Adults with cerebral palsy (CP) represent an increasing population whose health status and health care needs are poorly understood.¹ Mortality records reveal that death due to ischemic heart disease and cancer is higher among adults with CP²; however, there have been no national surveillance efforts to track disease risk in this population. We examined estimates of chronic conditions in a population-representative sample of adults with CP.

Objective To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission. Design Systematic review. Data source Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019. Eligibility criteria for selecting studies All studies of predictive models for 28 day or 30 day hospital readmission that used EMR data. Outcome measures Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models. Results Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval −0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77). Conclusions On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

This article provides a conceptual framework for understanding healthcare disparities experienced by individuals with disabilities. While health disparities are the result of factors deeply rooted in culture, life style, socioeconomic status, and accessibility of resources, healthcare disparities are a subset of health disparities that reflect differences in access to and quality of healthcare and can be viewed as the inability of the healthcare system to adequately address the needs of specific population groups. This article uses a narrative method to identify and critique the main conceptual frameworks that have been used in analyzing disparities in healthcare access and quality, and evaluating those frameworks in the context of healthcare for individuals with disabilities. Specific models that are examined include the Aday and Anderson Model, the Grossman Utility Model, the Institute of Medicine (IOM)'s models of Access to Healthcare Services and Healthcare Disparities, and the Cultural Competency model. While existing frameworks advance understandings of disparities in healthcare access and quality, they fall short when applied to individuals with disabilities. Specific deficits include a lack of attention to cultural and contextual factors (Aday and Andersen framework), unrealistic assumptions regarding equal access to resources (Grossman's utility model), lack of recognition or inclusion of concepts of structural accessibility (IOM model of Healthcare Disparities) and exclusive emphasis on supply side of the healthcare equation to improve healthcare disparities (Cultural Competency model). In response to identified gaps in the literature and short-comings of current conceptualizations, an integrated model of disability and healthcare disparities is put forth. We analyzed models of access to care and disparities in healthcare to be able to have an integrated and cohesive conceptual framework that could potentially address issues related to access to healthcare among individuals with disabilities. The Model of Healthcare Disparities and Disability (MHDD) provides a framework for conceptualizing how healthcare disparities impact disability and specifically, how a mismatch between personal and environmental factors may result in reduced healthcare access and quality, which in turn may lead to reduced functioning, activity and participation among individuals with impairments and chronic health conditions. Researchers, health providers, policy makers and community advocate groups who are engaged in devising interventions aimed at reducing healthcare disparities would benefit from the discussions. Implications for Rehabilitation Evaluates the main models of healthcare disparity and disability to create an integrated framework. Provides a comprehensive conceptual model of healthcare disparity that specifically targets issues related to individuals with disabilities. Conceptualizes how personal and environmental factors interact to produce disparities in access to healthcare and healthcare quality. Recognizes and targets modifiable factors to reduce disparities between and within individuals with disabilities.

Background: Digital replantation attempt and success rates have been declining in the United States. Regionalization of digit replantation has been proposed as a solution to improve both attempt and success rates of these procedures. There is limited information about which criteria could establish a hospital as a center specialized for digit replantation. The authors analyzed hospital replantation volume and patient factors associated with successful thumb/finger replantation. Methods: A retrospective study using data from the 2008 to 2012 State Inpatient Databases of the Health Care Cost and Utilization Project from five states (New York, California, North Carolina, Utah, and Florida) was performed. The generalized estimating equation method was used to examine the association between patient characteristics and hospital volume and success of thumb/finger replantation. A receiver operating characteristic curve and Youden’s J statistic were used to determine annual hospital replantation volume cutoff levels for success rates. Results: There were 3417 digit amputation injuries, with 631 replantation attempts (18 percent) and with an overall thumb/finger replantation success rate of 70 percent. The hospital annual replantation volume increased the odds of success (OR, 1.06; 95 percent CI, 1.02 to 1.10). The annual hospital volume of three replantations was needed to achieve a success rate of 70 percent. Conclusions: Practice patterns demonstrate that hospitals with higher annual volume have greater success. Identifying high-volume centers and regionalization of digit replantation should be considered a priority. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.