Many public health responses and modeled scenarios for COVID-19 outbreaks caused by SARS-CoV-2 assume that infection results in an immune response that protects individuals from future infections or illness for some amount of time. The presence or absence of protective immunity due to infection or vaccination (when available) will affect future transmission and illness severity. Here, we review the scientific literature on antibody immunity to coronaviruses, including SARS-CoV-2 as well as the related SARS-CoV, MERS-CoV and endemic human coronaviruses (HCoVs). We reviewed 2,452 abstracts and identified 491 manuscripts relevant to 5 areas of focus: 1) antibody kinetics, 2) correlates of protection, 3) immunopathogenesis, 4) antigenic diversity and cross-reactivity, and 5) population seroprevalence. While further studies of SARS-CoV-2 are necessary to determine immune responses, evidence from other coronaviruses can provide clues and guide future research. Antibody mediated immunity to SARS-CoV-2 will affect future transmission and disease severity. This systematic review on antibody response to coronaviruses, including SARS-CoV-2, SARS-CoV, MERS-CoV and endemic coronaviruses provides insights into kinetics, correlates of protection, and association with disease severity. Limited pre-existing immunity to this virus is thought to be responsible for the explosive increase in cases. Nearly all transmission models of SARS-CoV-2 assume that infection produces immunity to reinfection for durations of at least 1 year3. This assumption is relevant to public health officials implementing and managing various nonpharmaceutical interventions, the utility of sera from infected individuals as a therapeutic4, and the ability for serological tests to identify those who are immune5. The dynamics of immunity will also affect the performance of serological testing to quantify the extent of infection in populations. However, knowledge of the dynamics and nature of immune response to SARS-CoV-2 infection is limited, and the scientific basis for durable immunity, upon which these key public health and clinical strategies are dependent, is not well developed.A pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-29. Human coronaviruses (HCoVs) have been used in human challenge experiments since shortly after their discovery in 196511. These experiments, where individuals were intentionally infected with HCoV, provide some of the clearest characterization of human responses to coronaviruses and the potential for immune responses to limit infection and disease. Multiple human challenge studies measured antibody immunity before a coronavirus challenge and identified antibody responses that were associated with protection from infection, serological response, or symptoms12. The low severity of HCoV allowed for safe use of these viruses9 as human challenge. The greater likelihood of severe illness in SARS-CoV-2 limits the applicability of such experiments, although some have argued for their use in subsets of the population13.In the context of our current limited understanding of SARS-CoV-2 immunity, this review looks for insights from studies of the broader coronavirus family. Several authors have noted human experimental infection studies , suggesting that protection after coronavirus infections may last for only 1 or 2 years7, and so an understanding of the temporal dynamics of protective immunity is critical. As with other introductions of novel pathogens14, explosive outbreaks of SARS-CoV-2 across the globe may threaten its persistence by reducing the number of available hosts susceptible to infection. Immune interactions with endemic coronaviruses could theoretically affect the short- and long-term dynamics of SARS-CoV-2, and vice versa16 through cross-protection or antibody-dependent enhancement17, but these interactions and effects are not yet understood. If SARS-CoV-2 does become endemic, age-stratified seroprevalence studies of endemic coronaviruses may provide insight into incidence rates in the presence of higher levels of population immunity.The duration of immunity to SARS-CoV-2 will dictate the overall course of the pandemic and post-pandemic dynamicsHere, we describe the results of a systematic review of literature on antibody measures of immunity to coronaviruses, including endemic HCoV , SARS-CoV (severe acute respiratory syndrome coronavirus that emerged in 2002), MERS-CoV (Middle East respiratory syndrome coronavirus), and early work on SARS-CoV-2. We conceptualize the stages of exposure and infection at which immunity may play a role in the dynamics of SARS-CoV-2, and how literature describing work on this and other coronaviruses can provide insights into these stages, as follows 8 found similar dynamics in IgA and IgG across ten individuals experimentally infected with HCoV-229E: antibody levels increased after 8 days and peaked around 14 days, although significant variation between patients was reported. Yang et al.21, analyzing the data across 67 patients, found higher positive rates of IgM against SARS-CoV than IgG during the first month. The proportion of patients who seroconverted for IgM peaked 30 days after onset, followed by a gradual decrease of IgM levels, while IgG levels peaked by week 2521. Using sera from 18 SARS-CoV patients, Mo et al.23 noted that IgM, IgG, and neutralizing antibodies increased after day 15, and while IgM and neutralizing antibodies peaked on day 30, IgG peaked on day 60. Another study on 30 SARS-CoV-infected patients20 detected seroconversion of IgM, IgG, and IgA at similar times, indicating that the earliest they reached a peak was on average 15 days38. While it is currently too early to characterize how anti-SARS-CoV-2 antibodies will change over prolonged periods of time, preliminary studies have analyzed antibody changes in recent infections. Tan et al.39 found that IgM was detected on day 7 and peaked on day 28 (across 28 patients), and IgG appeared by day 10 and peaked on day 49 (45 patients), while Zhao et al.40 determined that seroconversion among 173 patients took place at median times of 12 (IgM), 14 (IgG), and 11  days.Callow et al.27. Most studies that examined antibody kinetics over extended periods of time focused on IgG48. Callow et al.8 found among experimental infections with HCoV-229E that after peaks in IgG and IgA, antibody levels waned, and between 11 weeks and 1 year post inoculation, were at similar levels to those found in inoculated but uninfected patients. Other studies46 reported detectable IgG levels in recovered MERS-CoV patients, respectively, at 5 months and 1 year after illness onset, while another21 detected IgG antibodies across 67 SARS-CoV patients after 82 weeks, the endpoint of the study. We found few studies that analyzed changes in antibody kinetics over the course of many years after illness onset43. The 18 SARS-CoV patients in Mo et al.23 were followed for 2 years; in that study, after peaking, IgM levels were undetectable by day 180. On the other hand, levels of IgG were still high on day 180 and gradually declined to still detectable levels by day 720, while neutralizing antibodies were detectable in 17 of 18 patients at day 720, but at low titers23. Cao et al.43 described similar long-term dynamics for IgG and neutralizing antibodies over the course of a 3-year study on SARS-CoV: titers for both peaked at month 4, and while they waned thereafter, 74.2% and 83.9% of patients had detectable levels of IgG and neutralizing antibodies, respectively, at month 36. Liu et al.47 found that a high proportion of 19 recovered SARS-CoV patients were positive up to 2 years post infection, with the percentage declining in the third year, a pattern similar to that found by Wu et al.49 in 18 patients50.Multiple studies reported that while IgM and IgG titers increased during the first week following symptom onset, IgM levels gradually waned (while remaining detectable) for SARS-CoV and MERS-CoV in comparison to IgG levels about a month post follow-up46. One study51 found that the rise in antibodies between acute and convalescent sera correlated positively with symptoms and clinical score in 15 patients experimentally infected with HCoV-229E  might be because these mainly infect the upper respiratory tract. Preliminary studies on SARS-CoV-2 point to a possibly contrasting pattern to MERS-CoV: while IgM antibodies appear at the same time in severe and nonsevere cases, IgG appears sooner in severe cases39. On the other hand, neutralizing antibody titers were higher in severe cases40.Some studies have explored a potential link between case severity and antibody response; however, the available information analyzing the nature of this relationship is uneven across viruses. Several studies on MERS-CoV as well as the first preliminary analyses on SARS-CoV-2 explicitly explore this connection, while fewer studies on endemic HCoVs and SARS-CoV do so. Several studies found that cases of varying severity  developed detectable antibodies against HCoV-229E, SARS-CoV, SARS-CoV-2, and MERS-CoVThe distributions of time points at which antibodies were detected (see “Methods”) in the digitized data are shown in Fig. Figure Identification of a correlate of protection requires characterization of immune responses prior to a known exposure or period of risk in which infection or illness outcomes are characterized. In our review, we found that this level of detail was only present in human challenge experiments with HCoVs. We identified 18 studies in which volunteers were exposed to experimental infections with HCoV. Of these, six associated pre-infection antibody measurements with virologic, serologic, or illness outcomes upon experimental infection  were more likely to experience cold (10/15) than those exposed to lower doses (<100.7 TCD50) (3/11).The earliest identified experimental infection study of coronaviruses found that seven of eight subjects with neutralizing titer <5 excreted virus after experimental exposure compared to only one of four subjects with pre-exposure titer of 40 or greater56 found that lower proportions of individuals with high neutralizing titer experienced significant cold upon viral challenge than individuals with low titer.Barrow et al.12 characterized IgA, IgG, and neutralizing antibodies in sera and nasal washings from 33 volunteers before they were experimentally exposed to 229E HCoV. She found that multiple antibody responses were associated with reduced risk of infection, seroconversion, and symptomatic illness upon challenge. Individuals who seroconverted to the experimental viral exposure (defined as a rise in ELISA IgG serum antibodies) had significantly higher serum IgG, neutralizing antibodies, and nasal IgA. Serum and mucosal IgA were associated with the duration of viral shedding post experimental infection, with those shedding for 5 days or more having statistically significantly less mucosal IgA than those shedding less than 5 days . Serum-neutralizing antibody was not found to be significantly associated with viral shedding duration. This study also showed protective associations of pre-infection serum-neutralizing antibody, serum IgG, and nasal IgA with clinical severity scores and nasal secretion weights (a measure of the severity of rhinorrhea symptoms).Callow57 reported detection of pre-existing neutralizing antibodies among medical students who had virus isolation  or seroconversion to HCoV-229E . Pre-existing neutralizing antibodies were inversely associated with increases in neutralizing antibodies after reinfection, but were not associated with reinfection events that were determined by CF seroconversion.Another prospective study9. On the first challenge, all six developed symptoms and detectable viruses, and five of six experienced a significant rise in titer. In