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Record W4283372505 · doi:10.1016/j.lanwpc.2022.100506

Monoclonal antibodies and Fc-fusion protein biologic medicines: A multinational cross-sectional investigation of accessibility and affordability in Asia Pacific regions between 2010 and 2020

2022· article· en· W4283372505 on OpenAlexaboutno aff
Xinning Tong, Xue Li, Nicole Pratt, Jodie Hillen, Ty Stanford, Michael Ward, Elizabeth E. Roughead, Edward Chia‐Cheng Lai, Ju‐Young Shin, Franco Wing Tak Cheng, Kuan Peng, Chak Sing Lau, Wai K. Leung, Ian Chi Kei Wong

Bibliographic record

VenueThe Lancet Regional Health - Western Pacific · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsnot available
FundersJanssen PharmaceuticalsHealth and Medical Research FundLi Ka Shing Faculty of Medicine, University of Hong KongNational Health and Medical Research CouncilMerck Sharp and DohmeGlaxoSmithKlineUniversity of Hong KongResearch Grants Council, University Grants CommitteeBristol-Myers SquibbElectrochemical SocietyNovartisEuropean CommissionAmgenPfizerBayer
KeywordsConsumption (sociology)Per capitaPopulationEconomicsBusinessMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Background: Monoclonal antibody (mAb) and Fc-fusion protein (FcP) are highly effective therapeutic biologics. We aimed to analyse consumption and expenditure trends in 14 Asia-Pacific countries/regions (APAC) and three benchmark countries (the UK, Canada, and the US). Methods: We analysed 440 mAb and FcP biological products using the IQVIA-MIDAS global sales database. For each year between 2010 and 2020 inclusive, we used standard units (SU) sold per 1000 population and manufacture level price (standardised in 2019 US dollars) to evaluate consumption (accessibility) and expenditure (affordability). Changes of consumption and expenditure were estimated using compound annual growth rate (CAGR). Correlations between consumption, country's economic and health performance indicators were measured using Spearman correlation coefficient. Findings: Between 2010 and 2020, CAGRs of consumption in each region ranged from 7% to 34% and the CAGRs of expenditure ranged from 9% to 31%. The median consumption of biologics was extremely low in lower-middle-income economies (0·29 SU/1000 population) compared with upper-middle-income economies (1·20), high-income economies (40·94) and benchmark countries (109·55), although the median CAGRs of biologics consumption in lower-middle-income economies (31%) was greater than upper-middle-income (14%), high-income economies (13%) and benchmark countries (9%). Consumption was correlated with GDP per capita [Spearman's rank correlation coefficient (r) = 0·75, p < 0·001], health expenditure as a percentage of total (r = 0·83, p < 0·001) and medical doctors' density (r = 0·85, p < 0·001). Interpretation: There have been significant increases in mAb and FcP biologics consumption and expenditure, however accessibility of biological medicines remains unequal and is largely correlated with country's income level. Funding: This research was funded by NHMRC Project Grant GNT1157506 and GNT1196900; Enhanced Start-up Fund for new academic staff and Internal Research Fund, Department of Medicine, LKS Faculty of Medicine, University of Hong Kong.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.098
GPT teacher head0.375
Teacher spread0.276 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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