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Record W3200608261 · doi:10.1016/s1470-2045(21)00463-0

Access to cancer medicines deemed essential by oncologists in 82 countries: an international, cross-sectional survey

2021· article· en· W3200608261 on OpenAlexafffund
Adam Fundytus, Manju Sengar, Dorothy Lombe, Wilma M. Hopman, Matthew Jalink, Bishal Gyawali, Dario Trapani, Felipe Roitberg, Elisabeth G.E. de Vries, Lorenzo Moja, André Ilbawi, Richard Sullivan, Christopher M. Booth

Bibliographic record

VenueThe Lancet Oncology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsQueen's University
FundersQueen's UniversityWorld Health Organization
KeywordsMedicineSnowball samplingEssential medicinesAccess to medicinesCross-sectional studyFamily medicineDeveloping countryDescriptive statisticsPublic healthAlternative medicineGlobal healthCancerEnvironmental healthNursingEconomic growthPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The WHO Essential Medicines List (EML) identifies priority medicines that are most important to public health. Over time, the EML has included an increasing number of cancer medicines. We aimed to investigate whether the cancer medicines in the EML are aligned with the priority medicines of frontline oncologists worldwide, and the extent to which these medicines are accessible in routine clinical practice. METHODS: This international, cross-sectional survey was developed by investigators from a range of clinical practice settings across low-income to high-income countries, including members of the WHO Essential Medicines Cancer Working Group. A 28-question electronic survey was developed and disseminated to a global network of oncologists in 89 countries and regions by use of a hierarchical snowball method; each primary contact distributed the survey through their national and regional oncology associations or personal networks. The survey was open from Oct 15 to Dec 7, 2020. Fully qualified physicians who prescribe systemic anticancer therapy to adults were eligible to participate in the survey. The primary question asked respondents to select the ten cancer medicines that would provide the greatest public health benefit to their country; subsequent questions explored availability and cost of cancer medicines. Descriptive statistics were used to compare access to medicines between low-income and lower-middle-income countries, upper-middle-income countries, and high-income countries. FINDINGS: 87 country-level contacts and two regional networks were invited to participate in the survey; 46 (52%) accepted the invitation and distributed the survey. 1697 respondents opened the survey link; 423 were excluded as they did not answer the primary study question and 326 were excluded because of ineligibility. 948 eligible oncologists from 82 countries completed the survey (165 [17%] in low-income and lower-middle-income countries, 165 [17%] in upper-middle-income countries, and 618 [65%] in high-income countries). The most commonly selected medicines were doxorubicin (by 499 [53%] of 948 respondents), cisplatin (by 470 [50%]), paclitaxel (by 423 [45%]), pembrolizumab (by 414 [44%]), trastuzumab (by 402 [42%]), carboplatin (by 390 [41%]), and 5-fluorouracil (by 386 [41%]). Of the 20 most frequently selected high-priority cancer medicines, 19 (95%) are currently on the WHO EML; 12 (60%) were cytotoxic agents and 13 (65%) were granted US Food and Drug Administration regulatory approval before 2000. The proportion of respondents indicating universal availability of each top 20 medication was 9-54% in low-income and lower-middle-income countries, 13-90% in upper-middle-income countries, and 68-94% in high-income countries. The risk of catastrophic expenditure (spending >40% of total consumption net of spending on food) was more common in low-income and lower-middle-income countries, with 13-68% of respondents indicating a substantial risk of catastrophic expenditures for each of the top 20 medications in lower-middle-income countries versus 2-41% of respondents in upper-middle-income countries and 0-9% in high-income countries. INTERPRETATION: These data demonstrate major barriers in access to core cancer medicines worldwide. These findings challenge the feasibility of adding additional expensive cancer medicines to the EML. There is an urgent need for global and country-level policy action to ensure patients with cancer globally have access to high priority medicines. FUNDING: None.

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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)0.003
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.231
GPT teacher head0.481
Teacher spread0.250 · 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

Citations171
Published2021
Admission routes2
Has abstractyes

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