Lessons learned for preventing health disparities in future pandemics: the role of social vulnerabilities among children diagnosed with severe COVID-19 early in the pandemic.

There are limited pediatric data regarding severe COVID-19 disease. Our study aims to describe the epidemiology and identify risk factors for severe COVID-19 disease in children. This is a retrospective cohort study among children with positive SARS-CoV-2 PCR from March to July 2020 at Children's Hospital Colorado. Risk factors for severe disease were analyzed as defined by hospital admission, respiratory support, or critical care. Univariable and multivariable analyses were conducted. Among 454 patients identified with SARS-CoV-2, 191 (42.1%) were females, median age 11 years. Fifty-five percent of all patients identified as Hispanic compared with 29% among all hospital visits in 2019 (P < 0.0001). In multivariable analyses, age 0-3 months or >20 years [adjusted odds ratio (aOR), 7.85; P < 0.0001 and aOR, 5.1; P = 0.03, respectively], preterm birth history (aOR, 3.7; P = 0.03), comorbidities [including immunocompromise (aOR, 3.5; P = 0.004), gastrointestinal condition (aOR, 2.7; P = 0.009), diabetes (aOR, 6.6; P = 0.04), asthma (aOR, 2.2; P = 0.04)], and specific symptoms at presentation were predictors for admission. Age 0-3 months or >20 years, asthma, gastrointestinal condition, and similar symptoms at presentation were also predictors for respiratory support. Elevated C-reactive protein was associated with the need for critical care with median of 17.7 mg/dL (IQR, 5.3-22.9) versus 1.95 mg/dL (IQR, 0.7-5.5) among patients requiring critical versus no critical care (OR, 1.2; P = 0.02). Extremes of age, comorbid conditions, and elevated CRP are predictors of severe disease in children. Findings from this study can inform pediatric providers and public health officials to tailor clinical management, pandemic planning, and resource allocation.

Greater social vulnerability, younger age, nonwhite race, Hispanic ethnicity, non-English speaking, Medicaid insurance, and milder glaucoma were associated with a higher no-show propensity, which worsened during the COVID-19 pandemic among those subjects who were older and more socially vulnerable. To identify sociodemographic risk factors for a higher likelihood to no-show among glaucoma subjects before and during the COVID-19 pandemic using the no-show propensity factor (NSPF), a novel attendance metric, which improves upon no-show percentages by adjusting for number of visits. We analyzed deidentified demographic, visit attendance, and social risk factor data [social vulnerability index (SVI) and area deprivation index (ADI) scores] of deidentified glaucoma subjects from the Bascom Palmer Glaucoma Repository, computed NSPF, and categorized scores as low, intermediate, or high by the 75th and 90th percentiles for the prepandemic and pandemic periods. We identified predictors of NSPF scores using univariable, multivariable, and logistic regression analyses. Of 15,342 subjects, 11,474, 2238, and 1630 subjects had low, intermediate, and high NSPF scores, respectively, with no-show rates of 9.5%, 39.2%, and 57.8%, respectively. Age (β=-0.039 per decade, P <0.001), Black race (β=0.152, P <0.001), Hispanic ethnicity (β=0.115, P <0.001), Medicaid (β=0.073, P <0.001), Spanish primary language (β=0.076, P <0.001), SVI scores (β=0.047 per 25% increase, P <0.001), ADI ranking (β=0.057 for highest quartile, P <0.001), and baseline moderate (β=-0.046, P <0.001) or severe (β=-0.077, P <0.001) glaucomatous disease were significant predictors of NSPF. Older age [odds ratio (OR) 1.15 per decade, P <0.001], higher overall SVI (OR: 1.09 per 25% increase, P <0.001), Medicare/Tricare insurance (OR: 1.13, P =0.044), and non-English/Spanish primary language (OR: 1.43, P =0.020) were associated with worsening NSPF during the pandemic. Younger age, nonwhite race, Hispanic ethnicity, non-English primary language, Medicaid, milder glaucoma, and residence in vulnerable areas are risk factors for greater propensity to no-show.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that causes coronavirus disease of 2019 (COVID-19) and has been reported in > 98,000 children in the U.S (5% of reported cases) as of early June 2020. Most published literature focuses on adults with COVID-19, but little is understood on the impact of SARS-CoV-2 in children. We created a database for children with COVID-19 at Children’s Hospital Colorado (CHCO), a large tertiary care pediatric hospital, to better understand the epidemiology and clinical outcomes of this disease in children.MethodsWe retrospectively reviewed the medical records of all pediatric and youth patients with positive SARS-CoV-2 PCR test results from March-May 2020. Univariate logistic regression models were used to identify predictors of hospital admission, need for critical care, and need for respiratory support among symptomatic patients, with p-values < 0.05 considered statistically significant.ResultsWe identified 246 patients with SARS-CoV-2 (age range: 17 days-25 years). We noted a Hispanic predominance with 68% of all patients with SARS-CoV-2 identifying as Hispanic or Latino, compared to 29% among all CHCO visits in 2019 (Figure 1). The most common symptoms at presentation were fever, cough, or shortness of breath in 94% of symptomatic patients. Sixty-eight patients (28%) were admitted, of which 7 (10%) required admission to the pediatric intensive care unit (PICU) for symptomatic COVID-19 disease (Figure 2). Age 0–3 months, certain symptoms at presentation, and several types of underlying medical conditions were predictors for both hospital admission and need for respiratory support (Figure 3). Initial and peak C-reactive protein (CRP) values were predictors for PICU admission with median peaks of 24.8mg/dL vs. 2.0mg/dL among PICU vs. non-PICU patients (OR 1.27, p=0.004).Figure 3: Predictors for Admission and Respiratory Support Requirement in CHCO Patients with SARS-CoV-2ConclusionThere is a wide spectrum of illness in children with SARS-CoV-2, ranging from asymptomatic to critical illness. Hispanic ethnicity was disproportionately represented in our cohort, which requires further evaluation. We found that young age, comorbid conditions, and CRP appear to be risk factors for severe disease in children.DisclosuresKelly E. Graff, MD, BioFire Diagnostics, LLC (Grant/Research Support)

