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Pharmaceutical policies: effects of regulating drug insurance schemes

2022· review· en· W4225408416 on OpenAlexaboutno aff
Tomás Pantoja, Blanca Peñaloza, Camilo Cid, Cristián Herrera, Craig Ramsay, Jemma Hudson

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2022
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsReimbursementMedicineMEDLINEPaymentCitationActuarial scienceAlternative medicineSystematic reviewFamily medicineDrugHealth careBusinessPharmacologyPolitical scienceComputer scienceWorld Wide WebPathology

Abstract

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BACKGROUND: Drug insurance schemes are systems that provide access to medicines on a prepaid basis and could potentially improve access to essential medicines and reduce out-of-pocket payments for vulnerable populations. OBJECTIVES: To assess the effects on drug use, drug expenditure, healthcare utilisation and healthcare outcomes of alternative policies for regulating drug insurance schemes. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, nine other databases, and two trials registers between November 2014 and September 2020, including a citation search for included studies on 15 September 2021 using Web of Science. We screened reference lists of all the relevant reports that we retrieved and reports from the Background section. Authors of relevant papers, relevant organisations, and discussion lists were contacted to identify additional studies, including unpublished and ongoing studies. SELECTION CRITERIA: We planned to include randomised trials, non-randomised trials, interrupted time-series studies (including controlled ITS [CITS] and repeated measures [RM] studies), and controlled before-after (CBA) studies. Two review authors independently assessed the search results and reference lists of relevant reports, retrieved the full text of potentially relevant references and independently applied the inclusion criteria to those studies. We resolved disagreements by discussion, and when necessary by including a third review author. We excluded studies of the following pharmaceutical policies covered in other Cochrane Reviews: those that determined how decisions were made about which conditions or drugs were covered; those that placed restrictions on reimbursement for drugs that were covered; and those that regulated out-of-pocket payments for drugs. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data from the included studies and assessed risk of bias for each study, with disagreements being resolved by consensus. We used the criteria suggested by Cochrane Effective Practice and Organisation of Care (EPOC) to assess the risk of bias of included studies. For randomised trials, non-randomised trials and controlled before-after studies, we planned to report relative effects. For dichotomous outcomes, we reported the risk ratio (RR) when possible and adjusted for baseline differences in the outcome measures. For interrupted time series and controlled interrupted time-series studies, we computed changes along two dimensions: change in level; and change in slope. We undertook a structured synthesis following the EPOC guidance on this topic, describing the range of effects found in the studies for each category of outcomes. MAIN RESULTS: We identified 58 studies that met the inclusion criteria (25 interrupted time-series studies and 33 controlled before-after studies). Most of the studies (54) assessed a single policy implemented in the United States (US) healthcare system: Medicare Part D. The other four assessed other drug insurance schemes from Canada and the US, but only one of them provided analysable data for inclusion in the quantitative synthesis. The introduction of drug insurance schemes may increase prescription drug use (low-certainty evidence). On the other hand, Medicare Part D may decrease drug expenditure measured as both out-of-pocket spending and total drug spending (low-certainty evidence). Regarding healthcare utilisation, drug insurance policies (such as Medicare Part D) may lead to a small increase in visits to the emergency department. However, it is uncertain whether this type of policy increases or decreases hospital admissions or outpatient visits by beneficiaries of the scheme because the certainty of the evidence was very low. Likewise, it is uncertain if the policy increases or reduces health outcomes such as mortality because the certainty of the evidence was very low. AUTHORS' CONCLUSIONS: The introduction of drug insurance schemes such as Medicare Part D in the US health system may increase prescription drug use and may decrease out-of-pocket payments by the beneficiaries of the scheme and total drug expenditures. It may also lead to a small increase in visits to the emergency department by the beneficiaries of the policy. Its effects on other healthcare utilisation outcomes and on health outcomes are uncertain because of the very low certainty of the evidence. The applicability of this evidence to settings outside US healthcare is limited.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0120.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.201
GPT teacher head0.407
Teacher spread0.206 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations11
Published2022
Admission routes1
Has abstractyes

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