Rasch Analysis of Postconcussive Symptoms: Development of Crosswalks and the Brain Injury Symptom Scale

What Affects the Quality of Score Transformations? Potential Issues in True-Score Equating Using the Partial Credit Model

This simulation study investigated to what extent departures from construct similarity as well as differences in the difficulty and targeting of scales impact the score transformation when scales are equated by means of concurrent calibration using the partial credit model with a common person design. Practical implications of the simulation results are discussed with a focus on scale equating in health-related research settings. The study simulated data for two scales, varying the number of items and the sample sizes. The factor correlation between scales was used to operationalize construct similarity. Targeting of the scales was operationalized through increasing departure from equal difficulty and by varying the dispersion of the item and person parameters in each scale. The results show that low similarity between scales goes along with lower transformation precision. In cases with equal levels of similarity, precision improves in settings where the range of the item parameters is encompassing the person parameters range. With decreasing similarity, score transformation precision benefits more from good targeting. Difficulty shifts up to two logits somewhat increased the estimation bias but without affecting the transformation precision. The observed robustness against difficulty shifts supports the advantage of applying a true-score equating methods over identity equating, which was used as a naive baseline method for comparison. Finally, larger sample size did not improve the transformation precision in this study, longer scales improved only marginally the quality of the equating. The insights from the simulation study are used in a real-data example.

Assessing the psychological impact of Beirut Port blast: A cross-sectional study

Beirut Port blast's magnitude is considered the third after Hiroshima and Nagasaki atomic bombings. This blast occurred in the densely populated section of Beirut, leaving more than six thousand injured patients. The psychological disturbances were assessed in the blast survivors who presented to the Emergency Department (ED) at the American University of Beirut Medical Center (AUBMC). This was a cross-sectional study at the ED of AUBMC. Identified patients were contacted and consented to participate in the study. Post-Traumatic Stress Disorder (PTSD) was selected as an outcome. Depression, PTSD, and concussion were assessed using patient health questionnaire (PHQ)-9, PTSD checklist for DSM-5 (PCL5), and brain injury symptoms (BISx) tools, respectively. The association of patients and injury characteristics with the study outcome was assessed using logistic regression. 145 participants completed the study procedures. The participants' average age was 39.8 ± 15.4 years, and 60% were males. Almost half of the participants showed depression on PHQ, and 2-thirds had PTSD. The participant's age was negatively associated with PTSD, whereas being a female, having depression, and having a concussion were positively associated with PTSD. The results of this study were in line with the previous literature report except for the association between younger age and PTSD, which warrants further investigations to delineate the reasons.

Functional Status Across Post-Acute Settings is Associated With 30-Day and 90-Day Hospital Readmissions

OBJECTIVE

To examine the association between cocalibrated functional scores across post-acute care settings and the subsequent risk of hospital readmission.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

We analyzed 781,021 fee-for-service Medicare beneficiaries discharged to either inpatient rehabilitation facilities (IRFs), skilled nursing facilities (SNFs), or home health agencies (HHA) after an acute hospital stay for stroke (N = 143,277), lower extremity joint replacements (512,577), and hip/femur fracture (125,167) between January 1, 2013, and August 31, 2014.

MEASURES

Functional items from IRF-PAI, MDS, and OASIS were categorized into self-care and mobility domains. We cocalibrated admission functional scores across post-acute settings and divided scores into 4 functional levels using quartiles (Q1-Q4, with Q4 representing the most independent function). The primary outcomes were 30-day and 90-day hospital readmissions (yes/no) after an initial post-acute stay.

RESULTS

Patients who were more dependent in self-care and mobility at the initial post-acute setting were significantly more likely to experience hospital readmission [eg, hazard ratios of 30-day readmission in stroke: 1.54 (95% confidence interval [CI] 1.47-1.61), 1.18 (95% CI 1.14-1.23), and 1.12 (95% CI 1.08-1.16) for Q1, Q2 and Q3, compared to Q4]. We found similar results for risk of 90-day hospital readmission across impairment conditions.

CONCLUSIONS AND IMPLICATIONS

Patients who were more functionally dependent at the initial post-acute setting had a higher risk to readmit to the hospitals after discharging from the post-acute setting for 30 and 90 days, compared with patients who were more functionally independent. This finding is consistent across impairment conditions and post-acute settings. Future research should determine effective strategies of maintaining and facilitating functional performance across post-acute settings to optimize long-term patient outcomes.

Detecting hospital behaviors of up-coding on DRGs using Rasch model of continuous variables and online cloud computing in Taiwan

BACKGROUND

This work aims to apply data-detection algorithms to predict the possible deductions of reimbursement from Taiwan's Bureau of National Health Insurance (BNHI), and to design an online dashboard to send alerts and reminders to physicians after completing their patient discharge summaries.

METHODS

Reimbursement data for discharged patients were extracted from a Taiwan medical center in 2016. Using the Rasch model of continuous variables, we applied standardized residual analyses to 20 sets of norm-referenced diagnosis-related group (DRGs), each with 300 cases, and compared these to 194 cases with deducted records from the BNHI. We then examine whether the results of prediction using the Rasch model have a high probability associated with the deducted cases. Furthermore, an online dashboard was designed for use in the online monitoring of possible deductions on fee items in medical settings.

RESULTS

The results show that 1) the effects deducted by the NHRI can be predicted with an accuracy rate of 0.82 using the standardized residual approach of the Rasch model; 2) the accuracies for drug, medical material and examination fees are not associated among different years, and all of those areas under the ROC curve (AUC) are significantly greater than the randomized probability of 0.50; and 3) the online dashboard showing the possible deductions on fee items can be used by hospitals in the future.

CONCLUSION

The DRG-based comparisons in the possible deductions on medical fees, along with the algorithm based on Rasch modeling, can be a complementary tool in upgrading the efficiency and accuracy in processing medical fee applications in the discernable future.