Numerous studies have demonstrated the potential association between rainfall and hand, foot and mouth disease (HFMD), but the results are inconsistent. This study aimed to quantify the relationship between rainfall and HFMD based on a multicity study and explore the potential sources of spatial heterogeneity.We retrieved the daily counts of childhood HFMD and the meteorological variables of the 143 cities in mainland China between 2009 and 2014. A common time series regression model was applied to quantify the association between rainfall and HFMD for each of the 143 cities. Then, we adopted the meta-regression model to pool the city-specific estimates and explore the sources of heterogeneity by incorporating city-specific characteristics.I2 = 52.75%, P < 0.001). By incorporating the city-specific characteristics into the meta-regression model, temperature and student density can explain a substantial proportion of spatial heterogeneity with I2 statistics that decreased by 5.29 and 6.80% at most, respectively.The overall pooled estimation suggested a nonlinear exposure-response relationship between rainfall and HFMD. Once rainfall exceeded 15 mm, the HFMD risk stopped increasing linearly and began to plateau with the excessive risk ratio (ERR) peaking at 21 mm of rainfall . We also found significant heterogeneity in the rainfall-HFMD relationships (Our findings verified the nonlinear association between rainfall and HFMD. The rainfall-HFMD relationship also varies depending on locations. Therefore, the estimation of the rain-HFMD relationship of one location should not be generalized to another location. Hand, foot and mouth disease (HFMD) is an acute highly contagious viral infection caused by enteroviruses and predominantly affects children under 5 years of age , 2. WhilMultiple studies suggested that the water environment plays a crucial role in the survival of enterovirus , thereby potentially affecting the transmission of HFMD. Water environments not only provide the major natural reservoir for enterovirus  but alsoAlthough a growing number of epidemiological studies have been recently conducted to quantify the associations between rainfall and HFMD, evidence from previous studies remains inconsistent. Studies in both southern China  and ThaiTo address the above research gap, we proposed a multicity two-stage time series study including 143 cities in mainland China. By estimating the associations between rainfall and HFMD for each of the 143 cities under a common modeling framework, we aimed to assess the spatial heterogeneity of rainfall-HFMD relationships and further explore the potential reasons.Details of data used in this study have been described previously , 23. BriGiven that most of HFMD cases came from urban areas, we aggregated and matched the daily time series of HFMD and meteorological variables of prefectural-level cities (corresponds to medium to large size cities under China’s administrative structure) in mainland China. Confining our analysis to prefectural-level cities also assures better data quality. Finally, we included 143 cities with successful matching in our final analysis. To further explore the sources of spatial heterogeneity, city-specific characteristics were collected from the China city statistical yearbook , includiWe adopted a multicity two-stage time series design to robustly estimate rainfall-HFMD relationships and explore potential heterogeneity . In the In summary, we applied a common quasi-Poisson time series regression with parametric splines to quantify the associations between rainfall and HFMD for each of the 143 cities. We used the quasi-Poisson distribution was assumed to account for overdispersion of disease counts. The natural cubic spline with 8 degrees of freedom (df) per year was used to control for the seasonality and long-term trends  . We alsoIn previous studies, rainfall was incorporated into models as either a continuous , 33 or cI2 statistics and the Cochran Q test [Corresponding to the two approaches used in the first-stage analysis, we applied different methods to obtain the overall rainfall-HFMD relationships depending on the variable types of rainfall. For continuous-scale rainfall, city-specific nonlinear exposure-response relationships between rainfall and HFMD were pooled by the multivariate meta-regression model with random intercepts  to obtain Q test .To further explore the reasons for potential heterogeneity, we incorporated the city-specific characteristics as meta-predictors into the above models with random intercepts. We included city-specific characteristics separately rather simultaneously (denoted as single predictor model) given that our main goal was to confirm potential effect modifiers rather than model predictions. Another added benefit of the single predictor model was the avoidance of collinearity issues due to high correlations among city-specific characteristics. Of note, the rainfall distribution was highly right skewed with sparse data in the high value range, which might lead to non-robust estimations of the right tail of exposure-response curves. Therefore, we only reported the results of meta-predictors for the categorical scale rainfall in the main text. Additional details of the corresponding results for continuous-scale rainfall are provided in Additional file To exclude the possibility that the model choices may substantially affect our conclusions, we implemented additional sensitivity analysis regarding key model choices in the time series regression models. The key model choices included the df of the natural cubic splines used to characterize the rainfall-HFMD relationships, the lag range of the rainfall-HFMD relationships and alternative cut-off points of rainfall groups . Additional details of our sensitivity analysis results are reported in Additional file dlnm, mvmeta and metaphor packages. Geographic maps were generated using ArcGIS Pro software .Statistical analyses were performed using R software (version 3.5.3), specifically From 2009 to 2014, by including 143 cities in mainland China, we retrieved a total of 3,200,157 HFMD clinical cases in children under 12 years old. Our study area covered the majority of mainland Chinese with great diversity in geographic and climatic conditions. We found that HFMD circulated widely throughout the whole country. In general, HFMD incidences steadily increased from north to south. We particularly observed a high incidence area in the southernmost part of China where the annual HFMD incidences for multiple cities exceeded 1000 cases per 100,000 persons . We observed an approximately linear increase in the HFMD risk as rainfall initially increased. Once rainfall exceeded 15 mm, the HFMD risk began to plateau and was even reduced as rainfall further increased. However, we would prefer not to overinterpret the results of extreme rainfalls due to limited data. Overall, we observed a nonlinear association between rainfall and HFMD, and the excessive risk ratio peaked at 21-mm rainfall .Based on continuous-scale rainfall data, we first pooled the city-specific estimates of exposure-response curves between rainfall and HFMD based on the multivariate meta-regression model Fig. . The oveI2 statistics increased from 24.98% in the low rainfall group to 45.66% in the extreme rainfall group.For categorical-scale rainfall, city-specific estimates for different levels of rainfall were pooled separately by univariate meta-regression model Fig. . The resI2 statistics decreased by 3.23 and 5.29%, respectively .Additional file 4: Figure S3. The overall pooled estimates of HFMD-rainfall relationships for different choice of lag range with the max lag time ranged from 7 to 21 days.Additional file 5: Table S2. The pooled estimates (measured by ERR) of low, high and extreme rainfall with alternative cut-off points .


