Evidence map›Paper›PMID 34871942›Full record

ArticleEpidemics2021

Coordinated support for local action: Modeling strategies to facilitate behavior adoption in urban-poor communities of Liberia for sustained COVID-19 suppression.

Laura A Skrip, Mosoka P Fallah, Jamie Bedson, Laurent Hébert-Dufresne, Benjamin M Althouse

Open access · goldAbstract read
In one paragraph

Article in Epidemics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 36% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. 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

5 authors at 4 institutions in 2 countries.

Laura A SkripInstitute for Disease Modeling, Global Health, Bill & Melinda Gates Foundation, Seattle, WA, USA.
Mosoka P FallahNational Public Health Institute of Liberia, Monrovia, Liberia.
Jamie BedsonIndependent Consultant, Seattle, WA, USA.
Laurent Hébert-DufresneVermont Complex Systems Center, University of Vermont, Burlington, VT, USA; Department of Computer Science, University of Vermont, Burlington, VT, USA.
Benjamin M AlthouseInstitute for Disease Modeling, Global Health, Bill & Melinda Gates Foundation, Seattle, WA, USA; University of Washington, Seattle, WA, USA; New Mexico State University, Las Cruces, NM, USA. Electronic address: balthouse@idmod.org.
Bill & Melinda Gates Foundation · USIndependent Sector · USNational Public Health Institute of Liberia · LRUniversity of Vermont · US

Funding

Using Dengue Controlled Human Infection Model to Identify Adaptive Immune Correlates of ProtectionP20GM125498 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Ellen Kozelka · 2018 to 2026
$24.9M
NIGMS NIH HHS P20 GM125498
6 · The paper itself

Abstract

backgroundLong-term suppression of SARS-CoV-2 transmission will involve strategies that recognize the heterogeneous capacity of communities to undertake public health recommendations. We highlight the epidemiological impact of barriers to adoption and the potential role of community-led coordination of support for cases and high-risk contacts in urban slums.

methodsA compartmental model representing transmission of SARS-CoV-2 in urban poor versus less socioeconomically vulnerable subpopulations was developed for Montserrado County, Liberia. Adoption of home-isolation behavior was assumed to be related to the proportion of each subpopulation residing in housing units with multiple rooms and with access to sanitation, water, and food. We evaluated the potential impact of increasing the maximum attainable proportion of adoption among urban poor following the scheduled lifting of the state of emergency.

resultsWithout intervention, the model estimated higher overall infection burden but fewer severe cases among urban poor versus the less socioeconomically vulnerable population. With self-isolation by mildly symptomatic individuals, median reductions in cumulative infections, severe cases, and maximum daily incidence were 7.6% (IQR: 2.2%-20.9%), 7.0% (2.0%-18.5%), and 9.9% (2.5%-31.4%), respectively, in the urban poor subpopulation and 16.8% (5.5%-29.3%), 15.0% (5.0%-26.4%), and 28.1% (9.3%-47.8%) in the less socioeconomically vulnerable population. An increase in the maximum attainable percentage of behavior adoption by the urban slum subpopulation was associated with median reductions of 19.2% (10.1%-34.0%), 21.1% (13.3%-34.2%), and 26.0% (11.5%-48.9%) relative to the status quo scenario.

conclusionsPost-lockdown recommendations that prioritize home-isolation by confirmed cases are limited by resource constraints. Investing in community-based initiatives that coordinate support for self-identified cases and their contacts could more effectively suppress COVID-19 in settings with socioeconomic vulnerabilities.

Indexed as

COVID-19Communicable Disease ControlEpidemiological ModelsHumansLiberiaSARS-CoV-2Vulnerable PopulationsCommunity engagementHealth behaviorLiberiaSARS-CoV-2Social ecologyUrban slum communities

Identifiers

PMID34871942
PMCPMC8641945
OpenAlexW3217629731

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.