Anatomical Therapeutic Chemical Code Research Articles

Prevalence and Incidence of Medication-Treated Diabetes and Pattern of Glucose-Lowering Treatment During the COVID-19 Pandemic: Real-World Data from the Electronic Greek Prescription Database.

This study aimed to investigate the prevalence and incidence of medication-treated diabetes mellitus and the evolving patterns of glucose-lowering treatments the year before and during the first two years of the coronavirus disease 2019 (COVID-19) pandemic. Data from the Greek electronic prescription database were analyzed for 2019, 2020, and 2021. The study population included individuals with active social security numbers. Prevalence and incidence rates were calculated based on the dispensing of glucose-lowering medications according to their unique anatomical therapeutic chemical (ATC) code. The study population comprised 10,289,140 individuals in 2019, 10,630,726 in 2020, and 11,246,136 in 2021. Diabetes prevalence rates were 8.06%, 6.89%, and 7.91%, and incidence rates were 16.8/1000, 8.6/1000, and 13.4/1000 individuals, respectively. Metformin was the most prescribed medication, and newer classes, like sodium-glucose cotransporter-2 inhibitors 2 (SGLT-2) inhibitors and glucagon like peptide-1 (GLP-1) receptor agonists exhibited increasing trends. The study identified a decrease in medication-prescribed diabetes prevalence and incidence during the initial year of the COVID-19 pandemic, attributed to healthcare access restrictions. Subsequently, figures returned close to baseline levels. Glucose-lowering medication trends reflected adherence to local and international guidelines, with metformin as the cornerstone, and increasing preference for newer classes such as GLP-1 receptor agonists and SGLT-2 inhibitors.

#1018 Profiles of medication use in dialysis patients globally

Abstract Background and Aims Global patterns of medication use in dialysis have not been defined so far. This project aims to provide a real-world view of medication use in dialysis and to characterize therapeutic classes and types of medications. An anonymized dialysis dataset of a global kidney care network called Apollo Dial DB was used. The most common prescribed medications administered at home by the patients were assessed. Method This is a multi-center, retrospective study using real-life data from adult dialysis patients captured in the first version of the Apollo Dial DB dataset. This dataset contains anonymized dialysis data (longitudinal observation level data) on more than 540,000 unique dialysis patients in 40 countries from Jan 2018-Mar 2021. Home medications were analyzed based on Anatomical Therapeutic Chemical (ATC) codes. Top (most common) 5 ATC codes for home medication are presented, once as 4-digit ATC code classes (e.g., A12A for Calcium) and once as full/exact ATC code (e.g., A12AA04 as 7-digit code for Calcium carbonate). Results In Apollo Dial DB 497,723 patients had 10,538,324 documented home medication entries (on average 21 medication prescriptions per patient outside the dialysis clinic), thereof 114,978 patients had 1,607,168 ATC code entries. Each ATC code entry represents a documented prescription of individual length. All percentages shown below are related to 1,607,168 ATC code entries (not to patients). The top 4-digit ATC code classes of home medication were Calcium (8.60%), Vitamin A and D, including combinations (7.33%), All other Therapeutic Products (6.84%), Antithrombotic Agents (5.89%) and Selective Calcium Channel Blockers with mainly vascular Effects (5.39%) (Fig. 1). Out of the ATC class Calcium, Calcium carbonate (4.89%) and Calcium acetate (3.22%) are listed as number 1 and 3 of the top 5 ‘full’ ATC codes. Alfacalcidol (3.36%) of ‘Vitamin A and D, including combinations’ is also number 2 of the ‘full’ ATC code list. Amlodipine (3.07%), number 4 of the full ATC code list, represents the majority of ‘Selective Calcium Channel Blockers with mainly vascular Effects’. Sevelamer of ‘All other Therapeutic Products’ is listed as number 5 in the ATC code list with 2.91% (Fig. 2). Conclusion The top 5 home ATC classes of medications account for 34% of all home prescriptions in dialysis patients. They largely represent phosphate binders, vitamin A & D, antithrombotics, and antihypertensives/antiarrhythmic medications. Although, outside the dialysis clinic each patient has on average 21 medication prescriptions, a lot of these prescriptions represent the major medication classes. It is worthwhile to mention the presented data are related to prescriptions and not to duration of use, e.g., both a medication prescribed for 6 months and a prescription for 1 week are documented only once, even if time on drug varies. Further research is needed to explore global patterns, including further ATC code groups particularly on a patient level, accounting for duration of use and medication formulations, real-world application of various drugs (e.g., GLP1 drugs, HIF PH inhibitor) and accounting for US data with NDC codes.

