Pediatric Anesthesiology

Prevalence of COVID-19 and Risk Factors for Infection Among Pediatric Anesthesia Patients: A Report From the PEACOC Research Network

Kato, Meredith A. MD^*; Zurakowski, David PhD^†; Adams, AmandaMarie BS^*; Soelberg, Julie CRNA/PhD^*; Staffa, Steven J. MS^†; Bradford, Victoria A. MD^‡; Efune, Proshad N. MD^§; Rodgers McCormick, Megan E. DO^‖; Grivoyannis, Anastasia D. MD^¶; Rossmann Beel, Elizabeth MD^#; Correll, Lynnie R. MD/PhD^**; Cheon, Eric C. MD^††; Tan, Gee Mei MD^‡‡; Thomas, James J. MD^‡‡; Fernandez, Allison M. MD/MBA^§§; Teng, Howard C. MD^‖‖; Khanna, Neha MBA^¶¶; Raman, Vidya T. MD^¶¶; Brzenski, Alyssa B. MD^##; Frugoni, Brian J. MD^##; Sheth, Michelle M. MD^***; Rugnath, Rahil M. BS^***; Meier, Petra M. MD^†

Author Information

From the ^*Department of Anesthesiology & Perioperative Medicine, Doernbecher Children’s Hospital, Oregon Health & Science University, Portland, Oregon;

^†Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts;

^‡Department of Anesthesiology, Albert B. Chandler Hospital, University of Kentucky, Lexington Kentucky;

^§Department of Anesthesiology and Pain Management, University of Texas Southwestern Medical Center, Dallas, Texas;

^‖Department of Anesthesiology, Rainbow Babies and Children’s Hospital, University Hospitals Cleveland Medical Center, Cleveland, Ohio;

^¶Department of Anesthesia & Critical Care Medicine, Johns Hopkins Hospital, John Hopkins University School of Medicine, Baltimore, Maryland;

^#Department of Pediatric Anesthesiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, Texas;

^**Department of Anesthesiology and Perioperative Medicine, Golisano Children’s Hospital, University of Rochester, Rochester, New York;

^††Department of Pediatric Anesthesiology, Ann & Robert H Lurie Children’s Hospital of Chicago, Chicago, Illinois;

^‡‡Department of Pediatric Anesthesiology, Children’s Hospital Colorado, Aurora, Colorado;

^§§Department of Anesthesia, Pain and Perioperative Medicine, Johns Hopkins All Children’s Hospital, St Petersburg, Florida;

^‖‖Department of Anesthesiology and Critical Care Medicine, Memorial Sloan Kettering Cancer Center, New York, New York;

^¶¶Department of Anesthesiology and Pain Medicine, Nationwide Children’s Hospital, Ohio State College of Medicine, Columbus, Ohio;

^##Department of Anesthesiology, Rady Children’s Hospital, University of California San Diego Medical Center, San Diego, California; and

^***Department of Anesthesiology, Children’s Hospital of Mississippi, University of Mississippi Medical Center, Jackson, Mississippi.

Accepted for publication August 2, 2022.

Funding: None.

The authors declare no conflicts of interest.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (www.anesthesia-analgesia.org).

Reprints will not be available from the authors.

Address correspondence to Meredith A. Kato, MD, Department of Anesthesiology & Perioperative Medicine, Doernbecher Children’s Hospital, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, BTE2, Portland, OR 97239. Address e-mail to [email protected].

Anesthesia & Analgesia ():10.1213/ANE.0000000000006227, October 21, 2022. | DOI: 10.1213/ANE.0000000000006227

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Abstract

KEY POINTS

Question: What is the prevalence of coronavirus disease 2019 (COVID-19) among pediatric patients presenting for anesthesia care between March 29, 2020 and June 30, 2020, and what are risk factors for infection?
Findings: We found that the prevalence of COVID-19 was low among pediatric patients presenting for anesthesia. Risk factors for infection included Black/African American race, Hispanic ethnicity, American Society of Anesthesiologists (ASA) physical status III or above, overweight and obese body mass index (BMI), orthopedic cases, abdominal cases, and emergency cases.
Conclusions: Rates of COVID-19 were very low in this early phase of the pandemic. Nine independent risk factors for infection were identified‚ and this is the first time surgical case type or emergency case status has been associated with COVID-19.

