Changing epidemiology of SARS-CoV-2 positivity rates in a diverse population of children and adults during variant evolution and progressive vaccination eligibility in New York City

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Velu, Charlene Thomas MS, Sophie Rand, Eddie Imada, Claudio Zanettini, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7104064/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 10 You are reading this latest preprint version Abstract Background The COVID-19 pandemic resulted in multiple waves of infections in New York City that were driven by evolving SARS-CoV-2 variants and shifting vaccine eligibility. We describe the trends in SARS-CoV-2 epidemiology in adults and children over consecutive waves during the height of the COVID-19 pandemic in New York City. Methods We analyzed SARS-CoV-2 PCR results, demographics, and vaccination data in adults and children in a multi-hospital network in New York City from 10/1/2020 to 9/19/2022. A subset of nasopharyngeal specimens underwent whole genome sequencing to determine the SARS-CoV-2 variant distribution in adults and children. Results There were 243,457 SARS-CoV-2 PCR tests performed in adults (89.2%) and 29,333 in children (10.8%) with overall positivity rates of 6.2% in adults and 5.9% in children during the study period. The highest overall positivity rate (12.1%) was seen during Wave 4 when the Omicron variant was predominant and positivity rates in children surpassed those in adults for the first time (children 15.6%, adults 11.7%, p < 0.001). During Wave 4, SARS-CoV-2 positivity was associated with pediatric age (aOR 1.12, 95% CI 1.01, 1.23), non-White race (aOR 1.37, 95% CI 1.26, 1.47), Hispanic ethnicity (aOR 1.53, 95% CI 1.38, 1.68), and unvaccinated status (aOR 1.52, 95% CI 1.42, 1.63). SARS-CoV-2 variant distribution did not differ over time between adults and children. Conclusions Our large cohort of SARS-CoV-2 testing over multiple COVID-19 waves in New York City demonstrated a shift in positivity rates when the Omicron variant was predominant, with disproportionate positivity in children, unvaccinated individuals, and specific racial and ethnic groups. As vaccination rates decline in response to changes in vaccine recommendations, this scenario may recur with the emergence of a new virulent SARS-CoV-2 variant or re-emergence of vaccine-preventable diseases. These findings highlight the need for targeted public health strategies that prioritize vulnerable populations during respiratory viral surges. COVID-19 SARS-CoV-2 variants epidemiology Figures Figure 1 Figure 2 Figure 3 Background The coronavirus disease 2019 (COVID-19) pandemic first emerged in the United States in New York City (NYC) in March 2020, and over the next two years, NYC would remain a focal point of the pandemic, experiencing multiple waves of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections [1,2,3,4,5]. Each wave presented unique challenges due to the emergence of SARS-CoV-2 variants that appeared to impact adults and children differently [6]. Despite early perceptions that children were not impacted by SARS-CoV-2 infections, we now know that children were not spared from infections, mortality, and poor outcomes due to COVID-19, especially when the Delta and Omicron variants predominated [7,8,9]. Against this backdrop, effective vaccines were made available and rolled out at varying stages by age group with eligibility for children following that of adults [10,11]. The epidemiology of waves in the United States were largely impacted by vaccination uptake with higher case rates seen in regions with low vaccination rates [12]. Although we are now in the post-pandemic era, SARS-CoV-2 continues to circulate resulting in recurrent waves throughout the year. These waves still result in substantial morbidity resulting in an estimated 230,000 hospitalizations in adults and ~ 10,000 hospitalizations in children ≤ 18 years during the 2024–2025 season in the U.S [13]. In addition to the effects of acute COVID-19, children are susceptible to the severe post-infectious complication multisystem inflammatory syndrome in children (MIS-C) [6,14,15], and both adults and children are at risk for developing long COVID-19 [15,16,17]. Despite continued impacts of COVID-19 infections, the FDA recently recommended against vaccinating healthy children and pregnant individuals [3]. Understanding the patterns and impacts of these prior COVID-19 waves may inform future prevention strategies and public health responses for future pandemics. Here we describe the changing epidemiology of SARS-CoV-2 testing and positivity rates in the context of vaccination and changing viral variants during the height of the COVID-19 pandemic in a large hospital system in New York City. Our objectives were (1) to delineate SARS-CoV-2 positivity rates by age, race and ethnicity, testing site, and vaccination status over 5 waves of SARS-CoV-2 infections, (2) describe vaccination rates over time in our study cohort, and (3) evaluate the distribution of SARS-CoV-2 variants in adults and children over time through whole genome sequencing. Methods Study design and data sources Specimens and data were sourced from four hospital systems within our hospital network. This included remnant nasopharyngeal (NP) swab viral transport media from routine clinical care specimens used for SARS-CoV-2 reverse transcriptase polymerase chain reaction (RT-PCR) testing and associated clinical data including specimen collection date, RT-PCR result, testing instrument, and RT-PCR testing location at time of test order. Data obtained from the electronic health record (EHR) included patient birth date, sex, race, ethnicity, and dates of COVID-19 vaccine administration. Patients were defined as pediatric ( 21 years old) based on the age at time of RT-PCR specimen collection. Defining waves of infection Waves of SARS-CoV-2 infections during the pandemic were defined using New York City Department of Health and Mental Hygiene daily case numbers to establish waves based on daily peaks and nadirs of infection [18,19]. There were five waves examined in this study, starting with Wave 2 from October 1, 2020 to June 30, 2020, Wave 3 from July 1, 2021 to December 1, 2021, Wave 4 from December 2, 2021 to March 5, 2022, Wave 5 from March 6, 2022 to June 12, 2022, and Wave 6 from June 13, 2022 to September 19, 2022. RT-PCR testing for SARS-CoV-2 RT-PCR tests collected from October 1, 2020 to September 19, 2022 were included in this study. To avoid overestimation of the positivity rate by patients who had repeat testing within a pandemic wave, only one test per unique patient per wave period was included. If a patient tested positive during the wave, the first positive test was included. If the patient did not test positive during a particular wave, the first negative test was used. Multiple RT-PCR testing platforms were used between the hospitals, and included the BioFire Respiratory Panel v.2, Cepheid Xpert Xpress SARS-CoV-2, Hologic Panther Fusion SARS-CoV-2 assay, Roche cobas 6800 SARS-CoV-2, Roche cobas SARS-CoV-2 & Influenza, and QIAstat-Dx Respiratory SARS-CoV-2 Panel. Whole genome sequencing and lineage determination of SARS-CoV-2 Whole genome sequencing (WGS) was performed at the New York Genome Center on remnant NP swabs in viral transport media. Samples with Ct values greater than 33 on RT-PCR testing were excluded. Briefly, nucleic acid was extracted on the KingFisher Flex Purification system (Thermofisher) using the MagMAX Viral Pathogen Nucleic Acid Isolation kit. After reverse transcription, the Molecular Loop Viral RNA Target Capture Kit (Molecular Loop) was used to prepare SARS-CoV-2 targeted libraries according to manufacturer recommendations. Libraries were pooled, quantified, and then sequenced on a NovaSeq 6000 sequencer with 2x150 base pair reads. Read pairs were processed and merged into single end reads that were mapped against the SARS-CoV-2 reference using BWA-MEM v0.7.17. The resulting alignments were processed and genome sequences were determined by molecule alignment pileup consensus calling with a minimum support of 5 unique reads. SARS-CoV-2 lineage and clade classification were determined by Phylogenetic Assignment of Named Global Outbreak (pangolin, https://covlineages.org/resources/pangolin.html ), pangolin v4.1.3 and pangoLEARN/USHeR 1.15.1. Vaccination status Vaccination status on all patients with RT-PCR testing was determined by cross-referencing patient information from the EHR with the New York Citywide Immunization Registry (CIR) and New York State Immunization Information System (NYSIIS). Vaccination status was defined as fully vaccinated if the patient had completed a full vaccination series (two doses for Pfizer or Moderna or one dose for Janssen/Johnson & Johnson) at least fourteen days before RT-PCR specimen collection; otherwise, the patient was considered unvaccinated at time of specimen collection. If the subject lived in New York and no vaccine data were available in CIR or NYSIIS, they were classified as unvaccinated. Vaccine status was classified as unknown if they lived outside NYS and no vaccine data was available on CIR or NYSIIS. Data and statistical analysis All data analyses and statistical calculations were performed using R v.4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Figures were generated using the package ggplot2 v.3.3.4 [20.21]. Descriptive statistics (i.e., median, interquartile range, frequency and percent) were calculated to describe demographic and clinical characteristics. Comparative analyses comparing categorical variables were conducted using a Pearson’s Chi-squared test. To evaluate factors associated with SARS-CoV-2 positivity during Wave 4 when differences were seen among patient populations, a multivariable logistic regression was performed controlling for age, race, ethnicity, and vaccination status and presented as adjusted odds ratios (aOR). All p-values were two-sided with statistical significance evaluated at the 0.05 alpha level. P-values were not adjusted for multiple comparisons. To assess trends in variant distribution over time in adults versus children, the Kolmogorov-Smirnov test was utilized. Results Demographics During the study period from October 1, 2020 to September 19, 2022, 243,457 tests were performed in adults (89.2%) and 29,333 tests were