There has been significant speculation regarding the association between the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pathogen, coronavirus disease (COVID-19) and smoking. We provide an overview of the available literature regarding the association between smoking, risk of SARS-CoV-2 infection, and risk of severe COVID-19 and poor clinical outcomes, with the aim of informing public health policy and practice, particularly in England.Publications were identified utilising a systematic search approach on PUBMED and Google Scholar. Publications presenting a systematic review or meta-analysis considering the association between smoking and SARS-COV-2 infection or COVID-19 outcomes were included.Eight studies were identified. One considered the relationship between smoking and the probability of SARS-CoV-2 infection, three considered the association between COVID-19 hospitalisation and smoking history, and six reviewed the association between smoking history and development of severe COVID-19. One study specifically investigated the risk of mortality. The studies considering risk of severe disease indicate that there is a significant association between COVID-19 and current or ever smoking.This is a rapidly evolving topic. Current analysis remains limited due to the quality of primary data, although, early results indicate an association between smoking and COVID-19 severity. We highly recommend public health messaging to continue focusing on smoking cessation efforts. The current coronavirus disease (COVID-19) pandemic presents a significant public health threat, posing an immediate risk to the health of the global population, and creating far-reaching, long-term consequences. As this new virus spreads, questions continue to emerge regarding risk factors. One widely debated question is the potential effect of smoking on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rates and on the clinical outcomes of the resulting disease, COVID-19.2, as are those exposed to secondhand smoke3. The World Health Organisation (WHO) has also highlighted smokers’ hand-to-mouth action, smoking-induced lung disease, and the sharing of tobacco products such as waterpipes, as factors which may increase a smoker’s vulnerability to SARS-CoV-2 infection and development of COVID-194. Evidence that chronic illnesses, especially respiratory and cardiovascular disease, are risk factors for worse outcomes in COVID-19 is accumulating6. Smoking contributes to the development of such long-term conditions2; conversely, it is reasonable to assume that smoking may increase risks of COVID-19. Early in the pandemic, it was argued that higher mortality among males in China may reflect and be partly explained by the gender disparity in smoking prevalence7. Nevertheless, smokers are not currently identified as a vulnerable group within the UK Government’s COVID-19 guidance on social distancing8.There is well-established evidence that smokers are more susceptible to both viral and bacterial respiratory infections9 and it appears SARS-CoV-2 not only utilises ACE2R as its receptor, but may do so more readily than SARS-CoV11. There are competing theories regarding the effects of smoking on the level of ACER2 expression in cells of the human respiratory tract13. Given the role of ACER2 in enabling viral entry into human cells, any change in ACER2 expression caused by exposure to tobacco smoke  may have implications for an individual’s susceptibility to infection.There may also be a specific mechanism through which exposure to tobacco smoke can influence infection with SARS-CoV-2. Research following the emergence of severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003 identified a viral binding site on the angiotensin-converting-enzyme 2 receptor (ACE2R)14.An opposing theory to this has been proposed, whereby the authors postulate that the nicotinic acetylcholine receptor (nACfR) acts as a co-receptor for viral cell entry within the respiratory tract and central nervous system. It is suggested that nicotine may compete with the SARS-COV-2 for the (nACfR) binding site, hence leading to a reduction in available viral adhesion sites. There is, however, no empirical evidence to support this at this time15, it is crucial to optimise policies influencing tobacco use during the pandemic. Therefore, we set out to review the findings of existing systematic reviews and meta-analyses regarding the association between smoking and risk of contracting SARS-CoV-2 and of developing severe outcomes once infected, with the intention of informing public health policy, with a particular focus on the public health system in England.Epidemiological evidence on the role of smoking in COVID-19 is emerging. Considering the wider implications of tobacco control on population health and the healthcare systemA search for existing systematic reviews and meta-analyses published between 1 January and 8 May 2020 was undertaken on PubMed, utilising the search terms: ‘Smoking’, ‘Nicotine’, ‘COVID-19’, ‘SARS-CoV-2’, and ‘Coronavirus’, in pairwise combinations. This was supplemented with an article title search on Google Scholar utilising the same terminology, without restriction on study type, from which meta-analyses and systematic reviews were extracted manually. Two independent researchers screened the publications for eligibility. Only publications in the English language were considered. Our method is explained schematically in 23, which formed the basis of our review . There was no significant difference in the risk of testing positive between former smokers and never smokers . Heterogeneity was very high in both meta-analyses .Across the eight selected studies, there were very limited data regarding the relationship between smoking history and risk of SARS-CoV-2 infection. Simons et al.In considering the relationship between smoking and COVID-19 outcomes, the eight studies included in our review took two general approaches:18;Using data presented in literature to produce pooled smoking prevalence estimates amongst hospitalised patients 23.Directly comparing rates of various clinical outcomes among patients hospitalised with COVID-19, according to their smoking history (which is variably defined between studies)17, whilst the third assessed the risk of hospital admission between smokers, ex-smokers and non-smokers18.Three meta-analyses considered hospital admission for COVID-19 and smoking. Two produced pooled estimates of smoking prevalence amongst smokers admitted to hospital with COVID-1916 found a pooled current smoking prevalence estimate of 6.5% (95% CI: 4.9– 8.2%) among hospitalised COVID-19 patients, which was lower than the estimated prevalence within the general population of the country. A secondary analysis designed to address possible misclassification of very recently quit smokers found a pooled estimate of ever smokers of 7.3% (95% CI: 5.7–8.9%). Emami et al.17 found a similar pooled estimate of smoking history of 7.63% [95% CI: 3.83–12.43%) based on a smaller sample of 2986 patients derived from six heterogeneous studies from China (I2=90.19%). Simons et al.18 conducted a rapid review attempting to answer several questions from the available literature. They performed a meta-analysis using five studies deemed ‘fair quality’, showing no difference in either risk of hospital admission between current smokers and never smokers , or former smokers and never smokers .Using data for 5960 patients across 13 studies in China, Farsalinos et al.23. These are described in chronological order of initial publication. Across these studies, the use of the term ‘severe disease’  was not consistently defined. Three of the studies acknowledged the variability of outcome measures used across the available primary research included within their pooled estimates22, but only two explicitly included detail of how they had defined ‘severe’ disease in the context of their data extraction process and analysis21. While the Chinese National Health Committee’s definition of severe COVID-19 (‘respiratory distress with respiratory rate ≥30/min or oxygen saturation ≤93% at rest or oxygenation index ≤300 mmHg’)22 was used by some of the primary research studies as their outcome of interest, other markers, including ICU admission and mechanical ventilation, or death were also used.Six of the studies selected investigated whether smoking was associated with poor clinical outcomes of COVID-1919 identified five studies from China, incorporating 1399 COVID-19 patients, 288 of whom were identified as having ‘severe disease’. One included study found active smoking to be a significant predictor of COVID-19 severity24, but the other four studies showed non-significant results. The pooled odds ratio for the 5 studies was found to be 1.69 (95% CI: 0.41–6.92), although this analysis has been heavily criticised for inclusion of erroneous data25.Lippi and Henry20 reviewed outcomes of SARS-CoV-2 in five Chinese studies. In three of the studies reviewed, no significant association could be established between smoking and outcomes by the authors. Results from one study24 indicated that smoking was significantly associated with disease progression . Analysis by Vardavas and Nikitara20 using raw data published by Guan et al.26, concluded that smokers were more likely to experience severe symptoms , and 2.4 times more likely to require ICU admission .Vardavas and Nikitara21 investigated the risk of development of ‘severe COVID-19’ in COPD patients and current smokers. They included 11 studies with a total of 2002 patients from China. Seven of these studies reported smoking history (n=1726). The pooled results showed an association between current smoking and development of severe COVID-19 . Zhao et al.21 reported moderate study heterogeneity . The association between smoking and development of severe COVID-19 was significant when all seven studies were included, however, upon removal of the most heavily-weighted study26, which had been found to be a source of heterogeneity, the results were non-significant . An updated version of this meta-analysis27, which included one additional study, showed a stronger association between current smoking and severe disease , and a reduction in study heterogeneity (I2=39%). This result remained significant on the removal of the most heavily-weighted study26 from the analyses 27.The meta-analysis of Zhao et al.22 published a meta-analysis of 19 studies with their main outcome measure defined as progression of COVID-19 among hospitalised patients. The data analysed included a total of 11590 patients, of which 2133 developed severe disease; 731 patients included were ever smokers. They found that disease progression was observed in 29.8% of ever smokers compared with 17.6% of never smokers . They reported moderate heterogeneity , and no significant publication bias . Subsequent sensitivity analysis using five of the 19 studies also found a significant association between current smoking and risk of disease progression compared to never smoking , with no evidence of significant heterogeneity or publication bias.Patanavanich and Glantz23 included five studies from China in their meta-analysis, incorporating data of 1980 patients of whom 268 experienced critical or fatal events. The authors reported a significant association between current smoking and disease progression amongst patients with COVID-19 . The authors reported no heterogeneity (I2=0%).Zheng et al.18 reviewed 33 studies investigating the association between disease severity and smoking status. They performed a meta-analysis of four selected ‘fair quality’ studies, showing that current smokers were more likely to develop severe COVID-19 illness compared to never smokers . No significant difference was observed between former and never smokers . Simons et al.18 treated mortality as a separate measure in their analysis, reviewing 17 studies, and conducting a meta-analysis using two studies deemed ‘fair quality’. These analyses found no significant difference between the risk of death from COVID-19 either between current and never smokers , or former and never smokers .Simons et al.There is some significant overlap between the studies used for the eight systematic reviews and meta-analyses considering: a) smoking prevalence amongst hospitalised patients, b) risk of hospitalisation, c) risk of disease progression, and d) risk of death . The totWe sought to review existing systematic reviews and meta-analyses regarding the association between smoking and risk of both contracting SARS-CoV-2, and COVID-19 disease severity and poor clinical outcomes. We found limited evidence suggesting that the risk of SARS-CoV-2 infection may be lower among smokers compared to non-smokers, albeit from highly heterogeneous studies. By contrast, however, there is growing evidence regarding the association between smoking status and COVID-19 severity and poor clinical outcomes, with an increasing