Evidence map›Paper›PMID 30626380›Full record

ArticleMalaria journal2019

Development of an agent-based model to assess the impact of substandard and falsified anti-malarials: Uganda case study.

Sachiko Ozawa, Daniel R Evans, Colleen R Higgins, Sarah K Laing, Phyllis Awor

Open access · goldAbstract read
In one paragraph

Article in Malaria journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 3 pooled it
2.9field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Tackling the blind spot of poor-quality medicines in Universal Health Coverage.Journal of pharmaceutical policy and practice · 2020
    Review
  13. Article
  14. Article
  15. Falsified and Substandard Drugs: Stopping the Pandemic.The American journal of tropical medicine and hygiene · 2019
    Review
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 3 countries.

Sachiko OzawaDivision of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, CB#7574, Beard Hall 115H, Chapel Hill, NC, 27599, USA. ozawa@unc.edu.ORCID http://orcid.org/0000-0001-7608-9038
Daniel R EvansDuke University School of Medicine, Durham, NC, USA.
Colleen R HigginsDivision of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, CB#7574, Beard Hall 115H, Chapel Hill, NC, 27599, USA.
Sarah K LaingDivision of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, CB#7574, Beard Hall 115H, Chapel Hill, NC, 27599, USA.
Phyllis AworDepartment of Community Health and Behavioural Sciences, Makarere University School of Public Health, Kampala, Uganda.
University of North Carolina at Chapel Hill · USDuke University · USMakerere University · UG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlobal efforts to address the burden of malaria have stagnated in recent years with malaria cases beginning to rise. Substandard and falsified anti-malarial treatments contribute to this stagnation. Poor quality anti-malarials directly affect health outcomes by increasing malaria morbidity and mortality, as well as threaten the effectiveness of treatment by contributing to artemisinin resistance. Research to assess the scope and impact of poor quality anti-malarials is essential to raise awareness and allocate resources to improve the quality of treatment. A probabilistic agent-based model was developed to provide country-specific estimates of the health and economic impact of poor quality anti-malarials on paediatric malaria. This paper presents the methodology and case study of the Substandard and Falsified Antimalarial Research Impact (SAFARI) model developed and applied to Uganda.

resultsThe total annual economic impact of malaria in Ugandan children under age five was estimated at US$614 million. Among children who sought medical care, the total economic impact was estimated at $403 million, including $57.7 million in direct costs. Substandard and falsified anti-malarials were a significant contributor to this annual burden, accounting for $31 million (8% of care-seeking children) in total economic impact involving $5.2 million in direct costs. Further, 9% of malaria deaths relating to cases seeking treatment were attributable to poor quality anti-malarials. In the event of widespread artemisinin resistance in Uganda, we simulated a 12% yearly increase in costs associated with paediatric malaria cases that sought care, inflicting $48.5 million in additional economic impact annually.

conclusionsImproving the quality of treatment is essential to combat the burden of malaria and prevent the development of drug resistance. The SAFARI model provides country-specific estimates of the health and economic impact of substandard and falsified anti-malarials to inform governments, policy makers, donors and the malaria community about the threat posed by poor quality medicines. The model findings are useful to illustrate the significance of the issue and inform policy and interventions to improve medicinal quality.

Indexed as

AntimalarialsArtemisininsChild, PreschoolCounterfeit DrugsDrug ResistanceFemaleHumansInfantMalariaMaleModels, TheoreticalPrivate SectorUgandaAntimalarialsartemisininArtemisininsCounterfeit DrugsAgent-based modelAntimalarialFalsifiedQualitySubstandardUganda

Identifiers

PMID30626380
PMCPMC6327614
OpenAlexW2912083894

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.