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BACKGROUND: Substantial socioeconomic differences in patient-reported outcome measures (PROMs) 3 months after stroke have recently been shown. We aimed to understand the underlying mechanisms and investigate potential interventional targets to equalize differences.

METHODS: All patients aged 18 to 64 years, independent in activities of daily living, registered with a first-time stroke in Riksstroke (the Swedish Stroke Register) from 2015 to 2017 were included. PROMs 3 months after stroke included activities of daily living status, mood, fatigue, pain, and general health. Socioeconomic status (SES) was measured on the basis of income and education. Using causal mediation analysis, we simulated the effect of interventions on the distributions of smoking, metabolic health (diabetes, antihypertensive treatment, statin treatment), atrial fibrillation, and stroke characteristics (stroke type, severity) on the absolute SES-related risk difference in PROMs.

RESULTS: Of 6910 patients, 8% had become dependent in activities of daily living, 13% reported low mood, 42% fatigue, 23% pain, and 17% poor general health 3 months after stroke. Adjusted for sex and age, low SES was associated with increased absolute risks of poor PROMs with between 6% and 18% compared with higher SES with the largest increase for general health (18.2% [95% CI, 13.5%-22.9%]). Intervening to shift the distribution of all mediators among patients with low SES to those of patients with higher SES potentially reduces SES disparities by a proportion of 14% to 45%. For most PROMs the most important intervention was reducing smoking and improving metabolic health.

CONCLUSIONS: Working-age patients with low SES report more severe outcomes 3 months after stroke than patients with higher SES. Targeted interventions reducing the prevalence of smoking, diabetes, hypertension, and high cholesterol in patients with low SES could mitigate these disparities.

BACKGROUND: There is a well-known association between low socioeconomic status (SES), poor survival, and clinician-reported outcomes after stroke. We aimed to assess socioeconomic differences in Patient Reported Outcome Measures 3 months after stroke.

METHODS: This nationwide cohort study included patients registered with acute stroke in the Swedish Stroke Register 2015-2017. Patient Reported Outcome Measures included activities of daily living (mobility, toileting, and dressing), and poststroke symptoms (low mood, fatigue, pain, and poor general health). Information on SES prestroke was retrieved from Statistics Sweden and defined by a composite measure based on education and income tertiles. Associations between SES and Patient Reported Outcome Measures were analyzed using logistic regression adjusting for confounders (sex and age) and additionally for potential mediators (stroke type, severity, cardiovascular disease risk factors, and living alone). Subgroup analyses were performed for stroke type, men and women, and younger and older patients.

RESULTS: The study included 44 511 patients. Of these, 31.1% required assistance with mobility, 18% with toileting, and 22.2% with dressing 3 months after stroke. For poststroke symptoms, 12.3% reported low mood, 39.1% fatigue, and 22.7% pain often/constantly, while 21.4% rated their general health as poor/very poor. Adjusted for confounders, the odds of needing assistance with activities of daily living were highest for patients with low income and primary school education, for example, for mobility, odds ratio was 2.06 (95% CI, 1.89-2.24) compared with patients with high income and university education. For poststroke symptoms, odds of poor outcome were highest for patients with low income and university education (eg, odds ratio, 1.79 [95% CI, 1.49-2.15] for low mood). Adjustments for potential mediators attenuated but did not remove associations. The associations were similar in ischemic and hemorrhagic strokes and more pronounced in men and patients <65 years old.

CONCLUSIONS: There are substantial SES-related differences in Patient Reported Outcome Measures poststroke. The more severe outcome associated with low SES is more pronounced in men and in patients of working age.

BACKGROUND AND OBJECTIVES: Low socioeconomic status (SES) is associated with increased risk of death and disability after stroke, but interventional targets to minimize disparities remain unclear. We aim to assess the extent to which SES-based disparities in the association between low SES and death and dependency at three months after stroke could be eliminated by offsetting differences in comorbidity, stroke severity, and acute care.

METHODS: This nationwide register-based cohort study included all 72 hospitals caring for patients with acute stroke in Sweden. All patients registered with an acute ischemic stroke in the Swedish Stroke Register in 2015-2016 who were independent in activities of daily living (ADL) at the time of stroke were included. Data on survival and SES the year before stroke were retrieved by cross-linkage with other national registers. SES was defined by education and income, and categorized into low, mid, and high. Causal mediation analysis was used to study the absolute risk of death and ADL-dependency at 3 months depending on SES, and to what extent hypothetical interventions on comorbidities, stroke severity, and acute care would equalize outcomes.

RESULTS: Of the 25,846 patients in the study, 6,798 (26.3%) were dead or ADL-dependent three months after stroke. Adjusted for sex and age, low SES was associated with an increased absolute risk of 5.4% (95% CI: 3.9%-6.9%; p<0.001) compared to mid SES, and 10.1% (95% CI: 8.1%-12.2%; p<0.001) compared to high SES. Intervening to shift the distribution of all mediators among patients with low SES to those of the more privileged groups would result in absolute reductions of these effects by 2.2% (95% CI: 1.2%-3.2%; p<0.001), and 4.0% (95% CI: 2.6%-5.5%; p<0.001), respectively, with the largest reduction accomplished by equalizing stroke severity.

