Evidence map›Paper›PMID 27906982›Full record

ArticlePLoS neglected tropical diseases2016

Using Community-Level Prevalence of Loa loa Infection to Predict the Proportion of Highly-Infected Individuals: Statistical Modelling to Support Lymphatic Filariasis and Onchocerciasis Elimination Programs.

Daniela K Schlüter, Martial L Ndeffo-Mbah, Innocent Takougang, Tony Ukety, Samuel Wanji, Alison P Galvani, Peter J Diggle

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2016. 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. AI sees an end to filariasis.PLoS neglected tropical diseases · 2024
    Article
  2. Article
  3. Article
  4. Scaling-Down Mass Ivermectin Treatment for Onchocerciasis Elimination: Modeling the Impact of the Geographical Unit for Decision Making.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Article
  5. Diagnosing risk factors alongside mass drug administration using serial diagnostic tests-which test first?Transactions of the Royal Society of Tropical Medicine and Hygiene · 2018
    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.

Daniela K SchlüterCHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom.ORCID 0000-0001-9018-0022
Martial L Ndeffo-MbahDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.
Innocent TakougangFoundation for Health Research & Development, Department of Public Health, University of Yaoundé 1, Yaoundé, Cameroon.
Tony UketyWorld Health Organisation, Geneva, Switzerland.
Samuel WanjiParasites and Vectors Research Unit, Department of Microbiology and Parasitology, University of Buea, Cameroon.
Alison P GalvaniDepartment of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.
Peter J DiggleCHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Lymphatic Filariasis and Onchocerciasis (river blindness) constitute pressing public health issues in tropical regions. Global elimination programs, involving mass drug administration (MDA), have been launched by the World Health Organisation. Although the drugs used are generally well tolerated, individuals who are highly co-infected with Loa loa are at risk of experiencing serious adverse events. Highly infected individuals are more likely to be found in communities with high prevalence. An understanding of the relationship between individual infection and population-level prevalence can therefore inform decisions on whether MDA can be safely administered in an endemic community. Based on Loa loa infection intensity data from individuals in Cameroon, the Republic of the Congo and the Democratic Republic of the Congo we develop a statistical model for the distribution of infection levels in communities. We then use this model to make predictive inferences regarding the proportion of individuals whose parasite count exceeds policy-relevant levels. In particular we show how to exploit the positive correlation between community-level prevalence and intensity of infection in order to predict the proportion of highly infected individuals in a community given only prevalence data from the community in question. The resulting prediction intervals are not substantially wider, and in some cases narrower, than the corresponding binomial confidence intervals obtained from data that include measurements of individual infection levels. Therefore the model developed here facilitates the estimation of the proportion of individuals highly infected with Loa loa using only estimated community level prevalence. It can be used to assess the risk of rolling out MDA in a specific community, or to guide policy decisions.

Indexed as

AnimalsCameroonCongoDemocratic Republic of the CongoElephantiasis, FilarialHumansLoaLoiasisModels, StatisticalOnchocerciasisResidence CharacteristicsRural Population

Identifiers

PMID27906982
PMCPMC5132393

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.