Pseudomonas aeruginosa biofilm infections. We showed that the pathways of CIP-resistance development during exposure of biofilms and planktonic P. aeruginosa populations to subinhibitory levels of CIP depend on the mode of growth. In the present study, we analyzed CIP-resistant isolates obtained from previous evolution experiments, and we report a variety of evolved phenotypic and genotypic changes that occurred in parallel with the evolution of CIP-resistance. Cross-resistance to beta-lactam antibiotics was associated with mutations in genes involved in cell-wall recycling ; and could also be explained by mutations in the TCA cycle (sdhA) genes and in genes involved in arginine catabolism. We found that CIP-exposed isolates that lacked mutations in quorum-sensing genes and acquired mutations in type IV pili genes maintained swarming motility and lost twitching motility, respectively. Evolved CIP-resistant isolates showed high fitness cost in planktonic competition experiments, yet persisted in the biofilm under control conditions, compared with ancestor isolates and had an advantage when exposed to CIP. Their persistence in biofilm competition experiments in spite of their fitness cost in planktonic growth could be explained by their prolonged lag-phase. Interestingly, the set of mutated genes that we identified in these in vitro-evolved CIP-resistant colonies, overlap with a large number of patho-adaptive genes previously reported in P. aeruginosa isolates from cystic fibrosis (CF) patients. This suggests that the antibiotic stress is contributing to the bacterial evolution in vivo, and that adaptive laboratory evolution can be used to predict the in vivo evolutionary trajectories.Ciprofloxacin (CIP) is used to treat  In addition, antibiotics at sublethal concentrations are responsible for increasing genetic variation4 by means of different pathways involving oxidative stress5, by inducing error-prone polymerases mediated by the SOS response6, by misbalancing nucleotide metabolism or acting directly on DNA7. In this respect, quinolones have been known for years to be mutagenic in bacteria8. This implies that antibiotic therapy, in some cases, may have the detrimental side-effect of accelerating the adaptation of pathogens as well as of the commensal strains (suggested to be a major reservoir of resistance).Sublethal concentrations of antibiotics are present in clinical settings at the infection site in certain tissues as a consequence of limited accessibility as well as in the environment. It has been shown that resistant mutants, even with high MICs can be selected at sublethal antibiotic concentrationsPseudomonas aeruginosa, that high-level CIP-resistant mutants with cross-resistance to beta-lactams developed quickly in the presence of a subinhibitory concentration of ciprofloxacin9, and a phenotypic shift of the P. aeruginosa populations evolved under constant exposure to subinhibitory concentrations of CIP10. Compared to the controls (CTRL), the populations evolved in the presence of sublethal concentrations of CIP showed decreased protease activity and swimming motility, higher levels of quorum-sensing (QS) signal molecules and upregulation of denitrification genes10. These observations suggested that evolution in the presence of sublethal concentrations of CIP has pleiotropic effects on the bacterial phenotypes and this might promote persistence of the resistant bacterial population.We have previously reported in a planktonic experimental evolution study conducted on more than 900 generations of 11. However, several recent studies have demonstrated the supplementary role played by mutational resistance in the survival of biofilms to antibiotic treatment13.Persistent infections are caused by biofilm-embedded bacteria which are characterized by multifactorial intrinsic tolerance (phenotypic resistance) to antibioticsP. aeruginosa biofilms exposed to subinhibitory concentrations of CIP and showed that the biofilm mode of growth promotes development of low-level mutational resistance14 and that this process was accelerated during evolution of a P. aeruginosa catalase mutant, as a surrogate of the oxidative stress environment present at the site of chronic infections15.We have recently investigated the evolution of mutational resistance in 10 encouraged us to investigate, the phenotypic and genomic evolution associated with CIP-resistance in biofilms15. The experimental set-up of the investigation of the phenotypic and genotypic landscape of evolution under exposure to subinhibitory levels of CIP is presented in Fig. The phenotypic shifts that we have previously observed in the experimental evolution of planktonic cultures exposed to sublethal levels of CIPP. aeruginosa biofilms to sub-MIC CIP led to mutations which were associated with extensive phenotypic changes. Cross-resistance between CIP and beta-lactam antibiotics such as aztreonam and ceftazidime, differential swarming and twitching motility compared to CTRL, growth curves with a prolonged lag phase, and metabolic shifts toward anaerobic respiration were observed. A fitness cost of CIP resistance in biofilms was observed in the absence of antibiotics. However, non-surprisingly, CIP-resistant colonies had increased fitness when the competition experiments were conducted in the presence of CIP. Overall, our data show that under antibiotic stress, mutational resistance to CIP associates with mutations in several genes that probably play a role in the persistence of biofilms during antibiotic treatment. Interestingly, the set of mutated genes that we identified in these in vitro-evolved CIP-resistant colonies, overlap with a large number of patho-adaptive genes previously reported in P. aeruginosa isolates from cystic fibrosis (CF) patients. This is identifying antibiotic stress as an important environmental factor that shapes the bacterial evolution in vivo. It can thus be speculated that this, together with the inflammatory response leads to the so-called chronic phenotype. Chronic phenotype occurs when