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The prolonged COVID-19 pandemic has created unique and complex challenges in operational and capacity planning for pediatric emergency departments, as initial low pediatric patient volumes gave way to unpredictable patient surges during Delta and Omicron variants. Compounded by widespread hospital supply chain issues, staffing shortages due to infection and attrition, and a concurrent pediatric mental health crisis, the surges have pushed pediatric emergency department leaders to re-examine traditionally defined clinical processes, and adopt innovative operational strategies. This study describes the strategic surge response and lessons learned by 3 major freestanding academic pediatric emergency departments in the western United States to help inform current and future pediatric pandemic preparedness.
OBJECTIVES/SPECIFIC AIMS: To build a multisite de-identified database of female adolescents, aged 12–21 years (January 2011–December 2012), and their subsequent offspring through 24 months of age from electronic health records (EHRs) provided by participating Community Health. METHODS/STUDY POPULATION: We created a community-academic partnership that included New York City Community Health Centers (n=4) and Hospitals (n=4), The Rockefeller University, The Sackler Institute for Nutrition Science and Clinical Directors Network (CDN). We used the Community-Engaged Research Navigation model to establish a multisite de-identified database extracted from EHRs of female adolescents aged 12–21 years (January 2011–December 2012) and their offspring through 24 months of age. These patients received their primary care between 2011 and 2015. Clinical data were used to explore possible associations among specific measures. We focused on the preconception, prenatal, postnatal periods, including pediatric visits up to 24 months of age. RESULTS/ANTICIPATED RESULTS: The analysis included all female adolescents (n=122,556) and a subset of pregnant adolescents with offspring data available (n=2917). Patients were mostly from the Bronx; 43% of all adolescent females were overweight (22%) or obese (21%) and showed higher systolic and diastolic blood pressure, blood glucose levels, hemoglobin A1c, total cholesterol, and triglycerides levels compared with normal-weight adolescent females (p<0.05). This analysis was also performed looking at the nonpregnant females and the pregnant females separately. Overall, the pregnant females were older (mean age=18.3) compared with the nonpregnant females (mean age=16.5), there was a higher percentage of Hispanics among the pregnant females (58%) compared with the nonpregnant females (43.9%). There was a statistically significant association between the BMI status of mothers and infants’ birth weight, with underweight/normal-weight mothers having more low birth weight (LBW) babies and overweight/obese mothers having more large babies. The odds of having a LBW baby was 0.61 (95% CI: 0.41, 0.89) lower in obese compared with normal-weight adolescent mothers. The risk of having a preterm birth before 37 weeks was found to be neutral in obese compared with normal-weight adolescent mothers (OR=0.81, 95% CI: 0.53, 1.25). Preliminary associations are similar to those reported in the published literature. DISCUSSION/SIGNIFICANCE OF IMPACT: This EHR database uses available measures from routine clinical care as a “rapid assay” to explore potential associations, and may be more useful to detect the presence and direction of associations than the magnitude of effects. This partnership has engaged community clinicians, laboratory, and clinical investigators, and funders in study design and analysis, as demonstrated by the collaborative development and testing of hypotheses relevant to service delivery. Furthermore, this research and learning collaborative is examining strategies to enhance clinical workflow and data quality as well as underlying biological mechanisms. The feasibility of scaling-up these methods facilitates studying similar populations in different Health Systems, advancing point-of-care studies of natural history and comparative effectiveness research to identify service gaps, evaluate effective interventions, and enhance clinical and data quality improvement.
Disaster recovery efforts focus on restoring basic needs to survivors, such as food, water, and shelter. However, long after the immediate recovery phase is over, some individuals will continue to experience unmet needs. Ongoing food insecurity has been identified as a post-disaster problem. There is a paucity of information regarding the factors that might place an individual at risk for continued food insecurity post disaster.
Using data from a sample (n=737) of households severely impacted by Hurricane Katrina, we estimated the associations between food insecurity and structural, physical and mental health, and psychosocial factors 5 years after Hurricane Katrina. Logistic regression models were fit and odds ratios (OR) and 95% CI estimated.
Nearly one-quarter of respondents (23%) reported food insecurity 5 years post Katrina. Marital/partner status (OR: 0.7, CI: 0.42, 0.99), self-efficacy (OR: 0.56, CI: 0.37, 0.84), sense of community (OR: 0.7, CI: 0.44, 0.98), and social support (OR: 0.59, CI: 0.39, 0.89) lowered the odds of food insecurity and explained most of the effects of mental health distress on food insecurity. Social support, self-efficacy, and being partnered were protective against food insecurity.
Recovery efforts should focus on fostering social-support networks and increased self-efficacy to improve food insecurity post disaster. (Disaster Med Public Health Preparedness. 2018;12:47–56)
Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster.
We developed a system dynamics computational model that predicts community functioning after a disaster. The computational model outputted the time course of community functioning before, during, and after a disaster, which was used to calculate resistance, recovery, and resilience for all US counties.
The conceptual model explicitly separated resilience from community functioning and identified all key components for each, which were translated into a system dynamics computational model with connections and feedbacks. The components were represented by publicly available measures at the county level. Baseline community functioning, resistance, recovery, and resilience evidenced a range of values and geographic clustering, consistent with hypotheses based on the disaster literature.
The work is transparent, motivates ongoing refinements, and identifies areas for improved measurements. After validation, such a model can be used to identify effective investments to enhance community resilience. (Disaster Med Public Health Preparedness. 2018;12:127–137)