Severe Acute Respiratory Syndrome (SARS) Coronavirus (CoV)-2 is a recently identified positive sense single-strand RNA (ssRNA) β-coronavirus. The viral spike proteins infect human hosts by binding to the cellular receptor angiotensin-converting enzyme 2 (ACE2). The infection causes a systemic illness involving cell metabolism. This widespread involvement is implicated in the pathophysiology of the illness which ranges from mild to severe, requiring multi organ support, ranging from oxygen supplementation to full cardiovascular and respiratory support. Patients with multiple co-existing comorbidities are also at a higher risk. The aim of this review is to explore the exact mechanisms by which COVID-19 affects patients systemically with a primary focus on the bleeding and thrombotic complications linked with the disease. Issues surrounding the thrombotic complications following administration of the ChAdOx1 nCoV-19 (Astra-Zeneca-Oxford) vaccine have also been illustrated. Risk stratification and treatment options in these patients should be tailored according to clinical severity with input from a multidisciplinary team. Severe Acute Respiratory Syndrome (SARS) Coronavirus (CoV)-2 is a recently identified positive sense single-strand RNA (ssRNA) β-coronavirus. The viral spike proteins infect human hosts by binding to the cellular receptor angiotensin-converting enzyme 2 (ACE2) . It is hThe consecutio temporum between transmission of SARS-CoV-2 infection and involvement of pulmonary, cardiac, endothelial, and blood cell metabolism assumes important relevance in preventing the devastating effects of the Covid-19 disease. After its transmission, which occurs primarily from inhalation of viral particles, the second stage involves localization in the cells of the respiratory tract . InfecteLaboratory tests performed on patients with Covid-19 have shown lymphopenia , mild thAmong patients with Covid-19 who required hospitalization due to their critical condtion, the most common haemostatic anomalies were mild thrombocytopenia  and incrChanges in blood coagulation status led to an increased risk of receiving mechanical ventilation, requiring hospitalization in an intensive care unit (ICU), or incurring death. A combination of a paucity of data alongside unclear and conflicting data supports the validity of other blood coagulation tests ,23. EvidEvidence suggesting a role for platelets in viral infections has long been proven. For example, the presence of influenza virus type A (IAV) particles was noted in the platelets of patients with acute influenza infection. Once the IAV was incorporated into the platelets, TLR7-dependent C3 was released with the subsequent activation of neutrophils and neutrophil extracellular traps (NETs) release . PlateleThe optimal therapy to be recommended for patients with severe Covid-19 disease has been tested and several drugs have been used to prevent the progression of the disease. The interactions that some of these drugs have with antiplatelet and anticoagulant agents have been clinically documented and considered important. The administration of experimental drugs has often been associated with excessive risk, as well as a reduced risk, of thrombotic events or thrombocytopenia in previous studies in non-Covid-19 populations and more recently in patients with Covid-19.Bevacizumab has been associated with the occurrence of cardiovascular adverse events including MI, stroke and VTE. Bevacizumab is a monoclonal antibody that interferes with vascular endothelial growth factor (VEGF) and is being investigated for COVID-19 ,39. AnotHydroxychloroquine, which has been the subject of open debates supported by opposing points of view, has since obtained FDA (food and drug administration) clearance for the treatment of COVID-19; however, it can potentially exert the antithrombotic action through interference with antiphospholipid antibodies .In this last year, researchers have directed a big commitment towards the study of drugs for the treatment of COVID-19 that may have interfered with oral antiplatelet drugs in oxidative processes at the level of hepatic cytochrome P450 (CYP) enzymes. The consequence is that the action of many antiviral drugs affects platelet metabolism. For example, the administration of lopinavir or ritonavir, protease inhibitors acting on CYP3A4 metabolism, may result in the inhibition of platelet activity at the level of CYP3A4 metabolism. As the active metabolite of clopidogrel is formed mainly by oxidation in CYP2C19, it leads to a reduction in the effective dose of clopidogrel. Instead, the oxidative process leading to the active ticagrelor metabolite occurs in CYP3A4 which is inhibited by lopinavir and ritonavir. The result is an increase in the effect of ticagrelor ,43,44,45The direct consequence of the combined use of these drugs is dysregulation of the haemostatic process which must be properly evaluated. Although the solution to this drawback is the use of P2Y12 platelet function tests to guide the use of clopidogrel or ticagrelor, there is limited clinical data available to support this strategy. A drug that is not affected by the action of lopinavir/ritonavir is prasugrel which may be a viable option in the absence of contraindications ,43,44,45We are aware of interactions between experimental drugs used for COVID-19 and therapies with oral anticoagulants administered for various cardiovascular diseases. Ritonavir use has also been shown to affect the choice and dosage of many anticoagulant agents. Careful surveillance after administration with vitamin K antagonists (VKAs), or apixaban and betrixaban, is required to make a dosage adjustment. The combination of drugs such as edoxaban and rivaroxaban and lopinavir/ritonavir administered contemporary is not recommended . AlthougWe are aware of a small number of studies that have reported the incidence of VTE in patients with Covid-19 ,48. A ChThe use of drugs for systemic anticoagulation has been a crucial point for the treatment of VTE. The choice of the most suitable drug requires clinical considerations factoring in comorbidities and possible impairment of renal or hepatic function, haematological disorders such as thrombocytopenia or that of the gastrointestinal system. Multiple therapies may be considered including a change in the anticoagulant pharmacological treatment in progress during the hospitalization period, due to the critical state at hand to a more suitable regiment at the time of discharge to adapt to the convalescence period.This pharmacological management used in critically ill hospitalized patients with VTE, in whom the administration of parenteral anticoagulants such as, for example, UFH may be preferred because it has good pharmacodynamic and pharmacokinetic requirements without known drug interactions with experimental Covid-19 therapies. It is important to underline that the effects of the therapy are obtained after a longer period with the administration of UFH (unfractionated heparin) because they depend on the achievement of a therapeutic aPTT. In addition, healthcare workers are more exposed to the risk of contamination due to frequent blood draws. Therefore, these aspects argue for the preferential choice in the administration of LMWH in patients in whom invasive procedures are not planned.The use of oral anticoagulation with DOAC is certainly advantageous because it does not involve the need for monitoring, easing the planning of discharge, and for the outpatient management of the patient. It is possible that the worsening of the patient’s clinical condition including, for example, the deterioration of the respiratory function in patients with Covid-19, may necessitate a switch between these classes of medications. Patients who are expected to be discharged are advised to use DOAC or LMWH to limit contact with healthcare personnel because controls for INR monitoring as for vitamin K antagonists (VKA) are not required.The vast majority of patients with Covid-19 and symptoms referable to acute DVT should be treated at home with anticoagulant therapy whenever possible. However, there are a number of patients for whom hospitalization is necessary to initiate acute endovascular techniques such as local fibrinolysis or embolectomy and patients with refractory symptoms . PatientRescue systemic fibrinolysis should be considered in patients with progressive deterioration, either systemically or by transcatheter approach. Instead, for patients with evident haemodynamic instability and high-risk PE ,58,59,60Patients with severe SARSCoV-2 infection and critical illness have a higher risk of VTE. Many factors contribute to the development of VTE which is often associated with worsening clinical condition and rapid deterioration of the patient. First, they have haemostatic imbalance coupled with a systemic inflammatory state and are forced into long immobility because they are assisted with mechanical ventilation. Second, the placement of central venous catheters contributes to increasing the risk of VTE in the ICU and infections ,62,63. TAdministration of anticoagulant therapy by systemic parenteral infusion is recommended in majority of patients whereby Covid-19 with acute thrombosis occurs. In these cases, the use of UFH can be used based on expected protocols, or in patients with deterioration of renal function. In the absence of an application for an urgent procedure, the use of LMWH is a reasonable alternative . AnticoaAvailable data are scarce to establish complication rates in SARS-CoV-2 patients, but thrombosis and haemorrhage rates can be high reaching the rate of 53% and 16% respectively as reported in other populations with respiratory failure . Even moPatients with Covid-19 and a lung infection who required hospitalization following clinical complications have an increased risk of VTE ,69. The Several studies have reported that early initiation of appropriate prophylactic anticoagulation reduces the risk of VTE in critically ill patients ,74,75. TIn January 2020 a WHO report recommended interim guidance for daily prophylaxis with low molecular weight heparin (LMWH) or unfractionated subcutaneous heparin (UFH) to be administered twice daily . In patiIt is important to point out that the problem of extended prophylaxis with LMWH  or direcPatients with clinically critical Covid-19 have a greater chance of developing disseminated intravascular coagulation (DIC) ,100 whicThe ISTH DIC score calculator is commonly used to establish the diagnosis of DIC . The worPatients with Covid-19 rarely suffer from clinically evident bleeding. However, significant blood loss is common in patients who develop DIC often when septic coagulopathy is associated . If blee9/L or in patients who have a high risk of bleeding because they require more invasive procedures to maintain a platelet count greater than 20 × 109/L. Second, the administration of fresh frozen plasma (15 to 25 mL/kg) in patients who experience active bleeding and whose PT or aPTT values are altered  or who have a decrease in fibrinogen (<1.5 g/L). Third, the administration of concentrated fibrinogen or cryoprecipitate for patients in whom the values of fibrinogen are low and persistent over time (<1.5 g/L) and the administration of prothrombin complex concentrate where transfusion of fresh frozen plasma is not possible. There are currently no indications for the routine use of tranexamic acid in patients with DIC associated with COVID-19 [First, the use of platelet concentrate is useful in patients who have DIC with active bleeding to maintain platelet counts greater than 50 × 10COVID-19 .Concerns about the triggering of changes in the hemostatic process may be also related to the direct effect of SARS-CoV-2 viral particles. Varga et al. demonstrated the presence of viral elements within endothelial cells due to the expression of ACE2 receptors. Also, inflammatory cells were demonstrated, corroborating evidence of a direct viral involvement towards endothelitis development, also noted in several organs .Preclinical analyses have also demonstrated the successful infection of organoids by viruses confirming their tropism for endothelial cells. Investigators used clinical-grade soluble human ACE2 to achieve the infection ,107. EndA further hypothesis for thrombosis generation is related to cytokine storm precipitating an initially controlled inflammation in the more complicated SIRS , as emerAs a consequence of dysregulated immune reactions, hypercoagulable states are established due to continued inflammation. Macrophages activation syndrome (MAS) and cytokines released play major roles by activating a pro-inflammatory cascade ,113 Fig.Recently, Webb et al.  proposedEndothelial dysfunction, which is the first recognizable step of Covid-19 thrombogenesis from an inflammatory cause, is first seen in atherosclerosis development. Metabolic derangements play main roles as atherogenic stimuli such as dyslipidemias, hypertension, and obesity which stimulate a series of changes in lesion-prone vascular endothelium, towards a vasoconstrictor, prothrombotic, proliferative, inflammatory phenotype. Pro-inflammatory cytokines, oxidized lipoproteins (ox-LDL), and advanced glycation end products (AGE), as well as disturbed blood flow associated with reciprocating, low shear stress  lead to not extracellular- lipid accumulation.According to the Virmani classification, the intimal xanthoma is the initial detectable small lesion constituted by foam cells with intracellular- and The endothelial expression of adhesion molecules VCAM-1 and ICAM-1 drives leukocytes and platelet recruitment. Also, smooth muscle cells coming from the tunica media, migrate to the plaque attracted by the PDGF stimulus and start to produce other inflammatory cytokines, such as IL-1 and IL-6.