Pregnant patients have increased morbidity and mortality in the setting of SARS-CoV-2 infection. The exposure of pregnant patients in New York City to SARS-CoV-2 is not well understood due to early lack of access to testing and the presence of asymptomatic COVID-19 infections. Before the availability of vaccinations, preventative (shielding) measures, including but not limited to wearing a mask and quarantining at home to limit contact, were recommended for pregnant patients. Using universal testing data from 2196 patients who gave birth from April through December 2020 from one institution in New York City, and in comparison, with infection data of the general population in New York City, we estimated the exposure and real-world effectiveness of shielding in pregnant patients. Our Bayesian model shows that patients already pregnant at the onset of the pandemic had a 50% decrease in exposure compared to those who became pregnant after the onset of the pandemic and to the general population. Pregnant patients make up a vulnerable patient population in any infectious disease outbreak. When New York City became the epicenter of COVID-19 pandemic in March 2020, the impact of SARS-CoV-2 infection on pregnant patients and their neonates was not well understood [During this period of uncertainty early in the pandemic, most national and regional public health authorities and medical care professionals advocated for the enforcement of protective measures including wearing masks, quarantining at home when possible, and keeping social distancing. These non-pharmaceutical interventions or shielding measures have been shown to be highly effective in mitigating epidemic curves in the larger population especially during different “lockdown” periods in myriad countries ,4,5,6 buStudies have shown that pregnant patients are at higher risk of getting seriously ill from SARS-CoV-2 compared to non-pregnant patients ,8. A metPregnant patients giving birth at a single New York City hospital between 20 April 2020 and 27 December 2020 were included in this study. 2682 pregnant patients with clinical data capture and sample capture could have had either RT-PCR testing or serology testing or were untested (unknown). Among these 2682 patients in terms of RT-PCR, 97.7% were tested and 2.3% were unknown; in terms of serology tests, 89.9% were tested and 10.1% were unknown. For testing results breakdown: 10% were RT-PCR negative but serology unknown; 0.3% were both RT-PCR and serology unknown; 8.1% were RT-PCR negative and serology positive, 0.075% were RT-PCR unknow and serology positive, 2.1% were both RT-PCR and serology positive, 77.18% were both RT-PCR and serology negative, 2.0% RT-PCR unknown and serology negative, and 0.56% were RT-PCR positive and serology negative. The demographics of these 2682 pregnant patients can be found in The serology was detected in the serum or plasma from peripheral blood collected during admission for delivery. The serology test was performed using the clinical testing Pylon 3D platform . The Pylon 3D platform  utilizesPregnant patients underwent RT-PCR testing for SARS-CoV-2 using nasopharyngeal swabs.The observed cross-sectional data for pregnant patients is restructured into four trajectories for model fitting: weekly proportion of RT-PCR and serology negative time-series, weekly proportion of RT-PCR positive and serology negative time-series, weekly proportion of RT-PCR positive and serology positive time-series and weekly proportion of RT-PCR negative and serology positive time-series.The seroprevalence data for general population in New York City Metro Area  from February 2020 to December 2020 and the daily total (including confirmed and probability) mortality data were extracted from US Department of Health and Human Services Centers for Disease Control and Prevention CDC Data Tracker . DetailsWe first develop a dynamic model diagramed in The initial conditions of In Equation (1), Following the dynamic model, we develop a Bayesian measurement model to model the data observation process so that the parameter estimation and model fitting can be conducted simultaneously using MCMC in Rstan . The modWe use Bayesian inference  in RStan to fit the model to RT-PCR and serology data by running four chains of 20,000 iterations each . We use 5% and 95% percentiles from the resulting posterior distributions for 90% CrI for the parameters. The Gelman–Rubin diagnostics  RT-PCR negative and serology negative without previous exposure (nfected) .We next defined four transmission quantities or parameters to link these above mentioned 5 time-based compartments: force of infection,  rate, β .The whole length of infectious period for pregnant patients can be therefore approximated by the sum of time delay between virus detectability and antibody detectability and the average time lag between antibody detectability and virus undetectability. We developed a dynamic model to study temporal changes of both RT-PCR and serology status in pregnant patients  with assWe modeled the exposure of 2196 pregnant patients who delivered at a New York City hospital from 20 April 2020 through 27 December 2020 based on SARS-CoV-2 testing performed on discarded samples obtained from birth admission using data from quantitative real-time polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 viral infection, or serology studies assaying levels of Immunoglobulin (Ig)G and IgM as a marker of the immune response to SARS-CoV-2 infection. Of the 2196 patients that had both RT-PCR and serology results available, 2.7% were positive and 97.3% were negative for RT-PCR testing results; and 11.2% were positive and 88.8% were negative and for serology testing results. For both tests combined, 2.2% were both positive for RT-PCR and serology, 0.5% were RT-PCR positive and serology negative, 9.0% were RT-PCR negative and serology positive, and 88.3% were both RT-PCR negative and serology negative.The test results of RT-PCR and serology allow us to divide our population of pregnant patients into four data-driven categories: (a) both RT-PCR negative and serology negative; (b) RT-PCR positive and serology negative; (c) both RT-PCR positive and serology positive; and d) RT-PCR negative and serology positive. The challenge in getting from test results to dynamic model compartments is that the first compartment (ic model  both manDifferent models (1–4) were used to analyze different assumptions about the force infection among pregnant patients. In model 1, we assumed the force of infection is constant over time, and then relaxed the assumptions by assuming a time-varying force of infection in models 2–4  days), and 18.8 days  days), respectively. These estimates are largely comparable with those for the general population ,24,25,26The estimated seroprevalence (proportion of pregnant patients who are seropositive) from each of the dynamic models  match thWe next compared cumulative level of exposure among pregnant patients with of the general population of New York City from the same time period. In brief, the levels of exposure in general population were estimated by applying our previously published inference methodology  to the eOur model was structured to recapitulate the average course of SARS-CoV-2 infection with turning RT-PCR positive occurring before becoming serology positive. However, not all disease courses follow this linear model structure. It is also possible that the state of pregnancy may alter the susceptibility to SARS-CoV-2 infection, although current evidence does not support that pregnancy increases the susceptibility of infection. In addition, the antibody decaying rate may differ during pregnancy. We should note that the thresholds of seropositive and seronegative assignment might vary between assays, and the performances of assays (including sensitivity and specificity) are different. Our study is not set up for longitudinal follow-up of our cohort, thus our data is not sufficient to evaluate the impact of pregnancy on the antibody decaying rate. While more detailed longitudinal serological data could be collected and modelled during pregnancy, incorporating antibody kinetics into transmission models may hinder the applicability of estimates resulting from different assays .In summary, we used a novel model to evaluate SARS-CoV-2 exposure levels in different populations using seroprevalence data and RT-PCR data, comparing exposure levels in pregnant patients in New York City to the levels in the general City population. This permits us to quantify the impact of shielding measures in preventing exposure during pregnancy across the first year of the pandemic. We estimate the impact of self-protection on reducing the level of exposure among pregnant patients during early 2020 who gave birth in this New York City hospital to be approximately 50%. These results, showing time-varying differences in exposure to SARS-CoV-2 in pregnant compared to non-pregnant populations, may have led to significant reduction in maternal morbidity and mortality in the early months of the pandemic. The estimated total exposure in pregnant patients and general population of New York City are both more than double the latest serology positive measurements.Positive results from RT-PCR testing and serology testing can both be used to identify infected or recently infected individuals. While an infected individual turns RT-PCR positive and then RT-PCR negative within the span of days to a week, a positive serology test result can serve as a maintained marker of infection that last for months. By capturing this dynamic