number of published primary research papers making larger, pooled sample sizes possible.22, which included the largest pooled sample size to date, suggests an increased risk of severe COVID-19 amongst ever smokers. While these are important findings, they must be considered in light of the limitations of the primary research.The studies considering risk of severe disease indicate that there is a significant association between COVID-19 and current or ever smoking. The meta-analysis by Patanavanich and Glantz20 and Lippi and Henry19 and concluded that early data from China did not provide clear evidence of an association between smoking and poorer COVID-1929 outcomes. However, the methodological soundness of the meta-analysis by Lippi and Henry19 has been challenged30. These criticisms include both the use of incorrect data in meta-analyses25 and the incorrect use of null hypothesis significance testing, leading to an incorrect conclusion regarding the absence of effect30. A repeat analysis by Guo25 using the correct data from the studies considered by Lippi and Henry19 found a significant association between active smoking and severe COVID-19 , supporting the view of Cattaruzza et al.7 that despite the uncertainty around data on the association between smoking and COVID-19, smoking may represent ‘the most important avoidable risk factor’. Our paper reviewed a significant amount of the available literature, and hence provides a comprehensive overview of the current evidence-base. It is, to our knowledge, one of the first review of reviews considering the association between SARS-COV-2, COVID-19 and smoking.Other descriptive reviews have acknowledged the contrasting findings presented by Vardavas and NikitaraThere are several limitations associated with the primary research used to inform the eight systematic reviews and meta-analyses considered within this report. Most of the primary research considered is based on data derived from small convenient hospital samples that are unlikely to be representative of the general population and will under-represent those populations that are unable or less likely to access healthcare.31. Similarly, ex-smokers are also likely to have smoking-related conditions such as cardiovascular illness and pulmonary pathology2 making them more vulnerable to severe COVID-1932. These smoking-related conditions may in fact be the reason why patients smoke less or quit. Thus, studies investigating current smoking may be subject to selection bias. Investigating the effect of being an ever smoker is less vulnerable to selection bias, though cannot address any hypotheses regarding the direct impact of current nicotine use on disease progression14. Some of these issues can be partly addressed with cohort study designs. Two such studies have been conducted in the UK34 and have found increased risks of hospitalisation and death among current and former smokers. Nevertheless, they also have major limitations, including lack of recent data on smoking status33 and unstable estimates in sensitivity analyses34.Furthermore, analysis of those, by definition, non-random samples raises significant risks of selection or collider bias through various pathways. For example, hospitalisation may be influenced both by smoking and COVID-19 severity. Studying hospitalised patients may lead to spurious or mask true associations between smoking and COVID-19 outcomesThe primary studies included in the reviews we assessed reported smoking history and outcomes inconsistently. Most studies do not define what is meant by ‘smoking’, and the definitions of current and former smoker are likely to vary. Some report ‘daily smokers’, while others may define former smoker differently, as the duration of abstinence is not clearly standardised. If investigating a correlation between nicotine and COVID-19, reporting of additional nicotine ingestion methods such as vaping, hookah and chewing tobacco should be considered. Definition of ‘severe’ outcomes also varied – often composite measures were used by the meta-analyses to produce a statistically viable sample size.18. However, this should be interpreted with caution because of likely selection bias, but also because smoking prevalence may be substantially underreported leading to misclassification. Several steps in the data acquisition process are prone to systematic error including self-reporting of smoking status, especially at a time when healthcare facilities are under pressure and no next-of-kin may be present to provide further information. Smoking data in health records is often incomplete or inconsistent36. It is also very challenging to determine how much data may be missing when deriving data from existing datasets and how missing data were treated in each study.Recording of smoking status on which many primary studies are based will be of variable quality, particularly at a time when clinical coding may not be a priority. The smoking rates reported are consistently lower than the population averagesAlthough most studies referenced by the assessed reviews and meta-analyses are from China, not all are, and this presents another difficulty in comparing datasets across different countries and in assessing generalisability of findings.37, the wider picture of the infection pyramid also requires consideration to determine the full picture of how smoking impacts on SARS-CoV-2 infection. Simons et al.38 attempted to address this; however, the primary studies available to inform their review were highly heterogeneous, which may decrease the reliability of the pooled estimates.Most of the reviews examined utilise data on hospitalised COVID-19 patients. Given that most SARS-CoV-2 infections do not result in medical interventionWe have reviewed the available international literature considering the association between SARS-CoV-2, COVID-19 and smoking in order to inform public health policy and practice, particularly in England. Although current evidence relating to smoking and COVID-19 is not conclusive, this should not preclude proactive efforts by public health systems to promote smoking prevention initiatives among never smokers, as well as to identify, advise and engage smokers in cessation attempts.39. Similarly, lower exposure to secondhand smoke can lead to rapid reductions in hospital admissions for asthma exacerbations amongst children40 and hospitalisation for myocardial infarction41. This latter point is particularly pertinent at this time given the UK Government’s social distancing guidance8, which has required people to remain at home for many weeks, potentially changing their smoking practices and leading to an increase in indoor smoking. Delivering these kinds of health improvements in the immediate-to short-term would not only impact on morbidity and mortality rates, but also contribute to a reduction in associated spells of primary, secondary and emergency care during a time in which the NHS is under unprecedented pressure.Regardless of any impacts on SARS-CoV-2 infection rates and COVID-19 outcomes, smoking cessation offers a range of benefits, both during the current pandemic and beyond. An obvious example is the reduction in incidence of smoking-related diseases, such as myocardial infarction42, and to face increased financial hardship resulting from the impact of lockdown measures and the long-term economic effects.The personal financial savings realised as a result of smoking cessation are also significant, particularly for those from deprived communities. Individuals from lower socioeconomic groups are more likely to smoke43. COVID-19 may present a ‘teachable moment’ 44 during which some smokers may be particularly susceptible to smoking cessation messages and motivated to make quit attempts. As such, this opportunity should be harnessed to maximum effect.Survey data suggest that smoking behaviours in England may already have started to shift as a result of the pandemic, though not in wholly positive ways. One survey from April 2020 found that 2% of former smokers surveyed had decided to quit during the previous four months at least in part due to COVID-19. However, in the same survey, 10% of current smokers reported smoking more indoors; 20% buying tobacco in larger quantities and 14% being less likely to try to quit, since the start of ‘lockdown’45. On-going reductions to public health funding pose a major challenge in ensuring equitable access to these services, with many local authorities having decommissioned or reduced smoking cessation services provision46. Urgent action should be taken to maximise visibility and reach of local, regional and national support that is available, and to enable easy access to pharmacological therapies locally.Specialist smoking cessation services providing psychological and pharmacological interventions are proven to be cost-effective, and improve success rates compared to unaided quit attempts47. This includes increasing capacity for telephone and digital support, facilitating remote access to nicotine replacement products, and pausing the use of carbon monoxide monitoring. While necessary, given current circumstances, these changes present a range of challenges to the effective identification of smokers, verification of self-reported quit attempts and maximisation of successful quit rates, and may contribute to widening health inequalities. Opportunities to share learning around the adoption of new approaches to delivering smoking cessation services should be maximised, including effective messaging to communicate risk around COVID-19 to smokers, and challenging disinformation that may be circulating. Smokers unable to quit should be encouraged to minimise the exposure of household members to secondhand smoke by smoking outside wherever possible, and to practice meticulous hand hygiene to reduce risk of transmission.Guidance issued by the National Centre for Smoking Cessation and Training aims to support UK smoking cessation services in significantly changing their traditional delivery models in response to COVID-1948. This may change as our understanding of a potential association develops. It is important that this relationship be investigated via studies that can address some of the underlying limitations of the existing evidence-base. This should include studies that actively collect  smoking status, rather than relying on existing health records. It also highly important to improve and standardise recording practices regarding smoking status in health and social care settings, to ensure that this cohort is easily identified and offered appropriate guidance.At the time of writing, smokers are not designated as high risk from COVID-19 within UK Government guidance‘smokers are at higher risk of developing severe disease and death’49. Therefore, public health messaging should strongly highlight the benefits of smoking cessation and not detract from the importance of this during the pandemic or in the aftermath.Though early primary research has been constrained by the emerging nature of the COVID-19 pandemic and inherent methodological challenges, more evidence is regularly being published. By considering the findings of eight systematic reviews and meta-analyses, we have conducted a sizeable review of the current evidence regarding the relationship between smoking and COVID-19. We highlight the importance of bespoke research utilising appropriate documentation of smoking status and robust study design. Despite some uncertainty regarding the exact nature and magnitude of the association between smoking and COVID-19, there is growing evidence to support the WHO’s position that


