Individual and Contextual Determinants of Regional Variation in Prescription Drug Use: An Analysis of Administrative Data from British Columbia
Bibliographic record
Abstract
BACKGROUND: Increasing attention is being paid to variations in the use of prescription drugs because their role in health care has grown to the point where their use can be considered a proxy for health system performance. Studies have shown that prescription drug use varies across regions in the US, UK, and Canada by more than would be predicted based on age and health status alone. In this paper, we explore the determinants of variations in the use of prescription drugs, drawing on health services theories of access to care. METHODS: We conducted a cross-sectional analysis using population-based administrative health care data for British Columbia (BC), Canada. We used logistic and hierarchical regressions to analyze the effects of individual- and area-level determinants of use of prescriptions overall and rates of purchase of prescriptions from five therapeutic categories representing a range of indications: antihypertensives, statins, acid reducing drugs, opioid drugs, and antidepressants. To indicate the relative scale of regional variations and the importance of individual- and area-level variables in explaining them, we computed standardized rates of utilization for 49 local health areas in BC. RESULTS: We found that characteristics of individuals and the areas in which they live affect likelihood of prescription drug purchase. Individual-level factors influenced prescription drug purchases in ways generally consistent with behavioral models of health services use. Contextual variables exerted influences that differed by type of drug studied. Population health, education levels, and ethnic composition of local areas were associated with significant differences in the likelihood of purchasing medications. Relatively modest regional variations remained after both individual-level and area-level determinants were taken into account. CONCLUSIONS: The results of this study suggest that individual- and area-level factors should be considered when studying variations in the use of prescription drugs. Some sources of such variations, including individual- and area-level socioeconomic status, warrant further investigation and possible intervention to address inequities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".