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Abstract To elucidate the specific and shared genetic background of schizophrenia (SCZ) and bipolar disorder (BPD), this study explored the association of polygenic liabilities for SCZ and BPD with educational attainment and cognitive aging. Among 106,806 unrelated community participants from the Taiwan Biobank, we calculated the polygenic risk score (PRS) for SCZ (PRSSCZ) and BPD (PRSBPD), shared PRS between SCZ and BPD (PRSSCZ+BPD), and SCZ-specific PRS (PRSSCZvsBPD). Based on the sign-concordance of the susceptibility variants with SCZ/BPD, PRSSCZ was split into PRSSCZ_concordant/PRSSCZ_discordant, and PRSBPD was split into PRSBPD_concordant/PRSBPD_discordant. Ordinal logistic regression models were used to estimate the association with educational attainment. Linear regression models were used to estimate the associations with cognitive aging (n = 27,005), measured by the Mini-Mental State Examination (MMSE), and with MMSE change (n = 6194 with mean follow-up duration of 3.9 y) in individuals aged≥ 60 years. PRSSCZ, PRSBPD, and PRSSCZ+BPD were positively associated with educational attainment, whereas PRSSCZvsBPD was negatively associated with educational attainment. PRSSCZ was negatively associated with MMSE, while PRSBPD was positively associated with MMSE. The concordant and discordant parts of polygenic liabilities have contrasting association, PRSSCZ_concordant and PRSBPD_concordant mainly determined these effects mentioned above. PRSSCZvsBPD predicted decreases in the MMSE scores. Using a large collection of community samples, this study provided evidence for the contrasting effects of polygenic architecture in SCZ and BPD on educational attainment and cognitive aging and suggested that SCZ and BPD were not genetically homogeneous.

Abstract Background It has been proposed that having a psychiatric disorder could increase the risk of developing a gastrointestinal disorder, and vice versa. The role of familial coaggregation and shared genetic loading between psychiatric and gastrointestinal disorders remains unclear. Methods This study used the Taiwan National Health Insurance Research Database; 4,504,612 individuals born 1970–1999 with parental information, 51,664 same-sex twins, and 3,322,959 persons with full-sibling(s) were enrolled. Genotyping was available for 106,796 unrelated participants from the Taiwan Biobank. A logistic regression model was used to examine the associations of individual history, affected relatives, and polygenic risk scores (PRS) for schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorder (MDD), and obsessive-compulsive disorder (OCD), with the risk of peptic ulcer disease (PUD), gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), and inflammatory bowel disease (IBD), and vice versa. Results Here we show that parental psychiatric disorders are associated with gastrointestinal disorders. Full-siblings of psychiatric cases have an increased risk of gastrointestinal disorders except for SCZ/BPD and IBD; the magnitude of coaggregation is higher in same-sex twins than in full-siblings. The results of bidirectional analyses mostly remain unchanged. PRS for SCZ, MDD, and OCD are associated with IBS, PUD/GERD/IBS/IBD, and PUD/GERD/IBS, respectively. PRS for PUD, GERD, IBS, and IBD are associated with MDD, BPD/MDD, SCZ/BPD/MDD, and BPD, respectively. Conclusions There is familial coaggregation and shared genetic etiology between psychiatric and gastrointestinal comorbidity. Individuals with psychiatric disorder-affected relatives or with higher genetic risk for psychiatric disorders should be monitored for gastrointestinal disorders, and vice versa.

Background: Social detachment includes the subjective aspect “loneliness” and the objective aspect “social isolation,” but tools to assess both dimensions are limited. This study aims to develop a questionnaire, the Social Detachment Questionnaire for Older Population (SDQO), that considers multiple dimensions of social detachment simultaneously. Methods: The study collected 600 valid samples from individuals aged 55 and above to examine the psychometric properties of the developed SDQO. Item analysis was conducted to assess the performance of each item, and exploratory factor analysis (EFA) was employed to analyze its initial structure and eliminate less ideal items. Subsequently, confirmatory factor analysis (CFA) was used to examine the model fit of the suggested structure by EFA, using different subsamples. Internal consistency, concurrent validity, and other analyses were also performed. Results: The original 27-item SDQO was reduced to 17 items after removing 4 questions in item analysis and 6 questions in EFA. The Cronbach's alpha for the 17-item version of SDQO was 0.80. Both EFA and CFA supported its 6-factor structure, with factors identified as community activities, loneliness, personal resources, leisure activities, friendship, and family resources. SDQO also demonstrated expected performance in concurrent validity. Conclusion: The 17-item version of SDQO exhibited good reliability and validity, measuring various aspects of social detachment behavior, feelings, and resources. It holds value for future research applications.