bacteria become well-adapted to the conditions of the respiratory tract of CF patients and, therefore, persists in spite of antibiotic therapy.We show here that prolonged exposure of katA) representing 57 isolates recovered from CIP-evolved populations and 35 isolates recovered from CTRL-evolved populations were analyzed.Growth curves of 92 CIP-resistant isolates .p = 0.002). For WT PAO1, the lag phase (median (range) in h) was 5.36 (3.5–13) vs. 3.5 (3.5–9.7) while for ΔkatA it was 6.84 (4.2–14) vs. 5.2 (4.8–6.5)  increase in the doubling time was found for the colonies isolates from CIP evolved compared to CTRL populations. The doubling time (median (range) in min) was 97.5 (71–893) vs. 69 (69–104). This was not observed for colonies isolated from the planktonic evolution experimentFor CIP-resistant colonies of the WT PAO1, a statistically significant  of 42 CIP-resistant colonies (24 colonies from CIP-evolved populations and 18 colonies from CTRL-evolved populations) , with the exception of CIP_Pl_10, CIP_Pl_11, and CIP_Pl_14. The highest fitness cost was observed for the CIP-resistant colonies isolated from the planktonic evolution of ΔR2 = 0.53) between the lag time and the planktonic FI was observed: the shorter the lag time, the better the fitness , growth advantage compared to ancestor colonies  Fig. .Fig. 4MoNo significant difference in swimming motility between the CIP and CTRL isolates was observed, though for both groups the CIP-resistant isolates were impaired in swimming compared to ancestor colonies.p = 0.0001) and higher twitching motility (p = 0.03) compared to CIP-evolved populations.CIP-resistant colonies from CTRL-evolved populations had significantly lower swarming motility  were determined and are presented in Fig. The level of resistance was higher in the CIP-resistant colonies from CIP-exposed populations compared to CTRL population and simultaneous resistance to beta-lactams such as aztreonam and ceftazidime was observed, in many cases with MICs above the clinically resistance breakpoints  and 18 CIP-resistant colonies recovered from CTRL-evolved populations (11 colonies isolated from biofilm populations and 7 colonies isolated from planktonic populations) for both WT PAO1 and N/dS  at high frequencies might explain the high-MIC values to ciprofloxacin. In addition, in one of the colonies CIP_Pl_10, mutations in PA3228 gene that encodes for a putative ATP-binding/permease fusion ABC transporter that may function as a multidrug transporter was observed with high frequencies (>80%).In CIP-resistant colonies from WT PAO1 planktonic populations, simultaneous mutations in gyrase nfxB gene, a negative regulator of efflux-pump MexCD-OprJ was repeatedly mutated as well as other efflux-pump regulator genes, such as mexR and mexS, at high frequencies. The MIC ciprofloxacin levels in these colonies are lower than in planktonic colonies with the exception of CIP_BF_5  had a 28 nucleotide insertion in mutL causing a hypermutable phenotype .In CIP-resistant colonies of henotype . In the A list of mutated genes that might explain for the observed cross-resistance of CIP-resistant colonies to beta-lactams, and especially aztreonam is presented in Fig. nfxB, nalC, nalD, and mexR) were identified. These mutations cause upregulation of the MexCD-OprJ and MexAB-OprM pumps which, besides ciprofloxacin, also accommodate beta-lactams. Interestingly, in addition we detected mutations in genes encoding for proteins involved in cell division (the divisome) , penicillin-binding protein pbpAC and amp genes involved in the regulation of AmpC beta-lactamase . It is noteworthy that these mutations are not present in WT CTRL-resistant colonies. These mutations might explain the increased doubling time of the CIP-resistant colonies.In addition, mutations in sdhA gene that encodes succinate dehydrogenase was mutated at frequencies higher than 80%. In addition, poxB and PA2290 genes that encodes pyruvate and glucose dehydrogenases were also found to be mutated.In some of the CIP-resistant colonies, mutations in genes related to TCA cycle were observed. In colonies no. CIP_BF_7, CIP_BF_21, and CIP_Pl_25, the ΔkatA-hypermutable CIP-resistant colonies, the genes related to iron storage and transport were mutated such as fecA, fecI, hasR, and PA2911 that encodes tonB dependent receptors at frequencies above 80%  during evolution experiments in the presence of antibiotics30.We have previously shown in planktonic experimental evolution studies that higher levels of QS molecules in P. aeruginosa populations evolved under sub-MIC ciprofloxacin compared to CTRL10.Swarming, which is QS regulatedP. aeruginosa exerts adaptive resistance to a number of antibiotics, including CIP, which might explain the maintenance of the swarming motility during evolution in the presence of CIP28.It has been shown that swarming pil genes encoding for type IV pili identified in CIP-resistant colonies. The association between CIP resistance and mutations in pil genes has been reported by other groups but the reason for this association is not completely clear24.The loss of twitching motility is correlated to the frequent mutations in aruL involved in the arginine decarboxylase and arcA involved in the arginine deaminase catabolic pathways were identified. Impairment of these pathways promotes the utilization of arginine by nitric oxide synthase to convert it to nitric oxide32. Nitric oxide will promote anaerobic respiration, and this is in agreement with previous studies that showed that anaerobic respiration is promoted in P. aeruginosa under CIP treatment33. Nitric oxide has been shown to be involved in the biofilm disruption34, but if this would occur as a consequence of the metabolic rewiring in CIP-treated biofilms remains to be investigated.Mutations in genes responsible for the catabolism of arginine such as P. aeruginosa mutants overexpressing efflux pumps were shown to rewire their metabolism to avoid fitness cost including increased expression of the anaerobic nitrate respiratory chain when cells are