Cell-free DNA (cfDNA) analysis is a minimally invasive method that can be used to detect genomic abnormalities by directly testing a blood sample. This method is particularly useful for immunosuppressed patients, who are at high risk of complications from tissue biopsy. The cfDNA tumor fraction (TF) varies greatly across cancer type and between patients. Thus, the detection of molecular alterations is highly dependent on the circulating TF. In our study, we aimed to calculate the TF and characterize the copy number aberration (CNA) profile of cfDNA from patients with rare malignancies occurring in immunosuppressed environments or immune-privileged sites. To accomplish this, we recruited 36 patients: 19 patients with non-Hodgkin lymphoma (NHL) who were either human immunodeficiency virus (HIV)-positive or organ transplant recipients, 5 HIV-positive lung cancer patients, and 12 patients with glioma. cfDNA was extracted from the patients’ plasma and sequenced using low-coverage whole genome sequencing (LC-WGS). The cfDNA TF was then calculated using the ichorCNA bioinformatic algorithm, based on the CNA profile. In parallel, we performed whole exome sequencing of patient tumor tissue and cfDNA samples with detectable TFs. We detected a cfDNA TF in 29% of immune-suppressed patients (one patient with lung cancer and six with systemic NHL), with a TF range from 8 to 70%. In these patients, the events detected in the CNA profile of cfDNA are well-known events associated with NHL and lung cancer. Moreover, cfDNA CNA profile correlated with the CNA profile of matched tumor tissue. No tumor-derived cfDNA was detected in the glioma patients. Our study shows that tumor genetic content is detectable in cfDNA from immunosuppressed patients with advanced NHL or lung cancer. LC-WGS is a time- and cost-effective method that can help select an appropriate strategy for performing extensive molecular analysis of cfDNA. This technique also enables characterization of CNAs in cfDNA when sufficient tumor content is available. Hence, this approach can be used to collect useful molecular information that is relevant to patient care. Patients infected with human immunodeficiency virus (HIV) or who are organ transplant recipients are at increased risk of developing several cancers compared to the general population . MoreoveThe cfDNA tumor fraction (TF), which is the proportion of tumor molecules that are present in a cfDNA sample, varies greatly across cancer type and between patients, and represents a clinically relevant biomarker . The detRecently, a cost-effective approach, ichorCNA11, was established to calculate the TF of cfDNA using low-coverage whole genome sequencing (LC-WGS). This method identifies segmental/arm-level copy number aberrations (CNAs), which enables the TF to be inferred .The aim of our study was to calculate the TF and determine the CNA profile of cfDNA in immunosuppressed patients or patients with tumors located in immune-privileged sites, such as the central nervous system (CNS) using LC-WGS and ichorCNA. This approach can guide the decision to subsequently perform a targeted PCR-based method, a targeted sequencing, or a WES, based on the circulating tumor content, to provide a more comprehensive molecular analysis of cfDNA in this specific, frail population.For this purpose, we recruited patients with cancers known to be associated with variable amounts of cfDNA (from low to high) in immunocompetent patients . SpecifiClinicalTrials.gov identifier: NCT03706625), a multidisciplinary, multicentric, translational research program that focuses on severe tumors that appear in immunosuppressed patients or in immune-privileged sites such as the CNS. The protocol was approved by the French national IRB (no. 2018-A01099-46) and the “Commission Nationale de l’Informatique et des Libertés” (CNIL no. 918222).Patients were enrolled in the IDeATIon project (Three types of rare and severe cancers were selected: (1) NHL  occurring in patients with HIV or in transplant recipients, (2) NSCLC in patients with HIV, and (3) glioma. The inclusion criteria were as follows: (1) male or female adults (age ≥ 18 years old), (2) confirmed histopathological diagnosis of one of the three types of cancer listed above, (3) anti-tumoral treatment-naive (except for some glioma patients), (4) hemoglobin level > 9 g/dl for lung cancer patients and > 7 g/dl for NHL and glioma patients, and (5) written informed consent. From July 2018 to December 2020, 118 patients were enrolled in the IDeATIon project. This study includes 36 of these prospectively enrolled patients .® CRL-5914Tm) and H1975 (ATCC® CRL-5908Tm), and healthy donor blood using a QIAamp DNA Blood Mini Kit (Qiagen). The DNA concentrations were quantified on a QuantusTm Fluorometer (Promega) using QuantiFluor® ONE dsDNA Dye (Promega). Genomic DNA was sheared to an average size of 150 bp using The Bioruptor® Pico (Diagenode). Fragment size was confirmed on a TapeStation 4200 electrophoresis system (Agilent). To create the spike-in mix, H2030 or H1975 DNA was mixed with healthy donor DNA to a final proportion of 5% or 15%, and the samples were analyzed by LC-WGS.Genomic DNA (gDNA) was isolated from two human NSCLC cell lines, H2030  following the manufacturer’s instructions and eluted in a volume of 35 μl. cfDNA was quantified using a QuantusTm Fluorometer (Promega). The size distribution of the cfDNA was checked on a TapeStation 4200 electrophoresis system (Agilent) using Cell-free DNA ScreenTape.Blood was collected in Cell-Free DNA Collection Tubes (ROCHE), which were then centrifuged for 10 min at 820 ® ThruPLEX® Tag-seq Kit (Takara Bio). Libraries were quantified using a QubitTm dsDNA HS Assay Kit (Thermo Fisher), and the fragment sizes were confirmed using a TapeStation 4200 electrophoresis system (Agilent). Equal amounts of the sequencing libraries were pooled to generate a single sample for sequencing. WGS at an average coverage of 4 × was performed on a Novaseq (Illumina) system using 2 × 150-bp paired-end sequencing. BCL2FASTQ (Illumina) was used to convert bcl files to fastq files. The coverage statistics are summarized in Sequencing libraries were prepared from 4 to 10 ng of cfDNA using the SMARTerBased on the pre-processed aligned reads, the TF in the cfDNA was calculated using the ichorCNA workflow, as described previously . BrieflyTumor gDNA was isolated from fresh or formalin-fixed paraffin-embedded (FFPE) diagnostic tissue biopsies using a QIAamp DNA Mini Kit (Qiagen) or a QIAamp DNA FFPE Tissue Kit (Qiagen), respectively, according to the manufacturer’s instructions.Libraries were prepared and hybrid-captured using the SeqCap EZ MedExome Enrichment Kit (Roche) with 200 ng of DNA input. Sequencing was performed on an Illumina Novaseq system with 150-bp paired-end reads. Raw paired-end fastq files were pre-processed using fastp  for qual1). Briefly, we first identified heterozygous single nucleotide polymorphisms (SNPs) from the matched normal germline blood sample using the Samtools mpileup command and the Exome captured regions bed file and computed read counts for the reference SNPs and alternative genotypes from the tumor biopsy samples. Next, we computed read counts for 50-kb bins using HMMcopy . The sequencing and bioinformatic analysis were performed as described above for tumor tissue, except that variant calling was performed on SNVs only.tximport R packages . Libraries were prepared from 500 ng of RNA. After end repair, A-tailing, ligation, and purification, sequencing was performed on an Illumina Novaseq with 150-bp paired-end reads. Reads were aligned to the human hg38 reference genome after an index was generated using STAR v2.7.2  by applypackages . Gene expackages  from totp-values were computed using Spearman’s test.We used R (version 4.0.0) to perform all statistical analyses and tests. The Wilcoxon test was used to compare ploidy across the exome and LC-WGS data. Copy number variation (CNV) segment tests and The clinical features of the 36 patients included in our cohort are shown in 2 ratio values after GC content correction.To establish the LC-WGS protocol and ensure reproducibility, we first performed a spike-in experiment by mixing gDNA from two lung cancer cell lines (H2030 and H1975) with known genomic profiles with DNA from a healthy donor at different ratios. We found that a detectable TF using our method was a TF > 5% . FollowiThe calculated TF values ranged from 8 to 70% . Thirty-Next, we compared TF values across different groups of patients with NHL, based on localization, International Prognostic Index (IPI) score, and type of immunodeficiency . Six of n = 23). These analyses involve comparison of the normalized log2 copy ratios between the two sample types from same patients. We found that the Mb-scale copy numbers correlated well in samples with a TF > 5% , TERT (5p), and EGFR (7q). Furthermore, we found amplification of MYC and heterozygous deletion of the PTEN tumor suppressor, located at 8q and 10q, respectively , followed by chromosome 15 loss (detected in 2/6 patients). These CNAs were mutually exclusive in our cohort . Focal cSTAT6, MDM2, and BCL7A are located. The second group (two patients with DLBCL) exhibited neutral chromosome 12 copy numbers but had gains in a region harboring the HLA locus, PRDM1, and POLG1  and one patient with Burkitt lymphoma] who exhibited gain of chromosome 12, which is where nd POLG1 . Interesnd POLG1 , such asTBL1XR1 and the proto-oncogene PIM1  on cfDNA and the somatic VAF in tumor tissue biopsy  on three of the cases with LC-WGS and tumor biopsy exome sequenced. We detected shared coding somatic SNV in cfDNA and tumor biopsy in FR-02-040-BP (16%), FR-02-056-LTT (28%), and FR-01-092-PA (83%). Across the common mutations, we identified mutations of known-driver genes in NSCLC as d MAP2K1  and recuStudies have shown that LC-WGS and ichorCNA analysis can accurately detect low fractions of circulating tumor DNA in blood plasma . CalculaOur pilot study is the first to calculate TFs and characterize somatic CNA profiles for cfDNA using LC-WGS and ichorCNA in immunosuppressed patients (HIV-positive or transplant recipients) with NHL  or lung cancer. Immunocompetent patients with glioma were also included, given the location of the tumor in an immunosuppressive environment, and, for the purposes of this pilot study, were expected to exhibit a low TF in the blood .A TF was detectable in 29% (7/24) of the immunodeficient patients in our cohort, with a TF range of 8% to 70%.No TFs or CNAs were detected in PCNSL and glioma patients. The failure to detect a TF in these patients was presumably caused by insufficient ctDNA levels in the blood that were below the limit of detection for ichorCNA. Indeed, the lower limit of ichorCNA sensitivity for detecting the presence of tumor is 3% . Glioma-In contrast, patients with a detectable TF should be selected for further larger-scale sequencing methods, such as WES, for which the calculated TF could help guide calibration of the sequencing depth.One immunosuppressed lung cancer patient included in our study had a TF of 23%. This patient had non-small-cell adenocarcinoma, was HIV-positive, and had advanced metastatic disease . The CNA profile derived from the tumor tissue biopsy taken from this patient correlated well with the findings from the corresponding liquid biopsy (ρ = 0.81). Moreover, the CNA profile was consistent with patterns that are typically observed in patients with NSCLC . DetermiIn our study population, the immunodeficient NHL patient group had the highest proportion of patients with detectable aberrations . The proportion of patients with detectable cancer-derived cfDNA in this group was