Abstract Background The co‐occurrence of depression and diabetes mellitus has been linked to an increased risk of developing cancer. This study aimed to investigate whether depression further amplifies the risk of cancer among individuals with diabetes. Methods This population‐based matched cohort study utilized Taiwan's National Health Insurance claims database. A total of 85,489 newly diagnosed diabetic patients with depressive disorders were selected, along with 427,445 comparison subjects. The matching process involved age, sex, and the calendar year of diabetes onset. The average follow‐up duration for the two cohorts was 6.4 and 6.5 years, respectively. The primary outcome of interest was the occurrence of overall cancer or cancer at specific anatomical sites. Results The adjusted hazard ratios for overall cancer incidence were 1.08 (95% CI, 1.05–1.11). For site‐specific cancers, depression exhibited significant associations with oropharyngeal, esophageal, liver, gynecological, prostate, kidney, and hematologic malignancies among patients with diabetes. Notably, a severity‐response relationship was observed, indicating that patients with recurrent episodes of major depressive disorders exhibited a higher incidence of cancer compared to those diagnosed with dysthymia or depressive disorder not otherwise specified. Furthermore, the strength of the association between depression and cancer risk was more pronounced among younger patients with diabetes as opposed to older adults. However, no significant relationship was observed between adherence to antidepressant treatment and cancer risk. Conclusions The findings of this study indicate a significant association between depression and an elevated risk of cancer among individuals diagnosed with diabetes. Future investigations should replicate our findings, explore the effects of pharmacological and non‐pharmacological treatments on cancer risk, and identify the underlying mechanisms.

Introduction: Social participation activities have a close association with health aging. However, the clinical significance of numbers of social participation activities and its cutoff value has not been defined. Methods: We recruited 516 people aged ≥55 years. Twelve social participation behaviors modified according to Taiwanese culture were investigated, and the adequacy of cutoff number was determined by the area under the receiver operating characteristic curve (AUC) according to the results of cluster analysis of individual activities and scores of the Brief Symptom Rating Scale-5 (BSRS-5) and the Chinese Happiness Inventory (CHI). Demographic, BSRS-5 and CHI data were then compared according to the candidate cutoff numbers. Results: The distribution of the numbers of social activities suggested that the highest partition of numbers of social activities was 3 in women and 4 in men. The AUC regarding the cluster of activity types was 0.917, with the highest Youden's J value located between 3 and 4. The AUC regarding the cluster of activity types and scores of the BSRS-5 and the CHI was 0.929, with similar cutoffs. If 3 and 4 were used as cutoffs, the between-group differences of both the CHI and the BSRS-5 were significant. More types of social activities had a different engaging frequency with the 3 and 4 cutoffs. Conclusion: Our findings found an adequate cutoff with better differential power in the psychopathology and happiness of older people that provided a basis for application in intervention and policy formation.

Summary: Background: Adult preventive health checkups with depression screening were launched in August 2011 in Taiwan; however, its impact has not yet been evaluated. This study aimed to use real-world data to assess the effectiveness of depression screening among middle-aged and older adults. Methods: A total of 4,972,228 adults aged 40 years and above who participated in a health checkup with depression screening between 2013 and 2019 and the same number of unscreened counterparts were included. The target trial emulation study was conducted to estimate the hazard ratios (HRs) for newly treated depression, psychiatric hospitalisation, and suicide. The changes in HRs during the study period were assessed using interval Cox models. Findings: The screening group had a higher rate of newly treated depression (HR 1.63 [95% CI 1.62, 1.64]) and a lower risk of psychiatric hospitalisation (HR 0.93 [95% CI 0.91, 0.95]). There was a null association between depression screening and suicide; however, a higher suicide risk was found in screened older adults aged 65 years and above. Only 10.8% received depression treatment during the study period among the screen-positive individuals. Interpretation: Health checkups with depression screening could potentially promote depression treatment and reduce the risk of psychiatric hospitalisation; however, there was no effect on suicide. The treatment rate for depression remained low after screening for depression. Further attention to enhance referral and treatment is required. Funding: The study was funded by the National Health Research Institutes, Taiwan.

