Impact of the COVID-pandemic on antidepressants, anxiolytics and hypnotics in Spain: an interrupted time series analysis (2015-2023)

Impact of the COVID-pandemic on antidepressants, anxiolytics and hypnotics in Spain: an interrupted time series analysis (2015-2023) | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 January 2025 V1 Latest version Share on Impact of the COVID-pandemic on antidepressants, anxiolytics and hypnotics in Spain: an interrupted time series analysis (2015-2023) Authors : Diana González Bermejo [email protected] , Raquel Gutiérrez Machín , Eva Angela Segovia Muñoz , and Edurne Lázaro Bengoa Authors Info & Affiliations https://doi.org/10.22541/au.173770596.62855565/v1 633 views 178 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Purpose This study aims to evaluate the impact of the COVID-19 pandemic on the consumption of antidepressants, anxiolytics, and hypnotics in Spain. Methods A quasi-experimental study was conducted using ecological data from 2015 to 2023 obtained from the National Health System’s drug consumption database. The number of Defined Daily Doses (DDD) dispensed per 1,000 inhabitants per day (DID) was calculated. Descriptive analyses were performed to assess percentage changes and the compound annual growth rate (CAGR) of drug consumption. Additionally, interrupted time series analysis was applied using segmented linear regression and autoregressive moving average models to quantify the pandemic’s impact. The year 2020 was selected as the intervention period to account for policies ensuring treatment availability and allowing patients to stockpile medications during the lockdown. Sensitivity analyses were conducted using joinpoint regressions. Results Antidepressant consumption showed a consistent increase throughout the study period, with a significant and sustained rise following the pandemic. In contrast, benzodiazepine anxiolytics exhibited a slight downward trend before the pandemic but experienced a sharp increase afterward, eventually returning to near pre-pandemic levels by the end of 2023. A similar pattern was observed for benzodiazepines and Z hypnotics after pandemic, with the difference that the pre-pandemic trend was increasing for benzodiazepines hypnotics. Conclusions A prolonged increase in antidepressant consumption was observed following the pandemic, while increases in anxiolytic and hypnotic use were temporary. Ongoing monitoring of these trends is essential to identify emerging patterns and ensure mental health needs are appropriately addressed over time. Title: Impact of the COVID-pandemic on antidepressants, anxiolytics and hypnotics in Spain: an interrupted time series analysis (2015-2023) Running head: Impact the COVID-pandemic on antidepressants, anxiolytics and hypnotics in Spain Authors Diana González Bermejo 1 ; Raquel Gutiérrez Machín 1 ; Eva Ángela Segovia Muñoz 1 ; Edurne Lázaro Bengoa 1 Pharmacoepidemiology and Pharmacovigilance Division. Medicines for Human Use Department. Spanish Agency for Medicines and Medical Devices (AEMPS). Madrid Spain. All authors: 1) Have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; 2) Been involved in drafting the manuscript or revising it critically for important intellectual content; 3) Given final approval of the version to be published. Each author has participated sufficiently in the work to take public responsibility for appropriate portions of the content; and 4) Agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Corresponding author Diana González Bermejo Pharmacoepidemiology and Pharmacovigilance Division, Medicines for Human Use Department. Spanish Agency for Medicines and Medical Devices (AEMPS). Calle Campezo 1, Edificio 8, E-28022 Madrid, Spain [email protected] PI statement: The authors confirm that the principal investigator for this paper is Diana González Bermejo. The content of the manuscript is original and that it has not been published or accepted for publication, either in whole or in part, other than on a preprint server, as a short abstract, communication, or conference proceeding. No part of the manuscript is currently under consideration for publication elsewhere. All authors have seen and approved the final version of the submitted paper. Conflicts of Interest/Disclosure: the authors have no conflicts of interest to declare. Disclaimer: This document expresses the opinion of the authors of the paper and may not be understood or quoted as being made on behalf of or reflecting the position of the Spanish Agency of Medicine and Medical Devices or its committees or working parties. Scientifically and Ethical approval statement The investigators had access only to aggregated and fully anonymized data and under this condition, no specific ethics review was required according to Spanish law. Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Keywords (from MeSH): COVID-19, antidepressants, anxiolytics, hypnotics, time series analysis Word count: 3416 Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Abstract Purpose This study aims to evaluate the impact of the COVID-19 pandemic on the consumption of antidepressants, anxiolytics, and hypnotics in Spain. Methods A quasi-experimental study was conducted using ecological data from 2015 to 2023 obtained from the National Health System’s drug consumption database. The number of Defined Daily Doses (DDD) dispensed per 1,000 inhabitants per day (DID) was calculated. Descriptive analyses were performed to assess percentage changes and the compound annual growth rate (CAGR) of drug consumption. Additionally, interrupted time series analysis was applied using segmented linear regression and autoregressive moving average models to quantify the pandemic’s impact. The year 2020 was selected as the intervention period to account for policies ensuring treatment availability and allowing patients to stockpile medications during the lockdown. Sensitivity analyses were conducted using joinpoint regressions. Results Antidepressant consumption showed a consistent increase throughout the study period, with a significant and sustained rise following the pandemic. In contrast, benzodiazepine anxiolytics exhibited a slight downward trend before the pandemic but experienced a sharp increase afterward, eventually returning to near pre-pandemic levels by the end of 2023. A similar pattern was observed for benzodiazepines and Z hypnotics after pandemic, with the difference that the pre-pandemic trend was increasing for benzodiazepines hypnotics. Conclusions A prolonged increase in antidepressant consumption was observed following the pandemic, while increases in anxiolytic and hypnotic use were temporary. Ongoing monitoring of these trends is essential to identify emerging patterns and ensure mental health needs are appropriately addressed over time. Key points • Antidepressant consumption consistently increased throughout the study period, with a significant and sustained rise observed after the pandemic. In contrast, the effects on anxiolytic and hypnotic use were temporary. • The use of robust long-term interrupted time series analyses and sensitivity analyses is critical for accurately capturing underlying trends. By designating the entire year 2020 as the intervention period and excluding these data from the analysis, we mitigate the impact of fluctuations caused by policies facilitating stockpiling. This approach minimizes biases and ensures a more accurate interpretation of the results by focusing on the true underlying trends. Plan Language Summary Since the onset of the COVID-19 pandemic, concerns have arisen regarding its impact on mental health. This study aims to assess the consumption patterns of antidepressants, anxiolytics, and hypnotics from 2015 to 2023 and evaluate the effects of the COVID-19 pandemic on these trends in Spain. To achieve this, we employed a methodology that quantifies the impact of the pandemic by comparing the observed drug use after the pandemic with the estimated use had the pandemic not occurred, based on the underlying pre-pandemic trend. Data from 2020 were excluded from the analysis to minimize the influence of fluctuations caused by stockpiling policies, thereby reducing biases and ensuring an accurate interpretation of the results. Antidepressant consumption showed a consistent increase throughout the study period, with a significant and sustained rise following the pandemic. In contrast, benzodiazepine anxiolytics exhibited a slight downward trend before the pandemic but experienced a sharp increase afterward, eventually returning to near pre-pandemic levels by the end of 2023. A similar pattern was observed for benzodiazepine and Z hypnotics after pandemic, with the difference that the pre-pandemic trend was increasing for benzodiazepine hypnotics. Ongoing monitoring of these trends is essential to identify emerging patterns and ensure that mental health needs are addressed effectively over time. INTRODUCTION In December 2019, an outbreak of a novel coronavirus (COVID-19) emerged in Wuhan, China. By early 2020, COVID-19 had spread worldwide, with Europe, and particularly Spain, becoming a significant hotspots of the pandemic. 1,2 Beyond the immediate medical risks, the pandemic is expected to have profound psychological and social impacts. The combined effects of confinement, changes in lifestyle behaviors, social isolation, fear, uncertainty about the future, stigmatization, and highly stressful work environments may have significantly increased the prevalence of mental disorders. 3,4 Additionally, COVID-19 itself and the treatments used to combat the disease have been associated with mental health issues. 