The questionnaire assessed information factors, predisposing factors, awareness factors, motivational factors, preparatory actions, and vaccination intention. Vaccine hesitant participants  had lower levels of education, more often paid work, and tended to have a religion other than Catholicism. They used written media less often and tended to visit websites of public health organizations less often, but used messaging services like WhatsApp more frequently. All participants had neutral intentions towards checking information credibility. Vaccine hesitant respondents had less knowledge about vaccination, lower perceived severity of getting sick and dying of COVID-19, and reported fewer exposures to cues about the advantages of COVID-19 vaccination. They were less convinced of the emotional and rational advantages of COVID-19 vaccination and expressed more negative feelings about it. They also reported more negative social norms concerning COVID-19 vaccination, and lower self-efficacy to get vaccinated and to cope with potential side-effects. The regression model explained 58% of the variance in vaccination intention. The results suggest that strategies are needed to: 1. Reduce fake news and stimulate information checking to foster well-informed decision-making; 2. Target both rational and emotional consequences of COVID-19, in addition to strategies for optimizing levels of knowledge. Campaigns should acknowledge the perceptions of the emotional disadvantages and increase perceptions of emotional advantages of COVID-19 vaccinations, such as reducing feelings of regret, and increasing feelings of freedom and reassurance.COVID-19 vaccine hesitancy may be regarded as a new pandemic hindering the elimination of or coping with COVID-19. This study assessed reasons for COVID-19 vaccine hesitancy using the I-Change Model (ICM) by considering the role of informational and psychosocial factors. A cross-sectional online survey using a convenience sample was conducted among Dutch adults ( The COVID-19 pandemic is increasingly resulting in high levels of mortality and morbidity. Many effective vaccines have been approved for use . Yet, COOne of the factors influencing VH includes the sources and types of information that serve as cues for people to think and act with respect to the COVID-19 vaccination. Exposure to social media has been shown to be an important cue for people to become aware of COVID-19 and of actions relevant to cope with it . An impoResearch indicates that higher educated adults gathered health information from various reliable sources, whereas adults that received a lower education were more likely to use family and friends as a source for health information , increasThis study uses the I-Change Model (ICM) to examine the influence of informational and personal motivational factors on COVID-19 VH . The ICMThe goal of this study is to analyse the role of informational and motivational factors associated with VH against COVID-19. Using the ICM, the objectives are to examine adults differing in COVID-19 VH concerning their (i) sociodemographic factors, (ii) information seeking behaviour, including actions to check information and source credibility, (iii) awareness, and (iv) motivational factors regarding the COVID-19 vaccine. This in-depth information may help to optimize communication about COVID-19 vaccination for this group. Specifically, it may aid in the development of tailored approaches that also aim at understanding VH rather than merely focusing on persuasion.A cross-sectional study was conducted in line with the ICM to measure vaccination intention, preparatory actions to combat fake news, information seeking behaviour, awareness factors, and motivational factors. The anonymized dataset is available upon request from the first author.The study used a quantitative method to gather data about psychosocial determinants that might have an association with vaccination intention. Data collection for this study took place online from April to May 2021. Before filling in the questionnaire, respondents were informed about the purpose of the study. Informed consent was obtained from all individuals involved in the study through an informed consent form at the start of the online questionnaire.This study used a convenience sample and focused on Dutch-speaking adults, aged 18+, as they were the target population for the vaccination campaign against COVID-19 in the Netherlands in which four types of vaccinations were used ; allocation of vaccines was decided top-down depending on the age of the person.The link to the survey was disseminated to the target population online via WhatsApp, LinkedIn, and Facebook. Participants were required to (i) be above 18 years of age and (ii) not have been vaccinated yet. Of the original 325 respondents, 40 were excluded because they indicated that they were partially or fully vaccinated, 39 because they did not fully complete the questionnaire (<90% finished), and six because their age was lower than 18. The anonymized data of the remaining 240 respondents were included in the study.Questionnaire development was informed by previous research using the I-Change Model   using seSocio-demographic factors were assessed using questions from the Dutch Health Monitor . ParticiInformation seeking behaviour was assessed by 12 questions (see Table 2 for the items) about the frequency with which someone used a prespecified media or interpersonal source . The list of sources was based on previous studies about information seeking behaviour ,27,28.Intentions for information quality checking were assessed using a 7-point Likert scale (−3 = surely not to +3 = surely yes) to assess whether the respondent had the intention to: (1) check whether the news they read was fake or not; (2) read sufficient information on websites or social media accounts of the government or public health organisations; (3) read sufficient information on other websites or social media accounts; (4) obtain sufficient information from their family and friends; (5) check if their information from websites or social media accounts of the government or public health organisations is reliable; (6) check if their information from other websites or social media accounts is reliable; (7) check if the information from family and friends is reliable; (8) check the correctness of the information from social media accounts of the government or public health organisations; (9) check the correctness of information of other websites or social media accounts; and (10) check the correctness of information from family and friends.Knowledge about the facts concerning COVID-19 vaccination was measured using seven items regarding the vaccine . Perceived susceptibility (α = 0.78) and perceived severity towards getting COVID-19 (α = 0.64) were both measured with four items on a seven-point Likert scale (−3 = extremely low to 3 = extremely high). Cues to action were measured by five questions asking about possible cues for vaccination  . The lowAttitude, self-efficacy, social norms, and intention were measured on 7-point Likert scales (−3 = completely disagree to +3 = completely agree). An overview of the items can be found in Table 3.Attitude beliefs were assessed as both rational and emotional outcome expectations . PotentiSocial norms towards getting vaccinated against COVID-19 were assessed using seven items on a 7-point Likert scale (α = 0.91). An additional response option was provided in case a specific item did not apply .Self-efficacy was assessed using a 7-point Likert scale for 12 items (α = 0.91). Questions were developed based on previous studies. These items were reverse scored, i.e., a higher score reflects a higher self-efficacy towards getting vaccinated.n = 113; 47.1%), the rest was somewhat hesitant  or hesitant .Data was analysed using SPSS version 27 . Descriptive analyses were used to describe the research population. Missing data only occurred in five individuals for preparatory actions and was not replaced. Participants were divided into three groups corresponding to their level of VH. To investigate differences between the hesitancy groups on psychosocial constructs of the ICM, a summary measure of intention for the specific vaccines was computed (α = 0.87). The non-hesitant group had an intention score of >0.99, the somewhat hesitant group had an intention score of 0.50–0.99 and the hesitant group had an intention score of <0.50. These cut-off points were based on the distribution of the mean of the vaccine-specific intention summary measure. Approximately half of the population was non-hesitant  to determine factors uniquely associated with vaccination intention and to identify how well the model explained the variance in intention.The data presented in Concerning risk perception, respondents all had moderate perceptions concerning their chances of getting COVID-19. The somewhat hesitant respondents showed the lowest perception of getting seriously sick and of experiencing permanent damage, which significantly differed from the non-hesitant group. Regarding the perceived severity, hesitant respondents showed significantly lower perceived severity concerning getting seriously sick from COVID-19 and of dying of COVID-19 than the other respondent groups. Lastly, hesitant respondents reported encountering cues of news about the advantages of COVID-19 vaccination significantly less often than non-hesitant respondents.As can be seen in As reported by the hesitant respondents, the social norms in their environment were more negative towards vaccination than for somewhat and non-hesitant respondents. Their partners, friends, family, and colleagues were all reported as less positive towards vaccination than those in the environment of the other two respondent groups. Hesitant respondents also expressed significantly lower levels of self-efficacy towards vaccination than the other two groups. Hesitant respondents had lower self-efficacy to get vaccinated than the other two groups regarding the obstacles of becoming sick of it, having to get two injections, finding it scary, having to go to the vaccination location alone, suffering from possible side-effects, having doubts about the effectiveness and duration of the protection, hearing contradicting information about vaccination, reading or hearing fake news about vaccination, getting told not to get vaccinated by people in their environment, and getting told that vaccination is pointless. Hesitant respondents had a significantly lower intention concerning the intention to get vaccinated with the AstraZeneca and Johnson & Johnson vaccine than somewhat hesitant and non-hesitant respondents.To identify how well the factors of the model explained the variance of the intention to get vaccinated, a multiple linear regression analysis was conducted see . InspectIn the first model, socio-demographic factors were entered, and it was revealed that people with paid work, a religion other than Catholicism, and lower educational level were more likely to be hesitant. Model 2 added the awareness factors of the ICM  to Model 1, revealing that lower levels of knowledge were uniquely associated with hesitancy. The socio-demographic factors were no longer significant, suggesting that their contributions were mediated by knowledge. Model 3 added the motivational factors of the ICM, revealing that emotional and rational disadvantages were uniquely associated with VH, whereas knowledge and positive emotional outcomes were positively associated with the intention to vaccinate. The final model accounted for 58 percent of the variance in vaccination intention.The first objective of this study was to determine possible differences in predisposing factors between hesitant, moderately hesitant, and non-hesitant individuals. Both the results of the chi-square analysis and the regression analysis suggest that in our sample, VH was more common among lower educated participants, which corresponds with findings from other studies regarding COVID-19 VH ,32,33. PThe second objective of this study was to examine possible differences between the groups in information factors and intentions to check information and source credibility. In line with prior findings, non-hesitant respondents made more use of written media than hesitant respondents . HesitanThe third objective of this study was to examine possible associations between awareness factors and COVID-19 VH. Significant differences were found between the hesitancy groups on almost all knowledge items. Hesitant respondents had less knowledge about COVID-19 and the vaccine than non-hesitant respondents. Knowledge was also found to have a unique association with intention in the linear regression analysis. Less knowledge about vaccination has previously been linked to increased VH for COVID-19 and other diseases ,54, but The fourth objective of this study was to research possible associations between motivational factors and COVID-19 VH. Significant differences were found between the groups on almost all attitude items, where hesitant individuals expressed a significantly lower attitude towards vaccination than other respondents. Similar trends were found in previous research ,32,59,60When assessing differences between groups on the intention items, hesitancy for either the AstraZeneca vaccine or the Johnson & Johnson vaccine was found to account for significant differences in the total VH score. This effect was expected due to the media coverage about the negative side effects of the latter two vaccines ,66. PfizA few limitations of this study should be considered when interpreting the present findings. First, due to the cross-sectional design, causation of the observed effects cannot be concluded. Future research should focus on these mechanisms, and also by employing longitudinal designs. Second, the research sample was predominantly female and included more highly educated individuals than nationally representative samples. This selection bias may be linked to the chosen recruitment channels and may have influenced the study outcomes, as a higher education has previously been linked to lower VH ,32,33. RDespite the limitations, the strength of this study is that it provides important insight into the psychosocial factors underlying COVID-19 VH. Using the ICM, a model that integrates various other models for understanding health behaviours , this st


