ArticleThe international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease2018
Spatial clusters of latent tuberculous infection, Connecticut, 2010-2014.
Article in The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Influence of tuberculosis knowledge on acceptance of preventive treatment and the moderating role of tuberculosis stigma among China's general population: cross-sectional analysis.BMC public health · 2024Article
- Article
- A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States.International journal of environmental research and public health · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
settingIn the United States, tuberculosis (TB) control is increasingly focusing on the identification of persons with latent tuberculous infection (LTBI).
objectiveTo characterize the local epidemiology of LTBI in Connecticut, USA.
methodsWe used spatial analyses 1) to identify census tract-level clusters of reported LTBI and TB disease in Connecticut, 2) to compare persons and populations in clusters with those not in clusters, and 3) to compare persons with LTBI to those with TB disease.
resultsSignificant census tract-level spatial clusters of LTBI and TB disease were identified. Compared with persons with LTBI in non-clustered census tracts, those in clustered census tracts were more likely to be foreign-born and less likely to be of white non-Hispanic ethnicity. Populations in census tract clusters of high LTBI prevalence had greater crowding, persons living in poverty, and persons lacking health care insurance than populations not in clustered census tracts. Persons with LTBI were less likely than those with TB disease to be of Asian ethnicity, and persons with LTBI were more likely than those with TB disease to reside in a clustered census tract.
conclusionsCharacterizing fine-scale populations at risk for LTBI supports effective and culturally accessible screening and treatment programs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.