Evidence map›Paper›PMID 30040843›Full record

ArticlePLoS medicine2018

High levels of sewage contamination released from urban areas after storm events: A quantitative survey with sewage specific bacterial indicators.

Hayley T Olds, Steven R Corsi, Deborah K Dila, Katherine M Halmo, Melinda J Bootsma, Sandra L McLellan

Abstract read
In one paragraph

Article in PLoS medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 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

38 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. An Urban Stormwater Contaminant Signature: Defining Priority Contaminants for Urban Stormwater Research.Water environment research : a research publication of the Water Environment Federation · 2025
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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

6 authors.

Hayley T OldsSchool of Freshwater Sciences, UW-Milwaukee, Milwaukee, Wisconsin, United States of America.ORCID 0000-0002-6701-6459
Steven R CorsiUnited States Geological Survey, Upper Midwest Water Science Center, Middleton, Wisconsin, United States of America.ORCID 0000-0003-0583-5536
Deborah K DilaSchool of Freshwater Sciences, UW-Milwaukee, Milwaukee, Wisconsin, United States of America.
Katherine M HalmoSchool of Freshwater Sciences, UW-Milwaukee, Milwaukee, Wisconsin, United States of America.
Melinda J BootsmaSchool of Freshwater Sciences, UW-Milwaukee, Milwaukee, Wisconsin, United States of America.
Sandra L McLellanSchool of Freshwater Sciences, UW-Milwaukee, Milwaukee, Wisconsin, United States of America.ORCID 0000-0003-3283-1151

Funding

Microbial Community Profiles Identify New Indicators of Waterborne PathogensR01AI091829 · NIAID · UNIVERSITY OF WISCONSIN MILWAUKEE · PI MCLELLAN, SANDRA L. · 2011 to 2019
$3.1M
NIAID NIH HHS R01 AI091829
6 · The paper itself

Abstract

backgroundPast studies have demonstrated an association between waterborne disease and heavy precipitation, and climate change is predicted to increase the frequency of these types of intense storm events in some parts of the United States. In this study, we examined the linkage between rainfall and sewage contamination of urban waterways and quantified the amount of sewage released from a major urban area under different hydrologic conditions to identify conditions that increase human risk of exposure to sewage. METHODS AND

findingsRain events and low-flow periods were intensively sampled to quantify loads of sewage based on two genetic markers for human-associated indicator bacteria (human Bacteroides and Lachnospiraceae). Samples were collected at a Lake Michigan estuary and at three river locations immediately upstream. Concentrations of indicators were analyzed using quantitative polymerase chain reaction (qPCR), and loads were calculated from streamflow data collected at each location. Human-associated indicators were found during periods of low flow, and loads increased one to two orders of magnitude during rain events from stormwater discharges contaminated with sewage. Combined sewer overflow (CSO) events increased concentrations and loads of human-associated indicators an order of magnitude greater than heavy rainfall events without CSO influence. Human-associated indicator yields (load per km2 of land per day) were related to the degree of urbanization in each watershed. Contamination in surface waters were at levels above the acceptable risk for recreational use. Further, evidence of sewage exfiltration from pipes threatens drinking water distribution systems and source water. While this study clearly demonstrates widespread sewage contamination released from urban areas, a limitation of this study is understanding human exposure and illness rates, which are dependent on multiple factors, and gaps in our knowledge of the ultimate health outcomes.

conclusionsWith the prediction of more intense rain events in certain regions due to climate change, sewer overflows and contamination from failing sewer infrastructure may increase, resulting in increases in waterborne pathogen burdens in waterways. These findings quantify hazards in exposure pathways from rain events and illustrate the additional stress that climate change may have on urban water systems. This information could be used to prioritize efforts to invest in failing sewer infrastructure and create appropriate goals to address the health concerns posed by sewage contamination from urban areas.

Indexed as

EstuariesFloodsRainWater MicrobiologyWater PollutionBacteriaDNA, BacterialEnvironmental MonitoringFecesHumansLakesPolymerase Chain ReactionRisk AssessmentRisk FactorsSewageTime FactorsDNA, BacterialSewage

Identifiers

PMID30040843
PMCPMC6057621

What OpenQuestion holds

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LicenceCC0
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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.