Predicting emergency department visits among children with asthma in two academic medical systems.

Objective: To develop and validate a predictive algorithm that identifies pediatric patients at risk of asthma-related emergencies, and to test whether algorithm performance can be improved in an external site via local retraining.Methods: In a retrospective cohort at the first site, data from 26 008 patients with asthma aged 2-18 years (2012-2017) were used to develop a lasso-regularized logistic regression model predicting emergency department visits for asthma within one year of a primary care encounter, known as the Asthma Emergency Risk (AER) score. Internal validation was conducted on 8634 patient encounters from 2018. External validation of the AER score was conducted using 1313 pediatric patient encounters from a second site during 2018. The AER score components were then reweighted using logistic regression using data from the second site to improve local model performance. Prediction intervals (PI) were constructed via 10 000 bootstrapped samples.Results: At the first site, the AER score had a cross-validated area under the receiver operating characteristic curve (AUROC) of 0.768 (95% PI: 0.745-0.790) during model training and an AUROC of 0.769 in the 2018 internal validation dataset (p = 0.959). When applied without modification to the second site, the AER score had an AUROC of 0.684 (95% PI: 0.624-0.742). After local refitting, the cross-validated AUROC improved to 0.737 (95% PI: 0.676-0.794; p = 0.037 as compared to initial AUROC).Conclusions: The AER score demonstrated strong internal validity, but external validity was dependent on reweighting model components to reflect local data characteristics at the external site.

Outpatient Follow-up Care After Hospital Discharge of Children With Complex Chronic Conditions at a Rural Tertiary Care Hospital.

OBJECTIVE: To determine factors associated with completion of recommended outpatient follow-up visits in children with complex chronic conditions (CCCs) following hospital discharge. METHODS: We retrospectively identified children aged 1 to 17 years diagnosed with a CCC who were discharged from our rural tertiary care children's hospital between 2017 and 2018 with a diagnosis meeting published CCC criteria. Patients discharged from the neonatal intensive care unit and patients enrolled in a care coordination program for technology-dependent children were excluded. RESULTS: Of 113 eligible patients, 77 (68%) had outpatient follow-up consistent with discharge instructions. Intensive care unit (ICU) admission (P = .020) and prolonged length of stay (P = .004) were associated with decreased likelihood of completing recommended follow-up. CONCLUSIONS: Among children with CCCs who were not already enrolled in a care coordination program, ICU admission was associated with increased risk of not completing recommended outpatient follow-up. This population could be targeted for expanded care coordination efforts.

COVID-19 Deaths in the United States: Shifts in Hot Spots over the Three Phases of the Pandemic and the Spatiotemporally Varying Impact of Pandemic Vulnerability.

The geographic areas most impacted by COVID-19 may not remain static because public health measures/behaviors change dynamically, and the impacts of pandemic vulnerability also may vary geographically and temporally. The nature of the pandemic makes spatiotemporal methods essential to understanding the distribution of COVID-19 deaths and developing interventions. This study examines the spatiotemporal trends in COVID-19 death rates in the United States from March 2020 to May 2021 by performing an emerging hot spot analysis (EHSA). It then investigates the effects of the COVID-19 time-dependent and basic social vulnerability factors on COVID-19 death rates using geographically and temporally weighted regression (GTWR). The EHSA results demonstrate that over the three phases of the pandemic (first wave, second wave, and post-vaccine deployment), hot spots have shifted from densely populated cities and the states with a high percentage of socially vulnerable individuals to the states with relatively relaxed social distancing requirements, and then to the states with low vaccination rates. The GTWR results suggest that local infection and testing rates, social distancing interventions, and other social, environmental, and health risk factors show significant associations with COVID-19 death rates, but these associations vary over time and space. These findings can inform public health planning.

COVID-19: A Vaccine Priority Index Mapping Tool for Rapidly Assessing Priority Populations in North Carolina.

BACKGROUND: The initial limited supply of COVID-19 vaccine in the U.S. presented significant allocation, distribution, and delivery challenges. Information that can assist health officials, hospital administrators and other decision makers with readily identifying who and where to target vaccine resources and efforts can improve public health response. OBJECTIVE: The objective of this project was to develop a publicly available geographical information system (GIS) web mapping tool that would assist North Carolina health officials readily identify high-risk, high priority population groups and facilities in the immunization decision making process. METHODS: Publicly available data were used to identify 14 key health and socio-demographic variables and 5 differing themes (social and economic status; minority status and language; housing situation; at risk population; and health status). Vaccine priority population index (VPI) scores were created by calculating a percentile rank for each variable over each N.C. Census tract. All Census tracts (N = 2,195) values were ranked from lowest to highest (0.0 to 1.0) with a non-zero population and mapped using ArcGIS. RESULTS: The VPI tool was made publicly available (https://enchealth.org/) during the pandemic to readily assist with identifying high risk population priority areas in N.C. for the planning, distribution, and delivery of COVID-19 vaccine. DISCUSSION: While health officials may have benefitted by using the VPI tool during the pandemic, a more formal evaluation process is needed to fully assess its usefulness, functionality, and limitations. CONCLUSION: When considering COVID-19 immunization efforts, the VPI tool can serve as an added component in the decision-making process.

Care Coordination Program for Children With Complex Chronic Conditions Discharged From a Rural Tertiary-Care Academic Medical Center.

OBJECTIVES: Hospital discharge offers an opportunity to initiate coordination of follow-up care, preventing readmissions or emergency department (ED) recidivism. We evaluated how revisits and costs of care varied in a 12-month period between children in a care coordination program at our center (enrolled after hospital discharge with a tracheostomy or on a ventilator) and children with complex chronic condition discharges who were not enrolled. METHODS: Children ages 1 to 17 years were retrospectively included if they had a hospital discharge in 2017 with an International Classification of Diseases, 10th Revision code meeting complex chronic condition criteria or if they were in active follow-up with the care coordination program. Revisits and total costs of care were compared over 2018 for included patients. RESULTS: Seventy patients in the program were compared with 56 patients in the control group. On bivariate analysis, the median combined number of hospitalizations and ED visits in 2018 was lower among program participants (0 vs 1; P = .033), and program participation was associated with lower median total costs of care in 2018 ($700 vs $3200; P = .024). On multivariable analysis, care coordination program participation was associated with 59% fewer hospitalizations in 2018 (incidence rate ratio: 0.41; 95% confidence interval: 0.23 to 0.75; P = .004) but was not significantly associated with reduced ED visits or costs. CONCLUSIONS: The care coordination program is a robust service spanning the continuum of patient care. We found program participation to be associated with reduced rehospitalization, which is an important driver of costs for children with medical complexity.