DISCUSSION: Low SES patients have substantially increased risks of death and ADL-dependency three months after stroke compared to more privileged patient groups. This study suggests that if we could intervene to equalize SES-related differences in the distributions of comorbidity, acute care, and stroke severity, up to 40 out of every 1000 patients with low SES could be prevented from dying or becoming ADL-dependent.

Causal mediation analysis is used to decompose the total effect of an exposure on an outcome into an indirect effect, taking the path through an intermediate variable, and a direct effect. To estimate these effects, strong assumptions are made about unconfoundedness of the relationships between the exposure, mediator and outcome. These assumptions are difficult to verify in a given situation and therefore a mediation analysis should be complemented with a sensitivity analysis to assess the possible impact of violations. In this paper we present a method for sensitivity analysis to not only unobserved mediator-outcome confounding, which has largely been the focus of previous literature, but also unobserved confounding involving the exposure. The setting is estimation of natural direct and indirect effects based on parametric regression models. We present results for combinations of binary and continuous mediators and outcomes and extend the sensitivity analysis for mediator-outcome confounding to cases where the continuous outcome variable is censored or truncated. The proposed methods perform well also in the presence of interactions between the exposure, mediator and observed confounders, allowing for modeling flexibility as well as exploration of effect modification. The performance of the method is illustrated through simulations and an empirical example. 

Background: Those with low socioeconomic status have an increased risk of stroke, more severe strokes, reduced access to treatment, and more adverse outcomes after stroke. The question is why these differences are present. In this study we investigate to which extent the association between low socioeconomic status and stroke severity can be explained by differences in risk factors and stroke prevention drugs.

Methods: The study included 86 316 patients registered with an ischemic stroke in the Swedish Stroke Register (Riksstroke) 2012–2016. Data on socioeconomic status was retrieved from the Longitudinal integrated database for health insurance and labour market studies (LISA) by individual linkage. We used education level as proxy for socioeconomic status, with primary school education classified as low education. Stroke severity was measured using the Reaction Level Scale, with values above 1 classified as severe strokes. To investigate the pathways via risk factors and stroke prevention drugs we performed a mediation analysis estimating indirect and direct effects.

Results: Low education was associated with an excess risk of a severe stroke compared to mid/high education (absolute risk difference 1.4%, 95% CI: 1.0%-1.8%), adjusting for confounders. Of this association 28.5% was an indirect effect via risk factors (absolute risk difference 0.4%, 95% CI: 0.3%-0.5%), while the indirect effect via stroke prevention drugs was negligible.

Conclusion: Almost one third of the association between low education and severe stroke was explained by risk factors, and clinical effort should be taken to reduce these risk factors to decrease stroke severity among those with low socioeconomic status.

Background: Although it has been established that low socioeconomic status is linked to increased risk of death after stroke, the mechanisms behind this link are still unclear. In this study we aim to shed light on the relationship between income level and survival after stroke by investigating the extent to which differences in stroke severity account for differences in survival.

Methods: The study was based on patients registered in Riksstroke (the Swedish stroke register) with first time ischemic stroke (n = 51,159) or intracerebral hemorrhage (n = 6777) in 2009–2012. We used causal mediation analysis to decompose the effect of low income on 3-month case fatality into a direct effect and an indirect effect due to stroke severity. Since causal mediation analysis relies on strong assumptions regarding residual confounding of the relationships involved, recently developed methods for sensitivity analysis were used to assess the robustness of the results to unobserved confounding.

Results: After adjustment for observed confounders, patients in the lowest income tertile had a 3.2% (95% CI: 0.9–5.4%) increased absolute risk of 3-month case fatality after intracerebral hemorrhage compared to patients in the two highest tertiles. The corresponding increase for case fatality after ischemic stroke was 1% (0.4–1.5%). The indirect effect of low income, mediated by stroke severity, was 1.8% (0.7–2.9%) for intracerebral hemorrhage and 0.4% (0.2–0.6%) for ischemic stroke. Unobserved confounders affecting the risk of low income, more severe stroke and case fatality in the same directions could explain the indirect effect, but additional adjustment to observed confounders did not alter the conclusions.

Conclusions: This study provides evidence that as much as half of income-related inequalities in stroke case fatality is mediated through differences in stroke severity. Targeting stroke severity could therefore lead to a substantial reduction in inequalities and should be prioritized. Sensitivity analysis suggests that additional adjustment for a confounder of greater impact than age would be required to considerably alter our conclusions.

To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assumptions about unconfoundedness are required. Since these assumptions cannot be tested using the observed data, a mediation analysis should always be accompanied by a sensitivity analysis of the resulting estimates. In this article, we propose a sensitivity analysis method for parametric estimation of direct and indirect effects when the exposure, mediator, and outcome are all binary. The sensitivity parameters consist of the correlations between the error terms of the exposure, mediator, and outcome models. These correlations are incorporated into the estimation of the model parameters and identification sets are then obtained for the direct and indirect effects for a range of plausible correlation values. We take the sampling variability into account through the construction of uncertainty intervals. The proposed method is able to assess sensitivity to both mediator‐outcome confounding and confounding involving the exposure. To illustrate the method, we apply it to a mediation study based on the data from the Swedish Stroke Register (Riksstroke). An R package that implements the proposed method is available.