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

Abstract

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.

Advancing convergent mixed methods design using the Diamond Approach for clinical multiple case studies: an example using data on time to treatment in aneurysmal subarachnoid haemorrhage

“…Our study consisted of a sample of 27 patient cases with aSAH purposefully sampled to maximise the diversity of patients' socio-economic backgrounds across two tertiary referral networks. Patient characteristics in our sample were similar to those from other aSAH studies: the proportion of female, of lower WFNS grade (1-3), and type of treatment (coiling/clipping) were all similar (Baltsavias et al, 2000;Dupont et al, 2009;Nichols et al, 2016;Rehman et al, 2020), suggesting our sample adequately reflects aSAH patients.…”

“…the proportion of female, of lower WFNS grade (1-3), and type of treatment (coiling/clipping) were all similar (Baltsavias et al, 2000;Dupont et al, 2009;Nichols et al, 2016;Rehman et al, 2020), suggesting our sample adequately reflects aSAH patients.…”

Associations between socioeconomic status and place of residence with survival after aneurysmal subarachnoid haemorrhage

“…13 This suggests that geographical location can be interrelated Survival post-subarachnoid haemorrhage with socioeconomic factors, to include locale and social structures, including healthcare access. 12,25 Our mortality rate (52.32%) at 12 months was higher than previously reported rates, although Duran et al 35 reported a mortality rate of 45.00% excluding delayed admissions. Beseoglu et al 36 reported a mortality rate of 14.4% for their hospital-based study.…”

“…A conceptual framework of rural vulnerability identifying proposed interrelationships between demographic, sociodemographic and individual risk factors linked to rurality has previously been described 12 and forms the empirical basis for this analysis. The Socioeconomic advantage/disadvantage Index for Areas (SEIFA), as well as the Accessibility/Remoteness Index of Australia (ARIA), 26 were assigned from geocoded residential street addresses using statistical area level 2 data for both indexes, which represents aggregated spatial areas with an average population of 10 000 individuals from the 2011 Australian Census.…”

“…9,10 The impact of population-level sociodemographic factors such as geographic location and associated social determinants of health are poorly understood. 4,11,12 Health outcomes post stroke in disadvantaged rural areas are also influenced by shifting demographics, ageing populations and an increased prevalence of modifiable risk factors. 13 Despite the correlations between socioeconomic disadvantage and outcome post stroke, the factors explaining this effect and associations in aSAH are to a large extent unknown.…”