effect of antibody waning in our models, we found that SARS-CoV-2 exposure estimates were much higher than the seroprevalence estimates for our sample of pregnant patients and the general public in New York City. These results confirm that previous studies looking at RT-PCR positive testing rates or seroprevalence alone will substantially underestimate population-level and subgroup exposure to SARS-CoV-2.We found that patients who gave birth between April and August of 2020 had lower levels of exposure to SARS-CoV-2 compared to the general population. In fact, in the first months of the pandemic (April and May 2020), the exposure levels of pregnant patients were half of the exposure levels of the general population in New York City, and half of the exposure levels in pregnant patients who gave birth by the end of 2020. To understand the possible variables that contribute to this lower exposure level in pregnant patients who gave birth early in 2020, we must take into account the distinctions between the experience of pregnant patients who gave birth in early 2020 vs. late 2020. Patients that gave birth before August 2020—before the level of exposure in pregnant patients became comparable to that of non-pregnant patients—were all at least in their mid to late first trimester by the time that the pandemic hit New York City. This means that most of these patients had a high probability of knowing about their pregnancy at the onset of the pandemic, and it is possible that this knowledge of pregnancy led to behavior changes that made them more cautious than the general population. In contrast, the patients giving birth towards the end of 2020 were not pregnant and/or did not know of their pregnancy before the onset of the pandemic and may not have behaved differently than the general population; in other words, they could be considered part of general population in early 2020. During the early part of the pandemic, the population only had access to shielding measures and other non-pharmaceutical measures for prevention of disease exposures . Thus, the reduction of exposure in pregnant patients by about half early in the pandemic may be attributed to effectiveness of shielding measures . Our curSuch a high-level reduction of exposure might have been associated with a reduction in infection and especially a reduction of severe COVID-19 illness and, consequently, in mortality in pregnant patients. A large-scale retrospective analysis from a database that covers about 20% of the American population and includes 406 446 patients hospitalized for childbirth (6380 (1.6%) of whom had COVID-19) compared outcomes for pregnant patients with and without COVID-19 from April–November 2020 . It concAfter the period included in our study, additional SARS-CoV-2 preventative measures in the form of vaccinations were introduced in 2021 although strict quarantine regulations were also lifted from the city by then. Pregnant patients were not included in studies testing the safety and efficacy of COVID-19 vaccines. Studies conducted since the start of vaccination distribution including those looking at the real-word implementation of vaccination have confirmed the safety and effectiveness of vaccines specifically for pregnant patients, their placentas, and their neonates ,33,34,35Our study has several important strengths, the two most important of which are robust data on a cohort of pregnant patients assessed over an extended period of time tested with both RT-PCR and serology throughout 2020, and the use of a novel model for reproducibly calculating disease exposure from testing data. While the lacuna in data capture in May and June could potentially influence the performance of parameter inference, varying model assumptions on the force of infection  found the estimated parameters and level of exposure to be robust and therefore clarified the likely minimal impact caused by missing data.While the pregnant patient population is from a single NYC institution which may not be representative of the broader population, this population allowed for uniformity in testing and the study of a large cohort of patients.


Objectives: To describe the prevalence and correlates of depressive symptoms, generalized anxiety disorder (GAD), and perceived negative mental health impact during the SARS-Cov-2 pandemic in Mexico City and evaluate their association with adherence to stay-at-home directives.Methods: Baseline data from a cohort study of 2,016 Mexico City government employees were analyzed using multivariable logistic regression models.Results: Among participants, 17.2% had clinically significant depressive symptoms, 21.6% had probable GAD, and 15.2% reported that the pandemic has had a major impact on their mental health. Factors including the presence of COVID-19 symptoms, self-isolation, and economic difficulties were associated with poor mental health. The presence of depressive symptoms and general anxiety were associated with non-adherence to public health directives, particularly among those who might have experienced these symptoms for the first time during the pandemic.Conclusion: Our study is one of the first to document the population mental health burden during the SARS-CoV-2 pandemic in Mexico and to provide evidence of the potential role of mental health in the adherence to public health measures. The SARS-Cov-2 pandemic has killed more than 185,000 people in Mexico as of March 1st, 2021 . BetweenIn addition to its impact on physical health, SARS-Cov-2 may affect mental health through direct  , 7 or inPoor mental health may play a role in shaping the spread of infectious disease through non-compliance with recommended medical treatment  and non-Thus, we aimed 1) to provide estimates of the prevalence and correlates of depressive symptoms, GAD, perceived negative mental health impact, and use of mental health services during the SARS-Cov-2 epidemic in a sample of Mexico City’s government workers and 2) to evaluate the relation between mental health conditions and adherence to stay-at-home directives., 2020, Mexico City government employees responded to an online questionnaire (platform developed by Amazon Web Services Mexico-Grupo SIAYEC) that assessed demographic characteristics, pre-existing health conditions, financial difficulties during the pandemic, COVID-19 symptoms, adoption of preventive and social distancing measures, knowledge of the virus, and mental health conditions. Potential participants included all employees with access to an institutional email–those performing any type of desk work in the central government . Employees from Mexico City’s Ministry of Health were not included in our sample. Invitations were sent to institutional emails of 14,361 eligible employees; 16.6%  participated in the study. Study participants were younger  and more likely to identify as women (49.8% vs. 41.2%) than in the eligible source. For the present cross-sectional analysis, we included participants with complete responses to the mental health questionnaire . Among them, a low proportion of missingness (≤2%) was observed for all covariates with no specific pattern ; missingness did not appear to depend on mental health outcomes  is a longitudinal cohort study of Mexico City’s government employees initiated in June 2020 to 1) evaluate health and behaviors during the SARS-Cov-2 epidemic and 2) investigate the impact of these factors on the adherence to mitigation strategies. Between June 4th and July 8thoutcomes . TherefoDepressive symptoms were measured using the Spanish version of the 7-item Center for Epidemiologic Studies Depression (CESD-7) scale  queryingThe prevalence of general anxiety symptoms were assessed using the Spanish version of the Generalized Anxiety Disorder-7 (GAD-7) scale , 22. ThePerceived negative mental health impact of the epidemic was evaluated based on a question used in the Kaiser Family Foundation Health Tracking Poll , “Do youParticipants were asked about the number of days they left the household in the previous week. Those who reported going out at least once were further asked about the reasons for leaving the household: groceries, work, medical attention, recreational physical activity, walking their pet, taking care of a relative, social gathering, boredom, not believing in social distancing, and others. Participants were additionally asked to report whether or not they received visits from non-household members in the week previous to the survey. Non-adherence to stay-at-home orders was defined as leaving household for non-essential activities  or home visits by non-household members.Measures of sociodemographic factors included age at the time of survey response, sex, education level , whether participants lived alone or with other household members, monthly household income in Mexican pesos , and pre-existing physician-diagnosed health conditions .Disease-specific stressors included the presence of at least one COVID-19 symptom in the week previous to the survey  and self-isolation due to participant symptomatology or recent contact with a symptomatic individual. Stressors related to mitigation strategies included whether or not participants went out to work in the previous week, presence of financial difficulties in the household during the pandemic , and inability to take care of themselves or family members .Participants were also asked about the source of information that they consider the most reliable during the pandemic traditional media , online media , personal sources , and official government websites.For description of the population, we calculated