Unused, expired pharmaceuticals and their disposal practices among the general public in Burdur-Türkiye: a cross-sectional study

BackgroundUnused pharmaceuticals are currently a public health problem. This study aimed to identify unused pharmaceuticals, research practices about the disposal methods, classify the medicines according to Anatomical Therapeutic Chemical codes (ATC) and, to determine the number of unused medicines.MethodsThe study was designed as a cross-sectional study. Data were collected between April and August 2023 in Burdur-Türkiye by non-probability sampling technique (convenience method). Pharmaceuticals were classified according to ATC. Statistical Package for Social Science SPSS (V.24) package program was used for data analysis.ResultsA total of 1120 people, 1005 in the first sample group and 115 in the second sample group, participated in the study. Findings of first sample group: A total of 4097 boxes of unused pharmaceuticals (4.7 ± 4.3 boxes/per capita) were detected. It was found that pharmaceuticals were stored in areas such as kitchens (59.1%) and refrigerators (38.6%), the reason for keeping them was reuse (41%), and the disposal practice was household garbage (81%). Paracetamol (648 boxes), Other cold preparation (303 boxes), Dexketoprofen (239 boxes), Diclofenac (218 boxes), Amoxicillin and beta-lactamase inhibitor (190 boxes) were found to be the most frequently unused pharmaceuticals. Using the unused medicines at home without consulting a physician was 94.1% (self-medication). Findings of second sample group: Of the 6189 dosage forms in 265 boxes pharmaceutical, 3132(50.6%) dosage forms were used and 3057(49.4%) were found to be unused.ConclusionThere is a significant amount and number of unused medicines in households, and self-medication is common. Medicines are not properly disposed of and some of them expire. Public information is needed. A “drug take-back system” for unused medicines can be useful in solving this problem.

Classifying Multimorbidity Using Drug Concepts via the Rx-Risk Comorbidity Index: Methods and Comparative Cross-Sectional Study.

The study objective was to update a method to identify comorbid conditions using only medication information in circumstances in which diagnosis codes may be undercaptured, such as in single-specialty electronic health records (EHRs), and to compare the distribution of comorbidities across Rx-Risk versus other traditional comorbidity indices. Using First Databank, RxNorm, and its web-based clients, RxNav and RxClass, we mapped Drug Concept Unique Identifiers (RxCUIs), National Drug Codes (NDCs), and Anatomical Therapeutic Chemical (ATC) codes to Rx-Risk, a medication-focused comorbidity index. In established rheumatoid arthritis (RA) and osteoarthritis (OA) cohorts within the Rheumatology Informatics System for Effectiveness registry, we then compared Rx-Risk with other comorbidity indices, including the Charlson Comorbidity Index, Rheumatic Disease Comorbidity Index (RDCI), and Elixhauser. We identified 965 unique ingredient RxCUIs representing the 46 Rx-Risk comorbidity categories. After excluding dosage form and ingredient related RxCUIs, 80,911 unique associated RxCUIs were mapped to the index. Additionally, 187,024 unique NDCs and 354 ATC codes were obtained and mapped to the index categories. When compared to traditional comorbidity indices in the RA cohort, the median score for Rx-Risk (median 6.00 [25th percentile 2, 75th percentile 9]) was much greater than for Charlson (median 0 [25th percentile 0, 75th percentile 0]), RDCI (median 0 [25th percentile 0, 75th percentile 0]), and Elixhauser (median 1 [25th percentile 1, 75th percentile 1]). Analyses of the OA cohort yielded similar results. For patients with a Charlson score of 0 (85% of total), both the RDCI and Elixhauser were close to 1, but the Rx-Risk score ranged from 0 to 16 or more. The misclassification and under-ascertainment of comorbidities in single-specialty EHRs can largely be overcome by using a medication-focused comorbidity index.

Development and validation of algorithms to predict left ventricular ejection fraction class from healthcare claims data.