5,6 Given the clinical, social, and economic consequences for a country affected by mental illness, it is essential to understand the current psychological status of the Spanish population and its evolution to guide effective interventions. To this end, a drug utilization study was conducted using interrupted time series analysis to estimate the impact of the COVID-19 pandemic on the use of antidepressants, anxiolytics, and hypnotic drugs in both the short and long term. This study provides relevant information to be considered by public health strategies to face ongoing and future challenges. METHOD Study design and source of data A quasi-experimental study was conducted from January 1, 2015, to December 31, 2023, using ecological data from a nationwide drug consumption database in Spain. Prescription-only medicines are partially reimbursed by the National Health System (NHS), with the percentage depending on patients’ age, income, and specific treatment categories. The Ministry of Health collects data on medicines dispensed by community pharmacies and reimbursed by the NHS, excluding hospital, private care, and over-the-counter medicines. While some drugs with multiple indications may not be reimbursed and thus not registered, this does not apply to the drugs under study. Definition of exposure Exposure was defined according to the ATC classification. 7 The study included all drugs listed under the codes N06A, N05B, and N05C that are marketed in Spain (see Table S1 and S2 on the drugs included in the study in the supplementary information online). Outcome definition The number of defined daily doses (DDD) dispensed per 1,000 inhabitants per day (DDD/1,000 inhabitants/day = DID) was calculated for each ATC group, using the DDD values set by the World Health Organization. Population denominators were obtained from the National Statistics Institute. This calculation represents the proportion of the study population treated daily with the drug or the average number of DDD used by 1,000 inhabitants on any given day of the period analysed (e.g.: year, quarter, month…). The percentage change in DID and the compound annual growth rate (CAGR) were estimated. The percentage change was calculated by comparing the end and beginning values of the period, dividing the difference by the initial value, and multiplying by 100. The CAGR, representing the average rate at which some value grows over a certain period assuming the value has been compounding over that time period, was calculated using the formula: CAGR = (Final value / Initial value) ^(1/n) – 1 where ( n ) is the number of periods. CAGR smooths out annual fluctuations, providing a theoretical annual growth rate for easier comparison over time. Statistical analysis First, we conducted descriptive analyses of yearly drug consumption and its increase, by medicine subgroups. Subsequently, interrupted time series (ITS) analysis were conducted using ordinary least squares regression models to capture quarterly drug use trends over the study period, accounting for the impact of the COVID-19 pandemic by modelling trends separately for the pre- and post-pandemic periods. 8 Results are shown in two dimensions: level and slope. The level change represents the immediate effect of the intervention, measured as the difference between the fitted value for the first post-intervention data point (one quarter after the intervention) and the predicted outcome based on the pre-intervention slope. The slope change reflects the long-term effect, indicating the trend change from pre- to post-intervention. We selected the entire year 2020 as the intervention period to remove the effects of policies that ensured treatment availability and allowed patients, especially those with chronic conditions, to stockpile medications during the lockdown. Consequently, any changes in drug consumption during 2020 were excluded from our analysis. The general formula for a segmented regression model corresponds to: Y(t) =𝛽 0 +𝛽 1 𝑡+𝜆 𝑖 𝑙𝑒𝑣𝑒𝑙 𝑖 (𝑡)+𝛼 𝑖 trend 𝑖 +𝜀 - Y (𝑡), is the dependent variable indicating the quarterly DID per 1,000 patients at time t;- 𝑡, is a discrete variable indicating the time in quarters. A sequential number starting from 1 (the first quarter, January-March 2015) to 36 (the last quarter, October-December 2023);- (𝑡), is a dummy variable indicating the time periods in which each intervention was in effect (0 until the date before the intervention and 1 from the date of the intervention);- (𝑡), is a discrete variable indicating the time after the intervention (0 until the quarter before the intervention, sequential number starting from the quarter of the intervention);- 𝛽 0 , estimates the baseline outcome (DID of drug use per quarter at time 0);- 𝛽 1 , estimates the baseline slope, i.e., the secular trend in DID before the intervention. The slope is the change in the outcome value per unit change in the time value. For this study, the slope represents the change in DID per quarter;- 𝜆 𝑖 , estimates the change after the intervention;- 𝛼 𝑖 , estimates the change in the slope after the intervention;- 𝜀, is the error term and it refers to the sum of the deviations within the regression line, which provides an explanation for the difference between the results of the model and actual observed results. The error term is also known as the residual. Autocorrelation and partial autocorrelation function plots were examined to identify patterns. If autocorrelation was detected, the model was re-estimated using generalized least squares, which addresses heteroscedasticity and incorporates an autoregressive moving average process. When multiple lags exhibited autocorrelation, the lowest lag