BACKGROUND: The Coronavirus disease of 2019 (COVID-19) pandemic and the corresponding mitigation measures have had a discernible impact on drug utilization among outpatients. However, limited research exists on the prescription trends in the elderly population during the pandemic period in Viet Nam.
OBJECTIVE: This study aims to analyze the effects of COVID-19 on outpatient drug utilization patterns at a national geriatric hospital in Ho Chi Minh City before and after the early onset of the pandemic.
METHODS: Data was collected from the prescriptions and administration claims, encompassing the period from January 2016 to December 2022. The dataset was divided into two periods: Period 1: January 2016 to December 2020 and Period 2: January 2021 to December 2022. The drug utilization was measured using DDD/1000P (defined daily doses-DDD per 1000 prescriptions) on a monthly basis. The analysis employed interrupted time series using Autoregressive Integrated Moving Average (ARIMA) to detect changes in drug use levels and rates.
RESULTS: A total of 1,060,507 and 644,944 outpatient prescriptions from Thong Nhat Hospital were included in Period 1 and Period 2, respectively. The median age of the patients were 58 in Period 1 and 67 years old in Period 2. The most common comorbidities were dyslipidemia, hypertension, and diabetes mellitus. In terms of medication utilization, cardiovascular drugs were the most frequently prescribed, followed by drugs active on the digestive and hormonal systems. The study observed significant surges in the number of prescriptions and the average number of drugs per prescription. However, there were no significant changes in the overall consumption of all drugs. Among the drug groups related to the cardiovascular system, three subgroups experienced a sudden and significant increase: cardiac therapy, beta-blocking agents, and antihypertensives, with increasing consumption levels of 1,177.73 [CI 95%: 79.29; 2,276.16], 73.32 [CI 95%: 28.18; 118.46], and 36.70 [CI 95%: 6.74; 66.66] DDD/1000P, respectively. On the other hand, there was a significant monthly decrease of -31.36 [CI 95%: -57.02; -5.70] DDD/1000P in the consumption of anti-inflammatory and antirheumatic products. Interestingly, there was a significant increase of 74.62 [CI 95%: -0.36; 149.60] DDD/1000P in the use of antigout preparations.
CONCLUSIONS: COVID-19 resulted in a sudden, non-significant increase in overall drug consumption levels among outpatients. Notably, our findings highlight significant increases in the utilization of three drug groups related to the cardiovascular system, specifically cardiac therapy, beta-blocking agents, and antihypertensives. Intriguingly, there was a statistically significant increase in the consumption of antigout preparations, despite a decline in the monthly consumption rate of non-steroidal anti-flammatory drugs (NSAIDs). Further studies in the following years are necessary to provide a more comprehensive understanding of the impact of COVID-19 on outpatient drug utilization patterns.

Drug utilization researchers are often interested in evaluating prescribing and medication use patterns and trends over a specified period of time. Joinpoint regression is a useful methodology to identify any deviations in secular trends without a preconceived notion of where these break points might occur. This article provides a tutorial on the use of joinpoint regression, within Joinpoint software, for the analysis of drug utilization data.
The statistical considerations for whether a joinpoint regression analytical technique is a suitable approach are discussed. Then, we offer a tutorial as an introduction on conducting joinpoint regression (within Joinpoint software) through a step-by-step application, which is a case study developed using opioid prescribing data from the United States. Data were obtained from public files available through the Centers for Disease Control and Prevention from 2006 to 2018. The tutorial provides parameters and sample data needed to replicate the case study and it concludes with general considerations for the reporting of results using joinpoint regression in drug utilization research.
The case study evaluated the trend of opioid prescribing in the United States from 2006 to 2018, where time points of significant variation (one in 2012 and another in 2016) are detected and interpreted.
Joinpoint regression is a helpful methodology for drug utilization for the purposes of conducting descriptive analyses. This tool also assists with corroborating assumptions and identifying parameters for fitting other models such as interrupted time series. The technique and accompanying software are user-friendly; however, researchers interested in using joinpoint regression should exercise caution and follow best practices for correct measurement of drug utilization.

