Incidence and Economic Burden of Adverse Drug Reactions among Elderly Patients in Ontario Emergency Departments
Bibliographic record
Abstract
BACKGROUND: The rapid rise in the availability and use of pharmaceutical agents, and particularly polypharmacy, directly increases the risk for patients to experience adverse drug reactions (ADRs). There are few studies on the overall incidence and costs of ADRs. OBJECTIVE: The aim of this study was to estimate the incidence and costs of emergency department (ED) visits related to ADRs for patients greater than 65 years of age using administrative data, and to describe risk factors for experiencing severe ADRs. METHODS: We employed a retrospective cohort design based on population-based healthcare administrative clinical databases. Identification of ADR-related ED visits from the administrative database was based on International Classification of Diseases, 10th Revision-Canadian Enhancement (ICD-10-CA) codes for each ED visit. The incidence and costs of ADR-related ED visits and subsequent hospital admissions were estimated for all adults aged 66 years and above for the period April 2003-March 2008. Costs were standardized and reported in 2008 Canadian dollars. Logistic regression was used to detect risk factors for severe ADRs. RESULTS: Approximately 0.75% of total annual ED visits among adults aged 66 years and above were found to be ADR-related, and among these patients 21.6% were hospitalized. In 2007, the cost of ADR-related visits was $333 per ED visit and $7528 per hospitalization for a total annual cost of $13.6 million in Ontario, or an estimated $35.7 million in Canada. Severe ADRs were associated with sex, age, comorbid disease burden, multiple drugs, multiple pharmacies, newly prescribed drugs, recent ED visit, recent hospitalization and long-term care (LTC) residence. CONCLUSIONS: ADRs are an important public health issue that threaten the safety of drug therapy and results in significant economic burden to the healthcare system. ED visits related to ADRs may be underestimated in retrospective studies using administrative data compared with prospective studies. Further research is needed to better understand the risk of experiencing severe ADRs among LTC residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".