The Amount of Peer-reviewed Research for Pediatric Patients as Compared With Adults

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Peer-reviewed

Inquiry Commodity

Prevalence of COVID-19 in adolescents and youth compared with older adults in states experiencing surges

• Barbara Rumain,
• Moshe Schneiderman,
• Allan Geliebter

x

• Published: March 10, 2021
• https://doi.org/10.1371/periodical.pone.0242587

Figures

Abstruse

Purpose

There has been considerable controversy regarding susceptibility of adolescents (ten–19 years) and youth (15–24 years) to COVID-19. However, a number of studies have reported that adolescents are significantly less susceptible than older adults. Summer 2020 provided an opportunity to examine data on prevalence since afterwards months of lockdowns, with the easing of restrictions, people were mingling, leading to surges in cases.

Methods

We examined data from Departments of Health websites in six U.Due south. states experiencing surges in cases to determine prevalence of COVID-nineteen, and two prevalence-related measures, in adolescents and youth as compared to older adults. The 2 other measures related to prevalence were: (Percentage of cases observed in a given historic period grouping) ÷ (percentage of cases expected based on population demographics); and percent divergence, or [(% observed—% expected)/ % expected] ten 100.

Results

Prevalence of COVID-19 for adolescents and for youth was significantly greater than for older adults (p < .00001), as was percentage observed ÷ percentage expected (p < .005). The percent deviation was significantly greater in adolescents/youth than in older adults (p < 0.00001) when there was an backlog of observed cases over what was expected, and significantly less when observed cases were fewer than expected (p< 0.00001).

Conclusions

Our results are contrary to previous findings that adolescents are less susceptible than older adults. Possible reasons for the findings are suggested, and we note that public health messaging targeting adolescents and youth might be helpful in curbing the pandemic. Also, the findings of the potential for loftier transmission amidst adolescents and youth, should exist factored into decisions regarding schoolhouse reopening.

Citation: Rumain B, Schneiderman M, Geliebter A (2021) Prevalence of COVID-19 in adolescents and youth compared with older adults in states experiencing surges. PLoS ONE xvi(3): e0242587. https://doi.org/x.1371/periodical.pone.0242587

Editor: Dong-Yan Jin, Academy of Hong Kong, HONG KONG

Received: November 16, 2020; Accepted: Feb 15, 2021; Published: March ten, 2021

Copyright: © 2021 Rumain et al. This is an open admission article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted apply, distribution, and reproduction in whatever medium, provided the original author and source are credited.

Data Availability: All data is publicly bachelor on the websites listed in the Supporting Information file.

Funding: The authors received no specific funding for this work.

Competing interests: The authors take declared no competing interests exist.

Introduction

In that location has been considerable controversy regarding susceptibility of adolescents and youth to COVID-nineteen. As per the WHO, adolescents are defined to be ages ten–19, and youth are 15–24 years of historic period. In the very early studies in Mainland china, Dong et al. [one] and Lu et al. [2] reported that adolescents were susceptible. Bi et al. [3] studied individuals from birth to 70+ in China and found the rate of infection in all age groups was similar. In contrast, Zhang et al. [4], studying individuals in Hunan province, China, ended that the infection rate in 0-14-yr-olds, as reported by the Hunan CDC, was 6.2% as compared to eight.six% amidst those 15–64 years and xvi.iii% amidst those 65 years and higher up, and that these differences were significant. Hence, older adults were accounted to be the most susceptible, and those in the first half of adolescence least susceptible, and youth (15–24) were intermediate in susceptibility. Geliebter, Rumain & Schneiderman [5] attempted to replicate Zhang et al.'south statistical analyses and obtained results in line with those of Bi et al., indicating a similar infection rate for all the historic period groups. In Europe, Kuchar et al. [6] found that adolescents were less decumbent to SARS-CoV-2 infection than adults during the COVID-19 pandemic in Warsaw. Similarly, in a large cross-sectional assay using data from primary intendance practices in England, de Lusignan et al. [seven] found increasing age was associated with increased odds of a positive SARS-CoV-2 exam.