Firefighters have increased cancer incidence and mortality rates compared to the general population, and are exposed to multiple products of combustion including known and suspected carcinogens.The study objective was to quantify fire response exposures by role and self-reported exposure risks.Urinary hydroxylated metabolites of polycyclic aromatic hydrocarbons (PAH-OHs) were measured at baseline and 2–4 h after structural fires and post-fire surveys were collected.Baseline urine samples were collected from 242 firefighters. Of these, 141 responded to at least one of 15 structural fires and provided a post-fire urine. Compared with baseline measurements, the mean fold change of post-fire urinary PAH-OHs increased similarly across roles, including captains (2.05 (95% CI 1.59–2.65)), engineers (2.10 (95% CI 1.47–3.05)), firefighters (2.83 (95% CI 2.14–3.71)), and paramedics (1.84 (95% CI 1.33–2.60)). Interior responses, smoke odor on skin, and lack of recent laundering or changing of hoods were significantly associated with increased post-fire urinary PAH-OHs.Ambient smoke from the fire represents an exposure hazard for all individuals on the fireground; engineers and paramedics in particular may not be aware of the extent of their exposure. Post-fire surveys identified specific risks associated with increased exposure. Firefighters in the United States have been shown to have a higher cancer incidence and mortality rate compared with the general population . During The largest cohort study of firefighters in the United States to date demonstrated an increased rate in lung cancer 10%), gastrointestinal cancer 30–45%), kidney cancer (29%), and mesothelioma (100%) deaths, with similar increases in cancer incidence, compared to the general population pyrene, dibenzanthracene, chrysene, benzo[a] anthracene, and naphthalene [Measurement of urinary hydroxylated metabolites of polycyclic aromatic hydrocarbons (PAH-OHs) concentrations has been used for the assessment of exposure to combustion products in previous studies of firefighters , 15. PAHhthalene , 19, 20.The study was approved by the University of Arizona Institutional Review Board, Protocol #1509137073, and all subjects provided informed consent. The study included collection of blood, buccal cells, and urine during annual medical surveillance examinations or during new recruit training for both incumbent and new recruit firefighters, and collection of urine after a structural fire. For subjects not able to provide a baseline urine during enrollment, it was collected after the post-fire urine sample. Inclusion criteria included being TFD uniformed personnel and responding to fires as part of their current duties. A survey evaluating firefighter demographics, medical and occupational history, and recent exposures, was collected at baseline. A survey evaluating actions at the fireground and recent exposures was collected at the fire scene during firefighter rehabilitation, and a second post-fire survey focusing on additional activities after the fire was completed after return to the station at the time of urine collection.Baseline urine samples were collected throughout the day and transported on ice to University of Arizona laboratory the day of collection. Based on an unpublished pilot study by University of Arizona and TFD evaluating exposure to a training fire with firefighters wearing SCBA at all times within the structure, urinary naphthol metabolite concentrations were found to peak 2–4 h following cessation of exposure. For the current study, post-exposure urine samples were therefore collected 2–4 h post-fire by TFD personnel and transported on ice to University of Arizona laboratory within 24 h. Urine was collected in a 120 mL polypropylene collection cup after providing instructions to the firefighter to wash their hands first, void into the container, and return the resealed collection cup to a research team member for refrigeration until processing. A water control was collected and processed in the same manner as the urine collection for each day of baseline and post-fire collections.Helix pomatia, and extracted using solid phase extraction. Prior to analysis on the gas chromatography tandem mass spectrometry, samples were derivatized. A surrogate standard mix of the deuterated PAH-OHs containing 1-Hydroxynaphthalene-d7, 2-Hydroxyphenanthrene-d9, 2-Hydroxyfluorene-d9, 1-Hydroxypyrene-d9, was added to each sample prior to the extraction.Upon arrival in the laboratory, specific gravity was recorded for each sample using the Atago Refractometer . Urine samples were centrifuged at 1900 rpm for 10 min, then 10 mL aliquots of the supernatant were frozen at −20 °C until PAH-OH analysis as previously described . This meDetection limits were determined to be 175 ng/L, 100 ng/L, 150 ng/L, and 200 ng/L for each of the naphthols, fluorenols, phenanthrols, and 1-hydroxypyrene, respectively. PAH-OH values were multiplied by a specific gravity factor calculated for each urine sample to correct for renal function and individual hydration levels 2−1SG−1. https://www.r-project.org) and Stata MP 14.1 (https://www.stata.com/stata14/). Longitudinal analyses were conducted by the R package lme4 [p < 0.05 was considered statistically significant.Non-detectable PAH-OHs were replaced by half the value of their respective detection limit. The PAH-OH concentrations were natural log-transformed to better fit the normal distribution. Univariate and multivariable analyses were performed using a linear mixed-effects model with random intercept to assess mean differences of log-transformed PAH-OHs between baseline and post-fire stratified by job types. The primary outcome was the sum of all PAH-OHs , and secondary outcomes included the sum of naphthols (1-naphthol and 2-naphthol), sum of phenanthrols , sum of fluorenols , along with the individual PAH-OHs. Assessment of model fit was performed by the analysis of residuals. All statistical analyses were performed using R version 3.5.3 . The outcomes were the differences of log-transformed PAH-OHs comparing baseline and post-fire urine samples.Subject consenting and baseline urine collection started on 10/6/2015 and continued through 11/21/2017. Post-fire urine collection started on 2/9/2016 and continued through 12/19/2016. During this interval, 242 firefighters provided a baseline urine and 141 of these firefighters provided at least one post-fire urine. Some firefighters provided post-fire samples from more than one fire event (range 2–6 fires). Of the 141 subjects in the study that provided both a baseline and post-fire urine, 83 provided the baseline urine after the post-fire urine. With the exception of one subject for whom the baseline urine was collected 48 h after the post-fire urine, all other subjects had at least a 14-day interval between baseline and post-fire urines. The absolute value of the time span between the baseline and post-fire urine samples averaged 135 days, with a maximum of 543 days.n = 141) are a subsample of those recruited at baseline, a bootstrap method was used to evaluate whether there were significant differences between the two groups. Out of 100 bootstrap sample replicates with 141 individuals, more than 95% showed that there were no significant differences of gender, race/ethnicity, age, BMI, smoker, and rank distributions. When comparing smokers  to nonsmokers (n = 226) at baseline, none of the quantified PAH-OHs were significantly different between the two groups (data not shown).Characteristics of the participating firefighters are listed in During the study period, 15 fires were studied . These fp < 0.05) concentration of the sum of urinary PAH-OHs post-fire compared to baseline. The results of multivariable models adjusting for baseline age, BMI, and smoking yielded similar results (data not shown). All groups except the investigators also had significant increases in the sum of naphthols, sum of fluorenols, and sum of phenanthrols comparing baseline and post-fire. Results for individual PAH-OHs are included in the The concentrations of the sum of urinary PAH-OHs at baseline and post-fire are presented in Evaluation of post-fire survey responses in relation to urinary PAH-OH concentrations is listed in Our study demonstrated significant increases in the sum of all urinary PAH-OHs following fireground operations in four main groups: firefighters, captains, engineers, and paramedics. These results also highlight the understudied exposure of engineers and paramedics to combustion emissions while providing nonentry support at fire incidents. Though fire investigators were not found to have a significant increase, only two post-fire urine samples were available for analysis.The baseline urinary PAH-OH concentrations found in our study are comparable to those of the general population whereas the post-exposure values are less than those seen in the most highly exposed workers. Compared to urinary PAH-OH levels of participants 18–65 years of age in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) multiplied by 1.48 to transform their creatinine corrected values to our specific gravity corrected values , the medOur study results are consistent with other recent studies of firefighter entry teams , 15. OttThe marked variability in post-fire urinary PAH-OH concentrations observed in our study is likely due to differences in exposures based on distinct job tasks within roles at a fire, the complex and evolving nature of each individual fire and differences in use of respiratory protection. Entry/fire attack or ventilation teams are made up of two firefighters and a captain serving as team lead. At times the captain sets up the fireground/tactical operations as the firefighters make the initial entry before the captain joins the firefighters inside the burning structure or on the roof. In addition, we have identified instances where captains removed their respirators to facilitate radio communication while outside of the burning structure but still in a smoky area . This inThe survey data revealed information which could be used to inform a firefighter job exposure matrix. Response to residential fires had increased exposures compared to commercial fires, although there was a great deal of variability within each group and the number of commercial fires was limited to three. A study of controlled experimental fires found a significant increase in median concentration of urinary PAH-OH metabolites from pre- to post- exposure on firefighters assigned to attack and search roles . In our The use of PPE while conducting certain duties was assessed using the surveys to identify its contribution to decreasing exposure. A significant decrease