+: the recognition of proteic antigens presented by macrophages on MHC type II molecules triggers a response for which Th1 cells produce IFN-ɣ, in turn activating macrophages and overly stimulating the synthesis of pro-atherogenic inflammatory cytokines. In this process, costimulatory and coinhibitory receptors on T cells direct T-cell function and determine T-cell fate. These co-signaling molecules are divided into pro-atherogenic and anti-atherogenic ones. It is known, in fact, at least in the pre-clinical setting, that CTLA-4 blocking accelerates atherosclerosis development and decreases luminal patency [An interesting role is one of the activated T cells, mainly CD4 patency ,119.The evolution of the plaque sequentially favours the formation of pathological intimal thickening (PIT) lesions consisting of macrophage foam cells and small extracellular lipid pools. The lesions eventually progress to fibrous cap atheroma (FCA) presenting with a fibro-adipous cap and necrotic cores from extracellular lipids, cholesterol crystals, and calcifications due to the accumulation of osteocalcin, osteopontin, and Bone Morphogenetic Protein (BMP). The last stages of atherogenesis involve mainly complicated lesions with fissuration and erosion resulting in thrombus which provokes a critical reduction of blood flow, thus occluding the vessel.+ and CD8+ T-cells but also on monocytes and activated B cells. Their anti-atherogenic role has been demonstrated by preclinical and clinical studies, without a clear elucidation of the mechanism of pathogenesis.During plaque progression, regulators of immune cells divide into pro-atherogenic immune checkpoint proteins CD28–CD80/86, OX40–OX40L, CD137–CD137L and CD30–CD30L and anti-atherogenic ones CTLA-4–CD80/CD86, PD-1–PD-L1/2, ICOS–ICOSL, GITR–GITRL, CD27–CD70 and TIM proteins . CTLA-4 Obesity, metabolic syndrome and type 2 diabetes mellitus (T2DM) are among the best-known risk factors for both thrombosis and severe Covid-19 infection since they promote a state of chronic inflammation and impaired fibrinolysis. Meta-inflammation, i.e., chronic inflammation driven by chronically altered metabolic pathways presents with increased release of cytokines from adipocytes. Consequently, a contribution comes from macrophages (recruited in inflamed sites) and transient hypoxia which is proper of visceral fat rather than subcutaneous one.Macrophages in particular, extend the secretion of cytokines ensuring the vascular endothelium is stimulated towards a pro-thrombotic state with upregulation of adhesion molecules and downregulation of coagulation inhibitory proteins.Consequently, thrombin generation is increased, platelets are further activated and TNF-α and IL-6 increase the expression of tissue factor, promoting the hypercoagulable state.Chronic inflammation is also associated with dysregulation of endogenous anticoagulant mechanisms, including tissue factor pathway inhibitor, antithrombin, and the protein C anticoagulation system.The second important mechanism is impaired fibrinolysis. Expression of PAI-1 is markedly upregulated in visceral adipose tissue and elevated levels of PAI-1, together with tissue plasminogen activator (tPA), have been found in severe Covid-19 patients . 118 hosp = 0.03) [Recent meta-analyses and case-control studies underlined the association between obesity and severe COVID-19. Also, they showed that obese patients had worse prognoses than non-obese patients  . In gene = 0.03) .As a causative factor, hyperinsulinemia is related to all the metabolic conditions associated with poor outcomes from Covid-19 infection. Again, the primary mechanism is impaired fibrinolysis, due to increased PAI-1 expression . It, theThe impairment of fibrinolysis by insulin is completely independent of glycemia while hyperglycemia produces effects on coagulation which is not related to insulinemia. Impaired fibrinolysis is consistently found in T2DM patients  Figure .Hyperglycemia instead stimulates clotting factors synthesis from the liver and inflammatory reaction by increasing the secretion of IL-6. Biochemical effects of hyperinsulinemia are several and entail β-oxidation and ketolysis inhibition .Due to the major depletion of nicotinamide adenine nucleotides (NAD+) by glucose oxidation rather than beta-oxidation and ketolysis, NAD+ availability for mitochondrial deacetylase sirtuin 3 (SIRT3) activity decreases . This prVitamin D hydroxylation requires magnesium which is notably depleted in hyperinsulinemic conditions. Other mechanisms responsible for decreased Vitamin D levels are increased renal excretion, reduced intracellular levels, and sequestration into adipocytes. The last process is related to lipogenesis which is already increased in hyperinsulinemic patients . ConsequEvidence of altered laboratory values of vitamin K have been demonstrated in severe Covid-19 cases. Other than coagulation factors, several other molecules, among which matrix Gla protein (MGP), a potent inhibitor of soft tissue calcification and elastic fibers degradation, are dependent on vitamin K.The proposed mechanism by Janssen et al. , implicaWith an increase in MGP synthesis, vitamin K is utilized for further molecule processing (MGP carboxylation) which decreases circulating vitamin K levels and increases damage to lung tissues. As a consequence, endothelial protein S is not sufficiently carboxylated and shifts the coagulation equilibrium towards thrombogenesis .An important proof that this mechanism may be deranged in Covid-19 thromboembolic events, is that diabetes, hypertension, and CVD, which are correlated with worse outcomes, are conditions related to chronic elastic fibers pathology.Gender differences in Covid-19 outcomes and cardiovascular diseases have raised many issues regarding the hormonal asset of patients and its interference with coagulopathy development.Endothelial dysfunction is found in the elderly, smokers, and patients with metabolic alterations such as dyslipidemias, T2DM and arterial hypertension ,134.An interesting mechanism is the presence of receptors on megakaryocytes and platelet membranes for both estrogen and androgens. Also, testosterone has been observed to increase the secretion of endothelial nitric oxide eNO, a potent inhibitor of platelet activation ,136.The function of estrogens, in women, is also directed to regulate and enhance platelet function according to the ovarian cycle release of hormones. On the contrary, lack of testosterone i.e., hypogonadism in the elderly produces a less protective effect on hypercoagulation . ConsequRecently, following the recording of thrombotic adverse events secondary to the administration of the ChAdOx1 nCov-19/AstraZeneca, interest has emerged in evaluating the contribution of antiphospholipid antibodies. A previous study of Zhang et al.  reportedIn March several cases of unexpected thrombotic events and thrombocytopenia that have developed after vaccination with the recombinant adenoviral vector encoding the spike protein antigen of SARS Cov 2  were brought to the attention of researchers ,139,140,Pathophysiological processes linking the inflammatory response from SARS Cov2 and changes in hemostasis remain unclear. They represent an important step both to prevent the most serious thrombotic complications of Covid-19 disease and to resolve the side effects associated with the administration of the ChAdOx1 nCov-19, AstraZeneca. Besides, whether antiphospholipid antibodies play an important role in either the pathophysiology of COVID-19-associated thrombosis or the hemostatic protuberant effects related to vaccine administration requires further investigation . SeveralThe effectiveness and safety associated with the use of vaccines to combat the 2019 coronavirus pandemic (Covid-19) have been established in several randomized blinded clinical trials ,149,150.From December 2020 to March 2021, the European Medicines Agency has established suitability for administration for four vaccines: two messenger RNA-based vaccines—BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna)—which encode the spike SARS-CoV-2 glycoprotein antigen, encapsulated in lipid nanoparticles; ChAdOx1 nCov-19 (AstraZeneca), a recombinant chimpanzee adenoviral vector encoding the SARS-CoV-2 spike glycoprotein; and Ad26.COV2.S (Johnson & Johnson/Janssen), a type 26 recombinant adenovirus vector encoding the SARS-CoV-2 spike glycoprotein. At the beginning of March 4, the EMA initiated an ongoing review of COVID-19 vaccines produced by Novavax, CureVac AG, and Sputnik V . To dateBeginning in late February 2021 several cases have been reported of patients who presented with venous thrombosis and thrombocytopenia 7 to 10 days after administration of the first dose of the ChAdOx1 nCoV-19 adenoviral vector vaccine against coronavirus disease 2019 (Covid-19) ,138.p < 0.05). Many of these reactions were reduced by the administration of prophylactic paracetamol. The immune response was effective in the ChAdOx1 nCoV-19 population, with excellent spike protein-specific T cell response peaking on day 14. Antibody response mediated by anti-spike IgG production was highest by day 28 and was boosted after a second dose. It is important to emphasize that after a booster dose, all participants had a neutralizing activity. Neutralizing antibody responses were strongly correlated with antibody levels measured by ELISA.The immunogenicity benefits associated with the use of ChAdOx1 nCoV-19 have been established in a landmark paper from the Laboratory of Virology, National Institute of Allergy and Infectious Diseases , and from the Jenner Institute, University of Oxford  and in an animal study on rhesus macaques ,154. TheWhile the RCT results on the safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine were awaited , the Phan = 300 and MenACWY n = 260) and reported 13 serious adverse events that occurred during the study period, none of which were considered to be related to either study vaccine. Regarding local and systemic reactions, they were more common in recipients of the ChAdOx1 nCoV-19 vaccine than in those treated with the control vaccine. The nature of the reactions was similar to that reported in the phase 2 study . Side effects were less common in older adults (age ≥ 56 years) than young adults. In recipients of two vaccine doses, median SARS-CoV-2 IgG anti-peak responses 28 days after the boost dose were similar in the three age cohorts. No differences in neutralizing antibody titers were reported after a boost dose which were similar across all age groups [In a short time, three independent studies