To the Editor—The novel coronavirus SARS-CoV-2 disease (COVID-19) emerged in China and has spread throughout the world.1 The first case of COVID-19 in Saudi Arabia was confirmed on March 2, 2020, and presently almost 200,000 people have been infected here.2 The Ministry of Health (MOH) has responded to the COVID-19 outbreak by designing clusters of governmental hospitals to accommodate the increased flow of patients. Although our bed-occupancy rates never exceeded 80% until 2019, the situation has changed dramatically since March 2020, when the intensive care unit (ICU) occupancy rates reached 100% due to the pandemic. Hence, our hospital has been under pressure to upgrade our ICU services. We have used crisis management tactics in configuring our medical city . Our main challenge has been to install new structures  in the pop-up units. We could not maintain single-patient occupancy; thus, we isolated cohorts of COVID-19 patients in multiple-occupancy glass rooms. Nursing stations have been set up outside these rooms; new circuits have been installed for the transmission of data and alarms; and new procedure carts have been arranged for each new unit. The ICU-bed triage and staff governance have been controlled by the CMT. Our CMT members have provided coverage 24 hours per day, 7 days per week to arrange the ICU admission flow and the transfer of patients to other hospitals based on the daily MOH plan. We have followed a tiered strategy in which we allocate experienced intensivists and nurses to supervise redeployed noncritical care physicians and nurses, and we also established back-up teams. The refinement of the respiratory and ICU care included changing the ventilator circuits and filters based on patient needs, avoiding nebulizers, creating specialized intubation and prone-positioning ventilation teams, and upgrading the oxygen supply system. The latter has been a major problem for our oxygen supply management team. Hence, we have promoted awake prone positioning and more oxygen-support therapies  to avoid mechanical ventilation if possible. Interventional therapies  have been carefully screened by expert teams to optimize resource utilization. The ICU pharmacy operations have been linked to the MOH central stock and supervised by pharmacists of the CMT to facilitate the prompt delivery of medications. Infection control measures have been strictly implemented in all hospital areas by creating specific zones and protocols for donning and doffing personal protective equipment, providing sanitizer dispensers, applying strict room-disinfection protocols, and providing safe waste handling.6 Moreover, we have utilized novel transportation capsule isolation technology to minimize the risk of SARS-CoV-2 acquisition during inter- and intrahospital transportation.7se COVID-9 emerged5 Stores of equipment, medications, and technical gadgets remains under CMT supervision. Continuous medical education of our staff about COVID-19 by our MOH could facilitate the management of future outbreaks. Regardless of the limitations in any healthcare system, hospitals should be prepared for future pandemics.New hospital communication systems have been installed in the pop-up ICUs. The communication between frontline staff and the CMT is continuous. Because visitors were not allowed, family meetings were organized via web-based applications to reduce patient and family stress. Training and emotional staff support have been provided on a daily basis. We have utilized daily COVID-19 training sessions to provide additional emotional reassurance . Moreover, ~600 COVID-19 patients have been hospitalized in our ICU over the past 4 months. Our staff’s nosocomial infection rate was ~3% during the early stages of the pandemic, and it has decreased to 0.5% since May 2020. As the current wave of COVID-19 subsides, we are focusing on maintaining our costly infrastructure upgrades. These could ensure that a proper set-up would be available to meet future needs.


Low-cost imaging systems that utilize exogenous fluorescent dyes, such as acridine orange (AO), have recently been developed for use as point-of-care (POC) blood analyzers. AO-based fluorescence imaging exploits variations in emission wavelength within different cell types to enumerate and classify leukocyte subpopulations from whole blood specimens. This approach to leukocyte classification relies on accurate and reproducible colorimetric features, which have previously been demonstrated to be highly dependent on the cell staining protocols . We have developed a light-sheet-based fluorescence imaging spectrometer, featuring a spectral resolution of 9 nm, with an automated spectral extraction algorithm as an investigative tool to study the spectral features from AO-stained leukocytes. Whole blood specimens were collected from human subjects, stained with AO using POC methods, and leukocyte spectra were acquired on a cell-by-cell basis. The post-processing method involves three steps: image segmentation to isolate individual cells in each spectral image; image quality control to exclude cells with low emission intensity, out-of-focus cells, and cellular debris; and the extraction of spectra for each cell. An increase in AO concentration was determined to contribute to the red-shift in AO-fluorescence, while varied pH values did not cause a change in fluorescence. In relation to the spectra of AO-stained leukocytes, there were corresponding red-shift trends associated with dye accumulation within acidic vesicles and at increasing incubation periods. The system presented here could guide future development of POC systems reliant on AO fluorescence and colorimetric features to identify leukocyte subpopulations in whole blood specimens. A leukocyte differential count is a subset of a complete blood count (CBC), which is among the most frequently ordered blood tests in clinical laboratories ,2,3. AbnIn recent years, point-of-care (POC) testing has aimed to increase test accessibility to a patient, reaching individuals in rural clinics, general practices, ambulatory care, and resource limited areas ,12,13 POmax = 525 nm), and red-shifts in emission when staining RNA via electrostatic interactions and when trapped in acidic vesicles (λmax = 650 nm) [Fluorescent dyes are being used in POC systems to aid in distinguishing these leukocyte populations since many are biocompatible, long-term chemically stable, and can be tailored to target specific molecules . The mos 650 nm) ,22,23. W 650 nm) ,25,26,27 650 nm) .Variations in AO concentration and incubation time can cause an emission wavelength shift in whole blood samples, which may result in inaccurate cell counts . Recent The fluorescence emission spectra of an AO-labeled cell can be acquired using a spectrometer, which measures the spectrum of emitted light using a diffraction grating . In contHere we present a light-sheet based imaging spectrometer fluorescence platform with an automated spectral extraction method as an investigative tool to extract spectral features from AO-stained leukocytes. This information will be used to further understand the colorimetric features implemented in POC devices to distinguish leukocyte subpopulations in a three-part leukocyte differential test. To replicate the conditions of live cell staining of whole blood using AO in POC devices, few pre-processing steps were applied (such as cell separation methods and RBC lysis) prior to collecting image data. Overall, we observed the location in the spectra, along with the shape of the spectral curve, where the red and green fluorescence in peaks used to calculate RG ratios occur. These results exhibit the specific details in the colorimetric features that contribute to the fluorescence emission in AO-stained leukocytes that are interpolated by the Bayer filter used in color cameras and in bandpass filters that target specific wavelengths. These results demonstrate the potential of imaging spectroscopy to elucidate the behavior of dyes within leukocytes and to guide future iterations of POC devices, such as determining the optimal optical and staining parameters or optimizing the bandpass filter selection to improve the separation between the three leukocyte populations. In order to quantify the spectrum of individual, AO-stained cells, we developed a light-sheet based imaging spectrometer fluorescence platform. A schematic of the light-sheet based imaging spectrometer is demonstrated in em = 480 nm, F21010, Molecular Probes) were used to calculate the spatial resolution of the system and illustrate the linear translation and spectral images acquired. Spectral images were acquired by a CMOS camera with the x-axis representing the linear position across the slide in microns and y-axis representing the spectra produced by the diffraction grating. As the slide is translated, spectral images are acquired at multiple linear sections of the sample, resulting in multiple images associated with individual objects  a. Green  objects b. For vi objects c. The exem = 515 nm, F6377, Sigma-Aldrich, St. Louis, MO, USA) at a concentration of 1 mg/mL in distilled water, rhodamine B  at a concentration of 5.8 × 10−4 g/mL in distilled water, and a 1:10 by volume mixture of both. Representative spectral images of each dye solution are shown in The fluorescence emission spectra of known fluorescent dyes were acquired using the light-sheet based imaging spectrometer and a commercial spectrometer . The fluorescent dyes included fluorescein sodium salt  were acquired using the light-sheet based imaging spectrometer to characterize the red-shift in fluorescence that occurs within AO-stained cells. Six solutions of 100 µg/mL acridine orange hemi (zinc chloride) salt  (AO) were prepared at different pH conditions . Since the pH of 3.8 and 7.3 have been used in studies involving AO-stained cells ,34, the Under the approval of the Institutional Review Board at the University of Arkansas (IRB #13-06-759) and following informed consent, whole blood was collected by a trained phlebotomist from 5 healthy volunteers over the age of 18. A maximum of 4 mL of whole blood was collected via venipuncture in a K2 EDTA 7.2 mg vacutainer collection tube . Eight µL of whole blood was mixed with 8 µL of 20 µg/mL acridine orange hemi(zinc chloride) salt  and incubated for 3, 5, and 7 min following the results reported by Powless, et al. Spectral images were acquired in 2 min intervals per incubation time, referred to as incubation periods henceforth . Image aA detailed flow diagram of the automated spectral extraction algorithm, designed to detect cells within an image and extract the spectra, is presented in The spectral images were processed to isolate the cells in each image and discard cellular debris. Initially, the spectral images were binarized to perform image segmentation techniques in MATLAB, such as “imclose” to fill in gaps within the cells and “bwareaopen” to exclude small cells associated with cellular debris. The location of the centroid and edge locations, called boundaries, of the cells were detected using the MATLAB function “regionprops”, which found the center of mass and edges in the binary image of the region that returned a value of 1. Additionally, the distance between the centroids were used to discriminate between cells in the same field of view. Any overlapping objects defined as being larger than 170 pixels wide, or objects with pixels touching the edge of the image, were excluded. The images were cropped to the boundary locations to isolate only the spectra associated with each cell. The top and bottom edges of the cropped image were fixed at the full length of the image to include the whole spectral range.x-axis) at a fixed position in all images to evaluate the focus of the image assuming cells in focus will have an abrupt gradient in the fluorescence signal between the background and edge. In-focus images were considered to have a slope greater than 0.7 based on the average for all images  ,16,17. AUsing our light-sheet based imaging fluorescence platform with an automated spectral extraction algorithm as an investigative tool, we have extracted the spectra associated with the colorimetric features used in fluorescence-based POC systems that apply AO as a contrast agent, as demonstrated in For the monocyte population, the spectra was expected to be in the middle of the other groups, described previously, since they are known to have similar morphological and colorimetric features to the other populations stained with AO. However, as a whole, the spectra for the presumed monocyte population were indistinguishable from the other populations. This could be related to the limited total cell count, which monocytes exhibit population fractions of 2% to 8% in normal samples . This chIn summary, we have developed a light-sheet based imaging spectrometer with an automated spectral extraction algorithm to quantify, on a cell-by-cell basis, the specific spectral changes seen in leukocytes when stained with AO under conditions which simulate POC testing. The results presented here could be used to guide future development of POC systems using AO, specifically by focusing on the spectral window around 650 nm which accounts for the majority of the differences among the three major leukocyte subpopulations. This approach also may suggest the potential of the light-sheet based imaging spectrometer architecture could be used to investigate individual intracellular changes within AO-stained leukocytes or the behavior of other dyes.AO is known to emit unique colorimetric features for three subpopulations of leukocytes , which are dependent on the cellular content of each population. This study represents the proof-of-concept of a research-specific technique to elucidate differences between the entire spectra of AO-stained leukocytes, rather than mean red and green fluorescence or RG ratio commonly used in POC blood analyzers. The results from the combination of our light-sheet based imaging spectrometer fluorescence platform and an automated spectral extraction algorithm demonstrate the potential of this system to be used as an investigative tool which could guide future POC devices or be built upon to explore individual intracellular changes within AO-stained leukocytes.


