Evidence map›Paper›PMID 29043784›Full record

ArticleEnvironmental science & technology2017

Estimating the High-Arsenic Domestic-Well Population in the Conterminous United States.

Joseph D Ayotte, Laura Medalie, Sharon L Qi, Lorraine C Backer, Bernard T Nolan

Abstract read
In one paragraph

Article in Environmental science & technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
116citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

116 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Article
  7. Associations Between Perceived Tap Water Safety and Dietary Outcomes Among US Adults.Journal of the Academy of Nutrition and Dietetics · 2026
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  11. Conserved Molecular Responses to Arsenite Exposure inbioRxiv : the preprint server for biology · 2026
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  16. Review
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56 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Joseph D AyotteU.S. Geological Survey, New England Water Science Center, New Hampshire - Vermont Office, 331 Commerce Way, Pembroke, New Hampshire 03301, United States.ORCID http://orcid.org/0000-0002-1892-2738
Laura MedalieU.S. Geological Survey, New England Water Science Center, New Hampshire - Vermont Office, 87 State Street, Montpelier, Vermont 05602, United States.
Sharon L QiU.S. Geological Survey, 1300 SE Cardinal Court Bldg., 10 Vancouver, Washington 98683, United States.
Lorraine C BackerCenters for Disease Control and Prevention, National Center for Environmental Health, 4770 Buford Highway NE, Chamblee, Georgia 30341, United States.
Bernard T NolanU.S. Geological Survey, National Water Quality Program, National Center 413, 12201 Sunrise Valley Drive, Reston, Virginia 20192, United States.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

Arsenic concentrations from 20 450 domestic wells in the U.S. were used to develop a logistic regression model of the probability of having arsenic >10 μg/L ("high arsenic"), which is presented at the county, state, and national scales. Variables representing geologic sources, geochemical, hydrologic, and physical features were among the significant predictors of high arsenic. For U.S. Census blocks, the mean probability of arsenic >10 μg/L was multiplied by the population using domestic wells to estimate the potential high-arsenic domestic-well population. Approximately 44.1 M people in the U.S. use water from domestic wells. The population in the conterminous U.S. using water from domestic wells with predicted arsenic concentration >10 μg/L is 2.1 M people (95% CI is 1.5 to 2.9 M). Although areas of the U.S. were underrepresented with arsenic data, predictive variables available in national data sets were used to estimate high arsenic in unsampled areas. Additionally, by predicting to all of the conterminous U.S., we identify areas of high and low potential exposure in areas of limited arsenic data. These areas may be viewed as potential areas to investigate further or to compare to more detailed local information. Linking predictive modeling to private well use information nationally, despite the uncertainty, is beneficial for broad screening of the population at risk from elevated arsenic in drinking water from private wells.

Indexed as

ArsenicWater Pollutants, ChemicalWater WellsLogistic ModelsUnited StatesWater SupplyArsenicWater Pollutants, Chemical

Identifiers

PMID29043784
PMCPMC8842838

What OpenQuestion holds

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Registered trials

None linked

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.