performed in children (10.8%), with overall positivity rates of 6.2% in adults and 5.9% in children. The testing distribution by age varied by wave, with the highest proportion of children tested during Waves 2 (11.4%), Wave 3 (14%), and Wave 4 (10.1%), and the lowest proportion of children tested in Wave 5 (5.9%) and Wave 6 (5.6%) (Table 1 ). Individuals tested identified as White (101,478, 37%), Black (32,688, 12%), Asian (36,217, 13%), Native Hawaiian or Other Pacific Islander (NH/PI) (291, 0.1%), American Indian or Alaska Native (AI/AN) (814, 0.3%) and 101,302 (37%) individuals did not have race data available. There were 40,580 (15%) individuals who identified as Hispanic, 166,272 (61%) as non-Hispanic, and 65,938 (24%) with unavailable ethnicity data. Table 1 Demographic characteristics of adults and children tested for SARS-CoV-2 by PCR during each pandemic wave. Characteristic Overall N = 272,790 Wave 2 N = 122,570 Wave 3 N = 61,038 Wave 4 N = 39,390 Wave 5 N = 32,334 Wave 6 N = 17,458 Age at Test (Median, (IQR)) 49 (33, 67) 49 (32, 66) 47 (31, 67) 48 (32, 67) 54 (36, 70) 56 (37, 72) Age Group (N, %) 0–1 years 3,579 (1.3%) 1,643 (1.3%) 904 (1.5%) 588 (1.5%) 288 (0.9%) 156 (0.9%) 1–4 years 7,688 (2.8%) 3,389 (2.8%) 2,782 (4.6%) 976 (2.5%) 361 (1.1%) 180 (1.0%) 5–12 years 7,544 (2.8%) 3,569 (2.9%) 2,326 (3.8%) 1,019 (2.6%) 412 (1.3%) 218 (1.2%) 12–16 years 4,225 (1.5%) 2,188 (1.8%) 1,039 (1.7%) 552 (1.4%) 301 (0.9%) 145 (0.8%) 16–21 years 6,297 (2.3%) 3,144 (2.6%) 1,476 (2.4%) 849 (2.2%) 542 (1.7%) 286 (1.6%) Adult 243,457 (89%) 108,637 (89%) 52,511 (86%) 35,406 (90%) 30,430 (94%) 16,473 (94%) Sex (N, %) (N = 272,627) Female 161,510 (59%) 71,658 (58%) 36,199 (59%) 23,723 (60%) 19,444 (60%) 10,486 (60%) Male 111,117 (41%) 50,851 (42%) 24,801 (41%) 15,622 (40%) 12,874 (40%) 6,969 (40%) Unknown 163 61 38 45 16 3 Race (N, %) American Indian or Alaska Native 814 (0.3%) 325 (0.3%) 208 (0.3%) 126 (0.3%) 103 (0.3%) 52 (0.3%) Asian 36,217 (13%) 12,382 (10%) 9,619 (16%) 5,862 (15%) 5,457 (17%) 2,897 (17%) Black or African American 32,688 (12%) 13,400 (11%) 7,765 (13%) 4,948 (13%) 4,164 (13%) 2,411 (14%) Native Hawaiian or Pacific Islander 291 (0.1%) 153 (0.1%) 45 (< 0.1%) 48 (0.1%) 27 (< 0.1%) 18 (0.1%) White 101,478 (37%) 45,487 (37%) 22,299 (37%) 15,096 (38%) 12,135 (38%) 6,461 (37%) Unknown or Declined 101,302 (37%) 50,823 (41%) 21,102 (35%) 13,310 (34%) 10,448 (32%) 5,619 (32%) Ethnicity (N, %) Hispanic or Latino 40,580 (15%) 16,572 (14%) 10,243 (17%) 5,670 (14%) 5,115 (16%) 2,980 (17%) Not Hispanic or Latino 166,272 (61%) 71,521 (58%) 37,632 (62%) 24,606 (62%) 21,126 (65%) 11,387 (65%) Unknown or Declined 65,938 (24%) 34,477 (28%) 13,163 (22%) 9,114 (23%) 6,093 (19%) 3,091 (18%) Setting (N, %) (N = 272,789) Emergency 51,866 (19%) 16,704 (14%) 17,131 (28%) 7,014 (18%) 7,295 (23%) 3,722 (21%) Inpatient 53,583 (20%) 17,880 (15%) 14,712 (24%) 7,795 (20%) 8,546 (26%) 4,650 (27%) Outpatient 167,340 (61%) 87,985 (72%) 29,195 (48%) 24,581 (62%) 16,493 (51%) 9,086 (52%) Change in Positivity Rates by Demographics, Testing Site, and Vaccine Status during Wave 4 The positivity rate changed over time by wave with the most pronounced shift during Wave 4, coinciding with the emergence of the Omicron variant in NYC (Fig. 1 ). The overall positivity rate during Wave 4 was 12.1% with a peak 14-day rolling overage of 27.7% on 1/8/2022. Positivity rates for children surpassed those in adults for the first time during Wave 4 (children 15.6%, adults 11.7%, p < 0.001) (Fig. 2 A). Among children, the highest positivity rate was seen in children 5–12 years (18.5%), followed by 1–4 years (15.9%), 12–16 years (15.2%), 16–21 years (14.4%), and 0–1 years (11.9%) (Fig. 2 B). Additionally, there was a shift in testing site, with an increase in outpatient testing and positivity rates observed in both adults and children during Wave 4 compared to Wave 3 (Table 1 , Fig. 2 C). During Wave 3 when the Delta variant was predominant, total outpatient testing represented only 48% of tests in adults and children, but increased to 62% and 70% in adults and children, respectively, in Wave 4. In children who tested positive, 80% of positive cases were outpatient in Wave 4 and positivity rate was higher in outpatient compared to ED and inpatient sites (outpatient 18%, ED 13%, inpatient 5.9%, p < 0.001) (Fig. 2 C). Among racial and ethnic groups, the highest positivity rates in Wave 4 were seen in individuals who identified as NH/PI (11/48 [22.9%]) and Black (793/4948, [16%]), and lowest in White (1533/13,563, [10.2%]) and Asian (644/5862, [11%]) individuals (p < 0.001) (Fig. 2 D). Higher positivity rates were also seen in Hispanic (879/5670, [16%]) compared to non-Hispanic individuals (2703/24606, [11%], p < 0.001) during Wave 4 (Fig. 2 E). To evaluate independent risk factors for testing positive during Wave 4 when positivity rates were highest and when disparities by race, ethnicity and age appeared to be most pronounced, we performed a multivariable logistic regression which demonstrated that pediatric age (aOR 1.12, 95% CI 1.01, 1.23), non-White race (aOR 1.37, 95% CI 1.26, 1.47), Hispanic ethnicity (aOR 1.53, 95% CI 1.38, 1.68), and unvaccinated status (aOR 1.52, 95% CI 1.42, 1.63) were all independent risk factors for SARS-CoV-2 positivity during Wave 4 (Table 2 ). Table 2 Multivariable logistic regression analysis of characteristics associated with SARS-CoV-2 positivity during Wave 4. Characteristic aOR 95% CI p-value Race White — — Non-White 1.37 1.26, 1.47 < 0.001 Unknown or Declined 1.07 0.97, 1.16 0.2 Ethnicity Not Hispanic or Latino — — Hispanic or Latino 1.53 1.38, 1.68 < 0.001 Unknown or Declined 1.24 1.13, 1.35 < 0.001 Vaccination Status Fully Vaccinated — — No Vaccination Data 0.91 0.74, 1.12 0.4 Unvaccinated/Partial 1.52 1.42, 1.63 < 0.001 Age Adult — — Pediatrics 1.12 1.01, 1.23 0.026 Abbreviations: aOR = adjusted Odds Ratio, CI = Confidence Interval Vaccination rates over time COVID-19 vaccine status was available for 233,640 (96%) adults and 28,001 (95.4%) children. Vaccination rates in adults and children increased over time as vaccine eligibility expanded although children remained significantly under-vaccinated compared to adults by the end of the study period (Fig. 1 ). During Wave 2, 13% of adults and 1% of children had received at least 2 doses of a COVID-19 vaccine, and by Wave 4, 74% of adults were fully vaccinated, with this rate sustained in Wave 5 (73%) and Wave 6 (74%). In children, full vaccination status continued to increase as the waves progressed (Wave 3: 11%, Wave 4: 22%, Wave 5: 29%, Wave 6: 32%) in line with later vaccine eligibility in children compared to adults. However, despite eligibility expanding to children 6 months to 4 years old on June 18, 2022 at the beginning of Wave 6, vaccination rates remained low in this age group by the end our study period in September 2022 with only 0.1% of 1–4 years with available vaccination data fully vaccinated at this time. Variant distribution Whole genome sequencing (WGS) on remnant nasopharyngeal swab specimens from RT-PCR testing was performed on a subset of our population (adult 2.1%, children 3.3%) to determine if differences in epidemiologic trends between adults and children could be explained by infections due to different variants. WGS of 5092 adult and 1084 pediatric SARS-CoV-2 specimens collected from December 1, 2020 to September 19, 2022 were performed. Most sequenced specimens were from patients tested in outpatient settings (72%). Predominant lineages during Wave 2 were B.1 lineages (47%), Iota/B.1.526 (20%), Alpha/B.1.1.7 (13%), and B.1.637 (6.5%). The predominant lineage during Wave 3 was Delta/B.1.617.2/AY lineages (87%), during Wave 4 was Omicron/BA.1 (77%), during Wave 5 was Omicron or BA.2 (81%), and during Wave 6 were Omicron/BA.5 (51%), Omicron/BA.4 (14%), and Omicron/BA.2 (14%). A Kolmogorov-Smirnov test demonstrated no difference in distribution of Alpha, Iota, B.1.637, Omicron, Delta, or General B lineages between adults and children during each wave (Fig. 3 ). Discussion In this large, urban, and demographically diverse cohort of adult and pediatric patients in New York City tested for SARS-CoV-2 by RT-PCR during several crucial waves of the COVID-19 pandemic, we observed dynamic shifts in positivity rates across age, race and ethnicity, testing site and vaccination status. These changes were most pronounced during Wave 4, when the Omicron variant emerged, resulting in the highest positivity rates across all groups, but with disproportionate impacts on young children, certain racial and ethnic groups, and unvaccinated individuals. Additionally, vaccination rates in children lagged despite increasing eligibility for younger age groups throughout the study period. As we anticipate future infectious threats in the setting of increasing vaccine hesitancy and loss of vaccine mandates [3], understanding trends in testing patterns, positivity rates, and vaccination across diverse populations will be essential to guiding future public health responses and vaccine efforts. As Omicron emerged in December 2021 and gained predominance, community positivity rates in both adults and children the United States climbed to unprecedented levels with both vaccinated and unvaccinated individuals developing infections as vaccine effectiveness against symptomatic infections decreased from 90.9% against Delta to 65.5% against Omicron [22]. Notably in our study, the positivity in children surpassed adults for the first time. Though vaccination continued to offer high protection against hospitalization and severe disease [23], young children who were still not eligible for vaccination were highly impacted during the Omicron wave with peak hospitalization rates up to six times higher than during the Delta wave [24,25,26]. This shift appeared to be driven by high positivity rates in unvaccinated young children 5–12 years old and 1–4 years old in emergency department and outpatient settings. Indeed, 74.7% of children with SARS-CoV-2 infections during this wave were unvaccinated in our cohort. Although inpatient positivity rates in children were lower compared to ED and outpatient, they increased substantially from 0.8% during the Delta wave to 5.9% during the Omicron wave in our cohort. This is consistent with the pediatric experience in New York City, as pediatric hospitalizations due to or with