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Record W2481684576 · doi:10.1111/ijpp.12288

Emerging trends on drug use globally

2016· editorial· en· W2481684576 on OpenAlexaboutno aff
Jack E. Fincham

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2016
Typeeditorial
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDrugEnvironmental healthPharmacology

Abstract

fetched live from OpenAlex

The importance of prescription medications has never been as significant globally as is currently the case. Major issues pertaining to prescription medications include their cost, access to necessary therapeutic agents, and counterfeiting and drug security. Each of these three factors influences individual countries, but their importance is of an international scope as described below. Firstly, there is the issue of cost. The lack of affordability of medications negatively impacts beneficial aspects of drug therapies. Parker-Lue et al.[1] have suggested the cost of drugs is a significant component of health-care costs. They also have called for greater multidisciplinary interactions between economists, clinicians and ethicists to address innovation and cost of therapy considerations.[1] These issues are international in scope. Reich and Shibuya[2] have written about the significant advances and progress made in Japan dealing with health issues. However, they note the rising cost of medications, including generic medications, has had a dramatic impact upon health expenditures in Japan and suggest increasing costs will adversely affect the spending for health care as a component of the Japanese gross domestic product (GDP).[2] In the lay press in the UK, a recent article by Bosely[3] in The Guardian pointed to the unavailability of needed cancer chemotherapies due to the reluctance of the UK NHS to pay the exorbitant prices charged by manufacturers. van Harten et al.,[4] when examining the costs of cancer chemotherapeutic agents in 15 European countries, note that 30% of hospital expenditures are for cancer chemotherapies. These authors point to the increasing scrutiny of pharmaceutical manufacturers in pricing decisions for such cancer therapies.[4] Dranitsarus and Papadopoulos[5] note that in governmental programmes in the UK, Canada and Australia, health outcome assessment programmes utilising cost-effectiveness and cost-utility techniques are used in evaluation of new chemotherapies for formulary inclusion. They point out that in the US, these health outcome measures (cost-effectiveness and cost-utility) are not utilised by governmental insurance programmes.[5] Ruggeri and Nolte[6] point to the use of external reference pricing as a means to assess how drugs should be priced. In the United States, the pricing of medications has been increased to an excess because of the impact of pharmacy benefit management (PBM) oversight of the drug pricing processes.[7] The use of cost outcome evaluative tools, such as cost-effectiveness and cost-utility need to be used, in my opinion, globally. As costs of drugs increase, cost-effectiveness and cost-utility assessments will be crucial for the appropriate evaluation of medications to assure health outcomes that patients deserve.[8,9] Access is another international issue. Access to essential medicines will increase exponentially in the next few years. Aitken[10] of the IMS Institute for Healthcare Informatics suggests the volume of medications used will reach 4.5 trillion doses by 2020 and cost $ 1.4 trillion, with the largest growth in pharmerging1 countries. IMS refers to these pharmerging countries in three tiers.[10] Tier one of these countries include China; tier 2, Brazil, India, Russia; tier 3, Algeria, Argentina, Bangladesh, Chile, Columbia, Egypt, Indonesia, Kazakhstan, Mexico, Nigeria, Pakistan, Philippines, Poland, South Africa, Saudi Arabia, Turkey and Vietnam.[10] Gray et al.[11] have noted that the World Health Organization has published its new model of essential medications. They[11] note the inclusion of new cancer chemotherapy and options for drug-resistant tuberculosis were important additions the essential list of medicines. Shulman et al.[12] have noted that a great proportion of the burden of cancer resides in low-income countries. Eniu et al.[13] have also noted the fact that more than 8 million patients died of cancer in 2013 with the majority of these patients residing in the developing world Finally, counterfeiting and drug security remains an issue. The impact of counterfeit medications on patients, healthcare systems and societies globally is disturbing, and is not isolated to one country, but is global. Kumar and Baldi[14] point to the worldwide expanse of counterfeiting and its influence. They[14] point to the detrimental impact of counterfeiting upon public health and the pharmaceutical industry internationally. Kelesidis and Falagas[15] describe the negative consequences of counterfeit antimicrobial drugs. Counterfeiting in individual countries such as Australia[16] and Italy[17] has been explored and chronicled. Nayyar et al.[18] suggest three main areas for international cooperation: research to develop affordable and accurate quality and quantity control measurement techniques, continuing international efforts to stem the tide of counterfeiting, and designating an international agency, such as the World Health Organization (WHO) to establish standards, training and drug quality surveillance globally. In conclusion, many issues will continue to impact healthcare delivery worldwide, with implications for patients, pharmacists, providers and payers. Research is needed to better understand the causes of these challenges and to propose solutions.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.406
Teacher spread0.348 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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