The use of large medical or healthcare claims databases is very useful for population-based studies on the burden of heart failure (HF). Clinical characteristics and management of HF patients differ according to categories of left ventricular ejection fraction (LVEF), but this information is often missing in such databases. We aimed to develop and validate algorithms to identify LVEF in healthcare databases where the information is lacking. Algorithms were built by machine learning with a random forest approach. Algorithms were trained and reinforced using the French national claims database [Système National des Données de Santé (SNDS)] and a French HF registry. Variables were age, gender, and comorbidities, which could be identified by medico-administrative code-based proxies, Anatomical Therapeutic Chemical codes for drug delivery, International Classification of Diseases (Tenth Revision) coding for hospitalizations, and administrative codes for any other type of reimbursed care. The algorithms were validated by cross-validation and against a subset of the SNDS that includes LVEF information. The areas under the receiver operating characteristic curve were 0.84 for the algorithm identifying LVEF≤40% and 0.79 for the algorithms identifying LVEF<50% and ≥50%. For LVEF≤40%, the reinforced algorithm identified 50% of patients in the validation dataset with a positive predictive value of 0.88 and a specificity of 0.96. The most important predictive variables were delivery of HF medication, sex, age, hospitalization, and testing for natriuretic peptides with different orders of positive or negative importance according to the LVEF category. The algorithms identify reduced or preserved LVEF in HF patients within a nationwide healthcare claims database with high positive predictive value and low rates of false positives.

Hoarding of insulin and oral antidiabetics in the first phase oftheCOVID-19 pandemic: Regulatory actions taken to avoid shortage.

To report on hoarding of prescribed medicines, with focus on insulins, in the early phase of the COVID-19 pandemic and on regulatory actions taken to avoid shortage. The National Prescribed Drug Register which utilizes the Anatomic Therapeutic Chemical (ATC) Classification System and covers the total Swedish population was used. We calculated the number of packages of insulins (ATC code A10A), oral anti-diabetics (A10B), and all medicines across all ATC codes combined (A-S) dispensed per week in 2019 and 2020. Correspondingly, the number of packages of glucose test strips dispensed was calculated using the data source Concise held by the Swedish eHealth Agency. Prompt increases in numbers of dispensed packages were observed in March, peaking at week 11/2020. The absolute numbers of packages dispensed in week 11/2019 and week 11/2020 were: insulin, 49,694 and 95,767, an increase by +92.7%; oral antidiabetics, 55,478 and 82,684, +47.1%; glucose test strips, 18,119 and 23,476, +29:6%; and all medicines across all ATC codes combined, 1,988,456 and 2,659,421, +33.7%. Voluntary restriction of dispensing and a rapid change to applicable regulation were implemented within 2 weeks. A steep decline occurred, which became more pronounced after temporary regulation came in force from April 1, then leveling out during the following months. A signal of insulin hoarding was detected early in the COVID-19 pandemic. A temporary regulation, reducing dispensing to a maximum supply of 3 months was rapidly implemented. A shortage of vitally important prescribed medicines was avoided.

Mental comorbidities in adolescents and young adults with juvenile idiopathic arthritis: an analysis of German nationwide health insurance data

BackgroundStudies on prevalence rates of mental comorbidities in patients with juvenile idiopathic arthritis (JIA) have reported varying results and provided limited information on related drugs. The purpose of this study was to determine the prevalence of selected mental health diagnoses and the range of associated drug prescriptions among adolescents and young adults (AYA) with JIA compared with general population controls.FindingsNationwide statutory health insurance data of the years 2020 and 2021 were used. Individuals aged 12 to 20 years with an ICD-10-GM diagnosis of JIA in ≥ 2quarters, treated with disease-modifying antirheumatic drugs and/or glucocorticoids were included. The frequency of selected mental health diagnoses (depression, anxiety, emotional and adjustment disorders) was determined and compared with age- and sex-matched controls. Antirheumatic, psychopharmacologic, psychiatric, and psychotherapeutic therapies were identified by Anatomical Therapeutic Chemical (ATC) codes and specialty numbers. Based on data from 628 AYA with JIA and 6270 controls, 15.3% vs. 8.2% had a diagnosed mental health condition, with 68% vs. 65% receiving related drugs and/or psychotherapy. In both groups, depression diagnosis became more common in older teenagers, whereas emotional disorders declined. Females with and without JIA were more likely to have a mental health diagnosis than males. Among AYA with any psychiatric diagnosis, 5.2% (JIA) vs. 7.0% (controls) received psycholeptics, and 25% vs. 27.3% psychoanaleptics.ConclusionsSelected mental health conditions among 12-20-year-old JIA patients are diagnosed more frequently compared to general population. They tend to occur more frequently among females and later in childhood. They are treated similarly among AYA regardless of the presence of JIA.