the second, zero of six experienced illness, detectable virus or significant rise in titer. Callow et al.8 rechallenged volunteers with the same dose of HCoV-229E, 1 year apart. Of nine volunteers who were infected in the first exposure, 6 (67%) were infected in the second exposure. However, none of these individuals developed respiratory illness symptoms and they experienced a mean duration of detectable virus of 2 days compared to a mean of 5.6 in the initial challenge. Of note, these experimental doses may differ from the amounts of virus that people are exposed to in natural infections.Several studies exposed volunteers to two viral challenges, some months apart. Reed 9 rechallenged six volunteers who had been experimentally infected with HCoV-229E 8–12 months previouslyWe identified 82 papers as related to cross-reactivity and/or antigenic diversity  infect both the upper and lower respiratory tract, resulting in more severe but rarely fatal disease58. Other CoVs have been associated with human disease, including enteric disease in infants and zoonotic infections from livestock, but seem rare and are not described here64.Within the 66. The S protein, which protrudes from the virus envelope, is immunodominant67 and consists of two subunits: the S1 protein, which contains the receptor-binding domain (RBD) and the S2 protein, which mediates cell membrane fusion69. The nucleocapsid protein, which is also immunogenic, is smaller than S, lacks a glycosylation site, and induces antibodies sooner than to S during infection, making it an attractive protein for diagnostic assay design70. Sequence homology for the N and S of SARS-CoV to other Betacoronaviruses is 33–47 and 29%, respectively, while homology to Alphacoronaviruses is lower (25–29% homology to N and 23–25% for S)70. SARS-CoV-2 is most similar to SARS-CoV, harboring sequence homology of 90% in N and 76% in S followed by MERS-CoV 71. Immunogenicity of other proteins were less studied. Studies in HCoV-229E72 and SARS-CoV73 suggest that M does have immunogenic epitopes, despite its relatively small size but with titers rising at times later than 21 days post infection, while responses to the E protein were rarely detected. In contrast, another study detected anti-M antibodies in over 80% of the individuals (n = 58) at 10 days post onset, while detection of anti-N and anti-S increased later74. The authors speculated these late detections as effects of their antigen-purification procedures where the N protein may not be able to exhibit its natural conformation as fragments of N were able to be detected earlier.Coronaviruses have four structural proteins: the spike protein (S), the nucleocapsid (N), the envelope protein (E), and the membrane protein (M)75. While volunteers experimentally inoculated with HCoV-229E-like strains were protected against challenge with the homologous strain at 1 year , volunteers experienced only partial protection against heterologous HCoV-229E-like strains (n = 5/12 protected). Increasing population immunity to HCoV-229E in the population was associated with less clinical disease upon challenge with HCoV-229E-like strains9. Studies of serological responses to HCoV N proteins point to cross-reactivity within Alpha-HCoVs (229E and NL63) and Beta-HCoVs (OC43 and HKU1), but not between Alpha- and Beta-HCoVs79. Consistent with observations in human challenge studies, children experiencing natural HCoV infections experience fourfold seroconversions to either HCoV-OC43 or HCoV-229E, but not both simultaneously80. However, a longitudinal study in newborns found that children seroconverted to either HCoV-NL63 or HCoV-229E but not both, although both viruses are Alpha-HCoVs81. A later study in newborns followed from age 0 to 20 months82 showed asymmetric interactions within Alpha-HCoVs and Beta-HCoVs: seroconversion to HCoV-NL63 was observed after HCoV-229E, but no recent HCoV-229E seroconversions had a recent infection by HCoV-NL63, suggesting that HCoV-NL63 provides at least short-term protective immunity against HCoV-229E. Similarly, HCoV-HKU1 seroconversion occurred prior to HCoV-OC43 but rarely after, suggesting that HCoV-OC43 protected against HCoV-HKU1.Natural and experimental infection studies in humans point to cross-reactivity within but minimal reactivity between endemic human Alpha- and Betacoronaviruses. Individuals experimentally inoculated with HCoV-229E and HCoV-229E-like strain LP experienced a >4-fold rise in neutralizing antibodies to both HCoV-229E and LP, while individuals inoculated with HCoV-OC43 did not83 by IFA or neutralization45). Healthy individuals with antibodies to HCoV-229E, HCoV-OC43, and other endemic HCoVs rarely had detectable antibodies that bound SARS-CoV-infected cells or SARS-CoV N protein85. HCoV-OC43 N and SARS-CoV N proteins have a subset of sites with shared homology, potentially explaining low-level false-positive results for N-based assays86. Blood donors in Southern China (n = 152) and Saudi Arabia (n = 130) did not have detectable binding (IFA) or neutralizing antibodies to either MERS-CoV or SARS-CoV88. Because children experience less severe disease during SARS-CoV infection than adults, it was hypothesized that childhood vaccination with non-CoVs provided cross-protection against SARS-CoV. However, binding and neutralizing antibodies and T-cell responses induced by routine childhood vaccinations ) did not cross-react with SARS-CoV in experimentally inoculated mice89.Infection with endemic HCoVs produces little cross-reactivity to emerging CoVs SARS-CoV and MERS-CoV. Individuals experiencing natural infections with HCoV-OC43 or HCoV-229E did not have detectable antibodies in acute or convalescent samples against SARS-CoV (to N proteinn = 12/20)45. In one study, the number of SARS patients who experienced a greater than fourfold rise in binding antibodies was greater to HCoV-OC43 (n = 10/11) than HCoV-229E (n = 5/11)84. Some SARS patients also showed a rise in antibodies to HCoV-229E and HCoV-OC43 N protein84 and HCoV-NL6376. Among SARS-CoV patients (n = 28), 60% had detectable IFA titers to MERS-CoV and 25% had anti-MERS-CoV- neutralizing antibodies. A subset with available paired samples experienced seroconversion to HCoV-OC43, but limited seroconversion to Alpha-CoVs. In the same study, animal handlers at a wildlife market in Guangzhou (n = 94) with low-level prevalence of antibodies to SARS-CoV  had detectable antibodies toward MERS-CoV (2.2% by IFA)87. A follow-up study found that the cross-reactivity between SARS-CoV and MERS-CoV was unlikely to be due to similarity in the RBD, as monoclonal antibodies (mAbs) raised to SARS-CoV RBD did not bind MERS-CoV RBD or neutralize MERS-CoV even at high concentrations90. MERS-CoV may produce less cross-reactivity against SARS-CoV. A subset of slaughterhouse workers in Saudi Arabia  had antibodies to MERS-CoV by IFA as well as to endemic HCoVs, but none had reactivity to SARS-CoV spike protein. MERS-CoV patients were observed to have low-level cross-reactivity to SARS-CoV88 and in one study HCoV-HKU1 N protein76. Because of this cross-reactivity, researchers have developed diagnostic assays with truncated SARS-CoV S, N, and M proteins94. A study with longitudinal follow-up suggests that the titers may indeed reflect stimulation of pre-existing antibodies from past infections as avidity of IgG antibodies to HCoV-OC43 and/or HCoV-229E in two of their SARS patients was high early on and remained high, while the avidity against SARS-CoV was initially low24. See Fig. Emerging HCoVs can induce cross-reactive binding antibodies toward endemic and other emerging HCoVs. SARS patients often experience a > 4-fold rise in antibodies to HCoV-229E, HCoV-NL63, and/or HCoV-OC43 in paired acute/convalescent samples  and camels102. These RBDs maintained functionality and induced potent neutralizing antibodies. When residues in their receptor-binding motifs were mutated to evade neutralization, cross-reactivity persisted, but binding affinity to DPP-4 (the main receptor for MERS-CoV) was lost, suggesting limited antigenic escape for MERS-CoV. A study of MERS-CoV isolates with distinct amino acid differences in the S and other replication proteins found differences in replication kinetics, but it is not clear that these were attributable to differences in S77.There is some evidence for antigenic evolution in the receptor-binding domain of emerging CoVs. A study of SARS-CoV strains from the zoonotic phase  to early and late in the SARS epidemic revealed that some escaped neutralization by mAbs targeting the spike RBD of SARS-CoVIn the initial review, 44 papers were identified as related to immunopathogenesis. Of these, we found 26 sufficiently relevant to review in Supplementary Table 103. In support of a priming role from pre-existing antibodies against endemic strains is the observation that older patients infected with SARS-CoV appeared to mount an earlier immune response with higher titers than younger patients103. Authors from another study pointed toward enhancement from antibodies within a single-infection episode17. Nasopharyngeal viral load increased in the first week and declined thereafter, but clinical worsening was seen in many of the patients at week 2, with virus shedding in stool and urine observed toward the end29. Many manifested with additional new lesions as original lesions improved. The timing of the appearance of new lesions correlated with IgG seroconversion and declined viral load, suggesting that pathology post week 1 was driven by the immune response rather than by uncontrolled viral replication. Others have argued that ADE due to neutralizing antibodies is unlikely as treatment of SARS-CoV with convalescent serum did not result in adverse effects103.Antibody-dependent enhancement or other antibody-mediated immunopathogenesis may be possible. Antibody-dependent enhancement (ADE), where pre-existing antibodies increase pathogenicity by facilitating viral entry into cells, a long hypothesized explanation for severe dengue infections, has been hypothesized to play a role in coronavirus pathogenesis specifically in patients who seroconverted early in SARS-CoV infection104. The authors noted similarity to dengue in humans where infants with mid-levels of maternal antibody to dengue  experience increased risk of disease105. The one study that we found that addressed this characterized endemic HCoV in infants106. They observed the highest lower respiratory tract infection burden (7.8%) at 6–23 months of age , while upper respiratory tract infection burden was close to uniform106. The heightened disease burden after maternal immunity waning to medium levels in the lower tract, and the absence of such observation in the upper tract where antibodies do not circulate, subtly supports the possibility of ADE in HCoVs.Immunopathogenesis has been observed in a coronavirus of cats, feline infectious peritonitis virus (FIPV). Kittens passively immunized with serum-containing antibodies to FIPV developed more rapid disease than controls not passively immunized108. Though replication is observed after entry, the virus does neither exit nor alter the expression of proinflammatory immune mediators  and apoptosis-inducing ligands (FasL)109. This contrasts with the effects in nonhuman primate studies where endocytosis into macrophages stimulated inflammation, which in turn drives severe lung injuries110. A study by another group in the human promonocyte cell line (HL-CZ), which expresses both ACE2 and Fc-γ-RII, demonstrated increased infectivity and virus-induced apoptosis when anti-SARS-CoV sera from patients were added at 100- to 2000-fold dilutions, while at higher concentrations neutralization occurred111. Upon infection, TNF-α, IL-4, and IL-6 expressions heightened, while IL-3 and IL-1β only