ArticleThe Science of the total environment2019
Confluent impact of housing and geology on indoor radon concentrations in Atlanta, Georgia, United States.
Article in The Science of the total environment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of statistical methods for the identification of buildings and areas with high radon levels.Frontiers in public health · 2024Pooled it
- Indoor Air Radon Testing Rate and Its Relationships with Various Socioeconomic and Public Health Factors in Georgia, USA.International journal of environmental research and public health · 2026Article
- Quantifying mean, variability, and uncertainty in indoor radon exposure in Pennsylvania using random forest and quantile regression forest models.Scientific reports · 2026Article
- High-resolution national radon maps based on massive indoor measurements in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Associations Between Gestational Residential Radon Exposure and Term Low Birthweight in Connecticut, USA.Epidemiology (Cambridge, Mass.) · 2024Article
- Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States.Environmental science & technology · 2023Article
- Geologic, seasonal, and atmospheric predictors of indoor home radon values.Environmental research, health : ERH · 2023Article
- Active Monitoring of Residential Radon in Rome: A Pilot Study.International journal of environmental research and public health · 2022Review
- A Geologically Based Indoor-Radon Potential Map of Kentucky.GeoHealth · 2020Article
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
Abstract
Radon is a naturally released radioactive carcinogenic gas. To estimate radon exposure, studies have examined various risk factors, but limited information exists pertaining to the confluent impact of housing characteristics and geology. This study evaluated the efficacy of housing and geological characteristics to predict radon risk in DeKalb County, Georgia, USA. Four major types of data were used: (1) three databases of indoor radon concentrations (n = 6757); (2) geologic maps of rock types and fault zones; (3) a database of 402 in situ measurements of gamma emissions, and (4) two databases of housing characteristics. The Getis-Ord method was used to delineate hot spots of radon concentrations. Empirical Bayesian Kriging was used to predict gamma radiation at each radon test site. Chi-square tests, bivariate correlation coefficients, and logistic regression were used to examine the impact of geological and housing factors on radon. The results showed that indoor radon levels were more likely to exceed the action level-4 pCi/L (148 Bq/m
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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.