supported this finding, not only in larger patient cohorts but also to show that the vaccine would immunize a large portion of the population. A single-blind, randomized study  was desie groups . The impe groups . We summn = 4440) efficacy was 62.1%  to ChAdOx1 nCoV-19 vaccine or control . Recipients who were managed with a low dose followed by a standard dose showed efficacy that was 90.0% . There were ten hospitalized cases for COVID-19 after the 21st day of the first dose and all were included in the control population. Of these two were classified as severe COVID-19, one died. There were 74,341 person-months of safety follow-up  with a total of 175 serious adverse events recorded in 168 participants. Of these 84 events occurred in the ChAdOx1 nCoV-19 group and 91 in the control group. Of note, three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 population, one in the control population, and one in a participant who remained masked by the group allocation [The second RCT  evaluatelocation .The third prospective cohort study  comparedDespite the efforts research has made in the past year to formulate optimal therapies for the treatment of SARS-CoV-2 infection, the results have been disappointing in both patients with mild COVID-19 and those hospitalized in the critical phase of the disease. Two very recent RCTs depict a frustrating scenario.The use of ivermectin for treatment was evaluated in a very recent RCT . Mildly n = 276) and was compared to those who were managed with standard prophylactic anticoagulation  (n = 286). The results did not record a significant difference considering the primary outcome set on composite of venous or arterial thrombosis, on treatment with extracorporeal membrane oxygenation, or mortality within 30 days among patients admitted to intensive care with COVID-19 between the groups. Regarding unselected patients admitted to intensive care with COVID-19, the evidence does not support the routine empirical use of prophylactic anticoagulation at intermediate doses [Thrombotic events are commonly reported in critically ill patients with Covid-19. As previously illustrated, there is limited data to guide antithrombotic prophylaxis. In the INSPIRATION investigation trial , authorste doses .Another concern has been for many critically ill hospitalized patients who required immediate surgery ,152,155.Mass vaccination however raises the possibility of preventing death and avoiding hospitalization in critical conditions. In this regard, data from Israel, which is currently leading the world in terms of percentage of the vaccinated population, are comforting . Covid-1The systemic complications of the COVID-19 infection is due to a combination of factors as elaborated above. Treatment should be tailored to the patient with an escalating plan dictated by clinical severity. There are several mechanisms to explain the potential thrombotic complications by both the COVID-19 infection and the vaccines which should be considered. Despite the thrombotic complications, vaccines have been shown to reduce hospital admissions and the severity of illness in robust studies. Long term effects of these however remain uncertain.


Our team has been working for some time on designing a new kind of biomimetic fixation of resurfacing endoprostheses, in which the innovative multi-spiked connecting scaffold (MSC-Scaffold) that mimics the natural interface between articular cartilage and periarticular trabecular bone in human joints is the crucial element. This work aimed to develop a numerical model enabling the design of the considered joint replacement implant that would reflect the mechanics of interacting biomaterials. Thus, quantitative micro-CT analysis of density distribution in bone material during the embedding of MSC-Scaffold in periarticular bone was applied. The performed numerical studies and corresponding mechanical tests revealed, under the embedded MSC-Scaffold, the bone material densification affecting its mechanical properties. On the basis of these findings, the built numerical model was modified by applying a simulated insert of densified bone material. This modification led to a strong correlation between the re-simulation and experimental results (FVU = 0.02). The biomimetism of the MSC-Scaffold prototype that provided physiological load transfer from implant to bone was confirmed based on the Huber–von Mises–Hencky (HMH) stress maps obtained with the validated finite element (FE) model of the problem. The micro-CT bone density assessment performed during the embedding of the MSC-Scaffold prototype in periarticular bone provides insight into the mechanical behaviour of the investigated implant-bone system and validates the numerical model that can be used for the design of material and geometric features of a new kind of resurfacing endoprostheses fixation. Among orthopaedic implants, joint replacements have the greatest market share . The hipIn a traditional total hip arthroplasty, the damaged bone and cartilage are removed and replaced with artificial prosthetic components, such as a hip endoprosthesis with a stem and acetabular (socket) cup system. The damaged femoral head is removed and replaced with a metal stem (cemented/non-cemented) that is placed into the femoral canal. In hip resurfacing, the femoral head is not removed but rather trimmed and capped with a smooth metal covering cap. The damaged bone and cartilage within the socket are removed and replaced with a metal shell, just as in a traditional total hip replacement .A hip replacement can be made of numerous materials, such as Ti-alloy, Co-Cr alloy, polyethylene, and poly(methyl methacrylate), all of which exhibit high mechanical strength and wear resistance. Over the past century, different combinations of materials have been developed as hybrid fixation implants. These implants provide sufficient mechanical strength to support the destroyed bones or anatomical structures as well as good wear resistance to realize long-term fixation stability ,5.One of the advantages of hip resurfacing over traditional total hip arthroplasty endoprostheses is that the former is easier to revise. If an implant fails, a revision arthroplasty operation is necessary, and it is more complicated than the initial operation. In hip resurfacing arthroplasty, less femur bone is removed as compared with a traditional hip replacement. Thus, many surgeons believe it is easier to exchange implants that fail after hip resurfacing .In the vast majority of cases, cement fixation is the worldwide recognised implant-bone fixation method for femoral components of resurfacing arthroplasty (RA) endoprostheses. To avoid problems with cement  ,8 and acOur research group designed, developed, and prototyped, through bioengineering research, the essential innovation for a fixation method of RA endoprostheses components in periarticular trabecular bone by means of an innovative multi-spiked connecting scaffold (MSC-Scaffold) ,10,11,12Implantation of calcium phosphate (CaP) surface-modified scaffold prototypes in an animal model has revealed the scaffolding effect . The majComputational studies on the influence of various geometrical features of an initially embedded MSC-Scaffold on mechanical stresses in peri-implant bone have revealed the features that determine the appropriate MSC-Scaffold design . Before Combinations of experiments with computational models, especially finite element (FE) models, have commonly been used to study bone and implant mechanics. For example, Enns-Bray et al.  used expIn this study, bone density micro-CT assessment during the embedding of the innovative multi-spiked connecting scaffold prototype in periarticular bone is performed to develop a validated numerical model for designing a new kind of biomimetic fixation of resurfacing endoprostheses.The FE model of the MSC-Scaffold prototypes for RA embedded in periarticular bone was validated in two experimental steps preceded by the preparation tasks, including computer-assisted design (CAD) modelling, selective laser melting (SLM) manufacturing , and bonDuring CAD modelling of the MSC-Scaffold prototype, directives from the previous works ,12 were 3. The SLM method was chosen because it is characterised by high precision in the manufacturing of porous or lattice structures [The manufacturing was subcontracted to the Centre of New Materials and Technologies at the West Pomeranian University of Technology in Szczecin, Poland. The following process parameters were applied during the SLM manufacturing: laser power, 100 W; layer thickness, 30 µm; laser spot size, 0.2 mm; scan speed, 0.4 m/s; laser energy density, 70 J/mmructures ,26.To remove the adhered powder aggregates from the spike surfaces after manufacturing, a manual blasting treatment was performed with an experimentally customised abrasive mixture. It was composed of equal proportions of F220 white aloxite (~53–75 μm), F320 white aloxite (~29.2 μm ± 1.5%), and blasting micro glass beads (~30 μm ± 10%). The surfaces were cleaned in an ultrasonic bath  using demineralised water with surfactants. The manufactured MSC-Scaffold prototype is shown in Hip joints of Polish Large White swine (aged between 8 and 10 months) were purchased from a local slaughterhouse . A swine bone was used because it is a good animal model for human bone. Indeed, the mechanical properties of swine articular bone are consistent with those of human articular bone . The femFor the FE model simulation, the CAD models of the MSC-Scaffold prototypes were adopted, as described in Due to the symmetry of the model, computational studies were performed for a quarter of the built FE model. The base of the bone material cylinder was assumed to be a fixed support . On the symmetry planes of the model, translations of the lateral surfaces in the normal directions were excluded. Contacts between the MSC-Scaffold and simulated bone material were set, and an augmented Lagrangian method was applied to the contact surfaces (contact penetration was present but controlled). This method is applicable for any type of contact behaviour and is commonly used for symmetric and asymmetric contacts, which are recommended for general frictionless or frictional contacts ,29. FiguThe simulated bone material was assumed to be a single-phase transversally isotropic elastic material. The values of the mechanical properties of the MSC-Scaffold ,30 and bComputational studies were performed to analyse the force required to embed the MSC-Scaffold in the periarticular bone at five different embedding levels. The force needed to embed the MSC-Scaffold in the periarticular bone was assumed to cause the limit stress value determined as the mean of the internodal stresses of the maximum values on the surface contacting with the apexes of the spikes. Additionally, the stress distribution was determined according to the Huber–von Mises–Hencky (HMH) theory.Mechanical tests of the MSC-Scaffold prototype quasi-static embedding in the swine periarticular bone samples were performed with a universal testing machine . Emery paper (120 grit) was fixed to the grips, which stabilised the bone sample and protected against slipping. The MSC-Scaffold prototype was placed on the bone sample and preloaded to reach the point of contact (bone-implant specimen). The MSC-Scaffold prototype quasi-static embedding tests were performed at the crosshead speed of 0.1 mm/s until the spikes of the prototypes reached the depth of 3.0 mm in the bone. Quasi-static loading of the prototype scaffold at a rate of 0.1 mm/s corresponds to bone elastic strain rates induced during peaceful gradual loading of an operated limb in the course of postoperative rehabilitation ,35. DuriMicro-CT-assisted mechanical tests were conducted using a micro-CT scanner  equipped with a specialised device that enabled mechanical tests designed and manufactured by our research team. This technique was performed to evaluate the density of bone material before testing. The CAD model of the executive system that mediated mechanical testing is presented in The bone-implant specimens were mounted on a rotary stage and scanned in their entirety. The scanning parameters were as follows: source voltage, 130 keV; source current, 125 mA; resolution, 17.5 μm; filter, 1.5 mm brass; exposure time, 300 ms; rotation, 180°, every 0.5°; scanning time, 20 min. The three-dimensional (3D) visualization and two-dimensional (2D) image analysis of the micro-CT reconstructed bone samples and bone-implant specimens were performed using dedicated micro-CT software. In the bone-implant 3D reconstructions, the area of interest was extracted for qualitative and quantitative analyses. The MSC-Scaffold prototype and trabecular bone were identified and distinguished based on radiological density. The soft tissue, including bone marrow, was hidden. A representative view of the reconstructed bone-implant specimen from the micro-CT examination is shown in For each level of embedding, trabecular bone areas of interest were chosen from the top of the apex spikes of the MSC-Scaffold. Vascular canals and osteocyte lacunae were extracted from the volume of interest using VG Studio Max software  and werebρ) was calculated using Equation (1) as follows:Cancellous bone tissue comprises a network of trabeculae and soft tissues that fill the intertrabecular space. The trabecular network and soft tissue are naturally combined in highly variable proportions in different skeletal regions. Thus, one cannot take a fixed reference to approach the volumetric trabecular bone density and have a wide range of values ,38. TherTρ is the volumetric trabecular bone density ;wρ is the marrow and soft tissues density ;φ is the marrow and soft tissue fraction.