COVID-19 increased 18-fold during the Omicron wave [27]. During the Omicron wave, children 0–4 years old were not yet eligible for vaccination, and only 22% of children in our cohort were vaccinated leaving them particularly vulnerable to infection. While there had been a jump in childhood vaccination rates after the openings of vaccine eligibility for the 12–15 years old and 5–11 years old age groups, a similar trend was not seen in younger children 6 months to 4 years when their eligibility expanded in June 2022 (Fig. 2 ). Vaccine coverage with COVID-19 vaccine has continued to be an issue in children in New York City and by September 2023, only 7% of 0–4 year olds have completed the primary series and only 1% received a bivalent dose as per NYC DOHMH. In 5–12 year olds, 51% completed the primary series and 6% received bivalent doses [28]. This trend is expected to continue, especially given the upcoming loss of a vaccine recommendation for routine COVID-19 vaccination in children [3]. Numerous studies and reports demonstrated racial disparities in COVID-19 infections during the first year of the pandemic, with Hispanic and non-Hispanic Black and non-Hispanic AI/AN experiencing higher infection and death rates compared to White individuals [18]. As the pandemic ensued in New York City, these disparities became further heightened particularly during the Omicron wave. The NYCDOH published a report in January 2022 [19] demonstrating disproportionately increased hospitalization rates in Black/African American New Yorkers. Similar to these reports, we uncovered disparities in SARS-CoV-2 infections in non-White and Hispanic individuals throughout the pandemic which were exacerbated during the peak of community positivity rates in the Omicron wave. Unvaccinated children and non-White individuals represented a high burden of positive tests. Ultimately, we found that pediatric age, non-White race, Hispanic ethnicity, and unvaccinated status were all independent risk factors for SARS-CoV-2 positivity during the Omicron wave. Recognizing such factors in pandemics in general can help with timely targeting of vulnerable populations and decreasing potentially avoidable healthcare utilization. Early in the pandemic, it was thought that children could have served as reservoirs for new variants that then disseminated into the adult population [29. 30]. The distribution of SARS-CoV-2 variants in a representative subset of our population was comparable with the variant distribution observed in the broader New York City area [31,32] and similar between adults and children over time. Only a few studies have compared the distribution of variants between children and adults, and have done so by comparing pediatric sequences against sequences from general populations that were previously collected or independently deposited in public databases, and found similar distributions between pediatric and adult populations [24,25]. Our study obtained and sequenced samples from adults and children within the same hospital system and time period and more directly suggests that specific SARS-CoV-2 variants are unlikely to explain differences in SARS-CoV-2 epidemiology between the two age groups during the different waves of the pandemic, or that children were reservoirs for new variants. Our study has multiple limitations. Clinical information was not included, and thus we could not evaluate or compare clinical severity of illness or other clinical features by wave or patient population. While testing site may provide a clue into clinical severity, this is an imperfect method given that the universal testing and pre-surgical screening for SARS-CoV-2 upon hospital admission was a common practice during the pandemic [33,34,35,36]. Additionally, WGS was performed in only a subset (2.2%) of our population, though it is likely representative of our study population since the resulting distribution was similar to that of New York City [37]. Finally, in later waves the wide availability of at-home SARS-CoV-2 antigen testing likely lowered the use of RT-PCR tests [38], thus community infections may have been underrepresented. Conclusion Retrospective analysis of the initial, more dynamic waves of the COVID-19 pandemic underscores the importance of monitoring epidemiologic trends across diverse populations to inform equitable public health responses, particularly in the face of emerging or re-emerging diseases and growing vaccine hesitancy. We hope that these data will serve as impetus to institute more timely and personalized action in future pandemics. Abbreviations COVID-19 coronavirus disease 2019 SARS-CoV-2 severe acute respiratory syndrome coronavirus 2 RT-PCR reverse transcriptase polymerase chain reaction Ct cycle threshold NYC New York City WGS Whole genome sequencing EHR electronic health record CIR Citywide Immunization Registry NYSIIS New York State Immunization Information System Declarations Ethics approval and consent to participate: This retrospective clinical study was approved by the Weill Cornell Medicine Institutional Review Board under Protocol # 20-03021671. The IRB granted a waiver of informed consent and a HIPAA waiver in accordance with U.S. federal regulations (45 CFR 46.116(d)) given the retrospective nature of the study and minimal risk to participants. The study was conducted in accordance with the Declaration of Helsinki. Clinical Trial: Not applicable Consent for publication: Not applicable Availability of data and materials: The viral genome sequences generated in this study have been deposited in GISAID under the accession numbers listed in Supplemental data. The data are publicly available at https://gisaid.org/. Competing interests: The authors declare they have no competing interests. Conflict of Interest Disclosures (includes financial disclosures): All authors have no conflicts of interest to disclose. Funding: This work was supported by the Weill Cornell Medicine Department of Pediatrics Pilot Award Program. Authors' contributions: All authors contributed to the study conception and design. Data acquisition and integration from multiple data sources was performed by SR. Statistical analyses were performed by CT. Data analysis and interpretation was performed by CT, PV, and KPA. Analysis of WGS data was performed by PV, EI, CZ, and LM. The first draft of the manuscript was written by PV and KPA. JYH, ZG, ELA, MC, CT, PV, KPA, EI, CZ, LM, and SR reviewed and approved the final manuscript. Acknowledgements: The authors would like to thank Samantha Fennessey from the New York Genome Center and Lucio Queiroz from Weill Cornell Medicine Department of Pathology and Laboratory Medicine for assistance with depositing viral genome data into the data repository and the Weill Cornell Institutional Biorepository Core for specimen processing and storage. Sequencing was performed by the New York Genome Center (NYGC) Sequencing Laboratory as part of the COVID-19 Genomic Research Network (CGRN) with funds generously provided by NYGC donors and the JPB Foundation. References Fried, M. W. et al. Patient Characteristics and Outcomes of 11 721 Patients With Coronavirus Disease 2019 (COVID-19) Hospitalized Across the United States. Clin Infect Dis 72 , e558–e565 (2020). West, A. P., Jr et al. 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Wickham, H. ggplot2: Elegant Graphics for Data Analysis . (Springer Science & Business Media, 2009). Wickham, H. & Grolemund, G. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data . (‘O’Reilly Media, Inc.’, 2016). Andrews, N. et al. Covid-19 Vaccine Effectiveness against the Omicron (B.1.1.529) Variant. N. Engl. J. Med. 386 , 1532–1546 (2022). Buchan, S. A. et al. Estimated Effectiveness of COVID-19 Vaccines Against Omicron or Delta Symptomatic Infection and Severe Outcomes. JAMA Netw Open 5 , e2232760 (2022). Han, M. S. et al. Distinct Clinical and Laboratory Features of COVID-19 in Children During the Pre-Delta, Delta and Omicron Wave. Pediatr. Infect. Dis. J. 42 , 423–428 (2023). Iuliano, A. D. et al. Trends in Disease Severity and Health Care Utilization During the Early Omicron Variant Period Compared with Previous SARS-CoV-2 High Transmission Periods - United States, December 2020-January 2022. MMWR Morb. Mortal. Wkly. Rep. 71 , 146–152 (2022). Marks, K. J. et al. Hospitalization of Infants and Children Aged 0–4 Years with Laboratory-Confirmed COVID-19 - COVID-NET, 14 States, March 2020-February 2022. MMWR Morb. Mortal. Wkly. Rep. 71 , 429–436 (2022). Website. https://health.ny.gov/press/releases/2022/docs/pediatric_covid-19_hospitalization_report.pdf. COVID-19: Data on Vaccines - NYC Health. https://www.nyc.gov/site/doh/covid/covid-19-data-vaccines.page#nyc. Yonker, L. M. et al. Virologic Features of Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Children. J. Infect. Dis. 224 , 1821–1829 (2021). Yonker, L. M. et al. Pediatric Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): Clinical Presentation, Infectivity, and Immune Responses. J. Pediatr. 