In late 2019, a new viral illness causing severe disease was putting stress on the medical systems in Wuhan, China.^¹ By January 2020, cases were detected in 18 countries outside of China, including the United States, and the World Health Organization (WHO) declared the outbreak a public health emergency of international concern, the organization’s highest level of alarm.^² The Coronaviridae Study Group of the International Committee on Taxonomy of Viruses named the new virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)^³ in March 2020 and by the end of the month, there were cases in all 50 states.^⁴ The WHO named the disease caused by this novel virus coronavirus disease 2019 (COVID-19).

Centers for Medicare and Medicaid Services issued a statement on March 18, 2020 to delay indefinitely elective surgical procedures^⁵ to preserve limited personal protective equipment (PPE) and prepare for a surge of patients. Only trauma surgery, cases addressing threat to life, limb or eyesight, urgent cancer surgery, and organ transplants were allowed to proceed.^⁶ Most hospitals in the country developed policies to test all surgical patients for COVID-19 to assess both clinical risk and risk to health care workers in aerosol generating procedures, such as intubation.

During these early days of the pandemic, the risk of COVID-19 to children was unclear. Expedient politicians minimized the risk to children, alarming most health care professionals.^⁷ Testing was limited, making assessment of risk to the pediatric population difficult. However, children undergoing surgery were tested. This offered a unique dataset of results in children who were tested regardless of symptomatology.

PEdiatric Anesthesia COvid-19 Collaborative (PEACOC) is a perioperative pediatric COVID-19 outcomes research network of academic centers. In the present study, we aim to calculate the prevalence of COVID-19 among children presenting for anesthesia services and to calculate positive rates in publicly available population data. We then propose to look at demographic and clinical features associated with positive test results in the pediatric cohort. Our aim is to quantify the infection burden on our population, compare it to publicly available population data and identify risk factors for infection.

METHODS

Ethics

Institutional review board (IRB) approval or exemption was achieved locally at each participating medical center using the Oregon Health & Science University protocol as a standard. The requirement for written informed consent was waived by the IRB at all centers. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were followed.^⁸ Outcomes were defined and established a priori at initiation of the study design. Statistical analysis and reporting adhered to the SAMPL Guidelines. Individual institutions (Supplemental Digital Content 1, Table 1, https://links.lww.com/AA/E50) began contributing data following approval by local institutional review boards or equivalents. Study data were collected and managed at the Doernbecher Children’s Hospital, Oregon Health & Science University. Individual institutions were not identifiable and were represented using an institution code only.

Population

Anesthesia records were extracted from electronic medical records using a standardized protocol. Anesthesia encounters for children ages 28 days to 18 years from March 29 to June 30, 2020 were included. All encounters with testing were included, even if the procedure was canceled. Both polymerase chain reaction and antigen detection technology were accepted. All patients were tested in the context of their hospital encounter within 72 hours of an anesthetic. For patients admitted to the centers, through the emergency room (ER) for instance, the test was administered as part of the preoperative work-up. If a patient was transferred from an ER of another hospital with a documented test result, the COVID test was not repeated so long as the test was within 72 hours of the procedure. If the surgical procedure needed to be performed and there was no test result, patients were tested during or just after their anesthetic and they were managed as a COVID-positive patient until definitive results were available. All centers had a protocol in place for universal testing. Data were collected from a given center starting on the date universal testing of anesthesia patients began at that center. Anesthesia encounter dates were coded by week, with each week representing a prevalence point. Data collected included state of residence, age, sex, race, ethnicity, American Society of Anesthesiologists (ASA) physical status, procedure, diagnosis, weight, height, and ambulatory or emergency procedure status. Emergency status was determined from the anesthesia billing designation of ASA physical status, ASA physical status IIIE for instance. For centers where this billing data were not available, emergency status was determined by the urgency of the case booking based on the severity of the disease process and the speed of predicted deterioration.

