Evidence map›Paper›PMID 40493553›Full record

ArticlePloS one2025

Impact of multiple consultations and switching in treatment-seeking in tuberculosis: Mathematical modelling and optimal control.

Preeti Saini, Samit Bhattacharyya

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Socioeconomic Interventions for WHO's End TB Strategy Targets: Insights from SIR Modelling in Kazakhstan.International journal of environmental research and public health · 2026
    Article
  2. 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

2 authors.

Preeti SainiDisease Modelling Lab, Department of Mathematics, School of Natural Sciences, Shiv Nadar Institution of Eminence (SNIoE), NH-91, Greater Noida, Uttar Pradesh, India.
Samit BhattacharyyaDisease Modelling Lab, Department of Mathematics, School of Natural Sciences, Shiv Nadar Institution of Eminence (SNIoE), NH-91, Greater Noida, Uttar Pradesh, India.ORCID https://orcid.org/0000-0002-7357-7949

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment-seeking behaviour significantly contributes to the worldwide tuberculosis (TB) burden, especially in Southeast Asia and African regions. At the onset of symptoms like coughing, fever, fatigue, and loss of appetite, individuals visit various clinics or alternative medicine centres, often switching multiple times before actually reaching a TB DOTS (Directly Observed Therapy Short Courses) centre. This, however, introduces a long delay in proper diagnosis and treatment of TB cases, which increases community transmission and the overall TB burden. By synthesizing data from various empirical studies, we develop an intricate mathematical model of such multiple consultations and aim to quantify the impact of such behavioural interactions on disease burden. Our SIR-based TB transmission framework quantifies the rise in active TB cases due to delays from multiple consultations before diagnosis and treatment at DOTS centres. We found that up to 2-3 consultations before diagnosis can substantially lower the overall TB burden. Using optimal control modelling, we propose targeted interventions - including enhanced TB awareness, early detection, and improved healthcare infrastructure. These findings offer valuable insights for policymakers and public health organizations to develop effective strategies for TB control in high-prevalent regions.

Indexed as

Models, TheoreticalPatient Acceptance of Health CareReferral and ConsultationTuberculosisDirectly Observed TherapyHumans

Identifiers

PMID40493553
PMCPMC12151365

What OpenQuestion holds

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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.