Evidence map›Paper›PMID 38055687›Full record

ArticlePLOS global public health2023

Application of diagnostic network optimization in Kenya and Nepal to design integrated, sustainable and efficient bacteriology and antimicrobial resistance surveillance networks.

Marie Brunetti, Amit Singh, Sheilla Chebore, Deepak Gyenwali, Nada Malou, Cecilia Ferreyra, Tulsi Ram Gompo, Sharmila Chapagain, Susan Githii, Evelyn Wesangula and 1 more

Abstract read
In one paragraph

Article in PLOS global public health, 2023. 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. Review
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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.

Marie BrunettiFIND, Geneva, Switzerland.ORCID https://orcid.org/0000-0001-7247-2531
Amit SinghFIND, Bangalore, India.
Sheilla CheboreFIND, Nairobi, Kenya.
Deepak GyenwaliFIND, Kathmandu, Nepal.
Nada MalouFIND, Geneva, Switzerland.
Cecilia FerreyraFIND, Geneva, Switzerland.ORCID https://orcid.org/0000-0001-5480-7940
Tulsi Ram GompoCentral Veterinary Laboratory, Kathmandu, Nepal.ORCID https://orcid.org/0000-0002-0973-0187
Sharmila ChapagainCentral Veterinary Laboratory, Kathmandu, Nepal.
Susan GithiiNational Antimicrobial Stewardship Interagency Committee, Nairobi, Kenya.
Evelyn WesangulaNational Antimicrobial Stewardship Interagency Committee, Nairobi, Kenya.
Heidi AlbertFIND, Cape Town, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial resistance (AMR) is a major global public health concern, particularly in low- and middle-income countries, which experience the highest burden of AMR. Critical to combatting AMR is ensuring there are effective, accessible diagnostic networks in place to diagnose, monitor and prevent AMR, but many low- and middle-income countries lack such networks. Consequently, there is substantial need for approaches that can inform the design of efficient AMR laboratory networks and sample referral systems in lower-resource countries. Diagnostic network optimization (DNO) is a geospatial network analytics approach to plan diagnostic networks and ensure greatest access to and coverage of services, while maximizing the overall efficiency of the system. In this intervention, DNO was applied to strengthen bacteriology and AMR surveillance network design in Kenya and Nepal for human and animal health, by informing linkages between health facilities and bacteriology testing services and sample referral routes between farms, health facilities and laboratories. Data collected from the target settings in each country were entered into the open-access DNO tool OptiDx, to generate baseline scenarios, which depicted the current state of AMR laboratory networks and sample referral systems in the countries. Subsequently, baselines were adjusted to evaluate changing factors such as samples flows, transport frequency, transport costs, and service distances. Country stakeholders then compared resulting future scenarios to identify the most feasible solution for their context. The DNO analyses enabled a wealth of insights that will facilitate strengthening of AMR laboratory and surveillance networks in both countries. Overall, the project highlights the benefits of using a data-driven approach for designing efficient diagnostic networks, to ensure better health resource allocation while maximizing the impact and equity of health interventions. Given the critical need to strengthen AMR laboratory and surveillance capacity, DNO should be considered an integral part of diagnostic strategic planning in the future.

Identifiers

PMID38055687
PMCPMC10699636

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.