was selected adhering to the principle of parsimony. The final model results are presented in figures and tables, with 95% confidence intervals (95% CI) to indicate estimate precision. Further, the long-term effect of the intervention was measured as the difference between the observed DID in the final quarter and the estimated DID had the intervention not occurred (counterfactual DID). This absolute effect was expressed as a proportion of the counterfactual DID (relative effect). R software version 4.3.3 (2024-02-29) Copyright (C) 2021 was used for statistical analysis. Sensitivity analysis We assessed the robustness of our findings using joinpoint regression analysis with a log-linear model to identify shifts in trends (joinpoints) independent of prespecified interventions. These joinpoints were compared to pandemic timings. When additional significant inflection points were found, analyses were replicated, treating both the pandemic and joinpoint-identified points as interventions. Accounting for these points is essential, as missing them could lead to inaccurate trend modeling and skewed estimates. The analysis was performed using Joinpoint software (version 4.7, https://surveillance.cancer.gov/joinpoint/), which applies permutation tests with Monte Carlo methods to determine the necessary number of joinpoints, with a significance level of 5%. Antidepressants Selective serotonin reuptake inhibitors (SSRI) were the most used antidepressants, accounting for 47.1% in 2015 and 57.6% in 2023. The second most consumed was the group known as other antidepressants (N06AX), representing 31.7% in 2015 and 38.1% in 2023. Non-selective monoamine reuptake inhibitors (NSMRI) accounted for 3.7% in 2015 and 3.4% in 2023, while monoamine oxidase A inhibitors (MAOIs) remained constant at 0.01%. The group of other antidepressants showed the most significant increase during the study period, with a 65.0% rise and a CAGR of 6.4%. Similarly, both SSRI and NSMRI experienced substantial growth, with increases of 23.9% and 22.5%, respectively, and CAGRs of 2.7% and 2.5% (Table 1). The pandemic significantly impacted antidepressant consumption patterns, especially SSRI, leading to an immediate increase of 3.305 DID (95% CI: 2.263 to 4.347) and a trend change of 0.218 DID per quarter (95% CI: 0.103 to 0.333). For NSRI, there was a significant level change of 0.213 DID (95% CI: 0.136 to 0.290) but no significant trend change (0.007 DID per quarter (95% CI: -0.001 to 0.015). In contrast, other antidepressants showed a non-significant level change of 0.353 DID (95% CI: -0.316 to 1.023) but a significant trend increase of 0.269 DID per quarter (95% CI: 0.195 to 0.343). By the end of the study period (4th quarter 2023), the consumption of all groups was higher than expected (Table 2 and Figures S3A, S3B, S3C). Anxiolytics Benzodiazepines consistently accounted for approximately 99% of all anxiolytics consumed during the entire study period, with the remaining percentage corresponding to hydroxyzine. The trend in benzodiazepines consumption remained largely unchanged, with a minimal decline of 0.1% and a negligible CAGR of -0.02%. Hydroxyzine consumption decreased by 18.5% during the study period, resulting in a negative CAGR of -2.5% (Table 1). Anxiolytics showed a pre-pandemic downward trend of -0.088 (95% CI: -0.121 to -0.034) for benzodiazepines and -0.009 (95% CI: -0.012 to -0.007) for hydroxyzine. The pandemic initially caused a significant increase in benzodiazepine consumption, with a rise of 5.049 DID (95% CI: 4.166 to 5.981). However, this was followed by a decreasing trend of -0.257 DID per quarter (95% CI: -0.359 to -0.154). For hydroxyzine, there was a significant rise of 0.087 DID (95% CI: 0.042 to 0.133) after the pandemic, with an upward trend of 0.010 DID per quarter bordering on statistical significance (95% CI: 0.005 to 0.015). For both groups of anxiolytics, the consumption was higher than expected by the end of the study period (Table 2 and Figures S3D, S3E). Hypnotics Benzodiazepines consistently held the highest share among all hypnotics, accounting for 74.2% in 2015 and 75.2% in 2023. Benzodiazepine-related drugs, such as Z-hypnotics, represented 25.1% in 2015 and 24.1% in 2023. The group of other hypnotics and sedatives, which includes clomethiazole and magnesium glutamate hydrochloride, comprised the remaining percentage. Benzodiazepines and Z-hypnotics increased 10.2% and 4.4% respectively, with CAGR of 1.2% and 0.5% (Table 1). Benzodiazepines showed a small but statistically significant increase of 1.205 DID (95% CI: 0.876 to 1.535) following the pandemic. However, a long-term downward trend of -0.082 DID per quarter (95% CI: -0.121 to -0.042) was observed. By the end of the study period, consumption was higher than expected, but the results were not statistically significant. For Z-hypnotics, the post-pandemic increase was 0.414 DID (95% CI: 0.285 to 0.543). Although the increase in trend was not significant, the consumption was significantly higher than expected by the end of the study period (Table 2 and Figures S3F, S3G). Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Sensitivity analysis For all medications included in the analysis, the joinpoint regression results were consistent with previous findings: except for hydroxyzine, an upward trend was identified around point 20, corresponding to the onset of the pandemic. Additionally, a further shift was detected in the fourth quarter of 2021, which moderated the upward trend for antidepressants. In contrast, for anxiolytics and hypnotics, this shift marked the beginning of a downward trend. (Figures S4A-S4G). As a result, all ITS analysis were replicated including this additional inflection point. Below, we present the results comparing two-intervention analysis with single-intervention analysis. When interpreting the findings, note that a new counterfactual was constructed using data from the 1st to 3rd quarter of 2021. Outcomes after the additional inflexion point showed either increases or decreases based on this new counterfactual. However, it is essential to contextualize these results with pre-pandemic levels, especially regarding absolute and relative effects at the study’s end. Antidepressants Selective serotonin reuptake inhibitors (SSRIs) showed a significant increase in both level and trend after the pandemic across both models. However, for NSMRIs and other antidepressants, the inclusion of the additional inflection point rendered the pandemic’s effects on level non-significant and moderated the growth across all groups starting from the fourth quarter of 2021. The trend turned negative relative to the new counterfactual. For all antidepressants, consumption was higher than expected in the third quarter of 2021. By the end of the study period, consumption was lower than expected based on the new counterfactual. Specifically, consumption in the last quarter of 2023 was below the level that would have been expected if the post-pandemic trend had continued (Table 2 and Figures S5A-S5C). Anxiolytics Benzodiazepine and hydroxyzine consumption levels increased following the pandemic in both models. However, the incorporation of an additional inflection point rendered the level effects non-significant, while the observed downward trend in benzodiazepine consumption remained significant. Consumption exceeded expectations in the third quarter of 2021. Nonetheless, by the end of the study period, consumption fell below the levels projected by the updated counterfactual model. (Table 2 and Figures S5D-S5E). Hy p notics Benzodiazepines showed a pronounced increase after the pandemic in the single-intervention model, but the addition of a second inflection point mitigated this rise, rendering the post-pandemic trend inconclusive. In the model with a single intervention, the trend was negative and statistically significant, indicating a sustained decline in consumption. However, in the model incorporating the additional inflexión point, the trend shifted to being positive and statistically significant. For Z-hypnotics, the increase after pandemic remained significant in both models. The upward trend reached statistical significance after incorporating the additional inflection point. For all antidepressants, the consumption was higher than expected in the third quarter of 2021. By the end of the study period, consumption was lower than expected based on the new counterfactual (Table 2 and Figures S5F-S5G). DISCUSIÓN Antidepressant consumption showed a consistent increase throughout the study period, with a significant and sustained rise following the pandemic. In contrast, benzodiazepine anxiolytics exhibited a slight downward trend before the pandemic but experienced a sharp increase afterward, eventually returning to near pre-pandemic levels by the end of 2023. A similar pattern was observed for benzodiazepine and Z hypnotics after pandemic, with the difference that the pre-pandemic trend was increasing for benzodiazepine hypnotics. By 2022, the pandemic’s impact began to moderate, and by the last quarter of 2023, consumption was lower than expected if the post-pandemic trend had been sustained. When introducing a second intervention into the model, the period following the first intervention was significantly shortened, reducing the number of observations from 12 to only 3. This reduction impacts the model’s ability to accurately estimate the effects of the first intervention, as fewer data points decrease the statistical power and increase the uncertainty of the estimated changes in level and trend. Additionally, the second intervention, placed at the fourth quarter of 2021, captures part of the effects that were originally attributed to the pandemic, causing shifts in the direction and significance of the estimated changes in level and trend. Furthermore, the inclusion of multiple interventions can introduce methodological challenges, such as redistributing the variance between the two events or even creating overlap between the variables, which can further complicate the interpretation of results. Despite these limitations, adding a second intervention provides a more accurate and realistic understanding of the data by accounting for another significant event that likely affected consumption trends. The changes in medication consumption reflect not only the pandemic’s direct effects but also treatment access and patient behavior during lockdowns. Policies that allowed patients, especially those with chronic conditions, to stockpile medications played a key role. This context might explain the increased prescription rates of antidepressants, anxiolytics, and hypnotics in March 2020, as reported by national and regional databases across Europe. 