growing under aerobic conditions35.Antibiotic-resistant 5 showing that mutants with impaired activity of TCA cycle enzymes were more tolerant to antibiotics due to decreased levels of bactericidal hydroxyl-radicals36.In accordance with the metabolic rewiring in the presence of ciprofloxacin, we have detected mutations in the enzymes involved in the TCA cycle in CIP-resistant colonies. This is in agreement with studiesP. aeruginosa isolates that have evolved in the lung of CF patients37 and associated to the impaired catabolic capacity of the isolates. We have previously reported that metabolic reduction is a common adaptive trait of the P. aeruginosa nonmucoid isolates in the CF lung38 which has probably an impact on the persistence of the bacteria in the lung, in spite of the antibiotic treatment.Interestingly, mutations in the enzymes of the TCA cycle have been reported in clinical σ54 (rpoN) and σS(rpoS) were found to be mutated in some CIP-biofilm resistant colonies and ΔkatA planktonic-resistant colonies and associated to impaired growth of the isolates. However, mutations in these genes were not detected in WT PAO1 planktonic-CIP and either of the CTRL-resistant colonies. Interestingly, rpoN mutants were found in P. aeruginosa from chronically infected patients39.In addition, RNA polymerase P. aeruginosa isolates from chronic lung infections of patients with CF18 revealed a large degree of overlap  were only mutated in clinical isolates suggesting the role of the immune system in the selection of these phenotypes, in accordance to our previous publication40.A comparison of the identified mutated genes in CIP-resistant colonies from in vitro-evolved populations with a list of patho-adaptive genes found to be mutated in several lap Fig.  suggestiIn the future, supplementation of the present results with gene expression data will improve the understanding of evolution processes in biofilms.P. aeruginosa biofilms support the mutagenic potential of CIP at subinhibitory concentrations41. Mutations in CIP-resistance determinants were associated with mutations conferring phenotypes supporting a general survival advantage in the presence of antibiotics, as they were affecting some of the common mechanisms of action described for antibiotics, such as switch of the metabolic pathway to anaerobic respiration or affecting the TCA cycle. Phenotypes such as increased lag phases will also impair the effect of other antibacterial agents, while loss of twitching might impair biofilm dispersal42. Overall, our data show that CIP-resistant mutants acquire several other mutations that might confer survival advantages during antibiotic treatment of biofilms.In conclusion, our data on the phenotypic and mutagenic evolution in P. aeruginosa PAO1 and its catalase mutant (ΔkatA) (MIC CIP = 0.125 µg/ml) have been used previously to start biofilm and planktonic evolution experiments in the presence of 0.1 mg/L ciprofloxacin (CIP) or without selection pressure (CTRL) for seven passages, as previously published15. The evolved populations were plated on LB plates containing ciprofloxacin   and three colonies from the plates with the highest ciprofloxacin concentration which allowed growth were isolated, passed twice on antibiotic-free media and stored at −80 oC for further analysis  see Fig. . CIP-resFor growth curves and motility assays all the available CIP-resistant colonies from CIP and CTRL evolution experiments, which are in total 92 isolates, were investigated.katA CIP-resistant colonies had hypermutable phenotype, as indicated in Supplementary Table For whole-genome sequencing, susceptibility testing to antipseudomonal antibiotics and fitness cost determinations in both planktonic and biofilm growth, a total of 42 colonies were investigated; 24 colonies from CIP-evolved populations (16 colonies isolated from biofilm populations and 8 colonies isolated from planktonic populations) and 18 colonies from CTRL-evolved populations (11 colonies isolated from biofilm populations and 7 colonies isolated from planktonic populations). The selection was randomly done and aimed at including colonies representing the different replicate lineages  were performed, as described in katA ancestor colonies. Genomic DNA was extracted from the colonies using Gentra puregene yeast/bacteria DNA purification kit (Qiagen). The DNA was prepared for sequencing using the Illumina TruSeq DNA Nano kit and sequenced on an Illumina MiSeq yielding a coverage of approximately 120×. Sequencing reads were mapped to the reference genome of P. aeruginosa PAO1 (GenBank accession. NC_002516), and single and multiple nucleotide variants (SNVs and MNVs) were called using CLC genomic workbench (Qiagen). Large indels and structural variants were also detected using using CLC genomic workbench. Mutations present in the CIP-resistant colonies were filtered out from the genome of the sequenced WT PAO1 and ΔkatA ancestor colonies using customized Linux and AWK scripts. R (version 3.2.5) was used for further statistical analysis of the mutations detected in colonies and all mutations occurring in >10% of the reads and at least 10 unique reads were included in the analysis.The whole-genome sequencing was performed for 42 CIP-resistant colonies (24 isolated from CIP-evolved population and 18 from CTRL-evolved populations) as well as for WT PAO1 and ΔN/dS, the ratio of the rate of nonsynonymous substitutions (dN) to the rate of the synonymous substitutions (dS), was calculated as a measure of the selection pressure acting on the protein-coding genome, as previously described, assuming that 25% of all single-nucleotide polymorphisms (SNP) result in synonymous changes33. dN/dS is expected to be >1 if natural selection promotes changes in protein sequences and <1 if natural selection suppresses changes.The Pseudomonas Genome Database was used for gene function analysis. dp value was ≤0.05.Graphs and statistical analysis were performed using GraphPad Prism 7, Statview software and R (version 3.2.5). Student t-test and Mann–Whitney for nonparametric were used for comparisons among resistant isolates for growth rates and motilities (comparing CIP populations to CTRL populations and CIP-biofilm populations to CIP planktonic populations). The differences were considered significant when the Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.Supplementary MethodsSupplementary DataReporting Summary