Statistical Analysis

Data were rigorously validated, cleaned, and coded according to a standard codebook. Body mass index (BMI), expressed in kg/m², was calculated from height and weight for children >2 years of age, then converted to z scores based on age and sex using the Centers for Disease Control and Prevention (CDC) growth reference and the Zanthro add on package for Stata software^⁹ (version 16.1, StataCorp LLC, College Station, TX). The z scores were used to generate BMI percentiles and children were categorized per the CDC definitions as underweight (<5th percentile), normal weight (5–85th percentile), overweight (85–95th percentile), and obese (>95th percentile). Diagnoses were queried for diseases in 4 binary categories: circulatory, including diseases of the heart and circulatory system; respiratory, including respiratory diseases; injury, including injury and trauma; and neoplastic, representing cancers and neoplasms. This was accomplished using groupers adapted from Clinical Classifications Software Refined from the Healthcare Cost and Utilization Project (HCUP) of the federal Agency for Healthcare and Research and Quality.^¹⁰ Procedures were categorized manually using groupers derived and created from the body of cases done by participating centers. Chest cases included cardiac and respiratory procedures; abdomen cases included general abdominal surgery, urology, and gynecology; head and neck included otolaryngology, oral surgery, dental, and ophthalmology; orthopedics included surgery on joints, tendons, bones, and spine for scoliosis; neurosurgery included intracranial procedures, vagal nerve stimulators, spine for unstable cervical spine, cord, or disk. Sedation for radiology included sedations for computed tomography, magnetic resonance imaging, nuclear medicine, positron emission tomography. Other included procedures in soft tissue, multisite, echocardiography, central venous access, lumbar puncture, and bone marrow biopsy. Procedures performed in interventional radiology were classified according to the nature of the procedure and the anatomy in question. For instance, a cecostomy tube exchange was considered an abdominal procedure, whereas a thoracentesis was considered a chest procedure. Geographic analysis utilized regions defined by the United States Census.^¹¹

Prevalence of COVID-19 among anesthesia patients was calculated as a straight ratio: number of positive cases divided by the total tested. Incident cases of COVID-19 infections were collected from publicly available data published by the CDC.^¹² These were expressed as a ratio of positive cases divided by total tests. The CDC data tracker was constantly updated. For consistency, all data were therefore downloaded on a single day, October 18, 2021.

Weekly positive test rates were plotted separately for the study and general populations and trends over the study period were assessed using the Cochran Armitage test for trend.^¹³ Case-level data were presented for positive and negative cases. All categorical variables were presented as frequencies and percentages. The χ² test or Fisher exact test were implemented for univariate comparisons between positive and negative cases. Fisher exact test was implemented where observed counts for a given variable were <10 within a given category. In this exploratory analysis, all variables were included in a multivariable logistic regression model for determining independent risk factors of a positive test, with results reported as adjusted odds ratios (OR), 95% confidence intervals (CI), and P values.^¹⁴ A sensitivity analysis was implemented while excluding patients with missing data on race. A forest plot was constructed for significant multivariable COVID-19 risk factors in exploratory analysis. A 2-tailed P < .05 was used as the criterion for statistical significance in this exploratory analysis. Stata software (version 16.1, StataCorp LLC, College Station, TX) was utilized for statistical analyses. Based on the number of positive test events and nonevents among our pediatric study population, the study had 80% power to detect an OR of 1.38 and 90% power to detect an OR of 1.44 using logistic regression with a 2-tailed α level of .05 (PASS software version 15, NCSS LLC, Kaysville, UT).

RESULTS

Prevalence Data of Pediatric Anesthesia Patients Versus General Population

A total of 33,357 anesthesia encounters met inclusion and exclusion criteria. Thirty-seven test results were inconclusive and therefore excluded, leaving 33,320 for analysis (Table 1). Patients represented 12 centers, 44 states, and the District of Columbia. There were 116 international patients. The prevalence among pediatric anesthesia patients over the entire study period was 0.79%. Publicly available population data from the CDC demonstrated a significant downward trend from 18.8% to 7% over the study period, whereas the pediatric anesthesia data were comparatively flat going from 1% to 1.7% (Figure 1A, B).

