Incidence of mental health diagnoses during the COVID-19 pandemic: a multinational network study.

AIMS: Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic. METHODS: By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions. RESULTS: A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021. CONCLUSIONS: Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.

Rates of Antipsychotic Drug Prescribing Among People Living With Dementia During the COVID-19 Pandemic.

Importance: Concerns have been raised that the use of antipsychotic medication for people living with dementia might have increased during the COVID-19 pandemic. Objective: To examine multinational trends in antipsychotic drug prescribing for people living with dementia before and during the COVID-19 pandemic. Design, Setting, and Participants: This multinational network cohort study used electronic health records and claims data from 8 databases in 6 countries (France, Germany, Italy, South Korea, the UK, and the US) for individuals aged 65 years or older between January 1, 2016, and November 30, 2021. Two databases each were included for South Korea and the US. Exposures: The introduction of population-wide COVID-19 restrictions from April 2020 to the latest available date of each database. Main Outcomes and Measures: The main outcomes were yearly and monthly incidence of dementia diagnosis and prevalence of people living with dementia who were prescribed antipsychotic drugs in each database. Interrupted time series analyses were used to quantify changes in prescribing rates before and after the introduction of population-wide COVID-19 restrictions. Results: A total of 857 238 people with dementia aged 65 years or older (58.0% female) were identified in 2016. Reductions in the incidence of dementia were observed in 7 databases in the early phase of the pandemic (April, May, and June 2020), with the most pronounced reduction observed in 1 of the 2 US databases (rate ratio [RR], 0.30; 95% CI, 0.27-0.32); reductions were also observed in the total number of people with dementia prescribed antipsychotic drugs in France, Italy, South Korea, the UK, and the US. Rates of antipsychotic drug prescribing for people with dementia increased in 6 databases representing all countries. Compared with the corresponding month in 2019, the most pronounced increase in 2020 was observed in May in South Korea (Kangwon National University database) (RR, 2.11; 95% CI, 1.47-3.02) and June in the UK (RR, 1.96; 95% CI, 1.24-3.09). The rates of antipsychotic drug prescribing in these 6 databases remained high in 2021. Interrupted time series analyses revealed immediate increases in the prescribing rate in Italy (RR, 1.31; 95% CI, 1.08-1.58) and in the US Medicare database (RR, 1.43; 95% CI, 1.20-1.71) after the introduction of COVID-19 restrictions. Conclusions and Relevance: This cohort study found converging evidence that the rate of antipsychotic drug prescribing to people with dementia increased in the initial months of the COVID-19 pandemic in the 6 countries studied and did not decrease to prepandemic levels after the acute phase of the pandemic had ended. These findings suggest that the pandemic disrupted the care of people living with dementia and that the development of intervention strategies is needed to ensure the quality of care.

Comparative Effectiveness and Safety Between Apixaban, Dabigatran, Edoxaban, and Rivaroxaban Among Patients With Atrial Fibrillation : A Multinational Population-Based Cohort Study.

BACKGROUND: Current guidelines recommend using direct oral anticoagulants (DOACs) over warfarin in patients with atrial fibrillation (AF), but head-to-head trial data do not exist to guide the choice of DOAC. OBJECTIVE: To do a large-scale comparison between all DOACs (apixaban, dabigatran, edoxaban, and rivaroxaban) in routine clinical practice. DESIGN: Multinational population-based cohort study. SETTING: Five standardized electronic health care databases, which covered 221 million people in France, Germany, the United Kingdom, and the United States. PARTICIPANTS: Patients who were newly diagnosed with AF from 2010 through 2019 and received a new DOAC prescription. MEASUREMENTS: Database-specific hazard ratios (HRs) of ischemic stroke or systemic embolism, intracranial hemorrhage (ICH), gastrointestinal bleeding (GIB), and all-cause mortality between DOACs were estimated using a Cox regression model stratified by propensity score and pooled using a random-effects model. RESULTS: A total of 527 226 new DOAC users met the inclusion criteria (apixaban, n = 281 320; dabigatran, n = 61 008; edoxaban, n = 12 722; and rivaroxaban, n = 172 176). Apixaban use was associated with lower risk for GIB than use of dabigatran (HR, 0.81 [95% CI, 0.70 to 0.94]), edoxaban (HR, 0.77 [CI, 0.66 to 0.91]), or rivaroxaban (HR, 0.72 [CI, 0.66 to 0.79]). No substantial differences were observed for other outcomes or DOAC-DOAC comparisons. The results were consistent for patients aged 80 years or older. Consistent associations between lower GIB risk and apixaban versus rivaroxaban were observed among patients receiving the standard dose (HR, 0.72 [CI, 0.64 to 0.82]), those receiving a reduced dose (HR, 0.68 [CI, 0.61 to 0.77]), and those with chronic kidney disease (HR, 0.68 [CI, 0.59 to 0.77]). LIMITATION: Residual confounding is possible. CONCLUSION: Among patients with AF, apixaban use was associated with lower risk for GIB and similar rates of ischemic stroke or systemic embolism, ICH, and all-cause mortality compared with dabigatran, edoxaban, and rivaroxaban. This finding was consistent for patients aged 80 years or older and those with chronic kidney disease, who are often underrepresented in clinical trials. PRIMARY FUNDING SOURCE: None.

Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study.

Many patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD's timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five "training" US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model's area under the curve (AUC) varied 0.633-0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570-0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.