The tissue from the dome of a right posterior inferior cerebral aneurysm was first mechanically characterized using biaxial tension and stress relaxation tests. Then, the load-dependent collagen fiber architecture of the aneurysm tissue was quantified using an in-house polarized spatial frequency domain imaging system. Finally, optical coherence tomography and histological procedures were used to quantify the tissue’s microstructural morphology. Mechanically, the tissue was shown to exhibit hysteresis, a nonlinear stress-strain response, and material anisotropy. Moreover, the unloaded collagen fiber architecture of the tissue was predominantly aligned with the testing Y-direction and rotated towards the X-direction under increasing equibiaxial loading. Furthermore, our histological analysis showed a considerable damage to the morphological integrity of the tissue, including lack of elastin, intimal thickening, and calcium deposition. This new unified characterization framework can be extended to better understand the mechanics-microstructure interrelationship of aneurysm tissues at different time points of the formation or growth. Such specimen-specific information is anticipated to provide valuable insight that may improve our current understanding of aneurysm growth and rupture potential.Intracranial aneurysms (ICAs) are focal dilatations that imply a weakening of the brain artery. Incidental rupture of an ICA is increasingly responsible for significant mortality and morbidity in the American’s aging population. Previous studies have quantified the pressure-volume characteristics, uniaxial mechanical properties, and morphological features of human aneurysms. In this pilot study,  This focal dilation of the artery will grow and remodel in response to the altered hemodynamics and may eventually rupture, leading to subarachnoid hemorrhage. A recent cohort study4 showed that 35% of ruptured ICAs caused death of the patients, and another 29% resulted in moderate-to-severe disability. Although relatively asymptomatic, ICAs can be detected by cranial imaging, such as digital subtraction angiography, computed tomographic angiography5, 3D rotational angiography6, multi-detector computed tomography7 and magnetic resonance angiography8. The two common clinical interventions include endovascular coil embolization and microsurgical clip ligation, which aim to mitigate blood flow into the aneurysm, and thus, prevent further growth of the ICA and reduce the rupture potential. However, endovascular coil embolization suffers from a low packing density of the aneurysm space and a high aneurysmal recurrence rate12, while surgical clip ligation carries high procedural risks associated with the required craniotomy14. A comprehensive understanding of the aneurysm tissue’s biomechanical properties is crucial to improve existing treatment methods and to develop new therapeutics and devices.An intracranial aneurysm (ICA) is a dilation of the cerebral artery wall that arises due to genetic characteristics25; however, these studies were largely limited to uniaxial or pressure-inflation mechanical characterizations. For example, Scott et al.21 quantified the pressure-volume relationship for artery segment specimens with and without aneurysms. They found that the aneurysm tissues were less extensible than the healthy counterparts, and that most of the changes in the tension-stretch curves between the two tissue groups were in the low-stress region, suggesting possible alterations in the elastin component of the tissue. Steiger et al.23 mechanically characterized tissues from both the aneurysm neck and the aneurysm dome in addition to control strips from the parent artery, in which their primary focuses were on the comparisons of failure stress and strain, defined as the yield stress and strain, of different tissues. They found that the failure strain was not significantly different between the aneurysm neck (or the aneurysm dome) and the arterial control. On the other hand, the failure stress was approximately 50% lower for the aneurysm dome compared to the aneurysm neck. These observations suggest that there are distinct microstructural changes, occurring near the aneurysm dome, which might be associated with wall thinning or the degradation of key constituents . More recently, Costalat et al.16 aimed to examine the differences in the mechanical properties between ruptured and unruptured ICA tissues. Their results showed that the unruptured aneurysms were stiffer than the ruptured ones, but there were no statistical differences in the wall thickness . Thus, aneurysm growth is likely a continuously-evolving process that degrades the structural integrity of the tissue until the aneurysm eventually ruptures. These three studies have made fundamental progress towards understanding the biomechanical properties of aneurysm tissues at different stages of aneurysm growth; however, the uniaxial and pressure-based loading may not be optimal, and planar biaxial loading may capture the physiological deformations more accurately.Biomechanical properties of ruptured and unruptured aneurysm tissues have been investigated in the literature27 or other alternative microscopy techniques such as two-photon microscopy28. For example, Frösen et al.26 classified 66 aneurysm dome tissues into four morphological groups based on qualitative histological assessments: Type A—endothelialized wall with linearly aligned smooth muscle cells; Type B—thickened wall with disorganized smooth muscle cells; Type C—hypocellular wall with either myointimal hyperplasia or organizing thrombosis; Type D—an extremely thin thrombosis-lined hypocellular wall. They believe that these morphological groups follow the growth and remodeling of the aneurysm as it progresses towards rupture, which is supported by the increased rupture incidence associated with each group: 42% for Type A, 55% for Type B, 64% for Type C, and 100% for Type D. Moreover, Hasegawa et al.27 quantified the morphological changes of the aneurysm tissue after treatment with clip blades. Specifically, the treated tissues had a thickened, fibrous intima layer that was infiltrated by lymphocytes and a negligible amount of elastin fibers. On the other hand, Signorelli et al.29 used two-photon microscopy to qualitatively show a disorganized collagen fiber architecture near the aneurysm rupture zone. Furthermore, Cebral et al.28 used multi-photon microscopy to quantify the recruitment of the collagen fibers in unruptured aneurysms in response to the applied uniaxial loading. These studies have shown the complex changes in the constituent morphology and collagen fiber architecture associated with the aneurysm growth and rupture processes. However, recent advances have resulted in new methods, such as optical coherence tomography (OCT) or polarized spatial frequency imaging (pSFDI), which allow for non-destructive quantifications of the microstructure that may be useful to the biomechanical understanding of aneurysms. For example, the OCT data, as verified by standard histology, can demonstrate the localized microstructural damage to the collagen fiber architecture, which could then be paired with the pSFDI-quantified load-dependent collagen fiber architecture to comprehend how those localized changes would alter the tissue’s microstructure-mechanics interrelationship.In addition to quantifying the mechanical differences associated with the aneurysm tissues, researchers have attempted to understand the tissue’s morphology at different phases of the aneurysm growth process, by using standard histological methodsload-dependent polarized spatial frequency domain imaging is performed to examine how the changes in the collagen fiber architecture of the aneurysm tissue in response to the mechanical loads. Finally, optical coherence tomography and standard histology are used to observe the aneurysm’s morphology and to establish connections to the bulk tissue’s mechanical properties.Thus, the objective of this study is to utilize a unified scheme to quantify the mechanical properties, collagen fiber architecture, and microstructural morphology of one resected human intracranial aneurysm tissue. This is accomplished by performing an extensive biaxial mechanical characterization of the aneurysm tissue to understand the tissue’s nonlinearity, anisotropy, and direction-coupling behaviors. The biaxial mechanical data is then used to evaluate the applicability of three common constitutive models. Next, A 49-year-old female with a history of migraines and smoking underwent a non-contrast head computed tomography (CT) that demonstrated a subarachnoid hemorrhage. Subsequently, she underwent CT angiography, which showed a right posterior inferior cerebellar aneurysm Fig. a. In addStandard microsurgical dissection was performed to isolate the aneurysm. Clips were placed at the neck of the aneurysm, and micro-Doppler was used to confirm patency of the parent and branch vessels and lack of patency of the aneurysm. Aneurysm occlusion was next confirmed with intraoperative digital subtraction angiography Fig. b. After At the time of testing, the tissue was thawed, and thickness was determined by averaging three measurements acquired using a caliper . The tissue specimen was then mounted to a commercial BioTester system  using four sets of four-tined Biorakes to create an effective testing region of For mechanical characterization, the aneurysm tissue was submerged in a bath of mentclass2pt{minimX- and Y-components of L is the initial specimen edge length, t is the initial specimen thickness, and X- and Y-directions, respectively. In addition, the small displacements The load cell readings in both tissue directions were used to calculate the first Piola-Kirchhoff stress tensor x-intercept of extension of the high-tensile modulus The biaxial mechanical data from the tension and stress relaxation protocols were further analyzed to determine key mechanics-related parameters of the stress-stretch responses. First, we quantified the low-tensile modulus mentclass2pt{minimmentclass2pt{minimmentclass2pt{minim30, i.e.,W is the strain-energy function, c, X- and Y-directions, respectively.Three common constitutive models were used to model the biaxial responses of human aneurysm tissue. The first was the simplified two-dimensional strain-based model proposed by Tong and FungW. The classical model proposed by Holzapfel et al.31  does not consider a dispersion of the collagen fibers. We adopted the form3233  has the formThe other two models use the first invariant entclass1pt{minima34, i.e.The constitutive model parameters for Eqs. –3) were were3) w35. Briefly, a LED-driven, micromirror-based pattern projection system  emitted cyan light (490 nm wavelength) through a rotating linear polarizer  onto the tissue specimen, which was then reflected back through the same polarizer and captured as an 8-bit grayscale image by a 5 megapixel CCD camera . This process was repeated with 535.Following the mechanical characterization, an in-house pSFDI device was used to quantify the load-dependent changes in the tissue’s collagen fiber architecture (CFA). Details of this system are described previouslyLoading Point 1—unloaded, Loading Point 2—prior to the modulus transition, Loading Point 3—after the modulus transition, and Loading Point 4—peak loading. The tissue was displaced to the specified size of each of the above 4 loading points, and the pSFDI was performed to quantify