BackgroundThe widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. ObjectiveThe aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. MethodsWe compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. ResultsThe developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of code-mixed training instances is essential for the model’s performance. Furthermore, the LLM-based deidentification method was a feasible and appealing approach that can be controlled and enhanced through natural language prompts. ConclusionsThe study contributes to understanding the underlying mechanism of PLMs in addressing the deidentification process in the code-mixed context and highlights the significance of incorporating code-mixed training instances into the model training phase. To support the advancement of research, we created a manipulated subset of the resynthesized data set available for research purposes. Based on the compiled data set, we found that the LLM-based deidentification method is a feasible approach, but carefully crafted prompts are essential to avoid unwanted output. However, the use of such methods in the hospital setting requires careful consideration of data security and privacy concerns. Further research could explore the augmentation of PLMs and LLMs with external knowledge to improve their strength in recognizing rare PHI.

PurposeThis study aimed to estimate the lifetime healthcare costs and loss of life expectancy (loss-of-LE) among patients with incident rheumatoid arthritis (RA) with and without depression.MethodsThis 18 years longitudinal cohort study used data from Taiwan’s National Health Insurance Research Database. In total, 43,311 patients with RA were included. Among them, 1,663 patients had depressive disorders in the year preceding the RA diagnosis. The survival function for patients with RA with or without depression was estimated and extrapolated over a lifetime using the rolling extrapolation algorithm. The loss-of-LE was calculated by comparing the sex, age, and calendar year-matched referents from vital statistics. The average monthly cost was calculated as the sum of the monthly costs for all patients divided by the number of surviving patients. Lifetime healthcare costs were estimated by multiplying the monthly average cost by the monthly survival probability.ResultsThe loss-of-LE for RA patients with and without depression was 5.60 years and 4.76 years, respectively. The lifetime costs of RA patients with and without depression were USD$ 90,346 and USD$ 92,239, respectively. However, the annual healthcare costs were USD$ 4,123 for RA patients with depression and USD$ 3,812 for RA patients without depression. Regardless of sex or age, RA patients with depression had higher annual healthcare costs than those without depression.ConclusionPatients with RA and depression have a high loss-of-LE and high annual healthcare costs. Whether treating depression prolongs life expectancy and reduces healthcare costs warrants further investigation.

Background/Purpose: The number of psychiatrists working in community clinics in Taiwan has increased dramatically in the recent decade. This study aimed to investigate the trend of prevalence and incidence of depressive disorders and assess the quality of depression care between 2007 and 2016 in Taiwan. Methods: We used the claims database derived from Taiwan's National Health Insurance (NHI) program, in which approximately 23.0 million individuals were enrolled, translating to a coverage rate of 99%. Patients with depressive disorders were identified based on International Classification of Diseases codes. The process indicators of depression care quality included visit, duration, and dose adequacy. The outcome indicators included the rate of psychiatric hospitalisation, emergency visit, self-harm hospitalisation, and suicide. Results: The prevalence of treated depressive disorders increased from 1.61% in 2007 to 1.92% in 2016, i.e., a 25% increase, whereas the incidence of first-ever or recurrent depressive disorder did not change significantly. The number of patients treated by psychiatrists and in community clinics also increased. The quality of depression care improved, the proportion of patients receiving minimum psychiatric clinic follow-up and adequate medication increased, and the rate of emergency visits, psychiatric hospitalisation, and self-harm hospitalisation declined. Conclusion: The community-based psychiatric services increased and the quality indicators of depression care in Taiwan improved during 2007–2016. The causality warrants further investigations.

