Evidence map›Paper›PMID 31522690›Full record

ArticleParasites & vectors2019

Determining post-treatment surveillance criteria for predicting the elimination of Schistosoma mansoni transmission.

Jaspreet Toor, James E Truscott, Marleen Werkman, Hugo C Turner, Anna E Phillips, Charles H King, Graham F Medley, Roy M Anderson

Abstract read
In one paragraph

Article in Parasites & vectors, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Improving the Cost-efficiency of Preventive Chemotherapy: Impact of New Diagnostics on Stopping Decisions for Control of Schistosomiasis.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024
    Article
  6. Article
  7. Article
  8. Translating From Egg- to Antigen-Based Indicators forFrontiers in tropical diseases · 2022
    Article
  9. Article
  10. What Can Modeling Tell Us About Sustainable End Points for Neglected Tropical Diseases?Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Article
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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

8 authors.

Jaspreet ToorLondon Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, St Mary's Campus, Imperial College London, Norfolk Place, London, W2 1PG, UK. j.toor@imperial.ac.uk.ORCID http://orcid.org/0000-0003-1510-397X
James E TruscottLondon Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, St Mary's Campus, Imperial College London, Norfolk Place, London, W2 1PG, UK.
Marleen WerkmanLondon Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, St Mary's Campus, Imperial College London, Norfolk Place, London, W2 1PG, UK.
Hugo C TurnerOxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam.
Anna E PhillipsLondon Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, St Mary's Campus, Imperial College London, Norfolk Place, London, W2 1PG, UK.
Charles H KingCenter for Global Health and Diseases and Department of Mathematics, Case Western Reserve University, 10900 Euclid Avenue LC: 4983, Cleveland, OH, 44106, USA.
Graham F MedleyCentre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK.
Roy M AndersonLondon Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, St Mary's Campus, Imperial College London, Norfolk Place, London, W2 1PG, UK.

Funding

Gates Foundation OPP1184344Medical Research Council MR/R015600/1
6 · The paper itself

Abstract

backgroundThe World Health Organization (WHO) has set elimination (interruption of transmission) as an end goal for schistosomiasis. However, there is currently little guidance on the monitoring and evaluation strategy required once very low prevalence levels have been reached to determine whether elimination or resurgence of the disease will occur after stopping mass drug administration (MDA) treatment.

methodsWe employ a stochastic individual-based model of Schistosoma mansoni transmission and MDA impact to determine a prevalence threshold, i.e. prevalence of infection, which can be used to determine whether elimination or resurgence will occur after stopping treatment with a given probability. Simulations are run for treatment programmes with varying probabilities of achieving elimination and for settings where adults harbour low to high burdens of infection. Prevalence is measured based on using a single Kato-Katz on two samples per individual. We calculate positive predictive values (PPV) using PPV ≥ 0.9 as a reliable measure corresponding to ≥ 90% certainty of elimination. We analyse when post-treatment surveillance should be carried out to predict elimination. We also determine the number of individuals across a single community (of 500-1000 individuals) that should be sampled to predict elimination.

resultsWe find that a prevalence threshold of 1% by single Kato-Katz on two samples per individual is optimal for predicting elimination at two years (or later) after the last round of MDA using a sample size of 200 individuals across the entire community (from all ages). This holds regardless of whether the adults have a low or high burden of infection relative to school-aged children.

conclusionsUsing a prevalence threshold of 0.5% is sufficient for surveillance six months after the last round of MDA. However, as such a low prevalence can be difficult to measure in the field using Kato-Katz, we recommend using 1% two years after the last round of MDA. Higher prevalence thresholds of 2% or 5% can be used but require waiting over four years for post-treatment surveillance. Although, for treatment programmes where elimination is highly likely, these higher thresholds could be used sooner. Additionally, switching to more sensitive diagnostic techniques, will allow for a higher prevalence threshold to be employed.

Indexed as

Disease EradicationEpidemiological MonitoringMass Drug AdministrationAdolescentAdultAgedAged, 80 and overAnimalsAnthelminticsChildChild, PreschoolDisease Transmission, InfectiousFemaleHumansInfantInfant, NewbornAnthelminticsElimination of transmissionMonitoring and evaluationPositive predictive valuePost-treatment surveillancePrevalence thresholdSchistosomiasisStochastic individual-based model

Identifiers

PMID31522690
PMCPMC6745786

What OpenQuestion holds

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LicenceCC BY
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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.