227 , 45–52.e5 (2020). Annavajhala, M. K. et al. Emergence and expansion of SARS-CoV-2 B.1.526 after identification in New York. Nature 597 , 703–708 (2021). Clinical Variant Data. Department of Health https://coronavirus.health.ny.gov/clinical-variant-data. Siegel, D. A. et al. Trends in COVID-19 Cases, Emergency Department Visits, and Hospital Admissions Among Children and Adolescents Aged 0–17 Years - United States, August 2020-August 2021. MMWR Morb. Mortal. Wkly. Rep. 70 , 1249–1254 (2021). Hatfield, K. M. et al. Assessment of Hospital-Onset SARS-CoV-2 Infection Rates and Testing Practices in the US, 2020–2022. JAMA Netw Open 6 , e2329441 (2023). Roberts, S. C., Peaper, D. R., Sussman, L. S., Martinello, R. A. & Pettker, C. M. Utility of Mass SARS-CoV-2 Testing of Asymptomatic Patients Before Ambulatory and Inpatient Preplanned Procedures Requiring Moderate Sedation or General Anesthesia. JAMA Netw Open 4 , e2114526 (2021). Evans, S., Naylor, N. R., Fowler, T., Hopkins, S. & Robotham, J. The effectiveness and efficiency of asymptomatic SARS-CoV-2 testing strategies for patient and healthcare workers within acute NHS hospitals during an omicron-like period. BMC Infect. Dis. 24 , 64 (2024). COVID-19: Data on Variants. https://www.nyc.gov/site/doh/covid/covid-19-data-variants.page. Website. Rader B, Gertz A, Iuliano AD, et al. Use of At-Home COVID-19 Tests — United States, August 23, 2021–March 12, 2022. MMWR Morb Mortal Wkly Rep 2022;71:489–494. DOI: http://dx.doi.org/10.15585/mmwr.mm7113e1. Additional Declarations No competing interests reported. Supplementary Files VeluGISAIDaccessionnumbers.xlsx Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 08 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviews received at journal 26 Aug, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers invited by journal 14 Aug, 2025 Editor assigned by journal 13 Aug, 2025 Editor invited by journal 13 Aug, 2025 Submission checks completed at journal 12 Aug, 2025 First submitted to journal 12 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7104064","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501961203,"identity":"ed68de4a-338d-4c9a-b65b-18362e481037","order_by":0,"name":"Priya D. Velu","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Priya","middleName":"D.","lastName":"Velu","suffix":""},{"id":501961204,"identity":"da7cbfb9-9a77-4e07-a881-0bc95282d36d","order_by":1,"name":"Charlene Thomas MS","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Charlene","middleName":"Thomas","lastName":"MS","suffix":""},{"id":501961205,"identity":"06570228-1605-4f40-88c0-0c4a63fe6660","order_by":2,"name":"Sophie Rand","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sophie","middleName":"","lastName":"Rand","suffix":""},{"id":501961206,"identity":"ed681666-f04f-4b41-bc47-172e58aa5e6c","order_by":3,"name":"Eddie Imada","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Eddie","middleName":"","lastName":"Imada","suffix":""},{"id":501961207,"identity":"fde8dcd0-0846-4f9c-84f3-b542119c8eaf","order_by":4,"name":"Claudio Zanettini","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Zanettini","suffix":""},{"id":501961208,"identity":"cc516da9-c3ff-438d-b056-758a9dfd7213","order_by":5,"name":"Jin-Young Han","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jin-Young","middleName":"","lastName":"Han","suffix":""},{"id":501961209,"identity":"d2f25912-3cca-44c6-a5da-aaab82247c4d","order_by":6,"name":"Zachary Grinspan","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zachary","middleName":"","lastName":"Grinspan","suffix":""},{"id":501961210,"identity":"ee026338-e09f-43c0-9334-e9327796ad82","order_by":7,"name":"Erika L. Abramson","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Erika","middleName":"L.","lastName":"Abramson","suffix":""},{"id":501961211,"identity":"f310c3a1-b9a3-4cc9-91aa-2d29904c8acd","order_by":8,"name":"Luigi Marchionni","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Luigi","middleName":"","lastName":"Marchionni","suffix":""},{"id":501961212,"identity":"9313906e-7803-430d-a90c-894272caed1d","order_by":9,"name":"Melissa Cushing","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"","lastName":"Cushing","suffix":""},{"id":501961213,"identity":"c83fbbea-e3a2-4274-bb14-26331f49b69f","order_by":10,"name":"Karen P. Acker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACCQYGxgcJBjZyQLYBRIiduYGBgQ2vFmaDDxVpxggtzIwEtbBJzjhzOLGBaC2Ss5ufSfO2MaevbW/ewPCj4l4eP0jLh7LDOLVIyxwztuZtY8vdduZYAWPPmeJiyWbGBsYZ53BrkZNIMLzN28aTu+1GjgEzY1tC4obDjA3MvG34tKR/ADpMIt3s/huIlv0gLX/xaJGWyDECet8gwewGD9QWoF+ADNxaJOecKQYGcoLhtjNpBQd7ziQkzgDacrDnXDpOLRK32zcCo/K/vNnxwxsf/KhISOxvbz744EeZNU4toIiBgwMYDIJaRsEoGAWjYBRgBQAR3lnHiwROyQAAAABJRU5ErkJggg==","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":true,"prefix":"","firstName":"Karen","middleName":"P.","lastName":"Acker","suffix":""}],"badges":[],"createdAt":"2025-07-11 18:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7104064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7104064/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-11990-4","type":"published","date":"2025-11-11T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89971697,"identity":"019aafd5-c63f-462d-8f10-7b22ead49652","added_by":"auto","created_at":"2025-08-27 05:38:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":394890,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of SARS-CoV-2 PCR testing, positive tests, positivity rate, and vaccination rates. Number of tested and positive cases presented as daily case counts. Positivity rates presented as 14-day rolling averages. Due to low testing numbers, rolling average of pediatric positivity rate was excluded in Waves 5 and 6. Vaccination rates indicate daily proportion of patients with 2 or more vaccines doses. Age labels in dark orange refer to dates of vaccine eligibility for those age groups (\u0026gt;18y 12/11/2020, 12-15y 5/10/21, 5-11y 10/29/21, 6mo-4y 6/18/2022).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7104064/v1/b5aa77e901d24ed8f481db0d.png"},{"id":89971723,"identity":"adca48f6-34f8-4707-a8a6-a9195d8ade35","added_by":"auto","created_at":"2025-08-27 05:38:38","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":662173,"visible":true,"origin":"","legend":"\u003cp\u003eSARS-CoV-2 positivity rates during each pandemic wave. By age group \u0026nbsp;(A, B), testing site (C), vaccine status (D), race (E), and ethnicity (F). Solid lines represent children and dotted lines represent adults. (C and D). C, Children; A, Adults (E) AI/AN, American Indian or Alaska Native; NH/PI, Native Hawaiian or Other Pacific Islander.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7104064/v1/33542c9eaf634c7f07ad2cb3.jpeg"},{"id":89971719,"identity":"4c62e07d-0665-43cb-ae8c-c3244546c289","added_by":"auto","created_at":"2025-08-27 05:38:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":198531,"visible":true,"origin":"","legend":"\u003cp\u003eWeekly distribution of SARS-CoV-2 variants in adults and children. Proportion of each SARS-CoV-2 variant indicated in colored bars and total number of sequenced specimens by black line. Variants indicated by WHO classification and include following Pango lineages: Delta (B.1.617.2, AY), Omicron (BA.1, BA.2), Alpha (B.1.1.7), Iota (B.1.526), B lineages (B.1/B1.1 lineages). Week 0 = December 1-7, 2020. The distribution of each variant was compared between adults and children over time using the Kolmogorov-Smirnov test and showed no difference between each variant (Alpha p=0.782, Delta p=0.255, Iota p=0.873, Omicron sublineages p=0.865).\u003c/p\u003e","description":"","filename":"floatimage37.png","url":"https://assets-eu.researchsquare.com/files/rs-7104064/v1/5f340e5d4562c84b292e74ca.png"},{"id":96105059,"identity":"8686ec36-c5cf-4a82-b29d-dbbdb2bbe9fa","added_by":"auto","created_at":"2025-11-17 16:07:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2189393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7104064/v1/1b8d89e4-00c2-4fbb-b851-c3435a39213b.pdf"},{"id":89971688,"identity":"3cb3dccd-e579-4d28-b7e3-d3483d8f28c2","added_by":"auto","created_at":"2025-08-27 05:38:35","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":121132,"visible":true,"origin":"","legend":"","description":"","filename":"VeluGISAIDaccessionnumbers.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7104064/v1/e23a6ac2b742765b3a79e1ce.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changing epidemiology of SARS-CoV-2 positivity rates in a diverse population of children and adults during variant evolution and progressive vaccination eligibility in New York City","fulltext":[{"header":"Background","content":"\u003cp\u003eThe coronavirus disease 2019 (COVID-19) pandemic first emerged in the United States in New York City (NYC) in March 2020, and over the next two years, NYC would remain a focal point of the pandemic, experiencing multiple waves of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections [1,2,3,4,5]. Each wave presented unique challenges due to the emergence of SARS-CoV-2 variants that appeared to impact adults and children differently [6]. Despite early perceptions that children were not impacted by SARS-CoV-2 infections, we now know that children were not spared from infections, mortality, and poor outcomes due to COVID-19, especially when the Delta and Omicron variants predominated [7,8,9]. Against this backdrop, effective vaccines were made available and rolled out at varying stages by age group with eligibility for children following that of adults [10,11]. The epidemiology of waves in the United States were largely impacted by vaccination uptake with higher case rates seen in regions with low vaccination rates [12].\u003c/p\u003e\u003cp\u003eAlthough we are now in the post-pandemic era, SARS-CoV-2 continues to circulate resulting in recurrent waves throughout the year. These waves still result in substantial morbidity resulting in an estimated 230,000 hospitalizations in adults and ~\u0026thinsp;10,000 hospitalizations in children\u0026thinsp;\u0026le;\u0026thinsp;18 years during the 2024\u0026ndash;2025 season in the U.S [13]. In addition to the effects of acute COVID-19, children are susceptible to the severe post-infectious complication multisystem inflammatory syndrome in children (MIS-C) [6,14,15], and both adults and children are at risk for developing long COVID-19 [15,16,17]. Despite continued impacts of COVID-19 infections, the FDA recently recommended against vaccinating healthy children and pregnant individuals [3]. Understanding the patterns and impacts of these prior COVID-19 waves may inform future prevention strategies and public health responses for future pandemics.\u003c/p\u003e\u003cp\u003eHere we describe the changing epidemiology of SARS-CoV-2 testing and positivity rates in the context of vaccination and changing viral variants during the height of the COVID-19 pandemic in a large hospital system in New York City. Our objectives were (1) to delineate SARS-CoV-2 positivity rates by age, race and ethnicity, testing site, and vaccination status over 5 waves of SARS-CoV-2 infections, (2) describe vaccination rates over time in our study cohort, and (3) evaluate the distribution of SARS-CoV-2 variants in adults and children over time through whole genome sequencing.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and data sources\u003c/h2\u003e\u003cp\u003eSpecimens and data were sourced from four hospital systems within our hospital network. This included remnant nasopharyngeal (NP) swab viral transport media from routine clinical care specimens used for SARS-CoV-2 reverse transcriptase polymerase chain reaction (RT-PCR) testing and associated clinical data including specimen collection date, RT-PCR result, testing instrument, and RT-PCR testing location at time of test order. Data obtained from the electronic health record (EHR) included patient birth date, sex, race, ethnicity, and dates of COVID-19 vaccine administration. Patients were defined as pediatric (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;21 years old) or adults (\u0026gt;\u0026thinsp;21 years old) based on the age at time of RT-PCR specimen collection.