lower than that described in the literature for NHL patients. Bohers et al. identified tumor-associated mutations in the cfDNA in 19/30 patients (63%), and Rossi et al., in 20/30 patients (66.6%) . HoweverInterestingly, 5/6 NHL patients with a TF > 5% had extranodal manifestations and a high IPI score. Given that our patients were untreated at the time of liquid biopsy, our results agree with a study concluding that ctDNA is a promising biomarker for identifying NHL patients at high risk of treatment failure and at risk of recurrence before clinical evidence of disease relapse .The potential utility of cfDNA as a biomarker and as a way to molecularly characterize tumors is supported by the concordance between the cfDNA and tumor tissue CNA profiles. Despite the low number of patients with cfDNA WES data, VAF comparison and purity estimation collectively suggests an accurate sensitivity of TF estimation using LC-WGS/IchorCNA. Surprisingly, the strongest correlation was found in the patient with a TF of 23% and not in the patient with a TF of 70%. This may be attributable to the fact that several CNA events were detected in the patient with the lower TF, while only chromosome 12 gain was found in the patient with the higher TF. However, this patient harbors coding variants with high VAFs in cfDNA  and “Commission Nationale de l’Informatique et des Libertés” (CNIL no 918222). The patients/participants provided their written informed consent to participate in this study.AB and KL processed the experimental data, performed the analysis, drafted the manuscript, and designed the tures. AB and SJ carried out the experiments. KL and P-YB performed the bioinformatic analyses. FCo, JD, and J-ML were involved in planning and supervised the work and the manuscript draft. J-PS and BA conceived of the project IDEATION study. MV is the project manager of IDEATION study. EG helped for experimental process. FCh and FB are anatomopathologists involved for selection of tumor tissue. MB, MT, FD, SC, VL, and JC aided in interpreting the results and worked on the manuscript. All authors discussed the results and commented on the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Despite COVID-19 vaccination programs, the threat of new SARS-CoV-2 strains and continuing pockets of transmission persists. While many U.S. universities replaced their traditional nine-day spring 2021 break with multiple breaks of shorter duration, the effects these schedules have on reducing COVID-19 incidence remains unclear. The main objective of this study is to quantify the impact of alternative break schedules on cumulative COVID-19 incidence on university campuses. Using student mobility data and Monte Carlo simulations of returning infectious student size, we developed a compartmental susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) model to simulate transmission dynamics among university students. As a case study, four alternative spring break schedules were derived from a sample of universities and evaluated. Across alternative multi-break schedules, the median percent reduction of total semester COVID-19 incidence, relative to a traditional nine-day break, ranged from 2 to 4%  and 8–16% . The maximum percent reduction from an alternate break schedule was estimated to be 37.6%. Simulation results show that adjusting academic calendars to limit student travel can reduce disease burden. Insights gleaned from our simulations could inform policies regarding appropriate planning of schedules for upcoming semesters upon returning to in-person teaching modalities. Undocumented infection found significantly among college students has also played an important role in disseminating disease in the past year3. In response, many U.S. universities replaced their traditional weeklong spring break with multiple breaks of shorter durations in spring 2021. The relative effect alternative break schedules might have on COVID-19 incidence, however, remains uncertain. Despite the development of vaccines, due to the difficulty of local-level implementation, the uncertainty about length of immunity due to vaccine, public hesitancy of vaccine, and the emergence of SARS-CoV-2 variants, the decision on a proper academic schedule is critical to prevent surges in campus communities. Modeling SARS-CoV-2 transmission influenced by student travel and disease prevalence at travel destinations can provide insights into the impact different break schedules have on COVID-19 incidence among on-campus university students4.The return of students to college campuses after scheduled breaks has resulted in dramatic surges of COVID-19 cases in college communities and has been linked to increases in county-level SARS-CoV-2 incidence5. Similarly, studies have used Apple mobility data to examine financial markets6, and Google mobility data from 130 countries to show that even slight reductions in visits to various locations results in fewer COVID-19 cases and deaths7. In another country-level analysis, using both Apple and Google mobility data, mobility explained a substantial proportion of COVID-19 transmission variability. Therefore, consideration of mobility patterns, including those of students during semester breaks, is critical for modeling COVID-19 incidences on college campuses8.Given the impact of COVID-19 on human mobility patterns and vice versa, others have examined how changes in mobility might impact spread of SARS-CoV-2. Since mobility can represent proxies for social distancing and stay-at-home interventions, these data sources may present approaches to examining impacts on disease transmission. For example, mobility networks derived from cell phone data predict that a small minority of ‘superspreader’ points of interest account for a large majority of infections14, none of the previous studies has explored the inter-relation among break schedule, travel behavior, and local COVID-19 surge. In this paper, we used mathematical and computational models to examine the effects of four commonly practiced alternative spring break schedules under five different realistic mean prevalence rates at travel destinations, on SARS-CoV-2 transmission among on-campus students . In particular, break schedules were derived from a sample of twenty-five universities that implemented altered spring break schedules in the spring 2021 semester. Analysis of mobility data, paired with results from Monte Carlo (MC) simulations, informed the relationship between travel and infections and were used directly in a variant of a SEIAR (susceptible-exposed-infectious-asymptomatic-recovered) type compartmental model to estimate total cases throughout the semester. We utilized both real-time and synthetic data to model the effects of breaks of different duration on college campuses. Furthermore, we performed thorough sensitivity analyses on those parameters that drive the results.While a few modeling studies have provided some insights into COVID-19 spread in college campusesWe analyzed the data from twenty-five sampled U.S. universities to observe the different break schedules, i.e., number of breaks and timing of the breaks, implemented in spring 2021. Our analysis led to the following four alternative spring break schedules: (1) the one-break schedule (nine-day break starting on day 72 of the semester), (2) the two-break schedule , (3) the three-break schedule , and (4) the four-break schedule . Note that each break considered was of the total length of nine days, which allowed us to perform a fair comparison among them.We performed MC simulations to identify the expected number of students who returned infected after each break. MC simulation results of infected percentage of returned students for various travel duration (three-day to nine-day break), various numbers of contacts during the break (10 daily contacts to 60 daily contacts), and various travel destination prevalence  are summarized in Fig. For each alternate break schedule identified, we used our transmission dynamics model to predict the temporal pattern of infectious student population for the entire semester  is shown in Fig. Regardless of schedule-prevalence pair and the number of students that travel, total cases and the percent reduction of total cases was always smallest under the scenarios where students maintained their contact rates while traveling at the same level as the during-school contact rates Fig. .15. In addition, concerns among students regarding vaccine safety and efficacy may create obstacles to achieving herd or sufficient population immunity at US university campuses16. Given current uncertainty on various aspects of COVID-19, it is critical to evaluate the effects of the semester break schedule on a COVID-19 surge among the student body and to identify whether any alternate schedules can be beneficial to avoid surges. Here, we developed a computational and mathematical model to study four alternate schedules that were implemented in some US universities during spring 2021.The threat of COVID-19 transmission has been even greater because of the emergence of novel viral strains such as lineages B.1.1.529 (Omicron variant), B.1.617.2 (Delta variant) and B.1.1.7 (Alpha variant)5. This result is in line with modeling estimates implying that counties with more early spring break students had a higher growth rate of cases than counties with fewer early spring break students in 202017.Our results over a wide range of parameter space show that the alternative spring break, i.e., multiple breaks of a shorter length, can significantly reduce semester-wide cumulative COVID-19 cases among college campus students. This was achieved by distributing the large surge due to the traditional longest one-break into multiple surges of significantly small size. We also note that the estimated effectiveness of alternative spring break schedules on COVID-19 case reduction was very much dependent on student movement/contact dynamics and travel destination prevalence rates. This is consistent with work showing the role of mobility reduction in reducing infections11. Our findings corroborate the importance of travel restrictions in limiting COVID-19 disease spread18. They are also in line with recent guidelines by the Centers for Disease Control and Prevention to ensure full vaccination prior to traveling, and to decrease travel among students who are at increased risk for severe COVID-1919. Along with knowledge of current COVID-19 transmission dynamics and understanding of student adherence to public health recommendations21, this analysis can aid potential risk assessments at universities across the country. For example, it is likely that the decline in infections at the university used for data calibration can be attributed to intervention protocols implemented locally at the university level, complementing other state and local measures22. These policies can enable universities to welcome students back to campus with fewer outbreaks and repercussions to surrounding communities23. In contrast, lack of intervention can contribute to increased peaks of infection among students two weeks after travel, suggesting secondary spread17.If a university has reason to believe that many of its students will not heed public health warnings and travel for recreation during scheduled break periods, the university may want to consider implementing alternative break schedules, along with other mitigation measures such as vaccination, masking, and surveillance testing, to help reduce the potential impact of prolonged break periods on virus transmission in their local community. Strong policy measures such as wearing facemasks, testing and reduced capacity have shown large reductions in