In a meta-assay of 32 studies, Viner et al. [8] found that adolescents ten–fourteen years of age take lower susceptibility to SARS-CoV-two infection than adults, with adolescent older than this actualization to have similar susceptibility to adults. Using a mathematical modeling approach with data from six countries (China, Italia, Japan, Singapore, Canada and South Korea), Davies et al. [9] fitted the information available from the six countries, and estimated that the susceptibility of adolescents 10-19-year-olds is approximately half that of older adults, ages lx+ (mean susceptibility for the former is .38 and for the latter is .81), and youth in their early on 20s have a susceptibility almost equal to that of older adults. However, no U.S. information were included in their model.

Regarding U.S. data on COVID-xix in adolescents, as of Apr 2, 2020, amongst 149,082 cases in all historic period groups for which patient historic period was known, only 2,572 (1.vii%) of these occurred in children aged <18 years, with nearly 60% of these cases occurring in adolescents x–17 years old [10]. Hence at that point, adolescents accounted for just 1% of the total cases. Merely by Sept 15, 2020, the number of cases in adolescents 10–19 years of age had climbed to 387,000 [11].

The bear witness in the The states that adolescents are susceptible was credible even earlier—in June 2020—when there was an outbreak in 260 individuals at a Georgia overnight campsite [12]. In the camp's 11-17-year-old adolescents, the attack rate was 44%, and in the 18-21-year-olds, information technology was 33%, where 'set on rate' is the number of persons with a positive test effect divided by the total number of attendees in that age group (including those who did not provide testing results). Following COVID-19's efficient spread in this youth-centric overnight setting, the CDC on July 31^st officially stated [12], "children of all ages are susceptible to SARS-CoV-2 infection and, opposite to early on reports might play an of import role in manual."

Summer 2020 presented a window of opportunity to examine data on prevalence in the U.S. since adolescents and youth were on vacation and probable to mingle with others, as were adults. After months of lockdowns, there was a general easing of restrictions, with people attending parties and large gatherings, and social distancing measures were non being adhered to, leading to surges. Nosotros examined data from U.S. states experiencing surges in the number of cases to determine prevalence of COVID-19, and 2 other measures related to prevalence, in adolescents and youth, as compared to older adults. The comparison grouping are older adults since Zhang et al. establish them to exist most susceptible, and Davies et al. institute them twice every bit susceptible as adolescents. Prevalence data for adolescents and youth would take important implications for school re-openings in middle school and beyond.

Methods

In that location were two criteria for inclusion of states in our study sample:

1. The country experienced a surge, defined as follows: After at least a 1-month plateau in the 7-day boilerplate of daily number of new cases, in that location is a dramatic increment of at least 100% in the 7-solar day average number of daily new cases from the plateau 2–3 months prior, which lasts at to the lowest degree i month, as reported for us in the New York Times "COVID Map and Case Count" [13]. For example, for Missouri, the case information are from Baronial 7^th when there was a surge. On that solar day, the 7-mean solar day average number of daily new cases was 1035. On June 7^th, 60 days prior, the 7-day daily average was 251. This is a 4-fold increment (i.e., 300%) over what it was on June 7^th. Moreover, for the months of Apr, May, and through June 6, the vii-day daily boilerplate of new cases had plateaued at approximately 200–300 cases per day. Also, the surge was at least 1 month in duration. Some other example is Florida, where the case data are from July xix^th. On that day the7-mean solar day daily average was 11,462. Lx days prior, i.eastward., on May 19^th, the vii-day daily boilerplate of new cases was 717. Thus, there was a 16-fold increase or 1500% increase in the seven-twenty-four hours daily boilerplate from May 19^th to July xix^th. Also prior to that, for the months of April, May and the beginning of June, the vii-day daily average of new cases had reached a plateau, fluctuating from 578–1100 per 24-hour interval. Even taking the maximum or 1100, the eleven,462 cases on July 19 is ten times that or a 900% increment. Likewise, the surge lasted at to the lowest degree a month from July 19, with August 19^thursday reporting a seven-day average of daily new cases at 4735.
2. The pediatric data were tabulated within distinct age brackets, and non amalgamated. Texas had over 650,000 cases, but age information was available on less than i-10th of these, and therefore could not be included. California lumped all child data together, and also could not be included every bit we are excluding children under age 10 since they are not yet adolescents. We therefore considered the post-obit six states: Florida, Tennessee, Missouri, Utah, Kansas, and Southward Dakota.