φ is the trabecular bone fraction.1 − E2, of periarticular bone in the regions of interest was calculated on the base of knowing bone density Longitudinal Young’s modulus, The simulated insert of densified bone material was introduced in the FE model. The mechanical properties of this insert were evaluated based on micro-CT imaging and the values of cancellous bone mechanical properties were calculated, as described in P < 0.05 were considered significant.All values of experimental results are expressed as means ± standard deviation (SD). Any differences with i-th value;i-th value;The linear regression model was used to analyse and compare experimental and simulation results. The fraction of variance unexplained (FVU) statistical test was performed to determine what part of the explanatory variable variation observed in the sample did not match the model ,40. The The embedding force–distance curves could be categorised into three regions. Region I, the initial phase of embedding the MSC-Scaffold into the periarticular bone, was characterised by a slight increase in the embedding force. Spikes of the MSC-Scaffold penetrated the intertrabecular space of the periarticular bone, and the embedding force increased as the spikes gradually made contact with the trabeculae. Region II showed the linear increase in the embedding force from the partially embedded MSC-Scaffold in the periarticular bone and the spikes in contact with the trabeculae. During this phase, the load was transferred from the spikes of the MSC-Scaffold prototype to the trabeculae of cancellous bone, a phenomenon that caused elastic deformation of this bone. Region III was characterised by embedding force changes due to the destruction of particular trabeculae and the densification of the trabecular bone.2 = 0.9932. The high value of the determination coefficient, R2, indicates a strong linear relationship.The results of embedding tests obtained in region II were averaged and presented as a mean line . The linFor each analysed scenario, the areas of stress concentration in the periarticular bone were localised around the spike apexes  prototype gives the possibility of a new kind of biomimetic fixation of resurfacing endoprostheses in periarticular cancellous bone. The implantation method of the RHA endoprosthesis with the biomimetic MSC-Scaffold involves the spikes press-fit insertion into the trabecular bone to the defined depth, allowing the limb loading directly after the resurfacing endoprosthesis implantation. The fixation procedure of RHA endoprosthesis with the biomimetic MSC-Scaffold proceeds in two steps as follows: (1) the mechanical insertion of the endoprosthesis components into the periarticular trabecular bone on the desired osteoconductive level by the operating surgeon and (2) the adaptive bone tissue ingrowth into the interspike space of the biomimetic MSC-Scaffold.In the present study, the state of loading of partially embedded implant  is examined. For this state, what is crucial is the determination of the critical load not causing further embedding of MSC-Scaffold during physiological loading of the operated limb. We used bone density micro-CT assessment during the mechanical embedding test of the MSC-Scaffold prototype in periarticular bone to develop a validated FE model, and therefore determined the most appropriate geometric features of the MSC-Scaffold and their values in relation to the preliminary patented version ,12,13,14Similar to the study by Cicciù et al. ,44, micrWe agree with the opinion of Marangalou et al.   was used, therefore:


By the end of spring (31 May), the COVID-19 death rate was remarkably unevenly distributed across the countries in Europe. While the risk of COVID-19 mortality is known to increase with age, age-specific COVID-19 death rates across Europe were similarly unevenly distributed. To explain these mortality distributions, we present a simple model where more favorable survival environments promote longevity and the accumulation of health frailty among the elderly while less favorable survival environments induce a mortality selection process that results in lower health frailty. Because the age-related conditions of frailty render the elderly less resistant to SARS-CoV-2, pre-existing survival environments may be non-obviously positively related to the COVID-19 death rate. To quantify the survival environment parameter of our model, we leveraged historic cohort- and period-based age-specific probabilities of death and life expectancies at age 65 across Europe. All variables are significantly correlated with indicators of frailty like elderly dependence on others for personal and household care for a subset of European countries. With respect to COVID-19 death rates, we find significant positive relationships between our survival indicators and COVID-19 death rates across Europe, a result that is robust to statistical control for the capacity of a healthcare system to treat and survive infected persons, the timing and stringency of non-pharmaceutical interventions, population density, age structure, case rates and the volume of inbound international travelers, among other factors. To address possible concerns over reporting heterogeneity across countries, we show that results are robust to the substitution of our response variable for a measure of cumulative excess mortality. Also consistent with the intuition of our model, we also show a strong negative association between age-specific COVID-19 death rates and pre-existing all-cause age-specific mortality rates for a subset of European countries. Overall, results support the notion that variation in pre-existing frailty, resulting from heterogeneous survival environments, partially accounts for striking differences in COVID-19 death during the first wave of the pandemic. At the end of spring (31 May), the COVID-19 death rate (deaths over population) was strikingly unevenly distributed across Europe . Among tr = 0.134, p = 0.480) and ≥75  years of age is statistically uncorrelated with the COVID-19 death rate during this phase of the pandemic.Because the risk of COVID-19 death is significantly higher among the elderly, one might assume that variation in the COVID-19 death rate across Europe in this period reflects differences in age structure . ConsideWhile the percent of elderly is statistically independent of the COVID-19 death rate, the risk of death by COVID-19 is manifestly related to age. Except for Ukraine, across the countries in Frailty is a notion used to describe a range of age-related conditions that render the elderly less resistant to health shocks ,4,5,6. SConsider two similarly economically developed countries with varying rates of survival to age 75. Suppose that one country has high and the other a low survival environment with respect to all causes of death. Persons surviving to 75 in the low survival environment are more positively selected on the underlying ability to persist from 1 year to the next—i.e., longevity. Given positive mortality selection, elderly persons over the age of 75 in the low survival country may be more likely to withstand adverse health shocks than similarly aged persons in the high survival country because of lower underlying frailty. Similarly, persons surviving to 75 in a high survival environment are more negatively selected on the underlying ability to persist, rendering such persons less likely to withstand adverse health shocks than similarly aged persons in a low survival environment because of higher underlying frailty.In this paper we develop a simple and highly stylized model of longevity and accumulated health frailty that captures the intuition of positive and negative mortality above more formally. Each country begins with an identical underlying distribution of health frailty among the population. Then, heterogeneous survival environments across countries induce mortality selection processes that generate varying amounts of accumulated health frailty in subsequent periods. Specifically, more favorable survival environments promote longevity and a larger amount of accumulated health frailty, while less favorable survival environments induce a relatively strict mortality selection process that results in a lower amount of accumulated health frailty among the elderly. With respect to an exogenous health shock that disproportionately targets the relatively health frail, like SARS-CoV-2, this simple model implies that countries with more favorable pre-existing survival environments will have larger susceptible  populations and, therefore, higher COVID-19 death rates.longevity-frailty model, we show a strong negative association between age-specific COVID-19 death rates and pre-existing all-cause age-specific mortality rates.To measure a survival environment, we collected data on pre-existing all-cause age-specific death rates (measured in both period and cohort-specific terms), data on life expectancy at age 65, and developed an index of survival that is integrative of these measures. All measures of the survival environment are highly correlated with more explicit indicators of elderly frailty, including percent of disabled persons ≥ 65 years that need the assistance of others, percent of persons ≥ 65 years that need help with personal care activities, and percent of persons ≥ 65 years that need help with household activities. Across measures of the survival environment, we show that a more favorable pre-existing survival environment is strongly positively associated with the COVID-19 death rate across Europe. These results are robust to statistical control for competing theories of COVID-19 prevalence and mortality, including the capacity of a healthcare system to manage and survive infected persons, the timing and stringency of non-pharmaceutical interventions, population density, age structure, and the volume of inbound international travelers. Results are also robust to the substitution of our response variable—the COVID-19 death rate—with a measure of cumulative excess mortality that addresses possible concerns of reporting heterogeneity across Europe. Finally, for a subset of European countries with readily available data, and compatible with our In the next section we describe our theoretical model, outlining the basic relationship between a country’s survival environment and the expected accumulation of health frailty among the elderly. In Consider a very simple model of survival with two periods, t = 1 with probability The likelihood that an individual survives to period Given the survival probability function, we can derive the expected accumulation of health frailty in period Thus, this simple model outlines a clear relationship between a country’s survival environment . First, we subset the HMD data for countries in our sample to all cohorts born between 1910 and 1960—i.e., data for individuals at least 60 years of age in the year 2020—and collect Cohort Survival. To obtain a cohort-based measure of survival environments across the countries of Europe we employ cohort death rate data from the Human Mortality Database (HMD). HMD data were accessed at To calculate . 