Evidence map›Paper›PMID 34643905›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2022

Methods and Approaches for Buruli Ulcer Surveillance in Africa: Lessons Learnt and Future Directions.

Joseph W S Timothy, Rachel L Pullan, Rie R Yotsu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Joseph W S TimothyDepartment of Disease Control, London School of Hygiene & Tropical Medicine, London, UK.
Rachel L PullanDepartment of Disease Control, London School of Hygiene & Tropical Medicine, London, UK.
Rie R YotsuSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan. yotsurie@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over 95% of the global burden of Buruli ulcer disease (BU) caused by Mycobacterium ulcerans occurs in equatorial Africa. National and sub-national programs have implemented various approaches to improve detection and reporting of incident cases over recent decades. Regional incidence rates are currently in decline; however, surveillance targets outlined in 2012 by WHO have been missed and detection bias may contribute to these trends. In light of the new 2030 NTD roadmap and disease-specific targets, BU programs are required to strengthen case detection and begin a transition towards integration with other skin-NTDs. This transition comes with new opportunities to enhance existing BU surveillance systems and develop novel approaches for implementation and evaluation.In this review, we present a breakdown and assessment of the methods and approaches that have been the pillars of BU surveillance systems in Africa: (1) Passive case detection, (2) Data systems, (3) Clinical training, (4) Active case finding, (5) Burden estimation, and (6) Laboratory confirmation pathways. We discuss successes, challenges, and relevant case studies before highlighting opportunities for future development and evaluation including novel data collection tools, risk-based surveillance, and integrated skin-NTD surveillance. We draw on both experience and available literature to critically evaluate methods of BU surveillance in Africa and highlight new approaches to help achieve 2030 roadmap targets.

Indexed as

Buruli UlcerMycobacterium ulceransAfricaHumansIncidenceAfricaBurdenBuruli ulcerCase detectionCase findingMethodologyMethodsMycobacterium ulceransReportingSurveillance

Identifiers

What OpenQuestion holds

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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.