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Objective To evaluate the association between antipsychotic use in pregnancy and the risk of congenital malformations in children. Data sources Searches of PubMed, EMBASE, PsycINFO and Cochrane Library were conducted from inception to 06 Jan 2020 using keywords: antipsychotics, pregnancy, pregnancy complication and congenital abnormalities. Study selection Of 38 reports initially identified as being of potential interest, 13 studies met our inclusion criteria: English observational studies that examined the association between gestational antipsychotic use and congenital malformations in children. Data extraction Data were extracted independently by two investigators including the publication year, study site, study period, data source, study design, sample size, medication exposure, exposure period and pregnancy definition, exposure as well as outcome ascertainment, selection of study and comparison group, confounding adjustment, statistical analysis, and method of linkage between mother and children. Risk estimates were pooled using a random-effect model and the I2 statistic was used to evaluate the degree of heterogeneity. Results 13 studies met our systematic review inclusion criteria. Six studies with a total of 2,515,272 pregnancy episodes were included in our meta-analysis which provided a pooled adjusted risk ratio of 1.23, 95% confidence interval: 0.96-1.58. The I2 result showed moderate heterogeneity between studies (I2 =35.2%, p=0.173). Conclusions We did not find strong evidence of an association between prenatal exposure to antipsychotic medications and the risk of congenital malformations in children. We recommend further studies investigate this association, focusing on specific medication classes and dose responses, which would help clinicians decide whether or not to prescribe certain antipsychotics during pregnancy.

Introduction: Information regarding availability of electronic healthcare databases in the Asia-Pacific region is critical for planning vaccine safety assessments particularly, as COVID-19 vaccines are introduced. This study aimed to identify data sources in the region, potentially suitable for vaccine safety surveillance. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE). Methods: Nineteen countries targeted for database reporting were identified using published country lists and review articles. Surveillance capacity was assessed using two surveys: a 9-item introductory survey and a 51-item full survey. Survey questions related to database characteristics, covariate and health outcome variables, vaccine exposure characteristics, access and governance, and dataset linkage capability. Other questions collated research/regulatory applications of the data and local publications detailing database use for research. Results: Eleven databases containing vaccine-specific information were identified across 8 countries. Databases were largely national in coverage (8/11, 73%), encompassed all ages (9/11, 82%) with population size from 1.4 to 52 million persons. Vaccine exposure information varied particularly for standardized vaccine codes (5/11, 46%), brand (7/11, 64%) and manufacturer (5/11, 46%). Outcome data were integrated with vaccine data in 6 (55%) databases and available via linkage in 5 (46%) databases. Data approval processes varied, impacting on timeliness of data access. Conclusions: Variation in vaccine data availability, complexities in data access including, governance and data release approval procedures, together with requirement for data linkage for outcome information, all contribute to the challenges in building a distributed network for vaccine safety assessment in the Asia-Pacific and globally. Common data models (CDMs) may help expedite vaccine safety research across the region. Refereed/Peer-reviewed

Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effects. Propensity score methods allow researchers to reduce bias from measured confounding by summarizing the distributions of many measured confounders in a single score based on the probability of receiving treatment. This score can then be used to mitigate imbalances in the distributions of these measured confounders between those who received the treatment of interest and those in the comparator population, resulting in less biased treatment effect estimates. This methodology was formalized by Rosenbaum and Rubin in 1983 and, since then, has been used increasingly often across a wide variety of scientific disciplines. In this review article, we provide an overview of propensity scores in the context of real-world evidence generation with a focus on their use in the setting of single treatment decisions, that is, choosing between two therapeutic options. We describe five aspects of propensity score analysis: alignment with the potential outcomes framework, implications for study design, estimation procedures, implementation options, and reporting. We add context to these concepts by highlighting how the types of comparator used, the implementation method, and balance assessment techniques have changed over time. Finally, we discuss evolving applications of propensity scores.

Purpose Comparative gastrointestinal bleeding (GIB) risk between rivaroxaban and low-dose aspirin is unknown in patients with atrial fibrillation (AF). This study investigated GIB risk with rivaroxaban vs aspirin among two separate AF cohorts in Hong Kong and the United Kingdom, using a common protocol approach. Methods This was a population-based cohort study using separate data from the Clinical Data Analysis and Reporting System (CDARS) of the Hong Kong Hospital Authority (2010-2018) and The Health Improvement Network (THIN) database in the United Kingdom (2011-2017). Patients with AF newly prescribed aspirin or rivaroxaban were included. Cox proportional hazards regression was used to compare GIB risks for rivaroxaban vs aspirin, accounting for confounders using propensity score fine stratification approach. Results In CDARS, 29 213 patients were included; n = 1052 (rivaroxaban), n = 28 161 (aspirin). Crude GIB event rates per 100 patient-years in CDARS were 3.0 (aspirin) and 2.6 (rivaroxaban). No difference in GIB risk was observed between rivaroxaban and aspirin overall (HR = 1.04, 95%CI = 0.76-1.42), and in dose-stratified analyses (HR = 1.21, 95%CI = 0.84-1.74 [20 mg/day]; HR = 0.80, 95%CI = 0.44-1.45 [≤15 mg/day]). In THIN, 11 549 patients were included, n = 3496 (rivaroxaban) and n = 8053 (aspirin). Crude GIB event rates were 1.3 (aspirin) and 2.4 (rivaroxaban) per 100 patient-years. No difference in GIB risk was observed between rivaroxaban and aspirin overall (HR = 1.40, 95%CI = 1.00-1.98) and low-dose rivaroxaban (≤15 mg/day) (HR = 1.00, 95%CI = 0.56-1.30), but increased GIB risk was observed for rivaroxaban 20 mg/day vs aspirin (HR = 1.57, 95%CI = 1.08-2.29). Conclusion In patients with AF, GIB risk was comparable between aspirin and rivaroxaban ≤15 mg/day. GIB risk for rivaroxaban 20 mg/day vs aspirin remains uncertain and warrants further investigation.

