Evidence map›Paper›PMID 32568035›Full record

ArticleEmerging infectious diseases2020

Efficient Surveillance of Plasmodium knowlesi Genetic Subpopulations, Malaysian Borneo, 2000-2018.

Paul C S Divis, Ting H Hu, Khamisah A Kadir, Dayang S A Mohammad, King C Hii, Cyrus Daneshvar, David J Conway, Balbir Singh

Open access · diamondAbstract read
In one paragraph

Article in Emerging infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% of its field
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 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Observational
  7. Proposed Integrated Control of ZoonoticTropical medicine and infectious disease · 2020
    Review
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

8 authors at 3 institutions in 2 countries.

Paul C S Divis
Ting H Hu
Khamisah A Kadir
Dayang S A Mohammad
King C Hii
Cyrus Daneshvar
David J Conway
Balbir Singh
Universiti Malaysia Sarawak · MYLondon School of Hygiene & Tropical Medicine · GBNational Health Service · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Population genetic analysis revealed that Plasmodium knowlesi infections in Malaysian Borneo are caused by 2 divergent parasites associated with long-tailed (cluster 1) and pig-tailed (cluster 2) macaques. Because the transmission ecology is likely to differ for each macaque species, we developed a simple genotyping PCR to efficiently distinguish between and survey the 2 parasite subpopulations. This assay confirmed differences in the relative proportions in areas of Kapit division of Sarawak state, consistent with multilocus microsatellite analyses. Analyses of 1,204 human infections at Kapit Hospital showed that cluster 1 caused approximately two thirds of cases with no significant temporal changes from 2000 to 2018. We observed an apparent increase in overall numbers in the most recent 2 years studied, driven mainly by increased cluster 1 parasite infections. Continued monitoring of the frequency of different parasite subpopulations and correlation with environmental alterations are necessary to determine whether the epidemiology will change substantially.

Indexed as

Plasmodium knowlesiBorneoDNA, ProtozoanGenetics, PopulationMalaysiaDNA, ProtozoanAnophelesmalariaMalaysiaMalaysian Borneomosquitoesparasitesparasitic diseasesPlasmodium knowlesisurveillancevector-borne infectionszoonosis

Identifiers

PMID32568035
PMCPMC7323547
OpenAlexW3035645156

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.