Nationwide prevalence of glucocorticoid prescriptions over 17 years and osteoporosis prevention among long-term users.

Glucocorticoid steroids are frequently prescribed, and side effects are well-known, such as glucocorticoid-induced osteoporosis. Our aim was to estimate the nationwide trend in the prevalence of glucocorticoid steroid prescriptions over 17 years and to elucidate the proportion of patients on long-term glucocorticoid steroid therapy who receive active bone protective therapy. As well as to examine which medical specialties prescribe glucocorticoid steroids the most. This study was a retrospective observational registry study extended over 17 years (2003-2020). Data were retrieved from the Icelandic Prescription Medicine Register on all delivered glucocorticoid steroids (Anatomic therapeutic chemical code: H02AB) for oral use. Long-term users were defined as those who annually received ⩾90 defined daily doses of glucocorticoid steroids. Annually, 3.8% of the population received oral glucocorticoid steroids, from 3.3% in 2006 to 4.3% in 2017. Prednisolone was most frequently prescribed. Females dispatched glucocorticoid steroid prescriptions more often than males (55.8%). Males and females reached their peak prevalence between the ages of 60 and 70. General practitioners most often prescribe glucocorticoid steroids, followed by physicians in training, rheumatologists, internists, and medical students. Of those who received prescriptions for glucocorticoid steroids, 12.2%-18.1% were classified as long-term users. A declining number of patients have been receiving bone-protective therapy in recent years. Only 13.0% of chronic users received bone protective therapy in 2020. The use of glucocorticoid steroids has increased during the last 2 decades despite improvements in treatment for inflammatory disorders. The prevalence of long-term users has remained stable. Meanwhile, the use of parallel active bone-protective therapy among long-term users of glucocorticoid steroids is declining. Thus, improvements in prophylaxis for corticosteroid-induced osteoporosis are urgently needed for patients who require long-term treatment with glucocorticoid steroids.

Association between Central Nervous System Drugs and Femoral Fracture Risk in Japanese Individuals ≥80 Years Old: A Case-crossover Study.

Objectives To assess the association between concomitant use of central nervous system drugs and femoral fracture risk in individuals ≥80 years old in Japan. Methods A case-crossover design was used, defining the case period as 3 days before the fracture diagnosis and the control period as 31-33, 34-36, and 37-39 days prior. The association between the daily intake of central nervous system drugs (Anatomical Therapeutic Chemical codes) and fracture risk was analyzed using conditional logistic regression. Patients Using the Japanese administrative claims database, we examined elderly patients diagnosed with femoral neck fractures between January 1, 2009, and December 31, 2020. Results In 255,875 patients, the concomitant use of central nervous system drugs increased the odds ratios of femoral fracture (3.41[95% confidence interval: 3.27-3.55], 3.69 [3.46-3.91], 3.76 [3.42-4.13], and 4.34 [3.86-4.86] for an intake of >0-1, >1-2, >2-3, and >3 central nervous system drugs, respectively). Conclusions The concomitant use of central nervous system drugs is associated with an increased risk of femoral fractures in individuals ≥80 years old in Japan.

The European List of Key Medicines for Medical Education: A Modified Delphi Study.

Rational prescribing is essential for the quality of health care. However, many final-year medical students and junior doctors lack prescribing competence to perform this task. The availability of a list of medicines that a junior doctor working in Europe should be able to independently prescribe safely and effectively without supervision could support and harmonize teaching and training in clinical pharmacology and therapeutics (CPT) in Europe. Therefore, our aim was to achieve consensus on such a list of medicines that are widely accessible in Europe. For this, we used a modified Delphi study method consisting of three parts. In part one, we created an initial list based on a literature search. In part two, a group of 64 coordinators in CPT education, selected via the Network of Teachers in Pharmacotherapy of the European Association for Clinical Pharmacology and Therapeutics, evaluated the accessibility of each medicine in his or her country, and provided a diverse group of experts willing to participate in the Delphi part. In part three, 463 experts from 24 European countries were invited to participate in a 2-round Delphi study. In total, 187 experts (40%) from 24 countries completed both rounds and evaluated 416 medicines, 98 of which were included in the final list. The top three Anatomical Therapeutic Chemical code groups were (1) cardiovascular system (n = 23), (2) anti-infective (n = 21), and (3) musculoskeletal system (n = 11). This European List of Key Medicines for Medical Education could be a starting point for country-specific lists and could be used for the training and assessment of CPT.