means and standard deviations for continuous variables, and percentages for categorical variables. Clinically significant depressive symptoms and probable GAD were operationalized based on previous benchmarks used in the Mexican population , 21. PerTo estimate the adjusted odds ratio of non-adherence to stay-at-home orders according to the presence of 1) clinically significant depressive symptoms and 2) probable GAD, we used logistic regression models adjusting for theoretically appropriate confounders . ConfounWe repeated analyses among participants with no previous medical diagnosis of depression or anxiety in order to isolate the estimated association among those with recent onset of mental health conditions. Since the pandemic could have a different impact in those with pre-existing mental health disorders , compareAll analyses were conducted using R version 3.6.1.Characteristics of study participants are presented in Mean (SD) CESD-7 and GAD-7 scores were 4.6 (4.5) and 5.9 (5.1), respectively. Among participants, 17.2% had clinically significant depressive symptoms, 21.6% had probable GAD, and 15.2% reported that the pandemic has had a major impact on their mental health. However, among those with clinically significant depressive symptoms, only 31.2% reported seeking any type of psychological care during the epidemic. Comparatively, 25.9% of those with probable GAD and 27.7% of those who reported major mental health impact reported seeking psychological attention.The prevalence of clinically significant depressive symptoms and probable GAD observed in this sample were higher than previous estimates for Mexico City and for the country . SpecifiPrevalence of mental health outcomes differed according to population subgroups . The burResults from multivariable logistic regression analyses were consistent with univariate results for depressive symptoms and probable GAD . The strAssociations between mental health conditions and non-adherence to stay-at home directives  revealedAnalyses restricted to participants with no previous medical diagnosis of depression or anxiety similarly revealed that having clinically significant depressive symptoms without history of previous medical diagnosis of depression was associated with non-adherence to stay-at-home directives. After multivariable adjustment, this association was stronger in magnitude than that observed in the complete sample ). Probable GAD was also significantly associated with non-adherence to stay-at-home directives among those with no previous medical diagnosis of depression or anxiety ).We further explored effect heterogeneity by history of medical diagnosis of depression or anxiety for the association between mental health conditions and non-adherence to stay-at-home directives. The results indicate that the association between mental health conditions and non-adherence to stay-at-home directives was stronger and only significant among those with no prior diagnosis of depression or anxiety .Our survey of government employees in Mexico City during the SARS-CoV-2 pandemic revealed several important findings. First, we found consistent indicators of poor mental health and suboptimal use of psychological services. Compared to previous literature, the prevalence of poor mental health in our study population was higher than previous population estimates in Mexico City, including in periods following major natural disasters. Second, clinically significant depressive symptoms and probable GAD were particularly elevated among those who had a medical diagnosis of depression or anxiety, those who had COVID-19 symptoms in the previous week, and those who had difficulties taking care of themselves or their family. Finally, we found that elevated depressive or general anxiety symptoms were associated with non-adherence to stay-at-home directives. Most striking, this association was strongest among those with no history of medical diagnosis of depression or anxiety for whom poor mental health might be of more recent onset.Our study is the first of which we are aware to document an association between poor mental health and adherence to public health directives during COVID-19, particularly among those with recent onset of these conditions. It has been previously proposed that, during epidemics, psychological reactions to stressors and public health directives may play a role in shaping the spread of disease and in worsening the mental health among vulnerable groups , 15. SomTo our knowledge, only two other studies have evaluated the impact of the pandemic on mental health in the Mexican population and were conducted primarily among students and employees of private universities , 12. RamWhile differences in measurements across studies may limit comparability of our prevalence estimates, the increased burden of poor population mental health in Mexico during the pandemic compared to previous estimates, including during other major disasters, is consistent with literature examining these trends in other settings , 30. ForLikewise, the disproportionate burden of mental health conditions across sociodemographic subgroups in our study population, namely among women and younger age groups, is also consistent with recent findings in other settings , 12, 31.The association between economic burden and inability to care of self or others with poor mental health is consistent with the literature in other settings  and provOur study has several limitations. First, the low response rate may reflect selection into the study population and might have biased our estimates. However, demographic characteristic of respondents appeared to be fairly similar to those of the source population and a study with similar online recruitment strategies in Mexico had comparable response rates (11–12%) . Second,Our observation that increases in depressive symptoms and general anxiety may affect non-adherence to public health directives, particularly among those who might have experienced these symptoms for the first time during the pandemic, has important implications. First, it provides empirical evidence on the potential role of mental health and stress-related behaviors on the adherence to public health measures during epidemics. This is in line with observations during the Ebola outbreak where fear-related behaviors, such as stigmatizing infected survivors and ignoring medical procedures, were reported to disrupt public health efforts . Second,Our study is one of the first to document the population mental health burden during the SARS-CoV-2 pandemic in Mexico and our work highlights many potential targets for public health interventions to support mental health. More work is needed to document the continued and potentially changing influence of the pandemic on Mexico’s population mental health; to elucidate the relationship between mental health, history of mental health, and adherence to public health directives; and to identify important areas and opportunities for public health interventions that can improve population health in Mexico in this difficult time.


Lentils are at risk of cross contact with gluten-containing grain. Consumers should continue to sort through lentils removing foreign grain, and rinse sorted lentils under running water to remove grain dust before cooking.Lentils are naturally gluten-free and are recommended for people with celiac disease and other gluten-related disorders. However, like oats, they appear to be at a heightened risk of cross contact with gluten-containing grains. The purpose of this study was to spot check for the presence of errant gluten-containing grains in a variety of brands of lentils purchased in 2021. Twenty-five bags of different dry lentil products representing 24 brands were purchased online and at various grocery stores. Each bag of lentils was individually hand sorted. Two of the 25 packages of lentils contained errant gluten-containing grains. One 16-ounce (454 g) bag contained a grain of wheat. Another 16-ounce (454 g) bag contained a grain of wheat and a grain of barley. For a product to be considered gluten-free in the United States, it must contain <20 mg of gluten per kilogram (or 20 parts per million of gluten). A product at the 20-ppm level of gluten should contain no more than 2 intact gluten-containing grains per kilogram or 35.27 ounces  of food (or 1 intact gluten-containing grain in 17.64 ounces [500 g] of food). Based on these calculations, a 16-ounce (454 g) bag of lentils containing 1 intact gluten-containing grain would  Lentils are naturally gluten-free. They are recommended for people with celiac disease and other gluten-related disorders. However, like oats, they appear to be at a heightened risk of cross contact with gluten-containing grains. This is due in part to farming practices. Lentils may come into contact with wheat, barley, or rye while being grown, harvested, and/or stored. The United States Department of Agriculture, Agricultural Marketing Service, Federal Grain Inspection Services standard for lentils allows for a certain percentage of foreign material  . AcceptaBetween March of 2016 and April of 2020, Gluten Free Watchdog (GFWD) received at least 17 complaints from consumers about errant grain in dry lentils. These complaints represented 9 different brands. All but one product were labeled gluten-free or certified gluten-free. In the US, all products under the labeling jurisdiction of the Food and Drug Administration (FDA) and labeled gluten-free, must comply with the FDA's gluten-free labeling rule, including containing a level of gluten below 20 parts per