Access and intensity of use of prescription analgesics among older Manitobans.
Bibliographic record
Abstract
BACKGROUND: Under-treatment of pain is frequently reported, especially among seniors, with chronic non-cancer pain most likely to be under-treated. Legislation regarding the prescribing/dispensing of opioid analgesics (including multiple prescription programs [MPP]) may impede access to needed analgesics. OBJECTIVE: To describe access and intensity of use of analgesics among older Manitobans by health region. METHODS: A cross-sectional study of non-Aboriginal non-institutionalized Manitoba residents over 65 years of age during April 1, 2002 to March 31, 2003 was conducted using the Pharmaceutical Claims data and the Cancer Registry from the province of Manitoba. Access to analgesics (users/1000/Yr) and intensity of use (using defined daily dose [DDD] methodology) were calculated for non-opioid analgesics, opioids, and multiple-prescription-program opioids [MPP-opioids]. Usage was categorized by age, gender, and stratified by cancer diagnosis. Age-sex standardized rates of prevalence and intensity are reported for the eleven health regions of Manitoba. RESULTS: Thirty-four percent of older Manitobans accessed analgesics during the study period. Female gender, increasing age, and a cancer diagnosis were associated with greater access and intensity of use of all classes of analgesics. Age-sex standardized access and intensity measures revealed the highest overall analgesic use in the most rural / remote regions of the province. However, these same regions had the lowest use of opioids, and MPP-opioids among residents lacking a cancer diagnosis. CONCLUSION: This population-based study of analgesic use suggests that there may be variations in use of opioids and other analgesics depending on an urban or rural residence. The impact of programs such as the MPP program requires further study to describe its impact on analgesic use.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".