Background Identification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients. Methods A retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration. Results Compared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77–0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71–0.78)). Conclusions The COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key points: Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77–0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71–0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.

While in-hospital outcomes and long-term results of craniosynostosis surgery have been described, no large studies have reported on postoperative readmission and emergency department (ED) visits. The authors conducted this study to describe the incidence, associated diagnoses, and risk factors for these encounters within 30 days of craniosynostosis surgery.Using 4 state-level databases, the authors conducted a retrospective cohort study of patients <3 years of age who underwent surgery for craniosynostosis. The primary outcome was any hospital based, acute care (HBAC; ED visit or hospital readmission) within 30 days of discharge. Multivariate logistic regression modeling was used to identify factors associated with this outcome.The final sample included 1120 patients. On average, patients were ages 4.6 months with the majority being male (67.3%) and having Medicaid (52%) or private (48.0%) insurance. Ninety-nine patients (8.8%) had at least 1 HBAC encounter within 30 days and 13 patients (1.2%) had 2 or more. The majority of encounters were managed in the ED without hospital admission (56.6%). In univariate analysis, age, race, insurance status, and initial length of stay significantly differed between the HBAC and non-HBAC groups. In multivariate analysis, only African-American race (adjusted odds ratio [AOR] = 5.98 [1.49-23.94]) and Hispanic ethnicity (AOR = 5.31 [1.88-14.97]) were associated with more frequent HBAC encounters.Nearly 10% of patients with craniosynostosis require HBAC postoperatively with ED visits accounting for the majority of these encounters. Race is independently associated with HBAC, the cause of which is unknown and will be the focus of future research.

Disparities in mortality after abdominal aortic aneurysm repair are linked to insurance status

Decision letter: Long COVID in cancer patients: preponderance of symptoms in majority of patients over long time period

Prognostic Significance of Hemoglobin Levels in Patients With Heart Failure

Factors associated with hospital admission for proximal humerus fracture

The Impact of Federal Poverty Level on the Association between Insurance Status and Overall Survival among Adults with Cancer

To be able to predict, at the time of triage, whether a need for hospital admission exists for emergency department (ED) patients may constitute useful information that could contribute to systemwide hospital changes designed to improve ED throughput. The objective of this study was to develop and validate a predictive model to assess whether a patient is likely to require inpatient admission at the time of ED triage, using routine hospital administrative data. Data collected at the time of triage by nurses from patients who visited the ED in 2007 and 2008 were extracted from hospital administrative databases. Variables included were demographics (age, sex, and ethnic group), ED visit or hospital admission in the preceding 3 months, arrival mode, patient acuity category (PAC) of the ED visit, and coexisting chronic diseases (diabetes, hypertension, and dyslipidemia). Chi-square tests were used to study the association between the selected possible risk factors and the need for hospital admission. Logistic regression was applied to develop the prediction model. Data were split for derivation (60%) and validation (40%). Receiver operating characteristic curves and goodness-of-fit tests were applied to the validation data set to evaluate the model. Of 317,581 ED patient visits, 30.2% resulted in immediate hospital admission. In the developed predictive model, age, PAC status, and arrival mode were most predictive of the need for immediate hospital inpatient admission. The c-statistic of the receiver operating characteristic (ROC) curve was 0.849 (95% confidence interval [CI] = 0.847 to 0.851). The goodness-of-fit test showed that the predicted patients' admission risks fit the patients' actual admission status well. A model for predicting the risk of immediate hospital admission at triage for all-cause ED patients was developed and validated using routinely collected hospital data. Early prediction of the need for hospital admission at the time of triage may help identify patients deserving of early admission planning and resource allocation and thus potentially reduce ED overcrowding.