in urinary 2-naphthol and 1- and 3- phenanthrol were observed in individuals who had their SCBA on greater than 60 percent of the time during fire attack and ventilation, respectively, in comparison to those individuals reporting SCBA use 60 percent or less of the time. These results are consistent with other research findings that increased use of SCBA while at a fire scene can decrease exposure to products of combustion , 28. OurThe survey data also identified that some self-reported exposures were associated with increased urinary PAH-OHs, including smoke odor on the skin. While these results are not surprising, we are not aware that they have been previously studied. A finding contrary to our expectations was that cleaning of the skin with water or a wipe while still on scene was associated with a significant increase in the sum of PAH-OHs. This finding could potentially be explained by a greater use of wipes when exposures had been higher, such as having visible soot on the skin. Dermal decontamination with wipes has been shown to reduce the amount of skin contamination , 21, andThe results of this study affirm the need for fireground exposure reduction interventions for firefighters. Previous studies have investigated various post-fire interventions conducted at the fire scene to reduce dermal and inhalation exposure. Interventions include decontamination of PPE with soap and water along with bagging of gear to reduce exposure from off gassing contaminants, along with cleaning of skin as soon as possible with wipes to reduce dermal absorption , 21, 33.Limitations of our study include exposure monitoring limited to PAH metabolites, as many other toxic chemicals are present in fire smoke. While this limited scope of exposure monitoring does not affect the study findings, the differential exposures identified may not be generalizable to chemical exposures beyond PAHs. The timing of urine collection at 2–4 h post-exposure was chosen both to maximize the urinary concentrations of 1- and 2- naphthol, as the combined naphthols had the highest concentration of the measured PAH metabolite groups, and because collection at this time period was acceptable by the fire service. However, measurement at this time period likely underestimates post-fire concentrations of PAH-OHs with a longer elimination half-life. The baseline urine was not collected immediately before the fireground response as this was not possible given the unpredictable timing of the fires and the rapid firefighter response to the fires. The urinary PAH-OH concentrations may have been influenced by exposures outside of the fireground, as we did not have any restriction on diet or smoking, both of which can contribute PAH exposures. Beyond smoke from fires, diesel exhaust is also a source of PAH exposure , which iIn conclusion, our study results showed that all fire service personnel at a fire scene are at risk for exposure to products of combustion. Characteristics of the fire, firefighter activities at the scene and self-reported exposures were all significantly associated with urinary sum of all PAH-OHs measurements. Specifically, residential fires, interior responses including duration of interior response, smoke odor on skin, and lack of recent laundering or changing of hoods were significantly associated with increased post-fire urinary sum of PAH-OHs. Fire departments should continue to implement measures to reduce dermal and respiratory exposures.Supplementary Information for: Evaluation of Fireground Exposures Using Urinary PAH Metabolites


The novel coronavirus disease 2019 (COVID-19) has infected more than 100 million people globally within the first year of the pandemic. With a death toll surpassing 500,000 in the United States alone, containing the pandemic is predicated on achieving herd immunity on a global scale. This implies that at least 70-80 % of the population must achieve active immunity against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), either as a result of a previous COVID-19 infection or by vaccination against SARS-CoV-2. In December 2020, the first two vaccines were approved by the FDA through emergency use authorization in the United States. These vaccines are based on the mRNA vaccine platform and were developed by Pfizer/BioNTech and Moderna. Published safety and efficacy trials reported high efficacy rates of 94-95 % after two interval doses, in conjunction with limited side effects and a low rate of adverse reactions. The rapid pace of vaccine development and the uncertainty of potential long-term adverse effects raised some level of hesitation against mRNA vaccines in the global community. A successful vaccination campaign is contingent on widespread access to the vaccine under appropriate storage conditions, deployment of a sufficient number of vaccinators, and the willingness of the population to be vaccinated. Thus, it is important to clarify the objective data related to vaccine safety, including known side effects and potential adverse reactions. The present review was designed to provide an update on the current state of science related to the safety and efficacy of SARS-CoV-2 mRNA vaccines. Due to the serious implications of this pandemic, a scientific focus on vaccine development for COVID-19 has been the forefront of a global initiative to combat this virus [Severe acute respiratory syndrome-2 virus (SARS-CoV-2) represents a highly contagious respiratory virus that is responsible for the current worldwide coronavirus disease 2019 (COVID-19) pandemic [is virus .The establishment of vaccines represents a vital instrument in reducing rates of infections and diseases globally . The oriVaccine development has persisted into the modern day, and while based on the same principles, the methods of antigen introduction have evolved . The vacThe concept of mRNA vaccines has been scientifically relevant since the early 21st century, however, the development of the Pfizer/BioNTech and Moderna COVID-19 vaccines presents the initial, large scale, application of this type of inoculation . Previouin vivo RNA delivery – shelter mRNA from degradation, and mediate endocytosis and endosomal escape [When specifically applying the mRNA vaccine platform to SARS-CoV-2 Fig. , it is cl escape . Positivl escape . The imml escape . This mel escape , 7.Fig.Development of a vaccine to combat COVID-19 has been of paramount importance since the onset of the pandemic . The scoThe Moderna vaccine (mRNA-1273) trial enrolled 30,420 volunteers, with half of the participants received the vaccine, while the other half received the placebo [As of publication of this review, there have been no serious side effects identified in the ongoing phase 3 clinical trials for both the Moderna and Pfzier/BioNTech mRNA vaccines , 30. MilThe initial published trials on mRNA vaccines against COVID-19 reported more local reactions in the recipients of the vaccine compared to the control group receiving placebo , 30. TheIn the same trials, the younger vaccine recipients (between 16 and 55 years of age) reported systemic events more frequently than their older counterparts (over 55 years of age) , 30. ThiAdverse events were more commonly reported among vaccine recipients 27 %) compared to the placebo group (12 %) , 30. The7 % compaThe most commonly reported side effects associated with the vaccine among those over the age of 55 years old were chills, headache, injection site pain, fatigue, and myalgias . A higheThere is limited data regarding the use of the Pfizer/BioNTech and Moderna COVID-19 mRNA vaccines during pregnancy. The Center for Disease Control (CDC) reports that about 25 % of women of reproductive age (15–49 years of age) hospitalized with COVID-19 between March 1 and August 22, 2020 were pregnant, and that pregnant women tended to require mechanical ventilation more than their nonpregnant counterparts . The CDCP = .64). There were also no stillbirths among women with SARS-CoV-2 during pregnancy [Until present, the FDA has not issued any explicit guidelines regarding the use of COVID-19 vaccines in pregnant women. Rather, the FDA refers to pregnant women in the EUA letters and factsheets provided to healthcare providers for the individual vaccines. The EUA letters for both the Moderna and the Pfizer/BioNTech vaccines contain provisions obliging post-authorization observational studies and label pregnant women as a “population of interest” for these studies, citing a lack of data regarding vaccine risk in pregnancy. The Moderna vaccine fact sheet specifically alludes to a reproductive toxicity study in female rats; adverse effects on fetal development, female fertility, and early offspring development were assessed, with no adverse outcomes observed. The Moderna vaccine also has a pregnancy exposure registry intended to monitor pregnancy outcomes in women who received the vaccine during pregnancy. To date, neither Moderna nor Pfizer have issued guidelines or guidance regarding their vaccines and pregnancy . The aforegnancy .Current evidence suggests that both the Moderna and the Pfizer/BioNTech vaccines elicit a strong humoral response due to the production of neutralizing antibodies coupled with a robust cellular response by inducing functional and pro-inflammatory CD4 + and CD8 + T cells and expression of Th1 cytokines . NotablyIndividual immunological status and circumstantial considerations notwithstanding, the available evidence appears to favor vaccination with either the Moderna or the Pfizer/BioNTech mRNA vaccines for the majority of the population. The vaccination program’s effectiveness depends upon convincing efficacy and safety data coupled with popular public acceptance and inoculation . HoweverVaccine hesitancy has been described as a “lack of confidence in vaccination and/or complacency about vaccination” that may result in deferment or failure to vaccinate in spite of accessible services . The swiIn order to address vaccine hesitancy and enhance COVID-19 vaccine adoption, multi-tiered, evidenced-based approaches must be implemented. They include evidence-based initiatives from behavioral, communication, implementation, and social sciences that can guide clinical programs at the organizational, relational, and individual levels to support public health initiatives and challenge COVID-19 vaccine hesitancy . ImportaNotably, a recent survey conducted by the Kaiser Family Foundation found that 29 % of healthcare providers themselves expressed hesitancy about receiving the COVID-19 vaccine. The same survey found that among the general public, the group that reported that they “definitely will not get vaccinated” may be the hardest to reach via most traditional public health means. Only two emissaries were reported as trustworthy sources by at least half the people in this group: their personal health care provider (59 %) and former President Trump (56 %). These findings suggest that individual health care provider endorsement and support may be one of the sole avenues for reaching this group with reliable and timely vaccine information .Practical limitations oblige organizations to choose from the available evidence-based approaches to identify strategies that are achievable and sufficient within their particular circumstances and settings. Initiatives designed to improve uptake of COVID-19 vaccination must therefore be chosen and customized to conform to the specific needs and resources of clinical environments and to tackle recognized obstacles to adoption. Thus, healthcare organizations must assess local contexts in order to appreciate pertinent roadblocks and resources. Moreover, framing initiatives within iterative assessment approaches can aid in effectively identifying and amending the appropriate strategies over time.The current data suggests that the currently approved mRNA-based COVID-19 vaccines are safe and effective for the vast majority of the population. Furthermore, broad-based vaccine uptake is critical for achieving herd immunity; an essential factor in decreasing future surges of COVID-19 infections. Ensuring sufficient COVID-19 vaccination adoption by the public will involve attending to the rising vaccine hesitancy among a pandemic-weary population. Evidence-based approaches at the federal, state, city, and organizational levels are necessary to improve vaccination efforts and to decrease hesitancy. Educating the general public about the safety of the current and forthcoming vaccines is of vital consequence to public health and ongoing and future large-scale vaccination initiatives.