Alexithymia, a personality trait characterized by difficulties interpreting one’s own emotional states, is commonly elevated in autistic adults, and a growing body of literature suggests that this trait underlies a number of cognitive and emotional differences previously attributed to autism, such as difficulties in facial emotion recognition and reduced empathy. Although questionnaires such as the twenty-item Toronto Alexithymia Scale (TAS-20) are frequently used to measure alexithymia in the autistic population, few studies have attempted to determine the psychometric properties of these questionnaires in autistic adults, including whether differential item functioning (I-DIF) exists between autistic and general population adults.We conducted an in-depth psychometric analysis of the TAS-20 in a large sample of 743 verbal autistic adults recruited from the Simons Foundation SPARK participant pool and 721 general population controls enrolled in a large international psychological study (the Human Penguin Project). The factor structure of the TAS-20 was examined using confirmatory factor analysis, and item response theory was used to further refine the scale based on local model misfit and I-DIF between the groups. Correlations between alexithymia and other clinical outcomes such as autistic traits, anxiety, and quality-of-life were used to assess the nomological validity of the revised alexithymia scale in the SPARK sample.The TAS-20 did not exhibit adequate global model fit in either the autistic or general population samples. Empirically driven item reduction was undertaken, resulting in an eight-item unidimensional scale (TAS-8) with sound psychometric properties and practically ignorable I-DIF between diagnostic groups. Correlational analyses indicated that TAS-8 scores meaningfully predict autistic trait levels, anxiety and depression symptoms, and quality of life, even after controlling for trait neuroticism.Limitations of the current study include a sample of autistic adults that was overwhelmingly female, later-diagnosed, and well-educated; clinical and control groups drawn from different studies with variable measures; and an inability to test several other important psychometric characteristics of the TAS-8, including sensitivity to change and I-DIF across multiple administrations.http://asdmeasures.shinyapps.io/TAS8_Score).These results indicate the potential of the TAS-8 as a psychometrically robust tool to measure alexithymia in both autistic and non-autistic adults. A free online score calculator has been created to facilitate the use of norm-referenced TAS-8 latent trait scores in research applications (available at  Alexithymia is a subclinical construct characterized by difficulties in identifying and describing one’s own emotional state , 2. IndiAlexithymia is a construct of particular interest in research on autism spectrum disorder (hereafter “autism), a condition frequently associated with difficulties in processing, recognizing, communicating, and regulating emotions –32. A rer = 0.92 and 0.81 for the TAS-20 and BVAQ-B total scores, respectively, with all subscale rs > 0.62). The internal consistency of the TAS-20 and its three subscales has also been reported in a sample of 27 autistic adults by Samson et al.  for t tests, r =  for bivariate correlations, and rp =  for partial correlations. Evidence both for or against this interval null hypothesis can be quantified by calculating the ROPE Bayes factor (BFROPE), which is defined as the odds of the prior effect size distribution falling within the ROPE divided by the odds of the posterior effect size distribution falling within the ROPE , BFROPE = 5.77 × 10–6), likely due to the absence of older adults  in our sample. Alexithymia also showed a nonzero negative correlation with education level, although the magnitude of this relationship was small enough to not be practically significant . Unlike in the general population, females in the SPARK sample had slightly higher TAS-8 scores , although this difference was small and not practically significant (BFROPE = 0.265). Additionally, there was an absence of practically significant differences in alexithymia by race/ethnicity . Lastly, age of autism diagnosis was positively correlated with TAS-8 scores , although this correlation was also small enough to not be practically significant (BFROPE = 0.014).The relationships between TAS-8 scores and demographic variables were also examined in order to determine whether relationships found in the general population apply to autistic adults. As hypothesized, TAS-8 scores showed a small and practically insignificant correlation with age  to be 10.2 . This estimate was several grades higher than that produced using the Flesch–Kincaid algorithm . Using the FORCAST algorithm, the TAS-8 items demonstrated a grade level of 8.8, indicating a moderate decrease in word difficulty. This decreased reading level compared to the TAS-20 was also reflected in the Flesch–Kincaid measures . Thus, in addition to improving the psychometric properties of the measure, our item reduction procedure seemingly improved the overall readability of the TAS.While alexithymia is theorized to account for many traits associated with the autism phenotype –51, studM = 0, SD = 1) and can similarly be scaled to the familiar T-score metric . As scores on the TAS-8 are both norm-referenced and psychometrically robust, we believe they present a viable alternative to TAS-20 total scores in any study protocol that includes the TAS-20 or one of its short forms . To facilitate the calculation and use of the TAS-8 latent trait scores in alexithymia research, we have created an easy-to-use online