infections in campus modelsWe acknowledge some limitations of our study. The model parameters were estimated based on limited data and published literature as the explicit semester-wide case data on college campuses were not available. Given uncertainty over parameters about SARS-CoV-2 transmission, the upper and lower bounds of the percent reduction may be subjective to the dynamics we chose. However, the scope of the sensitivity analysis of Fig. In summary, simulation results, based on computational and mathematical modeling, show that student travel does impact transmission dynamics on college campuses, and that adjusting academic calendars to limit student movement can reduce disease burden. Selecting the correct adjustment to simultaneously mitigate risk and provide students with adequate respite should factor in prevalence projections and the likelihood of the student body to follow recommended safety protocols.The timing and distribution of the four alternative break schedules were based on a sample of twenty-five universities/colleges that implemented an adjusted break schedule in spring 2021. Among the sample (n = 25), spring breaks were either shortened or rescheduled later in the semester , split into two break-periods , split into three break-periods , or split into four break-periods . Within each schedule-break-period, the start-day of the break relative to the first day of class was determined. These start-days were weighed by their respective duration and averaged to provide simulation start-days for each break-period within each alternative schedules. The duration of each break-period was predetermined such that each alternative schedule had a total of nine days off and was therefore comparable.To mimic the student travel volume observed in mobility data that increased as the break duration increased and saturated at some level, a sigmoid function was selected representing the relationship between break-duration and percent of students that travel Fig. , top. SaEvery simulation is associated with a student travel behavior curve, a mean daily contact rate, and a mean travel destination prevalence rate. For a given break-period, the percent of students who travel was determined from the student travel behavior curve. It was assumed that no student would travel for leisure if given a break-period of two days or less. The proportion of students infected while traveling was determined from Monte Carlo (MC) simulations. MC simulations output the proportion of traveling students infected given a break-duration, mean destination prevalence, and mean daily contacts. This percent was applied to the number of students traveling to determine total infections which were distributed uniformly over the break-period.i and a parameters in the SEIAR model. For each break-duration, mean destination prevalence rate, and mean daily contact parameter, 500 simulations of 1000 students were run totaling 105,000 simulations. The mean percent of infected students given a combination of break-duration, mean prevalence, and mean daily contact parameter was applied to traveling students to determine the number of traveling students that returned infected.Monte Carlo simulations were used to estimate the percent of students infected while traveling. The number of daily contacts per student was drawn from a Poisson distribution. The probability of contact with an infectious individual was based on the assumed mean prevalence rate. The probability of viral transmission during contact with an infectious individual was 0.366% and 0.270% from a symptomatic and asymptomatic person, respectively. These transmission rates were based on the estimated The schematic diagram of our model simulations is presented in Fig. i and a), students transitioned from susceptible to exposed and remained in exposed for A susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) compartmental model was used to simulate transmission dynamics during the regular-period of the semester. The SEIAR model assigned students into mutually exclusive compartments: susceptible (those susceptible to the virus), exposed (those with the virus but not yet capable of transmitting it), infectious (symptomatic persons capable of transmitting the virus to susceptible persons), asymptomatic  and recovered (those who had the virus before and are assumed immune). We assumed a closed student population with no deaths. Transmission occurred during contact between infectious/asymptomatic and susceptible compartments. After successful transmission (determined by transmission parameters The following equations were used to simulate transmission dynamics:Reduced SEIAR in Fig. Break S*E*I*A*R* in Fig. At the beginning of the break-periods, SEIAR model, respectively. Here, The equations for Regular SEIAR model.Upon return from travel after each break-period, students return to their current compartment in the COVID-19 dashboards from eight large universities were surveyed and revealed a common trend of a large surge of COVID-19 cases in the first three-weeks of the Fall 2020 semester followed by a steady decline throughout the semester. Transmission parameters between symptomatic and susceptible individuals, 24, 14-day infectious period (25, 40% of infections were asymptomatic (26, 75% relative risk of infection from an asymptomatic individual relative to a symptomatic individual (26. Initial population size of the S, E, I, A, and R compartments were 4767, 232, 1, 0, and 0, respectively.Remaining model parameters were taken from the literature and were as follows: 5-day incubation period (


Examination of within-person mean differences in 2015 Healthy Eating Index scores and nutrient intakes between ages 5 and 6 years revealed few statistically significant differences between the two groups: children in the post-ED group consumed slightly fewer vegetables (p = 0.02) and less sodium (p = 0.01) than their pre-ED peers. Findings suggest emergency efforts to maintain children’s nutrition were largely successful in the early months of the pandemic. Research is needed to understand the mechanisms by which emergency efforts contributed to these findings.Early in the COVID-19 pandemic, the U.S. Department of Agriculture (USDA), State governments, and school districts took unprecedented steps to mitigate the pandemic’s impact on students’ nutrition. To examine the effect of emergency responses on 6-year-old children’s nutritional outcomes, this study analyzed longitudinal data from a national study of children’s feeding practices, the Special Supplemental Nutrition Program for Women, Infants, and Children—Infant and Toddler Feeding Practices Study-2 (WIC ITFPS-2). Findings include no differences in food insecurity prevalence; however, there were shifts in sources of food, with children in the post-COVID-emergency-declaration (post-ED) group consuming more dietary energy from stores and community food programs and less from restaurants and schools than children in the pre-COVID-emergency-declaration (pre-ED) group ( On 13 March 2020, U.S. President Donald J. Trump declared a national emergency in response to the deepening COVID-19 health crisis [The U.S. Department of Agriculture (USDA) funds meal distribution to elementary, middle, and high school students through a number of programs, including the National School Lunch Program (NSLP), the School Breakfast Program (SBP), and the Summer Food Service Program (SFSP). The NSLP is the nation’s second-largest nutrition assistance program , and hisDuring the early months following the emergency declaration (ED), the USDA also continued to fund a national, longitudinal study of children who were previously enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), the WIC Infant and Toddler Feeding Practices Study-2 (WIC ITFPS-2). WIC is a nutrition assistance program tailored for pregnant and postpartum women and children up to age 5 who are at nutritional risk and whose families have low incomes. WIC ITFPS-2 had been following a cohort of about 3800 children since around the time of their births in 2013 and 2014. Between April 2019 and August 2020, the study children turned 6 years old and were reassessed.For families in the United States that have low financial resources, a child’s sixth year of life may introduce new feeding challenges. If the child’s birthday meets school age requirements, the child may be attending school and may be receiving free or reduced-price school meals. However, if the child’s birthday does not meet school age requirements, the child may not be attending school when he or she turns 6 and, therefore, may not be participating in federally funded school-based meals programs. If the child is not enrolled in a qualified childcare program, the child may not receive free meals from their childcare provider either. Because the child is no longer categorically eligible for WIC after turning 5 years old, there is the possibility of a gap in nutrition assistance, which may place young children in families with limited resources at risk of food insecurity. Food insecurity, defined by USDA as “the limited or uncertain availability of nutritionally adequate and safe food, or limited or uncertain ability to acquire acceptable foods in socially acceptable ways” , is a maWIC ITFPS-2 regularly collects dietary intake data on study children, including around their fifth and sixth birthdays. The sixth-year data provide valuable information on child nutrition and household food security status in the period after the window for WIC eligibility closes. In addition, because sixth-year data collection continued after the COVID-19 ED, the data offer insight into the influence of the pandemic response on children’s food intakes and nutritional outcomes during the initial months of the national emergency.When examining children’s dietary intakes, both diet quality and consumption of nutrients of public health concern are primary outcomes of interest. The 2015 Healthy Eating Index (HEI-2015) scores facilitate assessment of diet quality because they measure alignment of diets with the Dietary Guidelines for Americans (DGA). From a different perspective, nutrients of public health concern are those that are typically over- or underconsumed by Americans. Research indicates that increasing intakes of underconsumed nutrients of public health concern and limiting intakes of overconsumed nutrients reduce the risk of chronic disease later in life ,15,16,17Using nationally representative data from WIC ITFPS-2, this study creates two groups of children—those who took their 6-year interview before the COVID-19 ED and those who took their 6-year interview in the early months after the COVID-19 ED—and assesses differences in the groups’ dietary intakes. These two groups of children are referred to as temporal groups because their membership is determined by the date of their 72-month WIC ITFPS-2 interview relative to the time of the ED. The subsequent analysis describes the two groups’ sources of food, HEI-2015 scores, intakes of nutrients of public health concern, and daily total energy. Because young children’s dietary intakes change as they grow, the analytical approach focuses on statistical differences in group means of intrapersonal dietary changes between children’s fifth and sixth years; however, overall means at children aged 6 years for the two groups are also discussed.WIC ITFPS-2 is a prospective cohort study following children who enrolled in WIC around the time of birth through age 9 years (contract number AG-3198-B11-0020). Enrolled caregivers report on the study children at ages 1, 3, 5, 7, 9, 11, 15, 18, 24, 32, 36, 42, 54, 60, and 72 months, with a planned follow-up