Table 1. - Demographics and Patient Characteristics

Variable	All cases (N = 33,320)
Age, n (%)
Infant <1 y (28–364 d)	3333 (10)
Toddler 1–4 y	10,366 (31.1)
School age 5–11 y	10,547 (31.7)
Adolescent 12+ y	9074 (27.2)
Sex, n (%)
Female	14,446 (43.4)
Male	18,868 (56.6)
Other or unknown	6 (0.02)
Race, n (%)
American Indian or Alaska Native	85 (0.3)
Asian	1089 (3.3)
Black or African American	3232 (9.7)
Native Hawaiian or Pacific Islander	56 (0.2)
White	17,921 (53.8)
Multiracial	1168 (3.5)
Unknown/missing	9769 (29.3)
Ethnicity, n (%)
Hispanic	8875 (26.6)
Not Hispanic	23,306 (70)
Unknown	1139 (3.4)
ASA physical status, n (%)	n = 30,062
I/II	21,356 (71)
III or higher	8706 (29)
Procedure type, n (%)
Chest	1501 (4.5)
Abdomen	7772 (23.3)
Head and neck	10,409 (31.2)
Orthopedics	3409 (10.2)
Neurosurgery	731 (2.2)
Sedation for radiology	2834 (8.5)
Other	6663 (20)
BMI, n (%)	n = 23,637
Underweight	1587 (6.7)
Normal weight	13,716 (58)
Overweight	3603 (15.2)
Obese	4731 (20)
Ambulatory status, n (%)	24,195 (72.6)
Emergency status, n (%)	2149/30,747 (7)
Circulatory diseases, n (%)	4774 (14.3)
Respiratory diseases, n (%)	7165 (21.5)
Injury or trauma, n (%)	7336 (22)
Neoplastic diseases, n (%)	4486 (13.5)
Region, n (%)
Midwest	6053 (18.2)
Northeast	4680 (14.1)
South	12,203 (36.6)
West	10,384 (31.2)

Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index

Figure 1.:
Trend in COVID-19–positive cases among pediatric anesthesia patients and the general population data from the CDC data. A, COVID-19–positive test results in pediatric anesthesia patients. Pediatric rates were relatively flat over the study period, going from 1% to 0.5% the week of May 10th and rising to 1.7%. B, COVID-19–positive test results in the general population. Publicly available population data from the CDC are plotted. The general population data began at 18.8%, reached a nadir at 4.2% the week of June 7th, and increased to 7% by study end. C, COVID-19–positive test rates showing regional variation. The Northeast had the highest rates during the beginning of the pandemic and decreased to zero by study end. In contrast, the South saw a steep rise in cases in the last 2 weeks of the study. The Midwest and West were relatively flat throughout. D, Ratio of cases performed under emergency status. Proportion of emergency cases trended downward from 22% to 5% over the study period. This trend was statistically significant (P < .001) and was largely driven by the restart of elective case schedules. CDC indicates Centers for Disease Control and Prevention; COVID-19, coronavirus 2019.

Regional Variation in Positive COVID-19 Test Rates

The rates of positive cases varied by region (Figure 1C). The West and Midwest were relatively flat. The Northeast had a steep decrease in the early weeks, starting at 5.6% and tapering to zero. In contrast, the South started at 1.4%, stayed flat during the study period, and showed a steep increase to 3% by the end of the study period.

Emergency Cases

The proportion of cases classified by emergent case status decreased over the course of the study (Figure 1D). This downward trend was statistically significant going from a peak of 22% in the week beginning April 5, 2020‚ to a steady 5% during the last 5 weeks of the study period (P < .001).

Risk Factors for COVID-19

On univariate analysis, race, ethnicity, ASA, procedure type, BMI category, ambulatory status, emergency status, circulatory disease, respiratory disease, and region were all associated with positive status (Table 2). In an exploratory multivariable logistic regression model including all variables, Black/African American race, Hispanic ethnicity, ASA physical status III or above, overweight and obese BMI, orthopedic cases, abdominal cases, emergency cases, absence of injury and trauma, and West region were independently associated with positivity (P < .05; Table 3, Figure 2). In a sensitivity analysis using multivariable logistic regression excluding patients with missing or unknown race, significantly higher adjusted odds of COVID-19 infection was found for Black/African American race as compared to White race (adjusted OR = 2.96; 95% CI = 1.58–5.55; P = .001).

