Evidence map›Paper›PMID 31053068›Full record

ArticleBMC public health2019

Geographic disparities and temporal changes in risk of death from myocardial infarction in Florida, 2000-2014.

Evah W Odoi, Nicholas Nagle, Shamarial Roberson, Kristina W Kintziger

Abstract read
In one paragraph

Article in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Trends and Inequalities in the Incidence of Acute Myocardial Infarction among Beijing Townships, 2007-2018.International journal of environmental research and public health · 2021
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Disparities in Temporal and Geographic Patterns of Myocardial Infarction Hospitalization Risks in Florida.International journal of environmental research and public health · 2019
    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

4 authors.

Evah W OdoiComparative and Experimental Medicine, College of Veterinary Medicine, The University of Tennessee, 2407 River Drive, Knoxville, TN, 37996, USA.
Nicholas NagleDepartment of Geography, The University of Tennessee, 2407 River Drive, Knoxville, TN, 37996, USA.
Shamarial RobersonFlorida Department of Health, Bureau of Chronic Disease Prevention, 4052 Bald Cypress Way, Tallahassee, FL, USA.
Kristina W KintzigerDepartment of Public Health, The University of Tennessee, 1914 Andy Holt Avenue, Knoxville, TN, 37996, USA. kkintzig@utk.edu.ORCID http://orcid.org/0000-0003-3555-7805

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying disparities in myocardial infarction (MI) burden and assessing its temporal changes are critical for guiding resource allocation and policies geared towards reducing/eliminating health disparities. Our objectives were to: (a) investigate the spatial distribution and clusters of MI mortality risk in Florida; and (b) assess temporal changes in geographic disparities in MI mortality risks in Florida from 2000 to 2014.

methodsThis is a retrospective ecologic study with county as the spatial unit of analysis. We obtained data for MI deaths occurring among Florida residents between 2000 and 2014 from the Florida Department of Health, and calculated county-level age-adjusted MI mortality risks and Spatial Empirical Bayesian smoothed MI mortality risks. We used Kulldorff's circular spatial scan statistics and Tango's flexible spatial scan statistics to identify spatial clusters.

resultsThere was an overall decline of 48% in MI mortality risks between 2000 and 2014. However, we found substantial, persistent disparities in MI mortality risks, with high-risk clusters occurring primarily in rural northern counties and low-risk clusters occurring exclusively in urban southern counties. MI mortality risks declined in both low- and high-risk clusters, but the latter showed more dramatic decreases during the first nine years of the study period. Consequently, the risk difference between the high- and low-risk clusters was smaller at the end than at the beginning of the study period. However, the rates of decline levelled off during the last six years of the study, and there are signs that the risks may be on an upward trend in parts of North Florida. Moreover, MI mortality risks for high-risk clusters at the end of the study period were on par with or above those for low-risk clusters at the beginning of the study period. Thus, high-risk clusters lagged behind low-risk clusters by at least 1.5 decades.

conclusionMyocardial infarction mortality risks have decreased substantially during the last 15 years, but persistent disparities in MI mortality burden still exist across Florida. Efforts to reduce these disparities will need to target prevention programs to counties in the high-risk clusters.

Indexed as

AdultAgedBayes TheoremFemaleFloridaHealthcare DisparitiesHumansMaleMiddle AgedMyocardial InfarctionResidence CharacteristicsRetrospective StudiesRisk AssessmentRisk FactorsRural PopulationUrban PopulationDisparitiesGeographic clustersMyocardial infarction mortalityTemporal trend

Identifiers

PMID31053068
PMCPMC6499995

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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.