million (ppm). There are multiple organizations in the US certifying gluten-free foods. All have differing criteria for certification, including containing a level of gluten from below 5 ppm up to <20 ppm. Complaints submitted by consumers generally included photographs of the foreign material. In most cases, GFWD was able to determine that the errant grain included wheat and/or barley either through laboratory testing or by expert review of the grain photographs by farmers familiar with gluten-containing grain.GFWD also sorted an unopened bag of a brand of lentils reported by a consumer. It contained numerous errant grains. It was determined through laboratory testing that the grain found included gluten-containing grain . As a result of consumer complaints and testing commissioned by GFWD, GFWD has been advising consumers with celiac disease or another gluten-related disorder to carefully sort dry lentils removing any foreign grain and to rinse sorted lentils under running water to remove grain dust.Of the 8 products labeled gluten-free or certified gluten-free reported by consumers that contained errant grain, GFWD reported 6 to the FDA. Consumers were advised to report the other 2 products to an FDA consumer complaint coordinator. The 2 products with the most egregious misbranding in terms of the number of errant grain found, were recalled  Figure . To the The purpose of this study was to spot check for the presence of errant gluten-containing grains in a variety of brands of lentils purchased in 2021, and to assess whether there had been any changes to gluten-free claims on product packaging of the lentils originally reported by consumers to GFWD.During the first half of 2021, 25 bags of different dry lentil products representing 24 brands were purchased online through Amazon, Wal-Mart, and Vitacost, and at various grocery stores in Ohio, US. Products had best by dates ranging from July 2021 to February 2025. Products varied in weight from 10 ounces (283 g) to 35 ounces (992 g). Each bag of lentils was individually hand sorted. Lentils were scooped from product packaging in 2-tablespoon increments and poured onto a flat white tray. They were separated and sorted with a paring-style knife. This process was repeated until each bag of lentils was empty. All foreign material was set aside for later review. Seven of the 9 brands of lentils previously reported by consumers to GFWD were included in the sorting. Two brands could not be included due to the lack of availability. Product labels were also reviewed for gluten-free claims and other advisory statements.not be considered gluten-free. Packaging for both products included an allergen advisory statement for wheat . Non-gluten-containing foreign material was found in 6 additional bags of lentils. This material included split peas, mallow seeds, stones, and rice grain.Two of the 25 packages of lentils (8%) contained errant gluten grains . One of Nine of the 25 products (36%) were labeled gluten-free. Of these, 1 was labeled certified gluten-free. Twelve of the 25 products (48%) contained an allergen advisory statement for wheat or gluten, including 3 products labeled gluten-free. Eighteen of 25 products (72%) contained a statement advising consumers to sort and rinse lentils, including 5 products labeled gluten-free.Of the 9 brands of lentils reported to GFWD by consumers, 8 were labeled gluten-free, including 3 that carried a certified gluten-free mark on product packaging at the time the complaint was received. Six of the 8 brands originally labeled or certified gluten-free were reviewed in 2021. Two could not be reviewed because they were not available for retail purchase. Of the brands that could be compared, 3 of the 6 original consumer-reported products were no longer labeled gluten-free in 2021 . Three onot be considered gluten-free.Two, 16-ounce 454 g) bags sorted in 2021 were found to contain errant barley and/or wheat. One of the bags contained a single grain and the other bag contained 2 grains. According to calculations published by the Gluten-Free Certification Organization, one gluten-containing grain could contain upwards of 10.5 mg of protein, including 9.45 mg of gluten  sample of whole lentils is challenging, and similar to trying to evenly distribute a grain of wheat or barley within a sample of intact inherently gluten-free grains  . LentilsNone of the above is meant to suggest that those with celiac disease or other gluten-related disorders should not eat lentils. For the present study, 27 pounds of lentils were sorted. Only 3 errant gluten-containing grains were found. Given the farming practices for lentils, this suggests fairly robust sorting and cleaning before the lentils are packaged for the consumer. Regardless, the gluten-free consumer should continue hand-sorting lentils, removing any foreign material, and rinsing under running water using a fine-mesh colander to remove any grain dust. When available, consumers may want to choose lentils that are labeled gluten-free. Lentils grown under a gluten-free purity protocol, following steps similar to what is followed for gluten-free purity protocol oats   are a neManufacturers of gluten-free foods may use whole lentils or lentil flour in their products. Results of a search on the terms “gluten-free” and “lentil” on a popular mail order site in the US, included chips, crackers, pasta, ready-to-eat breakfast cereal, soup, and rice mixes. It would be prudent of manufacturers to ask suppliers for certificates of analysis for gluten for lentil ingredients. In addition, manufacturers should consider sorting whole lentils for gluten-containing grain and testing lentil flour for gluten.Lentils are at risk of cross contact with gluten-containing grain due to various farming practices. While there appears to be movement away from labeling lentils gluten-free, the vast majority of lentil products sorted in 2021 were free of wheat, barley, and rye grain. Regardless, due to the continued risk of cross contact, consumers should sort through and rinse lentils before cooking. When available, lentils labeled gluten-free should be chosen over products not labeled gluten-free. Consumers concerned about cross contact also may want to choose lentils grown and processed following a gluten-free purity protocol.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.All authors contributed to the study design, the interpretation of the data, drafting of the manuscript, and approve the submitted version.Gluten Free Watchdog, LLC, paid for the cost of the lentils.TT is the owner and founder of Gluten Free Watchdog, LLC. The remaining 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.


The trade-off between speed and precision is a key step in the detection of small polyps in wireless capsule endoscopy (WCE) images. In this paper, we propose a hybrid network of an inception v4 architecture-based single-shot multibox detector (Hyb-SSDNet) to detect small polyp regions in both WCE and colonoscopy frames. Medical privacy concerns are considered the main barriers to WCE image acquisition. To satisfy the object detection requirements, we enlarged the training datasets and investigated deep transfer learning techniques. The Hyb-SSDNet framework adopts inception blocks to alleviate the inherent limitations of the convolution operation to incorporate contextual features and semantic information into deep networks. It consists of four main components: (a) multi-scale encoding of small polyp regions, (b) using the inception v4 backbone to enhance more contextual features in shallow and middle layers, and (c) concatenating weighted features of mid-level feature maps, giving them more importance to highly extract semantic information. Then, the feature map fusion is delivered to the next layer, followed by some downsampling blocks to generate new pyramidal layers. Finally, the feature maps are fed to multibox detectors, consistent with the SSD process-based VGG16 network. The Hyb-SSDNet achieved a 93.29% mean average precision (mAP) and a testing speed of 44.5 FPS on the WCE dataset. This work proves that deep learning has the potential to develop future research in polyp detection and classification tasks. Gastrointestinal cancer is the third major cause of death worldwide . ColorecThe motivation of this study is to achieve an encouraging mean average precision (mAP) for polyp detection purposes while reducing the running time by implementing tremendous deep learning frameworks. For that reason, we redesigned a new SSD model using the inception-v4 (as a backbone), which can solve the problem of over-fitting and increased computation during the optimization process. Thus, the inception frameworks can capture more target information compared to the VGG networks without increasing network complexity. The inception-v4 has a more uniform simplified architecture and more inception modules than inception-v3. However, it may not be necessary to add more inception layers to obtain significant increases in the performances as stated in . TherefoInspired by the feature fusion single-shot multibox detector (FSSD)  and the The application of a lightweight version of inception v4 architecture as the backbone to improve the SSD detector ability in small polyp detection.A weighted mid-fusion block of two adjacent modules of the modified version of inception-A is used to replace the Conv4_3 layer of the VGG16 network; thus, tackling the problem of missing target details.The filter numbers of the first convolution layers in the stem part of the inception v4 backbone were modified from (32 to 64) to