Table 2. - Exploratory Comparison of COVID-19–Positive Versus Negative Cases, Univariate Analysis

Variable	Positive cases (n = 265)	Negative cases (n = 33,055)	P
Age, n (%)
Infant <1 y (28–364 d)	24 (9.1)	3309 (10)	.277
Toddler 1–4 y	82 (30.9)	10,284 (31.1)
School age 5–11 y	73 (27.6)	10,474 (31.7)
Adolescent 12 + y	86 (32.5)	8988 (27.2)
Sex, n (%)
Female	123 (46.4)	14,323 (43.3)	.588
Male	142 (53.6)	18,726 (56.7)
Other or unknown	0 (0)	6 (0.02)
Race, n (%)
American Indian or Alaska Native	0 (0)	85 (0.3)	<.001^a
Asian	7 (2.6)	1082 (3.3)
Black or African American	32 (12.1)	3200 (9.7)
Native Hawaiian or other Pacific Islander	1 (0.4)	55 (0.2)
White	105 (39.6)	17,816 (53.9)
Multiracial	7 (2.6)	1161 (3.5)
Unknown/missing	113 (42.6)	9656 (29.1)
Ethnicity, n (%)
Hispanic	113 (42.6)	8762 (26.5)	<.001^a
Not Hispanic	142 (53.6)	23,164 (70.1)
Unknown	10 (3.8)	1129 (3.4)
ASA physical status, n (%)	n = 165	n = 29,897
I/II	93 (56.4)	21,263 (71.1)	<.001^a
III or higher	72 (43.6)	8634 (28.9)	<.001^a
Procedure type, n (%)
Chest	7 (2.6)	1494 (4.5)	.001^a
Abdomen	67 (25.3)	7705 (23.3)
Head and neck	62 (23.4)	10,347 (31.3)
Orthopedics	29 (10.9)	3380 (10.2)
Neurosurgery	4 (1.5)	727 (2.2)
Sedation for radiology	17 (6.4)	2817 (8.5)
Other	79 (29.8)	6584 (19.9)
BMI category, n (%)	n = 157	n = 23,480
Underweight	13 (8.3)	1574 (6.7)	.009^a
Normal weight	71 (45.2)	13,645 (58.1)
Overweight	28 (17.8)	3575 (15.2)
Obese	45 (28.7)	4686 (20)
Ambulatory status, n (%)	170 (64.2)	24,025 (72.7)	.002^a
Emergency status, n (%)	32/256 (12.5)	2117/30,491 (6.9)	<.001^a
Circulatory diseases, n (%)	24 (9.1)	4750 (14.4)	.014^a
Respiratory diseases, n (%)	40 (15.1)	7125 (21.6)	.011^a
Injury or trauma, n (%)	47 (17.7)	7289 (22.1)	.091
Neoplastic diseases, n (%)	45 (17)	4441 (13.4)	.092
Region, n (%)
Midwest	34 (12.8)	6019 (18.2)	<.001^a
Northeast	31 (11.7)	4649 (14.1)
South	134 (50.6)	12,069 (36.5)
West	66 (24.9)	10,318 (31.2)

P values were calculated using the χ² test or Fisher exact test in the univariate analysis. Fisher exact test was implemented for variables with counts <10.

Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index; COVID-19, coronavirus disease 2019.

^aStatistically significant.

Table 3. - Exploratory Multivariable Logistic Regression of Positive COVID-19 Test

Covariate	Adjusted odds ratio (95% CI)	P
Age
Infant <1 y (28–364 d)
Toddler 1–4 y	1.15 (0.68–1.96)	.597
School age 5–11 y	Reference
Adolescent 12+ y	1.18 (0.76–1.83)	.45
Sex
Female	1.1 (0.75–1.61)	.628
Male	Reference
Race
American Indian or Alaska Native
Asian	0.45 (0.06–3.28)	.429
Black or African American	2.61 (1.43–4.78)	.002^a
Native Hawaiian or other Pacific Islander
White	Reference
Multiracial	1.05 (0.31–3.5)	.943
Unknown/missing	1.13 (0.64–1.99)	.672
Ethnicity
Hispanic	2.85 (1.62–5.01)	<.001^a
Not Hispanic	Reference	.
ASA physical status
I/II	Reference
III or higher	1.89 (1.25–2.85)	.003^a
Procedure type
Chest	0.33 (0.04–2.63)	.294
Abdomen	1.95 (1.06–3.57)	.031^a
Head and neck	Reference
Orthopedics	2.82 (1.41–5.63)	.003^a
Neurosurgery	0.79 (0.18–3.56)	.761
Sedation for radiology	0.68 (0.23–2.05)	.497
Other	1.61 (0.85–3.06)	.148
BMI category
Underweight	1.81 (0.9–3.64)	.095
Normal weight	Reference
Overweight	1.93 (1.17–3.19)	.011^a
Obese	1.86 (1.17–2.97)	.009^a
Ambulatory	0.7 (0.46–1.09)	.117
Emergent	2.12 (1.19–3.77)	.011^a
Circulatory diseases	0.67 (0.32–1.37)	.273
Respiratory diseases	1.1 (0.66–1.84)	.706
Injury and trauma	0.55 (0.33–0.92)	.022^a
Neoplastic diseases	1.41 (0.83–2.39)	.206
Region
Midwest	Reference
Northeast	2.5 (0.94–6.68)	.067
South	1.82 (0.8–4.13)	.15
West	2.47 (1.08–5.69)	.033^a

