Evidence map›Paper›PMID 41275220›Full record

ArticleBMC public health2025

Frontline workers in India's tuberculosis (TB) elimination efforts: a street-level bureaucracy perspective.

Vanita Singh, Rajesh Chandwani, Viswanath Pingali, Alpa Dalal

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Vanita SinghManagement Development Institute, C-19 Scholars Block, MDI Campus, Sector-17C, Sukhrali, Gurugram, Haryana, 122007, India. vanita.singh@mdi.ac.in.
Rajesh ChandwaniIndian Institute of Management Ahmedabad, Vastrapur, Ahmedabad, 380015, India.
Viswanath PingaliIndian Institute of Management Ahmedabad, Vastrapur, Ahmedabad, 380015, India.
Alpa DalalJupiter Hospital, Thane, Maharashtra, 400601, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIndia's fight against Tuberculosis (TB) has evolved from the National Tuberculosis (TB) Control Program (NTCP) in 1962 to the current National TB Elimination Program (NTEP), guided by the National Strategic Plan (2020-2025). Despite ambitious goals, TB incidence remains high at 199 cases per lakh, with only a 3% annual reduction, far from the 15% target. Systemic issues, especially in human resources, hamper effective policy implementation. Frontline workers (FWs), crucial to the program, face heavy workloads, inadequate upgradation of training, and limited support, yet their voices are rarely heard. This study uses Lipsky's Street-Level Bureaucracy (SLB) theory to explore FWs' challenges under NTEP and offers insights to strengthen India's TB elimination efforts.

methodTwenty-three in-depth interviews were conducted with frontline workers (FWs) in the Mumbai Metropolitan Region, a TB hotspot. Interviews were audio-recorded, transcribed verbatim, and thematically coded using a deductive approach informed by Lipsky's Street-Level Bureaucracy (SLB) theory.

resultsThree key themes emerged: (1) TB program-specific contextual challenges, (2) human resource constraints, and (3) leadership support. Consistent with street-level bureaucracy theory, frontline workers, despite the formal constraints of their contractual employment, exercised practical discretion to manage high workloads, adverse working conditions, and limited resources.. Supervisory leadership style influenced worker attitudes, transformational leadership fostered motivation and greater engagement with program activities , whereas transactional leadership was linked to dissatisfaction and program alienation.

conclusionDifficult working conditions coupled with resource deficits hinder effective program implementation. However, supervisory leadership significantly shapes FWs' engagement with program objectives, highlighting its critical role in advancing TB elimination efforts. We recommend improving working conditions for TB health workers by addressing staff shortages, offering risk-based incentives such as paid leave, medical insurance, and nutritional support, and ensuring safer workplaces. Additionally, effective leadership training for District TB Officers, City TB Officers, and Medical Officers, along with recognition and capacity-building for frontline workers such as TB Health Visitors (TBHVs), is crucial.

Indexed as

Disease EradicationTuberculosisAdultFemaleFrontline WorkersHumansIndiaInterviews as TopicMaleQualitative ResearchCommunity health workersIndiaNational tuberculosis elimination programmePolicy implementationTB workersTransformational leadership

Identifiers

PMID41275220
PMCPMC12754887

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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.