the corresponding CFA.For investigations of the load-dependent changes in the CFA, the pSFDI device was integrated with the BioTester. The data of the equibiaxial tension protocol were used to determine four loading points along the force-displacement curve Fig. e: LoadinLoading Point 1  were considered as the baseline, whereas the relative percent changes were calculated for Loading Points 2-4. Similar comparisons were made for a 3The predicted angle ion Fig. .36. After imaging, the 3D OCT data were post-processed to visualize the collagenous medial layer using a weighted mean filter38 and a weighted mean binarization algorithm39. This was performed for all 1280 cross-sectional images of the OCT data to create a 3D intensity map of the collagenous medial layer and to visualize the microstructural damage of the aneurysm tissue.An OCT system . The nonlinear mechanical response was consistent for both X- and Y-directions and throughout all loading protocols. Interestingly, both the low-tensile and the high-tensile moduli for the X-direction  was demonstrated by the larger Y-direction (1.30–1.60) compared to the X-direction (1.04–1.29) for all biaxial tension protocols. On the other hand, the directional coupling can be examined by means of the changes in X-direction, 1.04 vs 1.20) and increased in the direction of the consistent load  as compared to Protocol 1. This trend was consistent for all the non-equibiaxial loading protocols . This interesting directional coupling of the human aneurysm tissue is similar to the observations reported by May-Newman and Yin43 for the uniaxial and biaxial mechanical responses of porcine mitral valve leaflets.Thirdly, the mechanical response of the aneurysm tissue was anisotropic with a unique directional coupling Fig. b. Quantimentclass2pt{minimX- and Y-directions, respectively) compared to the initial 5 seconds of the stress relaxation testing  ICA dome tissues  than the Y-direction (0.89 vs 0.07-0.13). Interestingly, the ICA tissue in the present study had smaller values for the low-tensile modulus in the X-direction (35.7 kPa) and the Y-direction (22.5 kPa) than all three tissue groups in Costalat et al. (371-1645 kPa), but had comparable high-tensile modulus values to the intermediate group in the X-direction (3158 kPa vs 3656 kPa) and to the soft group in the Y-direction (1482 kPa vs 1148 kPa).Costalat et al.20 showed that the average peak failure strain for aneurysm tissue strips from the meridional orientation (X-direction (0.25) falls within this range whereas the Y-direction (0.89) does not, which further supports the idea that the X-direction of our tissue is approximately aligned near the meridional axis. On the other hand, Parshin et al.19 showed notably larger values for the peak failure strain of one posterior inferior cerebellar aneurysm sample compared to the average in Robertson et al. (2.86 vs 0.36), and the peak deformations in both directions of our aneurysm sample fall within this range. The large difference in the peak failure strains from these studies may suggest that Parshin et al. tested an aneurysm sample approximately oriented in the circumferential direction .Previous studies have also quantified the failure properties of aneurysm tissues. Robertson et al.in vivo configuration of the tested ICA tissue is unknown, the above comparisons lead to the hypothesis that the tested X-direction of our aneurysm sample may be more aligned with the meridional direction of the artery. It is also likely that our aneurysm sample has a soft-to-intermediate mechanical response using the classification presented by Costalat et al.16. However, as outlined in Study limitations and future extensions, comparisons with the data of ICA tissues characterized using this technique are required to determine the accuracy of these statements.Although the The three constitutive models exhibited excellent fits to the biaxial mechanical data Fig. a. The Hoentclass1pt{minimaon Table –S3. The Loading Point 1) had collagen fibers predominantly aligned close to the Y-direction  cells. Secondly, the media and adventitia became difficult to be differentiated for some regions of the tissue, while the intima became delaminated and had large variations in its relative thickness  for smaller aneurysms54. Computational modeling tools could provide more insight into this seemingly challenging and debatable aneurysm condition, assist in the decision for clinical intervention, and could be used to evaluate other rupture-related metrics . On the other hand, the developed simulation tools can act as a test bed for newly developed therapeutic options, such as shape memory polymer embolic devices57 or blood diverting stents60. Specifically, the fluid-structure interaction type of numerical simulations can be used to evaluate the altered hemodynamic characteristics and how the medical device interacts with the arterial and aneurysm tissues.The mechanical and structural database from these future characterizations can be used to inform constitutive models that capture the complex mechanicalin vivo orientation of the resected tissue was unknown, so the information from the present study cannot be fully understood within the context of the in vivo function. Regardless, we have successfully quantified the mechanical properties of the tissue and related it to the collagen fiber architecture acquired from the pSFDI imaging. With similar future characterizations of other resected aneurysm tissues, we could compare the collagen fiber architecture to re-address the orientation of the tissue in the present study. This would also allow an expansion of the limited sample size  to understand the viscoelastic properties of the tissue, and the creep testing modality  may provide additional insight. Fifthly, the load-dependent collagen fiber architecture was only quantified at four points in an equibiaxial loading scenario. However, our biaxial mechanical characterization results showed a unique directional coupling, suggesting that future extensions should explore the non-equibiaxial load-dependent collagen fiber architecture. Finally, the resected aneurysm tissue was temporarily stored in a freezer after surgical removal, and the effect of freezing on aneurysm tissues is not completely understood. Previous studies for other soft tissues found negligible differences before and after temporary freezing64, but future extensions would ideally use the fresh resected tissue for the characterizations.There are several limitations in the present study. Firstly, the exact the first of its kind, for aneurysm tissues including mechanical, collagen microstructural, and morphological characterizations. The extensions of this work include both experimental and computational topics. As for experimental extensions, the presented methods could be extended to consider other aneurysms from different locations in the cerebral vasculature and expand on the limited sample size (65. Ideally, these future extensions would include a healthy arterial control for comparisons, but this is difficult since cerebral arterial tissues would need to be acquired post-mortem. On the other hand, we have performed preliminary constitutive modeling using three common constitutive models. Future extensions should explore microstructurally informed constitutive models66 that incorporate the aneurysm collagen microstructure, morphology, and tissue-level mechanics to accurately describe the tissue’s mechanical response. Eventually, these constitutive models could be incorporated into computational models of the aneurysm fluid-structure interaction69 to better understand the growth process and rupture potential to better inform clinical decisions and treatment options.This pilot study has presented a multiscale characterization, X-direction, suggesting that there may be more non-viscoelastic components aligned close to the Y-direction. Furthermore, collagen microstructural characterizations have indicated that the collagen fibers of the aneurysm tissue were predominantly aligned near the testing Y-direction at the unloaded state. As the tissue was gradually loaded under equibiaxial loading, the collagen microstructure slightly rotated away from the Y-direction and became more aligned, illustrating the load-dependent properties of the collagen fiber architecture. Finally, in the histomorphological analysis, we have also found that there were significant changes to microstructure of the tissue, including potential adipose cell deposition in the adventitia, thickening of the intima, a lack of elastin, and deposition of calcium near the media/intima interface. These morphological features were supplemented by the OCT analysis that showed a non-uniform tissue thickness and localized damage to the collagenous medial layer. The comprehensive framework presented in this pilot aneurysm tissue characterization study can be extended to other aneurysm tissues from different cerebral arteries and ruptured/unruptured states to further understand the mechanics-microstructure interrelationship. The expanded mechanical and microstructural database could then be used to develop simulation tools for evaluating novel therapeutic options and enhancing the clinical understanding of aneurysm rupture-related risk.This novel multiscale characterization of a resected human aneurysm tissue has provided new insights into the tissue’s mechanical, collagen microstructural, and morphological properties. The biaxial mechanical characterizations of the aneurysm tissue have shown differences in the loading/unloading curves and that the tissue was nonlinear, anisotropic, and had unique directional coupling. The biaxial mechanical data was then fit with three common constitutive models, but future investigations are necessary to explore the use of microstructurally informed constitutive models that can leverage all the mechanical, microstructural, and morphological data. Moreover, the results of the stress relaxation testing have indicated that the aneurysm tissue stress relaxation was more prominent in the Supplementary Information