at child aged 9 years. Because women could enroll in the WIC ITFPS-2 prenatally or postnatally, each interview is in the field for approximately 15 months. Harrison et al. . The WesThis research involves secondary analysis of data collected at child aged 60 months and 72 months, referred to as the 60- and 72-month interviews. The interview window for both these interviews is a 6-week window around the study child’s birthday. Data collection for the 60-month interview spans April 2018 through August 2019, while data collection for the 72-month interview spans April 2019 through August 2020.WIC ITFPS-2 participants were enrolled in person at study-eligible WIC sites between July and November of 2013. To be eligible for WIC ITFPS-2, a mother had to speak English or Spanish, be at least 16 years old, and be enrolling in WIC for the first time for her current pregnancy or her newborn infant at the time of study enrollment. Mothers who postnatally enrolled in WIC were eligible for the study if, in addition to maternal requirements, their infant was less than 2.5 months old at the time of study enrollment. Mother–child dyads remained in the study regardless of whether they continued with WIC.Trained telephone interviewers conducted both the 60- and 72-month interviews. At each interview, mothers provided information on sociodemographic characteristics, including participation in school meal programs and WIC; family feeding practices; and other health-related behaviors. Both interviews also included the USDA six-item household food security module, which enquired about food shortage in the prior 12 months .Using the USDA’s scoring algorithm, household food security status was categorized as high/marginal, low, or very low. This study uses the USDA definitions of low and very low food security. Households experiencing low food security “report reduced diet quality and variety but few, if any, indications of reduced food intake,” while households experiencing very low food security “report multiple indications of reduced food intake and disrupted eating patterns, such as skipping meals” . In thisAs part of the 60- and 72-month interviews, caregivers provided 24-h dietary recall information on their children which was elicited using the USDA Automated Multiple Pass Method (AMPM) . This stHEI-2015 is the third and current version of the HEI, a widely recognized tool for assessing how closely food intakes align with the DGA . An HEI-For all dietary intake information, the reported foods, beverages, and dietary supplements were coded using the USDA Food and Nutrient Database for Dietary Studies (FNDDS) to estimate intakes of total energy and 64 nutrients . The USDIn both the pre- and post-ED periods, if the respondent indicated receipt of food from a school meal program such as the NSLP, interviewers coded the source as “school” regardless of whether the meal was distributed from a school cafeteria or eaten in one. In both periods, if indicated, interviewers categorized receipt of food directly from WIC as “community food program.” However, for food purchased from stores using WIC, SNAP, or P-EBT benefits, the respondent likely indicated “store” when asked about the source of the food, as these benefits are redeemed primarily only at stores.n = 1560; weighted n = 338,589), and the post-ED period used data from 22 March 2020 through 29 June 2020, the last date on which AMPM data were collected . Just over one-quarter (27%) of the post-ED interviews were conducted in March 2020. About half (52%) of the post-ED interviews were conducted in April 2020, and about one-fifth (20%) were conducted in May and June 2020.To examine the impact of responses to the COVID-19 ED on food intakes and nutritional outcomes, two temporal groups were created based on the timing of children’s 72-month interview relative to the ED: a sample of children whose 72-month interviews were conducted prior to the ED , and a sample of those whose interviews were conducted after the ED . Data from both samples were weighted to produce national estimates. Because regional responses to the pandemic varied greatly during March 2020, we excluded data from interviews conducted between 7 March and 21 March from the two groups to facilitate distinguishing the impact of the emergency response. Consequently, the pre-ED period used data from 30 April 2019 through 6 March 2020  was not systematically different from the two temporal groups that we planned to compare. We compared estimates for three groups: those who completed the 72-month interview before 7 March (the pre-ED group), those who completed the 72-month interview between 7 March and 21 March (the excluded group), and those who completed the 72-month interview after 21 March (the post-ED group). To ensure that dietary changes were not associated with underlying differences in these three groups, we compared the groups on several sociodemographic characteristics: mother’s educational attainment at child age 54 months; mother’s employment status at child age 54 months and at 72 months; maternal race, ethnicity, marital status, and self-reported weight status at child age 72 months; and household income relative to Federal poverty guidelines, food security status, and participation in nutrition assistance programs at child age 72 months. These sociodemographic characteristics were chosen because prior work with the WIC ITFPS-2 data has shown that they are associated with various aspects of children’s dietary intake [The study design did not originally envision temporal analysis of a single interview month. Accordingly, we undertook preliminary analyses to ensure that the temporal groups were not systematically different and that the group we planned to exclude from subsequent analyses . Subsequently, we examined dietary outcomes at 72 months, comparing levels of intakes for the two groups resulting from the changes in intakes between ages 5 and 6. In these analyses, the population ratio method was used .All of the descriptive analyses presented used weighted cases, with the statistical weights accounting for sample selection and nonresponse bias. Weighted findings reflect the entire study-eligible population temporally divided by whether their sixth-year interview occurred pre- or post-ED. There was a 97 percent overlap in participants reporting at both 60 and 72 months, so we used the 72-month cross-sectional sample statistical weights.t-tests were two-tailed unless otherwise indicated. Statistical significance was at the level of p < 0.05.SAS statistical software package version 9.4  was used for data analyses. All statistical tests appropriately accounted for the complex survey design employed in WIC ITFPS-2. All p < 0.01) and a slightly higher percentage had 6 or more persons (p = 0.02) at 72 months than at 60 months. As study children aged out of WIC and into formal schooling between the interviews, there was a large jump, nearly 15 percent, in school meal participation (p < 0.01) and a significant drop, nearly 20 percent, in household WIC participation (p < 0.01). At 72 months, a lower percentage had household incomes at or below 75 percent of the Federal poverty guidelines (p = 0.02) and a higher percentage had incomes above 130 percent of the guidelines (p = 0.01) than at 60 months. Concomitantly, the percentages of families reporting high or marginal household food security was higher at 72 months than at 60 months (p < 0.01), while the percentage of households reporting low or very low food security (combined) was lower (p < 0.01).p < 0.01). The percentage of households reporting incomes less than 75 percent of the Federal poverty guidelines also significantly differed between groups (p = 0.04), with a larger percentage of those in the post-ED group than in the pre-ED group reporting incomes below this level. Participation in the school meal programs  was more prevalent in the post-ED group than the pre-ED (p = 0.05), as was participation in WIC by mothers (p = 0.05) and infants (p = 0.04).Examination of the characteristics of the pre- and post-ED groups at 72 months revealed that full-time maternal employment was 10 percentage points lower and maternal unemployment was 10 percentage points higher in the post-ED group than in the pre-ED group. In both cases, the difference was statistically significant (p < 0.01). The opposite occurred when examining food obtained from restaurants and schools. Six-year-olds interviewed pre-ED generally obtained more dietary energy from restaurants and schools, while 6-year-olds interviewed post-ED obtained less (p < 0.01 for both comparisons). Children in the pre-ED group also generally obtained less dietary energy from community food programs while children in the post-ED group obtained more (p < 0.01). Energy obtained from childcare declined for both groups, on average, but the comparison of mean differences was not statistically significant between pre- and post-ED groups.p < 0.02), with the post-ED group exhibiting a small deterioration in scores, on average, and a small improvement in scores for the pre-ED group. The difference in mean HEI-2015 sodium component scores was also significant (p = 0.01), with the post-ED exhibiting an improvement in scores between 60 and 72 months, on average, and the pre-ED group exhibiting a small deterioration. There were no statistically significant differences between groups in intakes on a given day in the seven nutrients of public health concern; however, there was a significant difference in daily total energy intake on a given day (p = 0.02), with the post-ED group exhibiting a larger mean difference than the pre-ED group.Analyses of within-person differences may account for individual trajectories between the fifth and sixth years; however, levels of intakes at 72 months provide important indicators of diet quality after these changes. To assess whether the nutritional intakes of the pre- and post-ED groups were similar, we compared mean HEI scores and nutrient intakes using the population ratio method. As mentioned, because the pre- versus post-ED classification was not randomly assigned (but based on the timing of the 72-month interview), we first examined characteristics of the two groups to ensure there were no systematic differences that might confound the results of the HEI score comparison; other than employment status , no differences were found.p = 0.05), with the post-ED group scoring slightly lower than the pre-ED group, and in mean HEI sodium component scores (p < 0.01), with the post-ED group scoring slightly higher than the pre-ED group, where higher HEI sodium component scores indicated lower sodium intake given energy. However, this analytic approach also indicated significant differences in intakes of refined grains (p = 0.03) and total saturated fat (p = 0.04), with the post-ED group exhibiting higher scores for refined grains and lower scores for saturated fats than the pre-ED group. Among the seven nutrients of public health concern considered for comparison, significant differences were identified in vitamin D intakes, both in total grams (p < 0.01) and in grams per 1000 kilocalories (p < 0.01), with the post-ED group exhibiting higher intakes than the pre-ED group in both cases. Additionally, a significant difference was identified in intakes of saturated fat as a percentage of dietary energy (p = 0.01), with the post-ED group consuming a slightly larger percentage than the pre-ED group.There were no significant differences between the pre- and post-ED groups for the majority of HEI scores and nutrients analyzed . There wThis study used data from the WIC ITFPS-2 to examine changes in dietary intakes prior to and after the COVID-19 health