capture more patterns and relevant information similar to the original SSD.The modification of the inception v4 model through the reduction of layers is used to achieve faster speed while maintaining the computational cost.The Hyb-SSDNet uses a weighted mid-fusion block to add new convolution layers to construct the multi-scale feature pyramid differently from the conventional SSD.The Hyb-SSDNet model robustness is verified through repeated experimentation on three well-known datasets in the field  for a fair comparison with the competitor’s state-of-the-art methods.The discussion of the advantages and limitations of the proposed framework.The main contributions of this paper are as follows:The remainder of this paper is organized as follows. Automatically detecting polyps in WCE images is a challenging task because of their variations in terms of size, shape, and morphology. Recently, significant progress has been made in investigating handcrafted features for gastrointestinal classification purposes. In this regard, a framework-based pyramid histogram for polyp classification based on T-CWT and gamma parameters were presented in a previous study . UnderstPolyps localization in WCE/colonoscopy images by drawing a bounding box around the localized region is the purpose of this paper, whereas polyp classification is conducted directly as an internal stage of localization. The frameworks of domain-specific object detection methods are primarily categorized into two types. One is the two-stage algorithm that first generates region proposals and then classifies each proposal into different object categories . The seg et al.  presenteg et al.  proposedg et al.  proposedg et al. . A lightThe single-shot multibox detector (SSD) uses VGG16 as the backbone network and is truncated with other convolutional layers . SSD useFor object detection in medical imaging, various deep network architectures have been investigated to trade-off between precision and speed. Faster R-CNN  and R-FCA systemic overview of the Hyb-SSDNet architecture for polyp detection is shown in Different multi-modal fusions have been widely used in deep learning. Mid-fusion operates independently at each scale. It then merges and passes them to complete the network processes. However, information from multiple scales is treated equally during fusion. Capturing variance in visual patterns as well as more informative patterns in class prediction is one of the requirements of the complete classification approach. Besides, the traditional SSD-based VGG16 network only uses the The main fusion structure is shown in D is the feature concatenation operation, The d by the  connectiThis is motivated by the squeeze-and-excitation network’s success  in showicSE Block The squeeze-and-excitation network uses a squeeze operation as a primary process by applying a global average pooling  to spatially compress the input feature maps, and reduce the dimensionality from x is the vector output, x). Therefore, the channel-wise dependencies will not be well encoded. Hence, the squeeze-and-excitation network uses the excitation operation to solve the channel’s weight inefficiency. One of the more significant issues of the excitation operation is to properly learn the non-linear interaction between channels as they have to be activated simultaneously. Therefore, two fully-connected layers C and r mean the number of channels and the scaling parameters, respectively. To reduce the computational complexity of the modified inception-A modules, the r parameter is set to 2. Finally, a channel-wise recalibration of the output vector was performed with the input feature maps to be scaled consistently to original sizes. We consider sig as the sigmoid activation. The final step reconstructs the spatial squeeze and channel excitation block (cSE). The merging operation expressed in Equation  assume the importance of squeezing the input feature map along the channels and mutually exciting them spatially. Therefore, a convolutional layer is applied to compress the input feature map. The sigmoid function is activated to obtain spatially distributed information. Finally, the sSE block processed a concatenation operation for the feature map fusion with the input feature map, computed in Equation (Equation  as:(6)x¨E) block , which cThis is based on the experiments conducted in the work of . ConcateThis is motivated by FSSD ,25. The The Fist WCE dataset was acquired from PillCam©COLON 2 polyps . The oriQ is the number of queries in the set and q is the query for average precision.In this study, the mean average precision (mAP) metric was adopted to evaluate polyp detection performance. Using an intersection over union (IoU) of 0.5, the mAP is defined as the average of the average precision (AP) of all object categories. It is formulated by Equation .(8)mAP=precision, recall, and F1-score can be defined in Equations  is used to measure the detection speed and indicates the number of frames transmitted per second. More details regarding the evaluation metrics can be found in [Given a set of ground truth bounding boxes annotated by the experts, and a set of predicted bounding boxes produced by the network with a confidence score above a specific threshold , quations  and 10)precisionfound in .The experiments were performed using Colab Pro Plus solution provided by Google, with a maximum RAM of 52 Gb and a disk of 166,83 Gb. All experiments were conducted using CUDA 8.0.61-1, CuDNN6.0, Keras 2.0.5, Python 3.5, h5py 2.10.0, NumPy 1.16.3, TensorFlow 1.15, TensorFlow-GPU 1.15, and OpenCV 3.1. According to the ground truth bounding boxes of the small polyp region, the same aspect ratio within a range of 1–2 was maintained for all employed datasets.−8, and beta_2 = 0.999. Using Keras, the learning rate schedule is defined as follows: 0.0001 if epoch < 50, 0.00001 if epoch < 80, and 0.000001 otherwise. Hyb-SSDNet is an improved model for the in-depth feature fusion of SSD300. For a fair comparison with the state-of-the-art approaches, target polyp detection was used [To verify the capability of the Hyb-SSDNet model, various indicators and parameters in the field of medical imaging were investigated and adjusted. As a primary process, the surrounding black regions containing no useful information about the WCE polyp image were removed, as they may have degraded the performance of WCE polyp detection and increased the computation time. The input images were resized to 299 × 299 × 3, and the batch size was 32. Concerning data augmentation strategies , we inve studies ,57. We pwas used . The modwas used , the samented in .In this section, we investigate the effectiveness of the different components that affected the experimental results of the main structure of the Hyb-SSDNet detector through an ablation study on the WCE, CVC-ClinicDB, and ETIS-Larib colonoscopy datasets. Different Hyb-SSDNet model settings are shown in For fair comparison experiments with the proposed Hyb-SSDNet framework, we selected the most highlighted methods in the literature-based SSDs models, YOLOv3, and DSSD, as listed in The main objective is to prove the efficiency of the proposed Hyb-SSDNet framework, not only for WCE polyp images detection but also for colonoscopy polyp localization cases (CVC-ClinicDB and ETIS-Larib). Polyp detection involves the identification of polyp regions and the rejection of parts containing normal, blurry tissues, and others showing feces or water jet sprays to clean the colon. However, the proposed Hyb-SSDNet model successfully detected a hard polyp case in which the ground truth was not annotated by experts In this paper, an improved FSSD detection model (Hyb-SSDNet) was proposed for accurate polyp abnormality localization and detection in both WCE and colonoscopy datasets. In addition to the problem of medical ethics, researchers have been pushed to use their datasets for polyp detection owing to the lack of standard and public WCE datasets. Therefore, research in this field is limited and the performance metrics may be subjective. The lightweight version of the Hyb-SSDNet aims to model the visual appearance of small polyp regions via a weighted mid-fusion of inception modules for a polyp detection network using the inception v4 backbone. The mid-fusion block uses the modified inception-A modules to magnify the mid-level feature maps instead of using simple convolution layers. Therefore, the Hyb-SSDNet model shows promising results in the (mAP) metric with little speed drop. An efficient feature fusion module using a simple concatenation strategy and L2 normalization was applied to the SSD framework to combine the intermediate inception layers to further generate new pyramid feature maps. The effectiveness of the weighted feature fusion was confirmed on three annotated testing datasets: WCE, CVC-ClinicDB, and ETIS-Larib. The Hyb-SSDNet framework adequately integrates low- and mid-level details, and high-level semantic information using a novel top-down feature fusion module. It also adopts one horizontal connection to reduce the number of repetitive computations, which shortens the detection time. In the future, a filtration strategy will be investigated to regularize the intermediate layer and reduce the false negative rate. Although the proposed model achieves satisfactory detection precision, the presence of the circumstances  certainly influence the results as they cannot be completely omitted. This shortcoming should be examined through subsequent work, such as how to improve the performance of the model while maintaining a higher detection speed.