In this exploratory analysis, all variables were included in the multivariable logistic regression model. Odds ratios could not be estimated for categories with a very small number of events.

Abbreviations: ASA, American Society of Anesthesiologists; BMI, body mass index; COVID-19, coronavirus disease 2019.

^aStatistically significant.

Figure 2.:
Forest plots depicting 9 significant independent risk factors for positive COVID-19 test. In exploratory analysis, multivariable risk factors for a COVID-19–positive test were Black/African American race, Hispanic ethnicity, ASA physical status III or above, overweight and obese BMI, orthopedic cases, abdominal cases, emergency cases, absence of injury and trauma, and West region (all P < .05). Of these, Black/African American race (OR = 2.61, 95% CI = 1.43–4.78) and Hispanic ethnicity (OR = 2.85, 95% CI = 1.62–5.01) had the highest adjusted OR. ASA indicates American Society of Anesthesiologists; BMI, body mass index; CI, confidence interval; OR, odds ratio.

DISCUSSION

COVID-19 prevalence among pediatric anesthesia patients was lower than in the general population. We identified 9 independent risk factors for infection.

Prevalence

On analysis of 33,320 pediatric anesthesia encounters, the rate of COVID-19 was low at 0.79%. It was lower than the general population throughout the study period. This is consistent with de Lusignan et al,^¹⁵ who found that children aged 0 to 17 years had a lower infection rate, 4.6%, than any adult age group (12.6%–19.5%) between February and March 2020.

Societal factors may have contributed. The lowest rates of infection in our data set fell between April 5 and June 20, 2020, corresponding tightly to the dates of school closures. All US schools were closed by April 5.^¹⁶ This is consistent with Kaufman et al,^¹⁶ who demonstrated that interventions to mitigate the spread of COVID-19, including school closures, resulted in fewer infections and deaths. Further, we found regional variation (Figure 1C). The West and Northeast had higher rates overall (Table 2; Figure 1C), but the South saw a steep increase in the last weeks of the study. This corresponds temporally to the loosening of public health restrictions and suggests that state policy, such as mask mandates, indeed had an effect on infection rates.^¹⁶^,^¹⁷

Clinical and Demographic Risk Factors

This is the first study describing risk factors of COVID-19 infection in pediatric patients undergoing surgery. Our analysis reveals that patients undergoing abdominal and orthopedic procedures were at higher risk (Table 3; Figure 2). This may be due to the proportion of fractures and appendectomies. Both comprise generally healthy children who become acutely ill. They are active in their communities just before illness, which may put them at greater risk.

Children undergoing emergency surgery were at higher risk. This was surprising given that elective cases that were canceled due to COVID-19 were captured in our dataset. Bailey et al^¹⁸ found that children tested in an emergent setting were at increased risk, but do not specify why the children were tested. Presumably, a large proportion was tested for viral or respiratory symptoms. We know that patients in the present study were not tested for symptoms, rather in the context of an anesthetic. Yet, we too saw the association with that emergency context. As the elective surgeries resumed and the percentage of emergency cases decreased, the risk of COVID-19 actually increased in our data set. After April 26, 2020, emergency cases made up fewer than 10% of cases, but the children who presented with COVID-19 rose from a nadir of 0.5% the week of May 10th to 1.7% by the end of June (Figure 1A, D). This is important as many emergency surgeries proceed before the COVID-19 results are determined. Knowing that the emergency nature of a case increases the risk of infection can help direct clinical management. Universal testing created a unique opportunity to look for patterns for COVID-19 infection among surgery types and case urgency. As such, this is the first time emergency case status has been associated with the prevalence of an infectious disease.

