Neuropsychiatric Events Associated with Leukotriene-Modifying Agents: A Systematic Review.

INTRODUCTION: Leukotriene-modifying agents (LTMAs) including montelukast, zafirlukast, and zileuton are approved by the US Food and Drug Administration (FDA) for the treatment of asthma and allergic rhinitis. Various neuropsychiatric events (NEs) have been reported; however, the evidence of the association is conflicting. This systematic review investigates the association between NEs and LTMAs by assessing the relevant published literature. METHODS: PubMed, EMBASE, MEDLINE, and Cochrane Library were searched using keywords. Studies designed to investigate the association were eligible for inclusion without restriction to any study design or language. The primary outcome was defined as suicidal conditions, while secondary outcomes included all other NEs. RESULTS: Thirty-three studies were included for a narrative review. Four observational studies did not find a significant association, while ten pharmacovigilance studies using different global databases detected the signals. Notably, some studies suggest that the FDA warning issued in 2008 might have influenced the reporting rate of NEs as a result of increased awareness. LIMITATIONS: The risk of NEs was not quantified, because of the lack of randomized controlled trials and observational studies investigating the association. CONCLUSION: Many pharmacovigilance studies have been conducted to determine the association between NEs and LTMAs, but there is limited evidence from observational studies. High-quality epidemiological studies should be conducted to evaluate the association and quantify the risk, not only in children, but also in adults

COVID-19 Vaccination Preferences of University Students and Staff in Hong Kong

IMPORTANCE: COVID-19 has required universities to rapidly develop vaccination policies for students and staff, yet little is known about the preferences of these individuals toward vaccination. OBJECTIVE: To quantify student and staff preferences for COVID-19 vaccination at a university in Hong Kong. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional online survey study was conducted from July 20 to September 21, 2021, before the announcement of a campus-wide vaccine mandate. A survey of 42 451 eligible university students and staff used discrete-choice experiment methods to quantify 7 attributes of COVID-19 vaccination: risk of a mild or moderate adverse event after vaccination, risk of a severe adverse event after vaccination, efficacy against COVID-19 infection, efficacy against severe manifestation of COVID-19 infection, duration of protection after vaccination, incentive for completing vaccination, and out-of-pocket costs. MAIN OUTCOMES AND MEASURES: A mixed logit regression model was used to estimate the preferences of attributes for COVID-19 vaccines and marginal willingness to pay (mWTP) adjusted for background characteristics, role, vaccination, and COVID-19 infection status of family or friends, adverse event status after vaccination among family and friends of participants, and scenario block. RESULTS: Among 42 451 eligible university students and staff invited, 3423 individuals completed the survey (mean [SD] age, 27.1 [9.9] years; 2053 [60.0%] women). Participants included 2506 students (73.2%) and 917 staff (26.8%), with a response rate of 8.1%. Quarantine-free travel was preferred (β = 0.86; 95% CI, 0.72-0.99; mWTP: $235.9; 95$190.3-$294.2), followed by efficacy against any COVID-19 infection (β = 0.30; 95$84.1; 95% CI, $71.8-$100.8), against severe manifestation of COVID-19 infection (β = 0.25; 95% CI, 0.24-0.27; mWTP: $69.7; 95$465-$653), and risk of severe adverse events following vaccination (β = −0.24; 95$66.8; 95% CI, −$81.5 to −$55.3). Participants were less concerned about protection duration (β = 0.17; 95% CI, 0.15-0.18; mWTP: $46.0; 95$38.6-$56.2) and risk of mild to moderate adverse events (β = −0.12; 95$32.7; 95% CI, −$41.2 to −$26.4). CONCLUSIONS AND RELEVANCE: Preference of all attributes were significant and were considered important by the participants for vaccine decision-making. Insights drawn could assist policy makers in future vaccination decisions, such as campus vaccine mandate and requirement of a third dose

Towards a global partnership model in interprofessional education for cross-sector problem-solving