Objective: Mass vaccination planning is occurring at all levels of government in advance of regulatory approval and manufacture of a SARS-CoV-2 vaccine for distribution sometime in 2021. We outline a methodology in which both health insurance provider network data and publicly available data sources can be used to identify and plan for SARS-CoV-2 vaccinator capacity at the county level.Methods: Sendero Health Plans, Inc. provider network data, Texas State Board of Pharmacy data, US Census Bureau data, and H1N1 monovalent vaccine data were utilized to identify providers with demonstrated capacity to vaccinate the population in Travis County, Texas to achieve an estimated SARS-CoV-2 herd immunity target of 67%.Results: Within the Sendero network, 2,356 non-pharmacy providers were identified with 788 (33.4%) practicing in primary care and 1,569 (66.6%) practicing as specialists. Of the total, 686 (29.1%) provided at least one immunization between January 1, 2019 and September 30, 2020. There are 300 pharmacies with active licenses in Travis County with 161 (53.7%) classified as community pharmacies. We estimate that 1,707,098 doses of a 2-dose SARS-CoV-2 vaccine series will need to be administered within Travis County, Texas to achieve the estimated 67% herd immunity threshold to disrupt person-to-person transmission of the SARS-CoV-2 virus based on 2020 census data.Conclusion: A community-based health insurance plan can use data from its provider network and public data sources to support the CDC call to action to identify SARS-CoV-2 vaccinators in the community, including physicians, nurse practitioners, physician assistants, and pharmacies in order to provide macro level estimates of SARS-CoV-2 administration and throughput. The US Centers for Disease Control and Prevention (CDC) released guidance on August 31, 2020 outlining the nationwide process for distributing and administering the SARS-CoV-2 vaccine .The challenges related to vaccine distribution and administration of the SARS-CoV-2 vaccine should not be underestimated. Among the many challenges are: (1) distribution of vaccine quantities on a scale never attempted in the United States; (2) the likely need for two-dose administration of the vaccine; 3) an estimated coverage rate of 67.0% needed to achieve vaccine-induced herd immunity   provider network data, Texas State Board of Pharmacy data, US Census Bureau data, and H1N1 monovalent vaccine data.Implementing a mass vaccination strategy is a complicated process. Currently, state and local health departments across the United States are considering using a combination of open and closed Points of Distribution (POD) sites, mobile immunization teams for vulnerable populations, and private clinics and pharmacies to support mass vaccination activities. With regard to the latter, CDC guidance advises jurisdictions to identify providers in the community who can provide vaccination services when expanded quantities of vaccine are available beyond that required for critical workforce populations . CentersA five step methodology is outlined to estimate quantities of vaccine that a jurisdiction can be expected to provide to achieve herd immunity and to identify potential SARS-CoV-2 vaccinators in the community see . These sEstimating herd immunityEstimating vaccine coverage ratesImplementing the SARS-CoV-2 vaccine scheduleIdentifying providers with demonstrated capacity to administer the SARS-CoV-2 vaccineEstimating vaccine throughputHerd immunity is the level of protection against a pathogen that must be attained in the population to disrupt person-to-person transmission of a virus . It is a0) is confirmed.From a technical perspective herd immunity is the sum of naturally acquired immunity and vaccine induced immunity in a given population. The level of vaccine-induced immunity required for a particular population is therefore dependent on the level of naturally acquired immunity achieved during community transmission of the virus. For this paper we will assume that herd immunity and vaccine coverage for SARS-CoV-2 are equal because, while rigorous in its estimation, a precise herd immunity threshold cannot be calculated until the basic reproduction number  persons aged 6 months and older received the monovalent H1N1 vaccine in 2009/10 . Coverag.0% persoCurrent CDC planning guidance outlines two potential vaccine scenarios. Both scenarios involve vaccines that have a 2-dose schedule . VaccineFrom a practical perspective, this means that each person will receive two-doses of the SARS-CoV-2 vaccine. This doubles the volume of vaccine that must be distributed, stored, and administered when compared to the H1N1 single dose monovalent vaccine. The increased quantity of SARS-CoV-2 vaccine will have storage implications at the provider level and will require appropriate staffing levels to maintain vaccine administration throughput. In addition, the primary and secondary dose must be matched by vaccine manufacturer, presenting additional logistical requirements for ordering and storage.Jurisdictions are advised to develop a network of trained, competent, and accessible providers as part of the overall mass vaccination strategy. Centers for Disease Control and Prevention guidance advises jurisdictions to recruit—among others—doctor's offices, pharmacies, and occupational health settings of large employers (to develop closed PODs). This is largely a renewal of the successful strategy used during H1N1 to expand the number and locations of providers to vaccinate as many people as possible. Explicit in this guidance is that jurisdictions should build upon relationships with existing partners, including health insurance companies, to identify potential SARS-CoV-2 vaccinators. To assist in this call to action, Sendero used its provider network to identify potential SARS-CoV-2 vaccinators within Travis County, Texas. Providers were identified as follows:We identified all providers and pharmacies in the Sendero provider network who are eligible to submit a claim for vaccine administration.Providers included doctors of medicine, doctors of osteopath, nurse practitioners, and physician assistants.Providers were further classified as either a specialist or a primary care provider based on pre-identified designations.Of the providers identified in (1) we queried the Sendero claims database to determine how may of these providers administered at least one vaccine based on CPT codes 90460, 90461, 90471, 90472, 90473, 90474.Pharmacies were identified based on Texas State Board of Pharmacy Data. Pharmacies with active licenses were reviewed to determine if they are open to the public for retail services and prescriptions . Establishments identified as a community pharmacy were queried in the Sendero claims database to determine if they administered at least one vaccine based on a National Drug Code (NDC) for any vaccine.The date of service for all Sendero data queries was from January 1, 2019 through September 30, 2020.For data reporting, some data are stratified by individuals with health insurance and without health insurance. Stratifying by health insurance coverage status is designed to more accurately represent likely throughput by provider type. Indiviudals with health insurance are more likely to have a medical home and to use procedures associated with their medical home to obtain the SARS-CoV-2 vaccine when available. Those without health insurance typically do not have a medical home and are likely to utilize public health services to obtain the SARS-CoV-2 vaccine. The SARS-CoV-2 vaccine will be free for consumers at the point of service regardless of health insurance status or place of service. Providers in Texas will be allowed to charge an administration fee based on the state Medicaid schedule.A total of 2,356 non-pharmacy providers were identified in the Sendero network see  Primary n = 161; 53.7%) of these licensed pharmacies are open to the public for the purposes of dispensing medicaitons and providing pharmaceutical care. The remaining 139 pharmacies include specialty, compounding, hospital, and government pharmacies, all of whom do not dispense medications directly to the public. Of the 161 community pharmacies in Travis County, 93 (57.8%) administered at least one vaccine to a Sendero member during the observation period, with 87 of these pharmacies classified as a chain community pharmacy . Six independent community pharmacies in Travis County dispensed at least one medication to a Sendero member during the study period.The Texas State Board of Pharmacy reports 300 active, licensed pharmacies in Travis County . A revieUsing the methodology outlined above vaccine throughput by potential SARS-CoV-2 vaccinators can be estimated. The CDC Vaccine Scenario B is used in these estimates because it more accurately represents the technical specifications of the vaccine likely to be distributed to providers and pharmacies. One or more SARS-CoV-2 vaccines are expected to be approved by regulatory authorities with subsequent manufacturing and distribution in 2021. Centers for Disease Control and Prevention guidance lays out a process for these activities. Guidance documents note that successful implementation of a mass vaccination campaign will be highly dependent on identifying SARS-CoV-2 vaccinator capacity in the community and that “if a jurisdiction has a good understanding of its (SARS-CoV-2) vaccination providers and locations and their vaccine administration capacities, then planners can generate rough estimates of (SARS-CoV-2) vaccination capacity in their jurisdiction . Such utilization patterns may reflect ease of access, geographical preference, population density, and general changes in the marketplace that tend to favor chain community pharmacies over independent community pharmacies  how and where will additional staff be recruited to support surge capacity for SARS-CoV-2 vaccination?; (2) what are the plans to train allied health providers to serve as SARS-CoV-2 vaccinators beyond the already established local community capacity?; and (3) what plans and support are available to provide increased vaccine storage capacity at individual clinics or pharmacies to accommodate increased vaccine volume?Now is the time to prepare the infrastructure and operational activities for SARS-CoV-2 vaccination. The methodology outlined above provides a macro level estimate of vaccinator capacity, the number of doses to be administred, and throughput based on different delivery methods. At the macro level, the stakes are clear—over one million doses of vaccine will need to be administerd in Travis County, Texas in a short period of time in order to disrupt person-to-person transmission of this virus.This study has several limitations. Firstly, while our provider network data indicate which provider has administered a vaccine between January 1, 2019 and September 30, 2020, we do not know provider vaccine storage capacity, workforce capacity to administer the vaccine, or administrative staff capacity to record vaccine data in state immunization databases as required by the CDC; any limitations in these variables will likely reduce throughput. Secondly, the Sendero provider network does not represent all physicians within Travis County, so it is possible that there is additional SARS-CoV-2 vaccination capacity unaccounted for in this study; however, it would be naïve to think that all currently licensed providers will become SARS-CoV-2 vaccinators, particularly those who do not typically provide vaccines. Thirdly, the data in this paper estimates capacity based on assumed business practices. However, such capacity is subject to change as has been demonstrated by medical office closures for non-emergent procedures during the pandemic. Finally, calculations are based on the analysis using structures applied to a specific country and local jurisdiction within that country.This study does not address vaccine efficacy. Such information continues to be released as vaccine candidates progress through clinical trial and regulatory processes. Furthermore, this model does not consider infection-induced herd immunity, due to surveillance variability at the national, regional, and local level. Therefore, it is possible that persons who experience asymptomatic disease may choose to receive the vaccine. That said, infection-induced immunity will contribute, along with vaccine-induced immunity, to overall herd immunity within the community.This study also does not address vulnerability characteristics, social dynamics, and inequity associated with vaccine uptake. Our focus was to create a method to identify likely sources of vaccinator capacity with demonstrated experience to vaccinate the population of a large urban center in the United States. Future work is needed to better understand the dynamics associated with vaccine uptake across the population spectrum within a large urban center in the United States.A community-based health insurance plan can use data from its provider network and public data sources to support the CDC call to identify SARS-CoV-2 vaccinator capacity in the community, including physicians, nurse practitioners, physician assistants, and pharmacies. A model is proposed that can be used to develop population estimates of expected quantities of the SARS-CoV-2 vaccine to be received, distributed, and administered at the macro level to achieve different levels of vaccine-induced immunity. This model illustrates the importance of data on the operational and logistical components in preparation of the SARS-CoV-2 mass vaccination campaign.