Abstract Background Most individuals with major depressive disorder (MDD) receive either no care or inadequate care. The aims of this study is to investigate potential determinants of effective treatment coverage. Methods In order to examine obstacles to providing or receiving care, the type of care received, and the quality and use of that care in a representative sample of individuals with MDD, we analyzed data from 17 WHO World Mental Health Surveys conducted in 15 countries (9 high-income and 6 low/middle-income). Of 35,012 respondents, 3341 had 12-month MDD. We explored the association of socio-economic and demographic characteristics, insurance, and severity with effective treatment coverage and its components, including type of treatment, adequacy of treatment, dose, and adherence. Results High level of education (OR = 1.63; 1.19, 2.24), private insurance (OR = 1.62; 1.06, 2.48), and age (30–59yrs; OR = 1.58; 1.21, 2.07) predicted effective treatment coverage for depression in a multivariable logistic regression model. Exploratory bivariate models further indicate that education may follow a dose—response relation; that people with severe depression are more likely to receive any services, but less likely to receive adequate services; and that in low and middle-income countries, private insurance (the only significant predictor) increased the likelihood of receiving effective treatment coverage four times. Conclusions In the regression models, specific social determinants predicted effective coverage for major depression. Knowing the factors that determine who does and does not receive treatment contributes to improve our understanding of unmet needs and our ability to develop targeted interventions.

BackgroundAs a mental health issue, suicide is a growing global concern, with patients who have post-traumatic stress disorder (PTSD) being at particularly high risk. This study aimed to investigate whether the link between PTSD and suicidal ideation is mediated by trauma-related guilt.MethodsData were obtained from Wave 1, Time 1 (November 2016), and Time 2 (March 2017) of the National Survey for Stress and Health (NSSH) in Japan. The NSSH is an online longitudinal survey conducted on Japan’s national population aged 18 years and older. The cumulative response rate of the survey was 66.7% at Time 2. A total of 1,005 patients with PTSD were included for analyses. The severity of PTSD symptoms was assessed with PTSD DSM-5 Checklist, and the trauma-related guilt were assessed using the two subscales (hindsight-bias/responsibility and global guilt scale) of the trauma-related guilt inventory (TRGI). Suicidal ideation was evaluated using the suicidal ideation attributes scale (SIDAS). Pearson’s correlation was used to investigate the associations among PTSD symptoms, TRGI scores, and SIDAS scores. Causal mediation analysis was applied to evaluate the causal relationship between PTSD, trauma-related guilt, and suicidal ideation.ResultsPearson’s correlation did not show patients’ age, gender, and household income significantly associated with SIDAS scores. On the other hand, severities of PTSD symptoms (r = 0.361, p

Code-mixing is a phenomenon where at least two languages are combined in a hybrid manner in the context of a single conversation. The use of mixed language is widespread in multilingual and multicultural countries and poses significant challenges for the development of automated language processing tools. In Taiwan’s electronic health record (EHR) systems, unstructured EHR texts are usually represented in a mixture of English and Chinese which increases the difficulty for de-identification and synthetization of protected health information (PHI). We explored this problem by applying several state-of-the-art pre-trained mono- and multilingual language models and propose to exploit the principle-based approach (PBA) for the tasks of PHI recognition and resynthesis on a code-mixed EHR corpus annotated with 6 main categories and 25 subcategories of PHIs. A hierarchical principle slot schema is defined in the PBA to encode knowledge of code-mixed PHIs and utilize slots to learn from the training set to assemble principles for recognizing PHI mentions and synthesizing surrogates simultaneously. In addition, a semantic disambiguation process is implemented to disambiguate ambiguous PHI categories in the de-identification process and to dynamically extend the knowledge encoded in PBA during the knowledge augmentation process. The experiment results demonstrate that the proposed method can achieve the best micro- and macro-F-scores in comparison to the other mono- and multilingual language models fine-tuned on our code-mixed corpus.