PDATC-NCPMKL: Predicting drug's Anatomical Therapeutic Chemical (ATC) codes based on network consistency projection and multiple kernel learning

The development and discovery of new drugs is time-consuming and needs lots of human and material resources. Therefore, discovery of novel effects of existing drugs is an important alternative way, which can accelerate the process of designing “new” drugs. The anatomical Therapeutic Chemical (ATC) classification system recommended by World Health Organization (WHO) is a basic research area in this regard. A novel ATC code of an existing drug suggests its novel effects. Some computational models have been proposed, which can predict the drug-ATC code associations. However, their performance is not very high. There still exist spaces for improvement. In this study, a new recommendation system (named PDATC-NCPMKL), which incorporated network consistency projection and multi-kernel learning, was designed to identify drug-ATC code associations. For drugs or ATC codes, several kernels were constructed, which were fused by a multiple kernel learning method and an additional kernel integration scheme. To enhance the performance, the drug-ATC code association adjacency matrix was reformulated by a variant of weighted K nearest known neighbors (WKNKN). The reformulated adjacency matrix, drug and ATC code kernels were fed into network consistency projection to generate the association score matrix. The proposed recommendation system was tested on the ATC codes at the second, third and fourth levels in drug ATC classification system using ten-fold cross-validation. The results indicated that all AUROC and AUPR values were close to or exceeded 0.96. Such performance was higher than some existing computational models. Some additional tests were conducted to prove the utility of adjacency matrix reformulation and to analyze the importance of drug and ATC code kernels.

Early prediction of in-hospital mortality utilizing multivariate predictive modelling of electronic medical records and socio-determinants of health of the first day of hospitalization.

In France an average of 4% of hospitalized patients die during their hospital stay. To aid medical decision making and the attribution of resources, within a few days of admission the identification of patients at high risk of dying in hospital is essential. We used de-identified routine patient data available in the first 2 days of hospitalization in a French University Hospital (between 2016 and 2018) to build models predicting in-hospital mortality (at ≥ 2 and ≤ 30 days after admission). We tested nine different machine learning algorithms with repeated 10-fold cross-validation. Models were trained with 283 variables including age, sex, socio-determinants of health, laboratory test results, procedures (Classification of Medical Acts), medications (Anatomical Therapeutic Chemical code), hospital department/unit and home address (urban, rural etc.). The models were evaluated using various performance metrics. The dataset contained 123,729 admissions, of which the outcome for 3542 was all-cause in-hospital mortality and 120,187 admissions (no death reported within 30 days) were controls. The support vector machine, logistic regression and Xgboost algorithms demonstrated high discrimination with a balanced accuracy of 0.81 (95%CI 0.80-0.82), 0.82 (95%CI 0.80-0.83) and 0.83 (95%CI 0.80-0.83) and AUC of 0.90 (95%CI 0.88-0.91), 0.90 (95%CI 0.89-0.91) and 0.90 (95%CI 0.89-0.91) respectively. The most predictive variables for in-hospital mortality in all three models were older age (greater risk), and admission with a confirmed appointment (reduced risk). We propose three highly discriminating machine-learning models that could improve clinical and organizational decision making for adult patients at hospital admission.

Antimicrobial consumption surveillance in Uganda: Results from an analysis of national import data for the human health sector, 2018–2021

BackgroundThe surveillance of antimicrobial consumption (AMC) is critical to developing appropriate antimicrobial stewardship interventions. It is a key component of World Health Organization’s (WHO) Global Action Plan on Antimicrobial Resistance and the Uganda Antimicrobial Resistance National Action Plan 2018–2023. Our study’s objective was to determine the national consumption of all antimicrobials. MethodsData on all imported antimicrobials were retrieved from paper-based records and entered in the web-based National Drug Authority (NDA) management information system from 2021. The import data for the year is a proxy for nationwide consumption because they account for 95% of all medical products. The NDA authorizes all imports to the country regardless of final distribution in the supply chain. The data were analyzed in accordance with WHO Anatomical Therapeutic Chemical codes and defined daily dose (DDD) methodology. We also retrieved consumption data for 2018, 2019, and 2020 that were previously submitted by Uganda to WHO’s Global Antimicrobial Resistance and Use Surveillance System. ResultsIn 2021, the average DDD per 1000 inhabitants was 29.02 for all antimicrobials; 80.7% of antimicrobials consumed were oral. Penicillins (27.6%) were the most consumed antimicrobial class, followed by sulfonamides and trimethoprim (15.5%). Based on WHO’s Access, Watch, and Reserve (AWaRe) antibiotic classification, 62.91% of AMC was from the access class, with watch class averaging 14.51% in the period 2018–2021. Watch class AMC spiked in 2021 (34.2%) during COVID-19 pandemic compared to 2020 (24.29%). Azithromycin and ciprofloxacin were the most consumed watch class antimicrobials in 2021. ConclusionsThe relatively high consumption of injectable antimicrobials and year over year increase in watch class AMC requires urgent stewardship interventions. Further work is needed to establish a system for longitudinal AMC surveillance that is well resourced and funded to overcome the challenges of estimation and provide more accurate data on consumption and use patterns.

