Evidence map›Paper›PMID 41211307›Full record

ArticlePeerJ2025

Geographic disparities and predictors of suicide mortality risk in Florida: spatial scan statistics and negative binomial modeling.

Howard Onyuth, Corey Day, Nirmalendu Deb Nath, Agricola Odoi

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Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Howard Onyuth *Department of Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, TN, United States of America.
Corey DayDepartment of Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, TN, United States of America.
Nirmalendu Deb NathDepartment of Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, TN, United States of America.
Agricola Odoi *Department of Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, TN, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Florida ranks 17th in suicide mortality risk in the United States, with 14.8 suicide deaths per 100,000 persons. Geographic disparities in suicide mortality across the US are well-documented and are partly attributed to the uneven distribution of risk factors. However, limited data exist on such disparities and associated predictors within Florida, despite their importance for guiding targeted prevention efforts. This study aimed to investigate county-level geographic disparities in suicide mortality risk in Florida and identify predictors of these disparities. Methods: This retrospective ecological study used data from the Florida Department of Health. County-level age-adjusted suicide mortality risks and spatial empirical Bayesian-smoothed risks were calculated for three time periods: 2011-2013, 2014-2016, and 2017-2019. Tango's spatial scan statistics were applied to identify high-risk clusters. A negative binomial regression model was used to examine county-level predictors of suicide mortality risk for the 2017-2019 period. Results: Statewide age-adjusted suicide mortality risk increased from 22.6 to 24.3 per 100,000 persons over the study period. Counties in the northwest, northeast, southwest, and parts of central Florida consistently exhibited high mortality risks. Suicide mortality risk was significantly higher in counties with larger proportions of residents aged 45-64 years and ≥65 years, those reporting excessive drinking, frequent mental distress, or veteran status. Conclusion: This study identified geographic disparities and key predictors of suicide mortality risk across Florida counties. These findings can inform policymakers, healthcare providers, and community organizations in designing and implementing targeted suicide prevention programs tailored to the specific needs of high-risk communities.

Indexed as

SuicideAdultAgedBayes TheoremFemaleFloridaHumansMaleMiddle AgedRetrospective StudiesRisk FactorsGeographic disparitiesGeographic information systemsGISSpatial empirical Bayesian smoothingSuicideSuicide mortality riskTango’s flexible spatial scan statistics

Identifiers

PMID41211307
PMCPMC12593724

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