ED. Two temporal groups, a pre-ED group and a post-ED group, were compared on many sociodemographic characteristics to determine whether the approach was feasible. Based on data from the 72-month interview, only maternal employment and household income differed significantly. These were interpreted as effects of the pandemic rather than an underlying difference in the groups because it is well established that employment in the United States initially fell dramatically in response to COVID-19 mitigation strategies .Using the two groups identified, there were shifts in children’s sources of dietary energy after the 13 March 2020 health ED: a greater share of children’s dietary energy came from stores and community food programs, and less from restaurants and schools, in the post-ED period than in the pre-ED period. This finding aligns with work by Murphy et al. (2020). Using a cross-continental sample of adults from Ireland, Great Britain, the United States, and New Zealand, they found that all four regions had reductions in food from restaurants  because of the pandemic .Examination of children’s within-person changes in diet quality measures revealed that, compared to the pre-ED group, the post-ED group exhibited a slight decline in vegetable consumption and a small improvement in sodium consumption between 60 and 72 months. The finding for vegetable consumption is noteworthy given that previous literature suggests school meals tend to include more vegetables than those served at home . The finViewed in light of reduced prevalence of food insecurity at child aged 72 months compared to child aged 60 months, it is, perhaps, unsurprising that there were few significant differences between the pre- and post-ED groups in dietary outcomes between 60 and 72 months. Examination of within-person changes found that mean HEI total scores were similar between the groups, as were 11 of 13 component scores and all of the nutrients analyzed.The post-ED group had a much larger mean difference in dietary energy intake between 60 to 72 months than the pre-ED group ; howeverAnalysis of levels of intake at child aged 72 months indicated some statistically significant differences between the pre- and post-ED groups; however, magnitudes of differences were generally small. The differences found in nutrient intakes may be related to the types and/or amounts of foods consumed, and future research may want to examine specific foods consumed.One of the key findings from this study is that despite having a lower prevalence of maternal full-time employment and a higher prevalence of the lowest-income households within the post-ED group at 72 months, overall, a lower percentage of families represented in the longitudinal WIC ITFPS-2 data reported low or very low food security at 72 months than at 60 months. This finding contrasts with findings of increased food insecurity in the early months of the COVID-19 national emergency, particularly in households with children ,35,36; hThe results of this study suggest that, even with all of the hardships many families experienced at the onset of the pandemic, a high percentage of low-income 6-year-old children were able to access a similar quantity of dietary energy in the post-ED period as their pre-ED peers, with minimal differences in diet quality. Federal, State, and local emergency response efforts enacted in spring 2020 likely helped facilitate this, at least partially. Additional research is needed to understand the extent to which each of these efforts contributed to the decline in prevalence of food insecurity among low-income families with past exposure to Federal nutrition assistance programs, particularly WIC.The increases in the percentages of WIC ITFPS-2 families reporting participation in school meal programs and/or WIC between the pre- and post-ED groups further suggest that prior WIC participation for the study child may have been a protective factor as levels of unemployment rose, providing actionable knowledge on the process for receiving nutrition assistance through programs designed to serve families with children. In this context, we reiterate that the dietary recall data extended through the end of June 2020; therefore, the prevalence estimates for food insecurity are for the early months of the pandemic response. As the national health emergency continued, the prevalence of food insecurity in this vulnerable population may have changed notably.There are three notable limitations to this research. The first is that the study data do not permit exploration of how families acquired food and beverages. WIC ITFPS-2 did not collect financial information other than household income. As full-time employment fell, it is possible that study participants dipped into financial reserves. Additionally, research examining coping strategies suggests that participants may have tapped relationship assets . UnemploA second limitation is that while the AMPM is used extensively to assess dietary intake, it is subject to recall error. This is particularly important when considering the sources of food. For example, between the pre- and post-ED periods, federally funded school meals changed from service and consumption at school sites to distribution at schools and other sites for consumption outside of schools—primarily at home. In the post-ED period, once these distributed foods were in the home, it may have been challenging for respondents to identify exactly which foods or beverages came from school distributions and which were purchased at stores. This may have resulted in underreporting of foods from schools as a source and overreporting of foods from stores or community food programs.A third limitation is the inability to state whether changes in daily energy intakes were appropriate for the children. WIC ITFPS-2 does not collect detailed information on children’s physical activity levels or weight at the time of diet intake measurement; thus, it is not possible to assess energy intakes in relation to the child’s activity or requirements. Though the within-person findings indicate a larger change in daily total energy intake for the post-ED group than the pre-ED group, the changes left both groups at similar mean levels when the children were 6 years old.t-tests.The results of this study highlight the likely protective effect of Federal, State, and local government efforts on household food security during the early months of the pandemic. Using data from WIC ITFPS-2, this study found that, nationally, about 25 percent of households with 5-year-old children who had previously enrolled in WIC around the time of birth reported low or very low food security between April 2018 and August 2019, while only 20 percent of these households reported low or very low food security when the children were 6 years old, between April 2019 and August 2020. Analyses of household food security by whether the study child turned 6 years old prior to or after the COVID-19 ED (March 2020) indicated no difference in the percentages reporting low or very low food security (20.6% compared to 20.2%), even though maternal full-time employment was notably higher in the pre-ED period than the post-ED period (40.0% compared to 29.9%), based on pairwise t-tests of mean intrapersonal differences for the two groups indicated families relied more heavily on stores (−3.56% vs. 8.51%) and community food programs (−0.49% vs. 1.26%) as sources for children’s dietary energy, and less heavily on restaurants (0.98% vs. −5.24%) and schools (4.10% vs. −3.59%) in the post-ED period than in the pre-ED period.Consistent with the national emphasis on social distancing, which closed many businesses and schools, more of children’s dietary energy was consumed at home. Analysis revealed statistically significant differences in sources of dietary energy between ages 5 and 6 years for two groups of children—those who turned 6 years old before the U.S. COVID-19 ED and children who turned 6 years old after the ED. Pairwise t-tests. Future studies should not only continue to examine the ongoing impacts of the pandemic on food insecurity and diet quality of low-income families in the United States but should also focus on the mechanisms by which Federal, State, and local efforts may mitigate food insecurity.There were few statistically significant differences in mean dietary intakes of 6-year-olds assessed pre-ED compared to those assessed post-ED, and overall mean diet quality scores and energy intakes were similar based on


Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear, because abnormality is often absent in structural imaging findings. Previous studies have demonstrated that WM functional activity estimated from functional magnetic resonance imaging (fMRI) has a high sensitivity to postconcussion WM deficits and may be used to not only evaluate but guide treatment strategies, especially targeting brain areas involved in postconcussion cognitive decline. The purpose of the study was to determine whether machine learning-based models using fMRI biomarkers and demographic or neuropsychological measures at the baseline could effectively predict the 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically matched healthy controls enrolled between September 2015 and August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to their significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after a concussion. Approximately half of the patients experienced prolonged cognitive impairment at the 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy = 87.5%), 6 months (accuracy = 83.3%), and 1 year (accuracy = 83.3%) and performed worse at the 1-year follow-up compared to the baseline assessment (accuracy = 83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes. Mild traumatic brain injury (mTBI), commonly referred to as concussion, typically does not present with visual findings on structural magnetic resonance imaging (MRI) examinations, and therefore, providing neuroimaging evidence to support a diagnosis or therapeutic evaluation is difficult. Furthermore, mTBI can cause an array of postconcussive symptoms (PCS), most notably headaches, sleep deficit, fatigue, dizziness, depression, anxiety, and cognitive impairment . The aveN-back WM circuitry in patients with mTBI during both moderate and high WM load conditions compared with healthy controls (HCs), especially prominent under WM 2-back > 1-back conditions [Working memory (WM) involves the ability to transiently store and manipulate information to be used for cognitive or behavioral activities. WM deficit is one of the most common postconcussive cognitive impairments . Chen etnditions . Differenditions .N-back WM task (N = 1 and 2) was performed to obtain potential disease-related fMRI features, since WM 2-back > 1-back conditions can show the most prominent changes of impaired WM circuitry after mTBI [We hypothesized that long-term cognitive outcomes of mTBI can better be predicted using pooled fMRI, demographic, and neuropsychological biomarkers than by using neuropsychological evaluations alone. In this prospective observational study, our objectives were to identify fMRI, demographic, or neuropsychological biomarkers at the baseline that could best predict future cognitive changes during the year following a concussion and to construct machine learning-based predictive models to discriminate between patients at a high risk of poor long-term cognitive outcomes and patients with normal recovery. Specifically, the ter mTBI . AdditioAt present, for cognitive rehabilitation management, detailed holistic neuropsychiatric assessments are required to identify, establish, and develop adaptive general or