AIMS The aim of this study was to investigate the initial cardiovascular prescription patterns in patients after their first cardiovascular events, and to identify factors associated with cardiovascular polypharmacy. METHODS This was a cross-sectional study including patients aged ≥ 45 years with the first record of coronary heart disease (CHD) or stroke between 2007 and 2016 using The Health Improvement Network database. This study investigated the patterns of cardiovascular drugs prescribed during the first 90 days after the first cardiovascular events. Logistic regression was used to examine the association between patients' baseline characteristics and cardiovascular polypharmacy (≥5 cardiovascular drugs). RESULTS A total of 121,600 (59,843 CHD and 61,757 stroke) patients were included in the study. The mean age was 69.5 ± 11.9 years. The proportion of patients who were prescribed 0-1, 2-3, 4-5 drugs and ≥6 drugs were 11.0%, 29.8%, 38.6% and 20.5%, respectively. Factors associated with cardiovascular polypharmacy were sex (female: OR 0.74, 95% CI 0.72-0.76 vs male), age (75-84 years old: OR 0.50, 0.47-0.53 vs 45-54 years old), smoking status (current smoking: OR 1.29, 1.15-1.24 vs never), body mass index (obesity: OR 1.38, 1.34-1.43 vs normal), deprivation status (most deprived: OR 1.09, 1.04-1.14 vs least deprived) and Charlson comorbidity index (index ≥5: OR 1.25, 1.16-1.35 vs index 0). CONCLUSION Multiple cardiovascular drugs treatment was common in patients with CVD in the UK. High-risk factors of CVD were also associated with cardiovascular polypharmacy. Further studies are warranted to assess the impact of cardiovascular polypharmacy and its interaction on CVD recurrence and mortality.

Introduction The impact of the COVID‐19 pandemic on the global use of anti‐dementia medication is unknown. We aimed to determine the changes of anti‐dementia medication use in Europe (EU) and North America (NA) during the pandemic. Methods This is a cross‐sectional study using sales data of anti‐dementia medications in 2019 and 2020 from 34 EU and NA countries. The monthly uses of anti‐dementia medications from January through June in 2020 were compared to the corresponding months in 2019 for each country. Results In the pre‐pandemic period of January to March 2020, 70 out of 102 (3 months x 34 countries) measurements (68.6%) of monthly sales volume showed an increase. In contrast, 76.5% and 85.3% countries showed reduced sales in April and May 2020, respectively. Discussion These findings indicate changes in use of anti‐dementia medications during the pandemic. The delivery of pharmaceutical care for dementia patients may be heavily disrupted in certain countries.

Background Prescription sequence symmetry analysis (PSSA) is a signal detection method for adverse drug events. Its capacity to consistently detect adverse drug events across different settings has not been tested. We aimed to determine the consistency of PSSA results for detecting positive and negative control adverse drug events across different settings. Methods Using a distributed network model, we analyzed prescription dispensing data using PSSA in Australia, Hong Kong, Japan, Korea, and Taiwan. Positive control was amiodarone and thyroxine, as a marker of amiodarone-induced hypothyroidism, a known adverse event with a clear temporal relationship to amiodarone initiation. Negative controls were amiodarone and allopurinol, as a marker of amiodarone-induced gout and thyroxine and allopurinol, as a marker of thyroxine-induced gout. Gout is not recorded as an adverse event in product information for either medicine. Adjusted sequence ratios (ASR) were calculated for each country. Pooled estimates were obtained by using the generic inverse variance method. Results A positive association was identified between amiodarone and thyroxine in all settings with a pooled ASR 2.63 (95% confidence interval (CI) 1.47–4.72). Temporal analysis showed the effect occurred within the first few weeks of treatment. No significant associations were found for the negative controls in any setting; pooled ASR were 0.76 (95%CI 0.62–0.93) and 0.98 (95%CI 0.85–1.12) for amiodaroneallopurinol and thyroxine-allopurinol, respectively. Conclusion Despite different health settings, different populations, and different patterns of medicine utilization, PSSA gave consistent estimates across countries for a well-known positive association and two negative control adverse events. © 2015 The Authors Pharmacoepidemiology and Drug Safety Published by John Wiley & Sons Ltd. key words—prescription symmetry analysis; pharmacovigilance; amiodarone; hyperthyroidism; pharmacoepidemiology