Pharmacoepidemiology and Drug Safety最新文献

Background: Polypharmacy (PP) is common in elderly population and associated with some adverse clinical outcomes and increases healthcare burdens. We performed this systemic review and meta-analysis to estimate worldwide prevalence of PP and explore associated factors in the elderly.

Methods: The PubMed, Web of Science, Cochrane Library, and Ovid EMBASE databases were searched for studies published until May 30, 2022. We included observational studies representative of general patients aged ≥60 in which PP was defined as multiple drugs ≥5. Studies were excluded if only a particular group of the elderly population (e.g., with diabetes) were included. The primary outcome was the prevalence of PP. Random-effect models were employed to estimate the overall or variable-specific pooled estimates of PP. Secondary outcomes were hyperpolypharmacy (HPP, defined as multiple drugs ≥10) and PP prevalence based on different study years, genders, locations, populations, and so forth.

Results: We included 122 original observational studies with an overall population of 57 328 043 individuals in the meta-analysis. The overall prevalence of PP and HPP in the elderly population worldwide was 39.1% (95% confidence interval [CI], 35.5%-42.7%) and 13.3% (95% CI, 10.4%-16.5%), respectively. The prevalence of PP in Europe, Oceania, North America, Asia, and South America was 45.8% (95% CI, 41.5%-50.2%), 45.5% (95% CI, 26.7%-64.3%), 40.8% (95% CI, 29.8%-51.6%), 29.0% (95% CI, 20.0%-38.0%), and 28.4% (95% CI, 24.0%-32.8%), respectively (p < 0.01). Multivariate meta-regressions showed geographical regions of Europe or North America, age ≥70, and residence from nursing homes were independently associated with higher PP prevalence.

Conclusions: Nearly 40% of the elderly population is exposed to PP. The prevalence of PP is significantly higher in elderly individuals aged 70 or older, in developed regions and in nursing homes. It is important to focus on avoiding inappropriate PP in this population to address the growing burden of PP.

Purpose: Hypertension (HT), dyslipidemia (DL), and diabetes mellitus (DM) are major risk factors for cardiovascular diseases. Despite the wide availability of medications to reduce this risk, poor adherence to medications remains an issue. The aim of this study is to evaluate medication adherence of prevalent users in these disease medications (HT, DL, DM) using claims data. Factors associated with non-adherence were also examined.

Methods: Of 7538 participants of the Tsuruoka Metabolomics Cohort Study, 3693 (HT: 2702, DL: 2112, DM: 661) were identified as prevalent users of these disease medications. Information on lifestyle was collected through a questionnaire. Adherence was assessed by a proportion of days covered (PDC) and participants with PDC ≥0.8 were defined as adherent. Predictors of non-adherence were determined by performing multivariable logistic regression.

Results: Medication adherence differed by treatment status. Among those without comorbidities, those with HT-only showed the highest adherence (90.2%), followed by those with DM-only (81.2%) and those with DL-only (80.8%). Factors associated with non-adherence in each medication group were skipping breakfast and poor understanding of medications among those with HT medications, females, having comorbidities, having a history of heart disease, and drinking habit among those with DL medications, and good sleep quality and skipping breakfast among those with DM medications.

Conclusion: While participants showed high medication adherence, differences were observed across medication groups. The identified predictors of non-adherence could help target those in need of adherence support.