domain-specific interventions, whether adopting a nonpharmacologic or pharmacologic approach ; this isn = 34; 52.52 ± 6.95 days), 3 months , 6 months , and 1 year  after a concussion. In total, 24 patients (38.6%) completed the baseline and all four follow-up sessions. All participants had normal or corrected-to-normal visual activity and no history of neurological or psychiatric disorders. This study was approved by the Institutional Review Board of Taipei Medical University Hospital before data collection  and conducted according to the original and amended Declaration of Helsinki.Between September 2015 and August 2020, 70 right-handed patients with mTBI and 48 age-, sex-, and education-matched right-handed HCs consented to participate in the study at Taipei Medical University Hospital. Patients were followed up at 6 weeks , and the Wechsler Adult Intelligence Scale, fourth edition (WAIS-IV), were conducted by a clinical psychologist on the same days as the initial and follow-up MRI scans. The six types of clinical symptoms were assessed using the Glasgow Outcome Scale–Extended (GOSE), Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, Dizziness Handicap Inventory, Rivermead Post Concussion Symptoms Questionnaire (RPQ), Beck Anxiety Inventory, and Beck Depression Inventory, for which higher scores indicate greater symptomatology. The working memory (WM) ability was assessed using the WM index (WMI), arithmetic ability (AMT), and digit span score (DS) in the WAIS-IV test, for which the higher scores indicated better WM ability. Among them, the WMI is derived from the comprehensive performance of WM-related subtests and can be used as a general indicator representing a subject’s WM ability.3, matrix = 64 × 64 × 40, and 105 volumes) was performed. Participants were instructed to keep their eyes closed and not entertain any particular thoughts while remaining awake, alert, and as motionless as possible.MRI data were obtained using a 3T MRI scanner , and a 64-channel head coil was used to acquire the fMRI time series. Standard single-shot gradient-echo echo planar imaging-based fMRI  and is presented in N-back task contains three epochs, each composed of a 30-s task period and a 30-s fixation on a crosshair. The interstimulus interval between each trial during a task period was 2 s. In total, 45 trials were performed, with each task consisting of 80% nontarget trials and 20% target trials. In each run of the N-back tasks, participants were instructed to pay attention to a series of six-digit numerical stimuli and respond by using the right index finger to press the button whenever the current stimulus matched the number that had been presented N times previously (N = 1 or 2) [The experimental design of the 3, and matrix = 256 × 256 × 176) were obtained.For the co-registration and normalization of fMRI data, three-dimensional T1-weighted magnetization-prepared rapid gradient-echo images  for slice timing correction, realignment, spatial normalization to MNI space, and spatial smoothing with a 5-mm full-width-at-half-maximum Gaussian kernel. Furthermore, linear and quadratic trends of the fMRI time series were removed.N-back WM task, the experimental paradigm used the convolved canonical hemodynamic response function as the regressor in a general linear model. Six head motion parameters estimated through image realignment by using SPM12 were used as covariates and partially regressed out of the preprocessed fMRI time series. A contrast image corresponding to the main effects of the task performance was created and represented brain activity relative to the implicit baseline of unmodeled variance [t-test across all participants within each group.To calculate the brain activation and deactivation map during the variance . Group lt-test was used to determine the significance within the HC or mTBI group, and a two-tailed two-sample t-test was used to observe between-group differences. A two-tailed paired-sample t-test was conducted to examine the significance between the initial and follow-up data. The statistical tests were corrected for multiple comparisons by controlling the false discovery rate (FDR) to q = 0.05 to avoid errors related to multiple comparisons in these calculations.A one-sample p < 0.01, FDR corrected; The regions-of-interest ROIs;  were firt-test was applied to examine the significance between the baseline and follow-up data to identify the statistically significant progression of postconcussive cognitive decline at predetermined time points (periods). Specifically, these statistically significant time periods of cognitive changes were regarded as the meaningful time periods for the machine learning-based approach in terms of postconcussive cognitive progression prediction. Patients were further divided into “poor outcome” and “good outcome” groups according to the negative and positive slopes of cognitive changes within the specific time period.To determine the courses of the postconcussive WM changes, we first retrospectively assessed the 24 patients who completed the baseline and all four follow-up sessions to determine the status of WM decline or recovery at each time point during the 1-year follow-up period. A two-tailed paired-sample N-back WM fMRI features. Neuropsychological assessments  and the demographic data  were treated as the potential neuropsychological and demographic features, respectively. The candidate fMRI, demographic, and neuropsychological features were selected if there was a significant correlation with the estimated changes in the WMI during the specific time period to train the support vector machine (SVM) classifier with k-fold cross-validation (k = 10 in this study) for each prediction to achieve a reliable and unbiased estimate of the machine learning model performance on a limited dataset sample [The percentage signal change extracted from the ROIs for the WM task performed by each participant was used for the t sample . Specifin = 37), fall (n = 17), sports (n = 3), assault (n = 8), and other (n = 5). Among the patients, 24 completed the baseline, and all four follow-up sessions were selected for investigations regarding dynamic changes in cognitive functions after a concussion. The patients dropped out during follow-up visits mainly because of a failure to keep in touch through phone calls or e-mails or a change in residence or job. In total, 70 patients with mTBI  women) and 48 HCs  women) were recruited in this study . The reaN-back WM task fMRI exhibited increased signals in the bilateral frontal and parietal lobes  of patients exhibited no recovery in the WMI at 3 months after a mTBI.Seventy-five percent (18/24) of patients exhibited a decline in the WMI from 3 to 6 months after a mTBI.Thirty-eight percent (9/24) of patients exhibited no recovery in the WMI from 6 months to 1 year after a mTBI.Forty-six percent (11/24) of patients exhibited a worsened WMI at 1-year follow-up compared to the baseline.An assessment with the WAIS neuropsychological test revealed that the WM-related abilities, as indicated by the WMI A, AMT FB, and DSThe importance of the feature weighting derived from the candidate fMRI, demographic, and neuropsychological features to predict the estimated changes of the WMI between the baseline and 1-year after a concussion is shown in This study examined postconcussion cognitive changes during a 1-year follow-up period. Consistent with a previous study , in our Previous studies that aimed at predicting long-term postconcussive cognitive outcomes for mTBI generally adopted a multivariate approach encompassing patient demographics, clinical symptoms, and neuropsychological features, as well as other factors such as health care utilization and premorbid psychiatric conditions ,25. HoweNone of the baseline assessments of clinical neuropsychiatric symptoms  or the self-reported PCS burden (RPQ) correlated significantly with the WM changes between 6 weeks and 3 months, between 3 and 6 months, and between 6 months and 1 year after a concussion in this study. These results substantiate the idea that the baseline neuropsychological assessments and PCS burden may not be predictive of postconcussive cognitive outcomes. Moreover, this was true in a previous study, which demonstrated that a lower cognitive reserve, but not a worse PCS diagnosis, was associated with a poor cognitive outcome following mTBI . StudiesRecent studies have indicated the importance of age and sex effects in the context of mTBI, as the elderly and females are especially predisposed to postconcussive neurocognitive symptoms ,30,31,32In addition to considering WM functional activity as a potential fMRI biomarker for postconcussive WM decline, the WM task-induced default mode network (DMN) deactivation regions were considered in this research. The DMN, which is known to be active during rest and to deactivate during externally oriented tasks, may be essential for optimal WM operation . The faiThe systematic characterization of WM functional deficits may have crucial therapeutic implications in patients with postconcussive WM dysfunction, facilitating rehabilitation intervention planning in selected patients. Studies have suggested the potential application of brain functional activation and deactivation patterns in WM tasks for early neurocognitive training referral, training intensity planning, or even functional recovery prediction ,37. MoreThis study longitudinally tracked postconcussion WM functions in 70 patients and delineated the trends of dynamic changes in WM ability during a 1-year follow-up period. However, we note the considerable number of dropouts in our longitudinal data, which may be indicative of attrition bias, meaning that perhaps only patients who still felt uncomfortable at the designated follow-up time points would be motivated to continue participating in this study. Therefore, whether the dynamic changes in postconcussion WM functions in this study are representative of the patient population remains somewhat controversial. To mitigate the issue of attrition bias, only 24 patients who completed the baseline and all four follow-up sessions were selected for investigations regarding dynamic cognitive changes in the 1-year period. In fact, no significant within-group differences were observed in the demographics between patients who completed or did not complete the five visits during the 1-year period. In addition, our study showed similar trends of postconcussion cognitive changes with previous studies showing significant cognitive recovery within three months , and appIn this study, only right-handed patients and HCs were recruited in this study. Whether left-handed brains are structurally different from right-handed brains is still controversial and has spurred much research. Handedness is a form of functional hemispheric asymmetries that establish differences between the left and right sides of the brain . For theThis study depicts a framework for the precise individualized prediction of postconcussion cognitive outcomes based on demographic features and early fMRI biomarkers assessed at the baseline to fulfill the demands of early therapeutic intervention and individualized cognitive rehabilitation strategies. fMRI can be a useful tool in revealing the impairment and compensation of the brain cognitive network, which has the potential to guide treatment strategies, specifically targeting the brain areas involved in postconcussion cognitive decline at the individual level. Future prospective studies using this machine learning-based patient stratification strategy may refine and validate the prediction models and further facilitate the clinical applications of postconcussion cognitive decline.