This study determined if non-communicable disease status, HIV status, COVID-19 status and co-habiting were associated with COVID-19 test status in sub-Saharan Africa.Data of 5945 respondents age 18-years-old and above from 31 countries in sub-Saharan Africa collected through an online survey conducted between June and December 2020, were extracted. The dependent variable was COVID-19 status (testing positive for COVID-19 and having symptoms of COVID-19 but not getting tested). The independent variables were non-communicable disease status , HIV positive status, COVID-19 status (knowing a close friend who tested positive for COVID-19 and someone who died from COVID-19) and co-habiting (yes/no). Two binary logistic regression models developed to determine associations between the dependent and independent variables were adjusted for age, sex, employment, sub region and educational status.Having a close friend who tested positive for COVID-19 (AOR:6.747), knowing someone who died from COVID-19 infection (AOR:1.732), and living with other people (AOR:1.512) were significantly associated with higher odds of testing positive for COVID-19 infection, while living with HIV was associated with significantly lower odds of testing positive for COVID-19 infection (AOR:0.284). Also, respondents with respiratory conditions (AOR:2.487), self-reported depression (AOR:1.901), those who had a close friend who tested positive for COVID-19 infection (AOR:2.562) and who knew someone who died from COVID-19 infection (AOR:1.811) had significantly higher odds of having symptoms of COVID-19 infection but not getting tested.Non-communicable diseases seem not to increase the risk for COVID-19 positive test while cohabiting seems to reduce this risk. The likelihood that those who know someone who tested positive to or who died from COVID-19 not getting tested when symptomatic suggests there is poor contact tracing in the region. People with respiratory conditions and depression need support to get tested for COVID-19.The online version contains supplementary material available at 10.1186/s12879-022-07498-w. Sub-Saharan Africa is a low to middle income region with a high prevalence of communicable diseases , HIV infThe number of persons in sub-Saharan Africa who tested positive for COVID-19 has been low, and attributable to the low rate of COVID-19 testing . Many coThe region also has a very low coverage of COVID-19 vaccination , and togPrior studies have indicated that persons living with HIV are at higher risk of contracting and dying from COVID-19 infection , 15 as bMoreover, people living with certain non-communicable diseases like obesity , respiraThere is little information on the factors associated with the uptake of COVID-19 tests by people in sub-Saharan Africa living with non-communicable diseases and HIV infection. A prior study conducted in Nigeria indicated that significantly fewer people living with HIV tested positive for COVID-19 infection than those not living with HIV . Also, pThe aim of this study was to determine the factors associated with COVID-19 test status among residents in sub-Saharan Africa during the first wave of the COVID-19 pandemic. Specifically, the factors investigated were having a non-communicable disease, living with HIV, knowing someone with COVID-19 infection or who died from COVID-19 infection, and living arrangement. We hypothesised that people in sub-Saharan Africa living with non-communicable diseases or HIV will have higher odds of testing positive for COVID-19 than respondents who are not living with non-communicable diseases or HIV. Furthermore, we hypothesised that respondents who had a close friend who had tested positive for COVID-19 infection and who knew someone who died from COVID-19 may be at increased risk of exposure to COVID-19 infection and thus are more likely to have symptoms of COVID-19 infection and test positive for COVID-19. However, respondents who cohabitate are likely to be more careful and avoid putting others at risk for COVID-19 infection and are, therefore, less likely to test positive for COVID-19 infection.The study protocol was approved by the Human Research Ethics Committee at the Institute of Public Health, Obafemi Awolowo University, Ile-Ife, Nigeria (IPHOAU/12/1557). Informed consent was obtained from the study participants for the online survey by asking them to tick a checkbox that indicated consenting to study participation. Study participants could only proceed to the survey having ticked the checkbox. The study was performed in accordance with the National Health Research Ethics Code. All methods were carried out in accordance with National Health Research Ethics Code.The data for this cross-sectional study was extracted from a multi-country survey on the mental health and wellness of a global convenience sample of adults aged 18 years and older collected during the first wave of the pandemic  . There wThe data collection tool was developed for a study targeting a specific population in the United States  and was Respondents were asked if they had tested positive for COVID-19 or had COVID-19 symptoms but did not get tested. Response choices for these items were either ‘yes’ or ‘no’. This question was based on items from the mental health and wellness study .History of non-communicable diseases: Respondents were asked to identify if they had any of the 23 listed health conditions presented on a checklist in addition to other health conditions not listed. These included medical conditions which put individuals at higher risk for severe COVID-19 disease  and those that might put people at moderate risk of COVID-19 disease  [ression) .HIV status: As part of the 23 listed medical health conditions, participants were also asked about their HIV status. Respondents self-reported if they were living with HIV by ticking a checkbox to indicate yes. All respondents who did not tick the checkbox were categorised as not living with HIV.COVID-19 status: Respondents were asked if they had a close friend who had tested positive for COVID-19 or knew someone who died from COVID-19. Response choices for these items were ‘yes’ or ‘no’ [ or ‘no’ .Depression: Respondents were asked to indicate if they had experienced depression during the pandemic by checking a response box. A check indicated that the respondent self-reported depression. The question was adapted from the Pandemic Stress Index [ss Index .Co-habitation: Respondents were asked if they were living with other people  at the time of the survey.Respondents were asked about their country of residence during the pandemic, age (in years), sex at birth, the highest level of education attained , employment status  and the sub regions . Only tDescriptive analysis of all study variables was conducted. T-test and chi-square test were used to determine the associations between dependent, independent and confounding variables. Two binary logistic regression models were constructed to identify the independent variables significantly associated with the study dependent variables. The logistic regression models developed were adjusted for the sociodemographic status of the study participants . The adjusted odds ratios (AOR) and 95% confidence intervals (CIs) were calculated. Statistical significance was set at less than 5%.There were 21106 global participants who accessed the survey questionnaire of which 20083 (95.2%) consented to participate. Of the 20083 study participants, 5983 (29.8%) were from sub-Saharan Africa. Of the 5983 participants from sub-Saharan Africa, 5945 (99.4%) provided complete responses. Table Table In addition, significantly younger people (p < 0.001), more males than females (p = 0.002); more students compared to other professions (p = 0.014); people with heart conditions (p = 0.018), respiratory conditions (p < 0.001), and self-reported depression (p < 0.001); and respondents who had a friend who tested positive for COVID-19 (p < 0.001) and who knew someone who died from COVID-19 infection (p < 0.001) had COVID-19 symptoms but did not get tested.Table In addition, male respondents , respondents with respiratory conditions  and self-reported depression , respondents who had a close friend who tested positive for COVID-19 infection  and respondents who knew someone who died from COVID-19 infection  had significantly higher odds of having symptoms of COVID-19 infection and not getting tested. Also, younger respondents  and participants from the Eastern and Southern sub region of Africa  had significantly lower odds of having symptoms of COVID-19 infection and not getting tested. Data shown in Additional file The study found that respondents with non-communicable diseases did not have higher odds of testing positive for COVID-19 compared with those without non-communicable diseases. However, respondents with respiratory conditions and depression had significantly higher odds of not getting tested for COVID-19 infection despite having symptoms. On the other hand, respondents living with HIV had significantly lower odds of testing positive for COVID-19 but insignificantly higher odds of not getting tested for COVID-19 when they had symptoms. Respondents who had a friend who tested positive for COVID-19 and knew someone who died from COVID-19 infection had higher odds of testing positive for COVID-19 and having COVID-19 symptoms and not getting tested. Finally, respondents living with others had significantly higher odds of testing positive for COVID-19 while respondents living in Southern Africa had significantly lower odds of having COVID-19 symptoms and not getting tested. These study results partially support the study hypotheses.One of the strengths of the study is the contribution to understanding the epidemiology of the COVID-19 pandemic in sub-Saharan Africa, a region with a high prevalence of HIV and a growing prevalence of non-communicable diseases. This study provides information about the COVID-19 related behavioural responses of persons with HIV infection and those with non-communicable diseases, which helps to better understand the biological, clinical, and epidemiological relationship between both infections . The larDespite the limitations of the study, the findings highlighted some important information that can guide policy makers on the potential directions for further research and COVID-19 care measures. First, people who had friends with a history of COVID-19 infection and those who knew people who died from COVID-19 had significantly higher odds of a COVID-19 positive test and having COVID-19 symptoms but not getting tested. This implies that the surveillance system in the region may be weak resulting in poor contact