Evidence map›Paper›PMID 35997318›Full record

ArticleEmerging infectious diseases2022

Quantifying Population Burden and Effectiveness of Decentralized Surveillance Strategies for Skin-Presenting Neglected Tropical Diseases, Liberia.

Joseph W S Timothy, Emerson Rogers, Katherine E Halliday, Tarnue Mulbah, Michael Marks, Zeela Zaizay, Romeo Giddings, Marie Kempf, Estelle Marion, Stephen L Walker and 2 more

Open access · diamondAbstract read
In one paragraph

Article in Emerging infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

9 citing papers in PubMed, 10 citations in OpenAlex.

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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

12 authors at 3 institutions in 2 countries.

Joseph W S Timothy
Emerson Rogers
Katherine E Halliday
Tarnue Mulbah
Michael Marks
Zeela Zaizay
Romeo Giddings
Marie Kempf
Estelle Marion
Stephen L Walker
Karsor K Kollie
Rachel L Pullan
London School of Hygiene & Tropical Medicine · GBCentre Hospitalier Universitaire d'Angers · FRUniversité d'Angers · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We evaluated programmatic approaches for skin neglected tropical disease (NTD) surveillance and completed a robust estimation of the burden of skin NTDs endemic to West Africa (Buruli ulcer, leprosy, lymphatic filariasis morbidity, and yaws). In Maryland, Liberia, exhaustive case finding by community health workers of 56,285 persons across 92 clusters identified 3,241 suspected cases. A total of 236 skin NTDs (34.0 [95% CI 29.1-38.9]/10,000 persons) were confirmed by midlevel healthcare workers trained using a tailored program. Cases showed a focal and spatially heterogeneous distribution. This community health worker‒led approach showed a higher skin NTD burden than prevailing surveillance mechanisms, but also showed high (95.1%) and equitable population coverage. Specialized training and task-shifting of diagnoses to midlevel health workers led to reliable identification of skin NTDs, but reliability of individual diagnoses varied. This multifaceted evaluation of skin NTD surveillance strategies quantifies benefits and limitations of key approaches promoted by the 2030 NTD roadmap of the World Health Organization.

Indexed as

Buruli UlcerTropical MedicineHumansLiberiaNeglected DiseasesReproducibility of ResultsbacteriaBuruli ulcerdecentralized surveillance strategieseffectivenessleprosyLiberialymphatic filariasisneglected tropical diseasesNTDsparasitespopulation burdenskinskin NTDyaws

Identifiers

PMID35997318
PMCPMC9423900
OpenAlexW4292680970

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.