Abstract Aims We explored long-term employment status and income before and after depression diagnosis among men and women and at different working ages in Taiwan. Methods Data from 2006 to 2019 were obtained from the National Health Insurance Research Database (NHIRD). Individuals with newly diagnosed depressive disorder aged 15 to 64 years during the study period were identified. An equal number of individuals without depression were matched for their demographic and clinical characteristics. Employment outcomes included employment status, which was categorized into employed or unemployed, and annual income. Based on the occupation categories and monthly insurance salary recorded in the Registry for Beneficiaries of the NHIRD, a subject was defined as unemployed if he or she differed from the income earner or the occupation category was unemployed. Monthly income was defined as zero for unemployed subjects and proxied as monthly insurance salary for others. Annual income was the sum of monthly income in each observation year. Results A total of 420,935 individuals with depressive disorder were included in the study, and an equal number of individuals with not diagnosed depression served as controls. Employment rate and income were lower in the depression group than in the control group before the year of diagnosis, with a difference of 5.7% in employment rate and USD 1,173 in annual income. This gap increased considerably after the year of diagnosis (7.3% in employment rate and USD 1,573 in annual incomes) and further widened in the subsequent years (8.1% in employment rate and USD 2,006 in annual incomes in the 5th following year). The drops in the employment rate and income caused by depression were more evident in men and older age groups than in women and younger age groups, respectively. However, the reduction in employment rate and income in the following years after the diagnosis was more considerable among younger age groups. Conclusions The effect of depression on employment status and income was significant during the year of diagnosis and continued afterwards. The effect on employment outcomes varied between genders and across all age groups.

ObjectiveSeveral prognostic models of suicide risk have been published; however, few have been implemented in Japan using longitudinal cohort data. The aim of this study was to identify suicide risk factors for suicidal ideation in the Japanese population and to develop a machine-learning model to predict suicide risk in Japan.Materials and MethodsData was obtained from Wave1 Time 1 (November 2016) and Time 2 (March 2017) of the National Survey for Stress and Health in Japan, were incorporated into a suicide risk prediction machine-learning model, trained using 65 items related to trauma and stress. The study included 3,090 and 2,163 survey respondents >18 years old at Time 1 and Time 2, respectively. The mean (standard deviation, SD) age was 44.9 (10.9) years at Time 1 and 46.0 (10.7) years at Time 2. We analyzed the participants with increased suicide risk at Time 2 survey. Model performance, including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, were also analyzed.ResultsThe model showed a good performance (AUC = 0.830, 95% confidence interval = 0.795–0.866). Overall, the model achieved an accuracy of 78.8%, sensitivity of 75.4%, specificity of 80.4%, positive predictive value of 63.4%, and negative predictive value of 87.9%. The most important risk factor for suicide risk was the participants' Suicidal Ideation Attributes Scale score, followed by the Sheehan Disability Scale score, Patient Health Questionnaire-9 scores, Cross-Cutting Symptom Measure (CCSM-suicidal ideation domain, Dissociation Experience Scale score, history of self-harm, Generalized Anxiety Disorder-7 score, Post-Traumatic Stress Disorder check list-5 score, CCSM-dissociation domain, and Impact of Event Scale-Revised scores at Time 1.ConclusionsThis prognostic study suggests the ability to identify patients at a high risk of suicide using an online survey method. In addition to confirming several well-known risk factors of suicide, new risk measures related to trauma and trauma-related experiences were also identified, which may help guide future clinical assessments and early intervention approaches.

ObjectiveThis study applied an ecological-based analysis aimed to evaluate on a global scale the association between greenness exposure and suicide mortality.MethodsSuicide mortality data provided by the Institute for Health Metrics and Evaluation and the Normalized Difference Vegetation Index (NDVI) were employed. The generalized additive mixed model was applied to evaluate with an adjustment of covariates the association between greenness and suicide mortality. Sensitivity tests and positive-negative controls also were used to examine less overt insights. Subgroup analyses were then conducted to investigate the effects of greenness on suicide mortality among various conditions.ResultsThe main finding of this study indicates a negative association between greenness exposure and suicide mortality, as greenness significantly decreases the risk of suicide mortality per interquartile unit increment of NDVI (relative risk = 0.69, 95%CI: 0.59–0.81). Further, sensitivity analyses confirmed the robustness of the findings. Subgroup analyses also showed a significant negative association between greenness and suicide mortality for various stratified factors, such as sex, various income levels, urbanization levels, etc.ConclusionsGreenness exposure may contribute to a reduction in suicide mortality. It is recommended that policymakers and communities increase environmental greenness in order to mitigate the global health burden of suicide.