appeared in trace amounts. The difference could have resulted from the cell line differences or the set of mediators assessed109. Regarding regions of the spike protein that may induce antibodies with enhancement effects, both anti-S1a and anti-S1b mAbs showed mild-to-moderate effects111. Only one particular anti-S1b clone showed neutralization. No effect was seen for anti-N mAbs. There is limited evidence that these mechanisms are causal to inflammatory gene-expression differences among patients with differing severities112.Controlled in vitro experiments have explored the possible enhancing action of antibodies for HCoV infections. A series of studies by Yip et al. demonstrated that opsonization of anti-spike antibodies allowed SARS-CoV to enter non-ACE2-expressing immune cells that bear Fc-γ-RII (CD32)58. Increases in anticardiolipin antibodies were reported in 33.9% of 62 post-SARS osteonecrosis patients, but the lack of a comparison group of post-SARS patients without osteonecrosis meant that the link was inconclusive113. Anti-S2 IgG antibodies targeting uninfected lung epithelial cells (A549) were detected in SARS-CoV patients 20 days post symptom onset114, a reactivity not seen in serum from healthy individuals and non-SARS-CoV pneumonia patients. Complement inactivation only showed a cytotoxic effect when IgG was present/unbounded. The presence of anti-S2 antibodies also increased the binding of immune cells (PBMCs) to A549 cells treated with IFN-γ, replicating the conditions under which a cytokine storm would be observed. A separate study demonstrated colocalization of anti-S2 antibodies collected from serum of SARS patients ≥50 days post fever onset with annexin A2 and immunoprecipitated annexin A2 on A549 cell surfaces. Elevated expression of annexin A2 on the surface can be stimulated by IL-6 and IFN-γ, both cytokines induced by SARS-CoV, which in turn increases binding of anti-S2 antibodies to the cell. However, its pathogenic role was not explored115. Alternatively, similarity between viral and host epitopes (molecular mimicry) can generate cross-reactive antibodies. In mice116 and humans117, there is evidence of anti-N antibodies that cross-react with IL-11, an anti-inflammatory cytokine expressed in many tissues, including the lung and bone marrow. The authors suggest that high anti-N antibodies induced relatively early during infection may be involved in the thrombocytopenia and lymphopenia observed early in SARS-CoV infection116.While the link between antibody presence and enhanced severity via infection of immune cells remains unclear, some have suspected a role for autoreactive responses in increased severity of illness. Prolonged tissue destruction can increase presentation of host proteins to T- or B cells and result in an adaptive response against self-, i.e., epitope spreadingFrom the paper review, 68 papers were classified as having data on age and seroprevalence or seroincidence, of which 20 studies were confirmed on further review, and 14 had digitizable data. The age range across studies was 0 to ≥65, while the sample size of studies ranged from 69 to 19,974 (82–6400 for studies of endemic HCoVs). Supplementary Table 123. Trends from papers not displayed in the figure are largely the same125. Two studies show a marked decline in seroprevalence with age above 40119.For endemic coronaviruses, seroprevalence rises sharply in childhood, with little to no change in seroprevalence by age among adults. While the exact dynamics from 6 months to 20 years vary between studies, the general trend remains consistent. Figure 82. Zhou et al.’s serosurvey measured IgG and IgM separately, and showed very different patterns with age123. While IgG seroprevalence rose to high levels by age 10 and remained high into the adult population, IgM seroprevalence declined to zero for all individuals aged 14 and older. The authors interpreted this as evidence that first infections occurred before the age of 14 for all strains.The force of infection of endemic coronavirus strains is high, and the age at first infection low, but variable across studies. Simple catalytic models fit to the digitized data predict median total force of infection across studies of 0.21  among immunologically naive individuals  high enough to preclude significant rates of coronavirus infection among older adults if each serotype confers lifelong, complete homologous immunity Fig. .Fig. 7Ag125. Most studies showed no discernible difference in age-seroprevalence trends or in the overall seroprevalence by strain. Gao et al.78 found that seroprevalence of HCoV-229E and HCoV-HKU1 was significantly lower than seroprevalence of HCoV-OC43 and HCoV-NL63. In addition, Chan et al.122 found seroprevalence to HCoV-HKU1 to be low (21.6% in 31–40-year olds) in Hong Kong, and stated that this was expected from the low rates of HCoV-HKU1 among respiratory illnesses. Finally, most studies measured the presence of binding antibodies in the blood. Of the studies in Fig. 118 (red) measured seropositivity using a neutralization assay. That the seroprevalence is markedly lower in that study could indicate lower prevalences of neutralizing antibodies, lower sensitivity of neutralizing versus binding assays, or lack of correlation between neutralizing and binding antibodies against endemic coronaviruses.The review gave rise to several observations that were either consistent or conflicting across papers. Studies that enrolled children less than 6 months detected loss of maternal antibodies, representing a preliminary line of defense for newborn children133. Asymptomatic and subclinical rates of MERS-CoV are generally higher, but available serosurveys lack the power to draw strong inference about age trends. A large serosurvey conducted across Saudi Arabia found that the age of seropositive individuals was significantly lower than the age of clinical cases134, while another conducted across multiple countries in Africa and Asia found no trend in seroprevalence with age135. Studies of risk factors within camel workers have either not addressed age as a risk factor137 or not found an association138.Regarding the novel coronaviruses, serosurveys of SARS-CoV confirmed that the rate of asymptomatic or subclinical infection was very lowWe have presented a broad, comprehensive review of multiple aspects of the literature on antibody immunity to coronaviruses. We identified a number of key findings. The median time to detection was the shortest for SARS-CoV-2 , followed by SARS-CoV  and MERS-CoV . Most long-term studies found that SARS-CoV and MERS-CoV IgG waned over time , while others found detectable levels of IgG 3 years post symptom onset. Antibody kinetics varied across the severity gradient, with antibodies remaining detectable longer after illness with more severe symptoms. Human challenge studies with HCoV indicate that serum and mucosal immune responses  provide possible correlates of protection from infection and disease. However, repeat human challenge experiments with single HCoV suggest that individuals can be infected with the same HCoV 1 year after the first challenge, though perhaps experiencing lower severity. There is cross-reactivity within but minimal reactivity between Alpha- and Beta-CoVs. While endemic HCoVs rarely induce cross-reactive antibodies against emerging HCoVs, SARS-CoV and MERS-CoV stimulate antibodies induced by prior HCoV infections. Multiple mechanisms for immunopathology have been suggested, but no strong causal evidence exists. The extent to which antibody responses can in some circumstances contribute to disease severity is not known. Seroprevalence with the four major endemic HCoV strains rose rapidly during childhood and remained high in adults. Median age at first infection with any strain was 4.8 years (95% CI 2.5–11.2 years). There was no clear trend in seroincidence with age, and many studies have demonstrated incidence of coronavirus infections in elderly populations. These results suggest a measurable impact of immunity to coronaviruses on future risk, but this protection may be transient.We have suggested a set of search terms to identify relevant work, and others may expand on this search. Due to the speed of new research being produced, we limited our systematic review of SARS-CoV-2 papers to before March 20, 2020. Further evidence on immune responses to SARS-CoV-2 is likely contained in work that has since been published. To remain human-focused, we excluded from the review animal studies and studies of animal CoVs, but this literature is likely relevant to some areas of the review. We limited the scope of the review to antibody-mediated immunity, meaning that understanding of some areas of the review may be incomplete. Other aspects of immunity, including the innate and cellular responses, have been shown to be important in conferring protection at reexposure. Finally, digitized data could be affected by publication bias or other selection bias, meaning that it might not be representative of data from all studies. The aim of the pooled analyses was to summarize the array of studies rather than formally explore hypotheses, and as a result, the models used are simple. For example, in estimating force of infection from age-stratified seroprevalence data, we did not consider other features of a model, such as assay sensitivity, time- and age-varying force of infections, seroreversion, and cross-reactivity between strains, that might better explain patterns seen in the data. Albeit the limitations, there exist multiple implications for the ongoing SARS-CoV-2 pandemic responses and future outlooks.139, remains a key gap in the literature. Serological surveys to describe the extent of infection in particular populations should account for the dynamics of antibody and the potential for infections associated with different severities of illness to have different antibody responses in their analysis. Indications that less severe illnesses are associated with reduced antibody responses suggest that mild cases may pose challenges to serological assays.There is a need for development of serological assays with high sensitivity and specificity to understand the extent of infection in populations. Evidence from previous emerging CoVs suggests that false negativity is likely to result from waning of antibody levels. As most studies on antibody kinetics are on symptomatic patients, the kinetics in subclinical infections, a significant proportion of SARS-CoV-2 infectionsOn the other end, the proposed policy of immunity passports to allow individuals who are presumed to be immune to return to work once social distancing measures are relaxed requires highly specific serological tests to mitigate adverse outcomes. Evidence from past emerging HCoVs suggests low false positivity from cross-reaction with endemic HCoVs. However, antibody titers do not necessarily translate to immunity. Challenge studies indicate multiple candidates that may serve as correlates of protection, including serum and mucosal measures; however, these will need specific evaluation for SARS-CoV-2. The knowledge gap in correlates of protection and their durability must be filled before immunity passports are safe for general use.140, and polyclonal antibodies from SARS-CoV-2-recovered patients are being evaluated for treatment141. In this review, we have summarized concerns of theoretical dangers from antibodies in the immunopathogenesis of COVID-19 diseases. These concerns should not hinder the urgent development of vaccines and therapeutics; however, these hypothetical risks must be squarely addressed. Research in animals, in vitro, and in humans should be done on immunopathogenesis of SARS-CoV-2 as an integral component of the development of antibody therapeutics and vaccines. Compared to other