Evidence map›Paper›PMID 29095808›Full record

ArticleMMWR. Morbidity and mortality weekly report2017

Harmful Algal Bloom-Associated Illnesses in Humans and Dogs Identified Through a Pilot Surveillance System - New York, 2015.

Mary Figgatt, James Hyde, David Dziewulski, Eric Wiegert, Scott Kishbaugh, Grant Zelin, Lloyd Wilson

Abstract read
In one paragraph

Article in MMWR. Morbidity and mortality weekly report, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Article
  2. Expert insights on managing harmful algal blooms.Frontiers in freshwater science · 2024
    Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
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

7 authors.

Mary Figgatt
James Hyde
David Dziewulski
Eric Wiegert
Scott Kishbaugh
Grant Zelin
Lloyd Wilson

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cyanobacteria, also known as blue-green algae, are photosynthetic, aquatic organisms found in fresh, brackish, and marine water around the world (1). Rapid proliferation and accumulation of potentially toxin-producing cyanobacteria characterize one type of harmful algal bloom (HAB). HABs have the potential to cause illness in humans and animals (2,3); however, the epidemiology of these illnesses has not been well characterized. Statewide in 2015, a total of 139 HABs were identified in New York, 97 (70%) of which were confirmed through laboratory analysis; 77 independent beach closures were ordered at 37 beaches on 20 different bodies of water. To better characterize HAB-associated illnesses, during June-September 2015, the New York State Department of Health (NYSDOH) implemented a pilot surveillance system in 16 New York counties. Activities included the collection of data from environmental HAB reports, illness reports, poison control centers, and syndromic surveillance, and increased outreach to the public, health care providers, and veterinarians. During June-September, 51 HAB-associated illnesses were reported, including 35 that met the CDC case definitions*; 32 of the cases occurred in humans and three in dogs. In previous years, New York never had more than 10 HAB-associated illnesses reported statewide. The pilot surveillance results from 16 counties during a 4-month period suggest that HAB-associated illnesses might be more common than previously reported.

Indexed as

Harmful Algal BloomPopulation SurveillanceAdolescentAdultAnimalsChildChild, PreschoolDiseaseDog DiseasesDogsFemaleHumansMaleMiddle AgedNew YorkPilot Projects

Identifiers

PMID29095808
PMCPMC5689215

What OpenQuestion holds

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Registered trials

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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.