Socioeconomic disparities in first stroke incidence, quality of care, and survival: a nationwide registry-based cohort study of 44 million adults in England

Bray, Benjamin; Paley, Lizz; Hoffman, Alex; James, Martin; Gompertz, Patrick; Wolfe, Charles D A; Hemingway, Harry; Rudd, Anthony

doi:10.1016/s2468-2667(18)30030-6

Cited by 115 publications

(115 citation statements)

References 27 publications

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“…The importance of socio-economic status as predictor for stroke incidence and strokerelated mortality has been emphasised by many studies [23][24][25][26][27][28]. Previous research shows that health inequalities in stroke exist in low-and middle-income countries as well as in highincome countries [25].…”

Section: Introductionmentioning

confidence: 99%

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Income inequalities in stroke incidence and mortality: Trends in stroke-free and stroke-affected life years based on German health insurance data

et al. 2020

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BackgroundDue to substantial improvements in prevention and therapy, stroke incidence and mortality rates have decreased during the last decades, but evidence is still lacking on whether all socioeconomic groups benefited equally and how the length of life affected by stroke developed over time. Our study investigates time trends in stroke-free life years and life years affected by stroke. Special emphasis is given to the question whether trends differ between income groups, leading to decreasing or increasing social inequalities. MethodsThe analyses are based on claims data of a German statutory health insurance company of the two time periods 2006-2008 and 2014-2016. Income inequalities and time trends in incidence and mortality risks were estimated using multistate survival models. Trends in strokefree life years and life years affected by stroke are analysed separately for income groups by applying multistate life table analyses. ResultsStroke incidence and mortality risks decreased in men and women in all income groups. While stroke-free lifetime could be gained in men having higher incomes, improvements in mortality counterbalanced decreasing incidences, leading to increases in life years affected by stroke among men of the lower and higher income group. Among women, no significant changes in life years could be observed. ConclusionsChanges in stroke-affected life years occur among men in all income groups, but are more pronounced in the higher income group. However, irrespective of the income group the proportion of stroke-affected life years remains quite stable over time, pointing towards PLOS ONE | https://doi. Higher income0.96 (0.93-0.99) 0.88 (0.87-0.89) 0.94 (0.90-1.00) HR Hazard Ratio; 95%-CI bootstrapped (with replacement) using 1000 replications; all analyses are controlled for age in single-year age groups (as second-degree polynomial) and for period

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Section: Introductionmentioning

confidence: 99%

“…Low socioeconomic status is associated with a higher prevalence of risk factors (e.g. hypertension, smoking, BMI, physical inactivity, diabetes, diet, and alcohol intake) [24,25,29], with higher incidence risks, increased stroke severity, and higher stroke mortality [23- 28,30].…”

Section: Introductionmentioning

confidence: 99%

Income inequalities in stroke incidence and mortality: Trends in stroke-free and stroke-affected life years based on German health insurance data

et al. 2020

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“…Patients with low socioeconomic status (SES) are at an increased risk of death after stroke, even in countries with universal access to healthcare [5][6][7][8][9][10]. The causes behind these differences have not been established but are likely multifaceted, with aspects related both to patient characteristics (risk factors, comorbidities, etc.)…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic status and survival after stroke – using mediation and sensitivity analyses to assess the effect of stroke severity and unmeasured confounding

2020

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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.

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“…Third, there were disparities in the social development and economy between the four cities, with the city populations displaying different socioeconomic status. Low socioeconomic status patients had a higher stroke hospitalization risk and case fatality than those with a high socioeconomic status (45). Socioeconomic status differences varied even more signi cantly across the 333 cities than the four cities in our study.…”

Section: Discussionmentioning

confidence: 53%

Health care utilization for patients with stroke: A 3-year cross-sectional study of China’s two urban health insurance schemes across four cities

Yang

Nicholas²,

et al. 2020

Preprint

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Background Stroke is a devastating disease and a major cause of death and disability worldwide. We evaluated differences in stroke patients’ health care utilization by insurance type across four cities in China. Methods The data were a 5% random sampling from claims data of China Urban Employees’ Basic Medical Insurance (UEBMI) and Urban Residents’ Basic Medical Insurance (URBMI) from 2014 to 2016. The descriptive analyze and interaction actions were mainly conducted. Results We found that differences in healthcare utilization and medical expenses varied more across the four cities, Beijing, Shanghai, Tianjin, and Chongqing, than they did between the UEBMI and URBMI scheme. On average, UEBMI patients had a significantly larger number of outpatient (4.3) and inpatient (1.4) visits per year than the URBMI group (2.0 outpatient and 1.1 inpatient visits per year). UEBMI members’ average length of stay (ALOS) of 18.3 days was significantly longer than the 11 days ALOS of URBMI members. Importantly, the significant differences in healthcare utilization and healthcare expenditures were greater between cities than the average differences between UEBMI and URBMI. Beijing UEBMI outpatient (5.0) and inpatient (1.4) visits and Shanghai UEBMI outpatient (4.8) and inpatient (1.7) visits were significantly higher than Tianjin (3.4 outpatient and 1.4 inpatient visits) and Chongqing (1.8 outpatient and 1.1 inpatient visits). The divergences of UEBMI visits across the four cities were greater than the average UEBMI outpatient (4.3) and inpatient (1.4) visits. ALOS for URBMI Beijing patients (14.8 days) and Shanghai patients (27.7 days) were significantly longer than Tianjin (10.2 days) and Chongqing (10.1 days) URBMI patients. Medical costs, total OOP expenses and OOP reimbursement rates also varied more across the four cities than the average UEBMI or URBMI medical costs, total OOP expenses and reimbursement rates. Conclusions The health care utilization of patients with stroke varied by insurance type and city, and the differences in utilization and health care costs across cities was greater than the average difference between UEBMI and URBMI. Launching a new critical illness insurance scheme and further reforming the UEBMI and URBMI schemes would reduce these inter-city differences in health care utilization.

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Socioeconomic disparities in first stroke incidence, quality of care, and survival: a nationwide registry-based cohort study of 44 million adults in England

Cited by 115 publications

References 27 publications

Income inequalities in stroke incidence and mortality: Trends in stroke-free and stroke-affected life years based on German health insurance data

Income inequalities in stroke incidence and mortality: Trends in stroke-free and stroke-affected life years based on German health insurance data

Socioeconomic status and survival after stroke – using mediation and sensitivity analyses to assess the effect of stroke severity and unmeasured confounding

Health care utilization for patients with stroke: A 3-year cross-sectional study of China’s two urban health insurance schemes across four cities

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