Evidence map›Paper›PMID 35223209›Full record

ArticlePeerJ2022

Geographic disparities and predictors of vaccination exemptions in Florida: a retrospective study.

Corinne B Tandy, Agricola Odoi

Abstract read
In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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.

Corinne B Tandy *Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, TN, United States.
Agricola Odoi *Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, TN, United States.ORCID 0000-0001-5050-6737

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the United States, state-level policies requiring vaccination of school-going children constitute a critical strategy for improving vaccination coverage. However, policies allowing vaccination exemptions have also been implemented and contribute to reductions in vaccination coverage and potential increases in the burden of vaccine-preventable diseases. Understanding the geographic disparities in the distribution of vaccination exemptions and identifying high risk areas is necessary for guiding resource allocation and public health control strategies. This study investigated geographic disparities in vaccination exemptions as well as socioeconomic and demographic predictors of vaccination exemptions in Florida.

methodsVaccination exemption data were obtained from the Florida Department of Health's Florida HealthCHARTS web interface. Spatial patterns in geographic distribution of total and non-medical vaccination exemptions were assessed using county-level choropleth maps. Negative binomial models were used to identify significant predictors of county-level risks of both total and non-medical vaccination exemptions.

resultsTotal exemptions varied from 0 to 30.2 per 10,000 people. Nine counties had exemption risks in the top two classes (10.4-15.9 and 15.9-30.2 exemptions per 10,000 people). These counties were distributed in five distinct areas: Western Panhandle, central northern area, central, South-eastern coastal area, and the southern coastal border of the state. Non-medical exemptions varied from 0 to 10.4 per 10,000 people. Fifteen counties had exemption risks in the top two classes (3.7-5.6 and 5.6-10.4 exemptions per 10,000 people), and were located in six distinct areas: Western and Central Panhandle, Northeastern, Central-eastern coastal area, Central-western coastal area, and the South-western coastal border of the state. Predictors of high risk of total vaccination exemptions were high density of primary care providers (

conclusionsThere is evidence of county-level geographic disparities in both total and non-medical vaccination exemption risks in Florida. These disparities are explained by differences in county-level socioeconomic and demographic factors. Study findings are important in guiding resource allocation for health planning aimed at improving vaccination rates and reducing incidence of vaccine-preventable diseases.

Indexed as

Vaccine-Preventable DiseasesChildFloridaHumansRetrospective StudiesUnited StatesVaccinationVaccination CoverageDemographic predictorsFloridaGeographic disparitiesGeographic information systemsGISNegative binomial regressionSocio-demographic predictorsSocioeconomic predictorsVaccinationVaccination exemptions

Identifiers

PMID35223209
PMCPMC8877400

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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.