No study has evaluated the relationship between heavy rain disasters and influenza by comparing victims and non-victims, and we investigated the association between the 2018 western Japan heavy rain disaster and influenza.

All patients registered in the National Health Insurance Claims Database and treated in the Hiroshima, Okayama, and Ehime prefectures were included in this retrospective cohort study conducted 1-year post-disaster. A multivariate mixed-effects logistic regression analysis was used to assess the association between the disaster and anti-influenza drug prescribing. A difference-in-differences analysis was conducted to assess anti-influenza drug use for the 4-month period immediately before and every 4 months for a year post-disaster.

This study included 6 176 300 individuals (victims: 36 076 [0.60%]); 2573 (7.1%) and 458 157 (7.4%) in the victim and non-victim groups, respectively, used anti-influenza drugs in the year following the flood. The victims were significantly more likely than non-victims to use anti-influenza drug (risk ratio 1.18; 95% confidence interval [CI] 1.12-1.42). The victims used significantly more anti-influenza drugs in the 4 months immediately post-disaster compared with just before the disaster (odds ratio 3.62; 95% CI 1.77-7.41).

Anti-influenza drug use was higher among victims of the 2018 Western Japan heavy rain disaster than among non-victims.

Adults with mood and/or anxiety disorders have increased risks of comorbidities, chronic treatments and polypharmacy, increasing the risk of drug–drug interactions (DDIs) with antidepressants.

To use primary care records from the UK Biobank to assess DDIs with citalopram, the most widely prescribed antidepressant in UK primary care.

We classified drugs with pharmacokinetic or pharmacodynamic DDIs with citalopram, then identified prescription windows for these drugs that overlapped with citalopram prescriptions in UK Biobank participants with primary care records. We tested for associations of DDI status (yes/no) with sociodemographic and clinical characteristics and with cytochrome 2C19 activity, using univariate tests, then fitted multivariable models for variables that reached Bonferroni-corrected significance.

In UK Biobank primary care data, 25 508 participants received citalopram prescription(s), among which 11 941 (46.8%) had at least one DDI, with an average of 1.96 interacting drugs. The drugs most commonly involved were proton pump inhibitors (40% of co-prescription instances). Individuals with DDIs were more often female and older, had more severe and less treatment-responsive depression, and had higher rates of psychiatric and physical disorders. In the multivariable models, treatment resistance and markers of severity (e.g. history of suicidal and self-harm behaviours) were strongly associated with DDIs, as well as comorbidity with cardiovascular disorders. Cytochrome 2C19 activity was not associated with the occurrence of DDIs.

The high frequency of DDIs with citalopram in fragile groups confirms the need for careful consideration before prescribing and periodic re-evaluation.

Nowadays, both researchers and clinicians alike have to deal with increasingly larger datasets, specifically also in the context of mental health data. Sophisticated tools for dataset visualization of information from various item-based instruments, such as questionnaire data or data from digital applications or clinical documentations, are still lacking, specifically for an integration at multiple levels and for use in both data organization and appropriate construction for its valid use in subsequent analyses.

Here, we introduce ItemComplex, a Python-based framework for ex-post visualization of large datasets. The method exploits the comprehensive recognition of instrument alignments and the identification of new content networks and graphs based on item similarities and shared versus differential conceptual bases within and across data and studies.

The ItemComplex framework was evaluated using four existing large datasets from four different cohort studies and demonstrated successful data visualization across multi-item instruments within and across studies. ItemComplex enables researchers and clinicians to navigate through big datasets reliably, informatively, and quickly. Moreover, it facilitates the extraction of new insights into construct representations and concept identifications within the data.

The ItemComplex app is an efficient tool in the field of big data management and analysis addressing the growing complexity of modern datasets to harness the potential hidden within these extensive collections of information. It is also easily adjustable for individual datasets and user preferences, both in the research and clinical field.

Current approaches to identifying individuals at risk for psychosis capture only a small proportion of future psychotic disorders. Recent Finnish research suggests a substantial proportion of individuals at risk of psychosis attend child and adolescent mental health services (CAMHS) earlier in life, creating important opportunities for prediction and prevention. To what extent this is true outside Finland is unknown.

To establish the proportion of psychotic and bipolar disorder diagnoses that occurred in individuals who had attended CAMHS in Wales, UK, and whether, within CAMHS, certain factors were associated with increased psychosis risk.

We examined healthcare contacts for individuals born between 1991 and 1998 (N = 348 226), followed to age 25–32. Using linked administrative healthcare records, we identified all psychotic and bipolar disorder diagnoses in the population, then determined the proportion of cases where the individual had attended CAMHS. Regression analyses examined associations between sociodemographic and clinical risk markers with psychotic and bipolar disorder outcomes.