36–37) . Thus, fTo get an overall measure of the cohort-based survival environment across all age groups by country, we then perform the following calculationx will die within 1 year. Then, transforming this measure to Period Survival. We employ the most recently available all-cause age-specific death rate data from HMD life tables—2016 for all countries except for Ukraine where the most recently available data are 2013—to obtain our first period-based measure of the survival environment for the countries of Europe. Specifically, for each country-age group—0–110 years for each European country—we collect oflife expectancy at 65 years of age as a period-based measure of survival environment. This is obtained directly through the HMD life tables.Like our cohort-based measure of survival environment, we perform the area under the curve calculationSurvival Index. Our three indicators of survival (described above) are all highly correlated. Thus, using Principal Component Analysis (PCA), we combine our cohort- and period-based measures of survival environment into one integrative index. Our results indicate that roughly 94% of the variation observed across these three measures is captured through the first principal component. The standardized predicted PCA score for each country in our sample serves as an additional indicator of survival to be employed in analyses of COVID-19 death risk across the countries of Europe.Validation of Survival Environment Indicators. Our simple model outlines a clear relationship between a country’s survival environment and the accumulation of health frailty among the population. Insofar as our indicators of survival are an appropriate characterization of relative differences in survival environments across the countries of Europe, it follows that they can proxy, albeit roughly, for relative differences in the accumulation of health frailty.https://ec.europa.eu/eurostat/data/database (accessed on 15 June 2020). Data are available for 14 of the 30 countries in our sample—see Using data from the European Commission Eurostat database, we test the conceptual validity of our simple model and indicators of survival. Eurostat data were accessed at In https://github.com/CSSEGISandData/COVID-19 Unified-Dataset (accessed on 15 June 2020). Specifically, we combine COVID-19 death data with population data from the UN to calculate the daily cumulative COVID-19 death rate (deaths divided by population) over the period of 4 March–31 May 2020, corresponding to the end of spring and the first wave of the pandemic.Our measure of COVID-19 mortality risk comes from the John Hopkins University Center for System Sciences and Engineering COVID-19 Unified Dataset. JHU CSSE data were accessed at We note here that the COVID-19 death rate may be biased by testing regimes and capabilities. The issue of testing bias is particularly problematic for our analysis if COVID-19 testing regimes and capabilities are correlated with survival environments. For instance, one might expect that countries with greater COVID-19 testing capabilities are likely to be the same countries that have the healthcare infrastructure necessary to cultivate a relatively favorable survival environment. This would result in a positive relationship between the COVID-19 death rate and survival environment that is merely an illusion of testing capabilities. The standard econometric approach in this case would be to employ a fixed effects regression model, where unobserved heterogeneity in the response variable—such as testing capabilities—is controlled for through country fixed effects. However, given the time-invariant nature of our variables of interest, as well as the other control variables included in or model, we are unable to undertake this approach. Instead, we employ a second-best alternative—random effects regression model—which we discuss below. In the next section, we describe a series of control variables that account for these alternative explanations.The control variables included in our empirical models account for alternative hypotheses of variation in COVID-19 mortality risk. These hypotheses include the timing and strength of epidemic seeding from exported cases; the capacity of healthcare systems to manage and survive infected persons; the stringency of non-pharmaceutical interventions (NPI); population density; the confirmed case rate to account for epidemic intensity; age structure; and whether a country requires mandatory Bacille Calmette-Gu’erin (BCG) vaccination, suspected to ameliorate SARS-CoV-2 infection .To control for timing and strength of international seeding, we collected the most recently available data from the World Bank on the annual count of inbound international tourists (2018). The intuition being that a country with a larger number of inbound international tourists would likely have a stronger initial seeding of the virus, which would likely influence the trajectory of the pandemic in each country. Population density data are from the World Bank, capturing the count of persons per square kilometer as of 2019, proxying for risk of spread. We also include World Bank data on hospital beds per capita (2015/2016), physicians per capita (2015/2016), and nurses per capita (2015/2016) to control for the capacity of healthcare systems in each country to manage and survive infected persons. Our measure of NPI stringency comes from the Oxford COVID-19 Government Response Tracker and is collected from the John Hopkins University Center for System Sciences and Engineering COVID-19 Unified Dataset. The Oxford COVID-19 Government Response Tracker collects information on several common policy responses that governments have implemented to respond to the pandemic, such as school closures, workplace closures, cancellation of public events, and stay at home orders to name a few. We use the Stringency Index, which combines information about containment and closure policies with information about public information campaigns to generate a daily stringency score for each country that ranges from 0 to 100. Our models also control for the confirmed case rate, meant to proxy for epidemic intensity. These data are also from the John Hopkins University Center for System Sciences and Engineering COVID-19 Unified Dataset. Finally, with data from the World Bank, we include a control for age structure, captured as the percent of the population over the age of 65 years or older.t indexes refers to day. i.i.d.) with mean 0 and variance ησ2. The country-specific random effect is meant to account for omitted variables like cross-national differences in comorbidities like diabetes, cardiovascular disease, as well as demographic risk factors such as socioeconomic status linked to COVID-19 mortality.Due to the time-invariant nature of our survival environment measures of interest, as well as the cross-sectional time-series nature of the data, we estimate versions of the following random effects modelSEI), as a continuous variable—the natural log of life expectancy at 65 (lnLE65), the natural log of the cohort-based survival area (lnCS), the natural log of the period-based survival area (lnPS), and the natural log of the Survival Environment Index (lnSI)—so thatϵjt defined to follow the AR(1) process described by Equation (11). According to our theoretical intuition that links favorable survival environments with larger amounts of accumulated health frailty, we expect the estimated LE65, a 1% increase in life expectancy at 65 is associated with a We estimate three specifications of the general model presented above. First, we include our measures of interest, the indicators of survival environment (j is in the second (third) tercile group for the survival environment indicator of interest—e.g., life expectancy at 65—and 0 otherwise. Again, i.i.d.) with mean 0 and variance ησ2. Here, the reference group is all countries that fall into the first tercile range of the measure of interest. The interpretation of the β1) is the ratio of the expected geometric mean for the second tercile group over the expected geometric mean for the first tercile group, all else equal. Thus, [exp(β1) − 1] × 100 gives the associated percentage increase, or decrease if the difference is negative, in the COVID-19 death rate for the second tercile group relative to the first tercile group, all else constant  = 0, with a similar condition being required for the identification of the coefficients on the other covariates. In other words, jv is assumed to be the realization of an i.i.d. process with mean 0 and variance ησ2.SI) and in the absence of controls for alternative explanations of the uneven distribution in COVID-19 mortality during the first wave of the epidemic in Europe, we find that a standard deviation increase in the pre-existing survival environment increases the COVID-19 death rate by 73% . In column (8), we find that a standard deviation increase in the pre-existing survival environment increases the COVID-19 death rate by 23%, other variables held equal. For context, a standard deviation increase in the survival index, for example, is the equivalent of moving from Greece (SI = 1.28) to Spain (SI = 2.22). The estimate of ρ is reported for all models in As expected, all coefficients are positive and statistically distinguishable from chance. Starting with column (7), corresponding to our integrative survival index −1]×100 . SimilarNext, Z-scores from EuroMOMO. Excess mortality Z-scores were accessed at https://www.euromomo.eu/graphsand-maps (accessed on 15 July 2020). Each observation in the EuroMOMO data reflects the within country deviation from central tendency, or baseline, in all-cause mortality for a given week. Because of the within-country differenced nature of EuroMOMO’s excess mortality measure, we estimate the following panel-corrected standard errors modeljtEM which measures the cumulative excess mortality of country Given known inconsistencies in the reporting of COVID-19 deaths across countries—especially during the first wave of the pandemic—excess mortality can be used as a proxy for COVID-19 mortality that is potentially less biased by administrative differences that lead to under or over-reporting of COVID-19 deaths. We obtained weekly excess mortality https://dc-covid.site.ined.fr/en/data/ (accessed on 15 July 2020). We collapse age-specific COVID-19 deaths into common age intervals across the countries for which data are available and calculate corresponding pre-existing all-cause mortality rates using data from the Human Mortality Database (HMD). Age-specific COVID-19 death data is available for all age groups in England, France, Germany, Italy, the Netherlands, Spain, Sweden, and Ukraine. Denmark’s age-specific data is only available for those 60 years of age and older, Norway data is available for those 40 years of age and older, and Portugal’s data is top-coded at 80 years of age so that comparable data is only available for those 79 years of age and younger. Pre-existing age-specific all-cause mortality data are anchored on the most recently available common year of 2016. To test the statistical relationship between pre-existing all-cause mortality and the COVID-19 death rate, we estimate the following least squares modelCoV is the COVID-19 death rate for age group i in country j at time t, ACDR is the pre-existing all-cause death rate (2016), tW is a vector of week fixed effects. ε is the model disturbance term. Our theoretical interest is in the estimate of β, which we expect to be negative, implying that, other things equal, a 1% increase in the age-specific all-cause death rate is associated with a β percent decrease in the associated age-specific