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Record W2155469419 · doi:10.1371/journal.pmed.1001724

WHO Essential Medicines Policies and Use in Developing and Transitional Countries: An Analysis of Reported Policy Implementation and Medicines Use Surveys

2014· article· en· W2155469419 on OpenAlexaff
Kathleen Holloway, David Henry

Bibliographic record

VenuePLoS Medicine · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of TorontoPublic Health OntarioInstitute for Clinical Evaluative Sciences
FundersWorld Health Organization
KeywordsEssential medicinesDeveloping countryMedicineHealth policyAlternative medicineQuality (philosophy)Public economicsPolitical scienceEnvironmental healthEconomic growthPublic healthEconomicsNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Suboptimal medicine use is a global public health problem. For 35 years the World Health Organization (WHO) has promoted essential medicines policies to improve quality use of medicines (QUM), but evidence of their effectiveness is lacking, and uptake by countries remains low. Our objective was to determine whether WHO essential medicines policies are associated with better QUM. METHODS AND FINDINGS: We compared results from independently conducted medicines use surveys in countries that did versus did not report implementation of WHO essential medicines policies. We extracted survey data on ten validated QUM indicators and 36 self-reported policy implementation variables from WHO databases for 2002-2008. We calculated the average difference (as percent) for the QUM indicators between countries reporting versus not reporting implementation of specific policies. Policies associated with positive effects were included in a regression of a composite QUM score on total numbers of implemented policies. Data were available for 56 countries. Twenty-seven policies were associated with better use of at least two percentage points. Eighteen policies were associated with significantly better use (unadjusted p<0.05), of which four were associated with positive differences of 10% or more: undergraduate training of doctors in standard treatment guidelines, undergraduate training of nurses in standard treatment guidelines, the ministry of health having a unit promoting rational use of medicines, and provision of essential medicines free at point of care to all patients. In regression analyses national wealth was positively associated with the composite QUM score and the number of policies reported as being implemented in that country. There was a positive correlation between the number of policies (out of the 27 policies with an effect size of 2% or more) that countries reported implementing and the composite QUM score (r=0.39, 95% CI 0.14 to 0.59, p=0.003). This correlation weakened but remained significant after inclusion of national wealth in multiple linear regression analyses. Multiple policies were more strongly associated with the QUM score in the 28 countries with gross national income per capita below the median value (US$2,333) (r=0.43, 95% CI 0.06 to 0.69, p=0.023) than in the 28 countries with values above the median (r=0.22, 95% CI -0.15 to 0.56, p=0.261). The main limitations of the study are the reliance on self-report of policy implementation and measures of medicine use from small surveys. While the data can be used to explore the association of essential medicines policies with medicine use, they cannot be used to compare or benchmark individual country performance. CONCLUSIONS: WHO essential medicines policies are associated with improved QUM, particularly in low-income countries. Please see later in the article for the Editors' Summary.

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)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.026
GPT teacher head0.315
Teacher spread0.290 · 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 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

Citations91
Published2014
Admission routes1
Has abstractyes

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