Evidence map›Paper›PMID 33819266›Full record

ArticlePLoS neglected tropical diseases2021

Estimating true prevalence of Schistosoma mansoni from population summary measures based on the Kato-Katz diagnostic technique.

Oliver Bärenbold, Amadou Garba, Daniel G Colley, Fiona M Fleming, Rufin K Assaré, Edridah M Tukahebwa, Biruck Kebede, Jean T Coulibaly, Eliézer K N'Goran, Louis-Albert Tchuem Tchuenté and 3 more

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Improved Assessment ofOpen forum infectious diseases · 2024
    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

13 authors.

Oliver BärenboldSwiss Tropical and Public Health Institute, Basel, Switzerland.
Amadou GarbaDepartment of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland.
Daniel G ColleySchistosomiasis Consortium for Operational Research and Evaluation (SCORE), Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia, United States of America.ORCID 0000-0002-9381-8420
Fiona M FlemingSchistosomiasis Control Initiative, Imperial College, London, United Kingdom.ORCID 0000-0002-3866-2232
Rufin K AssaréSwiss Tropical and Public Health Institute, Basel, Switzerland.
Edridah M TukahebwaVector Control Division, Ministry of Health, Kampala, Uganda.ORCID 0000-0002-0624-2890
Biruck KebedeMinistry of Health, Addis Ababa, Ethiopia.
Jean T CoulibalySwiss Tropical and Public Health Institute, Basel, Switzerland.ORCID 0000-0002-3626-6068
Eliézer K N'GoranCentre Suisse de Recherches Scientifiques en Côte d'Ivoire, Abidjan, Côte d'Ivoire.
Louis-Albert Tchuem TchuentéLaboratory of Parasitology and Ecology, University of Yaoundé I, Yaoundé, Cameroon.ORCID 0000-0001-7365-7650
Pauline MwinziCentre for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya.
Jürg UtzingerSwiss Tropical and Public Health Institute, Basel, Switzerland.
Penelope VounatsouSwiss Tropical and Public Health Institute, Basel, Switzerland.ORCID 0000-0002-4904-5352

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundThe prevalence of Schistosoma mansoni infection is usually assessed by the Kato-Katz diagnostic technique. However, Kato-Katz thick smears have low sensitivity, especially for light infections. Egg count models fitted on individual level data can adjust for the infection intensity-dependent sensitivity and estimate the 'true' prevalence in a population. However, application of these models is complex and there is a need for adjustments that can be done without modeling expertise. This study provides estimates of the 'true' S. mansoni prevalence from population summary measures of observed prevalence and infection intensity using extensive simulations parametrized with data from different settings in sub-Saharan Africa. METHODOLOGY: An individual-level egg count model was applied to Kato-Katz data to determine the S. mansoni infection intensity-dependent sensitivity for various sampling schemes. Observations in populations with varying forces of transmission were simulated, using standard assumptions about the distribution of worms and their mating behavior. Summary measures such as the geometric mean infection, arithmetic mean infection, and the observed prevalence of the simulations were calculated, and parametric statistical models fitted to the summary measures for each sampling scheme. For validation, the simulation-based estimates are compared with an observational dataset not used to inform the simulation. PRINCIPAL

findingsOverall, the sensitivity of Kato-Katz in a population varies according to the mean infection intensity. Using a parametric model, which takes into account different sampling schemes varying from single Kato-Katz to triplicate slides over three days, both geometric and arithmetic mean infection intensities improve estimation of sensitivity. The relation between observed and 'true' prevalence is remarkably linear and triplicate slides per day on three consecutive days ensure close to perfect sensitivity. CONCLUSIONS/SIGNIFICANCE: Estimation of 'true' S. mansoni prevalence is improved when taking into account geometric or arithmetic mean infection intensity in a population. We supply parametric functions and corresponding estimates of their parameters to calculate the 'true' prevalence for sampling schemes up to 3 days with triplicate Kato-Katz thick smears per day that allow estimation of the 'true' prevalence.

Indexed as

Diagnostic Tests, RoutineModels, StatisticalAdolescentAfrica South of the SaharaAnimalsChemopreventionChildChild, PreschoolFecesFemaleHumansInfantMaleParasite Egg CountPoint-of-Care SystemsPrevalence

Identifiers

PMID33819266
PMCPMC8062092

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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.