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BACKGROUND: Low socioeconomic status (low education and income level) has been found to be associated with increased stroke mortality. However, findings from previous studies on the association between socioeconomic status and case fatality (survival) after stroke have been inconsistent.

AIMS: The study aims to explore the association between socio-economic status and survival after stroke using Riks-Stroke, the Swedish Stroke Register, with emphasis on changes in survival (in)equality with time after stroke.

METHODS: All 76 hospitals in Sweden admitting acute stroke patients participate in Riks-Stroke. Riks-Stroke data on 18- to 74-year-old patients with onset of first stroke during the years 2001-2009 were combined with data from other official Swedish registers. Case fatality was analyzed by socioeconomic status (education, income, country of birth, and cohabitation) and other patient characteristics.

RESULTS: Of the 62 497 patients in the study, a total of 6094 (9·8%) died within the first year after stroke. Low income, primary school education, and living alone were independently associated with higher case fatality after the acute phase. Differences related to income and cohabitation were present already early, at 8-28 days after stroke, with the gaps expanding thereafter. The association between education and case fatality was not present until 29 days to one-year after stroke. Dissimilarities in secondary preventative medications prescribed on discharge from hospital had only a minor impact on these differences.

CONCLUSIONS: Socioeconomic status had only a limited effect on acute phase case fatality, indicating minor disparities in acute stroke treatment. The survival inequality, present already in the subacute phase, increased markedly over time since the stroke event. The socioeconomic differences could not be explained by differences in secondary prevention at discharge from hospital. Large socioeconomic differences in long-term survival after stroke may exist also in a country with limited income inequity.

BACKGROUND: When profiling hospital performance, quality inicators are commonly evaluated through hospital-specific adjusted means with confidence intervals. When identifying deviations from a norm, large hospitals can have statistically significant results even for clinically irrelevant deviations while important deviations in small hospitals can remain undiscovered. We have used data from the Swedish Stroke Register (Riksstroke) to illustrate the properties of a benchmarking method that integrates considerations of both clinical relevance and level of statistical significance.

METHODS: The performance measure used was case-mix adjusted risk of death or dependency in activities of daily living within 3 months after stroke. A hospital was labeled as having outlying performance if its case-mix adjusted risk exceeded a benchmark value with a specified statistical confidence level. The benchmark was expressed relative to the population risk and should reflect the clinically relevant deviation that is to be detected. A simulation study based on Riksstroke patient data from 2008-2009 was performed to investigate the effect of the choice of the statistical confidence level and benchmark value on the diagnostic properties of the method.

RESULTS: Simulations were based on 18,309 patients in 76 hospitals. The widely used setting, comparing 95% confidence intervals to the national average, resulted in low sensitivity (0.252) and high specificity (0.991). There were large variations in sensitivity and specificity for different requirements of statistical confidence. Lowering statistical confidence improved sensitivity with a relatively smaller loss of specificity. Variations due to different benchmark values were smaller, especially for sensitivity. This allows the choice of a clinically relevant benchmark to be driven by clinical factors without major concerns about sufficiently reliable evidence.

CONCLUSIONS: The study emphasizes the importance of combining clinical relevance and level of statistical confidence when profiling hospital performance. To guide the decision process a web-based tool that gives ROC-curves for different scenarios is provided.

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.

Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and finite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

When performing a mediation analysis, i.e. estimating direct and indirect effects of a given exposure on an outcome, strong assumptions are made about unconfoundedness. 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 impact of violations. Lindmark et al. (2016) proposed a sensitivity analysis method for parametric estimation of conditional and marginal direct and indirect effects when the mediator and outcome are binary and modeled using probit models. In this paper we extend this to include cases with continuous mediators and outcomes and suggest extensions to the cases when the continuous outcome variable is censored or truncated. Three sensitivity parameters are used, consisting of the correlations between the error terms of the mediator, outcome and exposure assignment mechanism models. These correlations are incorporated into the estimation of the model parameters and sampling variability is taken into account through the construction of uncertainty intervals.