Among individuals diagnosed with a psychotic or bipolar disorder, 44.78% had attended CAMHS (hazard ratio = 6.28, 95% CI = 5.92–6.65). Low birth weight (odds ratio = 1.33, 95% CI = 1.15–1.53), out-of-home care experience (odds ratio = 2.05, 95% CI = 1.77–2.38), in-patient CAMHS admission (odds ratio = 1.49, 95% CI = 1.29–1.72) and attending CAMHS in childhood (in addition to adolescence; odds ratio = 1.16, 95% CI = 1.02–1.30) were all within-CAMHS risk markers for psychotic and bipolar disorders.

A substantial proportion (45%) of future psychotic and bipolar disorder cases emerge in individuals who had attended CAMHS, demonstrating large-scale opportunities for early intervention and prevention within CAMHS.

Physical activities are widely implemented for non-pharmacological intervention to alleviate depressive symptoms. However, there is little evidence supporting their genotype-specific effectiveness in reducing the risk of self-harm in patients with depression.

To assess the associations between physical activity and self-harm behaviour and determine the recommended level of physical activity across the genotypes.

We developed the bidirectional analytical model to investigate the genotype-specific effectiveness on UK Biobank. After the genetic stratification of the depression phenotype cohort using hierarchical clustering, multivariable logistic regression models and Cox proportional hazards models were built to investigate the associations between physical activity and the risk of self-harm behaviour.

A total of 28 923 subjects with depression phenotypes were included in the study. In retrospective cohort analysis, the moderate and highly active groups were at lower risk of self-harm behaviour. In the followed prospective cohort analysis, light-intensity physical activity was associated with a lower risk of hospitalisations due to self-harm behaviour in one genetic cluster (adjusted hazard ratio, 0.28 [95% CI, 0.08–0.96]), which was distinguished by three genetic variants: rs1432639, rs4543289 and rs11209948. Compliance with the guideline-level moderate-to-vigorous physical activities was not significantly related to the risk of self-harm behaviour.

A genotype-specific dose of light-intensity physical activity reduces the risk of self-harm by around a fourth in depressive patients.

Recent advances in natural language processing (NLP), particularly in language processing methods, have opened new avenues in semantic data analysis. A promising application of NLP is data harmonization in questionnaire-based cohort studies, where it can be used as an additional method, specifically when only different instruments are available for one construct as well as for the evaluation of potentially new construct-constellations. The present article therefore explores embedding models’ potential to detect opportunities for semantic harmonization.

Using models like SBERT and OpenAI’s ADA, we developed a prototype application (“Semantic Search Helper”) to facilitate the harmonization process of detecting semantically similar items within extensive health-related datasets. The approach’s feasibility and applicability were evaluated through a use case analysis involving data from four large cohort studies with heterogeneous data obtained with a different set of instruments for common constructs.

With the prototype, we effectively identified potential harmonization pairs, which significantly reduced manual evaluation efforts. Expert ratings of semantic similarity candidates showed high agreement with model-generated pairs, confirming the validity of our approach.

This study demonstrates the potential of embeddings in matching semantic similarity as a promising add-on tool to assist harmonization processes of multiplex data sets and instruments but with similar content, within and across studies.

Exposure to maternal mental illness during foetal development may lead to altered development, resulting in permanent changes in offspring functioning.

To assess whether there is an association between prenatal maternal psychiatric disorders and offspring behavioural problems in early childhood, using linked health administrative data and the Australian Early Development Census from New South Wales, Australia.

The sample included all mother–child pairs of children who commenced full-time school in 2009 in New South Wales, and met the inclusion criteria (N = 69 165). Univariable logistic regression analysis assessed unadjusted associations between categories of maternal prenatal psychiatric disorders with indicators of offspring behavioural problems. Multivariable logistic regression adjusted the associations of interest for psychiatric categories and a priori selected covariates. Sensitivity analyses included adjusting the final model for primary psychiatric diagnoses and assessing association of interest for effect modification by child's biological gender.

Children exposed in the prenatal period to maternal psychiatric disorders had greater odds of being developmentally vulnerable in their first year of school. Children exposed to maternal anxiety disorders prenatally had the greatest odds for behavioural problems (adjusted odds ratio 1.98; 95% CI 1.43–2.69). A statistically significant interaction was found between child biological gender and prenatal hospital admissions for substance use disorders, for emotional subdomains, aggression and hyperactivity/inattention.

Children exposed to prenatal maternal mental illness had greater odds for behavioural problems, independent of postnatal exposure. Those exposed to prenatal maternal anxiety were at greatest risk, highlighting the need for targeted interventions for, and support of, families with mental illness.