We accessed online tables containing COVID-19 case data from country Health Department websites when in that location was a surge, and tables for state population data by age group. Example data was downloaded for the summer months of July and Baronial, betwixt July quaternary and September quaternary (Labor Twenty-four hours weekend), at the time each of the states was experiencing a spike in cases. The websites are detailed in S1 Appendix and the tables/figures we relied upon are provided there.

Depending on how the data were tabulated, the case data for the six states are either for adolescents, for youth, or for both adolescents and youth combined. In South Dakota, data were tabulated by decade, and we used the 10-xix-year age bracket. Tennessee had a similar historic period bracket of eleven–20 years of age. Hence these two states provided data on adolescents. For Florida, the historic period brackets were 5–xiv, and ages 15–24 (youth, as defined by the WHO), and not for those 10–19. Therefore, we used only the latter grouping, the 15-24-yr-olds. Similarly, for Utah, 1-xiv-year-olds were amalgamated, so we focused on the next age group, xv–24. Thus, Florida and Utah provided the information on youth. For Kansas, since cases were reported for ten–17 years and for 18–24 years, which consists of age subclass demarcations different that in any of the other states, nosotros combined these to 10–24 years. For Missouri, nosotros also consider 10-24-twelvemonth-olds. Therefore, Kansas and Missouri provided information on adolescents and youth combined.

The case data from the Health Department websites was used to compute "Percentage of Cases Observed," where this is calculated as the number of cases in a item age group divided by the total number of cases for all ages in the state and then this ratio is converted into a percentage. The "Percent of Cases Expected" is adamant based on population demographics: For each historic period group, it is calculated equally the percentage of the population that the given historic period group comprises, multiplied by the total number of cases. The population demographic data was obtained from the land websites, and these are provided in Tables G–L in S1 Appendix.

We then calculated iii measures: 1) Prevalence, two) "Percent of Cases Observed" in a given historic period group ÷ "Percentage of Cases Expected" based on population demographics as noted above, and iii) Per centum Deviation, or [(% observed—% expected)/ % expected] ten 100.

Statistics: Nosotros performed chi-foursquare calculations to make up one's mind whether differences between the adolescent/youth groups and the older adults were meaning for the iii upshot measures. Significance level was based on ii-tailed α = .05.

Illustrative example using data for the state of Utah

As an example, the land Section of Wellness website for Utah was used to access the case data, and the URL is given in S1.1(iii) of the S1 Appendix:

https://coronavirus.utah.gov/example-counts/. The data from S1.iv(c) are reproduced in Table C in the S1 Appendix (on page v).

1. Prevalence. Prevalence is the proportion of a population who have a specific feature in a given time period, regardless of when they beginning developed the feature. The specific characteristic hither is having been infected with SARS-CoV-2, and the given time period is from the beginning of the pandemic through Summer 2020. Information technology is the cumulative number of individuals infected in a item historic period group out of the full number of individuals in that age group. For 15-24-yr-olds, Tabular array C in S1 Appendix shows that there are 10,674 infected xv–24-twelvemonth-olds. Table I in S1 Appendix, "Utah Demographics by Age, Sex, Race, and Ethnicity," shows the number of 15–24-year-olds in Utah is (245,404 + 232,671). Thus, the prevalence of COVID-19 in xv-24-yr-olds is 10,674 ÷ (245,404 + 232,671) = 2.2%. For the 65+ year-olds, Table C in S1 Appendix shows in that location are (3284 + 402) infected individuals out of 346,282 full individuals in Utah in the age group (see Tabular array I in S1 Appendix). Therefore, the prevalence in 65+ year-olds is 3644 ÷ 346,282 = 1.one%. Performing a chi-square adding comparing the 15-24-year-olds with the 65+ year-olds, yields χ2 = 1548.7, p<0.00001.
2. Percentage of Cases Observed ÷ Percentage of Cases Expected. For the 15-24-twelvemonth-olds, Tabular array C in S1 Appendix shows they correspond 23% of the total cases. To calculate the percentage of expected cases in this age grouping, Table I (in S1 Appendix) indicates they comprise 15% of the population (7.7% + 7.iii%). Therefore, we would expect 15% of the cases to be in xv-24-year-olds since they constitute 15% of the population. Instead, they comprise 23% of the cases. Therefore, Percentage of Cases Observed ÷ Percentage of Cases Expected = 23% ÷ 15% = 150%, i.due east., or there are i.5 times as many cases in xv-24-year-olds in Utah than we would expect based on population demographics.
3. Percentage Deviation. Using the formula to a higher place, the calculations are done in the same manner.