Objectives A partnership model in interprofessional education (IPE) is important in promoting a sense of global citizenship while preparing students for cross-sector problem-solving. However, the literature remains scant in providing useful guidance for the development of an IPE programme co-implemented by external partners. In this pioneering study, we describe the processes of forging global partnerships in co-implementing IPE and evaluate the programme in light of the preliminary data available. Methods This study is generally quantitative. We collected data from a total of 747 health and social care students from four higher education institutions. We utilized a descriptive narrative format and a quantitative design to present our experiences of running IPE with external partners and performed independent t-tests and analysis of variance to examine pretest and posttest mean differences in students’ data. Results We identified factors in establishing a cross-institutional IPE programme. These factors include complementarity of expertise, mutual benefits, internet connectivity, interactivity of design, and time difference. We found significant pretest–posttest differences in students’ readiness for interprofessional learning (teamwork and collaboration, positive professional identity, roles, and responsibilities). We also found a significant decrease in students’ social interaction anxiety after the IPE simulation. Conclusions The narrative of our experiences described in this manuscript could be considered by higher education institutions seeking to forge meaningful external partnerships in their effort to establish interprofessional global health education

Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer.

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to women of European ancestry. We generated genotypes for more than 11 million SNPs by imputation using the 1000 Genomes Project reference panel, and we identified 15 new loci associated with breast cancer at P < 5 × 10(-8). Combining association analysis with ChIP-seq chromatin binding data in mammary cell lines and ChIA-PET chromatin interaction data from ENCODE, we identified likely target genes in two regions: SETBP1 at 18q12.3 and RNF115 and PDZK1 at 1q21.1. One association appears to be driven by an amino acid substitution encoded in EXO1.BCAC is funded by Cancer Research UK (C1287/A10118, C1287/A12014) and by the European Community's Seventh Framework Programme under grant agreement 223175 (HEALTH-F2-2009-223175) (COGS). Meetings of the BCAC have been funded by the European Union COST programme (BM0606). Genotyping on the iCOGS array was funded by the European Union (HEALTH-F2-2009-223175), Cancer Research UK (C1287/A10710, C8197/A16565), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer program and the Ministry of Economic Development, Innovation and Export Trade of Quebec, grant PSR-SIIRI-701. Combination of the GWAS data was supported in part by the US National Institutes of Health (NIH) Cancer Post-Cancer GWAS initiative, grant 1 U19 CA148065-01 (DRIVE, part of the GAME-ON initiative). For a full description of funding and acknowledgments, see the Supplementary Note.This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ng.324

Trends in statin prescription prevalence, initiation, and dosing: Hong Kong, 2004-2015

Background and aims Clinical practice guidelines recommend specific statin doses for the primary and secondary prevention of cardiovascular disease. Little is known about how statin utilization and dosing have evolved over time in Hong Kong. The aim of this study was to describe trends in statin prevalence, initiation, and dosing from 2004 to 2015. Methods Patients receiving public health services, who were prescribed a statin, were included if they received a lipid test at a single center (n = 580,672). Using the territory-wide electronic health record, prescribed daily statin dose, nondaily dose frequency, and statin dose intensity were determined for statin prescriptions from 2004 to 2015. Statin prescription prevalence and initiation rates were estimated using the appropriate at-risk population in the hospital catchment area as the denominator. Prescribed daily doses were stratified by primary or secondary cardiovascular prevention status to assess changes in statin dosing over time. Results The prescription prevalence of statins was higher in 2015 (8.68%; 95% CI, 8.60%–8.75%) as compared with 2004 (1.82%; 95% confidence interval [CI], 1.78%–1.86%). Initiation rates for new statin users increased from 2004 to 2013. High-intensity statins were infrequently prescribed. New users generally had higher statin initiation rates for primary prevention. There were small increases in the prescribed daily doses of statins. Nondaily statin dosing was infrequent (0.42% of all prescriptions). Conclusions The prevalence and initiation of statin prescriptions increased in Hong Kong, and was in part driven by low-intensity statins for the primary prevention of cardiovascular disease

Greater variability in lipid measurements associated with cardiovascular disease and mortality: A 10-year diabetes cohort study.