scoring tool (available at http://asdmeasures.shinyapps.io/TAS8_Score) that converts TAS-8 item responses into general population-normed latent trait scores and corresponding T-scores.While the 20-item TAS possessed adequate composite score reliability in our sample, bifactor confirmatory factor models failed to support the theorized structure of the questionnaire in the autistic population. The TAS-20 items assessing the EOT facet of the alexithymia construct and the form’s reverse-coded items were particularly problematic, both exhibiting poor subscale reliabilities and contributing little common variance to the general alexithymia factor. These psychometric issues were further confirmed in our general population HPP sample, indicating that these problems were not unique to the autistic population. Removal of the EOT and reverse-coded items from the model greatly improved overall fit, but three additional items needed to be removed in order to meet our a priori standards of adequate IRT model fit and negligible I-DIF by diagnostic group. The final TAS-8 short form consisted of five DIF items , 13, 14 In addition to deriving a psychometrically robust short version of the TAS-20, the current study also sheds light on the areas of the form that are most psychometrically problematic, notably the EOT subscale. This subscale was the primary driver of poor TAS-20 model fit in the current study, and even when method factors were appropriately modeled, the reliability of the EOT subscale score was unacceptably low. Notably, it is not uncommon for researchers to perform subscale-level analyses using the TAS-20, examining correlations between DIF/DDF/EOT subscale scores and other constructs of theoretical interest . As the r|s < 0.2 and |d|s < 0.2). Moreover, despite a fairly large correlation between TAS-8 scores and neuroticism, partial correlation analyses demonstrated that alexithymia still explained substantial unique variance in autism symptomatology, depression, generalized anxiety, and quality of life over and above that accounted for by neuroticism. However, partial correlations with somatic symptom burden, social anxiety, and suicidal ideation failed to exceed the pre-specified interval null hypothesis, indicating that alexithymia in the autistic population only predicts these symptom domains insofar as it correlates positively with trait neuroticism. A particularly important future direction in alexithymia research will be to re-examine studies wherein alexithymia was found to be a “more useful predictor” of some clinical outcome when compared to autistic traits [Tests of convergent and divergent validity of the TAS-8 score were largely in line with prior results, indicating that self-reported alexithymia is moderately to strongly correlated with autistic traits, repetitive behaviors, internalizing psychopathology, suicidality, and poorer quality of life. Relationships were also observed between TAS-8 scores and sex, age of autism diagnosis, and education level, although these effects were small enough to be practically insignificant , which in addition to assessing the experience of undifferentiated emotions common in alexithymia also seemingly captures the phenomenon of medically unexplained symptoms. We confirmed that this was in fact the case in our SPARK sample, as the polyserial correlation between this item and PHQ-15 total scores was very high  and very minimally attenuated after controlling for overall alexithymia as measured by the TAS-8 latent trait score . Notably, a recent study has found that item 3 of the TAS-20 is the single most important item when discriminating individuals with a functional somatic condition  from healthy controls [One particularly surprising finding is the poor correlation between alexithymia and somatic symptom burden, given the theoretical status of alexithymia as a potential driver of somatization and a large literature showing relationships between these constructs . One parcontrols , providicontrols  or PAQ [controls , an obsecontrols , and an controls , althougd = 1.014) was approximately 15% larger than the same group difference in TAS-20 scores (d = 0.880), indicating that the TAS-8 is better able to discriminate between autistic and non-autistic adults than its parent form. Although the current study did not validate this form for use in other clinical populations where alexithymia is a trait of interest , future studies in these populations are warranted to determine whether the improved measurement properties of the TAS-8 are useful in improving inferences about alexithymia in those groups as well.This work has meaningful implications for the study of alexithymia in the autistic population and in general, as it provides strong psychometric support for the TAS-8 questionnaire as a general-purpose measure of alexithymia across multiple clinical and non-clinical populations. These findings are particularly useful for autism research, as they indicate that the TAS-8 can be used to compare levels of alexithymia between autistic and general-population samples without worry that differences in scores are significantly biased by qualitative differences in the ways individuals in each group answer the questionnaire items. Moreover, the between-group difference in TAS-8 scores , while both recruited online, were drawn from different studies with dissimilar protocols and different versions of the TAS questionnaire. The HPP sample completed the TAS-16 questionnaire, which omits four of the more poorly performing items of the original TAS-20. Thus, in order to estimate TAS-20 total scores in this group of individuals, we were required to impute those items for all 721 participants with an unknown degree of error. Interestingly, the HPP sample reported TAS-20 scores that were 1.5–6 points larger on average than previous large-scale general-population studies using the TAS-20 , 165, anAn additional limitation is that the HPP sample was not screened for autism diagnoses, and there remains a possibility that some of these individuals