Results

In all states, (1) prevalence of COVID-19 for adolescents and youth was significantly greater than for older adults, p < .00001, as was (two) the ratio of observed to expected cases, p < .005 (Table i). (3) In states where the number of observed cases exceeded the expected (Utah, Missouri and Kansas), the departure was significantly greater in adolescents/youth than in older adults, p < 0.00001. In states where observed cases were fewer than expected (Florida, Tennessee, South Dakota), the difference for older children/youth was significantly less than that for the older adults, p < 0.00001. We at present consider each developmental menstruation separately.

Boyhood (ten-19-year-olds): Data from S Dakota and Tennessee

The prevalence in adolescents was significantly greater than that in older adults (p<0.00001). This was as well true for the proportion of the ratio of cases expected based on population demographics to the observed number of cases, both for South Dakota (p = 0.005) and for Tennessee (p = 0.0039). The third prevalence-related measure, the divergence, was significantly more negative for older adults than for adolescents (p<0.00001). This means that the ratio of [(observed cases-expected cases)/expected cases] was significantly greater for older adults than for adolescents due mainly to the fewer number of observed cases relative to expected cases in the older adults.

Youth (15-24-twelvemonth-olds): Data from Utah and Florida

In both Utah and Florida, the prevalence of COVID-nineteen in youth was twice what it was in older adults 65+ (p<0.00001). In both states, the ratio of observed to expected cases for youth was twice what it was for older adults (150% vs. 73% in Utah; 132% vs.62% in Florida), p<0.0001. In Utah, at that place were 52% more cases than expected (based on population demographics) for youth, but 29% fewer cases than expected for older adults 65+, p< 0.00001. Similarly, in Florida, whereas there were 32.2% more case than expected for youth, at that place were 34% fewer cases than expected for older adults 65+, p< 0.0001.

Boyhood and youth combined (ages 10–24 years-old): Data from Kansas and Missouri

In both Kansas and in Missouri the data for adolescents and youth are combined. In both states, the prevalence of COVID-19 is significantly greater in the combined boyish plus youth group than in older adults 65+ (p< 0.00001). In both states, the ratio of observed to expected cases for the combined adolescent plus youth group was significantly greater than what it was for older adults 65+ (p< 0.00001). In Kansas, at that place were 17% more cases than expected (based on population demographics) for youth, merely xiii% fewer cases than expected for older adults 65+, p< 0.00001. Similarly, in Missouri, in that location were 7% more cases than expected for youth, and in that location were 3% fewer cases than expected for older adults 65+, p< 0.0001.

Discussion

We constitute that prevalence of COVID-19 in adolescence was significantly greater than in older adults, and similarly for the ii other prevalence-related measures. At that place was as well a higher prevalence in youth equally compared to older adults, and in adolescents and youth combined as compared to older adults. Again, the same findings held for the ii other prevalence-related measures. A possible factor is that adolescents have more contacts than adults [xiv], and another gene is that older adults, feeling vulnerable, may be more likely to adhere to masking and social distancing, which adolescents/youth may disregard. A third gene is that since 10-19-year-olds are adolescents, they may non exist fully appreciate the health consequences of not wearing a mask. A quaternary gene is that adolescents and youth, fifty-fifty if they recognize their potential for infection, may feel more compelled to have social interactions, regardless of the health consequences. All these factors likely acted in concert to yield the blueprint of results obtained. Public wellness messaging targeting adolescents and youth in particular, could exist helpful in addressing these factors.