disease systems , where risk from antibody-mediated immunopathogenesis is well supported with laboratory, clinical, and epidemiological data, this potential for immunopathogenesis in SARS-CoV-2 infections remains speculative145. Though evidence for immunopathogenesis remains limited146, it will be important to design human trials to provide valid evidence of a positive effect and detect negative effects should they occur. There are concerns with antibody treatment, where patients are already infected and ill-pose lesser concerns. Preventive measures, including vaccines or passively administered antibodies meant to lower risk, will require closer attention. Serological responses in vaccinees should be evaluated to detect potential immunopathogenic effects. Human vaccine trials might start with older individuals, who per infection are at higher risk of experiencing severe illness, and as such have a potential benefit-to-risk ratio from vaccines that is much higher than younger individuals. Consideration could also be given to post-licensing surveillance systems designed to detect the remote risks of waning immunity and subsequent adverse events due to vaccine-mediated immunopathogenesis.Numerous vaccines of varying antigenic compositions are currently under developmentKey questions for the long-term dynamics of SARS-CoV-2 include whether it is likely to be eradicated, whether widespread immunity will lead to transient decreases in incidence, and how long it will take for another large outbreak to occur Table . ModelinFinally, we note that the best evidence for immune responses to SARS-CoV-2 will come from studies of the virus itself. Such studies are being performed and reported with unprecedented speed, but in the absence of firm evidence, many are turning to the existing coronaviruses as a model. To that end, we have produced this review to aid researchers in understanding the scope of the evidence for immune responses to these coronaviruses.We conducted searches of the PubMed database on March 20, 2020 using the search term “coronavirus” and each of the following terms or phrases: “serolog*”, “serop*”, “cross reactivity”, “antibod* human coronavirus”, and “complement fixation”. We also searched for each of these terms with the search terms “SARS” and “MERS”. Articles were included in the search, regardless of publication date. Articles included electronic, ahead-of-print publications available in the PubMed database. We did not attempt to contact authors to obtain unpublished data. Each abstract was reviewed by two reviewers. Articles that were not considered applicable were excluded  study of antibody immune responses to human coronavirus, (2) study of human coronavirus, but not about antibody immunity, (3) study in nonhumans or basic biological study of virus–antibody interactions not characterizing actual humans, (4) study in humans not about coronavirus, (5) in another language, (6) duplicate of another record, or (7) dead link/bad return/noise. Disagreement between two reviewers on classification was resolved by a third reviewer. Papers classified in the first category (study of antibody immune responses to human coronavirus) were reviewed in their entirety and classified by one reviewer as relevant to one or more of the following: (1) antibody kinetics, (2) correlates of protection, (3) antigenic diversity and cross-reactivity, (4) immunopathogenesis, and/or (5) population seroprevalence or incidence. Papers that were not considered relevant to any of the preceding categories were excluded.Data were digitized from papers according to predefined criteria for each area of focus, related to accessibility, utility in answering key questions, and suitability for pooled analyses.For antibody kinetics and the association of antibody responses with clinical severity, we shortlisted papers that met the following criteria for data digitization: (i) antibody responses were provided for at least two distinct points in time and were relative to a point of infection or symptom onset, or (ii) explicitly discussed severity of symptoms in relation to antibody response. Data could consist of multiple measures of antibody kinetics: serological responses for individual patients (either quantified antibody levels or binary metric of seropositivity), and (cumulative) proportion of a group of patients that was seropositive or had seroconverted at different time points. Most studies used more than one assay and targeted more than one antibody; in these cases, we digitized all the data provided across assays and antibody types. For the pooled analysis, we excluded studies that summarized antibody responses across patients, but we nonetheless discussed their main findings. Where possible, severity associated with different (groups of) patients was extracted and standardized  and outcomes as either virologically confirmed or serologically confirmed infection.133; Degnah et al.135). If not reported, fold rises were calculated using lower ends of both time points. If measurements included the lowest reported, we assumed a titer of five to avoid having a zero as the denominator. All data points are accompanied by the type of test/assay performed.For cross-protection and antigenic diversity, instances of human infections with a particular HCoV and titers against itself and the others were summarized. Acute, convalescent, and fold rise in titers were digitized as brackets of possible values along with the number of individuals associated with those data points . For MERS-CoV and SARS-CoV, acute titers were assumed to be the lowest reported in that study (either <1:10 or <1:20) as prior exposure was unlikely  if sampling was performed independently of symptoms. Data extracted from the text, tables, or figures include strain/virus tested for; whether seropositivity, seroconversion, or incidence was measured; time period of the study for seroincidence studies; assay type; target antigen; cut point for defining seropositivity/seroconversion; bounds of age category; number of samples; number of positive samples. In plotting the data, if the upper bound of the highest age category could not be identified, we assumed that it was 20 years above the lower bound based on inspection of the highest age categories in other studies.For diagnostic serological assays and immunopathogenesis, we compiled and summarized findings from the categories without data extraction. Data extracted for correlates of protection and antigenic diversity were summarized and visualized without the attempt to draw a pooled conclusion.We conducted pooled analyses in two areas.To analyze the antibody kinetics and association of antibody responses with clinical severity, we summarized the approximate time to detection for three sets of studies. The first set includes studies with a cumulative number of positive cases over time. Each additional positive case added to the cumulative curve was assumed to be a new detected case at that time point. This is approximate only because cumulative curves sometimes decline  from the four major endemic strains by drawing 100,000 bootstrap samples of four strain-specific FOIs and calculating the sum across strains. Age at first infection is given by the inverse of the total FOI. Finally, we visually compared age-incidence trends to curves generated by catalytic models with four proposed mechanisms for CoV immunity (see “Supplementary Appendix” for details).Hypothetical age-seroincidence curves using different models for endemic HCoV immunity were built using extensions of the simple catalytic model in which each individual is exposed to a constant force of infection λ, and the probability of being seropositive at age a is given by S(a) = 1–e–λa, and the incidence rate from age a to a + 1 is given by I(a) =  e–λa–e–λ(a + 1). For example, if the first infection grants partial homologous immunity, reducing the hazard of infection by a factor ρ < 1, the incidence rate from age a to a + 1 is given by I(a)= e–λa(1–e–λ) + (1–e–λa)(1–e–ρ λa).Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting the true views of the Department of the Army or the Department of Defense.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


Membrane and secretory proteins are essential for almost every aspect of cellular function. These proteins are incorporated into ER-derived carriers and transported to the Golgi before being sorted for delivery to their final destination. Although ER-to-Golgi trafficking is highly conserved among eukaryotes, several layers of complexity have been added to meet the increased demands of complex cell types in metazoans. The specialized morphology of neurons and the necessity for precise spatiotemporal control over membrane and secretory protein localization and function make them particularly vulnerable to defects in trafficking. This review summarizes the general mechanisms involved in ER-to-Golgi trafficking and highlights mutations in genes affecting this process, which are associated with neurological diseases in humans. Approximately one-third of all proteins encoded by the mammalian genome are exported from the endoplasmic reticulum (ER) and transported to the Golgi apparatus, where they are sorted for delivery to their final destination in membrane compartments or secretory vesicles . SecretoThe stepwise process for segregating and exporting cargo from the ER is similar in yeast, plant, and mammalian cells and relies on several essential proteins . The first step involves the conversion of SAR1-GDP to SAR1-GTP by the guanine nucleotide exchange factor SEC12, which resides in the ER membrane ,6,7. AltUnlike other cell types, neurons consist of two compartments: the somatodendritic compartment and the axonal compartment. These compartments are separated by the axonal-exclusion zone located at the base of the axon, where proteins are either permitted into or excluded from the axon. Neurons are particularly sensitive to defects in trafficking given the long distances that must be traversed by proteins and organelles in axons and the need for precise spatiotemporal localization and function of membrane and secreted proteins to achieve synaptic plasticity. Indeed, many neurological diseases  are caused by impaired trafficking in neurons. Although few neuronal disorders are currently known to be caused by mutations in genes encoding accessory ER-to-Golgi trafficking proteins, the number may increase with the inclusion of whole-genome/exome sequencing in diagnostic evaluations as we develop a better understanding of the pathways and players involved. Neurons also engage in unconventional protein secretion pathways, wherein membrane and secretory proteins bypass the Golgi on their way to the membrane. Although this type of secretion is likely essential to neuronal growth, development, and function, the associated knowledge is limited. Therefore, we will focus on general COPII protein trafficking.Most secretory cargoes are sorted for ER export in COPII vesicles through either bulk flow or receptor-mediated transport. Proteins transported via bulk flow are incorporated into COPII vesicles by default if the proteins are properly folded and have no retention signal. They are sorted into COPII vesicles on the basis of their proximity to the budding vesicle. Because no receptors or export signals are needed for protein cargoes to be exported by bulk flow, the cargoes are not concentrated once inside the COPII vesicles . By contSec24c promoter rescues the embryonic lethality caused by Sec24c deficiency [SEC24 proteins are evolutionarily conserved cargo adapters. Cargo binding occurs upon recruitment of SEC23/24 heterodimers to the cytosolic face of the ER membrane by activated SAR1 ,16. SEC2ficiency .Cargo adapters, which are not part of the core COPII machinery, help to