SARS-CoV-2 has resulted in increased worldwide demand for personal protective equipment (PPE). With pressure from ongoing epidemic and endemic episodes, we assessed an adapted snorkel mask that provides full-face protection for healthcare workers (HCWs), particularly during aerosol-generating procedures. These masks have a custom-made adaptor which allows the fitment of standard medical respiratory filters. The aim of this study was to evaluate the fit, seal and clinical usability of these masks.in-situ simulation study recruited fifty-two HCWs to don and doff the adapted snorkel mask. Negative pressure seal checks and a qualitative fit test were performed. The HCWs completed intubation and extubation of a manikin in a university skills training laboratory, followed by a web-based questionnaire on the clinical usability of the masks.This multicentre, non-blinded Whilst fit and usability data were generally satisfactory, two of the 52 participants (3.8%) felt that the mask did not span the correct distance from the nose to the chin, and 3 of 34 participants (8.8%) who underwent qualitative testing with a Bitrex test failed. The majority of users reported no fogging, humidity or irritation. It was reportedly easy to speak while wearing the mask, although some participants perceived that they were not always understood. Twenty-one participants (40%) experienced a subjective physiological effect from wearing the mask; most commonly a sensation of shortness of breath.A fit-tested modified full-face snorkel mask may offer benefit as a substitute for N95 respirators and face shields. It is, however, important to properly select the correct mask based on size, fit testing, quality of the three-dimensional (3D) printed parts and respiratory filter to be used. Additionally, HCWs should be trained in the use of the mask, and each mask should be used by a single HCW and not shared. Despite several international studies on the efficacy and safety of the full-face snorkel mask as an alternative to N95 respirators, goggles and face shields, they are not commonly available or used in high-risk settings such as emergency centres and critical care units in Africa.•Adapted full-face snorkel masks can be used as PPE by HCWs. It is, however, important to examine the development of standardized procedures for donning, doffing and decontamination of these masks in the African context.•Local studies on these masks can contribute to creating awareness of safe and suitable alternatives amidst global shortages of traditional personal protective equipment.Since the first reported case of Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) in March 2020 in South Africa HCWs caring for COVID-19 patients are at high risk of being exposed to the SARS-CoV-2 virus during procedures that require protection against aerosols 10.Protection against aerosols requires both a filter to remove particles from inhaled air, as well as barrier protection for the eyes. This is particularly important if the HCW is close to the dispersion source, such as with endotracheal intubation or extubation. According to Tack and colleagues, several advantages can be noted when the full-face snorkel mask is compared to the standard protective measurements as recommended by the World Health Organization 2) build-up and clinical usability. 2 build-up were comparable to that of disposable N95 respirators and data from several studies suggested that the full-face snorkel mask with an airway filter meets OHSA N95 standards in protecting healthcare workers from aerosolised particles Initial anecdotal evidence on full-face snorkel masks suggested successful negative-pressure fit-checks , and qualitative fit-testing, but reported on failed quantitative fit-testing in-situ simulation study tested fit, seal, and clinical usability of full-face snorkel masks during two simulated aerosol-generating procedures, namely tracheal intubation and extubation. To standardize between the two study sites, uniform checklists for donning and doffing, 2), height and weight were collected.This multicentre, non-blinded, After Research Ethics Committee, we recruited a convenience sample of 52 clinician volunteers  from the departments of emergency medicine, internal medicine, anaesthesiology, family medicine, orthopaedics and critical care. We also included emergency care practitioners working for a private emergency service in South Africa. Each participant provided written informed consent. The study was performed in the midst of the first wave of infections in South Africa and as such all the participants were well versed in using typical PPE that consisted of a N95 mask, face shield/goggle, gowns, aprons and gloves. None of the participants had used a full-face snorkel mask for the purpose of PPE at the onset of the study.Van Wyk, Goussard and Meintjes report on the availability of three modified full-face snorkel masks in South Africa: SEAC Libera Med+, Mares Sea Vu Care and DiveTec Paladin, with significant differences between these masks Each mask was modified by removing the snorkel and fitting in a specialised adapter which allows connection of a standard respiratory filter to the inhalation and exhalation ports .Fig. B1AThe reusable adapter is 3D printed by the snorkel mask company and sold with the mask. The filter used was a hydrophobic microbial filter with >99.99% efficacy  which was recommended by the mask manufacturer . These fEach participant underwent fit and seal testing after a briefing on the protocol for donning, intubation, extubation and doffing. Face fit testing to determine the optimal size and strap position was conducted as per manufacturer guidelines. Participants were asked to inhale while occluding the inspiratory port with the palm of their hand. Report of a stifling sensation due to the vacuum created was considered a successful negative pressure seal test. For the qualitative fit test, participants then donned a hood from the test kit. A liquid aerosol with a bitter taste (Bitrex) was sprayed into the hood. Participants were requested to perform the following actions for 60 s each: normal breathing, deep breathing, turning their head from side to side, nodding their head up and down, reading the “Rainbow Passage” aloud, jogging on the spot, and return to normal breathing. The test was considered failed if the participant detected the bitter taste. [15]. Participants then completed a web-based, centrally monitored questionnaire to determine clinical usability  values Due to high demand during the pandemic, we were unable to obtain qualitative Bitrex fit tests The masks were well tolerated, with median values for user perception of seal and comfort of 4 and above on the 5-point Likert Scale .Table 2SThe average time that the mask was worn was 26.7 ± 6.5 min (mean ± SD). The self-reported scores on the clinical usability of the masks , showed Twenty-seven participants (51.9%) identified that they required assistance while donning, whilst only 10 (19.2%) participants required assistance while doffing. Using a 5-point Likert scale The race to generate sustainable PPE for frontline HCWs caring for suspected or proven COVID-19 patients during high-risk procedures has overwhelmed the scientific community with a variety of novel devices purported to contain infectious aerosols The SEAC Libera and Mares Sea Vu Care full-face snorkel masks were used in this study . We founThe majority of participants  did not find humidity to be a problem, while two participants (3.8%) reported perspiration on the forehead and mouth. We found comparable results to those previously reported for HCWs wearing spectacles, which can be attributed to factors inherent in the design which are not compatible with spectacles Similar to the study by Tack et al., a high percentage (32.7%) of participants reported increased respiratory effort after approximately 7 min wearing the mask. Although the study performed by Tack reported slight discomfort associated with longer wearing time, the masks were generally well tolerated Although these adapted full-face snorkel masks are under assessment to be included in recommendations for PPE strategies used during aerosol-generating procedures, many HCWs have already turned to using suchlike single-piece, reusable, eye, nose and mouth protection as a useful alternative during PPE shortages The masks were worn for the typical duration of airway management in COVID-19 patients, but not for longer periods of time required for ICU shifts or theatre cases. As such we only report on the usability of the full-face snorkel masks during airway management procedures and not clinical usability beyond the time duration in this study.versus the full-face snorkel mask. Furthermore, our study did not measure the level of procedural comfort for participants wearing spectacles and we suspect that wearers working shifts might not be able to use these masks. Procedural (intubation first-pass and extubation) times and success rates were not assessed in this study. However, participants mention neither any subjective prolongation of intubation time, nor unsuccessful attempts.Future studies should explore the real-world utility of these masks as well as preference of participants using typical PPE This study did not include objective measures of physiological discomfort. For example, a drop in oxygen saturation levels, increased inspiratory or end-tidal carbon dioxide, tachycardia or change in respiratory rate may be more objective measures of physiological impact. The study was unblinded which could result in some bias.Future research is needed into the impact on procedural time and success rates using the snorkel mask in comparison to standard COVID-19 PPE.Emergency centres and critical care units are generally considered high-stress environments. Intubation is often performed as a life-saving intervention. In light of the high-risk aerosol-generating nature of the procedure, novel devices that can provide a safe alternative during the COVID-19 pandemic must be investigated. Our research suggests that the adapted full-face snorkel masks were considered safe, easy to use and comfortable to wear, with limited chances of cross-contamination during donning, doffing, and when performing procedures. Finally, with the dramatic increases in scarcity and prices of N95 masks and face shields, these reusable masks are an affordable alternative.Results of the study will be disseminated through the academic institutions involved, and shared on various emergency medicine and critical care social media platforms so that potential users of this form of PPE may easily access the information. Feedback has been provided to each of the snorkel mask companies.Authors contributed as follow to the conception or design of the work; the acquisition, analysis, or interpretation of data for the work; and drafting the work or revising it critically for important intellectual content: RH contributed 30%; VL, VU, DJvT, and EdJ each contributed 10%; SS, WvdM, MdP, DS and FH contributed 3% and Hofmeyr 15%. All authors approved the version to be published and agree to be accountable for all aspects of the work.The authors declare no conflict of interest.