We identified factors independently associated with infection in our study population. These were Black/African American race, Hispanic ethnicity, increased BMI meeting overweight or obese criteria and ASA physical status III or higher (Table 3; Figure 2). Other studies similarly found that Black/African American race, Hispanic ethnicity, severe disease, and obesity were associated with a higher risk.^{^18–21} However, we found that merely overweight children were at a near 2-fold higher risk as well. Unlike previously published work, we did not find that Asian race or older children were at higher risk.^¹⁸^,^²⁰^,^²² Adult data demonstrated that male sex, Black/African American race, and increased BMI were associated with higher rates of infection.^¹⁵ We did not find that men were at increased risk, consistent with other pediatric studies,^¹⁸^,^²⁰ suggesting that the higher risk for men is conferred in adulthood.

None of the 4 disease categories demonstrated a higher risk of infection. This is in contrast to previous work finding higher infection rates in patients with malignancy, cardiac disease, and others.^¹⁸ Interestingly, there is no evidence in any literature that patients with respiratory disease, such as asthma, are at higher risk for infection.^²³ Patients with a diagnosis of injury or trauma actually had a lower risk of COVID-19. We suspect that true emergent trauma cases were washed out by other diseases included in this category by HCUP, such as wound infections. The difference between our findings and those of more general pediatric data may be due to the fundamental difference in our population. Here we evaluate children in preparation for surgery and anesthesia, whose risk profile is different than in the general pediatric population.

Study Limitations

Study limitations include a lack of a comparable population of anesthesia patients to compare to our pediatric population. We rely on publicly available data of incident cases in the general population. Despite the sample size, we are underpowered to detect differences among individual states or Native American, Native Hawaiians/Pacific Islanders. The multicenter sampling allows for data from all geographic regions throughout the country; however, it is not a sample chosen randomly. The exploratory univariate and multivariable analysis included many variables, which may lead to type I error inflation due to multiple testing. The data included a substantial amount of missing data, especially for patient race. The missing data may not be missing completely at random (MCAR) which introduces bias; however, the difference reported by race between White versus Black or African American were observed in multivariable exploratory analysis with unknown/missing included as a category.

CONCLUSIONS AND FUTURE WORK

This is the first study examining children who were tested for to assess risk for anesthesia and surgery. We compare our pediatric population to the general population. Identifying risk factors can guide clinical decisions. This work prompts additional questions. The underlying reasons for differences by surgical case type warrant further investigation. Work by the PEACOC Research Network to assess the risk of COVID-19 infection on pediatric surgical outcomes is underway.

ACKNOWLEDGMENTS

The PEACOC study team would like to thank Sharon Crabtree, MS (Senior Analytic Specialist Health Informatics Core, Johns Hopkins All Children’s Hospital, St Petersburg, FL), for extraction and cleaning of data; and Brian Chastain, BS (Medical Student, Department of Anesthesiology & Perioperative Medicine, Oregon Health & Science University, Portland, OR), for helping to gather publicly available data from the CDC.

DISCLOSURES

Name: Meredith A. Kato, MD.

Contribution: This author conceived the original concept for the study, designed the template for the data acquisition, participated in the analysis‚ and wrote the majority of the paper. This author made substantial contributions to the conception or design of the work; participated in the acquisition, analysis, or interpretation of data; and provided final approval of the version to be published.

Name: David Zurakowski, PhD.

Contribution: This author made substantial contributions to the conception or design of the work, participated in the acquisition, analysis, or interpretation of data; created figures and tables; revised the paper critically for important intellectual content; and provided final approval of the version to be published.

Name: AmandaMarie Adams, BS.

Contribution: This author made substantial contributions to the conception or design of the work; participated in the acquisition, analysis, or interpretation of data; revised the paper critically for important intellectual content; and provided final approval of the version to be published.

Name: Julie Soelberg, CRNA/PhD.

Name: Steven J. Staffa, MS.