The Characterization of Non-oncologic Chronic Drug Therapy in Bladder Cancer Patients and the Impact on Recurrence-Free and Cancer-Specific Survival: A Prospective Study

We aimed to characterize non-oncologic chronic drug therapy of bladder cancer (BC) patients and evaluate a possible impact on recurrence-free (RFS) and cancer-specific survival (CSS). Patients with a first diagnosis (FD) of BC or radical cystectomy (RC) were included in a prospective, monocentric, observational study. Drugs and medical data was assessed at start and three-monthly for 24 months. Drugs were classified by anatomical-therapeutic-chemical code (ATC). Endpoints for outcome analysis were RFS and CSS in univariate (Kaplan–Meier curves and log-rank test, Cox regression for Hazard Ratio (HR)) and multivariate (Cox regression models) analyses. Of 113 patients, 52 had FD and 78 RC. Median age was 74 and 72 years, 83% and 82% were male. Drugs of 114 ATC classes were taken by 48 (92%) FD patients (median number 4.5/IQR 2–7.5) and 73 (94%) of RC patients (median 5/IQR 2–9). In univariate analysis (log-rank test (p)/Cox regression (HR, 95% CI, p)), polypharmacy (p = 0.036/HR = 2.83, 95% CI = 1.02–7.90, p = 0.047), calcium channel blockers (p = 0.046/HR = 2.47, 95% CI = 0.97–6.27, p = 0.057) and proton pump inhibitors (p = 0.015/HR = 3.16, 95% CI = 1.18–8.41, p = 0.022) had a significant negative impact on RFS in RC patients, statins (p = 0.025/HR = 0.14, 95% CI = 0.02–1.06, p = 0.057) a positive effect on RFS in FD patients, angiotensin-converting enzyme inhibitors (p = 0.008/HR = 10.74, 95% CI = 1.20–96.17, p = 0.034) and magnesium (p = 0.042/HR = 5.28, 95% CI = 0.88–31.59, p = 0.067) a negative impact on CSS in FD patients. In multivariate analysis, the only significant drug effects were the negative impact of angiotensin-converting enzyme inhibitors (HR = 15.20, 95% CI = 1.30–177.67, p = 0.030) and magnesium (HR = 22.87, 95% CI = 1.57–333.81), p = 0.022) on CSS in FD patients, and the positive impact of statins (HR = 0.12, 95% CI = 0.01–0.97, p = 0.047) on RFS in FD patients. Impact of non-oncologic drugs on RFS and CSS was small in this prospective study. Thus, appropriate treatment of comorbidities is encouraged.

The Association between Birthweight and Use of Cardiovascular Medications: The Role of Health Behaviors.

There is limited evidence on the effect of low birthweight on the use of cardiovascular medications and the role of health behaviors. This study aims to determine the independent effect of low birthweight and its combination with adult health behaviors on the number of dispensed cardiovascular medications. We included 15618 participants with information on birthweight and self-reported health behaviors. Dispensed cardiovascular medications were identified from the Prescribed Drug Register based on a three-digit level Anatomical Therapeutic Chemical classification code (C01 to C10 and B01) and categorized into 0, 1, and ≥2 different types of medications. We applied multinomial logistic regression models estimating odds ratios (ORs) and 95% confidence intervals (CIs). Participants with low birthweight had a higher estimated OR of using ≥2 types of cardiovascular medications (OR = 1.46, 95% CI = 1.06, 2.01). Further, an increased risk for using ≥2 types of cardiovascular medications was found in participants with poor health behaviors for normal (OR = 2.17, 95% CI = 1.80, 2.62) and high (OR = 1.84, 95% CI = 1.29, 2.62) birthweight. The strongest effect on using ≥2 types of cardiovascular medications was found for low birthweight and poor health behaviors (OR = 3.14, 95% CI = 1.80, 5.50). This cohort study provides evidence that low birthweight increases the risk of using more types of cardiovascular medications in adulthood. This study also suggests that ideal health behaviors reduce this risk.

