Evidence map›Paper›PMID 25522751›Full record

ArticleMalaria journal2014

Pooled PCR testing strategy and prevalence estimation of submicroscopic infections using Bayesian latent class models in pregnant women receiving intermittent preventive treatment at Machinga District Hospital, Malawi, 2010.

Zhiyong Zhou, Rebecca Mans Mitchell, Julie Gutman, Ryan E Wiegand, Dyson A Mwandama, Don P Mathanga, Jacek Skarbinski, Ya Ping Shi

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Malaria journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 15% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Nested Group Testing Procedure.Communications in mathematics and statistics · 2022
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Determination of Varying Group Sizes for Pooling Procedure.Computational and mathematical methods in medicine · 2019
    Article
  14. Article
  15. Article
  16. 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

8 authors at 4 institutions in 2 countries.

Zhiyong ZhouMalaria Branch and Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA. zaz6@cdc.gov.
Rebecca Mans Mitchell
Julie Gutman
Ryan E Wiegand
Dyson A Mwandama
Don P Mathanga
Jacek Skarbinski
Ya Ping Shi
Center for Global Health · USCenters for Disease Control and Prevention · USDivision of Parasitic Diseases and Malaria · USUniversity of Malawi · MW

Funding

NCEZID CDC HHS 3U01CK000135NCEZID CDC HHS U01 CK000135
6 · The paper itself

Abstract

backgroundLow malaria parasite densities in pregnancy are a diagnostic challenge. PCR provides high sensitivity and specificity in detecting low density of parasites, but cost and technical requirements limit its application in resources-limited settings. Pooling samples for PCR detection was explored to estimate prevalence of submicroscopic malaria infection in pregnant women at delivery. Previous work uses gold-standard based methods to calculate sensitivity and specificity of tests, creating a challenge when newer methodologies are substantially more sensitive than the gold standard. Thus prevalence was estimated using Bayesian latent class models (LCMs) in this study.

methodsNested PCR (nPCR) for the 18S rRNA gene subunit of Plasmodium falciparum was conducted to detect malaria infection in microscopy-negative Malawian women on IPTp. Two-step sample pooling used dried blood spot samples (DBSs) collected from placenta or periphery at delivery. Results from nPCR and histology as well as previously published data were used to construct LCMs to estimate assay sensitivity and specificity. Theoretical confidence intervals for prevalence of infection were calculated for two-step and one-step pooling strategies.

resultsOf 617 microscopy-negative Malawian women, 39 (6.3%) were identified as actively infected by histology while 52 (8.4%) were positive by nPCR. One hundred forty (22.7%) individuals had past infection assessed by histology. With histology as a reference, 72% of women in the active infection group, 7.1% in the past infection group and 3.2% in histology-negative group were nPCR positive. Using latent class models without a gold standard, histology had a median sensitivity of 49.7% and specificity of 97.6% for active infection while PCR had a median sensitivity of 96.0% and specificity of 99.1%. The true prevalence of active infection was estimated at 8.0% (CI: 5.8-10.5%) from PCR. PCR also had similar sensitivity for detecting either peripheral or placental malaria for submicroscopic infections. One-step pooling would give similar confidence intervals for pool sizes less than 20 while reducing the number of tests performed.

conclusionsPooled nPCR testing was a sensitive and resource-efficient strategy and LCMs provided precise prevalence estimates of submicroscopic infections. Compared to two-step pooling, one-step pooling could provide similar prevalence estimates at population levels with many fewer tests required.

Indexed as

AntimalarialsBayes TheoremChemopreventionDNA, ProtozoanDNA, RibosomalFemaleHospitals, DistrictHumansMalaria, FalciparumMalawiMolecular Diagnostic TechniquesPolymerase Chain ReactionPregnancyPregnancy Complications, InfectiousPrevalenceRNA, Ribosomal, 18SAntimalarialsDNA, ProtozoanDNA, RibosomalRNA, Ribosomal, 18S

Identifiers

PMID25522751
PMCPMC4301903
OpenAlexW2131716216

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.