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefining waves of infection\u003c/h3\u003e\n\u003cp\u003eWaves of SARS-CoV-2 infections during the pandemic were defined using New York City Department of Health and Mental Hygiene daily case numbers to establish waves based on daily peaks and nadirs of infection [18,19]. There were five waves examined in this study, starting with Wave 2 from October 1, 2020 to June 30, 2020, Wave 3 from July 1, 2021 to December 1, 2021, Wave 4 from December 2, 2021 to March 5, 2022, Wave 5 from March 6, 2022 to June 12, 2022, and Wave 6 from June 13, 2022 to September 19, 2022.\u003c/p\u003e\n\u003ch3\u003eRT-PCR testing for SARS-CoV-2\u003c/h3\u003e\n\u003cp\u003eRT-PCR tests collected from October 1, 2020 to September 19, 2022 were included in this study. To avoid overestimation of the positivity rate by patients who had repeat testing within a pandemic wave, only one test per unique patient per wave period was included. If a patient tested positive during the wave, the first positive test was included. If the patient did not test positive during a particular wave, the first negative test was used. Multiple RT-PCR testing platforms were used between the hospitals, and included the BioFire Respiratory Panel v.2, Cepheid Xpert Xpress SARS-CoV-2, Hologic Panther Fusion SARS-CoV-2 assay, Roche cobas 6800 SARS-CoV-2, Roche cobas SARS-CoV-2 \u0026amp; Influenza, and QIAstat-Dx Respiratory SARS-CoV-2 Panel.\u003c/p\u003e\n\u003ch3\u003eWhole genome sequencing and lineage determination of SARS-CoV-2\u003c/h3\u003e\n\u003cp\u003eWhole genome sequencing (WGS) was performed at the New York Genome Center on remnant NP swabs in viral transport media. Samples with Ct values greater than 33 on RT-PCR testing were excluded. Briefly, nucleic acid was extracted on the KingFisher Flex Purification system (Thermofisher) using the MagMAX Viral Pathogen Nucleic Acid Isolation kit. After reverse transcription, the Molecular Loop Viral RNA Target Capture Kit (Molecular Loop) was used to prepare SARS-CoV-2 targeted libraries according to manufacturer recommendations. Libraries were pooled, quantified, and then sequenced on a NovaSeq 6000 sequencer with 2x150 base pair reads. Read pairs were processed and merged into single end reads that were mapped against the SARS-CoV-2 reference using BWA-MEM v0.7.17. The resulting alignments were processed and genome sequences were determined by molecule alignment pileup consensus calling with a minimum support of 5 unique reads. SARS-CoV-2 lineage and clade classification were determined by Phylogenetic Assignment of Named Global Outbreak (pangolin, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://covlineages.org/resources/pangolin.html\u003c/span\u003e\u003cspan address=\"https://covlineages.org/resources/pangolin.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), pangolin v4.1.3 and pangoLEARN/USHeR 1.15.1.\u003c/p\u003e\n\u003ch3\u003eVaccination status\u003c/h3\u003e\n\u003cp\u003eVaccination status on all patients with RT-PCR testing was determined by cross-referencing patient information from the EHR with the New York Citywide Immunization Registry (CIR) and New York State Immunization Information System (NYSIIS). Vaccination status was defined as fully vaccinated if the patient had completed a full vaccination series (two doses for Pfizer or Moderna or one dose for Janssen/Johnson \u0026amp; Johnson) at least fourteen days before RT-PCR specimen collection; otherwise, the patient was considered unvaccinated at time of specimen collection. If the subject lived in New York and no vaccine data were available in CIR or NYSIIS, they were classified as unvaccinated. Vaccine status was classified as unknown if they lived outside NYS and no vaccine data was available on CIR or NYSIIS.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData and statistical analysis\u003c/h2\u003e\u003cp\u003eAll data analyses and statistical calculations were performed using R v.4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Figures were generated using the package ggplot2 v.3.3.4 [20.21]. Descriptive statistics (i.e., median, interquartile range, frequency and percent) were calculated to describe demographic and clinical characteristics. Comparative analyses comparing categorical variables were conducted using a Pearson\u0026rsquo;s Chi-squared test. To evaluate factors associated with SARS-CoV-2 positivity during Wave 4 when differences were seen among patient populations, a multivariable logistic regression was performed controlling for age, race, ethnicity, and vaccination status and presented as adjusted odds ratios (aOR). All p-values were two-sided with statistical significance evaluated at the 0.05 alpha level. P-values were not adjusted for multiple comparisons. To assess trends in variant distribution over time in adults versus children, the Kolmogorov-Smirnov test was utilized.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eDemographics\u003c/h2\u003e\u003cp\u003eDuring the study period from October 1, 2020 to September 19, 2022, 243,457 tests were performed in adults (89.2%) and 29,333 tests were performed in children (10.8%), with overall positivity rates of 6.2% in adults and 5.9% in children. The testing distribution by age varied by wave, with the highest proportion of children tested during Waves 2 (11.4%), Wave 3 (14%), and Wave 4 (10.1%), and the lowest proportion of children tested in Wave 5 (5.9%) and Wave 6 (5.6%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Individuals tested identified as White (101,478, 37%), Black (32,688, 12%), Asian (36,217, 13%), Native Hawaiian or Other Pacific Islander (NH/PI) (291, 0.1%), American Indian or Alaska Native (AI/AN) (814, 0.3%) and 101,302 (37%) individuals did not have race data available. There were 40,580 (15%) individuals who identified as Hispanic, 166,272 (61%) as non-Hispanic, and 65,938 (24%) with unavailable ethnicity data.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of adults and children tested for SARS-CoV-2 by PCR during each pandemic wave.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;272,790\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWave 2\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;122,570\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWave 3\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;61,038\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWave 4\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;39,390\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWave 5\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;32,334\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWave 6\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;17,458\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at Test (Median, (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (33, 67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49 (32, 66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (31, 67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (32, 67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54 (36, 70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56 (37, 72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Group (N, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;1 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,579 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,643 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e904 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e588 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e288 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e156 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;4 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,688 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,389 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,782 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e976 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e361 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e180 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;12 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,544 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,569 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,326 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,019 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e412 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e218 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;16 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,225 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,188 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,039 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e552 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e301 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e145 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u0026ndash;21 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,297 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,144 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,476 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e849 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e542 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e286 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e243,457 (89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108,637 (89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52,511 (86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35,406 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30,430 (94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16,473 (94%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (N, %)\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;272,627)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161,510 (59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71,658 (58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36,199 (59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23,723 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19,444 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10,486 (60%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111,117 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50,851 (42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24,801 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15,622 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12,874 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6,969 (40%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace (N, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e814 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e325 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e208 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e126 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e103 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36,217 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12,382 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,619 (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,862 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,457 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,897 (17%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack or African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32,688 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,400 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,765 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4,948 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,164 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,411 (14%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNative Hawaiian or Pacific Islander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e291 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101,478 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45,487 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22,299 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15,096 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12,135 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6,461 (37%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown or Declined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101,302 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50,823 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21,102 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13,310 (34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10,448 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5,619 (32%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity (N, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,580 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16,572 (14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10,243 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,670 (14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,115 (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,980 (17%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Hispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166,272 (61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71,521 (58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37,632 (62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24,606 (62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21,126 (65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11,387 (65%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown or Declined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65,938 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34,477 (28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13,163 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9,114 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,093 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3,091 (18%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSetting (N, %) (N\u0026thinsp;=\u0026thinsp;272,789)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmergency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51,866 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16,704 (14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17,131 (28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7,014 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7,295 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3,722 (21%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInpatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53,583 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17,880 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14,712 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7,795 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8,546 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4,650 (27%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutpatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167,340 (61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87,985 (72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29,195 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24,581 (62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16,493 (51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9,086 (52%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eChange in Positivity Rates by Demographics, Testing Site, and Vaccine Status during Wave 4\u003c/h2\u003e\u003cp\u003eThe positivity rate changed over time by wave with the most pronounced shift during Wave 4, coinciding with the emergence of the Omicron variant in NYC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The overall positivity rate during Wave 4 was 12.1% with a peak 14-day rolling overage of 27.7% on 1/8/2022. Positivity rates for children surpassed those in adults for the first time during Wave 4 (children 15.6%, adults 11.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Among children, the highest positivity rate was seen in children 5\u0026ndash;12 years (18.5%), followed by 1\u0026ndash;4 years (15.9%), 12\u0026ndash;16 years (15.2%), 16\u0026ndash;21 years (14.4%), and 0\u0026ndash;1 years (11.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Additionally, there was a shift in testing site, with an increase in outpatient testing and positivity rates observed in both adults and children during Wave 4 compared to Wave 3 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). During Wave 3 when the Delta variant was predominant, total outpatient testing represented only 48% of tests in adults and children, but increased to 62% and 70% in adults and children, respectively, in Wave 4. In children who tested positive, 80% of positive cases were outpatient in Wave 4 and positivity rate was higher in outpatient compared to ED and inpatient sites (outpatient 18%, ED 13%, inpatient 5.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Among racial and ethnic groups, the highest positivity rates in Wave 4 were seen in individuals who identified as NH/PI (11/48 [22.9%]) and Black (793/4948, [16%]), and lowest in White (1533/13,563, [10.2%]) and Asian (644/5862, [11%]) individuals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Higher positivity rates were also seen in Hispanic (879/5670, [16%]) compared to non-Hispanic individuals (2703/24606, [11%], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) during Wave 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate independent risk factors for testing positive during Wave 4 when positivity rates were highest and when disparities by race, ethnicity and age appeared to be most pronounced, we performed a multivariable logistic regression which demonstrated that pediatric age (aOR 1.12, 95% CI 1.01, 1.23), non-White race (aOR 1.37, 95% CI 1.26, 1.47), Hispanic ethnicity (aOR 1.53, 95% CI 1.38, 1.68), and unvaccinated status (aOR 1.52, 95% CI 1.42, 1.63) were all independent risk factors for SARS-CoV-2 positivity during Wave 4 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression analysis of characteristics associated with SARS-CoV-2 positivity during Wave 4.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWhite\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNon-White\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.26, 1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUnknown or Declined\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97, 1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNot Hispanic or Latino\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHispanic or Latino\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.38, 1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUnknown or Declined\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13, 1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVaccination Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFully Vaccinated\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo Vaccination Data\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74, 1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUnvaccinated/Partial\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.42, 1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdult\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePediatrics\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01, 1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: aOR\u0026thinsp;=\u0026thinsp;adjusted Odds Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eVaccination rates over time\u003c/h2\u003e\u003cp\u003eCOVID-19 vaccine status was available for 233,640 (96%) adults and 28,001 (95.4%) children. Vaccination rates in adults and children increased over time as vaccine eligibility expanded although children remained significantly under-vaccinated compared to adults by the end of the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). During Wave 2, 13% of adults and 1% of children had received at least 2 doses of a COVID-19 vaccine, and by Wave 4, 74% of adults were fully vaccinated, with this rate sustained in Wave 5 (73%) and Wave 6 (74%). In children, full vaccination status continued to increase as the waves progressed (Wave 3: 11%, Wave 4: 22%, Wave 5: 29%, Wave 6: 32%) in line with later vaccine eligibility in children compared to adults. However, despite eligibility expanding to children 6 months to 4 years old on June 18, 2022 at the beginning of Wave 