ArticleScientific reports2025
Quantifying undetected tuberculosis in Ethiopia using a novel geospatial modelling approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB) is the leading infectious cause of death globally, with approximately three million cases remaining undetected, thereby contributing to community transmission. Understanding the spatial distribution of undetected TB in high-burden settings is critical for designing and implementing geographically targeted interventions for early detection and control. This study presents the first estimates of numbers of undetected TB cases in Ethiopia at national and local levels using novel geospatial method. We employed a Bayesian geostatistical modelling framework, incorporating national TB prevalence survey and TB notification data together with climatic and environmental variables, to estimate the number of undetected TB cases at district and national levels. Spatial clustering of undetected TB cases was assessed using Moran's Index statistic and Local Indicator of Spatial Autocorrelation (LISA). A Bayesian Poisson regression model with conditional autoregressive (CAR) prior structure was developed to identify drivers of the clustering. We estimated a total of 51,041 undetected TB cases (95% CI: 50,599, 51,486) in Ethiopia, with the majority of these cases predicted in the Oromia region (20,440), Amhara region (9614), and South Ethiopia region (6061). Spatial clustering of undetected TB cases was observed in districts near the international borders, including the Ethiopia-Somalia and Ethiopia-Kenya border regions, as well as in several districts of Southern Ethiopia. The number of undetected TB cases was negatively associated with the proportion of the population with good mass media exposure (Incidence rate ratio (IRR): 0.67 95% CI: 0.56, 0.80) and the proportion of the population with high wealth index (IRR: 0.73, 95% CI: 0.60, 0.90). Our findings revealed a high burden of undetected TB in Ethiopia, with spatial clustering in border regions and areas with limited healthcare access. Targeted TB screening interventions to communities with low socioeconomic status along with improving mass media exposure in these regions, could significantly reduce the burden of undetected TB in Ethiopia.
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.