Contribution: This author made substantial contributions to the conception or design of the work; participated in the acquisition, analysis, or interpretation of data, created figures and tables; revised the paper critically for important intellectual content; and provided final approval of the version to be published.

Name: Victoria A. Bradford, MD.

Name: Proshad N. Efune, MD.

Name: Megan E. Rodgers McCormick, DO.

Contribution: This author made substantial contributions to the conception or design of the work; participated in the acquisition, analysis, or interpretation of data; revised the paper critically for important intellectual content; and and provided final approval of the version to be published.

Name: Anastasia D. Grivoyannis, MD.

Name: Elizabeth Rossmann Beel, MD.

Name: Lynnie R. Correll, MD/PhD.

Contribution: This author made substantial contributions to the conception or design of the work, revised the paper critically for important intellectual content, and provided final approval of the version to be published.

Name: Eric C. Cheon, MD.

Name: Gee Mei Tan, MD.

Contribution: This author participated in the acquisition, analysis, or interpretation of data, revised the paper critically for important intellectual content, and provided final approval of the version to be published.

Name: James J. Thomas, MD.

Contribution: This author participated in the acquisition, analysis, or interpretation of data; revised the paper critically for important intellectual content; and provided final approval of the version to be published.

Name: Allison M. Fernandez, MD/MBA.

Name: Howard C. Teng, MD.

Name: Neha Khanna, MBA.

Name: Vidya T. Raman, MD.

Name: Alyssa B. Brzenski, MD.

Name: Brian J. Frugoni, MD.

Name: Michelle M. Sheth, MD.

Name: Rahil M. Rugnath.

Name: Petra M. Meier, MD.

Contribution: This author led the consortium and recruited centers to participate. She made substantial contributions to the conception or design of the work; participated in the acquisition, analysis, or interpretation of data; wrote drafts of segments of the paper; revised the paper critically for important intellectual content; and provided final approval of the version to be published.

This manuscript was handled by: James A. DiNardo, MD, FAAP.

^GLOSSARY

ASA =: American Society of Anesthesiologists
BMI =: body mass index
CDC =: Centers for Disease Control and Prevention
CI =: confidence interval
COVID-19 =: coronavirus disease 2019
ER =: emergency room
HCUP =: Healthcare Cost and Utilization Project
MCAR =: missing completely at random
OR =: odds ratio
PEACOC =: Pediatric Anesthesia COVID-19 Collaborative
PPE =: personal protective equipment
SARS-CoV-2 =: severe acute respiratory syndrome coronavirus 2
STROBE =: Strengthening the Reporting of Observational Studies in Epidemiology
WHO =: World Health Organization

REFERENCES

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Supplemental Digital Content

AA_2022_08_03_KATO_AA-D-22-00454_SDC1.docx; [Word] (15 KB)

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Prevalence of COVID-19 and Risk Factors for Infection Among Pediatric Anesthesia Patients: A Report From the PEACOC Research Network

Abstract

BACKGROUND:

METHODS:

RESULTS:

CONCLUSIONS:

METHODS

Ethics

Population

Statistical Analysis

RESULTS

Prevalence Data of Pediatric Anesthesia Patients Versus General Population

Regional Variation in Positive COVID-19 Test Rates

Emergency Cases

Risk Factors for COVID-19

DISCUSSION

Prevalence

Clinical and Demographic Risk Factors

Study Limitations

CONCLUSIONS AND FUTURE WORK

ACKNOWLEDGMENTS

DISCLOSURES

^GLOSSARY

REFERENCES

Supplemental Digital Content

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Prevalence of COVID-19 and Risk Factors for Infection Among Pediatric Anesthesia Patients: A Report From the PEACOC Research Network : Anesthesia & Analgesia

Abstract

BACKGROUND:

METHODS:

RESULTS:

CONCLUSIONS:

METHODS

Ethics

Population

Statistical Analysis

RESULTS

Prevalence Data of Pediatric Anesthesia Patients Versus General Population

Regional Variation in Positive COVID-19 Test Rates

Emergency Cases

Risk Factors for COVID-19

DISCUSSION

Prevalence

Clinical and Demographic Risk Factors

Study Limitations

CONCLUSIONS AND FUTURE WORK

ACKNOWLEDGMENTS

DISCLOSURES

GLOSSARY

REFERENCES

Supplemental Digital Content

^GLOSSARY