Evidence map›Paper›PMID 27484009›Full record

ArticleBMC public health2016

Explaining large mortality differences between adjacent counties: a cross-sectional study.

M Schootman, L Chien, S Yun, S L Pruitt

Abstract read
In one paragraph

Article in BMC public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

M SchootmanDepartment of Epidemiology, Saint Louis University College for Public Health and Social Justice, 3545 Lafayette Avenue, St. Louis, MO, 63104, USA. schootm@slu.edu.ORCID 0000-0003-1162-8824
L ChienUniversity of Texas School of Public Health at San Antonio Regional Campus, Department of Biostatistics, San Antonio, TX, USA.
S YunMissouri Department of Health and Senior Services, Office of Epidemiology, Jefferson City, MO, USA.
S L PruittDepartment of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Funding

Geographic Disparities in Colorectal Cancer SurvivalR01CA137750 · NCI · WASHINGTON UNIVERSITY · PI SCHOOTMAN, MARIO · 2009 to 2014
$2.0M
NCI NIH HHS R01 CA137750
6 · The paper itself

Abstract

backgroundExtensive geographic variation in adverse health outcomes exists, but global measures ignore differences between adjacent geographic areas, which often have very different mortality rates. We describe a novel application of advanced spatial analysis to 1) examine the extent of differences in mortality rates between adjacent counties, 2) describe differences in risk factors between adjacent counties, and 3) determine if differences in risk factors account for the differences in mortality rates between adjacent counties.

methodsWe conducted a cross-sectional study in Missouri, USA with 2005-2009 age-adjusted all-cause mortality rate as the outcome and county-level explanatory variables from a 2007 population-based survey. We used a multi-level Gaussian model and a full Bayesian approach to analyze the difference in risk factors relative to the difference in mortality rates between adjacent counties.

resultsThe average mean difference in the age-adjusted mortality rate between any two adjacent counties was -3.27 (standard deviation = 95.5) per 100,000 population (maximum = 258.80). Six variables were associated with mortality differences: inability to obtain medical care because of cost (β = 2.6), hospital discharge rate (β = 1.03), prevalence of fair/poor health (β = 2.93), and hypertension (β = 4.75) and poverty prevalence (β = 6.08).

conclusionsExamining differences in mortality rates and associated risk factors between adjacent counties provides additional insight for future interventions to reduce geographic disparities.

Indexed as

Cause of DeathHealth Status DisparitiesPatient DischargeBayes TheoremCosts and Cost AnalysisCross-Sectional StudiesFemaleHealth Services AccessibilityHumansHypertensionMaleMissouriPovertyPrevalenceRisk FactorsSpatial AnalysisBayesian analysisNeighborhood effectsSpatial statistics

Identifiers

PMID27484009
PMCPMC4970203

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LicenceCC BY
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Registered trials

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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.