Socioeconomic Risk Factors for Poor Outcomes of Developmental Dysplasia of the Hip

Payor Type Is Associated with Increased Rates of Reoperation and Healthcare Utilization following Rotator Cuff Repair: A National Database Study.

BackgroundHospital overcrowding and operating above capacity have occurred frequently throughout the COVID-19 pandemic. Both phenomena can lead to worsened patient outcomes; thus, it is imperative to find solutions that tackle both. Our goal was to create a treatment protocol for a subset of patients with mild to moderate COVID-19 infection that would combat inpatient overcrowding by diverting these patients to an emergency department (ED) observation unit (EDOU). This protocol was based on dynamic treatment guidelines and required regular updates to allow our team to provide the most up-to-date care throughout the pandemic.MethodsThis study is a retrospective chart review of all adult patients seen at two large suburban EDs for symptoms related to COVID-19 from April 2020 to January 2022. We subsequently identified adult patients who met the criteria for treatment with our COVID-19 protocol and were placed in our observation unit. These patients were identified using a flag for the protocol order set within our electronic medical record. Primary outcomes include the need for hospital admission, bounce back rate, and death rate.ResultsA total of 2,417 patients were treated in our ED observation units using our COVID-19 protocol. Our study population was evenly divided by gender, while a majority self-identified as white (76%). Five hundred two patients (20.8%) required admission to the hospital, and of these, 55 (11%) patients required intensive care unit (ICU) level of care. A total of 27 (1.1%) patients died. No deaths occurred for patients that remained within our ED observation units. Bounce back rates at the 48-hour, 72-hour, and seven-day marks were 3.6%, 4.6%, and 7.9%, respectively. Finally, we calculated a total of 284 inpatient days saved with the implementation of our protocol.ConclusionThis study shows that our newly created protocol is effective in that it reduces the need for inpatient hospital admissions and results in low bounce back rates. Protocol-driven care in ED observation units can be a powerful tool against hospital overcrowding. Creating such protocols offers opportunities for hospital systems to provide efficient care at a significant cost savings without sacrificing quality of care. Our COVID-19 treatment protocol can be replicated by other hospital systems within their own ED observation units should any future similar outbreaks occur.

Patients with Medicaid or self-pay insurance status are more likely to present to the emergency department (ED) for uncomplicated acute rhinosinusitis (ARS). Our aim was to determine if this pattern holds true for both pediatric and adult visits. Cross-sectional study using the 2005 to 2012 National Hospital Ambulatory Medical Care Surveys and National Ambulatory Medical Care Surveys. We included all visits with International Classification of Diseases, Ninth Revision codes for ARS and without codes for ARS complications. We tested for associations between insurance type and presentation to an ED versus a primary care physician (PCP), stratifying children versus adults. We used univariate and multivariable logistic regression modeling, controlling for clinical and demographic characteristics for analysis. There were 51,579,977 uncomplicated ARS visits to PCPs (48,213,335 visits) and EDs (3,366,642 visits). Medicaid and uninsured patients were under-represented for ARS visits. Medicaid insurance was significantly associated with ED presentation for ARS for both children (adjusted odds ratio [OR] = 7.0, P < 0.001) and adults (adjusted OR = 6.8, P < 0.001). Children with ARS and self-pay insurance status were much more likely to present to the ED (adjusted OR = 48.8, P < 0.001) than adults (adjusted OR = 5.2, P < 0.001); this difference between children and adults with self-pay insurance was significant (P = 0.001). With respect to absolute numbers of visits, patients with Medicaid or no insurance use less care overall for uncomplicated ARS than do privately insured patients. Medicaid is associated with ED presentation for ARS for pediatric and adult visits. Self-pay insurance status is strongly associated with ED presentation for adult and pediatric visits, and is significantly more common for children. These results suggest limitations in primary care access for uncomplicated ARS based on insurance status, particularly for uninsured pediatric patients. 4. Laryngoscope, 128:299-303, 2018.