could have met diagnostic criteria for autism or had a first-degree relative on the autism spectrum. However, previous studies have indicated that a small portion of autistic individuals  in an oIn addition to the limitations of the HPP sample, several limitations of the better-characterized SPARK sample were also present. As discussed in our previous work with this sample , it is nshould change over time, incorporating relevant findings such as empirical tests of latent variable models. Future research in alexithymia would greatly benefit from additional psychometric studies that aim to generate optimal instruments to measure all facets of the alexithymia construct, coupled with tests of the incremental validity of the EOT/DFAN trait facets over and above a score composed of solely DIF/DDF items.Another limitation concerns the correspondence of the TAS-8 to the theoretical alexithymia construct itself, as initially proposed by Sifneos and colleagues , 170. AsA final limitation of our study is the fact that we were unable to test all meaningful psychometric properties of the TAS-8. In particular, our study was cross-sectional, necessarily prohibiting us from assessing test–retest reliability, temporal stability, and I-DIF across repeated test administrations. Additionally, as alexithymia appears to be amenable to change with psychological interventions , 176, fuhttps://asdmeasures.shinyapps.io/TAS8_Score/). Although the measurement properties of the TAS-8 were strong in this study we stress that this single measure should not be considered the “gold standard” of alexithymia measurement in autism or any other population. In agreement with the original authors of the TAS [The TAS-20 is a widely used measure of alexithymia that has more recently become the de facto measure of choice for this construct in the autism literature. However, this measure has so far lacked robust psychometric evidence for its reliability and validity in the population of autistic adults. Leveraging two large datasets of autistic and general-population adults, we performed an in-depth investigation of the TAS-20 and its measurement properties in autistic adults, revealing several psychometric shortcomings of this commonly used questionnaire. By reducing the number of items on the measure, we were able to produce a unidimensional short form, the TAS-8, which exhibited superior psychometric properties to the TAS-20 in samples of both autistic and non-autistic adults. Furthermore, in order to allow others to utilize the population-normed latent trait scores generated by our IRT model, we have created a user-friendly online score calculator for the TAS-8 that is freely available to interested researchers (Additional file 1. Supplementary Materials and TAS-8 Questionnaire.


The emergence of SARS-CoV-2 virus and its associated disease COVID-19 have triggered significant threats to public health, in addition to political and social changes. An important number of studies have reported the onset of symptoms compatible with pneumonia accompanied by coagulopathy and lymphocytopenia during COVID-19. Increased cytokine levels, the emergence of acute phase reactants, platelet activation and immune checkpoint expression are some of the biomarkers postulated in this context. As previously observed in prolonged sepsis, T-cell exhaustion due to SARS-CoV-2 and even their reduction in numbers due to apoptosis hinder the response to the infection. In this review, we synthesized the immune changes observed during COVID-19, the role of immune molecules as severity markers for patient stratification and their associated therapeutic options. The infection caused by this virus leads to a primary viral pneumonia identified as COVID-19, which resembles the Severe Acute Respiratory Syndrome (SARS) According to data obtained from numerous cohorts, the main causes of death by COVID-19 include respiratory failure and the onset of sepsis. In fact, sepsis has been observed in nearly all deceased patients in many of the reported cohorts in vitro and in a primate model against SARS-CoV-2 Current treatments employed against SARS-CoV-2 include a number of antiretroviral therapies recycled from other infections such as ribavirin, lopinavir/ritonavir, oseltamivir, hydroxychloroquine and remdesivir In this review, we summarize the immune changes observed in SARS-CoV-2 infection, with an emphasis on the similarities with sepsis, and the role of immune compounds as severity markers and therapeutic targets.SARS-CoV-2 is a β-coronavirus included in the sarbecovirus subgenus, orthocoronavirinae subfamily, which is broadly distributed in humans and other mammals SARS-CoV-2 shows close similarities with SARS1 and MERS in terms of pathogenicity and has largely overtaken the death toll of the latter two. As with SARS1 and MERS Nidovirales, the viruses' molecular structures show similarities, although there are slight differences, which will be discussed below. Basically, SARS-CoV-2 is an enveloped, non-segmented, positive-sense RNA virus that has four main structural proteins: the spike (S) glycoprotein, the small envelope (E) glycoprotein, the membrane (M) glycoprotein, and the nucleocapsid (N) protein. In addition, SARS-CoV-2 has several accessory proteins that are relevant to the host immune process, as will be discussed below.Since they belong to the order According to reported data, angiotensin-converting enzyme 2 (ACE2) has been identified as a functional receptor for SARS1 Structural and functional analyses have shown that SARS-CoV-2 also binds to ACE2 Lung-resident macrophages are located on the apical side of the epithelium and, together with dendritic cells, act as innate immune cells to attack viruses until adaptive immunity is involved. It is still an open question as to how SARS-CoV-2 interacts with and eventually enters immune cells. Both ACE2 and dipeptidyl peptidase 4 are expressed in these cells but to a limited extent Successful virus-host cell fusion requires other molecules. Several research groups have attempted to inhibit