9 Consequently, finding comparable short-term studies that eliminate stockpiling effects (as in our study) and robust long-term analyses remains challenging. It is also important to consider pre-pandemic prescription trends. Antidepressant use had been steadily rising across the EU, including Spain, over the past decade, with exceptions like Denmark and Hungary, which had stable rates. Countries with lower prescription rates were more affected by the pandemic. For example, Denmark saw a rise in antidepressant prescriptions in late 2020, while Sweden, with higher pre-pandemic rates, did not. 10 The post-pandemic increase in antidepressants observed in our study was driven mainly by SSRIs, followed by the group of other antidepressants (which include Serotonin-Norepinephrine Reuptake Inhibitors-SNRI like venlafaxine and duloxetine) and NSMRI (like the tricyclics amitriptyline). Usage of these three groups peaked by 2022 and then grew more slowly, reflecting both clinical preferences and the pandemic’s societal impact. SSRI were likely favoured due to their safety profile and broad indications. 11 Furthermore, fluvoxamine was tested for their potential to reduce hospitalization risk in COVID-19 patients 12 . SNRI may have been used for severe depression, anxiety, and pain-related conditions worsened by the pandemic due to stress and lifestyle changes. 14 In the case of neuropathic pain, COVID-19 itself could also be a contributing factor. 15 Tricyclics, despite declining use, were still prescribed for chronic pain and sleep disorders. Despite declining use due to side effects like sedation and weight gain, tricyclic antidepressants remain in use, particularly for chronic pain and sleep disorders. 16 The sustained rise in antidepressant use post-pandemic aligns with French studies that extended over two years and utilized ITS analysis. 17,18 In contrast, other European studies reported varied trends, often conducted early in the pandemic and potentially biased by stockpiling, with many failing to account for pre-pandemic trends, making their findings inconclusive. 19-27 The slower growth of antidepressant uses post-2022 may be attributed to better pandemic control, lifted restrictions, vaccination, and improved mental health services. However, the continued increase suggests chronic conditions requiring long-term care, underscoring the need for a comprehensive healthcare response. The slight pre-pandemic decline in anxiolytic use in Spain mirrored trends across Europe, reflecting shifts in clinical practice away from benzodiazepines due to concerns about dependence, and towards safer alternatives like SSRI and SNRI 28 . The temporary rise in benzodiazepine use in early 2021, likely linked to increased social anxiety, was also noted in France. 17,18 Other anxiolytics saw marginal use, likely due to their slower effects. The long-term decline may signal a return to non-benzodiazepine treatments and improved resilience post-pandemic. However, proper follow-up is essential to avoid withdrawal or untreated anxiety. Overall, literature offers no clear pandemic impact on anxiolytic use in other European countries, with limited pre- and post-pandemic periods and lack of a robust methodology complicating interpretation of results. 9, 10,20,22,24,27 The steady rise in benzodiazepine hypnotics consumption before the pandemic reflects trends across Europe 28 and is linked to increasing sleep disorders driven by stress, digital device use, and irregular work patterns. Benzodiazepines are more frequently prescribed than Z-hypnotics due to their familiarity, broader efficacy, and versatility in treating both insomnia and anxiety. While Z-hypnotics were intended to be safer, they can also cause dependence and tolerance, reducing their perceived benefits. 29 The marginal use of other hypnotics, such as clomethiazole, may stem from safety concerns and its specific indications for treating alcohol withdrawal. The observed increase during the pandemic aligns with rising sleep disturbances from anxiety and disrupted routines 30-32 , while the decline post-2022 suggests a return to normalization of daily routines and a focus on safer long-term management strategies. Methodological issues in existing studies on hypnotics hinder comparability and lead to inconclusive findings. 