Although previous evidence suggests that the infection fatality rate from COVID-19 varies by age and sex, and that transmission intensity varies geographically within countries, no study has yet explored the age-sex-space distribution of excess mortality associated with the COVID pandemic. By applying the principles of small-area estimation to existing model formulations for excess mortality, this study develops a novel method for assessing excess mortality across small populations and assesses the pattern of COVID excess mortality by province, year, week, age group, and sex in Italy from March through May 2020. We estimate that 53,200 excess deaths occurred across Italy during this time period, compared to just 35,500 deaths where COVID-19 was registered as the underlying cause of death. Out of the total excess mortality burden, 97% of excess deaths occurred among adults over age 60, and 68% of excess deaths were concentrated among adults over age 80. The burden of excess mortality was unevenly distributed across the country, with just three of Italy’s 107 provinces accounting for 32% of all excess mortality. This method for estimating excess mortality can be adapted to other countries where COVID-19 diagnostic capacity is still insufficient, and could be incorporated into public health rapid response systems. After that peak, deaths declined for the following two months, and lockdown restrictions were gradually eased starting in late May2. In total, between February and August 2020, approximately 35,500 COVID-19 deaths were registered across Italy, equivalent to approximately 60 deaths per 100,000 people3.Italy received international attention as one of the first countries outside of China to experience a major COVID-19 outbreak. On March 9, 2020, Italy announced a nationwide lockdown to stem community transmission, and deaths peaked two weeks later during the week of March 254, and that cause-specific mortality from COVID-19 varied by age and sex5. Registered COVID-19 deaths were highest in the northern regions of the country, particularly in the region of Lombardy, where the registered COVID-19 death total amounted to over 160 deaths per 100,000 population  are measured as the ratio between the observed death count and the expected baseline12. Excess mortality analyses of the COVID-19 pandemic are becoming widely used in the media: among others, the Economist, the Financial Times and the New York Times have estimated excess mortality across dozens of countries in Europe, the Americas, and Asia15. Previous investigations have also examined how excess mortality analysis can capture deaths caused by COVID-19 but attributed to other causes, as well the indirect mortality burden of the COVID-19 pandemic16.Istat and other research groups drew conclusions about the mortality burden of the COVID-19 pandemic based on a series of excess mortality analyses. Excess mortality analyses attempt to measure the net effect of a discontinuity, such as a COVID-19 outbreak, on all-cause mortality. This is a two-step process: first, the investigator constructs an estimated baseline number of deaths expected during the period in question. While various methods have been used in the past to construct this baseline17. Any suitable approach for estimating small-group excess mortality must quantify uncertainty due to stochastic variation as well as limited data informing the baseline. Even at the national level, weekly estimates of COVID-19 excess mortality presented without uncertainty intervals can leave viewers confused about what constitutes a meaningful departure from the baseline.This study explores another central issue for excess mortality estimation: how can we detect increases in mortality at the local level or across multiple age groups, where the expected number of baseline deaths in each subpopulation of interest is relatively low? This question addresses a tension central to many forms of public health surveillance, where the imperative to identify clustering and high-risk subgroups in health surveillance data must be balanced against reduced study power and possible biases associated with small samples9; a structured province-year-age random effect that draws power from local correlations in mortality across those three dimensions, based on disease mapping principles18; and a Fourier curve-fitting method to fit capture seasonal trends in age-specific all-cause mortality7. Once baseline mortality was estimated for each week and subpopulation in the study period, we compared these estimates to observed counts from vital records over the same period while preserving uncertainty in the expected baseline mortality.To better understand the relationship between registered COVID-19 deaths and excess mortality across Italy, we developed a model to estimate excess mortality by age group, sex, and week across the country’s 107 provinces. Our approach estimated counterfactual baseline mortality rates for each of these groups from March through August 2021, based on mortality rates and predictive covariates observed from January 2015 through February 2021. Our baseline mortality model combined elements from a widely-used Poisson generalized linear modeling (GLM) strategy for mortality estimationThis study contributes two methodological advancements to existing literature on excess mortality analysis and mortality mapping. First, this is the first study to our knowledge to apply disease mapping methods to estimate excess mortality. Second, whereas previous studies have applied Fourier curve-fitting methods to estimate seasonality among large populations at the national and state levels, this study combines Fourier seasonality analysis with a space–time smoothing model to estimate seasonality of mortality among small populations. We apply these modeling methods to estimate excess mortality and SMRs across Italy at an unprecedented level of detail. Here, we present major findings on excess deaths (the difference between observed and baseline death counts) as well as SMRs (the ratio between observed and expected deaths) across subpopulations of Italy.2. At no point between June and August 2020 did the observed death count exceed the upper bound of the 95% uncertainty interval for baseline deaths: as a result, the remainder of this section will focus on excess mortality during the 13-week period from 26 February through 26 May 2020. During these 13 weeks, we estimate that 53,200 excess deaths  were associated with the COVID-19 pandemic, compared with 35,500 deaths registered with COVID-19 as the underlying cause during the same period.At the national level, the aggregated results from our model generally agreed with previous national studies on the timing and magnitude of excess deaths associated with the COVID-19 pandemic across Italy. Figure Our findings can be aggregated across sexes and provinces to understand the age structure of excess mortality across Italy. Figure From March through May 2020, 18 province-age groupings observed significantly fewer deaths than the expected baseline. Of these significantly lower mortality observations, 11 were among the 0–59 age group. These negative excess deaths were included in calculations of total excess mortality nationwide, but do not significantly change the national totals or the age distribution of excess mortality. Tables summarizing excess mortality by age and province grouping have been provided in Supplementary Appendix The outsized proportion of excess deaths observed within older age categories reflects two aspects of age-structured mortality across Italy during this period: baseline mortality is highest in older age groups even in normal years, and these age groups also experienced a larger relative increase in mortality during the study period. Figure This model is also able to characterize spatial variation in excess mortality during the first wave of the COVID-19 epidemic in Italy. From March through May 2020, just three provinces—Milan, Bergamo, and Brescia, all in the Lombardy region—accounted for 32% of all excess deaths in Italy, with just 9% of the country’s total population. By including just four other provinces across the Piedmont, Emilia-Romagna, and Liguria regions in northern Italy, nearly one-half of all excess deaths are captured in a region that makes up just 16% of Italy’s population. Expanding further, the 22 provinces with the highest number of excess deaths account for three-quarters of all excess mortality, but just 30% of Italy’s national population. Figure Comparing Figs. A province-level analysis also demonstrates that within high-burden regions, neighboring provinces experienced varying patterns of excess mortality during the first wave of COVID-19. The bottom two panels of Fig. In this study, we explored whether an application of small-area methods to excess mortality analysis could identify previously unreported trends in the pattern of excess mortality across Italy during the first wave of the COVID-19 pandemic. Our findings suggest that a small-area model yields estimates of excess mortality that are consistent with alternative calculation strategies at the national level, while offering new insights into the uneven distribution of excess mortality by age group, sex, province, and week across the country. Excess mortality estimates generated by this model suggest that a disproportionate majority of excess deaths occurred in adults age 60 and older, due to both the higher level of baseline mortality in these age groups and higher elevation of mortality above baseline during the first three months of the COVID-19 epidemic. This analysis also revealed a highly uneven spatial distribution of excess mortality: half of excess deaths were contained within just seven of Italy’s 107 provinces, accounting for less than 16% of the population. While the general nationwide pattern of excess mortality reflected the timing and geographical concentration of registered COVID-19 deaths, regional analyses obscure meaningfully different excess mortality trends across neighboring provinces within a region.19. This method was found to substantially outperform a simpler approach, in which average death counts across past years are used as the baseline, for predicting weekly excess mortality. It appears that a structured space–time approach stabilizes stochastic variation across relatively small death counts by province and week, producing a smoother mortality risk surface while still accounting for meaningful trends captured by covariates. Because this approach structures uncertainty in a way that allows for principled aggregation, the results can indicate high-risk subgroups by age or location, and identify local variation in excess mortality that might be masked at a less detailed level, without overstating the confidence of findings for individual subpopulations.This study extends regression-based methods for estimating age-structured baseline mortality by incorporating location, year, and