WeChat Mini Program
Old Version Features

Prevalence and Predictors of Sub-Optimal Laboratory Monitoring of Selected Higher Risk Medicines in Irish General Practice: a 5-Year Retrospective Cohort Study of Community-Dwelling Older Adults

BMJ OPEN(2025)

RSCI Univ Med & Hlth Sci

Cited 0|Views6
Abstract
ObjectivesTo describe the prevalence of sub-optimal monitoring for selected higher-risk medicines in older community-dwelling adults and to evaluate patient characteristics and outcomes associated with sub-optimal monitoring.Study designRetrospective observational study (2011–2015) using historical general practice-based cohort data and linked dispensing data from a national pharmacy claims database.SettingIrish primary care.Participants625 community-dwelling adults aged ≥70 years and prescribed at least one higher-risk medicine during the 5-year study period.Primary and secondary outcome measuresThe primary outcome was the prevalence of sub-optimal laboratory monitoring using a composite measure of published medication monitoring indicators, with a focus on commonly prescribed higher-risk medicines such as diuretics and anticoagulants. Poisson regression was used to assess the patient characteristics associated with sub-optimal monitoring and explanatory variables included the number of medicines, age, sex, deprivation and anxiety/depression symptoms. Logistic regression was used to explore the association between baseline sub-optimal monitoring and the odds of adverse health outcomes (unplanned healthcare utilisation, adverse drug reactions and mortality).ResultsOf 625 participants, the mean age was 77.7 years, 53% were female, the mean number of drugs was 7.3 (SD 3.3) and 499 (79.8%) had ≥1 unmonitored dispensing over 5 years. The number of drugs, deprivation and anxiety/depression symptoms were significantly associated with sub-optimal monitoring, with the strongest association seen for anxiety/depression symptoms (incidence rate ratio: 1.33, 95% CI 1.05 to 1.68). There was a small but significant association between baseline sub-optimal monitoring and emergency department visits at follow-up, but no evidence of an association with unplanned hospital admissions, mortality or adverse drug reactions.ConclusionThe prevalence of sub-optimal medication monitoring was high, and number of drugs, deprivation and anxiety/depression symptoms were significantly associated with sub-optimal monitoring. However, the public health impact of these findings remains uncertain, as there was no clear evidence of an association between sub-optimal monitoring and adverse health outcomes. Further research is needed to evaluate the effect of improved monitoring strategies and the optimal timing for drug monitoring of higher risk medications.
More
Translated text
Key words
Prescriptions,Polypharmacy,Primary Care,Safety
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined