Demand Side Cost-Sharing and Prescription Drugs Utilization: Evidence From a Quasi-Natural Experiment
Bibliographic record
Abstract
In this paper we investigate the eects of introduction of lump sum copayments on the utilization of prescription drugs by elderly patients.We make use of an unique dataset and analyze the policy change that implemented patient cost-sharing in the Czech Republic starting in 2008.After the introduction of copayments the number of prescriptions lled decreased by 29%.At the same time, however, total expenditures on prescription drugs dropped only in the rst quarter of the postintroduction period and then returned to previous levels.This was partially due to behavioral responses of patients and physicians: strategic shift of prescription purchases to the time right before the introduction of reform, prescription of more packages on one prescription and an upward shift in the price composition of prescribed drugs.Moreover, patients in general decided to forego those types of drugs that did not cause immediate worsening of health status.Abstrakt V na²em £lánku zkoumáme efekt zavedení regula£ních poplatk• na spot°ebu lék• na p°edpis.Na²e analýza se soust°edí zejména na pacienty star²í 64 let.K identikaci vyuºíváme zm¥nu zákona, která zavedla povinnou spoluú£ast pacient• v eské republice v roku 2008.Na²e výsledky ukazují, ºe po zavedení poplatk• se po£et vybraných recept• sníºil o 20 procent.Naproti tomu, celková cena p°edepsaných lék• se sníºila jenom v následujícím kvartálu a pak se vrátila na stejnou rostoucí trajektorii.Bylo to sp•sobeno t°ema druhy behaviorální odezvy pacient• a léka°•: posun nákupu lék• do období t¥sn¥ p°ed zavedením poplatk•, p°edpisovaní více balení na jeden recept (poplatek je placen za kaºdý recept), a p°edepisování draº²ích lék•.Mlad²í pacienti byli více ochotní omezit svou spot°ebu neº star²í.Pacienti ale celkov¥ omezili p°edev²ím spot°ebu t¥ch typ• lék
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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".