Evidence map›Paper›PMID 36689419›Full record

ArticlePloS one2023

Leveraging interactive voice response technology to mitigate COVID-19 risk in refugee settlements in Uganda: Lessons learned implementing "Dial-COVID" a toll-free mobile phone symptom surveillance and information dissemination tool.

Robin E Klabbers, Timothy R Muwonge, Phuong Pham, Andrew Mujugira, Patrick Vinck, Sukanya Borthakur, Monisha Sharma, Numan Mohammed, Rosalind Parkes-Ratanshi, Connie Celum and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.5field-weighted citation impact, top 21% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. 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

11 authors at 4 institutions in 3 countries.

Robin E KlabbersDepartment of Emergency Medicine, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-1462-0046
Timothy R MuwongeInfectious Diseases Institute, Makerere University, Kampala, Uganda.ORCID 0000-0002-8478-9234
Phuong PhamHarvard Humanitarian Initiative, Harvard University, Cambridge, Massachusetts, United States of America.
Andrew MujugiraDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.
Patrick VinckHarvard Humanitarian Initiative, Harvard University, Cambridge, Massachusetts, United States of America.
Sukanya BorthakurMedical Teams International, Kampala, Uganda.
Monisha SharmaDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-7599-1561
Numan MohammedViamo, Ghana.
Rosalind Parkes-RatanshiInfectious Diseases Institute, Makerere University, Kampala, Uganda.
Connie CelumDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-8813-3419
Kelli N O'LaughlinDepartment of Emergency Medicine, University of Washington, Seattle, Washington, United States of America.
University of Washington · USHarvard University · USInfectious Diseases Institute · UGUniversity of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPersons living in refugee settlements in sub-Saharan Africa may be at increased risk for COVID-19 and experience barriers to accessing COVID-19 information. We aimed to evaluate the implementation of "Dial-COVID" a multi-lingual, toll free, telephone platform that uses interactive voice response (IVR) to track COVID-19 symptoms/exposure and disseminate COVID-19 health information in refugee settlements in Uganda. We hypothesized that IVR could provide an alternative way to screen for COVID-19 and communicate public health information to humanitarian populations when physical access and testing capacity were limited.

methodsThe Dial-COVID IVR platform was created in ten languages and advertised by community health workers in refugee settlements for participants to call into toll free. In a recorded IVR symptom survey, participants were screened for COVID-19 symptoms/exposures and based on their responses, received tailored public health messages about COVID-19 risk mitigation in accordance with Uganda Ministry of Health guidelines. Here we report the challenges and lessons learned implementing this research during the pandemic.

resultsBetween February 2021 and March 2022, 15,465 calls were received by the Dial-COVID platform from all 31 refugee settlements in Uganda through which 6,913 symptom surveys were completed and 10,411 public health messages were disseminated in all study languages. Uptake of Dial-COVID fluctuated with the national COVID-19 caseload and was impacted by phone ownership and connectivity in refugee settlements. Intensified advertising efforts promoted Dial-COVID uptake. Flexibility to adapt IVR messages was contingent on translation capacity.

conclusionRefugees living in refugee settlements across Uganda accessed Dial-COVID to share and obtain COVID-19 information suggesting that IVR holds potential for rapid information dissemination and screening of humanitarian populations during future infectious disease outbreaks and may be a valuable tool for routine public health programs. IVR adaptation flexibility and reach are influenced by language constraints and by contextual factors related to platform access. REGISTRATION DETAILS: World Pandemic Research Network- 490652.

Indexed as

Cell PhoneCOVID-19RefugeesHumansInformation DisseminationTechnologyUganda

Identifiers

PMID36689419
PMCPMC9870132
OpenAlexW4317762036

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.