DOI: 10.5061/dryad.q2bvq83gv
title: Data from: Pediatric intensive care unit admissions for COVID-19: insights using state-level data

description: Introduction Intensive care has played a pivotal role during the COVID-19
 pandemic as many patients developed severe pulmonary complications. The
 availability of information in pediatric intensive care (PICUs) remains
 limited. The purpose of this study is to characterize COVID-19 positive
 admissions (CPAs) in the United States and to determine factors that may
 impact those admissions.   Materials and Methods This is a
 retrospective cohort study using data from the COVID-19 dashboard virtual
 pediatric system) containing information regarding respiratory support and
 comorbidities for all CPAs between March and April 2020. The state level
 data contained 13 different factors from population density, comorbid
 conditions and social distancing score. The absolute CPAs count was
 converted to frequency using the state’s population. Univariate and
 multivariate regression analyses were performed to assess the association
 between CPAs frequency and endpoints.   Results A total of 205
 CPAs were reported by 167 PICUs across 48 states. The estimated CPAs
 frequency was 2.8 per million children. A total of 3,235 tests were
 conducted with 6.3% positive tests. Children above 11 years of age
 comprised 69.7% of the total cohort and 35.1% had moderated or severe
 comorbidities. The median duration of a CPA was 4.9 days [1.25-12.00
 days]. Out of the 1,132 total CPA days, 592 [52.2%] were for mechanical
 ventilation. The inpatient mortalities were 3 [1.4%]. Multivariate
 analyses demonstrated an association between CPAs with greater population
 density [beta-coefficient 0.01, p&lt;0.01] and increased percent of
 children receiving the influenza vaccination [beta-coefficient 0.17,
 p=0.01].   Conclusions Inpatient mortality during PICU CPAs is
 relatively low at 1.4%. CPA frequency seems to be impacted by population
 density while characteristics of illness severity appear to be associated
 with ultraviolet index, temperature, and comorbidities such as Type 1
 diabetes. These factors should be included in future studies using
 patient-level data.
description: This study utilized only publicly available, deidentified,
 state-level data. As such, no institutional review board review or
 approval was sought.  
 <i>Endpoint identification and data
 collection</i> The following data was identified
 for collection regarding the CPAs themselves: number, duration, need for
 various ventilatory support measures, severity of comorbidities, and the
 total number of COVID-19 tests conducted. The following data was collected
 regarding US states: pediatric population, state population (pediatric and
 adult) density, air and drinking water quality, average temperature,
 average ultraviolet index, prevalence of pediatric obesity, type 1
 diabetes mellitus (DM) and asthma, the proportion of children who smoke
 cigarettes, received the influenza vaccine, had health insurance, and
 received home health care, race, percent of households with children below
 the poverty line, highest education level of adults in homes with
 children, and the social distancing score by global positional satellite
 data (Supplementary Table 1).   The
 data regarding the CPAs themselves was collected from the publicly
 available COVID-19 dashboard provided by the Virtual Pediatric System
 (VPS), which collects data from several PICUs in the US. COVID-19 data was
 collected from March 14<sup>th</sup> through April
 14<sup>th</sup>, 2020, in order to represent one full month of
 data. Data regarding number of centers, number of tests, and number of
 CPAs was captured in absolute counts. Data regarding CPAs duration was
 collected in days. The respiratory support modalities for which data was
 available were room air (RA), nasal cannula (NC) and for the advanced
 respiratory support modalities (i.e. other than RA and NC) there was
 available data for high flow nasal cannula (HFNC), non-invasive positive
 pressure ventilation (NIPPV), conventional mechanical ventilation (MCV),
 high frequency oscillatory ventilation (HFOV), and extracorporeal membrane
 oxygenation (ECMO), and was captured in duration (days) of their use. Data
 regarding severity of comorbidities is reported in the VPS dashboard and
 the percentage of CPAs with moderate or severe degree of comorbidities was
 collected.   State-wide data for the
 analyses were collected from a variety of sources with the complete list
 of sources provided as Supplementary Material 1. Children’s population
 data and pediatric comorbidity data was obtained from 2018, as these were
 the most recent and comprehensive data available. The sources for these
 other data points were generally US government-based efforts to capture
 state-level data on various medical issues, however, not all states
 reported data for all the endpoints (Supplementary Table 2).
   Endpoints were assigned to the authors for
 collection. One author was responsible for collecting data for each state
 for the variables assigned. Once these data were collected a different
 author, who did not primarily collect data for that specific endpoint,
 verified the numbers for accuracy. Finally, values in the top and bottom
 10<sup>th</sup> percentile were identified and verified by a
 third author.   <i>Statistical
 analyses</i> As the data was collected for each
 state and intended for state-level analyses, and each state has a
 different pediatric population, the absolute numbers of CPAs for each
 state were not directly comparable. Thus, the absolute CPAs count for each
 state was first converted to a frequency of CPAs per 1,000,000 children
 using the specific state’s population. This CPAs frequency was then used
 as the dependent variable in a series of single-independent variable
 linear regressions to determine the univariate association between CPAs
 frequency and the other endpoints. Multivariate regression was conducted
 with CPAs frequency as the dependent variable and with other variables
 entered as independent variables. Forward stepwise regression was utilized
 with the model with greatest R-squared value being used for the
 analyses.   Next, a composite
 endpoint called “percent of PICUs days requiring advanced respiratory
 support” was created. This consisted of the total duration of HFNC, NIPPV,
 MCV, HFOV, and ECMO divided by the total PICUs admission duration. This
 was then modeled similarly to CPAs frequency. Next, a composite outcome
 called “percent of PICU days requiring intubation” was created. This
 consisted of the total duration of MCV and HFOV divided by the total PICU
 admission duration. This, too, was then modeled similarly as CPA
 frequency. Lastly, an endpoint called “PICUs duration per admission” was
 created for each state and consisted of the total CPAs PICUs duration for
 that specific state divided by the number of CPAs reported by that state.
 This was also then modeled similarly to CPA frequency.
   All statistical analyses were done using the
 user-coded, syntax-based interface of SPSS Version 23.0. A p-value of 0.05
 was considered statistically significant. All statistical analyses were
 done at the state-level with state-level data. Analyses were not conducted
 at a patient-level with patient-level data. Any use of the word
 significant here-on in the manuscript refers to “statistically
 significant” unless explicitly specified otherwise.