COVID-19 death rate.The National Institute for Demographic Studies (INED) collects age- and sex-specific COVID-19 death data for select European countries. INED age- and sex-specific COVID-19 death data were accessed at In column (3) of the table we reduce the sample to intervals ≥ 60 years of age. The qualitative nature of the estimates is maintained, though the negative association between pre-existing all-cause mortality and the COVID-19 death rate appears to be stronger among the elderly. Specifically, a 1% increase in the pre-existing all-cause death rate is associated with an approximate 4.3% decrease in the COVID-19 death rate for those ≥ 60 years of age, other things equal. In column (4) we restrict the sample to intervals ≤ 49 years of age. Relative to column (3), which restricts to intervals ≥ 60 years of age, the coefficient estimate in column (4) intuitively deflates, reinforcing that the pre-existing death rate effect is stronger among the elderly and compatible with a longevity-frailty model.In this paper we developed a simple model of longevity and accumulated health frailty to explain observed differences in COVID-19 mortality across the countries of Europe during the first wave of the pandemic. The model describes how heterogeneous survival environments across countries induce mortality selection processes that generate varying amounts of accumulated health frailty among elderly populations, accounting for the surprising statistical independence of age structure and COVID-19 death rates but also for the known increase in the risk of COVID-19 mortality with age. With respect to an exogenous health shock that disproportionately targets the relatively health frail, like SARS-CoV-2, our model implies that countries with a more favorable survival environment will have a larger susceptible  population and, therefore, a higher COVID-19 death rate.To operationalize a country’s survival environment—a key parameter of our model—we collected data on pre-existing all-cause age-specific death rates (measured in both period and cohort-specific terms), life expectancy at age 65, and an index of survival that is integrative of these variables. We showed that our various indicators of the survival environment are correlated with more explicit measures of elderly frailty see , servingWe then statistically linked cross-national variation in survival environments to the puzzling distribution of COVID-19 death rates in Europe at the end of spring, finding that a more favorable survival environment, as indicated by lower pre-existing all-cause mortality rates, is positively associated with COVID-19 death rates. Specifically, we found that a standard deviation increase in the pre-existing survival environment increased the COVID-19 death rate by 23%, other variables held equal. Coupled with analyses involving categorical measurement of the elderly survival environment, the COVID-19 death rate appeared to increase monotonically with pre-existing elderly survival over the first wave of the pandemic. Importantly, these relationships are robust to statistical control for the inbound international travelers, the capacity of a healthcare system to manage and survive infected persons, population density, percent elderly, epidemic intensity, and the timing and stringency of non-pharmaceutical interventions, among other factors.We followed with analyses of cumulative excess mortality instead of the COVID-19 death rate. Results behaved similarly, implying that reporting differences across European countries is an unlikely source of our observed correlations between pre-existing survival environments and COVID-19 mortality. We closed analyses showing a strong negative association between age-specific COVID-19 death rates and pre-existing all-cause age-specific mortality rates for a subset of European countries with readily available data.Our results are consistent with other known facts concerning COVID-19 mortality during the first wave of the pandemic. Specifically, end-of-spring data show that a large percentage of COVID-19 deaths occurred among frail populations in long-term care facilities—Belgium (53%), Denmark (34%), France (51%), Germany (36%), Hungary (19%), Ireland (60%), Norway (60%), Portugal (40%), and Sweden (45%). These data come from the International Long-Term Care Policy Network [In sum, our results support the hypothesis that variation in pre-existing frailty, stemming from differences in pre-existing survival environments, partially determined the striking differences in COVID-19 death risk observed during the first wave of the European pandemic. Our findings also provide general intuition as to how similarly novel causes of death might operate across countries via the heterogeneous accumulation of health frailty among their populations. It is interesting that some reports suggest that the aged population in developing countries show a lower COVID-19 mortality rate than seen in Europe, which would be predicted by our model due to the poor survival environment that the aged face in these countries .


The COVID-19 pandemic has led to drastic increases in the prevalence and severity of insomnia symptoms. These increases in insomnia complaints have been paralleled by significant decreases in well-being, including increased symptoms of depression, anxiety, and suicidality and decreased quality of life. However, the efficacy and impact of early treatment of insomnia symptoms on future sleep and well-being remain unknown.Here, we present the framework and protocol for a novel feasibility, pilot study that aims to investigate whether a brief telehealth insomnia intervention targeting new insomnia that developed during the pandemic prevents deterioration of well-being, including symptoms of insomnia, depression, anxiety, suicidality, and quality of life.The protocol details a 2-arm randomized controlled feasibility trial to investigate the efficacy of a brief, telehealth-delivered, early treatment of insomnia and evaluate its potential to prevent deterioration of well-being. Participants with clinically significant insomnia symptoms that began during the pandemic were randomized to either a treatment group or a 28-week waitlist control group. Treatment consists of 4 telehealth sessions of cognitive behavioral therapy for insomnia (CBT-I) delivered over 5 weeks. All participants will complete assessments of insomnia symptom severity, well-being, and daily habits checklist at baseline (week 0) and at weeks 1-6, 12, 28, and 56.The trial began enrollment on June 3, 2020 and closed enrollment on June 17, 2021. As of October 2021, 49 participants had been randomized to either immediate treatment or a 28-week waitlist; 23 participants were still active in the protocol.To our knowledge, this protocol would represent the first study to test an early sleep intervention for improving insomnia that emerged during the COVID-19 pandemic. The findings of this feasibility study could provide information about the utility of CBT-I for symptoms that emerge in the context of other stressors before they develop a chronic course and deepen understanding of the relationship between sleep and well-being.ClinicalTrials.gov NCT04409743; https://clinicaltrials.gov/ct2/show/NCT04409743DERR1-10.2196/34409 The COVID-19 pandemic and resulting mass home confinement have led to a significant increase in insomnia complaints . Recent The observed increases in insomnia complaints are paralleled by significant decreases in well-being, including increased depression, anxiety, and suicidality and decreased quality of life -21. CritCognitive behavioral therapy for insomnia (CBT-I) is the gold standard, first-line, nonpharmacological treatment for chronic insomnia recommended by the American College of Physicians . It has Typically, individuals with sleep disturbance do not seek treatment unless their condition develops into chronic insomnia. This delay in seeking insomnia treatment makes parsing temporal, mechanistic relationships between insomnia and well-being nearly impossible. However, lifestyle changes and stress associated with the COVID-19 pandemic created large-scale disturbances in psychological well-being and sleep. These circumstances provided a novel opportunity to study the relationship between sleep and well-being by deploying an early sleep intervention to treat insomnia symptoms that have not yet developed into chronic insomnia. These circumstances have thus provided a unique opportunity through a pilot study to respond to a public health crisis and explore the temporal interrelationship between new sleep disturbances and deterioration in well-being as well as to assess whether intervening early in sleep disturbances is enough to alter these trajectories. The findings of the pilot study, the protocol of which is detailed in this paper, will be integral for guiding larger-scale trials in nonpandemic settings.Our feasibility, pilot study investigates the viability of an early treatment for insomnia symptoms to treat insomnia symptoms arising during the COVID-19 pandemic and determine pandemic-related risk factors for worsening well-being and sleep outcomes. We also assess the impact of the intervention on insomnia severity and well-being across 28 weeks.We will accomplish these objectives by conducting a waitlist-controlled trial across 28 weeks to address 3 aims.Aim 1 is to determine whether a brief, telehealth CBT-I reduces insomnia symptoms arising during the COVID-19 pandemic. We hypothesize that CBT-I will lead to improvements in insomnia severity, as measured by the Insomnia Severity Index (ISI) ,36, acroAim 2 is to determine whether brief, telehealth CBT-I mitigates negative mental health outcomes arising during the COVID-19 pandemic. We hypothesize that, compared with the waitlist control group, the CBT-I group will have an improved trajectory of well-being across 28 weeks and will have better well-being at weeks 7, 12, and 28. Additionally, we hypothesize improvements in insomnia symptoms will mediate improvements in well-being from baseline to weeks 6, 12, and 28.Aim 3 is to determine whether risk factors for insomnia that might be aggravated during the COVID-19 pandemic predict worse insomnia and negative mental health outcomes at follow-up. We hypothesize that self-reported high levels of social isolation, perceived stress, sleep reactivity, and screen time and low physical activity at baseline will predict worse long-term outcomes at 12 and 28 weeks across both study groups.We designed a 2-arm randomized controlled feasibility trial to investigate the efficacy of an early, brief, telehealth-delivered insomnia treatment to prevent adverse sleep and well-being outcomes. Participants with clinically significant insomnia symptoms  that began during the pandemic were randomized to either a treatment group or a waitlist control group. Treatment consists of 4 telehealth sessions of CBT-I delivered over 5 weeks. Participants in the waitlist control group do not receive any study interventions during the 28-week primary assessment period. All participants complete assessments of insomnia symptom severity, depressive symptom severity, anxiety symptom severity, quality of life, and pandemic-related risk factors at baseline (week 0), weeks 1-6, week 12, week 28, and week 56 . PrimaryThis study design creates 2 study phases: a 28-week waitlist-controlled (primary assessment) period, which allows the assessment of the therapy compared with a treatment-naive group and a delayed-start (secondary assessment) period, during which participants originally assigned to the waitlist control group receive the study therapy and participants assigned to the treatment group no longer receive study treatment . This alParticipants were randomized into either CBT-I or a 28-week waitlist control condition using stratification by biological sex at birth  and baseline insomnia severity  in a 1:1 ratio. Participants who dropped out of the study after randomization, but before the week 1 visit, were replaced in the randomization matrix. Our sample size estimates  accounted for this replacement.t test of equal means based on the difference between CBT-I and waitlist control