Evidence map›Paper›PMID 36548696›Full record

ArticleTropical medicine and infectious disease2022

Individuals' Vulnerability Based Active Surveillance for TB: Experiences from India.

Shibu K Balakrishnan, Rakesh P Suseela, Sunilkumar Mrithyunjayan, Manu E Mathew, Suresh Varghese, Shubin Chenayil, Suja Aloysius, Twinkle Prabhakaran, Sreenivas A Nair

Abstract read
In one paragraph

Article in Tropical medicine and infectious disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Active Case Finding among Adults using an Individual's Vulnerability Score for Pulmonary Tuberculosis in a Rural Village of Goa: A Cross-sectional Study.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine
    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

9 authors.

Shibu K BalakrishnanWorld Health Organization Technical Support Network (NTEP), Thiruvananthapuram 695001, India.
Rakesh P SuseelaWorld Health Organization Technical Support Network (NTEP), Thiruvananthapuram 695001, India.ORCID 0000-0002-6774-4114
Sunilkumar MrithyunjayanState TB Elimination Program, Kerala State Health Services Department, Directorate of Health Services, Thiruvananthapuram 695001, India.
Manu E MathewWorld Health Organization Technical Support Network (NTEP), Thiruvananthapuram 695001, India.ORCID 0000-0001-6324-0376
Suresh VargheseState TB Elimination Program, Kerala State Health Services Department, Directorate of Health Services, Thiruvananthapuram 695001, India.
Shubin ChenayilState TB Elimination Program, Kerala State Health Services Department, Directorate of Health Services, Thiruvananthapuram 695001, India.
Suja AloysiusState TB Elimination Program, Kerala State Health Services Department, Directorate of Health Services, Thiruvananthapuram 695001, India.
Twinkle PrabhakaranState TB Elimination Program, Kerala State Health Services Department, Directorate of Health Services, Thiruvananthapuram 695001, India.
Sreenivas A NairCountry and Community Support for Impact, Stop TB Partnership Secretariat, 1218 Geneva, Switzerland.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Community-based active TB case finding (ACF) has become an essential part of TB elimination efforts in high-burden settings. In settings such as the state of Kerala in India, which has reported an annual decline of 7.5% in the estimated TB incidence since 2015, if ACF is not well targeted, it may end up with a less-than-desired yield, the wastage of scarce resources, and the burdening of health systems. Program managers have recognized the need to optimize resources and workloads, while maximizing the yield, when implementing ACF. We developed and implemented the concept of 'individuals'-vulnerability-based active surveillance' as a substitute for the blanket approach for population/geography-based ACF for TB. Weighted scores, based on an estimate of relative risk, were assigned to reflect the TB vulnerabilities of individuals. Vulnerability data for 22,042,168 individuals were available to the primary healthcare team. Individuals with higher cumulative vulnerability scores were targeted for serial ACF from 2019 onwards. In 2018, when a population-based ACF was conducted, the number needed to screen to diagnose one microbiologically confirmed pulmonary TB case was 3772 and the number needed to test to obtain one microbiologically confirmed pulmonary TB case was 112. The corresponding figures in 2019 for individuals'-vulnerability-based ACF were 881 and 39, respectively. Individuals'-vulnerability-based active surveillance is proposed here as a practical solution to improve health system efficiency in settings where the population is relatively stationary, the TB disease burden is low, and the health system is strong.

Indexed as

active case findinghealth system efficiencyTB risk factorsvulnerability reduction

Identifiers

PMID36548696
PMCPMC9781449

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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.