week structured random effects within a Bayesian hierarchical framework21. This limitation is partly addressed by the separate harmonic seasonality fits for each age group. This study is also limited to the set of covariates which can be estimated by province and year: other covariates that may be predictive of all-cause mortality, such as the prevalence of environmental and occupational risk factors, were excluded due to lack of availability at the province level. While the population groupings reported in this study could be divided into even more granular units, any small-area investigation must protect the privacy rights of individuals22. Finally, as described in the Introduction, findings from excess mortality analyses must be carefully interpreted due to the many possible sources for changing mortality which are not accounted for in the modeling strategy.This method for measuring excess mortality also has several limitations that should be noted. Because the process for estimating baseline deaths is more complicated and requires additional inputs compared to a simpler averaging method, it is less accessible to a wide range of users. The model for estimating baseline mortality assumes the same relationship between each covariate and mortality across age groups. In reality, some covariates may have a differential effect by age—for example, temperature may have more of an impact on mortality in older age groups due to the greater prevalence of risk factors that inhibit the body’s thermoregulatory response23, but where increases in COVID-19 mortality may have outstripped diagnostic capacity early in the pandemic25. Combining this excess mortality data with cause-of-death information by province would also reveal new insights about the local drivers of excess mortality. We hope that this study provides a new avenue to convert excess mortality analysis into a tool for decision-making in public health.Additional mechanisms for public health monitoring are needed to catch future resurgences of COVID-19 and future epidemics. In the context of high-income countries such as Italy, where high-quality mortality data has been rapidly prepared and cleaned for public use, this approach to small-area excess mortality analysis could be employed as a routine surveillance tool, allowing health officials to identify high-mortality subgroups in a population and to introduce intervention measures in a timely manner. This approach could also be applied to assess patterns of excess all-cause mortality across countries such as Brazil, Mexico, and Colombia, which maintain nearly complete vital registration systemsWe fit a spatially-explicit hierarchical model with fixed effects by age group and for seven covariates, correlated age-province-year structured random effects, and harmonic curves capturing seasonal variation for each age group and province. Separate models were fit for each sex. We fit this model using mortality and population data from 1 January 2015 through 25 February 2020, then generated 1000 predictive samples of the baseline mortality rate for each sex, age group, and province for the weeks of 26 February through 31 August 2020. For each of the 1000 sampled draws, we compared the baseline mortality rate with the observed mortality rate to estimate a Standardized Mortality Ratio, and compared the predicted baseline deaths with observed deaths to estimate excess deaths from all causes.https://github.com/njhenry/covidemr.All methods were carried out in accordance with the relevant guidelines and regulations governing the use of public data sources. The code used to produce this model can be accessed online at 26. The number of deaths over this time period were recorded by year, month, day, Italian municipality, sex, and five-year age group. For the purposes of analysis, these observations were aggregated by sex, Italian province, age group, and week of the year. The five age groups used in this analysis were 0–59 years, 60–69 years, 70–79 years, 80–89 years, and 90+ years of age. These age groups were chosen based on the prior knowledge that the large majority of both all-cause mortality and registered COVID-19 deaths occurred among adults aged 60 and above. Weeks of the year were assigned based on the numeric day of the year, where January 1st of each year was assigned as the first day of the first week. The 365th and 366th days of the year were assigned to week 52, with the hierarchical model adjusting for observed weeks with more than seven days.All-cause mortality and population data were downloaded from Istat, the Italian National Institute of Statistics. As of 22 October 2020, complete mortality data covering all provinces and municipalities of Italy over the time period 1 January 2015 through 31 August 2020 was available for download from Istat27. Population counts were aggregated by sex, province, year, and the five age groups listed above.Population data by sex, age, and province for the years 2015 through 2020 was downloaded from the Istat web data portalWe downloaded or extracted data for each of seven covariates, listed below in Table To construct a mortality baseline for the months of March through August 2020 that incorporated multiple sources of uncertainty, we fit a small area model with age and covariate fixed effects, correlated province-year-age errors, and harmonic terms to capture seasonality within each age grouping and province. Because the age structure of mortality might differ by sex in Italy, two models were fit for males and females. For a particular sex, the number of deaths in a given province In the formulation above, 29. In this specification, 19.The first three terms on the right-hand side of this equation capture age and covariate fixed effects, corresponding to a discrete-time proportional hazards model where the baseline hazard varies by age group30, while the age and temporal covariance structures both correspond to discrete autoregressive processes of order 1. Separable covariance structures have been widely used in the fields of ecology and public health to construct models across space, time, and other dimensions32, and have been found to fit a wide variety of space–time covariance structures33.The term The term 7. In Serfling’s original formulation as well as more recent excess mortality papers, seasonality was fit using two Fourier terms34. We performed five-fold cross-validation estimate the best grouping variables and harmonic terms for seasonal curve fits. Based on the metrics of out-of-sample mean squared error and coverage, we found that the model performed best when seasonal curves were fit separately by province and age group, using two Fourier terms.This harmonic series, which adapts principles from Fourier analysis, is the basis for a classic model for predicting seasonality in flu mortality developed by Robert Serfling36. The model was fit in R v.4.0.3 using the package Template Model Builder v.1.7.1837.We assigned priors to all model parameters and then fit the model using the Laplace approximation for mixed-effect parameter estimationmaximum a posteriori predictions and joint precision matrix for all parameters, we generated 1000 samples for all model parameters using a multivariate-normal approximation of the posterior predictive distribution. These parameter samples were then entered into the original model to construct 1000 draws or “candidate maps” estimating the mortality rate across all provinces, age groups, and weeks in the study period38. Although the model was fit to data from 1 January 2015 through 25 February 2020, the fitted parameter fixed effects, random effects, and seasonality terms could all be applied forward to estimate 1000 draws of predicted baseline mortality from 26 February through 31 August 2020. All subsequent calculations were performed across draws to preserve the correlation structure within draws as well as the model uncertainty across draws.Using the We compared the distribution of predicted mortality rates with observed mortality rates, calculated as observed deaths divided by population, to calculate 1000 draws of standardized mortality ratios (SMRs) for each province-age-sex-year-week grouping We also multiplied the predicted mortality rates by the population in each province-age-sex-year grouping to calculate predicted baseline death counts for each draw. We then calculated 1000 draws of excess deaths for each grouping:In the results section below, draws for predicted mortality, SMRs, and excess deaths are summarized using the mean and 95% uncertainty interval bounds. The 95% uncertainty interval is reported as the 2.5th percentile and 97.5th percentile of values across 1000 draws.We used five-fold cross validation to compare predictive performance across multiple model specifications and to compare predictive performance with simpler models for calculating excess mortality. Each fold was created by fitting the model without data from the weeks in March through December for each of the years 2015 through 2019, then comparing predicted values for the held out weeks with the observed values. This holdout strategy mirrors the process we hope to capture in the months of March through August 2020 in the counterfactual where COVID-19 did not change the pattern of mortality across Italy.Because the expected number of deaths in a given province-age-sex-year-week groupings can be very low, particularly in lower age groups, we aggregated all out-of-sample observations across four-week intervals while preserving the other groupings. We then calculated the difference between the out-of-sample recorded deaths and the modeled mortality, and calculated summary metrics: root mean squared error, coverage of the 95% uncertainty intervals, and relative squared error when compared to a simpler model that uses the average mortality rate across all other years.We found that the out-of-sample root mean squared error for the best-performing model was 2.32E−5, compared to an average weekly mortality rate of 2.05E−4 across all age groups, suggesting a reasonably good fit for the model’s mean estimates. The out-of-sample relative squared error was 0.330 compared to the simple method of averaging weekly values across other years, suggesting that this predictive model substantially outperformed the simpler alternative for the years 2015–2019 even when an entire year of data was held out. The in-sample relative squared error compared to the simpler averaging method was 0.273, a much lower ratio of error, which indicates that the model provides a more flexible fit to the data than the simpler averaging strategy. The out-of-sample coverage of the 95% uncertainty interval was 99.1%, indicating that the predicted uncertainty bounds are conservative. The procedure for out-of-sample validation and results are discussed in more detail in Supplementary Appendix ggplot2 package in R v.4.0.339.All figures and maps in this study were created using the Supplementary Information.


