Background and Purpose— The theory of cognitive reserve (CR) was introduced to account for individual differences in the clinical manifestation of neuropathology. This study investigated whether CR has a modulating effect on cognitive impairment and recovery after stroke. Methods— This study is an interim analysis of the Korean Stroke Cohort for Functioning and Rehabilitation. A total of 7459 patients with first-ever stroke were included for analysis. Education, occupation, and composite CR scores derived from those 2 variables were used as CR proxies. Scores from the Korean version of the Mini-Mental State Examination analyzed for 30 months after stroke onset were analyzed. Results— Lower CR increased the risk of cognitive impairment after stroke. The odds ratio was 1.89 (95% CI, 1.64–2.19) in patients with secondary education and 2.42 (95% CI, 2.03–2.90) in patients with primary education compared with patients with higher education. The odds ratio was 1.48 (95% CI, 1.23–1.98) in patients with a skilled manual occupation and 2.01 (95% CI, 1.42–2.83) in patients with a nonskilled manual occupation compared with patients with a managerial or professional occupation. In the multilevel model analysis, the Korean version of the Mini-Mental State Examination total score increased during the first 3 months (1.93 points per month) and then plateaued (0.02 point per month). The slopes were moderated by the level of education, occupation, and composite CR score: the higher the level of education, occupation, or CR score, the faster the recovery. In the older adult group, the Korean version of the Mini-Mental State Examination scores showed a long-term decline that was moderated by education level. Conclusions— Education and occupation can buffer an individual against cognitive impairment caused by stroke and promote rapid cognitive recovery early after stroke. In addition, higher education minimizes long-term cognitive decline after stroke, especially in older patients. Clinical Trial Registration— URL: https://www.clinicaltrials.gov . Unique identifier: NCT03402451.

Assessment of functional independence and residual disability is very important for measuring treatment outcome after stroke. The modified Rankin Scale (mRS) and the modified Barthel Index (MBI) are commonly used scales to measure disability or dependence in activities of daily living (ADL) of stroke survivors. Lack of consensus regarding MBI score categories has caused confusion in interpreting stroke outcomes. The purpose of this study was to identify the optimal corresponding MBI and modified Rankin scale (mRS) grades for categorization of MBI. The Korean versions of the MBI (K-MBI) and mRS were collected from 5,759 stroke patients at 3 months after onset of stroke. The sensitivity and specificity were calculated at K-MBI score cutoffs for each mRS grade to obtain optimally corresponding K-MBI scores and mRS grades. We also plotted receiver operating characteristic (ROC) curves of sensitivity and specificity and determined the area under the curve (AUC). The K-MBI cutoff points with the highest sum of sensitivity and specificity were 100 (sensitivity 0.940; specificity 0.612), 98 (sensitivity 0.904; specificity 0.838), 94 (sensitivity 0.885; PLOS ONE | https://doi.

In auto insurance, a Bonus-Malus System (BMS) is commonly used as a posteriori risk classification mechanism to set the premium for the next contract period based on a policyholder's claim history. Even though recent literature reports evidence of a significant dependence between frequency and severity, the current BMS practice is to use a frequency-based transition rule while ignoring severity information. Although Oh et al. (2019) claim that the frequencydriven BMS transition rule can accommodate the dependence between frequency and severity, their proposal is only a partial solution, as the transition rule still completely ignores the claim severity and is unable to penalize large claims. In this study, we propose to use the BMS with a transition rule based on both frequency and size of claim, based on the bivariate random effect model, which conveniently allows dependence between frequency and severity. We analytically derive the optimal relativities under the proposed BMS framework and show that the proposed BMS outperforms the existing frequency-driven BMS. Later numerical experiments are also provided using both hypothetical and actual datasets in order to assess the effect of various dependencies on the BMS risk classification and confirm our theoretical findings.

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