ObjectivePrior studies have shown that greenness can reduce the burden of depressive disorders. However, most were focused on local-scale analyses while limited evaluated globally. We aimed to investigate the association between greenness and the burden of depressive disorders using data from 183 countries worldwide.MethodsWe used the normalized difference vegetation index (NDVI) to estimate greenness. Country-level disability-adjusted life year (DALY) loss due to depressive disorders was used to represent depressive disorder burdens. A generalized linear mixed model was applied to assess the relationship between greenness and depressive disorders after controlling for covariates. Stratified analyses were conducted to determine the effects of greenness across several socio-demographic levels.ResultsThe findings showed a significant negative association between greenness and the health burden of depressive disorders with a coefficient of −0.196 (95% CI: −0.356, −0.035) in the DALY changes per interquartile unit increment of NDVI. The stratified analyses suggested beneficial effects of greenness on depressive disorders across sex, various age groups especially for those aged

The introduction of pre-trained language models in natural language processing (NLP) based on deep learning and the availability of electronic health records (EHRs) presents a great opportunity to transfer the “knowledge” learned from data in the general domain to enable the analysis of unstructured textual data in clinical domains. This study explored the feasibility of applying NLP to a small EHR dataset to investigate the power of transfer learning to facilitate the process of patient screening in psychiatry. A total of 500 patients were randomly selected from a medical center database. Three annotators with clinical experience reviewed the notes to make diagnoses for major/minor depression, bipolar disorder, schizophrenia, and dementia to form a small and highly imbalanced corpus. Several state-of-the-art NLP methods based on deep learning along with pre-trained models based on shallow or deep transfer learning were adapted to develop models to classify the aforementioned diseases. We hypothesized that the models that rely on transferred knowledge would be expected to outperform the models learned from scratch. The experimental results demonstrated that the models with the pre-trained techniques outperformed the models without transferred knowledge by micro-avg. and macro-avg. F-scores of 0.11 and 0.28, respectively. Our results also suggested that the use of the feature dependency strategy to build multi-labeling models instead of problem transformation is superior considering its higher performance and simplicity in the training process.

[This corrects the article DOI: 10.1371/journal.pone.0179424.].

Background Medical treatment should be tailored to an individual's characteristics to optimize treatment benefits. We examined whether case‐only analyses from spontaneous reporting systems can detect host‐medication interactions in oral antidiabetic drug‐associated myocardial infarction. Methods and Results Interaction between sex and use of oral antidiabetic drugs was mined among patients with myocardial infarction in the US Food and Drug Administration Adverse Event Reporting System from 2004 to 2014, including 55 718 males and 42 428 females. The odds ratio (OR) of multiplicative interactions was used to estimate sex‐drug interaction. Detected signs of these interactions were then validated by a nested case‐control study utilizing a healthcare record database, Taiwan's National Health Insurance Research Database, from 2001 to 2014, including 31 585 cases and 126 340 controls. In the US Food and Drug Administration Adverse Event Reporting System, a higher proportion of male than female patients used metformin (10.32% in males versus 7.82% in females) and sulfonylureas (4.75% in males versus 3.43% in females); after adjusting for patients’ pharmacy‐based chronic disease score, males had a higher risk of metformin‐associated (OR=1.07; 99% confidence interval, 1.00–1.14) and sulfonylureas‐associated (OR=1.21; 99% confidence interval, 1.10–1.33) myocardial infarction than females. Detected signs of sex‐drug interactions were validated in the National Health Insurance Research Database (OR for metformin=1.14; 99% confidence interval, 1.03–1.26; OR for sulfonylureas=1.13; 99% confidence interval, 1.02–1.25). Conclusions Males have a higher risk of metformin‐ and sulfonylureas‐associated myocardial infarction than females, which suggests that sex‐drug interactions are a key issue in diabetes mellitus treatment plan development. This case‐only approach using information from spontaneous reporting systems may be a potential tool for screening host‐medication interactions that cause adverse events.