group at 28 weeks, at a 5% level of significance. With 25 per group, the study is 80% powered to detect a large effect size (Cohen d=0.80).Due to the anticipated difficulties in recruitment that will likely arise from the complexities of running a study during a pandemic and the time-sensitive nature of implementing an early sleep intervention relative to both the start of the pandemic and insomnia symptoms, we view this trial as a feasibility study. Therefore, for this feasibility trial, we aimed to recruit a total of 50 subjects, which would result in 25 subjects in each study group. With this sample size, our power calculations were derived using the 2-group CBT-I is a comprehensive, multimodal approach that addresses maladaptive cognitions and behaviors that contribute to and maintain sleep difficulties. Treatment consists of education about the 2-process model of sleep (the homeostatic and circadian processes ) and theThe study treatment protocol was adapted from Edinger’s 4-session, open-source CBT-I manual . TreatmeWe will compare the CBT-I group to a 28-week waitlist control group. Participants assigned to the waitlist control group do not engage in any study interventions while on the waitlist (during the primary assessment period) but complete study assessments at weeks 1-6, 12, and 28. After completion of all primary study time points (28-week follow-up), those assigned to the waitlist control group receive the same 4 telehealth CBT-I sessions delivered over 5 weeks as did the CBT-I group (secondary assessment period). The waitlist control group also completes additional questionnaires during their 4 treatment sessions, but the collected data will not be included in primary analyses.This study is conducted through Stanford Zoom and Stanford RedCap. The Stanford RedCap platform is developed and operated by Stanford Medicine Research Information Technology team. The RedCap platform services at Stanford are subsidized by (1) Stanford School of Medicine Research Office and (2) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085. All self-reported data are collected through questionnaires built on Stanford RedCap. Data from clinical interviews are entered into RedCap during the interview and then reviewed by the interviewer after the session. Once a participant is deemed eligible by the study team, they are randomized and scheduled for a second Zoom meeting with study staff. At this second Zoom meeting, they are notified of their study arm assignment by a research coordinator and complete week 1 questionnaires. If they are assigned to the treatment group, they meet with the therapist after completing week 1 questionnaires. For each following treatment session (weeks 2-5), participants meet with a research coordinator before meeting with the therapist. At the posttreatment time point (week 6), participants are sent a survey link to complete online questionnaires. If the participant is assigned to the waitlist control group, the week 1 session ends after completing the questionnaires. Participants in the waitlist control group are sent an email with a link to weekly surveys for each subsequent weekly session (weeks 2-6). Once the participant completes the week 1 questionnaires, they are considered enrolled in the study.Participants are adults in the United States aged 18 years or older who experienced new sleep disturbances after the start of the COVID-19 pandemic. Participants were recruited nationally through online postings, newsletters, and social media. A subset of participants was recruited from an ongoing survey-based observational study about sleep and well-being during the COVID-19 pandemic. All participants met the inclusion and exclusion criteria outlined in Interested participants completed online prescreening questions  assessinDuring the screening session, sleep disturbance was assessed using the Duke Structured Interview for Sleep Disorders (hereafter referred to as Duke)  and ISI.Participants with current or past psychosis, bipolar disorder, or epilepsy were excluded from the study due to safety concerns. Manipulating sleep increases the risk of seizures , mania , and psyParticipants currently abusing substances or taking over-the-counter or prescribed medications for sleep were not permitted in the study. To participate, all other medications must have been stable for at least 3 weeks, and medical conditions must have been deemed stable for at least 3 months by the study clinicians. However, hypnotics and other medications or supplements used to treat sleep disturbance were not permitted to be used during participation in the study. We collected information about current medication use as part of a basic medical history and assessed substance abuse or dependence using the MINI.For eligible participants, data collected at the screening time point (including surveys after the session) are used as baseline measures.Inclusion CriteriaAge 18 years or olderHaving access to the internet and an email addressAcute subjective complaint of sleep disturbance (Insomnia Severity Index [ISI] before the pandemic <10 and current ISI ≥10) that began after March 1, 2020 or the COVID-19 pandemic (as reported during interview)Living in the United StatesLiterate and fluent in EnglishWillingness to participate in the study, sign the consent, and complete majority of questionnairesExclusion CriteriaPresence of suicidal ideation representing high risk as measured by Sheehan-Suicide Tracking Scale (S-STS)Use of medication specifically prescribed for sleep disturbance and unwilling or unable to discontinue more than 1 week prior to baseline data collectionCurrent or lifetime history of bipolar disorder or psychosisCurrent substance abuse or dependenceNot able to verbalize understanding of involvement in research and provide written, informed consentUnstable pharmacotherapy for other mental health disorders (<3 weeks since beginning new medication)Severe impediment to vision, hearing, or hand movement likely to interfere with the ability to complete assessments or are unable or unlikely to follow study protocolsWorking rotating shift that overlaps with midnightWe employ several precautionary measures to reduce attrition and retain participants. First, at the beginning of the study, participants met with a member of the study team to discuss study procedures and answer questions. Participants are encouraged to ask questions throughout their involvement in the study. Participants were assigned an assessor who administers their clinical interviews at every time point, to encourage familiarity and build rapport with study staff. Participants voluntarily provided multiple different types of contact information  for appointment reminders, and sessions are scheduled based on participants’ time preferences. Study staff is persistent in attempting to recontact and engage noncompliant participants. Lastly, study assessments and data collection were carefully designed to minimize barriers to participation. We carefully curated the RedCap database so that participants do not have to re-enter information previously provided, and we eliminated long, superfluous questionnaires. We also added automated features to reduce the need for technical knowledge to navigate the questionnaires.All enrolled participants’ depressive and anxiety symptoms are monitored by a trained clinical psychologist who reviews all adverse events and any significant changes in well-being. Unlike many research studies that recruit locally and conduct study visits in person, the present study enrolls participants across the nation who may suffer from severe depressive symptoms such as suicidal ideation. To address this unique challenge, we developed a robust distressed patient protocol adapted from the 2009 model developed by Draucker et al . Our proStudy staff follow detailed instructions on how to proceed after a determination is made. If imminent threat is not determined, study participants are encouraged to follow up with their own mental health providers and are given contact information for their local emergency room, the National Suicide Prevention Lifeline, and the study psychologist. If imminent risk is determined, a warm transfer is provided to the National Suicide Prevention Lifeline. The visit provider then contacts the study psychologist, and together, they determine if contact of additional parties, including the participant’s emergency contact or local sheriff’s department, is warranted. At each stage, the study psychologist and principal investigator are apprised of steps taken, appropriate documentation is completed, and the institutional review board (IRB) is notified of any adverse events.In addition to the aforementioned risk mitigation protocol, additional risk management strategies are utilized that leverage built-in, automated systems in RedCap if risk is detected during online survey completion. In RedCap, we established branching logic that automatically classifies subjects as low, moderate, or high suicide risk and sends precomposed emails based on risk level. Participants flagged as low, moderate, or high risk are sent direct emails with study staff contact information, as well as the National Suicide Prevention Lifeline. Those flagged as high risk are also contacted directly by the study psychologist who conducts a full risk assessment via telephone to ensure participant safety to self and others. Emergency contacts or local sheriff’s departments are contacted if a participant is deemed to be high-risk and cannot be directly reached.During the screening session, sleep disturbance was assessed using the Duke  and ISI.The ISI is a 7-item, self-report measure of insomnia severity. The items consist of severity of early, middle, or late insomnia; sleep dissatisfaction; interference with daytime functioning; perception of sleep problems by others; and distress caused by sleep difficulties. Items are scored from 0 to 4, with 0 indicating no problem and 4 indicating a very severe problem. Score ranges for insomnia are as follows: 0-7, absent; 8-14, subthreshold; 15-21, moderate; and 22-28, severe.We administered the MINI  to screeSuicidal ideation and behaviors are assessed using the self-report version of the S-STS . The S-SPrimary outcomes of insomnia, well-being, and predictors of treatment response are collected at baseline (week 0), weeks 6, 12, 28, and 56. Primary outcomes of insomnia (ISI) and well-being were also collected at weeks 1-5. The primary time points are weeks 6, 12, and 28.The change in clinically significant insomnia symptoms (meeting criteria for insomnia disorder diagnosis) and subjective ratings of current insomnia symptoms are primary measures of insomnia. Insomnia disorder diagnosis is assessed using the Duke insomnia disorder module. Subjective sleep complaints are assessed using ISI report of current symptoms over the past 2 weeks. The Duke insomnia disorder module is only collected at weeks 0, 12, 28, and 56.We measure change in depressive symptoms as primary outcomes of well-being. Depressive symptoms are evaluated using the Patient Health Questionnaire-9 (PHQ-9) . The PHQBaseline levels of sleep reactivity and pandemic-related risk factors, including loneliness, perceived stress, screen time, social connection, and physical activity, are measured as predictors of long-term treatment outcomes.Loneliness is measured using the University of California, Los Angeles (UCLA) Loneliness Scale . This seSleep reactivity is measured by the Ford Insomnia Response to Stress Test (FIRST) . This 9-Perceived stress is measured using the Perceived Stress Scale (PSS) . The PSSScreen time is measured through modified self-report questions in the Coronavirus Health Impact Survey (CRISIS) . ParticiSocial connection is measured by the Social Network Index (SNI) . The SNIPhysical activity is evaluated using the International Physical Activity Questionnaire (IPAQ) . The IPAAll secondary outcomes of insomnia and well-being are collected at weeks 0, 12, 28, and 56. Secondary measures of insomnia and the Generalized Anxiety Disorder-7 (GAD-7) are also collected at weeks 1-6.Changes in sleep onset latency (SOL), number of