6, vaccination rates remained low in this age group by the end our study period in September 2022 with only 0.1% of 1\u0026ndash;4 years with available vaccination data fully vaccinated at this time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eVariant distribution\u003c/h2\u003e\u003cp\u003eWhole genome sequencing (WGS) on remnant nasopharyngeal swab specimens from RT-PCR testing was performed on a subset of our population (adult 2.1%, children 3.3%) to determine if differences in epidemiologic trends between adults and children could be explained by infections due to different variants. WGS of 5092 adult and 1084 pediatric SARS-CoV-2 specimens collected from December 1, 2020 to September 19, 2022 were performed. Most sequenced specimens were from patients tested in outpatient settings (72%). Predominant lineages during Wave 2 were B.1 lineages (47%), Iota/B.1.526 (20%), Alpha/B.1.1.7 (13%), and B.1.637 (6.5%). The predominant lineage during Wave 3 was Delta/B.1.617.2/AY lineages (87%), during Wave 4 was Omicron/BA.1 (77%), during Wave 5 was Omicron or BA.2 (81%), and during Wave 6 were Omicron/BA.5 (51%), Omicron/BA.4 (14%), and Omicron/BA.2 (14%). A Kolmogorov-Smirnov test demonstrated no difference in distribution of Alpha, Iota, B.1.637, Omicron, Delta, or General B lineages between adults and children during each wave (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, urban, and demographically diverse cohort of adult and pediatric patients in New York City tested for SARS-CoV-2 by RT-PCR during several crucial waves of the COVID-19 pandemic, we observed dynamic shifts in positivity rates across age, race and ethnicity, testing site and vaccination status. These changes were most pronounced during Wave 4, when the Omicron variant emerged, resulting in the highest positivity rates across all groups, but with disproportionate impacts on young children, certain racial and ethnic groups, and unvaccinated individuals. Additionally, vaccination rates in children lagged despite increasing eligibility for younger age groups throughout the study period. As we anticipate future infectious threats in the setting of increasing vaccine hesitancy and loss of vaccine mandates [3], understanding trends in testing patterns, positivity rates, and vaccination across diverse populations will be essential to guiding future public health responses and vaccine efforts.\u003c/p\u003e\u003cp\u003eAs Omicron emerged in December 2021 and gained predominance, community positivity rates in both adults and children the United States climbed to unprecedented levels with both vaccinated and unvaccinated individuals developing infections as vaccine effectiveness against symptomatic infections decreased from 90.9% against Delta to 65.5% against Omicron [22]. Notably in our study, the positivity in children surpassed adults for the first time. Though vaccination continued to offer high protection against hospitalization and severe disease [23], young children who were still not eligible for vaccination were highly impacted during the Omicron wave with peak hospitalization rates up to six times higher than during the Delta wave [24,25,26]. This shift appeared to be driven by high positivity rates in unvaccinated young children 5\u0026ndash;12 years old and 1\u0026ndash;4 years old in emergency department and outpatient settings. Indeed, 74.7% of children with SARS-CoV-2 infections during this wave were unvaccinated in our cohort. Although inpatient positivity rates in children were lower compared to ED and outpatient, they increased substantially from 0.8% during the Delta wave to 5.9% during the Omicron wave in our cohort. This is consistent with the pediatric experience in New York City, as pediatric hospitalizations due to or with COVID-19 increased 18-fold during the Omicron wave [27].\u003c/p\u003e\u003cp\u003eDuring the Omicron wave, children 0\u0026ndash;4 years old were not yet eligible for vaccination, and only 22% of children in our cohort were vaccinated leaving them particularly vulnerable to infection. While there had been a jump in childhood vaccination rates after the openings of vaccine eligibility for the 12\u0026ndash;15 years old and 5\u0026ndash;11 years old age groups, a similar trend was not seen in younger children 6 months to 4 years when their eligibility expanded in June 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Vaccine coverage with COVID-19 vaccine has continued to be an issue in children in New York City and by September 2023, only 7% of 0\u0026ndash;4 year olds have completed the primary series and only 1% received a bivalent dose as per NYC DOHMH. In 5\u0026ndash;12 year olds, 51% completed the primary series and 6% received bivalent doses [28]. This trend is expected to continue, especially given the upcoming loss of a vaccine recommendation for routine COVID-19 vaccination in children [3].\u003c/p\u003e\u003cp\u003eNumerous studies and reports demonstrated racial disparities in COVID-19 infections during the first year of the pandemic, with Hispanic and non-Hispanic Black and non-Hispanic AI/AN experiencing higher infection and death rates compared to White individuals [18]. As the pandemic ensued in New York City, these disparities became further heightened particularly during the Omicron wave. The NYCDOH published a report in January 2022 [19] demonstrating disproportionately increased hospitalization rates in Black/African American New Yorkers. Similar to these reports, we uncovered disparities in SARS-CoV-2 infections in non-White and Hispanic individuals throughout the pandemic which were exacerbated during the peak of community positivity rates in the Omicron wave. Unvaccinated children and non-White individuals represented a high burden of positive tests. Ultimately, we found that pediatric age, non-White race, Hispanic ethnicity, and unvaccinated status were all independent risk factors for SARS-CoV-2 positivity during the Omicron wave. Recognizing such factors in pandemics in general can help with timely targeting of vulnerable populations and decreasing potentially avoidable healthcare utilization.\u003c/p\u003e\u003cp\u003eEarly in the pandemic, it was thought that children could have served as reservoirs for new variants that then disseminated into the adult population [29. 30]. The distribution of SARS-CoV-2 variants in a representative subset of our population was comparable with the variant distribution observed in the broader New York City area [31,32] and similar between adults and children over time. Only a few studies have compared the distribution of variants between children and adults, and have done so by comparing pediatric sequences against sequences from general populations that were previously collected or independently deposited in public databases, and found similar distributions between pediatric and adult populations [24,25]. Our study obtained and sequenced samples from adults and children within the same hospital system and time period and more directly suggests that specific SARS-CoV-2 variants are unlikely to explain differences in SARS-CoV-2 epidemiology between the two age groups during the different waves of the pandemic, or that children were reservoirs for new variants.\u003c/p\u003e\u003cp\u003eOur study has multiple limitations. Clinical information was not included, and thus we could not evaluate or compare clinical severity of illness or other clinical features by wave or patient population. While testing site may provide a clue into clinical severity, this is an imperfect method given that the universal testing and pre-surgical screening for SARS-CoV-2 upon hospital admission was a common practice during the pandemic [33,34,35,36]. Additionally, WGS was performed in only a subset (2.2%) of our population, though it is likely representative of our study population since the resulting distribution was similar to that of New York City [37]. Finally, in later waves the wide availability of at-home SARS-CoV-2 antigen testing likely lowered the use of RT-PCR tests [38], thus community infections may have been underrepresented.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRetrospective analysis of the initial, more dynamic waves of the COVID-19 pandemic underscores the importance of monitoring epidemiologic trends across diverse populations to inform equitable public health responses, particularly in the face of emerging or re-emerging diseases and growing vaccine hesitancy. We hope that these data will serve as impetus to institute more timely and personalized action in future pandemics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecoronavirus disease 2019\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSARS-CoV-2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esevere acute respiratory syndrome coronavirus 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRT-PCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereverse transcriptase polymerase chain reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCt\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecycle threshold\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNYC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNew York City\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWGS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhole genome sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eelectronic health record\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCitywide Immunization Registry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNYSIIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNew York State Immunization Information System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This retrospective clinical study was approved by the Weill Cornell Medicine Institutional Review Board under Protocol # 20-03021671. The IRB granted a waiver of informed consent and a HIPAA waiver in accordance with U.S. federal regulations (45 CFR 46.116(d)) given the retrospective nature of the study and minimal risk to participants. The study was conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe viral genome sequences generated in this study have been deposited in GISAID under the accession numbers listed in Supplemental data. The data are publicly available at https://gisaid.org/.