Mobilizing innovative strategies to improve TB diagnosis and retention in the TB care cascade for migrant communities in new york city

The National Institute of Allergy and Infectious Diseases awarded The Trustees of Columbia University in the City of New York $426,746 on September 9, 2025, under the Allergy and Infectious Diseases Research program (CFDA 93.855) to evaluate innovative methods for improving tuberculosis detection, prevention, and treatment among migrant communities in New York City.

Columbia's Health Sciences Division will implement the SPOT-TB intervention (Screening with Portable X-rays for Rapid Recognition of TB), which deploys mobile diagnostic teams integrating ultraportable digital chest radiography with artificial intelligence-assisted interpretation, upfront testing for TB and HIV using interferon gamma release assays, and community engagement strategies. The intervention aims to increase diagnostic completion and retention in the TB care cascade compared to conventional clinic-based surveillance approaches. Three specific aims guide the work: identifying active and latent TB in migrant communities using the mobile platform; determining feasibility and acceptability of SPOT-TB through mixed methods evaluation; and assessing structural and behavioral barriers to TB care in NYC migrant populations. Performance extends from September 9, 2025, through August 31, 2027, at New York, NY 10032.

Two sub-recipients participate in the award. New York City Health and Hospitals Corporation, through Dr. Mary Salvatore as co-investigator, will oversee radiologic components including expert over-reads of AI-generated chest radiograph interpretations and refinement of imaging methodologies within the SPOT-TB workflow. Touro University's Graduate School of Social Work, through Dr. Jennifer Zelnick, will contribute mixed methods feasibility and acceptability assessment, apply qualitative methods to assess stigma and barriers to testing and care engagement, train social workers as part of the intervention, and support research design development and data analysis.

Abstract

Since 2021, TB incidence in new york city (NYC) has surged partly due to decreased attenɵon during the covid-19 pandemic and in part due to increased migraɵon across the southern US border. Approximately 205,000 migrants from TB-endemic regions arrived in NYC since april 2022. In 2023, TB incidence in NYC rose by 28%, the highest rates in over a decade, with 89% of cases among individuals born outside the US. Our proposal aims to evaluate innovaɵve methods to improve TB detecɵon, prevenɵon, and treatment among migrants in NYC. Iniɵal work by our team in NYC migrant shelters has demonstrated high numbers of persons with of latent TB infecɵon (ltbi) and acɵve TB. Current clinic-based approaches to TB surveillance are ineffecɵve since few high-risk persons complete TB screening and fewer are retained in the TB cascade of care. The proposed intervenɵon, spot-TB (screening with portable x-rays for rapid recognition of TB), evaluates the use of mobile diagnosɵc teams integraɵng ultraportable digital chest radiography paired with arɵficial intelligence-assisted interpretaɵon, upfront tesɵng for TB (interferon gamma release assay) and HIV, and community engagement to improve iniɵaɵon and retenɵon in the TB cascade of care compared to convenɵonal, clinic- based approaches to TB surveillance. Aim 1 is to idenɵfy acɵve and latent TB in migrant communiɵes using this mobile detecɵon platiorm, hypothesizing that IT will increase the proporɵon of migrants compleɵng diagnosɵc evaluaɵon compared to convenɵonal approaches. Aim 2 is to determine the feasibility and acceptability of the spot-TB strategy using mixed methods, hypothesizing that IT will be highly feasible and acceptable to both care workers and parɵcipants. Aim 3 is to evaluate structural and behavioral barriers to TB care in NYC migrant communiɵes, hypothesizing that unstable housing will be a fundamental barrier and that perceived TB sɵgma will predict subsequent loss to retenɵon in the care cascade. This project employs mixed methods to evaluate the intervenɵon's feasibility, acceptability, and effecɵveness, uɵlizing advanced staɵsɵcal techniques to understand complex relaɵonships. The scalable spot-TB model aims to provide a robust framework for improving TB care in urban settings, with data supporɵng future NIH R01 submissions for larger trials. By enhancing TB detecɵon and care retenɵon, the project seeks to reduce TB transmission and improve health outcomes for NYC's migrant populaɵon.