Prevalence and Factors that Influence Potentially Inappropriate Medication Use among Thai Elderly in Primary Care Settings
Bibliographic record
Abstract
BACKGROUND: Older age increases the likelihood of chronic diseases and polypharmacy with the likelihood of potentially inappropriate medications (PIMs) in secondary and tertiary care levels, but in the primary care settings of Thailand there still is a need for more evidence. This study aimed to examine the prevalence of PIM in primary care settings, and to identify factors that influence the use of PIM. METHODS: A cross-sectional retrospective study was conducted in 2017. Eight primary care units from four regions of Thailand were randomly selected. People aged ≥ 60 years in the eight units were studied as participants. The List of Risk Drugs for Thai Elderly (LRDTE) was used as the reference. Multivariate logistic regression was carried out to identify factors that influence. RESULTS: A total of 4,848 patients aged ≥60 years with 20,671 prescriptions were studied. The mean age was 70.7±8.3 years for males, and 61.2% for females. A little more than 5% (5.1%) had ≥ 3 chronic diseases and 15.0% received polypharmacy (≥5 medications). The prevalence of prescriptions with PIMs was 65.9%. The most frequent PIMs were antidepressants: amitriptyline (28.1%), antihistamines: dimenhydrinate (22.4%) and chlorpheniramine maleate (CPM) (11.2%); and Benzodiazepines: lorazepam (6.5%). Three factors that significantly influenced prescribing of PIMs were polypharmacy (adjusted OR 3.51; 95% CI 2.81-4.32), having ≥3 chronic diseases (adjusted OR 1.44; 95% CI 1.04-2.01), and age ≥75 years (adjusted OR 1.18; 95% CI 1.01-1.38). CONCLUSION: More than two-thirds of elderly Thai patients in the primary care settings were prescribed PIMs. Multidisciplinary prescription review and PIM screening in patients aged ≥75 years who have ≥3 chronic diseases or polypharmacy should be implemented in primary care and supportive computerized PIMs alert system is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".