Journal of Neurosciences in Rural Practice
 
REVIEW ARTICLE
Year : 2016  |  Volume : 7  |  Issue : 4  |  Page : 559-565

Determining rural risk for aneurysmal subarachnoid hemorrhages: A structural equation modeling approach


1 School of Health Sciences, Faculty of Health Science, University of Tasmania, Hobart Tasmania, Australia
2 Menzies Institute for Medical Research, University of Tasmania, Hobart Tasmania, Australia

Correspondence Address:
Linda Jayne Nichols
School of Health Sciences, University of Tasmania, 71 Brooker Ave Glebe 7001, Hobart Tasmania
Australia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0976-3147.188627

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An aneurysmal subarachnoid hemorrhage (aSAH) carries a high disability burden. The true impact of rurality as a predictor of outcome severity is unknown. Our aim is to clarify the relationship between the proposed explanations of regional and rural health disparities linked to severity of outcome following an aSAH. An initial literature search identified limited data directly linking geographical location, rurality, rural vulnerability, and aSAH. A further search noting parallels with ischemic stroke and acute myocardial infarct literature presented a number of diverse and interrelated predictors. This a priori knowledge informed the development of a conceptual framework that proposes the relationship between rurality and severity of outcome following an aSAH utilizing structural equation modeling. The presented conceptual framework explores a number of system, environmental, and modifiable risk factors. Socioeconomic characteristics, modifiable risk factors, and timely treatment that were identified as predictors of severity of outcome following an aSAH and within each of these defined predictors a number of contributing specific individual predictors are proposed. There are considerable gaps in the current knowledge pertaining to the impact of rurality on the severity of outcome following an aSAH. Absent from the literature is any investigation of the cumulative impact and multiplicity of risk factors associated with rurality. The proposed conceptual framework hypothesizes a number of relationships between both individual level and system level predictors, acknowledging that intervening predictors may mediate the effect of one variable on another.


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