Over the past 20 years, excessive antibiotic use has led to serious antimicrobial resistance (AMR) worldwide, and the phenomenon is particularly serious in China. To this end, the Chinese health sector took a series of measures to promote rational antibiotic use. In this study, to reveal the impact of policies on antibiotic use, we explored the long-term trend and patterns of antibiotic use at public health care institutions from 2012 to 2020 in northwest China, taking Gansu Province as an example.
Antibiotic procurement data were obtained from the provincial centralized bidding procurement (CBP) platform between 2012 and 2020. Antibiotic use was quantified using the Anatomical Therapeutic Chemical (ATC)/defined daily doses (DDD) methodology and standardized using the DDD per 1000 inhabitants per day (DID). Twelve relevant quality indicators were calculated for comparison with the European Surveillance of Antimicrobial Consumption (ESAC) project monitoring results.
Total antibiotic use increased from 18.75 DID to 57.07 DID and then decreased to 19.11 DID, a turning point in 2014. The top three antibiotics used were J01C (beta-lactam antibacterials, penicillins), J01F (macrolides, lincosamides and streptogramins), and J01D (other beta-lactam antibacterials, cephalosporins), accounting for 45.15%, 31.40%, and 11.99% respectively. The oral antibiotics used were approximately 2.5 times the parenteral antibiotics, accounting for 71.81% and 28.19%, respectively. Different use preferences were shown in public hospitals and primary health care centres (PHCs), and the latter accounted for more than half of total use. The absolute use of all classes of antibiotics in Gansu is almost higher than any of the 31 European countries included in the ESAC, but the relative use of some focused antibiotics is lower than theirs.
The intervention policies of the health department reduced antibiotic use in Gansu Province, but the proportion of broad-spectrum and parenteral antibiotics was still high. It is necessary to further improve the quality of antibiotic prescriptions and pay more attention to the rationality of antibiotic use in PHCs.

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UNASSIGNED: Drug interactions are important causes of adverse events. Assessments of pharmacological interactions outside healthcare services settings are scarce.
UNASSIGNED: To assess the frequency and factors associated with these potential interactions in adults living in Manaus, Amazonas, Brazil.
UNASSIGNED: We conducted a case-control study in 2019 with residents who had taken two or more medicines two weeks before the interview. The cases involved people with potential drug interaction, according to Micromedex™, and adults without drug interactions formed the control group. The factors associated with interaction were identified by multivariate logistic regression.
UNASSIGNED: 752 adults out of 2321 interviewed were using two or more medicines and were included. The prevalence of potential drug interactions was 30.2% (95% CI: 26.9; 33.5%). We identified 457 drug interactions, more frequently one interaction per person (49.7%), of major severity (61.9%), and with fair documentation (61.7%); three individuals were using contraindicated associations. Individuals aged 45-59 years (OR 1.88, 95% CI 1.03-3.42), using 3 or more drugs simultaneously (p-value<0.001), had higher chance of drug interactions.
UNASSIGNED: Drug interaction was common in among adults living in Manaus, mostly of major severity. The odds of interaction increased with age and number of concomitantly medicines.

Conflicting information on potential benefits of drugs as well as reports on hypothetical harm of commonly used drugs in COVID-19 treatment have challenged clinicians and healthcare systems. We analyzed the change in ambulatory drug utilization before, during, and after the first wave of the pandemic in 2020.
We explored dispensing data of nearly 19 000 pharmacies at the expense of the statutory health insurance funds covering 88% of Germany\'s population. We analyzed utilization of publicly discussed drugs with conflicting information. Drug utilization as number of packages dispensed per week from January to June 2020, reflecting 314 million claims, was compared with 2019.
Utilization of hydroxychloroquine increased +110% during March 2020 and then slightly decreased until week April 13-19. Renin-angiotensin-aldosterone system inhibitors and simvastatin/atorvastatin increased, +78% and +74%, respectively, and subsequently decreased below 2019 levels. Utilization of azithromycin and all systemic antibiotics decreased continuously from March 2-8 until June to levels considerably lower compared to 2019 (June 22-28: azithromycin: -55%, all systemic antibiotics: -27%). Pneumococcal vaccines utilization initially increased +373%, followed by supply shortages. Paracetamol utilization showed an initial increase of +111%, mainly caused by an increase of over-the-counter dispensings.
Apart from the pandemic itself, the data suggest that dissemination of misinformation and unsound speculations as well as supply shortages influenced drug prescribing, utilization, and purchasing behavior. The findings can inform post-pandemic policy to prevent unfounded over- and underprescribing and off-label use as well as drug shortages during a public health crisis.

