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Health technology assessment and judicial deference to priority-setting decisions in healthcare: Quasi-experimental analysis of right-to-health litigation in Brazil

2020· article· en· W3093830251 on OpenAlexafffund
Daniel Wang, Natália Pires de Vasconcelos, Mathieu J. P. Poirier, Ana Luiza Chieffi, Cauê Mônaco, Lathika Sritharan, Susan Rogers Van Katwyk, Steven J. Hoffman

Bibliographic record

VenueSocial Science & Medicine · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Health in Brazil
Canadian institutionsImpactCentre for Global Health ResearchMcMaster UniversityYork University
FundersCanadian Institutes of Health ResearchWellcome Trust
KeywordsDeferenceLegitimacyGovernment (linguistics)Health careJudicial deferenceHealth policyHealth technologyPublic administrationLawPolitical scienceMedicinePolitics

Abstract

fetched live from OpenAlex

The constitutional right to health in Brazil has entitled patients to litigate against the government-funded national health system (SUS), claiming access to various health treatments including those excluded from the health system's benefits package. Courts have tended to rely on a single medical prescription to judge these cases in favor of individual patients and against the health system. The large volume of cases has had a substantial financial impact on the government's health budget and has created unfairness in accessing healthcare. To change courts' behavior, a new health technology assessment (HTA) body - CONITEC - was created in 2011. Its creation was accompanied by an administrative procedure that made decisions about the health system's benefits package more transparent, accountable, participative and evidence-informed. It was expected that this HTA system would bring more legitimacy to the government's priority-setting decisions and promote deference from the courts. This study tests whether Brazil's new HTA system succeeded in encouraging judicial deference by analyzing a stratified random sample of 13,263 court decisions for whether the existence of a CONITEC report resulted in less frequent court orders to provide treatment for individual litigants. The results show that the creation of CONITEC did not change courts' behavior; courts still decide in favor of patients in most cases. Indeed, even when there was a CONITEC report recommending against government funding for a particular healthcare treatment, the vast majority of the relatively few patients who were unsuccessful in obtaining a health benefit at their first court hearing later obtained a favorable decision after appealing to a higher court. This finding was confirmed through an interrupted time-series analysis that did not find an impact of having a CONITEC report on courts' willingness to override a government priority-setting decision. In fact, CONITEC was rarely cited in court decisions, even when litigants mentioned the existence of a CONITEC report.

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 imitation

Not 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.

metaresearch head score (Codex)0.053
metaresearch head score (Gemma)0.154
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.154
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.052
GPT teacher head0.477
Teacher spread0.425 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2020
Admission routes2
Has abstractyes

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