Development and validation of a case definition for problematic menopause in primary care electronic medical records

BackgroundMenopause is a normal transition in a woman’s life. For some women, it is a stage without significant difficulties; for others, menopause symptoms can severely affect their quality of life. This study developed and validated a case definition for problematic menopause using Canadian primary care electronic medical records, which is an essential step in examining the condition and improving quality of care.MethodsWe used data from the Canadian Primary Care Sentinel Surveillance Network including billing and diagnostic codes, diagnostic free-text, problem list entries, medications, and referrals. These data formed the basis of an expert-reviewed reference standard data set and contained the features that were used to train a machine learning model based on classification and regression trees. An ad hoc feature importance measure coupled with recursive feature elimination and clustering were applied to reduce our initial 86,000 element feature set to a few tens of the most relevant features in the data, while class balancing was accomplished with random under- and over-sampling. The final case definition was generated from the tree-based machine learning model output combined with a feature importance algorithm. Two independent samples were used: one for training / testing the machine learning algorithm and the other for case definition validation.ResultsWe randomly selected 2,776 women aged 45–60 for this analysis and created a case definition, consisting of two occurrences within 24 months of International Classification of Diseases, Ninth Revision, Clinical Modification code 627 (or any sub-codes) OR one occurrence of Anatomical Therapeutic Chemical classification code G03CA (or any sub-codes) within the patient chart, that was highly effective at detecting problematic menopause cases. This definition produced a sensitivity of 81.5% (95% CI: 76.3-85.9%), specificity of 93.5% (91.9-94.8%), positive predictive value of 73.8% (68.3-78.6%), and negative predictive value of 95.7% (94.4-96.8%).ConclusionOur case definition for problematic menopause demonstrated high validity metrics and so is expected to be useful for epidemiological study and surveillance. This case definition will enable future studies exploring the management of menopause in primary care settings.

Belgian medication exposure during pregnancy (BeMeP), a new nationwide linked database: Linkage methods and prevalence of medication use.

This study aimed to describe the implementation of a new retrospective Belgian national cohort of pregnant women, the Belgian Medication Exposure during Pregnancy (BeMeP). We linked the national dispensing data to birth and death certificates and hospital stay data for a 7-year period between 2010 and 2016 for the first time in Belgium. We presented the characteristics of pregnancy events associated with the mothers enrolled in the linkage study. Next, we constructed a cohort of pregnancies and compared some characteristics computed using the BeMeP database with the national statistics. Finally, we described the use of medications during pregnancy based on the first level of the Anatomical Therapeutic Chemical (ATC) classification. We included 630 457 pregnant women with 900 024 pregnancy-related events (843 780 livebirths, 1937 stillbirths, 6402 ectopic events, and 47 905 abortions) linked to medication exposure information. Overall, 96.3% of live births and 83.5% of stillbirths (national statistics as reference) were captured from the BeMeP. During pregnancy, excluding the week of birth, 78.9% of live birth pregnancies and 79.6% of stillbirth pregnancies were exposed to at least one medication. The most frequently dispensed medications were anti-infectives (ATC code J = 50.2%) for live births and for stillbirths (44.0%). We linked information on pregnancies, all reimbursed medications dispensed by community pharmacists, all medications dispensed during hospitalization, sociodemographic status, and infant health to create the BeMeP database. The database represents a valuable potential resource for studying exposure-outcome associations for medication use during pregnancy.