In order to compile an inventory of national data sources for drug utilization research (DUR) in Argentina and to verify publicly available data sources, we performed a cross-sectional study that sought to identify national and provincial databases of drug use. In July 2020, we searched the websites of government institutions, carried out a systematic query of bibliographic databases for \"drug utilization research\" conducted in Argentina, and conducted a survey with local experts. Data collected included: the institution responsible for the database, population covered, accessibility, source of the data, healthcare setting, geographic information, and whether data were individual or aggregated. Descriptive analyses were then performed. We identified 31 data sources for DUR; only one was publicly and conveniently accessible. Five published aggregated data and provide more detailed access by formal request. Only seven sources (23%) reported national data, and most (n=29) included only data from the public healthcare sector. Although data sources for DUR have been found in Argentina, limited access by researchers and policymakers is still an significant obstacle. Increasing health data transparency by making data sources publicly available for the purpose of analyzing public health information is crucial for building a stronger health system.
Para realizar un inventario de fuentes de datos nacionales sobre utilización de medicamentos en Argentina y verificar las fuentes de datos disponibles públicamente, llevamos a cabo un estudio transversal que investiga la existencia de bases de datos nacionales y provinciales sobre utilización de medicamentos. En julio de 2020, realizamos una búsqueda en sitios web de instituciones gubernamentales, una búsqueda sistemática en bases de datos bibliográficas sobre “drug utilization research” en Argentina y una encuesta de expertos. Se identificaron 31 fuentes de datos de utilización de medicamentos, solo una era de acceso público y conveniente, cinco publicaban datos agregados y proporcionaban un acceso más detallado mediante solicitud formal, solo siete fuentes (23%) informaban datos nacionales, y la mayoría de ellas (n=29) incluían solo datos del sector público de salud. Aunque se han encontrado fuentes de datos de utilización de medicamentos en Argentina, el acceso a investigadores y legisladores sigue siendo una barrera importante. Aumentar la transparencia de los datos de salud a través de fuentes disponibles públicamente para analizar la información de salud pública es crucial para construir un sistema de salud más sólido.

Given the discrepancies between PDDs (prescribed daily doses) and DDDs (defined daily doses), we aimed to assess the extent of error in the results of an 18-year population-level study on statin utilization in Portugal.
The Portuguese regulatory agency provided data for the period 2000-2018 on statin dispensing (C10AA). The DDDs were gathered from the ATC/DDD database. DDDs were calculated by the DDD year-by-year approach (DDDYEAR ) and by the DDD last-year approach (DDDLAST ). PDDs were calculated according to the year-by-year approach (PDDYEAR ). Statin annual utilization rates per 1000 inhabitants per day were also calculated. Percent errors were calculated for PDDYEAR and DDDYEAR units.
The DDDYEAR approach revealed decreases in the consumption of atorvastatin, fluvastatin, lovastatin, pravastatin and simvastatin in 2009, when their DDD was modified. Conversely, the results from both DDDLAST and PDDYEAR approaches indicated gradual changes in the actual consumption of all statins in Portugal. Before 2009, atorvastatin, pravastatin and simvastatin utilization was greatly overestimated by DDDYEAR /1000 inhabitants/day. The average dose of lovastatin prescribed in the past 18 years (20 mg) was below the assigned DDDs during the study period, varying from 30 mg to 45 mg. Conversely, the PDD for fluvastatin was above the DDD values (ranging from 40 mg in 2000 to 70 mg in 2016). For atorvastatin, pravastatin and simvastatin, national PDDs were above the assigned DDD until the DDD modification in 2009.
A more dynamic system, based on national and annually updated DDDs, should be able to reduce discrepancies between DDDs and PDDs and the bias in utilization studies.

To explore how many pre-school aged children with autism spectrum disorder (ASD) used psychotropic medication, child and geographic factors associated with psychotropic medication use, and how many children who used psychotropic medication did or did not ever receive behavior therapy.
Children 2-5 years of age were enrolled from 2012 to 2016 in a multisite case-control study designed to investigate the development and risk factors of ASD. Children with a positive ASD screen or ASD diagnosis upon enrollment were asked to complete a comprehensive evaluation to determine ASD status and developmental level. Caregivers completed a Services and Treatments Questionnaire and multiple self-administered questionnaires to determine child use of psychotropic medication, ever receipt of behavior therapy, and presence of co-occurring symptoms.
There were 763 children who were classified as ASD and had data collected on the Services and Treatments Questionnaire. Of those, 62 (8.1%) used psychotropic medication to treat behavioral symptoms and 28 (3.7%) were ≤3 years of age when medication was first started. Attention problems (aOR, 7.65; 95% CI, 3.41-16.1; P < .001) and study site (aOR, 2.62; 95% CI, 1.04-6.56; P = .04) were significantly associated with psychotropic medication use after controlling for maternal race/ethnicity. More than one-half (59.7%) of those who used psychotropic medication did not ever receive behavior therapy.
Many preschool-aged children with ASD who use psychotropic medication do not receive behavior therapy. Pediatricians are an important resource for children and families and can help facilitate behavioral treatment for children with ASD and other disorders.

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