9, 20-24, 33-36 Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex CONCLUSIONS The pandemic prompted a significant shift in the consumption patterns of antidepressants, anxiolytics, and hypnotics reflecting both immediate needs and long-term trends. While the increased reliance on SSRIs and benzodiazepines addressed acute mental health needs, the subsequent decline in benzodiazepines use and the moderation of the initial impact of the pandemic for SSRI post-2022 suggests a shift toward more sustainable management strategies. Ongoing monitoring of these trends is essential to identify emerging patterns, and ensure that mental health needs are addressed appropriately over time. LIMITATIONS Our study has several strengths, including the large and representative population included in the study and the use of a robust methodology with sensitivity analysis. However, it was not designed to evaluate demographic and regional differences. While DDD serve as a strong proxy for prescription volume and mental health condition prevalence, it does not necessarily reflect actual doses consumed or changes in the prevalence of disorders like depression or anxiety. We cannot determine whether the observed increase is due to more individuals being exposed to these medications or the same individuals receiving higher doses. Additionally, prescriptions from hospital settings and private centres were excluded, although their impact is likely minimal due to the NHS’s universal coverage (97% of the population) and the fact that antidepressants, anxiolytics, and hypnotics are primarily prescribed in outpatient settings. The ITS design effectively differentiates the impact of interventions from underlying trends, controlling for short-term fluctuations and secular trends. However, healthcare systems are complex and heterogeneous, requiring tailored solutions. This variability in clinical practice often cannot be fully captured by statistical modeling, making it difficult to accurately extrapolate the pre-intervention trend as the counterfactual rate of drug prescribing. REFERENCES 1. WHO COVID-19 Dashboard. 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Antidpressants, anxiolytics and hypnotics consumption per year in terms of DID (2015-2023) All (N06A) DID NSMRI (N06AA) DID (%) a SSRI (N06AB) DID (%) a MAOI (N06AG) DID (%) a Others (N06AX) DID (%) a All (N05B) DID Benzodiazepines (N05BA) DID (%) b Hydrozyzine (N05BB) DID (%) b All N05C DID Benzodiazepines (N05CD) DID (%) Z-hypnotics (N05CF) DID (%) Others (N05CM) DID (%) 2015 73.1 2.7 (3.7) 47.2 (64.5) 0.01 (0.01) 23.2 (31.7) 56.6 55.7 (98.6) 0.8 (1.4) 31.0 23.0 (74.2) 7.8 (25.1) 0.2 (0.7) 2016 75.5 2.7 (3.6) 48.1 (63.7) 0.01 (0.01) 24.7 (32.6) 56.9 56.1 (98.7) 0.8 (1.3) 31.5 23.5 (74.5) 7.8 (24.8) 0.2 (0.7) 2017 77.1 2.8 (3.6) 48.4 (62.7) 0.01 (0.01) 26.0 (33.7) 57.2 56.6 (98.8) 0.7 (1.2) 31.7 23.7 (74.8) 7.8 (24.5) 0.2 (0.7) 2018 79.6 2.9 (3.) 49.1 (61.7) 0.01 (0.01) 27.6 (34.7) 55.8 55.1 (98.8) 0.7 (1.2) 31.8 23.9 (75.0) 7.7 (24.2) 0.2 (0.7) 2019 82.5 2.9 (3.5) 50.3 (60.9) 0.01 (0.01) 29.3 (35.5) 55.3 54.6 (98.8) 0.7 (1.2) 32.1 24.1 (75.1) 7.8 (24.2) 0.2 (0.7) 2020 86.2 3.0 (3.5) 52.1 (60.5) 0.01 (0.01) 31.1 (36.0) 57.6 56.9 (98.9) 0.7 (1.1) 33.4 25.1 (75.2) 8.0 (24.1) 0.2 (0.7) 2021 91.5 3.2 (3.5) 55.1 (60.2) 0.01 (0.01) 33.2 (36.3) 59.2 58.5 (98.9) 0.7 (1.1) 35.1 26.7 (76.1) 8.2 (23.3) 0.2 (0.6) 2022 96.5 3.3 (3.4) 57.1 (59.2) 0.01 (0.01) 36.1 (37.4) 58.2 57.5 (98.8) 0.7 (1.2) 34.2 25.8 (75.5) 8.2 (23.9) 0.2 (0.7) 2023 99.3 3.4 (3.4) 57.6 (58.1) 0.01 (0.01) 38.3 (38.5) 56.3 55.7 (98.8) 0.7 (1.2) 33.7 25.4 (75.2) 8.2 (24.2) 0.2 (0.6) Increase in DID d 2015-2023 35.9 23.9 22.2 - 65.0 -0.4 -0.1 -18.5 8.7 10.2 4.5 - CAGR (%) e 3.9 2.7 2.5 - 6.4 -0.05 -0.02 -2.5 1.1 1.2 0.5 - Abbreviations: DID: number of defined daily doses dispensed per 1,000 inhabitants per day; NSMRI: Non-selective monoamine reuptake inhibitors; SSRI: Selective serotonin reuptake inhibitors; CAGR: compound annual growth rate. a,b,c Percentage calculated respect to the total of antidepressants, anxiolytics and hypnotics respectively. d Increase in DID for the period 2015-2023 calculated by comparing the end and beginning values of DID for the period 2015-2023, dividing the difference by the initial value, and multiplying by 100. e The CAGR reprenting the average rate at which some value grows over a certain period of time assuming the value has been compounding over that time period, was calculated using the formula: CAGR = (Final value / Initial value) ^(1/n) – 1 where ( n ) is the number of periods. CAGR smooths out annual fluctuations, providing a theoretical annual growth rate for easier comparison over time. Table 2. Impact of the COVID-19 pandemic on antidepressants, anxiolytics, and hypnotics consumption (DID per quarter): A) Single-intervention analysis (pandemic period: 2020) vs. B) Dual-intervention analysis (pandemic period: 2020 and additional inflection point in Q4 2021 from sensitivity analysis) NSMRI (N06AA) (SSRI) N06AB Others N06AX Benzodiazepines (N05BA) Hydrozyzine (N05BB) Benzodiazepines (N05CD) Z-hypnotics (N05CF) Expected DID at baseline 2.662 (2.619 to 2.704) 46.922 (46.370 to 47.473) 22.074 (27.703 to 22.445) 56.246 (55.730 to 56.763) 0.823 (0.790 to 0.856) 22.935 (22.757 to 23.114) 7.788 (7.716 to 7.859) Prepandemic trend 0.012 (0.009 to 0.016) 0.163 (0.117 to 0.210) 0.382 (0.351 to 