223 Chronic pain is frequently under-treated among older people in Ireland: data from TILDA

Abstract Background Chronic pain can have significant adverse effects on quality-of-life, functional status, and well-being in later life. Concerns exist regarding under-treatment of pain among older people, however. The aim of this study, therefore, is to ascertain the prevalence of chronic pain among older people in Ireland, including prescribed pharmacological treatment. Methods Participants ≥70 years at TILDA Wave 1 were asked ‘Are you often troubled with pain?’ and if yes, were then asked ‘How bad is it most of the time? Mild, moderate, or severe?’. Medication lists were examined by anatomical therapeutic chemical code for analgesics (N02), opioids (N02A), and non-opioid analgesics (N02B). Frailty was assessed by Fried Frailty Scale. Data was analysed descriptively. Chi-square tests assessed differences in binary variables; t-tests for continuous variables. Results Over 1/3 of participants (n = 2,308; mean 77 years; 53% female) reported chronic pain (839/2,308), with 10% (222/2,308) reporting severe pain. Over 60% of participants with chronic pain (517/839) reported pain made it difficult to complete day-to-day activities. Almost two thirds of participants with moderate–severe pain (407/618) were not prescribed analgesia. Participants with untreated chronic pain were more likely to be female (62% vs 52%, p &amp;lt; 0.001), have functional impairment (22% vs 13%, p &amp;lt; 0.001) and to be frail (14% vs 6%, p &amp;lt; 0.001). Over 98% of participants with untreated pain had contact with healthcare (GP practice or ED) in the preceding 12 months. Conclusion In this population-representative analysis, chronic pain affects 1/3 older people in Ireland, with 1/10 reporting severe pain. Chronic pain often limits day-to-day activities yet is frequently under-treated or not treated at all, particularly in frail, older females, despite contact with the healthcare service. It is imperative, therefore, that we empower older people to report chronic pain to a healthcare professional and we facilitate appropriate assessment and treatment of their pain.

Impact of the COVID-19 pandemic on community antibiotic consumption in the EU/European Economic Area: a changepoint analysis.

A decrease in community antibiotic consumption in Europe has been observed during the COVID-19 pandemic. The magnitude of this decrease, how fast after the outbreak it occurred, whether it was sustained during the pandemic and whether the seasonal variation in antibiotic consumption was affected, have not yet been evaluated in detail. Data on community antibiotic consumption were available from the European Surveillance of Antimicrobial Consumption Network for 28 EU/European Economic Area (EEA) countries between 2010 and 2021. Antibiotic consumption was expressed as DDDs per 1000 inhabitants per day (DID). The impact of the pandemic on antibiotic consumption was investigated using descriptive statistics and non-linear mixed changepoint models for quarterly and yearly data. The decrease in overall antibiotic consumption between 2019 and 2020 (-3.4 DID; -18.6%) was mainly due to a decrease in the consumption of penicillins [Anatomical Therapeutic Chemical (ATC) code J01C] (-1.9 DID; -23.0%), other β-lactam antibacterials (J01D) (-0.6 DID; -25.8%) and macrolides, lincosamides and streptogramins (J01F) (-0.5 DID; -17.4%) and was sustained during 2021. The changepoint analysis of yearly data (28 countries) estimated a decrease of 3.3 DID in overall antibiotic consumption (J01) between 2019 and 2020. The analysis of quarterly data (16 countries) estimated a decrease in overall antibiotic consumption (J01) of 4.0 DID and a decrease in seasonal variation of 1.2 DID between the first and second quarters of 2020. The changepoint analysis indicated a significant, sudden and steep decrease in community antibiotic consumption in the EU/EEA immediately after the start of the COVID-19 outbreak in Europe, as well as a decrease in its seasonal variation.

Unlicensed drugs in Iceland in the years 2020 and 2021 with comparison to Sweden in 2020

Prescriptions of unlicensed drugs along with public discussion have increased in recent years. The cause of this increase is unclear. This is a retrospective descriptive study of applications of unlicensed drugs in Iceland in the years 2020 and 2021, as well as applications in Sweden for the year 2020. Information was collected on unlicensed prescription applications, registered drugs and stock-out time. Information was analyzed and categorized to describe the scope of unlicensed prescriptions in the two countries during the study time. In Iceland, 49.161 applications were approved in 2020 and 46.581 in 2021. The most common reason for using unlicensed products was that no registered drug was on the market with the same ATC-number (Anatomical Therapeutic Chemical code) and formulation. Shortage of drug was the reason for unlicensed prescription in 8.8% of cases in 2020 and 7.6% in 2021. The list of the 50 most prescribed drugs included the same drugs in 70% of the cases in both years. The five most prescribed drugs were the same in both years. In Sweden, 38.458 applications were approved in 2020. Of the most prescribed unlicensed drug, 46% were as no registered drug with same ATC-number and dosage form was marketed. Many unlicensed drugs, prescribed in Iceland, are the same year after year. Only a small part of the applications approved were due to shortage of drug. The use of drugs without marketing license in Iceland in 2020 was higher than in Sweden when adjusted for the size of the market and population. Getting the five most prescribed unlicensed drugs licensed in Iceland could reduce largely, the total of prescription of unlicensed drugs in Iceland.