The EPH/ephrin system constitutes a bidirectional signaling pathway comprised of a family of tyrosine kinase receptors in tandem with their plasma membrane-bound ligand (ephrins). Its significance in a wide variety of physiologic and pathologic processes has been recognized during the past decades. In carcinogenesis, EPH/ephrins coordinate a wide spectrum of pathologic processes, such as angiogenesis, vessel infiltration, and metastasis. Despite the recent advances in colorectal cancer (CRC) diagnosis and treatment, it remains a leading cause of death globally, accounting for 9.2% of all cancer deaths. A growing body of literature has been published lately revitalizing our scientific interest towards the role of EPH/ephrins in pathogenesis and the treatment of CRC. The aim of the present review is to present the recent CRC data which might lead to clinical practice changes in the future. The EPH/ephrin signaling system is part of the broad group of tyrosine kinases, acting as regulators of a wide variety of physiologic processes . Knowledge of its contribution to carcinogenesis has emerged during the past decade ,2,3. ItsAll the above highlight the concern regarding the orderly operation of this signaling network. Nine EPHAs and five EPHBs binding invariably five glycosylphosphatidylinositol (GPI)-anchored ephrin-A-ligands and three transmembrane ephrin-B ligands, respectively, have been documented. An emphatic characteristic of EPH/ephrin signaling is its bidirectional nature. The onward signaling is mediated by the EPH kinase activity in the EPH-expressing cells while the backward signaling in the ligand-expressing cells is mediated by the Src kinase family . The bidEPHA1, EPHA4, and EPHA7 genes are principally expressed in the crypts while the EPHA2, EPHA5, and the EFNA1 coding genes are abundantly expressed on the villi. Nevertheless, the whole spectrum of the implications of this gradient in the structure and the maintenance of the gut epithelium is still lacking [The EPH–ephrin signaling system is fundamental for the establishment of the crypt-villous axis and the location arrangement of the cellular populations. The progenitor cells populate the bottleneck of the crypt, stem cells and Paneth cells reside in the bottom of the crypt, and differentiated ones inhabit the villi. The underlying, regulatory mechanism that governs the process is the expression gradient of EPHBs and ephrins in the crypt-villous compartment. Sine qua non, EPHB2 and EPHB3 are maximally expressed in cells that reside in the lowest part of the crypt. Τheir expression declines ascendingly, while the expression of ephrin-B1 and ephrin-B2 has the exact opposite trajectory. It is maximal in the top of the villi and declines towards the crypt . Over an lacking . These aActivation by a ligand triggers the initiation of the EPH’s intracytoplasmic region RTK activity, which in turn activates molecular cascades that further transmit the signal, a process called forward signaling. GTPases of the Rho and Ras family, focal adhesion kinase (FAK), and the pathways of the Janus kinase (JAK)-signal transducer and activator of transcription (STAT), as well as the phosphoinositide 3-kinase (PI3K), have been observed to participate in forward signaling. EPH–ephrin signaling is bidirectional, as a response is also triggered in the ephrin-expressing cell . The latEPHB2 gene  have been reported in Finnish and British populations, none of them conferred a predisposition to developing CRC [EPHB2 gene, seems to affect the OS rather than affecting the development of CRC due to its role in angiogenesis [Colorectal carcinoma (CRC) is the third most commonly diagnosed cancer in both sexes and, despite the extensive research in diagnostics and treatment, it still constitutes the second most fatal malignancy. An epidemiologic dichotomy among developed and transitioning countries with an approximately ten-fold variance in their incidences is documented. Well-recognized risk factors include, among others, the shift towards a more sedentary way of living with the consequent rising wave of obesity and a diet enriched in animal proteins and fats and deprived of whole grain and fibers . Τhe conping CRC . In the ogenesis . Finallyogenesis .The EPHA2-ephrin-A1 interplay is of critical importance for eschewing contact-inhibition. In spheroid cultures of HT-29 cells, in which the maximal cell–cell contact is applied, the downregulation of ephrin-A1 resulted in a mitigation of growth rate, interfering with the tyrosine phosphorylation of EPHA2, E-cadherin, and b-catenin, as indicated by their hyperphosphorylation under the influence of EFNA1-Fc . ErikssoMin/+ mice have a non-functional Apc (adenomatous polyposis coli) protein due to the existence of the MIN  allele which encodes a nonsense mutation at codon 850 [Min/+ mice, the effects of EPHB2 and EPHB3 expression in CRC by creating mutant receptors lacking their cytoplasmic tail. DcyEPHB2 ApcMin/+ mice had ten times the number of tumors in the distal colon with a profoundly more invasive phenotype, as indicated by the desmoplastic reaction, in comparison with the controls. In EphB3−/−; ApcMin/+ mice, they documented 30% more colorectal tumors, a two-fold increase in tumor size >5 mm in diameter, and a three-fold increase in the percentage of invasive disease in comparison with EphB3+/−ApcMin/+. The differences among EPHB3−/−ApcMin/+ and EPHB3+/+ApcMin/+ more emphatically highlight the contribution of EPHBs’ downregulation in disease progression [Initial reports concerning the expression of EPHB2, EPHB3, EPHB4, and ephrin-B2 mRNA document significant alterations in the expression patterns among cancerous colonic cancer cells lines, surgical specimens, and the contiguous healthy tissue, with ephrin-B2 being the most variably expressed . Accordiodon 850 . These aodon 850 ,40. Battgression . Still, gression .Min/+ mice. They reported that ApcMin/+ c-myc+/− mice had less small intestine tumors and colon polyps with a smaller diameter than ApcMin/+ c-myc+/+ mice, which accounted for their OS time. VEGF, EPHA2, and ephrin-B2 had a significant downregulation of their expression correlating with the c-myc expression levels in the small intestinal tumors [Min mice resulted in a nearly ten-fold increase in the number of small and large intestinal adenomas populated by cells from the bottom of the crypts due to Wnt signaling hyperactivation. Summarizing a series of more sophisticated experiments, they suggested that the loss of Mule, which orchestrates the lysosomal and proteosomal degradation of various molecules, leads to the ectopic overexpression of EPHB3 and the aberrant c-myc-mediated expression of Paneth cells and stem cells. The end result constituted the formation of the abovementioned adenoma [Taking it a step further, Yekkala K. et al. explored the contribution of c-myc signaling in CRC carcinogenesis utilizing Apcl tumors . Domingu adenoma .Min mice are inclined to develop highly invasive, villus neoplasms with reduced ephrin-B1 expression [Εphrin-B1 and ephrin-B2 are preferentially incorporated in CRC-derived exosomes  while onpression . Ζhu Y. pression . As mentpression . Liu W. pression . Furtherpression . The datThe contribution of EPH/ephrin signaling in the developmental process of embryonic vasculature is well-established in the literature, constituting the foundations for the conceptualization of EPH/ephrin involvement in tumor neovascularization. Oqawa K. et al., experimenting with xenografts from human-derived breast cancer and Kaposi’s sarcoma cell lines, documented the expression of ephrin-A1 and EPHA2 all over the endothelial lining and cancer cells, demonstrating analogous results in surgically resected CRC specimens . It is cThe downregulation of EPHA1 is suggestive of lower overall survival (OS) , in accoAccording to Laiho P. et al., EPHBs exhibit a significant variance in their expression among serrated and typical CRC tissues, possibly contributing to the pathogenesis of disease . EPHB2 eEpigenetic mechanisms also contribute to colorectal carcinogenesis; namely, miR-645 downregulating the ephrin-A5 mRNA influences tumor growth and metastasis .The abovementioned data are documented in detail in The various roles of the EPH/ephrin system in CRC carcinogenesis are presented in Although several approaches to therapeutically target the EPH/ephrin system have been developed, currently clinical studies regarding the CRC do not exist. Any detailed narration is beyond the scope of this text and has been done elsewhere ,67, and Chemotherapeutics  have constituted the mainstay of therapeutic options in the systemic treatment of CRC. Their clinical utility has been extended from high-risk stage II disease  to stage III surgically resected tumors as an adjuvant treatment. In conjunction with biologic agents, chemotherapeutics exhibit notable efficiency in the management of metastatic disease . Their uRAS and RAF status is of paramount importance, since any downstream mutations can affect the responsiveness to biologics [+/+ cells were exposed to cetuximab and EPHA2 was significantly downregulated, the expression of the EPHA2 gene in NRASQ61K/+cells remained unaffected, indicating a functional relationship among the EPHA2 and EGFR [TGFB2 gene and regulates cellular proliferation and the sensitivity to 5-fluorouracil (5-FU) [Another therapeutic contribution might be the enhancement of sensitivity of established therapeutic agents. The introduction of anti-EGFR agents has improved the outcomes of CRC patients. Characteristically, the combination of chemotherapy plus an anti-EGFR agent exerts its superiority over the combination of chemotherapy plus an anti-VEGF. These effects are more emphatic in the left-sided tumors over the right-sided ones, highlighting their biologic differences . The mutiologics ,75. The and EGFR . Furtherand EGFR . Finallyl (5-FU) . The docMetastasis represents a significant cause of mortality in CRC patients. The current therapeutic approach includes the use of chemotherapy in conjunction with biologics . Ephrin-It is evident that EPH/ephrin signaling has an instrumental role in CRC carcinogenesis. Currently, the genetic studies concerning EPH/ephrin signaling remain limited and have failed to demonstrate a predisposing relationship between specific gene variants and CRC ,31. EPHAIt is becoming evident that therapeutic interventions in the EPH/ephrin system could reshape the architecture of colorectal carcinoma treatment and their efficiency should be validated in clinical trials in humans. Further research could enlighten our unaddressed inquiries towards the molecular mechanisms that govern the EPH/ephrin signaling system in CRC carcinogenesis.