Our findings in the six U.S. states are contrary to those of Zhang et al. in People's republic of china who constitute that the infection rate in older adults, ages 65+, exceeded that in adolescents and youth, and to those of Wu et al. [15], who constitute that of 44,672 confirmed cases of COVID in communist china, only 1% were in adolescents ages 10–19 years of historic period. It is likewise contrary to the model of Davies et al., which estimates the susceptibility of 10-nineteen-yr-olds to be half that of older adults. I reason for the differences could be that these before studies were conducted when schools were closed, reducing the number of contacts adolescents and youth were exposed to, and thus the number of cases. As well, testing was less available early on, and adolescents tend to accept milder cases of COVID-xix that might exist missed without the availability of widespread testing. As we noted earlier, on Apr 2, 2020, even in the U.S., adolescents accounted for just i% of the cases, again likely too due to the school closures and the lack of widespread testing.

1 limitation of this study is that case data on state website are presumably based on people tested considering they were either symptomatic, or they were exposed to someone who was idea to be infected, or they were seeking medical handling for some other condition and the medical facility required COVID-19 testing. That still leaves some infected individuals who were asymptomatic but did not get tested because they did non fall into the latter two categories. Would their inclusion alter our results? At that place are two possibilities regarding asymptomatic individuals: (i) Either the number of asymptomatic infections is a constant role of the number of symptomatic ones regardless of age, as the CDC argument on June 25^th [16] unsaid "Our all-time estimate correct now is that for every instance that'southward reported, there actually are ten other infections." In such a scenario, the conclusions regarding our prevalence data would be unaffected since the relative proportions would remain the same. (2) Or, that the manifestation of clinical symptoms is age-dependent equally Davies et al. maintain in a part of their model that deals with the clinical fraction of cases that are symptomatic vs. asymptomatic. They estimate that clinical symptoms manifest in 21% of adolescents simply in 63–69% of older adults ages 60+. This would imply that at that place are many more than asymptomatic adolescents than asymptomatic older adults: Appropriately, if asymptomatic individuals were added to our information set, our conclusions that prevalence in adolescents is significantly greater than in older adults, would be even more pronounced. A second limitation is that the conclusions are for the six states and may not generalize to the unabridged country. A third limitation is that proportions in unlike age groups may be affected by local epidemiology, access to care and public health policy.

Our data are consistent with the most recently available pediatric case data in a recently released report, issued by the American Academy of Pediatrics. The study used publicly reported information from 49 states, NYC, DC, Puerto Rico, and Guam, and noted that every bit of January 7, 2021, there were two,299,666 total child COVID-19 cases [17], although united states of america had varying definitions of what constituted a "child" and adolescents were oft grouped together with younger children. Age ranges reported for "children" varied by state (0–14, 0–17, 0–18, 0–19, and 0–20 years), but the key message is that children and adolescents are quite susceptible. What our study adds is that the prevalence of infection in the half dozen states we examined, was significantly greater in adolescents and youth than information technology was in older adults.

Regarding transmissibility, a large South Korean epidemiological study [18] found that adolescents ages 10–19 were more likely to spread the virus than adults. Moreover, we know from the incident with the spread in the overnight Georgia camp that adolescents and youth can efficiently transmit the disease. Since the Southward Korean written report and the rapid spread at the Georgia camp show that adolescents are quite capable of transmitting COVID-nineteen and our study shows COVID-19 to be quite prevalent in these age groups, all three studies taken together bear witness high prevalence combined with high transmissibility.

Conclusions

Our results contrast with previous findings that adolescents are less susceptible than older adults. Possible reasons for the high prevalence of COVID-nineteen and other prevalence-related measures in adolescents and youth are suggested. We also note that public wellness messaging targeting adolescents and youth might be an avenue to pursue.

The age groups studied, 10-19-twelvemonth-olds and fifteen-24-yr-olds, are typically students in middle schoolhouse, high school, college, and the beginning of graduate/professional school. Our findings signal the potential for high transmission among adolescents and youth, which should be factored into decisions regarding school reopening. In places, where schools take reopened, the high prevalence of COVID-xix highlights the necessity of students, faculty and staff wearing masks, social distancing, and washing hands regularly.

Supporting information

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Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242587

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