Low- and middle-income countries are implementing COVID-19 vaccination strategies in light of varying vaccine efficacies and costs, supply shortages, and resource constraints. Here, we use a microsimulation model to evaluate clinical outcomes and cost-effectiveness of a COVID-19 vaccination program in South Africa. We varied vaccination coverage, pace, acceptance, effectiveness, and cost as well as epidemic dynamics. Providing vaccines to at least 40% of the population and prioritizing vaccine rollout prevented >9 million infections and >73,000 deaths and reduced costs due to fewer hospitalizations. Model results were most sensitive to assumptions about epidemic growth and prevalence of prior immunity to SARS-CoV-2, though the vaccination program still provided high value and decreased both deaths and health care costs across a wide range of assumptions. Vaccination program implementation factors, including prompt procurement, distribution, and rollout, are likely more influential than characteristics of the vaccine itself in maximizing public health benefits and economic efficiency. Cost, supply and logistics present challenges to COVID-19 vaccine rollout in low and middle income countries. Here, the authors model vaccination programmes in South Africa and demonstrate the importance of the pace of vaccine rollout, with even moderately efficacious vaccines likely to be cost-effective. Even before the efficacy and safety of the leading vaccine candidates were established, many high-income countries (HICs) pre-emptively procured stocks of doses in excess of population need5. By contrast, most low- and middle-income countries (LMICs) do not have access to sufficient quantities of vaccine due to cost, limitations in available doses, and logistical challenges of production, distribution, and storage6. Meanwhile, the Africa Centres for Disease Control and Prevention have announced a goal of vaccinating 60% of Africans by the end of 20227.The development and licensure of coronavirus disease 2019 (COVID-19) vaccines offer a critically important opportunity to curtail the global COVID-19 pandemic11. How these program implementation factors will affect the clinical and health economic consequences of COVID-19 in LMICs has not been well-defined. This is a particularly urgent question given the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, such as B.1.351 in South Africa, which appear to partially reduce efficacy of some vaccines15.There has been much discussion about reported efficacies and costs of different vaccines. However, factors specific to implementation, including vaccine supply, vaccination pace, and acceptance among communities, are increasingly recognized to be crucial to the effectiveness of a vaccination program in promoting epidemic control in HICs—in some cases, even more so than vaccine efficacyIn this work, we use a microsimulation model to estimate the clinical and economic outcomes of COVID-19 vaccination programs in South Africa, examining different implementation strategies that policymakers could directly influence. We simulate COVID-19-specific outcomes over 360 days, including daily and cumulative infections (detected and undetected), deaths, years-of-life lost (YLL) attributable to COVID-19 mortality, resource utilization , and health care costs from the all-payer (public and private) health sector perspective. We examine different strategies of vaccination program implementation under multiple scenarios of vaccine effectiveness and epidemic growth, thereby projecting which factors have the greatest impact on clinical and economic outcomes and cost-effectiveness. Our goal was to inform vaccination program priorities in South Africa and other LMICs.Re) varies over time to produce two waves of SARS-CoV-2 infections.To understand the trade-offs inherent to policy decisions regarding the total vaccine supply to purchase and the speed with which to administer vaccinations, we compared the clinical and economic outcomes of different strategies of population coverage (vaccine supply) and vaccination pace. We determined the incremental cost-effectiveness ratio (ICER) of each strategy as the difference in health care costs (2020 USD) divided by the difference in years-of-life saved (YLS) compared with other strategies of supply and pace. We considered multiple scenarios of epidemic growth, including a scenario in which the effective reproduction number  and deaths , and highest costs (~$1.69–1.77 billion) over the 360-day simulation period  than the 80% supply strategy and the latter had an ICER of $4270/YLS compared with the 40% supply strategy. In the two-wave epidemic scenario, the 67% and 80% supply strategies had ICERs of $1,990/YLS and $2,600/YLS, respectively. A vaccine supply of 20%, while less efficient than higher vaccine supply levels, still reduced deaths by 72–76% and reduced costs by 15–32% compared with no vaccination. The highest vaccination pace, 300,000 vaccinations daily, resulted in the most favorable clinical outcomes and lowest costs compared with lower paces in both the Re = 1.4 and the two-wave epidemic scenarios (Table In both the Supplementary Table Re), a faster vaccination pace decreased both COVID-19 deaths and total health care costs, whereas the impact of a higher vaccine supply on deaths and costs varied  and slower pace . At a vaccination pace of 300,000/day, increasing the vaccine supply from 40% to 67% was cost-saving in the two-wave epidemic scenario, whereas it resulted in ICERs of $520/YLS when Re = 1.4, $1160/YLS when Re = 1.8, and $85290/YLS when Re = 1.1.When varying both vaccine supply and vaccination pace across different scenarios of epidemic growth , we performed sensitivity analyses in which we varied each of these factors. In each alternative scenario, we projected clinical and economic outcomes and determined the ICER of a reference vaccination program  compared with no vaccination programIn one-way sensitivity analysis, the reference vaccination program remained cost-saving compared with a scenario without vaccines across different values of effectiveness against infection, effectiveness against mild/moderate disease, effectiveness against severe/critical disease, and vaccine acceptance Table . When inThe reference vaccination program had an ICER < $100/YLS or was cost-saving compared with a scenario without vaccines across different values of prior immunity up to 40%), initial prevalence of active COVID-19, reduction in transmission rate among vaccinated but infected individuals, and costs of hospital and ICU care Table . When th0%, initiRe had the greatest influence on both infections and deaths, whereas varying the cost per person vaccinated had the greatest influence on health care costs. Vaccine effectiveness against infection and effectiveness against severe disease requiring hospitalization were more influential than effectiveness against mild/moderate disease in terms of reductions in deaths and costs.The influence of different scenarios into which the vaccination program would be introduced on cumulative infections, deaths, and health care costs is depicted in Fig. In a multi-way sensitivity analysis in which we simultaneously varied vaccine effectiveness against infection and cost per person vaccinated, the reference vaccination program was cost-saving compared with a scenario without vaccines when cost per person vaccinated was $14.81, even when effectiveness against infection was as low as 20%  across most epidemic scenarios, including various rates of epidemic growth and a broad range of prevalence of prior population immunity. Although there is no consensus on an ICER threshold for cost-effectiveness in South Africa, for context, the country’s gross domestic product per capita in 2019 was ~$6000 and a published South Africa cost-effectiveness threshold from an opportunity cost approach was ~$2950 (2020 USDs) per disability-adjusted life-year averted20.Using a dynamic COVID-19 microsimulation model, we found that vaccinating 67% of South Africa’s population, meeting the government’s goal for 202115. However, we found that, even with substantially lower vaccine efficacy than reported in clinical trials, vaccination programs would prevent the majority of COVID-19 deaths compared to scenarios without vaccines. For example, decreasing vaccine effectiveness against mild/moderate disease and severe/critical disease requiring hospitalization to 40% still reduced COVID-19 deaths by 65,800 (74%) compared with a scenario without vaccines. Although efficacy against symptomatic and severe disease have been the focus of vaccine trials, these parameters were less influential on population-wide health and cost outcomes than efficacy against infection, which is less commonly reported in trials4. Nonetheless, the effectiveness ranges we examined in sensitivity analysis include the point estimates of efficacy against symptomatic and severe disease reported in clinical trials of the AstraZeneca ChAdOx1, Moderna mRNA-1273, and Pfizer-BioNTech mRNA BNT162b2 vaccines3. This suggests that all of these vaccines are likely to have both health and economic benefits. Furthermore, our sensitivity analysis examining different Re scenarios likely captures the potential influence of more contagious SARS-CoV-2 variants such as δ.Much has been made about differences in the leading vaccine candidates and the impact of variants, such as the B.1.351 (β) variant, which eventually accounted for over 90% of SARS-CoV-2 infections in South Africa and the B.1.617.2 (δ) variant, on vaccine effectiveness23. Achieving the government’s goal of vaccinating 67% of South Africans within 1 year will depend at least partially on global vaccine supplies and may require global policymakers to better fund and facilitate