In pharmacoepidemiology, robust data are needed to judge the impact of drug treatment on pregnancy, pregnancy outcomes and breast-fed infants. As pregnant and breastfeeding women are usually excluded from randomised clinical trials, observational studies are required. One of those data sources are pregnancy registers specifically developed to focus on certain diseases or disease groups. The German Rhekiss register investigates pregnancies in women with chronic inflammatory rheumatic diseases (IRD). Rhekiss is a nationwide, multicentre, longitudinal study, in which women aged 18 years or older with an underlying IRD can be enrolled by a rheumatologist either when planning a pregnancy or in the first half of pregnancy. Data are collected prospectively at regular follow-up visits. Rheumatologists and patients provide information in a web-based system before conception (if enrolment was at the time of pregnancy planning), during and after pregnancy. A smartphone app is available for patients. Maternal and clinical information, general laboratory markers, treatment with antirheumatic and other drugs, adverse events, items related to course and outcome of pregnancy and the health of the child are uniformly assessed for all diseases. Individual information on the IRD includes classification criteria, diagnosis-specific laboratory parameters, clinical parameters and validated instruments to measure disease activity or damage. Furthermore, patient-reported outcome measures are captured. A total of 2013 individual patients have been enrolled in the register, and data on 1801 completed pregnancies are available. In summary, Rhekiss is a comprehensive and complex register that can answer various research questions about pregnancy in women with chronic IRDs.

Purpose: Vaccine-associated enhanced disease (VAED) is a theoretical concern with new vaccines, although trials of authorized vaccines against SARS-CoV-2 have not identified markers for VAED. The purpose of this study was to detect any signals for VAED among adults vaccinated against coronavirus disease 2019 (COVID-19).

Methods: In this cross-sectional study, we assessed COVID-19 severity as a proxy for VAED among 400 adults hospitalized for COVID-19 from March through October 2021 at eight US healthcare systems. Primary outcomes were admission to an intensive care unit (ICU) and severe illness (score ≥6 on the World Health Organization [WHO] Clinical Progression Scale). We compared the risk of outcomes among those who had completed a COVID-19 vaccine primary series versus those who were unvaccinated. We incorporated inverse propensity weights for vaccination status in a doubly robust regression model to estimate the causal average treatment effect.

Results: The causal risk ratio in vaccinated versus unvaccinated was 0.36 (95% confidence interval [CI], 0.15-0.94) for ICU admission and 0.46 (95% CI, 0.25-0.76) for severe illness.

Conclusion: Among hospitalized patients, reduced disease severity in those vaccinated against COVID-19 supports the absence of VAED.

Purpose: Concomitant use of hormonal contraceptive agents (HCAs) and enzyme-inducting antiepileptic drugs (EIAEDs) may lead to contraceptive failure and unintended pregnancy. This review identified and evaluated concordance and quality of clinical treatment guidelines related to the use of HCAs in women with epilepsy (WWE) receiving EIAEDs.

Methods: Relevant clinical guidelines were identified across four databases and were independently evaluated for quality utilizing the AGREE-II protocol instrument. Quality in this context is defined as the rigor and transparency of the methodologies used to develop the guideline. Guidelines were further assessed in terms of concordance and discordance with the latest body of knowledge concerning the use of hormonal contraception in the presence of EIAEDs.

Results: A total of n = 5 guidelines were retrieved and evaluated. Overall guideline scores ranged from 17% to 92%, while individual domain scores ranged from 0% to 100%. Contraceptive guidelines consistently recommended the use of intrauterine systems and long-acting injectables in the presence of EIAEDs, recommended against the use of oral, transdermal, and vaginal ring contraceptives, and differed regarding recommendations related to implants. Guidelines agreed regarding recommendations that women treated with EIAEDs should receive intrauterine systems and long-acting injectables; however, the suggested frequency of administration of injectable contraceptives differed. The use of intrauterine systems in this population is supported by evidence, but there is uncertainty surrounding the use of long-acting injectables and contraceptive implants.

Conclusions: To mitigate the risk of unintended pregnancy and its consequences, recommendations related to implants and long-acting injectable contraceptives should be evidence-based.

Background: The Medicines Intelligence (MedIntel) Data Platform is an anonymised linked data resource designed to generate real-world evidence on prescribed medicine use, effectiveness, safety, costs and cost-effectiveness in Australia.

