Evidence map›Paper›PMID 38033120›Full record

ArticlePloS one2023

Optimizing diagnostic networks to increase patient access to TB diagnostic services: Development of the diagnostic network optimization (DNO) approach and learnings from its application in Kenya, India and the Philippines.

Heidi Albert, Sidharth Rupani, Enos Masini, Jeremiah Ogoro, Maureen Kamene, Donna Geocaniga-Gaviola, Eddie Sistoso, Celina Garfin, Sarabjit Chadha, Nishant Kumar and 2 more

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

12 authors.

Heidi AlbertFIND South Africa, Cape Town, South Africa.ORCID 0000-0002-8595-4123
Sidharth RupaniLLamasoft, Inc., Ann Arbor, Michigan, United States of America.
Enos MasiniWorld Health Organization, Nairobi, Kenya.
Jeremiah OgoroNational Tuberculosis Leprosy and Lung Disease Programme, Nairobi, Kenya.
Maureen KameneNational Tuberculosis Leprosy and Lung Disease Programme, Nairobi, Kenya.
Donna Geocaniga-GaviolaNational TB Control Program, Department of Health, Manila, Philippines.
Eddie SistosoNational Tuberculosis Reference Laboratory, Metro Manila, Filinvest, Philippines.
Celina GarfinNational TB Control Program, Department of Health, Manila, Philippines.
Sarabjit ChadhaFIND, New Delhi, India.
Nishant KumarCentral TB Division, Ministry of Health and Family Welfare, Government of India, New Delhi, India.
Kekeletso KaoFIND, Campus Biotech, Geneva, Switzerland.
Zachary KatzFIND, Campus Biotech, Geneva, Switzerland.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Diagnostic network optimization (DNO) is an analytical approach that enables use of available country data to inform evidence-based decision-making to optimize access to diagnostic services. A DNO methodology was developed using available data sources and a commercial supply chain optimization software. In collaboration with Ministries of Health and partners, the approach was applied in Kenya, India and the Philippines to map TB diagnostic networks, identify misalignments, and determine optimal network design to increase patient access to TB diagnostic services and improve device utilization. The DNO analysis was successfully applied to evaluate and inform TB diagnostic services in Kenya, India and the Philippines as part of national strategic planning for TB. The analysis was tailored to each country's specific objectives and allowed evaluation of factors such as the number and placement of different TB diagnostics, design of sample referral networks and integration of early infant diagnosis for HIV at national and sub-national levels and across public and private sectors. Our work demonstrates the value of DNO as an innovative approach to analysing and modelling diagnostic networks, particularly suited for use in low-resource settings, as an open-access approach that can be applied to optimize networks for any disease.

Indexed as

Diagnostic ServicesReferral and ConsultationHumansIndiaKenyaPhilippines

Identifiers

PMID38033120
PMCPMC10688908

What OpenQuestion holds

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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.