further expand the repertoire of cargo eligible for concentrative export. For instance, ERGIC-53 is one of the best-studied adapters for glycoproteins, such as α-antitrypsin and GABA receptor ,27. AnotIn mammals, COPII vesicle formation occurs in ribosome-free regions of the ER, unlike in yeast where it takes place throughout the ER membrane . The larAfter the capture of cargo, the next main events contributing to the budding of vesicles involve membrane curvature and scission. The progressive membrane curvature observed during vesicle budding is most likely produced by the physical properties of the SEC23/24 and SEC13/31 heterotetramers. The lattice formed by these COPII components can adopt multiple geometries to accommodate cargoes of various sizes and shapes, including large cargoes such as procollagen ,7,33. AfThe typical end products of this stepwise budding process are COPII vesicles of 50 to 200 nm in cultured cells. To accommodate bulky cargoes such as collagen, which are fibrillar in shape and can be more than 300 nm long, specific cargo adapters, such as TANGO1, functioning with cTAGE5, promote the formation of large cargo carriers ,29,35. TTFG) competitively binds SEC23 and promotes disassembly of the outer SEC13/31 coat [COPII-mediated protein delivery relies on binding of the COPII coat to tethering and regulatory proteins. For vesicles to fuse, their outer coats must disassemble. This disassembly is catalyzed by the hydrolysis of SAR1-GTP and regulated through multiple mechanisms. Tropomyosin-receptor kinase–fused gene  through SEC23/24 . TRAPPI Precise spatiotemporal control over the production and delivery of adhesion molecules, neurotransmitter receptors, ion-channels, and secreted trophic factors is essential for neuronal growth and synaptic transmission. The expansive neuronal membrane and its compartmentalization by countless synapses with diverse compositions and activities place unique demands on the secretory pathway. As such, the secretory pathway in neurons has evolved to allow trafficking of synaptic components over long distances and local control of membrane protein composition . The genAs a consequence of the lack of Golgi in distal dendrites and axons, secretory proteins must be either synthesized and processed in the soma and then trafficked along the axon (which can be as long as 1 m) or dendrite or synthesized locally from mRNA that is actively trafficked along dendrites or axons for local synthesis ,71. MembFinally, post-ER carriers originating in dendrites and axons must either be transported long distances back to the somatic Golgi or bypass the Golgi by utilizing ERGICs for subsequent processing and sorting. Although most of the proteins synthesized in the dendritic compartment appear to be sorted back into the somatic Golgi apparatus for further processing , the GolSEC31A that leads to nonsense-mediated decay of its transcript is linked to a recessive neurological syndrome characterized by intrauterine growth retardation, marked developmental delay, and epilepsy [SEC24B in humans are associated with neural tube defects [Sec24b in mice also causes neural tube closure defects [TECPR2 has been identified in patients with hereditary spastic paraplegia (HPS) [TECPR2 mutation exhibit reduced SEC24D level and delayed ER export [The great length of neuronal projections and the requirement for precise spatiotemporal control over membrane and secreted protein localization render neurons particularly vulnerable to defects in the ER export and trafficking. Mutations in the genes encoding or directly affecting the function of essential components of the COPII machinery are linked to various neurological diseases. For example, a mutation in epilepsy . Loss ofepilepsy . Loss-of defects . The ina defects . Deletin defects . A homozia (HPS) . TECPR2 R export .cTAGE5 in murine brains leads to severe developmental defects and impaired dendrite outgrowth [TFG in mice is embryonic-lethal, and TFG point mutations have been identified in several patients with neurological diseases, including HSP, Charcot-Marie-Tooth disease, and hereditary motor and sensory neuropathy [TFG mutation R106C show defective axon pathfinding, decreased ER protein export, and elevated levels of ER stress [TRAPPC6A, TRAPPC6B, and TRAPPC9 are observed in patients with neurodevelopmental disorders [TRAPPC2L and TRAPPC12 mutations cause encephalopathy [TRAPPC12 mutations showed delayed ER-to-Golgi transport and Golgi fragmentation, which is readily rescued by reintroducing wild-type TRAPPC12, suggesting that these mutations cause loss of function [Additional mutations in genes involved in regulating ER-to-Golgi trafficking have been linked to neurological disorders. For example, a proline-to-alanine substitution in cTAGE5 (P521A) is a risk factor for familial idiopathic basal ganglia calcification (or Fahr disease) . Fahr diutgrowth . Additiouropathy ,88,89,90R stress . Many muisorders ,92,93,94alopathy ,96. InteRAB1, a modulator of ER-to-Golgi trafficking, as the most prominent modifier of α-Synuclein toxicity. Overexpression of RAB1 mitigates neuronal loss in animal models of Parkinson’s disease [Defects in ER-to-Golgi trafficking arising as a secondary event may also contribute to neurodegeneration. For example, the cytotoxicity associated with the accumulation of α-Synuclein, the most abundant component of Lewy bodies  has been partially attributed to a block in ER-to-Golgi trafficking ,98. Gene disease ,98, highTREM2, an innate immunity receptor expressed on microglia, cause the mutant protein to cycle abortively between the ER and ERGIC, impairing delivery to the cell surface [TREM2 are linked to a syndrome characterized by early-onset frontotemporal dementia and recurrent bone fractures  [Finally, many neurological disease-causing mutations affect the delivery of specific membrane proteins  to the cell surface; however, only a few have been shown to directly impact ER-to-Golgi trafficking of the targeted proteins. For example, disease-associated mutations in  surface ,100,101.lopathy) ,103.ER-to-Golgi trafficking is a conserved process that fulfills numerous physiological functions. Our understanding of this process has advanced tremendously during the past several decades. Future studies to elucidate the regulatory mechanisms underlying various physiologic and pathologic conditions will shed light on how dysfunction of ER-to-Golgi trafficking contributes to diseases, such as neurodegeneration, and should facilitate the development of effective cures for these devastating diseases.


Mental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable.To develop and validate a population-level prediction model for need for early intervention in psychosis (EIP) care for first-episode psychosis (FEP) in England up to 2025, based on epidemiological evidence and demographic projections.We used Bayesian Poisson regression to model small-area-level variation in FEP incidence for people aged 16–64 years. We compared six candidate models, validated against observed National Health Service FEP data in 2017. Our best-fitting model predicted annual incidence case-loads for EIP services in England up to 2025, for probable FEP, treatment in EIP services, initial assessment by EIP services and referral to EIP services for ‘suspected psychosis’. Forecasts were stratified by gender, age and ethnicity, at national and Clinical Commissioning Group levels.A model with age, gender, ethnicity, small-area-level deprivation, social fragmentation and regional cannabis use provided best fit to observed new FEP cases at national and Clinical Commissioning Group levels in 2017 . By 2025, the model forecasted 11 067 new treated cases per annum (95% CI 10 383–11 740). For every 10 new treated cases, 21 and 23 people would be assessed by and referred to EIP services for suspected psychosis, respectively.Our evidence-based methodology provides an accurate, validated tool to inform clinical provision of EIP services about future population need for care, based on local variation of major social determinants of psychosis. Effects on longer-term outcomes, particularly in the absence of sustained intervention, are less apparent,14 and are a challenge to providing sustainable care models for people experiencing psychosis.The past decade has witnessed an unprecedented transformation in public perception of mental health,15 incorporating epidemiological knowledge to aid decision-making, to effectively allocate resources to EIP services based on anticipated local need in different populations. For example, age, gender and urbanicity have been consistently associated with the incidence of psychotic disorders in the UK, Europe and potentially beyond,16 and their local distributions could therefore inform population-level need for treatment each year. Such population-based approaches are increasingly recognised as vital to effective public mental health, but few examples are available to inform local and national mental health policy.17Optimising this care requires a broader evidence-based, pragmatic approach,19 Based on empirical epidemiological estimates of the incidence of first-episode psychosis (FEP) by age, gender, ethnicity and selected area-level characteristics such as deprivation, this model predicted the incidence (number and rate of new cases) in different population groups and regions based on the population structure of the 2011 Census of Great Britain. This model has subsequently informed national policy-making in the UK,21 including implementation guidelines for the landmark ‘Access and Waiting Time’ standard for EIP care in 2016,20 as part of the National Health Service and Department of Health's ‘Five-Year Forward View for Mental Health’,2 which legislated commitment to parity of esteem between physical and mental health.22 This standard mandated that at least 60% of all people aged 14–65 years who are referred to EIP services for ‘suspected psychosis’ must have been assessed, and where suitable, commenced a National Institute of Health and Care Excellence (NICE)-concordant,23 evidence-based25 package of EIP care within 14 days. However, although there is emerging evidence that most EIP services in England are already exceeding the waiting time target,26 achieving fidelity to other targets in the standard, including offering cognitive–behavioural therapy for psychosis, family interventions and supported employment and education programmes, has proved challenging,26 and may erode potential beneficial outcomes expected for patients available when full-fidelity EIP care is offered.17 A prerequisite to achieving such fidelity is that mental health service policy makers, commissioners and providers have access to timely and accurate information about future local need for EIP care, so that services can be appropriately resourced to deliver effective, optimised evidence-based care.One example previously applied to population-level need for EIP care in England is known as the PsyMaptic model (Psychiatric Mapping Translated into Innovations for Care).18 had limited generalisability to forecast future need for EIP care. For example, it was restricted to (a) a static denominator population, using the 2011 Census; (b) incidence data ascertained before the national introduction of EIP services in 2002; (c) social determinants of health data at a local authority level  and (d) ICD-10 diagnostic criteria for psychotic disorders, and not the broader need for assessment and treatment generated by referrals to EIP services for suspected psychosis.20Nonetheless, the previously published PsyMaptic tool28 and ‘Mental Health Implementation Plan in England’,29 which have recently reaffirmed and strengthened their commitment to EIP care, taking into account projected changes in the population at risk