tracing and case detection and increased risk of ongoing COVID-19 transmission within the community as earlier noted . The surSecond, stigma and discrimination may be a concern in the region and a reason why people with COVID-19 symptoms do not getting tested. This is reflected in the study findings that indicates that people living with HIV had higher odds of having symptoms for COVID-19 and not getting tested; and lower odds of testing positive for COVID-19 infection. Concerns about stigma and discrimination for a positive COVID-19 result are salient  especialThird, people with depression were more likely to have COVID-19 symptoms without getting tested. The pandemic itself is a risk factor for depression , and depFourth, respondents in the present study who reported respiratory conditions had higher odds of symptoms without getting tested for COVID-19. The reasons for this remain unclear. Symptoms of COVID-19 may be misdiagnosed as respiratory disorders . Having Finally, demographic factors like living arrangements, age, sex, and region of residence were significantly associated with COVID-19 status. We observed that people who live with others had higher odds of testing positive for COVID-19. This may be because people who live in the same close space are less likely to use COVID-19 protective measures like face masks and social distancing thereby increasing the risk for cross infections especially when the ventilation is poor . This stAlso, we observed that younger respondents are less likely to have symptoms and take a COVID-19 test. Older people have a higher risk of co-morbidities and contracting COVID-19 infection and dying from the infection . TherefoPrior studies had also indicated that men are more vulnerable to COVID-19 than women  and haveThe regional difference in COVID–19 status is also important. This study provides evidence to suggest that the surveillance system instituted in Southern African may be more effective in promptly identifying and ensuring access of people with COVID-19 symptoms to testing when compared with other regions in sub-Saharan Africa. The surveillance system may however, have not been sufficient enough to mitigate and prevent COVID-19 transmission  as indicOur study provides some insight into behavioural reasons that may explain these gender differences in the COVID-19 epidemiological profile: men were less likely to take a COVID-19 test when they have symptoms thereby delaying diagnosis and increasing the risk for severe diseases. This is consistent across various health conditions and comparable to previous findings in the region that have indicated that men are less likely to take a HIV test than women even when they have symptoms . It is iThe study findings also have implications for COVID-19 public health response in the sub-Saharan Africa. The region has the highest burden of those living with HIV, the highest rate of mortality associated with HIV infection and high donor investment in HIV treatment programs for people living with HIV. It is therefore pertinent that programmes addressing the needs of people living with HIV should address barriers and challenges with uptake of COVID-19 tests when symptomatic to reduce the risk of mortality. The surveillance system also need to be strengthened to be cost effective by reaching out to all persons who tested positive to or died of COVID-19 infection. It is likely that the positivity yield will be much higher through this targeted testing. A surveillance system using this targeted testing may be more effective in timely containment of the pandemic in future. Strategic actions also need to be taken to identify and support people who are depressed and who have respiratory conditions to get tested. Future studies are needed to understand the challenges associated with and the barriers to accessing COVID-19 tests by those people who are depressed and who have respiratory conditions so as to design effective mitigations strategies.The findings suggest that residents of sub-Saharan Africa who had non-communicable diseases did not have a higher risk of testing positive for COVID-19. However, people at risk of COVID-19 infection and COVID-19 related deaths, including individuals with respiratory conditions and depression, were more likely to have symptoms of COVID-19 infection without getting tested. There are also indications that the COVID-19 surveillance system in the region is poor though the system in the Southern Africa sub region may be stronger than that in other sub regions. Finally, compared with those who live alone, people who cohabitate appear to take more COVID-19 precautionary measures to reduce their risk of contracting COVID-19 infection. Therefore, future research should further explore the motivations behind health-related decision-making practices among various at-risk populations to inform new regional-specific and population-targeted public health campaigns.Additional file 1. List of countries and number of respondents from the country. This file contains the list of the 31 countries in sub-Saharan Africa from where study participants were recruited. It also contains details on the number and percentage of participants recruited from these countries.Additional file 2. Study Questionnaire. This is the complete questionnaire used to collect data for the study.Additional file 3. Binary logistic regression separating Eastern and Southern Africa. This file contains details of the outcome of a Binary logistic regression to determine factors the association between HIV testing status, COVID-19 status non-communicable disease and HIV status for residents of sub-Saharan Africa. It is a supplemental analysis that included Eastern and Southern Africa as independent variables.


An increasing number of observational studies have reported the persistence of symptoms following recovery from acute COVID-19 disease in non-cancer patients. The long-term consequences of COVID-19 are not fully understood particularly in the cancer patient population. The purpose of this study is to assess post-acute sequelae of SARS-CoV-2 infection (PASC) in cancer patients following acute COVID-19 recovery.We identified cancer patients at MD Anderson Cancer Center who were diagnosed with COVID-19 disease between March 1, 2020, and September 1, 2020, and followed them till May 2021. To assess PASC, we collected patients reported outcomes through questionnaires that were sent to patients daily for 14 days after COVID-19 diagnosis then weekly for 3 months, and then monthly thereafter. We also reviewed patients’ electronic medical records to capture the persistence or emergence of new COVID19-related symptoms reported during any clinic or hospital encounter beyond 30 days of the acute illness and up to 14 months.We included 312 cancer patients with a median age of 57 years (18–86). The majority of patients had solid tumors (75%). Of the 312 patients, 188 (60%) reported long COVID-19 symptoms with a median duration of 7 months and up to 14 months after COVID-19 diagnosis. The most common symptoms reported included fatigue (82%), sleep disturbances (78%), myalgias (67%), and gastrointestinal symptoms (61%), followed by headache, altered smell or taste, dyspnea (47%), and cough (46%). A higher number of females reported a persistence of symptoms compared to males . Cancer type, neutropenia, lymphocytopenia, and hospital admission during acute COVID-19 disease were comparable in both groups. Among the 188 patients with PASC, only 16 (8.5%) were re-admitted for COVID-related reasons.More than one out of two cancer patients, and more likely females, report PASC that may persist beyond 6 months and even 1 year. The most common symptoms are non-respiratory and consist of fatigue, sleep disturbance, myalgia, and gastrointestinal symptoms. Most of the cancer patients with PASC were managed on outpatient basis with only 8.5% requiring a COVID-19-related re-admission.This research is supported by the National Institutes of Health/National Cancer Institute under award number P30CA016672, which supports the MD Anderson Cancer Center Clinical Trials Office. The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication. Clinical outcomes of COVID-19 in patients with cancer remains an area of active study with several studies showing more severe outcomes and associated risk factors such as pre-existing comorbidities, cancer stage and therapy, and immunocompromised state, compared to the non-cancer population .The earliest studies characterized the acute and sub-acute effects of COVID-19 on multiple organ systems  while moThe long-term consequences of COVID-19 remain to be fully understood and there is no unequivocal consensus on the definition of post-acute sequelae of SARS-CoV-2 infection (PASC), with one adopted definition being the persistence of symptoms or new delayed complications that develop at least 4 weeks after a COVID-19 diagnosis . The repSeveral studies suggest the persistence of symptoms past 30 days in patients with severe initial COVID-19 symptoms and those that were hospitalized due to COVID-19 , while oWe identified patients with cancer receiving care at the University of Texas MD Anderson Cancer Center who were also diagnosed with COVID-19 disease between March 1, 2020, and September 1, 2020. None of the patients were vaccinated during the period of initial COVID diagnosis. We followed these patients as longitudinal cohort through patient-reported outcomes (PRO)-based remote symptom monitoring along with usual care with clinician visits. Patients were followed from March 2020 until May 31, 2021. Patient questionnaires were sent out remotely daily for 14 days after COVID-19 diagnosis then weekly for 3 months, and then monthly thereafter. Chart reviews were conducted for each patient’s encounter that included visits to our acute cancer care center, hospital re-admission, or clinic visit. Re-admissions were classified as either related or non-related to COVID-19 based on the reason for hospitalization as well as the reported signs and symptoms. PASC or long COVID-19 was defined as the persistence of COVID-19-related symptoms beyond 30 days of diagnosis or the emergence of new COVID19-related symptoms reported during a hospital or clinic encounter throughout the follow-up period that extended up to 14 months. COVID-19-related symptoms included, among others, fatigue, cough, chest tightness, dyspnea, headache, fever, altered smell or taste, myalgias, gastrointestinal symptoms , sleep disturbance, and limitations with activities of daily living. This study was approved by the institutional review board at MD Anderson. A waiver of informed consent was obtained since this