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Background: Risk-prediction indices are one category of the many tools implemented to guide efforts to decrease readmissions. However, using fied models to predict a complex process can prove challenging. In addition, no risk-prediction index has been developed for patients undergoing colorectal surgery. Therefore, we evaluated the performance of a widely utilized simplified index developed at the hospital level - LACE (length of stay, acute admission, Charlson comorbidity index score, and emergency department visits) and developed and evaluated a novel index in predicting readmissions in this patient population., Methods: Using a retrospective split-sample cohort, patients discharged after colorectal surgery were identified within the inpatient databases of the Healthcare Cost and Utilization Project for the states of New York, California, and Florida (2006-2014). The primary outcome was death or readmission within 30 days after discharge. Multivariable logistic regression models incorporated patient comorbidities, postoperative complications, and hospitalization details, and were evaluated using the C statistic., Results: A total of 440,742 patients met eligibility criteria. The rate of death or readmission within 30 days after discharge was 14.0% (n = 61,757). When applied to surgical patients, the LACE index demonstrated a poor model fit (C = 0.631). The model fit improved significantly-but remained poor (C = 0.654; P < .001)-with the addition of the following variables, which are known to be associated with readmission after colorectal surgery: age, indication for surgery, and creation of a new ostomy. A novel, simplified model also yielded a poor model fit (C = 0.660)., Conclusion: Postdischarge death or readmission after colorectal surgery is not accurately modeled using existing, modified, or novel simplified risk prediction models. Payers and providers must ensure that quality improvement efforts applying simplified models to complex processes, such as readmissions following colorectal surgery, may not be appropriate, and that models reflect the relevant patient population., (Published by Elsevier Inc.)

The National Center for Children's Vision and Eye Health (NCCVEH) at Prevent Blindness partnered with the National Association of School Nurses (NASN) to provide guidance for school nurses responsible for screening the vision of preschool and K-12 students. Goals of this national partnership are to (1) standardize approaches to vision health, (2) facilitate follow up to eye care for students who do not pass vision screening, (3) provide family/caregiver friendly educational information, and (4) consult with leading pediatric eye care experts to promote evidence-based best practices. The NCCVEH/NASN partnership created a Vision and Eye Health page on the NASN website ( https://www.nasn.org/nasn-resources/practice-topics/vision-health ). This resource is organized according to the 12 Components of a Strong Vision Health System of Care. The 12 components emerged as the NCCVEH considered vision screening from a systems perspective. This systems perspective addresses key activities along the entire spectrum of care that supports a child's vision health-beginning with parent/caregiver education and ending with an annual evaluation of the school's vision health system. Each of these 12 components will be described in 4 installments of NASN School Nurse in 2019. This installment describes the first two components: Family Education and a Comprehensive Communication/Approval Process.

Data on the national and state levels is often used to inform policy decisions and strategies designed to reduce racial disparities in obesity. Obesity-related health outcomes are realized on the individual level, and policies based on state and national-level data may be inappropriate due to the variations in health outcomes within and between states. To examine county-level variation of obesity within states, we use a small-area analysis technique to fill the void for county-level obesity data by race. Five years of Behavioral Risk Factor Surveillance System data are used to estimate the prevalence of obesity by county, both overall and race-stratified. A modified weighting system is used based on demographics at the county level using 2010 census data. We fit a multilevel reweighted regression model to obtain county-level prevalence estimates by race. We compare the distribution of prevalence estimates of non-Hispanic Blacks to non-Hispanic Whites. For 25 of the 26 states included in our analysis there is a statistically significant difference between within-state county-level average obesity prevalence rates for non-Hispanic Whites and non-Hispanic Blacks. This study provides information needed to target disparities interventions and resources to the local areas with greatest need; it also identifies the necessity of doing so.