in England over the next 5 years.To overcome these limitations and facilitate generalisability of our methodology to other settings, we developed a new, population-based prediction model applied to psychosis care in England, validated against observed national routine data. Our methodology forecasted small-area need for EIP provision up to 2025 to inform the ‘NHS Long-Term Plan’https://doi.org/10.1192/bjp.2021.18).We followed the transparent reporting of multivariable prediction modell for individual prognosis or diagnosis reporting guidelines for prediction modelling (Supplementary Box 1 available at 33 (Supplementary Section 1): the Aetiology and Ethnicity in Schizophrenia and Other Psychoses (ÆSOP),30 the East London First Episode Psychoses (ELFEP)33 and Social Epidemiology of Psychoses in East Anglia (SEPEA)31 studies.Empirical incidence data were pooled from the three largest epidemiological catchment area studies of psychotic disorders in England in the past 25 years, which used similar methodologies, as described in detail elsewhere31) in each catchment area for suspected psychosis, and who later met research-based criteria for a diagnosis of for non-affective psychotic disorder (ICD-10 codes F20–29), affective psychotic disorder (ICD-10 codes F30–33) or substance-induced psychotic disorder (ICD-10 code F1X.5). Individuals with an organic basis to their disorder or profound intellectual disability were excluded.These studies included incidence data on all people aged between 16–64 years who presented to services . We stratified our seed data-set by age group , gender and self-assigned ethnicity  at ward level. We recorded the count of FEP cases and person-years at risk in each strata, estimated from the nearest decennial census to the case ascertainment period (Supplementary Section 3). Ward- and regional-level covariates were appended to this data-set.Participants were linked to their small-area neighbourhood of residence at first presentation . For each ward, we obtained measures of relevant socioenvironmental data, guided by previous models and the wider literature,a priori knowledge about the epidemiology of psychotic disorders, specified as prior probability distributions on the covariates to be estimated, informed by published evidence37 . We fitted our models by using integrated nested Laplace approximation38 to estimate the posterior distribution for the relevant parameters. Then, to fully characterise the uncertainty in the estimates and the predictions, we used Monte Carlo simulation to randomly sample 1000 values from the joint posterior distributions of each model parameter, simultaneously accounting for any correlation present between parameters in the model. This allowed us to capture the full uncertainty around the effect of each potential covariate on FEP incidence rates, which we expressed as posterior relative risks with their 95% credible interval estimates.We modelled the count of new (incidence) cases of psychotic disorders by using Poisson regression in a Bayesian framework. A Poisson distribution was preferred to a negative binomial distribution as there was no evidence of overdispersion within the seed data-set (Supplementary Table 1). The Bayesian approach allowed us to combine this data-set with a priori candidate models. We included age group, gender, their interaction and ethnic group in all models, with different permutations of deprivation, population density, social fragmentation and cannabis use fitted in each model. Person-years at risk were treated as an offset in the models.Initial modelling suggested that ethnic density  and inequality  were not associated with FEP incidence in our seed data-set, and were removed from further consideration. From the remaining covariates, we considered six N = 7678) between 2017–2025, stratified by age group, gender and ethnicity (as before). Projected population estimates up to 2025 were obtained from demographic forecasts with an established methodology,40 using 2011 Census data as a baseline, accounting for year-on-year changes in birth and death rates, immigration and emigration flows, and including assumptions about the impact of Brexit beyond 202040 (Supplementary Section 5). Population projections for the City of London (four wards) and Isles of Scilly (one ward) were excluded because of substantial overestimation of the ward population, when compared with Office for National Statistics 2017 mid-year population estimates.41 For socioenvironmental covariates, we used the latest available routine data at the time of model development for deprivation (2015),42 social fragmentation (2011), population density  and regional cannabis prevalence (2014)43 (Supplementary Section 2).We estimated the projected population at risk, aged 16–64 years, for all Census merged wards in England (44Posterior relative risk coefficients from each model were applied to the projected denominator data in a given year (2017–2025) at ward level, to estimate the predicted number of incident FEP cases in each age-gender-ethnic stratum in each ward. Within our Bayesian framework, we performed 1000 model simulations, sampling from the posterior distribution to yield the mean predicted FEP cases per stratum, and corresponding 95% confidence interval, derived from the 2.5% and 97.5% quantiles. This allowed us to aggregate prediction data to Clinical Commissioning Group (CCG) level and the national level by gender, broad age group  and ethnicity for any given year (2017–2025) (Supplementary Section 6). CCGs are autonomous, clinically led, statutory National Health Services (NHS) bodies responsible for the planning and commissioning of health services in local areas.45Apparent validity of the six candidate models was evaluated with the deviance information criterion (DIC), with a smaller value indicating better fit.46 The MHSDS records data from all health records for people in contact with mental health services in England. From this data-set, we extracted data for each CCG in England (N = 207) on the total number of new people (a) referred to mental health services for suspected psychosis; (b) accepted for assessment by EIP services; (c) who commenced EIP treatment for FEP and (d) who were probable FEP cases, based on empirical evidence that approximately 14% of those who commenced treatment for FEP in EIP services do not go onto to fulfil operationalised criteria for ICD-10 psychotic disorder.31 Those not meeting criteria for probable FEP in categories a–c implicitly included people meeting the clinical high risk for psychosis (CHR-P) criteria, who are not currently separately disaggregated within the MHSDS data-set. Formal definitions of each of these nested levels, henceforth referred to as referred/assessed/treated/probable FEP case-loads, are given in Supplementary Section 7.To externally validate our models, we compared predicted FEP data based on the 2017 denominator population, with observed national data available from NHS Digital's Mental Health Services Dataset (MHSDS) in the closest  year available . Comparisons were matched for the EIP age range served by each CCG in 2017 (Supplementary Section 7).r) between observed and predicted probable FEP counts, the r2 statistic and root-mean-square error (RMSE), with higher r2 and lower RMSE indicating better model fit. We inspected calibration plots of predicted versus observed FEP counts and compared the difference in predicted and observed FEP counts, expressed as a rate per 100 000 person-years at the CCG level. At the national level, we compared the difference between predicted and observed case-loads, expressed as a percentage, for the total count and by broad age group, gender and ethnic group. Choice of our best-fitting model was determined by consensus agreement between our authorship group, after evaluation of all apparent and external validation metrics.We inspected external validity at the CCG level by estimating correlation coefficients  because of problems with data validity in the MHSDS during this reporting period . These CCGs were reinstated for prediction forecasting (see below) between 2019 and 2025. MHSDS data were complete with respect to national- and CCG-level totals and by broad age group (16–35 and 36–64 years). Ten (0.1%) observed probable FEP cases were missing information on gender, and were excluded from gender-specific validation. Ethnicity data were missing for 820 (10.2%) observed probable FEP cases, which would have affected model validity by ethnicity without imputation, which we handled via imputation in sensitivity analyses (Supplementary Section 8).Our best-fitting model was used to forecast predicted probable FEP case-loads in England between 2019–2025, using the relevant denominator projections for each year. Predictions and their 95% confidence intervals, stratified by broad age group, gender and ethnicity, were aggregated to 2019 CCG and national levels for dissemination. We also forecasted new referred, assessed and treated case-loads  per annum by multiplying predicted probable FEP case-loads by the ratios derived from the observed data in the 2017 MHSDS (Supplementary Section 9). All prediction forecasts were expressed as incidence counts, and rates per 100 000 persons-years.47 prediction modelling was conducted with the integrated nested Laplace approximation package in R version 3.5 for Windows ,38 and out-of-sample prediction using matrix multiplication was performed in R.48Data-set generation and validation were performed in Stata version 15 for Windows,The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation, and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human patients were approved by NHS Health Research Authority . Consent was not required for this study, which was based on secondary use of aggregated, anonymised data from the ÆSOP, ELFEP and SEPEA studies and similarly aggregated NHS MHSDS data.34 most cases were male (62%), younger than 30 years old at first contact  and with an overrepresentation of participants from Black and minority ethnic backgrounds relative to the local at-risk population (varying from 24% in SEPEA49 to 77% in ELFEP33).From our seed data-set, we included 1638 cases of FEP from 4 515 379 person-years at risk . As typimodel 4 4.80, 95% CI 4.08–5.63) and Black African  ethnicity, consistent with the available literature.34 Population density was weakly associated with FEP incidence . Deprivation showed a non-linear relationship with FEP incidence, with exponentially higher rates at increasingly levels of deprivation . In contrast to expectations, a 1-s.d. increase in social fragmentation was associated with lower incidence . As expected, and driven by prior information,35 lifetime self-reported cannabis use was strongly associated with psychosis incidence .Apparent validation of our models suggested that inclusion of area-level predictors . Full mon = 20 492) were subsequently accepted for EIP assessment, 35.2% (n = 9346) commenced EIP treatment and 35.2%  met our definition of probable FEP. For the same period, model 4  predicted 8112 FEP cases per annum (95% CI 7623–8597), giving the lowest error of any model at the national level , compared with the observed probable FEP case-load , and, correspondingly, overpredicted case-loads aged 36–64 years  compared with observed probable FEP case-loads in the MHSDS data-set.All models performed similarly against national observed probable FEP case-loads stratified by major age group, gender and ethnic group (Supplementary Tables 7–9). Model 4 , for exa2r correlation coefficients (with models 2 and 3). For all models, calibration slopes were positive , indicating good agreement between observed and predicted probable FEP case-loads. For model 4, 60% (123 of 205) of predicted probable FEP case-loads at the CCG level