Results: The platform comprises Medicare-eligible people who are ≥18 years and residing in New South Wales (NSW), Australia, any time during 2005-2020, with linked administrative data on dispensed prescription medicines (Pharmaceutical Benefits Scheme), health service use (Medicare Benefits Schedule), emergency department visits (NSW Emergency Department Data Collection), hospitalisations (NSW Admitted Patient Data Collection) plus death (National Death Index) and cancer registrations (NSW Cancer Registry). Data are currently available to 2022, with approval to update the cohort and data collections annually. The platform includes 7.4 million unique people across all years, covering 36.9% of the Australian adult population; the overall population increased from 4.8 M in 2005 to 6.0 M in 2020. As of 1 January 2019 (the last pre-pandemic year), the cohort had a mean age of 48.7 years (51.1% female), with most people (4.4 M, 74.7%) residing in a major city. In 2019, 4.4 M people (73.3%) were dispensed a medicine, 1.2 M (20.5%) were hospitalised, 5.3 M (89.4%) had a GP or specialist appointment, and 54 003 people died. Anti-infectives were the most prevalent medicines dispensed to the cohort in 2019 (43.1%), followed by nervous system (32.2%) and cardiovascular system medicines (30.2%).

Conclusion: The MedIntel Data Platform creates opportunities for national and international research collaborations and enables us to address contemporary clinically- and policy-relevant research questions about quality use of medicines and health outcomes in Australia and globally.

Background: The association between opioid use and the risk of ventricular arrhythmias (VA) is poorly understood.

Aims: The objective of this study was to synthesize the evidence on the risk of VA associated with opioid use.

Materials & methods: We systematically searched the Cochrane Library, Embase, MEDLINE, and CINAHL databases in July 2022. Risk of bias was assessed using the Cochrane risk for bias tool for randomized controlled trials (RCTs) and ROBINS-I for observational studies. Certainty of evidence was assessed using GRADE.

Results: We included 15 studies (12 observational, 2 post hoc analyses of RCTs, 1 RCT). Most studies focused on opioid use for maintenance therapy (n = 9), comparing methadone to buprenorphine (n = 13), and reported QTc prolongation (n = 13). Six observational studies had a critical risk of bias, and one RCT was at high risk of bias. Two studies could not be included in the meta-analysis as they reported a different outcome and studied an opioid antagonist. Meta-analysis of 13 studies indicated that the use of methadone was associated with an increased risk of VA compared to the use of buprenorphine, morphine, placebo, or levacetylmethadol (risk ratio [RR], 2.39; 95% CI, 1.31-4.35; I² = 60%). The pooled estimate varied greatly between observational studies (RR, 2.12; 95% CI, 1.15-3.91; I² = 62%) and RCTs (RR, 14.09; 95% CI, 1.52-130.61; I² = 0%), but both indicated an increased risk.

Conclusion: In this systematic review and meta-analysis, we found that methadone use is associated with more than twice the risk of VA compared to comparators. However, our findings should be interpreted cautiously given the limited quality of the available evidence.

Purpose: In clinical practice, a discrepancy may exist between the prescribed amount of a drug and the commercially available pack sizes in the pharmacy, potentially contributing to drug waste. This study aimed-as an example of this phenomena-to quantify leftover of amoxicillin suspension prescribed to children, due to discrepancies between physician-prescribed and pharmacy-dispensed amounts.

Methods: We performed a retrospective cohort study including amoxicillin suspension dispensations for patients aged 0-12 years between 2017 and 2019 utilizing the Dutch PHARMO database. Leftover amount of amoxicillin was estimated by assessing the discrepancy between the prescribed and dispensed amounts. Extrapolated amoxicillin weight and economic spillage estimates for the Netherlands were determined. The impact of two theoretical interventions on leftover amount was assessed: (1) introducing vials with half the volume of the current 100 and 30 mL vials and (2) a combination of the first intervention with a maximum of 10% round-down by the dispensing pharmacy of the prescribed dose.

