Evidence map›Paper›PMID 37461011›Full record

ArticleBMC proceedings2023

A novel adaptation of spatial interpolation methods to map health attitudes related to COVID-19.

Raisa Behal, Kenneth Davis, Jeffrey Doering

Open access · goldAbstract read
In one paragraph

Article in BMC proceedings, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 42% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
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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

3 authors at 2 institutions in 1 country.

Raisa BehalFraym, 3101 Wilson Blvd., Suite 300, Arlington, VA, 22201, USA.
Kenneth DavisFraym, 3101 Wilson Blvd., Suite 300, Arlington, VA, 22201, USA. k.davis@fraym.io.ORCID http://orcid.org/0009-0008-9504-7616
Jeffrey DoeringJohnson and Johnson, 1 Johnson and Johnson Plaza, New Brunswick, NJ, 08933, USA.
Fracture Analysis Consultants (United States) · USJohnson & Johnson (United States) · US

Funding

Johnson and Johnson PO: 994459249
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic presented substantial challenges to public health stakeholders working to vaccinate populations against the disease, particularly among vaccine hesitant individuals in low- and middle-income countries. Data on the determinants of vaccine hesitancy are scarce, and often available only at the national level. In this paper, our goal is to inform programmatic decision making in support of local vaccine uptake. Our analytical objectives to support this goal are to (1) reliably estimate attitudinal data at the hyperlocal level, and (2) estimate the loss of data heterogeneity among these attitudinal indicators at higher levels of aggregation. With hyperlocal attitudinal data on the determinants of vaccine hesitancy, public health stakeholders can better tailor interventions aimed at increasing uptake sub-nationally, and even down to the individual vaccination site or neighborhood.

methodsWe estimated attitudinal data on the determinants of vaccine hesitancy as framed by the WHO's Confidence, Complacency, and Convenience ("3Cs") Model of Vaccine Hesitancy using a nationally and regionally representative household survey of 4,922 adults aged 18 and above, collected in February 2022. This custom survey was designed to collect information on attitudes towards COVID-19 and concerns about the COVID-19 vaccine. A machine learning (ML) framework was used to spatially interpolate metrics representative of the 3Cs at a one square kilometer (1km

resultsSpatial interpolated hyperlocal estimates of the 3Cs captured significant information on attitudes towards COVID-19 and COVID-19 vaccines. The interpolated estimates held increased heterogeneity within each subsequent level of disaggregation, with most variation at the 1km

conclusionsOur findings demonstrate that a) attitudinal data can be successfully estimated at the hyperlocal level, and b) the determinants of COVID-19 vaccine hesitancy have large spatial variance that cannot be captured through national surveys alone. Access to community level attitudes toward vaccine safety and efficacy; vaccination access, time, and financial burden; and COVID-19 beliefs and infection concerns presents novel implications for public health practitioners and policymakers seeking to increase COVID-19 vaccine uptake through more customized community-level interventions.

Indexed as

Behavior ChangeGeospatialImmunizationMachine LearningMisinformationVaccine Hesitancy

Identifiers

PMID37461011
PMCPMC10351110
OpenAlexW4384524198

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.