were within ±10 cases per 100 000 person-years of the observed case-loads, with 87% (179 of 205) within ±20 cases per 100 000 person-years . For 2020, our model predicted a new referred case-load of 24 499 (95% CI 22 930–25 991), an assessed case-load of 22 033 (95% CI 20 622–23 375), a treated case-load of 10 516 (95% CI 9843–11 157) and a probable FEP case-load of 9066 (95% CI 8485–9618) aged 16–64 years . Between 2019 and 2025, predicted treated case-loads increased by 6.2%, on average, but we could not exclude the possibility of no change or a small decline in case-loads over time .www.psymaptic.org via an open-access web visualisation platform and downloadable data-sets.Forecasted FEP incidence varied geographically , covering 14.5% of CCGs in England. Predictions could be less accurate in areas that have very different sociodemographic or socioeconomic characteristics from those in our seed data-set regions. However, our best-fitting model displayed acceptable external validity to a national data-set at the CCG level, for total, gender- and ethnicity-specific case-loads in people aged 16–64 years. Second, our models under- and overestimated probable FEP case-loads in people aged 16–35 and 36–64 years, respectively, compared with observed data. This may have arisen from inclusion of epidemiological data in our seed data-set collected before the introduction of EIP services in England;33 there is some evidence that age at first contact with mental health services may have fallen since their introduction.31 Third, external validity of our models was reliant on the reliability and validity of observed FEP data from the MHSDS. Sensitivity analyses allowed us to validate our models in the presence of missing observed data on ethnicity. Uncertainty in either our predictions or the observed data could explain deviance in our calibration plots at the CCG level. Since the MHSDS only provides information on who commenced EIP treatment, and not the subset of individuals who met diagnostic criteria for FEP, we revised observed treated case-loads downward by 14% to obtain a comparable FEP sample, in line with empirical evidence.31 This highlights the importance of recording reliable symptomatic and likely diagnostic outcomes in routine practice. Future models should include explicit predictions of people who meet CHR-P criteria for psychosis, implicitly included in our forecasts of new referred, assessed and treated case-loads per annum, but who could not be disaggregated because there is currently no provision to identify these groups separately in the MHSDS, upon which our forecasts were based. Fourth, our models did not account for predicted need in people aged <16 years , or homeless or prison populations.Prediction models are influenced by their underlying assumptions and limitations. Four issues are of particular note. First, our seed data included incident cases from approximately 10% of 2011 Census merged wards (16 Applied to FEP in England, our model provides healthcare commissioners with a decision-making tool to support the allocation of finite resources for EIP services, based on forecasts of need likely to arise in local populations, enhancing an earlier, more limited tool.18 Providing commissioners with access to evidence-based forecasts of such need should help optimise and fully implement NICE-concordant care for psychosis.22 This would provide the foundation for realising the demonstrable, evidence-based improvements in clinical and social outcomes for individuals experiencing FEP, as well as the cost-effectiveness benefits on which they were established.52 Moreover, optimal resource allocation in one mental health domain should propagate benefits to other areas of care for which mental health commissioners and planners are responsible. Our predictions also allow care providers to understand the likely sociodemographic and environmental characteristics of their case-loads, which may be useful for providing locally and regionally sensitive modes of care delivery across diverse communities.Our Bayesian population-level prediction model could be readily adapted and updated to forecast need beyond 2025, in other countries or for other disorders, where sufficiently reliable risk estimates and denominator data are available. Application to other countries, particularly the devolved nations in the UK, is both vital for public mental health and also offers the opportunity to establish the robustness of our models to populations with potentially different distributions of underlying risk factors for psychosis.53 Our prediction methodology could be readily generalised to other settings or mental health problems underpinned by a precise, reliable epidemiology. Priorities here would include extension to predict people at clinical high risk of psychosis, ongoing recalibration in England, and extension and validation to other countries. Applied to psychosis care in England, our platform provides commissioners and providers with an open-access, evidence-based toolkit to optimise need for EIP care as and when it arises.Overall, we have developed and validated a generalisable, translational tool that used epidemiological data to forecast accurate estimates of future incidence of first-episode mental health disorders in different communities. The tool currently informs recently-updated NICE guidelines in England for EIP service provision.


Introduction: Traumatic cervical artery dissections are associated with high mortality and morbidity in severely injured patients. After finding even higher incidences than reported before, we decided to incorporate a dedicated head-and-neck computed tomography angiogram (CT-A) in our imaging routine for patients who have been obviously severely injured or, according to trauma mechanism, are suspected to be severely injured. Materials and Methods: A total of 134 consecutive trauma patients with an ISS ≥ 16 admitted to our level I trauma center during an 18 month period were included. All underwent standardized whole-body CT in a 256-detector row scanner with a dedicated head-and-neck CT-A realized as single-bolus split-scan routine. Incidence, mortality, patient and trauma characteristics, and concomitant injuries were recorded and analyzed in patients with carotid artery dissection (CAD) and vertebral artery dissection (VAD). Results: Of the 134 patients included, 7 patients had at least one cervical artery dissection . Six patients (85.7%) had carotid artery dissections, with one patient having a CAD of both sides and one patient having a CAD and contralateral VAD combined. Two patients (28.6%) showed a VAD. Overall mortality was 14.3%, neurologic morbidity was 28.6%. None of the patients showed any attributable neurologic symptoms on admission. The new scanning protocol led to further 5 patients with suspected CeAD during the study period, all ruled out by additional magnetic resonance imaging with angiogram (MRI/MR-A). Conclusion: A lack of specific neurologic symptoms on admission urges the need for a dedicated imaging pathway for severely injured patients, reliable for the detection of cervical artery dissections. Although our modified CT protocol with mandatory dedicated CT-A led to false positives requiring additional magnetic resonance imaging, it likely helped reduce possible therapeutic delays. Overlooked injuries in severely injured patients are feared complications in the acute care of patients. Euler et al. were able to show that in 25% of cases at least one diagnosis remains undiscovered in severely injured patients . With a In the daily routine, a contrast-enhanced multidetector computed tomography (CT) of the head, neck, thorax, abdomen, and pelvis according to the S3 guideline is used in severely and multiply injured patients and offers a demonstrable survival advantage , even whHowever, there are no specific guidelines on how to identify vascular injuries in the neck area as accurately as possible. In the current S3 guidline for the care of severely injured patients, for example, only vague recommendations for head-and-neck injuries are made . Even hiIn single-bolus contrast-enhanced whole-body CT scans, traumatic lesions of the carotid and vertebral arteries often go unrecognized because of the low arterial contrast and contrast-enhanced parallel veins and soft tissues. Beam hardening artifacts at the skull base further worsen the detectability of such lesions, which mostly are discreet. If suspicion is raised, further diagnostic work-up is needed fast, causing additional radiation and contrast-agent dose in case of a CT-angiogram (CT-A) and time, cost, and delay in therapy of concomitant injuries. There is no widely accepted standard for addressing this dilemma in between overdiagnostics firsthand or missed diagnosis and stepped-up diagnostics later on. As a first step, the implementation of a non-dedicated angiogram revealed incidences of 6.5% for cervical artery dissection (CeAD), which were above reported values of 1.7% and 4.9% ,10.We thus decided that any patient qualified for a standard trauma CT scan, who has either been obviously severely injured or, according to trauma mechanism, is suspected to be severely injured, will receive a dedicated head-and-neck CT-A in between the non-contrast-enhanced brain scan and the delayed phase whole-body scan. As any injury to the head-and-neck region, especially vessel injuries such as carotid (CAD) or vertebral artery dissections (VAD), is of highest relevance for mortality, morbidity, and immediate therapeutic decisions, we accepted the additional radiation and contrast-agent trade-off based on an interdisciplinary consensus in our trauma center; this decision is in accordance with current recommendations of the Deutsche Röntgengesellschaft . After For this study, all patients admitted to the emergency room of our level I trauma center requiring a whole-body CT scan since November 2018 with an ISS ≥ 16 after complete diagnostic work-up were included. The decision for the CT scan is based upon the trauma mechanism and assumptions of its severity, e.g., based on estimated driving speed in car accidents, ejection from vehicle, falling from a great height (>3 m), burial trauma, death of co-driver, the observable injuries and circulation parameters in the emergency room, patient’s age, and assumed frailty, in accordance with national recommendations and guidelines as cited above. The decision is made by the leading trauma surgeon and radiologist on duty as a consensus. In November 2018, the whole-body CT scanning protocol was modified to a single-bolus split-scan approach including a dedicated contrast-enhanced head-and-neck CT-A triggered in the Aorta ascendens. The aim of this modification was to further optimize the detection rate of cervical artery dissections as we already had a non-dedicated head-and-neck angiogram as a mandatory component of our scanning protocol  with fixThe independent Ethics Committee at the Regensburg University confirmed that, for this specific scientific project, no dedicated ethics approval or commission’s opinion was necessary according to our applicable laws and guidelines. The consultation was filed under no. 20-1914-104.All patients were examined using a 2 × 128-detector row dual-energy scanner , fulfilling the requirements stated in the national S3 guideline “Polytrauma/Schwerverletzten—Behandlung” .First, a non-contrast-enhanced scan of the neurocranium was acquired in 0.75 mm slices . The second scan is the contrast-enhanced head-and-neck CT-A, triggered in the A. ascendens. It reaches from the aortic arch covering the outlets of the supra-aortic branches up to the vertex. The volume and concentration of the contrast agent used  were standardized to a total of 120 mL, with 12 injected for the head-and-neck CT-A. The contrast agent injection was followed by a flush with 20 mL of saline, both contrast agent and saline flush were injected with rates of 3.5 mL/s. ECG-gating is not routinely used in our protocol. The third scan was a venous phase delayed scan, covering the thorax, abdomen, pelvis, and upper half of the thigh; in select cases, the scan length was modified to cover the knees. It was scanned with a fixed delay of 30 s after the CT-A. The standard field of view was 500 mm, the slice thickness was 5.0 mm, the increment was 5.0 mm, the pitch was 0.60, kV and mAs were calculated and set automatically  based on the scan topogram. A soft-tissue kernel with medium edge attenuation (B26f medium) was used for the calculation of axial, coronal, and sagittal views of the head and neck. Additional thick-slab axial maximal intensity projection (MIP) was rendered (10 mm) axial, sagittal, and coronal. Additional reconstructions were rendered using a B60f kernel for lung tissue , a B60f sharp kernel for bones , and a B30f kernel for soft tissue . Further reconstructions were calculated on the radiologists’ discretion depending on the findings or suspicions drawn from the standard datasets.Image interpretation was performed using a standard three-monitor workstation using the Syngo and Syngo.via picture archiving and communication system . All imaging studies were read and validated by a board-certified radiologist. Additional magnetic resonance angiography (MR-A) and/or ultrasound were performed if the diagnosis of a cervical artery dissection remained unclear after the CT-A. Both MR-A and ultrasound were not mandatory parts of our diagnostic routine in the time period covered in this study.For statistical calculations, analysis, and plotting, GraphPad Prism Version 5.00a for Mac  and Microsoft Excel Version 16.38  were used. Arising from the observational character of this study, mainly, descriptive statistics was used. We did not calculate comparative statistics due to the small cohort size. Demographics and injury severity were compared against the cohort of all severely injured patients recorded in our database and/or the cohort of patients with CeAD before the most recent modification of our CT scan protocol, as reported before . We calcSince introduction of our new CT scan protocol, 134 severely injured patients with an ISS ≥ 16 underwent the standardized whole-body computed tomography including a dedicated head-and-neck CT-A. Thereof, 7 patients were found with at least one cervical artery dissection . Six patients  had carotid artery dissections, with one patient having a CAD of both sides, and one patient having a CAD and contralateral VAD combined. Two patients  showed a VAD. ISS and mean age as well as gender distribution were comparable to past time frames .Another five patients underwent additional MRI including MR-A after CT with CT-A, which did not confirm a suspected cervical artery dissection. In 2 out of 7 cases with CT-A-diagnosed CeAD reported here, MRI confirmed the diagnosis. In 5 cases, CT-A diagnosis required no further imaging work-up. In all, 3 of 7 patients had an ultrasound of the extracranial vessels but none was able to identify the dissection; two of them had an MR-A-confirmed diagnosis of CeAD.On admission, none of the patients showed any neurologic symptoms attributable to a CeAD. Five patients were put on anticoagulation (71.4%), two patients were not eligible for anticoagulation due to either intracerebral hemorrhages or a severe distraction-flexion-type spinal fracture C4–7. Two patients developed serious neurologic symptoms  due to carotid artery dissections, yielding a morbidity of 28.6%. One patient died, yielding a mortality of 14.3%.The trauma mechanism was a fall from large height in one case (14.3%) and traffic accidents in all other cases . Concomitant spinal fractures were found in 4 cases (57.1%). One patient had a complex midface fracture reaching into the skull, one patient had no fracture of the spine or thorax . Three pIn severely injured patients, rates of cervical artery dissections are reported to be as low as 0.9% based on ultrasound  and saidDifferent conclusions can be drawn from the observations made: first, the true rate of cervical artery dissections in severely injured patients is likely to be in between our reported values of 6.5 and 5.2% in our trauma center and seems to vary rather widely within different populations as reported from different trauma centers worldwide; the Wilson score interval, which is a variant of the normal approximation 95% confidence interval more suitable for small sample sizes and edge proportions near 0 as in our case, yields a 95% CI range between 2.6% and 10.4% for CeAD in this cohort.Second, dedicated CT-A did not lead to more dissections revealed in our cohort but did elicit uncertain or suspicious cases requiring additional imaging .Third, the most powerful predictor for cervical artery dissections seems to be any kind of high kinetic trauma mechanism, especially traffic accidents including cars, motorcycles/-scooters, bikes, and e-bikes, which is also true for aortic arch injuries due to a “whiplash”-type motion of fast deceleration and re-acceleration ,15. SomeFourth, large multicenter or registry-based studies are required to accurately assess cervical artery dissection risk factors, outcomes, and characteristics, as it will remain a low-incidence injury and rates between 1%  and 6.5%Fifth, multimodal work-up of suspicious cases is highly relevant as with a dedicated head-and-neck CT-A a tendency toward false positives can be seen. With ultrasound, which often is used as the first add-on diagnostic due to fast availability and low cost, false negatives are very likely , being uDespite the low incidence, the diagnosis of any artery dissection has to be made fast due to the severe consequences from any therapeutic delay, especially in cervical vessels as central nervous ischemia or embolization can lead to devastating and irreversible neurologic sequelae. In a population aged 45 and younger, 20% of ischemic strokes are due to traumatic and non-traumatic CeAD . MoreoveInjuries of the carotid or vertebral artery are reportedly associated with both high mortality rates and neurological morbidity rates, as again seen in the cohort reported here. In severely injured patients, diagnoses and decisions only based on history and clinical examination are prone to error with missed injuries in up to 47%. As none of the patients with CeAD in this population showed symptoms hinting specifically at a cervical vessel injury on admission, these diagnoses most likely would have gone unrecognized until the development of neurologic symptoms. Interestingly, a significant number of (66–73%) patients with blunt carotid injury may be asymptomatic upon initial presentation, developing delayed neurologic symptoms anywhere from 1 h to 7 days after injury. The onset of ischemic events can range from a few minutes to 31 days after injury, with the majority (82%) occurring within the first 7 days ,22.This delay in therapy needs to be avoided under all circumstances as secondary worsening or complications such as cerebral embolization could be prevented. Occlusive CeAD, multiple CeAD, and vertebral artery dissections result in an increased risk for delayed stroke, as reported by Lichy et al. .Comparing the rates reported here with larger studies, our values are above those of others, possibly resembling missed injuries due to non-specific imaging protocols. Nevertheless, our values are lower than those reported from clinically preselected cohorts and our own past cohort. As a comparison, Lee et al. report an average annual incidence rate for CeAD of any cause of 2.6 per 100,000 population  . Major tAs cervical artery dissections are highly relevant for the severely injured individual admitted to any emergency room, we wanted to report here the first results from our modifications of our diagnostic routine with the intent to nurture further discussions on the best diagnosis and treatment regimes in these uncommon but regularly to be seen cases. Thus, some relevant limitations are inherent to our study design.First, as a single-center study reporting from an 18 month period, only few cases could be found and included. This needs to be addressed by further multicenter or registry-based examinations. As a direct consequence, we decided to report descriptive statistics and to not include comparative statistics, as this could tempt readers to draw conclusions that are not backed by sufficient data as this is a preliminary report. Second, in our level I trauma center, severely injured patients are admitted at a rate above the nationwide average in both severity and frequency, due to geographic and strategic reasons within the local emergency medicine services. Considering this, other hospitals will likely find lower or even higher incidences of CeAD in their population. Third, the true incidence of cervical artery dissections still remains unknown; no case showed neurologic symptoms on admission, some dissections remain clinically inapparent (3 out of 7 in this study), and a definite multimodality diagnostic work-up for suspected cases or even all severely injured patients is not yet established. In consequence, further studies should address the question of true incidence of CeAD by multimodality work-up including MR-A. However, capped capacities for additional MR-A work-up especially in smaller hospitals is another limitation that needs to be considered in setting up a diagnostic pathway for severely injured individuals.The sensitivity (64–100%), specificity (67–100%), positive predictive value (65–100%), and negative predictive value (70–100%) of CT-A for the detection of CeAD compared with those of DSA vary widely with later studies indicating values close to those of DSA due to essential technical improvements made in the last few years ,27. MR-AOne key finding of the results reported here is the need for a diagnostic routine in severely injured patients that is able to reliably detect cervical artery injuries, as none of the patients covered in the recent 18 month period showed symptoms on admission. Whenever possible, we put patients with CeAD on anticoagulation based on a case-by-case interdisciplinary consensus. Whether the low morbidity and mortality in comparison to cohorts reported before can be attributed to our imaging pathway cannot be answered from this study reasonably due to the limitations discussed before. As a first measure, any suspected case of cervical artery dissection should be ruled out by MRI/MR-A and/or qualified ultrasound within a defined and short timeframe  based on the high mortality and morbidity risk in un- or delayed diagnosed cervical artery dissections.Modification of the trauma imaging routine to include a dedicated head-and-neck CT-A realized as a single-bolus split-scan protocol showed an incidence of 5.2% for cervical artery dissections after the first 18 months under operation, which is slightly below values reported before, as are both mortality (14.3%) and morbidity (28.6%) in this cohort. We repeatedly encountered false-positive findings requiring further diagnostic work-up with MR-A. Ultrasound did not help substantially to make or rule out the diagnosis of CeAD in our setting. Further studies will need to address the value of multimodality diagnostics and the role of state-of-the-art ultrasound techniques for extra- and intracranial vessel diagnostics. As none of the detected cases of cervical artery dissections showed neurologic symptoms on admission, a reliable, fast, and multimodal imaging pathway for the detection of cervical artery dissections in severely injured patients is of utmost importance in trauma centers of any size to avoid any therapeutic delay.