awakenings, wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE) over time are measured as secondary measures of insomnia symptoms using sleep diaries; 7 days of sleep diaries are collected at baseline and weeks 1-5, 12, 28, and 56. Sleep diaries collect information about sleep and rise times, time in and out of bed, number of middle-of-the-night awakenings, duration of these awakenings, sleep quality, nap frequency and duration, and caffeine and alcohol consumption. SOL is the time in minutes from “lights out” to sleep onset. WASO is the sum of the total number of minutes of wakefulness occurring after sleep onset and before final awakening (sleep offset). TST is the total time spent asleep, from the start of sleep onset to sleep offset, subtracting any periods of wakefulness. SE is calculated as TST divided by total time spent in bed, multiplied by 100.Secondary measures of well-being include measures of anxiety symptoms, suicidal ideation, and quality of life, as well as an additional measure of depressive symptoms.Anxiety symptoms over time are assessed using the GAD-7 . The GADSuicidal ideation and behaviors over time are measured by the S-STS . For thiQuality of life is assessed using the 36-Item Short Form Health Survey (SF-36) . The SF-The Beck Anxiety Inventory (BAI)  is used The Beck Depression Inventory-II (BDI-II) ,60 is usGiven the nature of this feasibility study, for all our analyses and interpretation, we will place a primary emphasis on estimation of effect sizes and confidence intervals rather than on testing statistical significance.Statistical analyses aim 1 is to determine whether a brief, telehealth CBT-I reduces insomnia symptoms arising during the COVID-19 pandemic. All primary analyses will be performed using the intention-to-treat principle. We will test whether CBT-I is superior to a waitlist control in reducing insomnia symptoms by using a mixed effects linear model with autoregressive error structure using intention-to-treat analysis with outcomes at time points 6, 12, and 28. Insomnia severity as measured by the ISI will be entered as the dependent variable with randomization group (2 levels), time point , and group-by-time interaction included as fixed effects. The model will also include a random slope. Several hypotheses (1.1a-d) will be tested using the described mixed effects model.Hypothesis 1.1a is that individuals assigned to the CBT-I group will experience an improved trajectory of insomnia symptoms during the waitlist-controlled 28-week period relative to those assigned to the waitlist control group. This hypothesis will be tested using a likelihood ratio test of the coefficients of the time and group-by-time interaction of the aforementioned mixed effects linear model.1 in 2 in 3 in Hypotheses 1.1b-1.1d are that individuals assigned to the CBT-I group during the waitlist-controlled period will have lower insomnia symptoms compared with those assigned to the waitlist-control group immediately posttreatment  and at the short-term  and long-term  follow-ups. These hypotheses will be tested using the aforementioned mixed effects linear model with independent likelihood ratio tests using the treatment coefficient for the posttreatment  and one using long-term  follow-up insomnia diagnosis, as defined by the Duke, as the dependent variable, with treatment  as the independent variable.Statistical analyses aim 2 is to determine whether a brief, telehealth CBT-I improves well-being during the COVID-19 pandemic. We will test whether immediate CBT-I is superior to a waitlist control in improving depressive symptoms as the primary measure of well-being during the waitlist-controlled period (28-week duration) by using a mixed effects linear model with autoregressive error structure using intention-to-treat analysis. Depression severity, as assessed by the BDI-II, will be entered as the dependent variable with baseline covariates used for stratified randomization , group (immediate CBT-I or waitlist control), time point , and group-by-time interaction included as fixed effects. The model will also include a random slope. Separate, secondary analyses will be conducted on the secondary measures relating to well-being described in the Secondary Outcomes section . Several hypotheses will be tested using this model.Hypothesis 2.2a is that individuals assigned to the CBT-I group will have an improved trajectory of depressive symptoms across the 28 weeks relative to individuals assigned to the waitlist control group. This hypothesis will be tested using a likelihood ratio test of the coefficients of the time and group-by-time interaction terms of the aforementioned mixed effects linear model.Hypotheses 2.2b-2.2d are that individuals assigned to the CBT-I group will have lower depressive symptoms compared who those who were assigned to the waitlist control group immediately posttreatment  and at the short-term  and long-term  follow-ups. These hypotheses will be tested using the aforementioned mixed effects linear model with independent likelihood ratio tests using the treatment coefficient for the posttreatment , short-term , and long-term  follow-up time points.Hypothesis 2.3 is that improvement in insomnia symptom severity (measured by the ISI) will mediate subsequent improvement in well-being from baseline (week 0) to posttreatment (week 6), short-term follow-up (week 12), and long-term follow-up (week 28). Using the approach described by Kraemer at al  and ManbStatistical analyses aim 3 is to determine whether risk factors for insomnia that are aggravated during the COVID-19 pandemic predict worse insomnia and well-being outcomes at follow-up.Hypothesis 3 is that high levels of social isolation, perceived stress, sleep reactivity, and screen time and low levels of physical activity caused by the COVID-19 pandemic will collectively predict a worse long-term outcome across both intervention arms. Two separate linear mixed models will be conducted for each outcome variable (insomnia severity and depressive symptoms). In each model, the outcome variable at 12 weeks and 28 weeks will be entered as the dependent variable with social isolation, perceived stress, sleep reactivity, screen time, and physical activity measures at baseline as well as experimental arm entered as predictors.Secondary analyses will be conducted. (1) Analytic methods described in the primary aims will be repeated but applied to secondary measures of sleep disturbance and well-being outcomes as described in the Secondary Outcomes section. (2) Analytic methods described in the primary aims will be repeated but applied to outcome measures collected at week 56. (3) Sparse unsupervised clustering and principal component analysis analyses will be used to identify cohesive factors of dysfunction in sleep complaints and patterns of mental health outcomes at baseline. Regression models will be used to quantify the relationships within and between sleep complaints and patterns of mental health outcomes. (4) Age and sex differences in treatment response and as moderators of relationships between sleep complaints, insomnia risk factors, and mental health outcomes will be explored.The IRB of Stanford University  approved the study, which is performed following the rules of the seventh edition (2013) of the Declaration of Helsinki (IRB-55940). It received initial approval from the IRB on April 30, 2020.The trial began recruitment on June 4, 2020, and the first participant was consented on June 10, 2020. As of October 2021, 794 subjects had completed prescreening, and 96 subjects had completed a Zoom screening session. A total of 49 participants were randomized to a study group (26 to the CBT-I group and 23 to the waitlist control group). Overall, 38 participants had completed the week 6 follow-up, 37 had completed the week 12 follow-up, 34 had completed the week 28 follow-up, and 15 had completed the week 56 follow-up. At the time of this writing, there were an additional 3 participants awaiting the 28-week follow-up and a total of 23 participants awaiting the week 56 follow-up (13 in CBT-I and 10 in waitlist control group). The study closed to enrollment of new subjects on June 17, 2021. As of the writing of this paper, due to the outstanding data collection of the remaining 28- and 56-week data, we had conducted interim analyses for the week 6 follow-up but had not tested the results of our primary hypotheses in full. We expect primary results of the study to be published in 2022.Here, we outline the protocol of an innovative research project responding to the mental health crisis related to the COVID-19 pandemic. We aim to address several gaps in the literature by investigating the use of an early, brief, nonpharmacological insomnia intervention delivered via telehealth to treat insomnia symptoms arising during a stressful life event and prevent worsening insomnia or mental health outcomes. This study represents multiple levels of innovation. First, to the best of our knowledge, this is the only project clinically responding to the large-scale increase in sleep disturbance during the COVID-19 pandemic. Although many studies are documenting the robust changes in sleep during the pandemic, this project is the first to test an intervention and examine pandemic-related predictors of intervention response. Second, on a broader scale, this is one of only a few studies testing the prospective temporal relationship between sleep disturbance and well-being. Lastly, to our knowledge, this is the only study of early deployment of CBT-I to treat insomnia symptoms arising from a stressful global event.This study was conceived and launched in response to the global pandemic of COVID-19, which, while rendering it novel, presents unique challenges. First, to address our scientific questions, we sought to recruit participants early in the course of their sleep disturbance, causing a significant urgency to launch the study. This time pressure led us to conduct this study with limited resources, which impacted our recruitment efforts. We were unable to offer compensation for participation. We relied heavily on recruiting participants through free online platforms, such as social media posts, online message boards, and electronic newsletters. Due to these recruitment concerns and the resulting decreased statistical power for the planned analysis, we view this as a feasibility study. Therefore, the planned analyses and publications resulting from this protocol will utilize effect size estimates, rather than statistical significance, to provide pilot and feasibility data to inform future hypotheses. A second potential limitation is the unequal attrition rate in the treatment versus the waitlist control group. Since participants are not financially compensated, the primary motivation of participation is meeting with a therapist on a weekly basis to address their sleep concerns. Thus, it is possible that individuals in the waitlist control group were more likely to decline to participate in follow-up time points due to loss of interest or spontaneous improvement in symptoms over time. Although this could potentially affect the ability to detect significant differences in between-group comparisons at the long-term follow-up time points due to inadequate power, our analytic approach using regression models has the advantage of increased efficiency and power over unadjusted analyses ,64. We wAlthough the unique conditions created by the COVID-19 pandemic posed many novel challenges, it also raises the potential significance of this protocol and feasibility study: It is unlikely that we or another group will be able to replicate this study in the future. Therefore, although the sample will be smaller than nonpandemic trials, it will likely be the largest sample to report on a sleep intervention deployed during the acute stage of a global pandemic. Supporting our hypothesis, the findings of a recent, large-scale clinical trial indicate that treatment of insomnia with CBT-I has an overall benefit in the prevention of incidence and recurrence of major depression in older adults with insomnia disorder. However, this trial utilized an in-person intervention in subjects with chronic insomnia . Our stu