AIM: To examine the associations between variability in lipids and the risk of cardiovascular disease (CVD) and mortality in patients with type 2 diabetes based on low-density lipoprotein-cholesterol (LDL-C), the total cholesterol (TC) to high-density lipoprotein-cholesterol (HDL-C) ratio and triglycerides (TG). MATERIALS AND METHODS: A retrospective cohort study included 125 047 primary care patients with type 2 diabetes aged 45-84 years without CVD during 2008-2012. The variability of LDL-C, TC to HDL-C and TG was determined using the standard deviation of variables in a mixed effects model to minimize regression dilution bias. The associations between variability in lipids and CVD and mortality risk were assessed by Cox regression. Subgroup analyses based on patients' baseline characteristics were also conducted. RESULTS: A total of 19 913 CVD events and 15 329 mortalities were recorded after a median follow-up period of 77.5 months (0.8 million person-years), suggesting a positive linear relationship between variability in lipids and the risk of CVD and mortality. Each unit increase in the variability of LDL-C (mmol/L), the TC to HDL-C ratio and TG (mmol/L) was associated with a 27% (HR: 1.27 [95% CI: 1.20-1.34]), 31% (HR:1.31 [95% CI: 1.25-1.38]) and 9% (HR: 1.09 [95% CI: 1.04-1.15]) increase in the risk of composite endpoint of CVD and mortality, respectively. Age-specific effects were also found when comparing LDL-C variability, with patients aged 45-54 years (HR: 1.70 [95% CI: 1.42-2.02]) exhibiting a 53% increased risk for the composite endpoints than those aged 75-84 years (HR: 1.11 [95% CI: 1.01-1.23]). Similar age effects were observed for both the TC to HDL-C ratio and TG variability. Significant associations remained consistent among most of the subgroups. CONCLUSIONS: Variability in respective lipids are significant factors in predicting CVD and mortality in primary care patients with type 2 diabetes, with the strongest effects related to LDL-C and the TC to HDL-C ratio and most significant in the younger age group of patients aged 45-54 years. Further study is warranted to confirm these findings

Mortality-causing mechanisms and healthcare resource utilisation of treatment-resistant depression: a six-year population-based cohort study

Background: Few studies investigated the mechanisms of treatment-resistant depression (TRD) leading to the worsened survival outcome, and economic evidence was mostly restricted to short follow-ups. We aimed to examine the association and potential mediators between TRD and all-cause mortality, and estimate a longer-term associated health resource utilisation pattern. Methods: This was a population-based cohort study using territory-wide electronic medical records in Hong Kong. Incident depression patients diagnosed in 2014 were followed up from the first diagnosis to death or December 2019 for TRD identification. We matched the TRD cohort 1:4 to the non-TRD cohort on propensity scores estimated by age, sex, history of physical disorders, and history of psychiatric conditions before depression diagnoses. Findings: 18% of incident patients developed TRD within six years of follow-up. Cox model showed that patients with TRD had 1⋅52-fold (95% CI: 1⋅14–2⋅02) greater risk of all-cause mortality, compared with non-TRD patients. Path analysis suggested that post-TRD psychiatric conditions significantly mediated 41⋅6% of mortality in patients with TRD (p=0.003). TRD was associated with 1⋅8-fold (95%CI: 1⋅63–2⋅00) higher healthcare costs compared to non-TRD patients over six years in negative binomial regression, with higher costs for both psychiatric and non-psychiatric services utilisation in all settings. Interpretation: Identifying patients with TRD and subsequent monitoring for post-TRD psychiatric diagnoses could be a way to reduce premature mortality. Multidisciplinary care involving both psychiatric and general medical professionals is also warranted to relieve the multifaceted impacts on healthcare resources and overall cost. Funding: Unconditional educational grant from Janssen

Logit tree models for discrete choice data with application to advice-seeking preferences among Chinese Christians

Logit models are popular tools for analyzing discrete choice and ranking data. The models assume that judges rate each item with a measurable utility, and the ordering of a judge’s utilities determines the outcome. Logit models have been proven to be powerful tools, but they become difficult to interpret if the models contain nonlinear and interaction terms. We extended the logit models by adding a decision tree structure to overcome this difficulty. We introduced a new method of tree splitting variable selection that distinguishes the nonlinear and linear effects, and the variable with the strongest nonlinear effect will be selected in the view that linear effect is best modeled using the logit model. Decision trees built in this fashion were shown to have smaller sizes than those using loglikelihood-based splitting criteria. In addition, the proposed splitting methods could save computational time and avoid bias in choosing the optimal splitting variable. Issues on variable selection in logit models are also investigated, and forward selection criterion was shown to work well with logit tree models. Focused on ranking data, simulations are carried out and the results showed that our proposed splitting methods are unbiased. Finally, to demonstrate the feasibility of the logit tree models, they were applied to analyze two datasets, one with binary outcome and the other with ranking outcome