0.413) -0.078 (-0.121 to -0.034) -0.009 (-0.012 to -0.007) 0.067 (0.051 to 0.082) -0.001 (-0.007 to 0.004) Immediate level change in DID after pandemic 0.213 (0.136 to 0.290) 3.305 (2.263 to 4.347) 0.353 (-0.316 to 1.023) 5.049 (4.116 to 5.981) 0.087 (0.042 to 0.133) 1.205 (0.876 to 1.535) 0.414 (0.285 to 0.543) Change in trend after pandemic (DID per quarter) 0.007 (-0.001 to 0.015) 0.218 (0.103-0.333) 0.269 (0.195 to 0.343) -0.257 (-0.359 to -0.154) 0.010 (0.005 to 0.015) -0.082 (-0.121 to -0.042) 0.001 (-0.012 to 0.016) Absolute effect last quarter 2023 0.301 (0.200 to 0.401) 5.927 (4.558 to 7.297) 3.586 (2.707 to 4.465) 1.962 (0.739 to 3.186) 0.213 (0.139 to 0.286) 0.220 (-0.239 to 0.679) 0.454 (0.329 to 0.580) Relative effect last quarter 2023 0.096 (0.061-0.131) 0.112 (0.084 to 0.140) 0.100 (0.073 to 0.126) 0.036 (0.013 to 0.060) 0.456 (0.239 to 0.673) 0.008 (-0.009 to 0.027) 0.059 (0.042 to 0.076) A) Analysis with two interventions: pandemic a and additional inflexión point Antidepressants Anxyolitics Hypnotics NSMRI (N06AA) (SSRI) N06AB Others N06AX Benzodiazepines (N05BA) Hydrozyzine (N05BB) Benzodiazepines (N05CD) Z-hypnotics (N05CF) Expected DID at baseline 2.662 (2.625 to 2.698) 46.878 (46.423 to 47.333) 22.074 (21.722 to 22.426) 56.246 (55.789 to 56.704) 0.823 (0.709 to 0.856) 22.980 (222.885 to 23.075) 7.788 (7.722 to 7.853) Prepandemic trend 0.012 (0.009 to 0.015) 0.170 (0.131 to 0.208) 0.382 (0.353 to 0.412) -0.078 (-0.116 to -0.039) -0.009 (-0.012 to -0.007) 0.062 (0.054 to 0.070) -0.001 (-0.007 to 0.004) Immediate level change in DID after pandemic 0.090 (-0.016 to 0.197) 1.656 (0.318 to 2.993) -0.530 (-1.553 to 0.492) 3.477 (2.147 to 4.806) 0.075 (0.005 to 0.145) 0.320 (-0.167 to 0.808) 0.229 (0.037 to 0.421) Change in trend after pandemic (DID per quarter) 0.051 (0.015 to 0.086) 0.846 (0.432 to 1.260) 0.611 (0.271 to 0.951) 0.353 (-0.088 to 0.795) 0.015 (-0.007 to 0.038) 0.271 (0.085 to 0.457) 0.082 (0.018 to 0.146) Absolute effect 3 rd quarter 2021 0.244 (0.179 to 0.310) 4.196 (3.349 to 5.044) 1.303 (0.673 to 1.932) 4.536 (3.718 to 5.354) 0.121 (0.071 to -0.171) 1.135 (0.922 to 1.347) 0.477 (0.359 to 0.595) Relative effect 3 rd quarter 2021 0.081 (0.058 to 0.104) 0.0815 (0.064 to 0.098) 0.040 (0.020 to 0.060) 0.083 (0.067 to 0.099) 0.218 (0.113 to 0.323) 0.046 (0.037-0.054) 0.061 (0.045 to 0.077) Immediate level change after the additional inflexión point 0.010 (-0.080 to 0.100) -0.460 (-1.443 to 0.523) -0.208 (-1.074 to 0.657) -0.400 (-1.526 to 0.724) 0.003 (-0.047 to 0.053) -0.206 (-0.629 to 0.215) -0.145 (-0.307 to 0.017) Change in trend after the additional inflexión point -0.056 (-0.094 to -0.019) -0.756 (-1.240 to -0.273) -0.399 (-0.757 to -0.404) -0.709 (-1.175 to -0.243) -0.006 (-0.029 to 0.016) -0.403 (-0.585 to 0.221) -0.080 (-0.147 to -0.013) Absolute effect at last quarter 2023 b -0.443 (-0.769 to -0.117) -6.515 (-10.611 to -2.419) -3.404 (-6.528 to -0.280) -6.075 (-10.136 to -2.013) -0.049 (-0.264 to 0.164) -3.434 (-5.178 to -1.690) -0.791 (-1.376 to -0.205) Relative effect at last quarter 2023 b -0.166 (-0.192 to -0.039) -0.100 (-0.158 to -0.042) -0.079 (-0.150 to -0.009) -0.099 (-0.158 to -0.039) -0.068 (-0.327 to 0.190) -0.119 (-0.173 to -0.065) -0.088 (-0.148 to -0.028) Data are presented with a 95% confidence interval. Abbreviations: DID: number of defined daily doses in a quarter dispensed per 1,000 inhabitants per day; NSMRI: Non-selective monoamine reuptake inhibitors; SSRI: Selective serotonin reuptake inhibitors. a The entire year 2020 was selected as the intervention period to eliminate the effects of policies that ensured treatment availability and allowed patients, to stockpile medications during the lockdown. Consequently, any changes in drug consumption during 2020 were excluded from our analysis. b When interpreting the findings, note that a new counterfactual was constructed using data from the 1st to 3rd quarter of 2021. Outcomes after the additional inflexion point showed either increases or decreases based on this counterfactual Information & Authors Information Version history V1 Version 1 24 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords antidepressants anxiolytics covid-19 hypnotics time series analysis Authors Affiliations Diana González Bermejo [email protected] Agencia Espanola de Medicamentos y Productos Sanitarios View all articles by this author Raquel Gutiérrez Machín Agencia Espanola de Medicamentos y Productos Sanitarios View all articles by this author Eva Angela Segovia Muñoz Agencia Espanola de Medicamentos y Productos Sanitarios View all articles by this author Edurne Lázaro Bengoa Agencia Espanola de Medicamentos y Productos Sanitarios View all articles by this author Metrics & Citations Metrics Article Usage 633 views 178 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Diana González Bermejo, Raquel Gutiérrez Machín, Eva Angela Segovia Muñoz, et al. Impact of the COVID-pandemic on antidepressants, anxiolytics and hypnotics in Spain: an interrupted time series analysis (2015-2023). Authorea . 24 January 2025. 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