Results: We included 79 512 amoxicillin suspension dispensations for 62 252 patients. The mean leftover amount of amoxicillin suspension per dispensing was 27%. The yearly amount of amoxicillin leftover was 49.8 kg in the study cohort, equivalent to yearly 633 kg and €621 000 when extrapolated to the Netherlands. Employing the first theoretical intervention reduced the mean leftover per dispensing to 20%, reducing the yearly leftover to 31.6 kg amoxicillin in the study cohort, and to 400 kg and €400 000 extrapolated. The second theoretical intervention further reduced leftover to 17%, reducing the yearly leftover to 24.3 kg amoxicillin in the study cohort, and to 300 kg and €300 000 extrapolated.

Conclusion: Approximately a quarter of amoxicillin suspension remains as leftover per dispensing. Applying different theoretical intervention shows the potential for a significant reduction of amoxicillin leftover.

Background: Oncology databases that integrate genomic and clinical data have become valuable resources for precision medicine. However, the generalizability of these databases has not been comprehensively assessed.

Objectives: To describe the demographics, clinical characteristics, treatments, and overall survival of breast cancer cohorts in GENIE-BPC and three other databases.

Methods: This study utilized GENIE-BPC, SEER, SEER-Medicare, and Merative MarketScan Research Databases. Women with invasive breast cancer were identified through EHR, cancer registries or ICD-9/10-CM codes. The ages were 18+ years or per database requirement. Treatments were based on EHR or HCPCS/NDC codes in claims. Overall survival was estimated as time from diagnosis to death.

Results: Of female breast cancer patients in GENIE-BPC (n = 775), SEER (n = 548 336), SEER-Medicare (n = 68 914), and Marketscan (n = 109 499) databases, the median ages at initial diagnosis were 44, 62, 74, and 57 years, respectively. A greater proportion of patients in GENIE-BPC, compared to SEER/SEER-Medicare, had higher nuclear grades (%III-%IV: 57% vs. 26%/24%), advanced disease stage (%IV: 25.3% vs. 5%/3.6%), percent of triple negative breast cancer (19.7% vs. 10.2%/8.5%), and receipt of chemotherapy (85.0% vs. NA/22.3%). The 1-, 3-, and 5-year overall survival rates were lower in GENIE-BPC (78.5%, 60.5%, 55.5%) than in SEER (95.8%, 89.5%, 85.5%) and SEER-Medicare (91.6%, 81.4%, 75.0%).

Conclusion: Breast cancer patients in GENIE-BPC were younger, had more advanced disease, had a higher proportion of triple negative breast cancer and recipients of chemotherapy, and had poorer overall survival. Researchers must use statistical adjustment when extrapolating results (e.g., biomarker prevalence) from GENIE-BPC to the larger breast cancer population.

Purpose: We aimed to validate and, if performance was unsatisfactory, update the previously published prognostic model to predict clinical deterioration in patients hospitalized for COVID-19, using data following vaccine availability.

Methods: Using electronic health records of patients ≥18 years, with laboratory-confirmed COVID-19, from a large care-delivery network in Massachusetts, USA, from March 2020 to November 2021, we tested the performance of the previously developed prediction model and updated the prediction model by incorporating data after availability of COVID-19 vaccines. We randomly divided data into development (70%) and validation (30%) cohorts. We built a model predicting worsening in a published severity scale in 24 h by LASSO regression and evaluated performance by c-statistic and Brier score.

Results: Our study cohort consisted of 8185 patients (Development: 5730 patients [mean age: 62; 44% female] and Validation: 2455 patients [mean age: 62; 45% female]). The previously published model had suboptimal performance using data after November 2020 (N = 4973, c-statistic = 0.60. Brier score = 0.11). After retraining with the new data, the updated model included 38 predictors including 18 changing biomarkers. Patients hospitalized after Jun 1st, 2021 (when COVID-19 vaccines became widely available in Massachusetts) were younger and had fewer comorbidities than those hospitalized before. The c-statistic and Brier score were 0.77 and 0.13 in the development cohort, and 0.73 and 0.14 in the validation cohort.

Conclusion: The characteristics of patients hospitalized for COVID-19 differed substantially over time. We developed a new dynamic model for rapid progression with satisfactory performance in the validation set.