Treatment-specific interrupted time series analyses of judicial deference to health technology assessment in Brazil
Bibliographic record
Abstract
BACKGROUND: The use of health rights litigation as a parallel decision-making venue to bypass health technology assessments (HTA) has resulted in unintended inequitable impacts on Latin American health systems since the 1990s. Brazil created a new HTA body in 2011 to promote a transparent and evidence-informed process in the Ministry of Health´s decisions about treatment coverage, but its impact on the judicial system’s provision of specific treatments to patient litigants has not yet been quantitatively evaluated. METHODS: We leverage a unique dataset of 3,774 judicial claims for ten of Brazil’s most frequently litigated treatments to conduct the first quasi-experimental evaluation of treatment-specific changes in judicial decisions providing treatments through the national health system. Interrupted time series analyses using ordinary least-squares and logistic fractional response regressions were used to determine if an HTA recommendation was significantly associated with a change in court decisions in favour of litigants following a positive or negative recommendation for national coverage for the ten most litigated treatments in the country. RESULTS: We find no evidence of a statistically significant change in court decisions using ordinary least-squares regression and decreases of smaller than 2.1% in positive court decisions using logistic fractional regression, regardless of whether HTA recommended for or against coverage. Among treatments recommended for coverage, three treatments experienced decreases in positive court decisions ranging from 3.1 to 26.8%. Among treatments recommended against coverage, two treatments experienced decreases in positive decisions ranging from 5.2 to 14.2%, and one treatment experienced a 9.6% increase in positive decisions. CONCLUSIONS: Our results demonstrate that nearly all court claims filed for the ten most litigated treatments in Brazil were granted, and HTA recommendations had almost no impact on judicial decisions to grant patient petitions for coverage. Policymakers should be aware that the creation of an HTA does not guarantee that its recommendations will produce a change in court decision making on patient petitions for treatment coverage. To realize the promise of basing difficult decisions on the provision and allocation of health technologies on principles of clinical utility, cost-effectiveness, and equity, the failure to meaningfully incorporate HTA in judicial processes must be addressed.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".