study posed no risk to the patients given that no clinical or laboratory interventions were done.Categorical variables were compared using chi-square or Fisher’s exact test, as appropriate. Continuous variables were compared using Wilcoxon rank sum test. All the tests were two-sided with a significance level of 0.05. The statistical analyses were performed using SAS version 9.4 .Among the 602 patients who were diagnosed with COVID-19 during the study period, longitudinal data was collected on 312 patients that included 188 patients who developed PASC having reported symptoms that persisted at least 30 days after COVID-19 diagnosis and 124 who did not. The remaining patients (290) could not be followed or assessed beyond 30 days. The female gender rate was significantly higher in the PASC group compared to the non-PASC group . The age and race were similar in both groups. The median age was 57, and 27% of both groups were above 65 years of age. While the rate of hypertension was higher in the non-PASC group , the rate of other comorbidities such as COPD and congestive heart failures were similar in both groups. Furthermore, the type of underlying cancer was similar in both groups with the majority of patients having solid tumors. The type of underlying malignancy  as well as those with metastatic diseases were similar in both PASC and non-PASC groups. However, patients with PASC were significantly less likely to have refractory or relapse diseases (19% vs. 29%) In addition, the rate of neutropenia, lymphocytopenia, lower respiratory tract infections, hypoxia, oxygen requirement, inflammatory biomarkers, COVID-19-related hospital admissions, multiorgan failure as well as medical management of COVID-19 were similar in both groups . DespiteThe most commonly reported symptoms among the 188 patients who developed PASC consisted of fatigue (82%), sleep disturbance (78%), myalgias (67%), gastrointestinal symptoms (62%), headache (47%), altered smell and taste (47%), dyspnea (47%), and cough (46%) .Among the 188 cancer patients who reported PASC, 59 patients (31%) were re-admitted to the hospital during the follow-up period and beyond 30 days of acute illness, only 16 (8.5%) of the patients with PASC had a COVID-related re-admission.Our data showed that 60% of 312 cancer patients diagnosed with COVID and followed up for a median duration of 7 month (up to 14 month) developed PASC. Females were more likely to report persistence of symptoms compared to males. However, cancer type, age, neutropenia, lymphocytopenia, hypoxia, severe disease, multiorgan failure, various interventions, or hospital admission during acute COVID-19 disease were not associated with higher risk of long COVID in our study group, while hypertension was more significantly associated with no PASC during follow-up. Furthermore, high-risk patients with baseline relapsing and refractory malignancy were associated with more acute severe COVID and higher mortality but less long-term COVID. The most common PASC symptoms were fatigue, sleep disturbances, myalgias, and gastrointestinal symptoms. Furthermore, among PASC patients who were re-hospitalized for any cause during the follow-up, the admission was COVID-related in only 27%.In a manner similar to our current study, the CDC reported that 60% of patients with immunosuppressive conditions continue to have COVID-19 symptoms beyond the acute illness . In the The existing literature has also shown a higher prevalence of PASC and a longer time to recovery among those with more severe COVID-19 who required hospital admission and medical interventions . HoweverIn the general population and following acute COVID-19, PASC symptoms occurred at a variable rate ranging from 10% to 87% . In a maIn our cancer patient population, PASC was not associated with a high rate of COVID 19-related hospital admissions during the follow-up. All cause admissions occurred at a rate of 31% among our cancer patients with PASC and among those who were admitted only 27% were admitted because of COVID-19-related reasons. Hence, among all PASC patients in our study only 8.5% were admitted for COVID 19-related reasons. Furthermore, some of those few that were admitted for COVID-19-related reasons could have had reinfection with a new variant during the prolonged follow-up period or reactivation related to their immunosuppressed status. Hence, even in our high-risk cancer patient population, most of the symptoms associated with PASC were managed for the most part on an outpatient basis without the need for hospital admission.Our study has its limitations. First, the retrospective nature of this study limits any firm conclusions about factors directly contributing to PASC in our COVID-19 cancer patients. Second, this is a single center study which could also limit the generalization of our results. Third, the subjective nature of surveys in the absence of quality of life assessment scales and other verified objective scales to assess reported PASC criteria could also limit our conclusions. Finally, the presence of several confounding causes of hospital admissions and overlapping symptoms in cancer patients that may be due to their underlying disease, aggressive treatments, and comorbidities present their own challenge in identifying PASC among cancer patients post their COVID-19 diagnosis.In conclusion, long COVID occurred in the majority of our cancer patients diagnosed with acute COVID-19 with a preponderance of symptoms  over a long time period. Long COVID was less likely in patients who experienced severe acute COVID at diagnosis and who had a higher mortality rate. Most of the patients with long COVID/PASC were fairly and adequately managed on outpatient basis without the need for hospital admission. Besides the female gender, we found no other underlying condition or severity of illness during acute COVID-19 that would predict PASC. The authors provide valuable results from an observational study of 312 cancer patients to assess post-acute sequelae of SARS-CoV-2 infection (PASC). Although primarily descriptive and based on a relatively small sample, the study represents a solid expansion of the existing knowledge on persistent symptoms among cancer patients. The work will be of interest to oncologists working on long COVID. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. The evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Nawale Hajjaji.Thank you for submitting your article "Long COVID in Cancer patients: Preponderance of Symptoms in Majority of Patients over Long Time Period" for consideration by eLife, the reviewers have discussed their critiques with one another. What follows below is the Reviewing Editor's edited compilation of the essential and ancillary points provided by reviewers in their critiques and in their interaction post-review. Please submit a revised version that addresses these concerns directly. Although we expect that you will address these comments in your response letter, we also need to see the corresponding revision clearly marked in the text of the manuscript. Some of the reviewers' comments may seem to be simple queries or challenges that do not prompt revisions to the text. Please keep in mind, however, that readers may have the same perspective as the reviewers. Therefore, it is essential that you attempt to amend or expand the text to clarify the narrative accordingly.As is customary in Essential revisions:Overall this was a good manuscript that could be improved by kindly addressing the concerns of the reviewer below.The following comments could be helpful to strengthen the work:1) Whether cancer stage or the type of anticancer therapy are associated to PASC is of great interest; these data should be within reach and added to Table 1.2) Some patients might have participated in a vaccination trial with mRNA vaccine between March and September 2020. If this is the case, this information should be added to Table 1.3) As mentioned by the authors in the introduction section line 94 "there is limited data available on PASC in cancer patients and how it affects their cancer progression, care and treatment". The addition of these analyses would greatly strengthen the article.4) It could be interesting to distinguish severe and mild covid at diagnosis to compare their pattern of long covid. In fact, the frequency of long covid reported in cancer patients with mild covid was only 22% (doi: 10.3389/fonc.2022.901426), much less than the frequency reported in this article. The following comments could be helpful to strengthen the work:1) Whether cancer stage or the type of anticancer therapy are associated to PASC is of great interest; these data should be within reach and added to Table 1.The type of underlying malignancy  as well as those with metastatic diseases were similar in both PASC and non-PASC groups. However, patients with PASC were significantly less likely to have refractory or relapse diseases (19% vs 29%) Added to the Results section. High risk patients with baseline relapsing and refractory malignancy tend to be associated with more acute severe COVID and higher mortality but less long term COVID. Added to the Discussion section.2) Some patients might have participated in a vaccination trial with mRNA vaccine between March and September 2020. If this is the case, this information should be added to Table 1.None of our patients were vaccinated between March and September 2020 (added in methods).3) As mentioned by the authors in the introduction section line 94 "there is limited data available on PASC in cancer patients and how it affects their cancer progression, care and treatment". The addition of these analyses would greatly strengthen the article.We thank the reviewer for their comments. We extracted additional data and found that the rates of delay in cancer treatment after COVID were similar in both PASC and non-PASC patients . Mortality beyond 30 days of initial COVID diagnosis was significantly higher in patients without PASC compared to those with PASC . Added in the results and Discussion section We have added the name of an author (Patrick Chaftari) who helped collect the additional data.4) It could be interesting to distinguish severe and mild covid at diagnosis to compare their pattern of long covid. In fact, the frequency of long covid reported in cancer patients with mild covid was only 22% (doi: 10.3389/fonc.2022.901426), much less than the frequency reported in this article.We thank the reviewers for their interesting comments. Patients without long COVID were more likely to have more acute severe COVID at diagnosis than patients who developed long COVID . Added in results and Discussion section.