and Equipment: The illness-death model is a multistate model that relates prevalence, incidence, mortality, and remission. A basic recursion formula that considers prevalence, incidence, mortality among the susceptible, and mortality among the diseased can be applied to irreversible chronic diseases such as diabetes. Among several mental disorders, remission plays a key role and thus an extended recursion formula taking remission into account is derived. Methods: Using the Global Burden of Disease Study 2019 data and population projections from the Federal Statistical Office of Germany, a total number of individuals with anxiety disorders by sex in Germany from 2019 to 2030 was projected. Regression models were fitted to historical data for prevalence and incidence. Differential mortality risks were modeled based on empirical evidence. Remission was estimated from prevalence, incidence, and mortality, applying the extended recursion formula. Sex- and age-specific prevalence of 2019 was given as the initial value to estimate the total number of individuals with anxiety disorders for each year up to 2030. Projections were alsomade through simple extrapolation of prevalence for comparison. Results: From 2019 to 2030, we estimated a decrease of 52,114 (−1.3%) individuals with anxiety disorders among women, and an increase of 166,870 (+8.5%) cases among men, through the illness-death model approach. With prevalence extrapolation, an increase of 381,770 (+9.7%) among women and an increase of 272,446 (+13.9%) among men were estimated. Discussion: Application of the illness-death model with discrete time steps is possible for both irreversible chronic diseases and diseases with possible remissions, such as anxiety disorders. The technique provides a framework for disease burden prediction. The example provided here can form a basis for running simulations under varying transition probabilities

Blockchain-Based Innovations for Population-Based Registries for Rare Neurodegenerative Diseases

Rare diseases are difficult if not impossible to study outside of population-based registries. Particularly in the context of rare neurodegenerative diseases characterized by case heterogeneity, difficult differential diagnosis by specialists, and small numbers of patients, registries make otherwise unfeasible incidence studies cost-effective and manageable. Building up and maintaining such registries is challenging and requires strong, active, and collaborative networks. Centralization around a leading institution provides structure and consistency, but this single-site storage leads to inefficiency and bottlenecks and is prone to failures, attacks, and manipulation. Furthermore, a substantial amount of trust is required between parties sharing data in a traditional registry. Patients are increasingly reluctant to share data in light of regular news reports about healthcare data breaches. Underfunded rare disease specialized centers are also hesitant to exchange with the leading institution out of fear that the low numbers of patients may seek treatment elsewhere. A lack of electronic health records and information system interoperability in certain settings leads to information silos and only further exacerbate the other issues. Blockchain technology may provide unique, innovative solutions to many of these challenges. Specifically, through digital trust and the use of an immutable distributed ledger, automated data transaction processing, guaranteed integrity, and enhanced security, blockchain technology seems to be perfectly suitable to optimize current population-based rare neurodegenerative disease registry construction and maintenance

Zwei Jahre Corona-Pandemie: Kritische Aspekte zur Modellierung von Erkranktenzahlen und zur notwendigen Datenerhebung

Erkrankungsmodelle und deren Ergebnisse haben während der Pandemie eine überragende Rolle gespielt. Folglich stellt sich eine Reihe wichtiger erkenntnistheoretischer Fragen, die wir in diesem Artikel behandeln. Wir widmen uns der Frage, was ein Erkrankungsmodell ist und wollen wissen, wo die Schwierigkeiten beim Betreiben des Modells liegen und welche Grenzen die Interpretation der Ergebnisse solcher Modellierungen haben. Trotz aller Kritikwürdigkeit von Erkrankungsmodellen wollen wir auf die Notwendigkeit von Modellierungen eingehen und für mehr Zurückhaltung bei der Interpretation und Kommunikation der Ergebnisse werben. Disease models and their results have played a predominant role during the pandemic. Consequently, a number of important epistemological questions arise, which we address in this article. We address the question of what a disease model is and want to know what the difficulties are in using the model and what the limitations are in interpreting the results of such modelling. Despite all the criticism of disease models, we want to address the necessity of modelling and advocate for more restraint in the interpretation and communication of the results