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosures (includes financial disclosures):\u0026nbsp;\u003c/strong\u003eAll authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Weill Cornell Medicine Department of Pediatrics Pilot Award Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e All authors contributed to the study conception and design. Data acquisition and integration from multiple data sources was performed by SR. Statistical analyses were performed by CT. Data analysis and interpretation was performed by CT, PV, and KPA. Analysis of WGS data was performed by PV, EI, CZ, and LM. The first draft of the manuscript was written by PV and KPA. JYH, ZG, ELA, MC, CT, PV, KPA, EI, CZ, LM, and SR reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Samantha Fennessey from the New York Genome Center and Lucio Queiroz from Weill Cornell Medicine Department of Pathology and Laboratory Medicine for assistance with depositing viral genome data into the data repository and the Weill Cornell Institutional Biorepository Core for specimen processing and storage. Sequencing was performed by the New York Genome Center (NYGC) Sequencing Laboratory as part of the COVID-19 Genomic Research Network (CGRN) with funds generously provided by NYGC donors and the JPB Foundation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFried, M. W. \u003cem\u003eet al.\u003c/em\u003e Patient Characteristics and Outcomes of 11 721 Patients With Coronavirus Disease 2019 (COVID-19) Hospitalized Across the United States. \u003cem\u003eClin Infect Dis\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, e558\u0026ndash;e565 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWest, A. P., Jr \u003cem\u003eet al.\u003c/em\u003e Detection and characterization of the SARS-CoV-2 lineage B.1.526 in New York. \u003cem\u003eNat Commun\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 4886 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrasad, V. \u0026amp; Makary, M. A. An Evidence-Based Approach to Covid-19 Vaccination. \u003cem\u003eN Engl J Med\u003c/em\u003e (2025) doi:10.1056/NEJMsb2506929.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai, K. \u003cem\u003eet al.\u003c/em\u003e Community transmission of SARS-CoV-2 during the Delta wave in New York City. \u003cem\u003eBMC infectious diseases\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTandon, P. \u003cem\u003eet al.\u003c/em\u003e The fourth wave: vaccination status and intensive care unit mortality at a large hospital system in New York City. \u003cem\u003eAcute Crit Care\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 339\u0026ndash;346 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePavia, A. T. 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S. \u003cem\u003eet al.\u003c/em\u003e Distinct Clinical and Laboratory Features of COVID-19 in Children During the Pre-Delta, Delta and Omicron Wave. \u003cem\u003ePediatr. Infect. Dis. J.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e, 423\u0026ndash;428 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIuliano, A. D. \u003cem\u003eet al.\u003c/em\u003e Trends in Disease Severity and Health Care Utilization During the Early Omicron Variant Period Compared with Previous SARS-CoV-2 High Transmission Periods - United States, December 2020-January 2022. \u003cem\u003eMMWR Morb. Mortal. Wkly. Rep.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 146\u0026ndash;152 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarks, K. J. \u003cem\u003eet al.\u003c/em\u003e Hospitalization of Infants and Children Aged 0\u0026ndash;4 Years with Laboratory-Confirmed COVID-19 - COVID-NET, 14 States, March 2020-February 2022. \u003cem\u003eMMWR Morb. Mortal. Wkly. Rep.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 429\u0026ndash;436 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWebsite. https://health.ny.gov/press/releases/2022/docs/pediatric_covid-19_hospitalization_report.pdf.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCOVID-19: Data on Vaccines - NYC Health. https://www.nyc.gov/site/doh/covid/covid-19-data-vaccines.page#nyc.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYonker, L. M. \u003cem\u003eet al.\u003c/em\u003e Virologic Features of Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Children. \u003cem\u003eJ. Infect. Dis.\u003c/em\u003e \u003cb\u003e224\u003c/b\u003e, 1821\u0026ndash;1829 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYonker, L. M. \u003cem\u003eet al.\u003c/em\u003e Pediatric Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): Clinical Presentation, Infectivity, and Immune Responses. \u003cem\u003eJ. Pediatr.\u003c/em\u003e \u003cb\u003e227\u003c/b\u003e, 45\u0026ndash;52.e5 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnnavajhala, M. K. \u003cem\u003eet al.\u003c/em\u003e Emergence and expansion of SARS-CoV-2 B.1.526 after identification in New York. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e597\u003c/b\u003e, 703\u0026ndash;708 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClinical Variant Data. \u003cem\u003eDepartment of Health\u003c/em\u003e https://coronavirus.health.ny.gov/clinical-variant-data.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSiegel, D. A. \u003cem\u003eet al.\u003c/em\u003e Trends in COVID-19 Cases, Emergency Department Visits, and Hospital Admissions Among Children and Adolescents Aged 0\u0026ndash;17 Years - United States, August 2020-August 2021. \u003cem\u003eMMWR Morb. Mortal. Wkly. 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The effectiveness and efficiency of asymptomatic SARS-CoV-2 testing strategies for patient and healthcare workers within acute NHS hospitals during an omicron-like period. \u003cem\u003eBMC Infect. Dis.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 64 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCOVID-19: Data on Variants. https://www.nyc.gov/site/doh/covid/covid-19-data-variants.page.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWebsite. Rader B, Gertz A, Iuliano AD, et al. Use of At-Home COVID-19 Tests \u0026mdash; United States, August 23, 2021\u0026ndash;March 12, 2022. MMWR Morb Mortal Wkly Rep 2022;71:489\u0026ndash;494. DOI: http://dx.doi.org/10.15585/mmwr.mm7113e1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, SARS-CoV-2 variants, epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-7104064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7104064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe COVID-19 pandemic resulted in multiple waves of infections in New York City that were driven by evolving SARS-CoV-2 variants and shifting vaccine eligibility. We describe the trends in SARS-CoV-2 epidemiology in adults and children over consecutive waves during the height of the COVID-19 pandemic in New York City.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed SARS-CoV-2 PCR results, demographics, and vaccination data in adults and children in a multi-hospital network in New York City from 10/1/2020 to 9/19/2022. A subset of nasopharyngeal specimens underwent whole genome sequencing to determine the SARS-CoV-2 variant distribution in adults and children.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThere were 243,457 SARS-CoV-2 PCR tests performed in adults (89.2%) and 29,333 in children (10.8%) with overall positivity rates of 6.2% in adults and 5.9% in children during the study period. The highest overall positivity rate (12.1%) was seen during Wave 4 when the Omicron variant was predominant and positivity rates in children surpassed those in adults for the first time (children 15.6%, adults 11.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). During Wave 4, SARS-CoV-2 positivity was associated with pediatric age (aOR 1.12, 95% CI 1.01, 1.23), non-White race (aOR 1.37, 95% CI 1.26, 1.47), Hispanic ethnicity (aOR 1.53, 95% CI 1.38, 1.68), and unvaccinated status (aOR 1.52, 95% CI 1.42, 1.63). SARS-CoV-2 variant distribution did not differ over time between adults and children.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur large cohort of SARS-CoV-2 testing over multiple COVID-19 waves in New York City demonstrated a shift in positivity rates when the Omicron variant was predominant, with disproportionate positivity in children, unvaccinated individuals, and specific racial and ethnic groups. As vaccination rates decline in response to changes in vaccine recommendations, this scenario may recur with the emergence of a new virulent SARS-CoV-2 variant or re-emergence of vaccine-preventable diseases. These findings highlight the need for targeted public health strategies that prioritize vulnerable populations during respiratory viral surges.\u003c/p\u003e","manuscriptTitle":"Changing epidemiology of SARS-CoV-2 positivity rates in a diverse population of children and adults during variant evolution and progressive vaccination eligibility in New York City","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 05:38:11","doi":"10.21203/rs.3.rs-7104064/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T08:28:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-31T08:31:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T03:29:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216391020988792811315318851415700421778","date":"2025-08-23T15:45:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115063345568460821627765384447465164892","date":"2025-08-16T14:56:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-15T03:56:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T09:19:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-13T04:04:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T15:20:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-08-12T15:18:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e416a837-04e9-4de6-ae90-e17413396acc","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:01:54+00:00","versionOfRecord":{"articleIdentity":"rs-7104064","link":"https://doi.org/10.1186/s12879-025-11990-4","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-11-11 15:57:54","publishedOnDateReadable":"November 11th, 2025"},"versionCreatedAt":"2025-08-27 05:38:11","video":"","vorDoi":"10.1186/s12879-025-11990-4","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11990-4","workflowStages":[]},"version":"v1","identity":"rs-7104064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7104064","identity":"rs-7104064","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}