Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Aims: Polypharmacy serves as a quality indicator in residential aged care facilities (RACFs) due to concerns about inappropriate medication use. However, aggregated polypharmacy rates at a single time offer limited value. Longitudinal analysis of polypharmacy patterns provides valuable insights into identifying potential overuse of medicines. We aimed to determine long-term trajectories of polypharmacy (≥9 medicines) and factors associated with each polypharmacy trajectory group.
Methods: This was a longitudinal cohort study using electronic data from 30 RACFs in New South Wales, Australia. We conducted group-based trajectory modelling to identify and characterize polypharmacy trajectories over 3 years. We evaluated the model fitness using the Bayesian Information Criterion, entropy (with a value of ≥0.8 considered ideal) and several other metrics.
Results: The study included 2837 permanent residents (median age = 86 years, 61.7% female and 47.4% had dementia). We identified five polypharmacy trajectory groups: group 1 (no polypharmacy, 46.0%); group 2 (increasing polypharmacy, 9.4%); group 3 (decreasing polypharmacy, 9.2%); group 4 (increasing-then decreasing polypharmacy, 10.0%), and group 5 (persistent polypharmacy, 25.4%). The model showed excellent performance (e.g., entropy = 0.9). Multinomial logistic regressions revealed the profile of each trajectory group (e.g., group 5 residents had higher odds of chronic respiratory disease compared with group 1).
Conclusions: Our study identified five polypharmacy trajectory groups, including one with over a quarter of residents following a persistently high trajectory, signalling concerning medication overuse. Quality indicator programs should adopt tailored metrics to monitor diverse polypharmacy trajectory groups, moving beyond the current one-size-fits-all approach and better capturing the evolving dynamics of residents' medication regimens.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602946 | PMC |
http://dx.doi.org/10.1111/bcp.16220 | DOI Listing |
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