vaccine distribution and accessible pricing for LMICs, in addition to local attention to delivery infrastructure and community outreach. Although these expenses may increase program costs, we found that the vaccination program would remain cost-saving at a vaccination cost up to $25/person and likely cost-effective even at per-person vaccination cost up to $75/person (ICER $1500/YLS). This is due to cost offsets in preventing hospitalizations.Similarly, we found that vaccination programs remained economically favorable even at relatively high vaccination costs. Although we did not explicitly account for all implementation and scale-up costs of a vaccination program, our estimates of cost per person vaccinated were based on reported costs of both vaccine and delivery in South AfricaRe = 1.1), which approximates the basic reproduction number in the intra-wave periods in South Africa, a faster pace remained preferable from a clinical and economic standpoint. However, with the faster vaccination pace, increasing the proportion of the population vaccinated from 40% to 67% resulted in higher costs and only modestly fewer YLL, with an ICER of $85,290/YLS, well above commonly reported willingness-to-pay thresholds in South Africa27. By contrast, when a higher epidemic growth rate is seen (Re = 1.8), as was documented during the first and second waves in South Africa, a faster vaccination pace remained highly preferable, and increasing the proportion of the population vaccinated from 40% to 67% resulted in fewer YLL and higher costs with a much lower ICER of $1160/YLS. Overall, these results demonstrate the importance of rolling out vaccinations quickly, particularly ahead of any future waves of the epidemic. Consequently, policymakers should invest in establishing a vaccine distribution and administration system, to ensure vaccines will be administered as promptly as possible. All available distribution channels, including those in public and private sectors, should be leveraged.A faster pace of vaccination consistently decreased infections, deaths, and costs across a range of epidemic growth scenarios. Yet, this was not always true of a higher vaccine supply. With lower epidemic growth . We assessed costs from a health care sector perspective and did not account for other sector costs associated with lockdowns and failure to achieve epidemic suppression 35. Excluding these costs may underestimate the true value of COVID-19 vaccination to society. We did not explicitly model the use of non-pharmaceutical interventions (NPIs) as a standalone strategy or in combination with vaccination. However, the evaluation of various transmission scenarios  captures the potential impacts of different levels of NPI implementation on clinical outcomes. As with all modeling exercises, our results are contingent on assumptions and input parameters. Primary assumptions in our model included initial prevalence of COVID-19, prevalence of prior immunity, time to vaccine rollout, and vaccine efficacy against asymptomatic infection.These results should be interpreted within the context of several limitations. We assumed that vaccine effectiveness was constant starting 14 days after administration and continuing throughout the 360-day simulation. Early data suggest that post-vaccination immunity lasts at least for monthsGiven data limitations and the uncertainty in making long-term projections, we limited the time horizon of our analysis to 1 year. The sustainability and cost-effectiveness of vaccination beyond 1 year is likely to depend on the duration of protection conferred by existing vaccines, their effectiveness against emergent variants, and the costs, effectiveness, and frequency of potential booster shots—factors that remain unknown as of June 2021. If SARS-CoV-2 becomes endemic, cost-effectiveness analysis will become increasingly critical for integrating vaccination programs within health program budgets.10. Policymakers can use our results to guide decisions about vaccine selection, supply, and distribution to maximally reduce the deleterious impact of the COVID-19 pandemic in South Africa.In summary, we found that a COVID-19 vaccination program would reduce infections and deaths, and likely reduce overall health care costs in South Africa across a range of possible scenarios, even with conservative assumptions around vaccine effectiveness. Our model simulations underscore that in South Africa and similar settings, acquisition and rapid distribution of vaccines should be prioritized over relatively small differences in vaccine effectiveness and price. The pace of vaccination is as or more important than population coverage and, therefore, attention to vaccination program infrastructure is critical. Non-pharmaceutical practices such as mask wearing and physical distancing remain crucial to reduce epidemic growth, while vaccination programs are being implemented36. We previously used the CEACOV model to project COVID-19 clinical and economic outcomes in a variety of settings, including an analysis of non-pharmaceutical public health interventions in South Africa39.We used the Clinical and Economic Analysis of COVID-19 Interventions (CEACOV) dynamic state-transition Monte Carlo microsimulation model to reflect COVID-19 natural history, vaccination, and treatment40. Outside the model, we calculated the mean lifetime YLS from each averted COVID-19 death during the 360-day model horizon, stratified by age  divided by the difference in life-years between different vaccination strategies. Our ICER estimates include health care costs during the 360-day model horizon and YLS over a lifetime from averted COVID-19 deaths during the 360-day model horizon24. “Cost-saving” strategies were those resulting in higher clinical benefits (fewer life-years lost) and lower costs than an alternative.Starting with SARS-CoV-2 active infection prevalence of 0.1% , we projected clinical and economic outcomes over 360 days, including daily and cumulative infections, deaths, hospital and ICU bed use, and health care costs without discounting41.In each simulation, we assumed a fixed supply of vaccines that would be administered to eligible and willing individuals regardless of history of SARS-CoV-2 infection. Available vaccine doses would first be offered to those aged ≥60 years, then to those aged 20–59 years, and finally to those aged <20 years42. To reflect possible implementation of other vaccines, as well as published data and uncertainties around the type of protection provided by each vaccine, we varied vaccine effectiveness against SARS-CoV-2 infection, effectiveness against mild/moderate COVID-19 disease, and effectiveness against severe COVID-19 disease requiring hospitalization. We assumed that a single vaccine dose would be given and did not explicitly model a two-dose schedule.In the base case, we applied characteristics of Ad26.COV2.S (Johnson & Johnson/Janssen), a single-dose vaccine for which administration in South Africa began through a phase 3b study in health care workers in February 202124.At model initiation, each individual is either susceptible to SARS-CoV-2, infected with SARS-CoV-2, or immune (by way of prior infection). Each susceptible individual faces a daily probability of SARS-CoV-2 infection. Once infected, an individual moves to the pre-infectious latency state and faces age-dependent probabilities of developing asymptomatic, mild/moderate, severe, or critical disease . IndividSupplementary Methods43. We assumed in the base case that, at model initiation, 30% had prior infection and were immune to repeat infection. This assumption was based on an estimate of the proportion of South Africa’s population that had been exposed to the B.1.351 variant by 30 January 2021  pace of vaccination was 150,000 doses/day18. Our comparisons of different vaccination program strategies included varying the vaccine supply to that sufficient to cover 0–80% of South Africa’s population and increasing the pace of vaccination up to 300,000 doses/day. In the base case, we assumed that vaccine uptake among those eligible was 67%, accounting for vaccine hesitancy and failure to reach some individuals48. Vaccine effectiveness was 40% against infection, 51% against mild/moderate disease, and 86% against severe or critical disease requiring hospitalization. The latter two were based on reported efficacies of the Johnson & Johnson/Janssen vaccine ≥14 days post vaccination in South Africa4.In the reference vaccination program strategy, we assumed the following: (a) there would be a sufficient supply of vaccine doses to fully vaccinate 67% of South Africa’s population (~40 million vaccinated people)Re) = 1.4 in the base case49. We also simulated alternative epidemic growth scenarios with lower or higher initial Re and a scenario in which there were episodic surges above a lower background basic reproduction number (R0), as observed in the South Africa epidemic over the past year ; prior immunity (10–50% of population); reduction in transmission rate among vaccinated but infected individuals (0–50%); and hospital and ICU daily costs (0.5×–2.0× base case costs). The ranges of vaccine effectiveness against mild/moderate disease and severe/critical disease requiring hospitalization were based on efficacies and 95% confidence intervals reported in the Johnson & Johnson/Janssen vaccine trial  were excluded. We performed multi-way sensitivity analyses in which we simultaneously varied parameters influential in one-way sensitivity analyses.We used sensitivity analysis to examine the relative influence on clinical and cost projections of various parameters around vaccine characteristics and epidemic growth Table . Specifine trial 4. We alsFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary