Evidence map›Paper›PMID 33585154›Full record

ArticleHealth and technology2021

Exploring the effectiveness of telehealth interventions for diagnosis, contact tracing and care of Corona Virus Disease of 2019 (COVID19) patients in sub Saharan Africa: a rapid review.

Tarisai Kudakwashe Manyati, Morgen Mutsau

Abstract read
In one paragraph

Article in Health and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

2 authors.

Tarisai Kudakwashe ManyatiCollege of Economic and Management Sciences, University of South Africa, Pretoria, South Africa.
Morgen MutsauCentre for Applied Social Sciences, University of Zimbabwe, Harare, Zimbabwe.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The efficacy of leveraging telehealth services on clinical outcomes remains scarcely documented. We conducted a rapid review to explore the effectiveness of telehealth interventions for the diagnosis, contact tracing and care of the corona virus disease of 2019 in sub Saharan Africa. Using MEDLINE, Science Direct and Cochrane Library databases, a review was conducted during the month of July 2020 of peer reviewed articles reporting on the use of telehealth interventions in sub-Saharan Africa. All the studies were assessed against the inclusion criteria by two independent reviewers. The 7 studies included in the synthesis were conducted in 2 countries [Nigeria (× 1), Zimbabwe (× 1)], and commentaries covering the entire sub Saharan Africa in general (× 4) and to Uganda (× 1). All the included articles and commentaries were published in 2020. We established that mobile applications are effective in providing information for referrals of potential patients infected by COVID 19 and provides convenient access to routine care without the risk of exposure through close contact. In countries such as Nigeria, mobile positioning data significantly improved decision making, capacity and scope of contact tracing and surveillance of known contacts of confirmed cases. We noted that collaborations between the government, mobile network operators and technology companies were utilised for successful mobile positioning data tracing interventions for COVID patients. Mobile applications such as the Vula platform in South Africa were also noted as effective in providing psychological support to health care workers attending to patients.

Indexed as

CareContact tracingCOVID19Sub Saharan AfricaTelehealth

Identifiers

PMID33585154
PMCPMC7870280

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.