BACKGROUND:The association between antipsychotic use and the risk of stroke in schizophrenic patients is controversial. We sought to study the association in a nationwide cohort with schizophrenia. METHODS:Using a retrospective cohort of patients with schizophrenia (N = 31,976) derived from the Taiwan National Health Insurance Research Database, 802 new-onset cases of stroke were identified within 10 years of follow-up (from 2000 through 2010). We designed a case-crossover study using 14-day windows to explore the risk factors of stroke and the association between antipsychotic drugs and the risk of stroke. We analyzed the risks of individual antipsychotics on various subgroups of stroke including ischemic, hemorrhagic, and other strokes, and the risks based on the antipsychotic receptor-binding profile of each drug. RESULTS:Use of any second-generation antipsychotic was associated with an increased risk of stroke (adjusted risk ratio = 1.45, P = .009) within 14 days while the use of any first-generation antipsychotic was not. Intriguingly, the use of any second-generation antipsychotic was associated with ischemic stroke but not hemorrhagic stroke. The antipsychotic receptor-binding profile analysis showed that the antihistamine 1 receptor was significantly associated with ischemic stroke (adjusted risk ratio = 1.72, P = .037), and the sensitivity analysis based on the 7-day window of exposure validated the association (adjusted risk ratio = 1.87, P = .015). CONCLUSIONS:Use of second-generation antipsychotic drugs appeared to be associated with an increased risk of ischemic stroke in the patients studied, possibly mediated by high affinity for histamine-1 receptor blockade. Further research regarding the underlying biological mechanism and drug safety is suggested.

PurposeThe aim of this study was to evaluate the concordance between claims records in the National Health Insurance Research Database and patient self-reports on clinical diagnoses, medication use, and health system utilization.MethodsIn this study, we used the data of 15,574 participants collected from the 2005 Taiwan National Health Interview Survey. We assessed positive agreement, negative agreement, and Cohen's kappa statistics to examine the concordance between claims records and patient self-reports.ResultsKappa values were 0.43, 0.64, and 0.61 for clinical diagnoses, medication use, and health system utilization, respectively. Using a strict algorithm to identify the clinical diagnoses recorded in claims records could improve the negative agreement; however, the effect on positive agreement and kappa was diverse across various conditions.ConclusionWe found that the overall concordance between claims records in the National Health Insurance Research Database and patient self-reports in the Taiwan National Health Interview Survey was moderate for clinical diagnosis and substantial for both medication use and health system utilization.

Substance misuse is associated with cognitive dysfunction. We used a stop signal task to examine deficits in cognitive control in individuals with opioid dependence (OD). We examined how response inhibition and post-error slowing are compromised and whether methadone maintenance treatment (MMT), abstinence duration, and psychiatric comorbidity are related to these measures in individuals with OD.Two-hundred-and-sixty-four men with OD who were incarcerated at a detention center and abstinent for up to 2 months (n = 108) or at a correctional facility and abstinent for approximately 6 months (n = 156), 65 OD men under MMT at a psychiatric clinic, and 64 age and education matched healthy control (HC) participants were assessed. We computed the stop signal reaction time (SSRT) to index the capacity of response inhibition and post-error slowing (PES) to represent error-related behavioral adjustment, as in our previous work. We examined group effects with analyses of variance and covariance analyses, followed by planned comparisons. Specifically, we compared OD and HC participants to examine the effects of opioid dependence and MMT and compared OD sub-groups to examine the effects of abstinence duration and psychiatric comorbidity.The SSRT was significantly prolonged in OD but not MMT individuals, as compared to HC. The extent of post-error slowing diminished in OD and MMT, as compared to HC (trend; p = 0.061), and there was no difference between the OD and MMT groups. Individuals in longer abstinence were no less impaired in these measures. Furthermore, these results remained when psychiatric comorbidities including misuse of other substances were accounted for.Methadone treatment appears to be associated with relatively intact cognitive control in opioid dependent individuals. MMT may facilitate public health by augmenting cognitive control and thereby mitigating risky behaviors in heroin addicts.