Evidence map›Paper›PMID 39313254›Full record

ArticleBMJ global health2024

Value for money of medicine sampling and quality testing: evidence from Indonesia.

Sara Valente de Almeida, Katharina Hauck, Sarah Njenga, Yunita Nugrahani, Ayu Rahmawati, Rahmi Mawaddati, Stanley Saputra, Amalia Hasnida, Elizabeth Pisani, Yusi Anggriani and 1 more

Abstract read
In one paragraph

Article in BMJ global health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Protocol deviations in medicine quality tests.Bulletin of the World Health Organization · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. 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

11 authors.

Sara Valente de AlmeidaDepartment of Infectious Disease Epidemiology, Imperial College London School of Public Health, London, UK svalente@ic.ac.uk.ORCID http://orcid.org/0000-0001-9178-2105
Katharina HauckDepartment of Infectious Disease Epidemiology, Imperial College London School of Public Health, London, UK.
Sarah NjengaNuffield Department of Population Health, University of Oxford, Oxford, UK.
Yunita NugrahaniCenter for Pharmaceutical Policy, Management and Services Studies, Faculty of Pharmacy - Universitas Pancasila, Jakarta, Indonesia.
Ayu RahmawatiCenter for Pharmaceutical Policy, Management and Services Studies, Faculty of Pharmacy - Universitas Pancasila, Jakarta, Indonesia.
Rahmi MawaddatiCenter for Pharmaceutical Policy, Management and Services Studies, Faculty of Pharmacy - Universitas Pancasila, Jakarta, Indonesia.
Stanley SaputraCenter for Pharmaceutical Policy, Management and Services Studies, Faculty of Pharmacy - Universitas Pancasila, Jakarta, Indonesia.
Amalia HasnidaErasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands.ORCID http://orcid.org/0000-0002-9264-334X
Elizabeth PisaniErasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands.ORCID http://orcid.org/0000-0002-3674-631X
Yusi AnggrianiCenter for Pharmaceutical Policy, Management and Services Studies, Faculty of Pharmacy - Universitas Pancasila, Jakarta, Indonesia.
Adrian GheorgheDepartment of Infectious Disease Epidemiology, Imperial College London School of Public Health, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSubstandard and falsified medicines (SFMs) are a public health concern of global importance. Postmarket surveillance in the form of medicine sampling and quality testing can prevent and detect SFM, however, there is remarkably scarce evidence about the cost and value for money of these activities: how much do they cost and how effective are they in detecting SFM?

methodsBetween February and October 2022, Systematic Tracking of At Risk Medicines (STARmeds) collected and analysed for quality 1274 samples of 5 medicines from physical and online retail outlets in 7 Indonesian districts. We collated data on the resources consumed by STARmeds, related to all stages of medicines sampling and quality testing including design, fieldwork and laboratory analysis. We used activity-based costing principles to calculate the financial and economic cost of medicine quality surveillance from the perspective of a hypothetical medicines' regulator. We calculated the cost per day and per week of fieldwork, per sample collected and per substandard sample. We used bootstrapping to capture uncertainty in the number of samples collected, by seller location type (urban, rural and online).

resultsThe total cost of sampling and testing medicines from the market was US$712 964 (current 2022 values). Laboratory costs represented the largest share (70%), followed by other direct costs (12%) and indirect costs (7%). On average, it costs STARmeds US$479 (95% CI US$462 to US$516) to collect one medicine sample and US$5990 (95% CI US$5601 to US$6258) to identify one substandard sample.

conclusionOur findings bring urgently needed and novel information on the cost and value for money of medicine quality surveillance. These may support planning and budgeting of the Indonesian pharmaceutical regulator, but also of regulators and researchers elsewhere, particularly in low-income and middle-income settings, as well as international organisations with health regulation and quality of care remits.

Indexed as

Counterfeit DrugsCost-Benefit AnalysisHumansIndonesiaProduct Surveillance, PostmarketingQuality ControlSubstandard DrugsCounterfeit DrugsSubstandard DrugsCross-sectional surveyGlobal HealthPharmacologyPublic Health

Identifiers

PMID39313254
PMCPMC11429347

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.