Healthcare providers are vulnerable in the fight against COVID-19 and may experience significant psychological and mental health consequences. This study aimed to compare the levels of depressive symptoms among frontline and non-frontline healthcare providers in response to the COVID-19 pandemic.t-test and ANCOVA were performed using SPSS version 26.A comparative cross-sectional study was conducted in two government hospitals managing COVID-19-related cases in Kelantan, Malaysia from May to July 2020 to identify and compared depressive symptoms levels of frontline and non-frontline healthcare providers. Convenient sampling was applied in the selection of eligible participants and those diagnosed as having any psychiatric illnesses were excluded. The self-administered questionnaires for the Malay versions of the Hospital Anxiety and Depression Scale to measure depressive symptoms score and the Medical Outcome Study Social Support Survey to measure social support score as an important confounder. A descriptive analysis, independent A total of 306 respondents from healthcare providers were recruited which 160 were frontline healthcare providers and 146 were non-frontline healthcare providers. The level of depressive symptoms (HADS score >8) was 27.5% for the frontline healthcare providers and 37.7% for the non-frontline healthcare providers. The mean depressive symptoms score for the non-frontline healthcare providers was 0.75 points higher than that of the frontline healthcare providers after adjusting for gender, duration of employment and social support.Non-frontline healthcare providers are also experiencing psychological distress during the COVID-19 pandemic even though they do not have direct contact with COVID-19 patients. Although a year has passed since the first case of Corovavirus disease 2019 (COVID-19) was diagnosed, the case numbers are continuing to increase, with more than 50 million people globally having been infected and more than one million death [Many researchers have studied mental health problems due to COVID-19. Anxiety, depression, distress, sleep problems and somatic symptoms are among the common mental health problems experienced by healthcare providers during disease outbreaks . These pA recent study reported the protective factors for mental health problems among healthcare providers . These iOne parameter for mental health problems is depression. Depression has been reported in many studies of healthcare providers during the COVID-19 pandemic. The reported pooled prevalence of depression among healthcare providers in 11 studies from China, West Bengal, India and Singapore was 30.2%  . Being fWorking as a highly exposed group to COVID-19 infection, healthcare providers were at higher risk of psychological symptoms . WhetherThis study was a part of a large study on the psychological impact of the COVID-19 pandemic in Kelantan. The included psychological impacts were vicarious traumatization, anxiety and depressive symptoms. Each of the studies had shared the same methodology and respondents with the difference in the study’s objective. The studies of vicarious traumatization and anxiety  were peer-reviewed and published in journals recently ,15.This comparative cross-sectional study was conducted among healthcare providers in two hospitals managing COVID-19-related cases from May to July 2020 in Kelantan, Malaysia. These government hospitals were designated as COVID-19 hospitals for managing COVID-19 patients throughout the state of Kelantan. Healthcare providers, such as medical specialists, medical officers, medical assistants, nurses, and health care assistants were included in the study. Medical specialists and medical officers were grouped into medical staffs; meanwhile, medical assistants, nurses, and healthcare assistants were grouped into paramedics. Those who had been diagnosed with any psychiatric illnesses were excluded. Frontline and non-frontline healthcare providers were identified and invited to participate in this study. Convenience sampling was applied in the selection of eligible participants ,15.The sample size was calculated by comparing two means using the Power and Sample Size Calculation software version 3.0.43 . This study was part of a larger study on the psychological impact of the COVID-19 pandemic. The variable with the largest sample size was anxiety. In the previous study , the staAn online method was used to invite eligible respondents. A Google Form was distributed through a WhatsApp group application. The respondents who agreed to participate in the research were requested to respond via a virtual consent form and to complete the self-administered questionnaires. They were informed that their participation in the research was voluntary and that they could withdraw their participation at any time. Sign-in to a Google account was not required to complete the survey. The participants were anonymous to reduce method and social desirability biases ,15.The case report form consisted of several parts, including a questionnaire on the healthcare providers’ socioeconomic data, the Malay version of the Hospital Anxiety and Depression Scale (HADS) questionnaire  and the The Malay version of Hospital Anxiety and Depression Scale (HADS) was used in this study . The queThe responses of the HADS-D subscale comprise one of four choices: never (0), mild (1), moderate (2) and severe (3). To assess levels of depressive symptoms, the possible scores of HADS-D range from 0 to 21. Higher scores denote more depressive symptoms . The besThe Malay-version Medical Outcome Study (MOS) Social Support Survey consists of 16 items and four dimensions, namely, emotional/informational (six items), tangible (three items), affectionate (three items) and positive social interaction support (four items) . Items at-test and analysis of covariance (ANCOVA) were performed [The data entry and analysis were conducted using IBM SPSS Statistics version 26.0 . The data were checked and cleaned prior to the analysis. The distributions and frequencies were examined. All continuous variables were expressed as mean and 95% CI. Meanwhile, frequency and percentage for categorical variables. An independent erformed ,15.Ethics approvals were obtained from the Universiti Sains Malaysia Human Research Ethics Committee (USM/JEPeM/COVID19-10) and the Ministry of Health Medical Research Ethics Committee (NMRR-20-703-54576). The questionnaire was anonymous, and the responses were confidential. The respondents were asked to answer the questionnaire as truthful as possible. They were to indicate their experiences and feelings, and there were no right or wrong answers.The response rate of the study was 95.6%. A total of 306 healthcare providers participated in this study: frontline healthcare providers (n = 160) and non-frontline healthcare providers (n = 146). The majority of the healthcare providers were female, Malay, had diploma education, married, paramedics, working at the medical department and no working shift .The HADS items for depressive symptoms (HADS-D) showed that the mean score for the non-frontline group was higher than that of the frontline group for most of the scale items. For items 4 and 12, the mean score of the frontline group was higher compared to that of the non-frontline group .t-test analysis showed a significant difference in depressive symptoms scores (p = 0.043), with non-frontline healthcare providers showing a 0.69 mean score difference compared to the frontline healthcare providers. ANCOVA showed a significant difference in the estimated marginal mean of the depressive symptoms scores between the non-frontline and frontline healthcare providers, with a mean difference of 0.75 after adjusting for gender, duration of employment and social support  after adjusting for gender, duration of employment and social support (The frequency of mean HADS score for depressive symptoms (score >8) was 99 (32.4%): 44 (27.5%) for the frontline healthcare providers and 55 (37.7%) for the non-frontline healthcare providers. The overall mean (SD) HADS score of depressive symptoms for healthcare providers was 7.17 (2.98). The mean score for the frontline healthcare providers was normally distributed, ranging from 1 to 15, with a mean (SD) of 6.84 (2.98). The mean score for the non-frontline healthcare providers was also normally distributed and ranged from 1 to 15 with a mean (SD) of 7.53 (2.95). The independent  support .Healthcare providers bear a high burden of responsibility, particularly in the public health response to the COVID-19 pandemic. This responsibility may have a significant impact on their physical and psychological health. Depressive symptoms is one of the common psychological impacts experienced by healthcare providers who respond to outbreaks and pandemics. In this study, the overall mean score of HADS-D indicated that healthcare providers had lower levels of depressive symptoms. However, when a comparison was made between frontline and non-frontline group, the frontline healthcare providers had lower levels of depressive symptoms compared to the non-frontline healthcare providers.Although there was a significant difference, the levels for both groups were lower than those required to denote clinical depressive symptoms, as indicated by the mean scores for both groups, which were less than eight. This might be influenced by lower cases of COVID-19. The total reported COVID-19 cases in that state was 160 cases until the end of July 2020 .A study in Singapore had a similar result: the non-medical healthcare workers had a higher mean score for depression compared to the medical healthcare workers . HoweverThe Malaysian study has also found evidence that depression is strongly intercorrelated with anxiety, stress and chronic fatigue . A shortStaff shortages caused the management to reshuffle placements and rescheduled the working hours. Changing in the working environment with lack of skills and less experiences may affect their working performance and may cause depression if they failed to achieve the job requirement. This situation will escalate if these non-frontline healthcare providers need to work longer hours and take on a greater workload in terms of shifts and patients  as this Tan et. al.  determinNon-frontline healthcare providers may have less medical information regarding COVID-19 and less intensive training on personal protective equipment and infection control measures . MedicalAs limitations of the study, a cross-sectional design study cannot be generalized to other population. However, this study gave evidence that non-frontline healthcare providers experienced more depressive symptoms than frontline healthcare providers. Meanwhile, a self-administered questionnaire applied in this study to reduce face-to-face interviews may produce common method bias and social desirability bias. Anonymous survey was used with the hope it may reduce such biases. There were no psychometric properties of the Hospital Anxiety and Depression Scale (HADS) conducted among healthcare providers in Malaysia. Future study of the validity and reliability of the HADS among healthcare providers in Malaysia is needed since the mental health of the healthcare providers is a concern and received increased attention during the pandemic.Depressive among healthcare providers–whether from frontline or non-frontline groups–needs to be addressed to prevent some of the established negative ramifications of depressive symptom. These groups need each other in complementing health services. Depressive score was higher in non-frontline healthcare providers compared to frontline healthcare providers. Non-frontline healthcare providers were affected by more workload burden and fear of been infected with COVID-19 as the workplace was a red zone of COVID-19. Both groups must be supported psychologically and treated equally based on their conditions. Improving the healthcare system in these ways would be beneficial in maintaining the quality of healthcare services, especially during the current COVID-19 pandemic.