A systematic umbrella review of the association of prescription drug insurance and cost-sharing with drug use, health services use, and health
Bibliographic record
Abstract
BACKGROUND: Increasing spending and use of prescription drugs pose an important challenge to governments that seek to expand health insurance coverage to improve population health while controlling public expenditures. Patient cost-sharing such as deductibles and coinsurance is widely used with aim to control healthcare expenditures without adversely affecting health. METHODS: We conducted a systematic umbrella review with a quality assessment of included studies to examine the association of prescription drug insurance and cost-sharing with drug use, health services use, and health. We searched five electronic bibliographic databases, hand-searched eight specialty journals and two working paper repositories, and examined references of relevant reviews. At least two reviewers independently screened the articles, extracted the characteristics, methods, and main results, and assessed the quality of each included study. RESULTS: We identified 38 reviews. We found consistent evidence that having drug insurance and lower cost-sharing among the insured were associated with increased drug use while the lack or loss of drug insurance and higher drug cost-sharing were associated with decreased drug use. We also found consistent evidence that the poor, the chronically ill, seniors and children were similarly responsive to changes in insurance and cost-sharing. We found that drug insurance and lower drug cost-sharing were associated with lower healthcare services utilization including emergency room visits, hospitalizations, and outpatient visits. We did not find consistent evidence of an association between drug insurance or cost-sharing and health. Lastly, we did not find any evidence that the association between drug insurance or cost-sharing and drug use, health services use or health differed by socioeconomic status, health status, age or sex. CONCLUSIONS: Given that the poor or near-poor often report substantially lower drug insurance coverage, universal pharmacare would likely increase drug use among lower-income populations relative to higher-income populations. On net, it is probable that health services use could decrease with universal pharmacare among those who gain drug insurance. Such cross-price effects of extending drug coverage should be included in costing simulations.
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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 | 0.022 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.036 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".