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.This project is exempt from Institutional Review Board approval because it does not report on personal health information of Sendero members.JL, RT, NT, and WD substantial contribution to study conception. JL, RT, and NT design of the work, data acquisition or analysis, and interpretation of data. JL and RT drafted work or substantially revised it. All authors contributed to the article and approved the submitted version.WD is employed by Sendero Health Plans. The remaining authors are contractors with Sendero Health Plans. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Musculoskeletal injuries (MSI) remain a concerning cause of racehorse morbidity and mortality with serious ethical and welfare consequences. Previous research examining risk factors for injuries report inconsistent findings. Age is thought to affect the risk of injury, but, to date, there have been no prospective studies specifically comparing injuries between two-year-old versus older horses. We aimed to: (1) determine the incidence of injuries for two-year-old and older horses, and whether this was affected by training track, season, or rainfall, and (2) determine the types of injuries affecting two-year-old and older horses, and whether horses trialled or raced after injury. Data were collected through personal structured weekly interviews with participating trainers over a 13-month period. Data were analysed using Poisson regression. The incidence of MSI in the current study was low (0.6%). The incidence of MSI in two-year-old horses was higher than older horses. Types of MSI varied between two-year-old and older horses and affected whether horses subsequently trialled or raced from 11 to 23 months after injury. A larger proportion of two-year-old horses had dorsal metacarpal disease and traumatic lacerations. A smaller proportion of two-year-old horses had suspensory desmitis, superficial digital flexor tendonitis, proximal sesamoid bone fractures, and fetlock joint injuries than older horses. Training track and rainfall did not affect the incidence of injuries. The season affected the incidence of injuries in two-year-old horses but not in older horses.p < 0.001). Types of MSI varied between two-year-old and older horses (p < 0.001) and affected whether horses subsequently trailed or raced from 11 to 23 months after injury (p < 0.001). A larger proportion of two-year-old horses had dorsal metacarpal disease and traumatic lacerations. A smaller proportion of two-year-old horses had suspensory ligament desmitis, superficial digital flexor tendonitis, proximal sesamoid bone fractures, and fetlock joint injuries than older horses. Training track and rainfall did not affect MSI. The season affected MSI in two-year-old horses (p < 0.001) but not older horses. The major limitation was that trainers in this study were metropolitan (city) and our findings may not be generalisable to racehorses in regional (country) areas. Another significant limitation was the assumption that MSI was the reason for failure to trial or race after injury. In conclusion, the incidence of MSI was low in the current study and the types and the risk factors for MSI are different for two-year-old and older horses.Musculoskeletal injuries (MSI) remain a concerning cause of racehorse morbidity and mortality with important ethical and welfare consequences. Previous research examining risk factors for MSI report inconsistent findings. Age is thought to affect MSI risk, but, to date, there have been no prospective studies comparing MSI in two-year-old versus older horses. This study aimed to: (1) determine the incidence of MSI for two-year-old and older horses, and whether this was affected by training track, season, or rainfall, and (2) determine the types of MSI affecting two-year-old and older horses, and whether horses trialled or raced after injury. A prospective survey was conducted with data collected through personal structured weekly interviews with participating trainers over a 13-month period. Data were analysed using Poisson regression. The incidence of MSI in the current study was low (0.6%). The incidence of MSI in two-year-old horses was higher than older horses ( Despite over three decades of active research, musculoskeletal injuries (MSI) remain a global problem for the thoroughbred (TB) racing industry ,2,3. TheThe impact of MSI may be under-reported due to case definition. Studies with a case definition of race-day MSI will underestimate the problem because a large proportion of MSI and fatalities occur during training rather than during racing ,24,25,26It has been proposed that two-year-old horses may be more susceptible to MSI due to their immaturity, even though there is little scientific information to support this claim. One study investigated reported a significantly higher incidence of carpal problems in horses that commenced training while the distal radial epiphyses were still open. However, this was not significant for injuries overall . Other sStudy design and execution can also result in under-reporting or inaccurate reporting of MSI. Retrospective studies inherently contain errors due to incomplete or misclassified data ,36. ProsFurthermore, there are no prospective studies that provide a detailed description of all injury types with most studies adopting a more broad outcome classification ,42,43, oWe address these data gaps through a prospective survey conducted over a 13-month period. Our aims were to: (1) determine the incidence of MSI for two-year-old and older horses and whether this was affected by training track, season, or rainfall, and (2) determine the types of MSI affecting two-year-old and older horses and whether horses trialled or raced after MSI.A summary of the structure of thoroughbred racing in Queensland, Australia is provided in Recruitment of horses was performed by recruiting trainers and enrolling all the horses under their care. The first author invited all licenced trainers at Eagle Farm, Doomben, and Deagon with three or more horses in work at the time of recruitment to participate in the study. A minimum of three horses was selected to ensure efficient data collection and trainer capacity to supply sufficient horses throughout the study.A prospective survey with the first author completing in-person structured weekly interviews over a 13-month period (56 weeks) with participating trainers or their forepersons. Details of the interview are described in (1)the total number of horses in training (number at risk of MSI)(2)the number of two-year-old horses in training(3)the number of new cases and the type of MSI and whether horses trialled or raced after injury(4)the number of fatalitiesThe outcomes of interest were:A horse in training was defined as a thoroughbred registered with the Australian Stud Book or equivalent international organization that was participating in race training under the care of the licenced trainer participating in the study. These horses were assumed to be healthy and capable of completing their training and racing exercise without clear lameness or medical conditions. The total number of horses in training were collected for each trainer every week. The unit of time at risk was one week. Horses were considered to be at risk for the week if they were in training for a minimum of five days. De-identified count data was collected rather than individual horse details.A horse was defined as a “two-year-old” until 1 August of its third year of life. August first is the date where thoroughbred horses officially increase a year of age regardless of their actual date of birth. This definition incorporates all racehorses two years of age and younger, as racehorses are often younger than two years when they commence race training.A MSI was defined as any clinically relevant injury to the musculoskeletal system, incorporating orthopaedic and soft tissue injuries, which prevented the horse from training for 7 days. A 7-day period was chosen in order to be consistent with previous studies ,40. Our Musculoskeletal injuries were diagnosed by a veterinarian to avoid measurement and ascertainment bias. Horses in the study were under the close care of racetrack veterinarians registered in Queensland. Trainer permission was granted to obtain clarification from the attending veterinarian if required. A horse was defined as not trialling or racing again after an MSI if it did not participate in a trial or race for a minimum of 12 months after injury, which was confirmed by the Racing Australia (RA) public database .The total number of fatalities for the week and the reason were reported. A fatality was defined as any illness or injury resulting in the death of a horse or necessitating euthanasia on humane grounds.The total rainfall for every month of the study period was obtained from the Bureau of Meteorology public database .® . Continuous data were assessed for normality using histograms. Normally distributed data were presented as mean and standard deviation. Non-normally distributed data were presented as a median, interquartile range unless otherwise indicated. The weekly number of older horses in training was calculated by subtracting the number of two-year-old horses from the total number of horses. The median numbers of total horses, two-year-old horses, and older horses in training over 56 weeks were then calculated. The incidence of MSI was reported as weekly percentages of new MSI. Weekly percentages of MSI were calculated as the number of new cases of MSI divided by the number of horses in training  each week, multiplied by 100. The median percentages of MSI per week at risk over the 56 weeks were then calculated.Data analysis was performed using Stata 15.1The weekly percentages of MSI for two-year-old and older horses were compared using the Wilcoxon rank sum test. Pearson’s Chi-squared test of proportions was used to compare the types of MSI between two-year-old and older horses, whether horses trialled or raced after injury, and whether the injuries were fatal. If the value in any cell was less than 5, Fisher’s exact test was used.p < 0.2. Multivariable analyses were performed using a backward stepwise procedure to obtain a parsimonious model. Goodness-of-fit was tested using post-estimation deviance statistics and Pearson statistics. Significance was set at p < 0.05 for all tests.Poisson regression was used to assess the effect of training track, season, and rainfall on the number of counts of MSI, with the exposure variable showing the total count of horses at risk each week used as the offset to determine our incidence rate ratio. Analysis was performed to provide the Incidence Rate Ratio and the 95% Confidence Intervals for the counts of MSI observed in horses of all age groups, then repeated for MSI in older horses, and, finally, for two-year-old horses. Clustered models were used to correct for misspecification due to the known and unknown differences between trainers. Variables were considered in the multivariable model if the univariable Poisson model was significant at Forty trainers  were eligible for recruitment. Twenty-seven trainers  consented to participate. Details are shown in Data were collected weekly over a 13-month (56 week) period from November 2017 to December 2018 for 26/27 (96%) of trainers. One trainer who did not complete the study contributed six months of data before retiring from training.p < 0.001).There were a total of 202 MSI occurring in 195 horses. Ninety-seven of these 195 horses (50%) were older than two years and ninety-eight of these 195 horses (50%) were two years old. Seven horses (4%) experienced a second MSI during the study period. Three out of seven (43%) of these horses with a second MSI were older than two years and four out of seven (57%) were two years of age at the time of the first injury. Five out of seven horses (71%) with a second MSI had a recurrence of the original injury , humeral stress fracture (n = 1), and dorsal metacarpal disease (n = 1)). The number of horses in training per week, the number of MSI, and the percentage of horses affected by MSI, stratified by age, is summarized in The types of MSI varied between two-year-old and older horses. A larger proportion of two-year-old horses compared with older horses had dorsal metacarpal disease and traumatic lacerations. A smaller proportion of two-year-old horses had suspensory ligament desmitis, superficial digital flexor tendonitis, proximal sesamoid bone fractures, and fetlock joint injuries than older horses .p < 0.001).Of the 195 horses with injuries, 82 horses (42%) did not trial or race again after injury. The follow-up period was between 12 and 23 months after injury. Of the horses that did not trial or race again after injury, 61/82 (74%) were older horses and 21/82 (26%) were two-year-old horses. Two-year-old horses were more likely to trial or race after MSI than older horses  of these. The older horse group sustained 61/83 (73%) of the injuries after which horses did not race or trial and the two-year-old group sustained 22/83 (27%).The type of MSI also significantly affected whether horses trialed or raced again after injury. Horses were more likely to trial or race after dorsal metacarpal disease and traumatic injuries and less likely to trial or race after suspensory desmitis, superficial digital flexor tendonitis, and proximal sesamoid bone fractures .There were 14 fatalities during this 13-month study period of which most were due to musculoskeletal injuries .p = 0.01). Humeral fractures were the most common fatal MSI 3/6 (50%) in two-year-old horses and sesamoid fractures were the most common fatal MSI in older horses 5/6 (83%). Most fatal MSIs occurred during training 9/12 (75%) rather than during a race 3/12 (25%). One two-year-old horse died after throwing the rider and galloping head-first into a barrier. Another two-year-old horse that was initially sound had to be euthanised due to rapid onset of severe neurological signs and cervical stenotic myelopathy being diagnosed.Twelve of 202 (6%) MSI were fatal in 12 horses and 6/12 (50%) of these were in two-year-old horses. The types of fatal injuries differed between two-year-old and older horses (p = 0.03). The track that horses trained on and the monthly rainfall did not affect the number of MSI  relative to winter (June to August) . The training track and monthly rainfall had no effect on the number of MSI  agreed to be involved. Of these 26 (96%) participated throughout the longitudinal study period, reflecting the very level of enthusiasm and commitment. Participating trainers represented a convenience sample and, therefore, may not be representative of all trainers. A priori power analyses and sample size estimates were not performed in the design of this study because the study was descriptive in nature and because the design involved all eligible trainers being invited to participate and participating trainers enrolled all trained horses in the study. The high participation rate and very low drop-out rate provide increased confidence that the findings are likely to be generally representative of the population of racehorses in training in Queensland.A small proportion (0.56%) of horses in training every week were affected by MSI. Comparing the impact of MSI between studies is problematic because of variation in outcome measures between studies. Most cross-sectional studies report prevalence (a “snapshot” of existing and new cases/number at risk), while cohort studies report either incidence rates  or cumulative incidence (number of new cases/number at risk) ,37,52. NPossible explanations for the low incidence of MSI and fatalities in this study include volunteer bias, close veterinary involvement, and active engagement of training modalities including water-walkers and treadmills. Trainers in this study all had ready access to veterinarians and diagnostic equipment, which is an important factor in monitoring horses and preventing MSI. Water-walkers and treadmills provide a short-term decrease in training intensity yet avoid the longer convalescent period and reduction in bone density associated with traditional paddock rest. Further investigation into the effect of water-walkers, treadmills, and equivalent training modalities on MSI is warranted.In the current study, there was a higher incidence of MSI in two-year-old horses when compared to older horses. We suspect that the difference between two-year-old and older horses reflects two possible mechanisms of injury: (1) low training intensity in poorly adapted tissue, and (2) high training intensity in well-adapted tissue . It was Despite the low incidence of MSI in this study, the consequences for affected horses were severe. In fact, 12/195 (6%) of horses with MSI were euthanised or died and MSI accounted for 12/14 (86%) of the total deaths. In this study, eleven horses were euthanised on humane grounds due to the severity of injury and poor prognosis deemed by the attending veterinarian or consulting specialist surgeon. One horse died as a result of a traumatic head injury. This finding is consistent with other studies reporting MSI are the most common cause of death, comprising over 70% of TB racehorse fatalities ,16,17,18The types of MSI differed between two-year-old and older horses. Two-year-old horses were more likely to sustain dorsal metacarpal disease and traumatic injuries and were more likely to trial or race again after injury than older horses. Dorsal metacarpal disease reflects poor adaptation of bone to withstand the normal stresses of racing , is someThe types of fatal MSI also differed between two-year-old and older horses and reflect different mechanisms of injury. Humeral fractures were the most common fatal MSI in two-year-old horses and these result from catastrophic progression of stress fractures . ProximaThe training track did not affect the number of MSI for two-year-old or older horses. Previous studies examining the effect of the racetrack on MSI risk have reported inconsistent results ,26,76,77Seasonal variation strongly affected the number of MSI in two-year-old horses (three times the rate of MSI in the summer than in the winter) but not in older horses. The seasonal effect in this study was not due to rainfall as the total rainfall per month did not affect the number of MSI. We have two main hypotheses why two-year old horses experienced a higher number of MSI while older horses did not. First, the poorly adapted bones of two-year-old horses may be less resilient to the firmer track conditions in the summer . Second,The higher incidence of MSI in the summer for two-year-old horses is consistent with previous studies with a higher incidence of MSI in the summer in the USA  and a loThe predominant strength of this research was high quality data resulting from the unique access to a large number of trainers through personal weekly interviews over a 13-month period. Twenty-seven of 40 (67.5%) eligible trainers responded including 26 for the whole duration of the study. We feel this provided a representative sample of the horses of interest for the time period of the study. Personal interviews ensured that the data collected were both complete and accurate. Relying on trainers to complete standardized questionnaire results has resulted in incomplete data and the subsequent elimination of up to 29% of trainers from previous studies ,40,41. TA further strength of this study was that case definition reflected the full extent of MSI, incorporating racing, training, and traumatic  injuries in training stables. The impact of MSI is often under-reported due to case definition and study design. Studies with a case definition of race day MSI will capture only a small component of the problem. There is good evidence that racing MSI are repetitive stress injuries and the damage occurs during training rather than during the race day ,30,31,32Our inclusion and exclusion criteria were also developed to capture all MSI that potentially affect racehorses in training. For example, some studies have excluded osteochondral fracture fragments less than 5 mm from their case definition ,22,89. HTraumatic injuries  have also been excluded from the case definitions of previous studies ,22,54,89The main limitation of this study is that this study represents a subset of the Australian racing industry and our results are not necessarily globally applicable. Furthermore, there is inherent volunteer bias due to the voluntary nature of participation and self-selection of trainers . The metWe also analysed count data rather than data for individual horse-time-at risk. This facilitated rapid population level data collection for a large number of horses. However, this method provides a less accurate measure of occurrence than collecting individual horse information . UnfortuFurthermore, there are serious limitations for assuming that MSI was the reason that horses did not trial or race after injury. Affected horses may have sustained paddock accidents during recuperation or returned to training but were subsequently retired for other reasons. In the current study, there were horses that did not race or trial following very minor injuries with a good prognosis including dorsal metacarpal disease and muscle injuries. It is unlikely that these injuries were the reason for retirement. Further scrutinization of the public database  and the In conclusion, we reported a low incidence of MSI compared to previous studies and the reasons behind this finding warrants further investigation. The types of MSI, outcome, and risk factors for MSI vary between two-year-old and older horses. Consequently, future investigations into MSI should address these different populations of horses separately. A particular focus on two-year-old training practices is required since they are at higher risk of MSI and it is possible that early training exercise may decrease the risk of MSI. Thoroughbred racing is a highly contentious, high profile, and lucrative industry and, as such, attracts significant media and public opinion. It is vital that the industry is focused on understanding and mitigating the risks of MSI.6. Patents.