host proteases, such as cathepsins and TMPRSS2, a protease that processes S protein and favors virus entry into cells In terms of the circulatory system, several reports have demonstrated a direct association between proinflammatory cytokine levels in plasma and lung injury during infection by coronaviruses such as SARS1 Table 1) Innate immune system (IIS) cells, mainly monocytes and macrophages, are the principal players in orchestrating the host's inflammatory response by activating the nuclear factor kappa B (NF-κB) and interferon regulatory factor (IRF) pathways According to several studies, the inflammatory phase for patients with severe COVID-19 is limited to the initial period of the disease Various clinical trials have included IL-6 and IL-6R-blocking antibodies to prevent this anaphylactic toxicity observed in patients with COVID-19 An increasing number of reports have indicated that not only the IIS but also the adaptive system becomes deregulated during COVID-19 infection. The approximately 83% of patients who have shown lymphocytopenia at admission illustrates this point A number of analyzed cohorts have confirmed that the severe phase appears in these patients approximately 8-11 days after the onset of the disease, when proinflammatory cytokines reach their peak expression, probably due to an exacerbated innate immune response P53 activation Similarly, an obvious depletion of lymphocytes during sepsis has been demonstrated, which compromises the adaptive response in the second phase of the disease Fig. 1). Likewise, the potential role of ICs and their ligands in lymphocytopenia observed in patients with severe COVID-19 should be a focus of research on SARS-CoV-2 infection. There is a crucial need for thorough studies on the development of clinical tools for personalized medicine for this infection. IC levels and IC-ligand expression would not only help physicians stratify patients at admission but also serve as pharmacological targets for those patients with a poor prognosis. Due to the impact of IC inhibitors in cancer therapies, several antibodies against ICs have been approved by the US Food and Drug Administration for use in certain malignancies The collective data available point to an exaggerated innate response followed by an inappropriate switch to the adaptive response, which leads to immune system exhaustion during SARS-CoV-2 infection  and IgG production. The kinetics reported for SARS1-specific IgM antibodies indicate that IgMs disappear after 12 weeks of disease onset. However, IgGs can last a long time and play a protective role Generating recombinant human monoclonal antibodies could be another method for neutralizing SARS-CoV-2. CR3022, a SARS coronavirus-specific human monoclonal antibody, has been reported capable of binding to SARS-CoV-2-RBD in vivo TF expression plays a pivotal role in the activation of blood coagulation in the setting of sepsis and endotoxemia Since the 1993 report on tissue factor (TF) synthesis by activated macrophages Proinflammatory cytokines induce the production of C-reactive protein (CRP) and haptoglobin by hepatocytes. A number of reports have indicated high CRP and haptoglobin levels in patients with COVID-19, with higher levels in those patients with a severe condition Figure 1 summarizes the progression of infected patients who usually require hospitalization from days 5 to 6 since the start of their infection. Depending on their immune response, patients might experience an exacerbated cytokine production that compromises their vital functions and leads to a state of exhaustion that hinders the activation of the adaptive response.COVID-19 has emerged as a complex disease that shares clinical characteristics with sepsis. A number of published studies have reported the onset of symptoms compatible with pneumonia accompanied by coagulopathy and lymphocytopenia during COVID-19 infection. However, there are still no solid markers to predict these patients' progression. Cytokine elevation rates, the presence of acute phase reactants such as D-dimer, platelet activation and IC expression are some of the biomarkers proposed in this context. Given the similarity of COVID-19 infection to sepsis, it is possible that testing for the early expression of ICs and their ligands on innate immune cells such as monocytes and macrophages could serve as a tool for classifying patients on admission, thereby opening up new avenues for treatment. As has been observed in prolonged sepsis, T-cell exhaustion due to SARS-CoV-2 and even reduced T-cell counts due to apoptosis hinder the host's response to infection. In such a scenario, new infections might emerge, increasing the risk of mortality.Figure 2). The first phase is strongly characterized by a disproportionate IIS reaction that causes a cytokine storm and, subsequently, generates significant damage to the body. In addition, inflammation induces the production of a significant number of factors related to the coagulation cascade, resulting in the onset of thrombi and associated DIC, a condition that highly resembles that observed during sepsis.According to data generated on COVID-19 since its emergence, this disease has shown two potentially overlapping phases (Table 2). To date, we have only partial results, and exact answers have not been found in the clinics. In fact, there have been cases of conflicting results from clinical trials and on the use of drugs that have had deleterious effects The urgency and severity of COVID-related events in various countries is facilitating a significant number of clinical trials  are overexpressed from the onset of COVID-19 infection. As mentioned previously, IC ligand levels could help stratify patients during admission and prevent the disease from progressing. A massive international study approach could be useful for developing this tool. Given that COVID-19 is a dynamic disease, however, we will need to study patients at different stages of the disease to identify predictive factors and appropriate targets. A single picture of cytokine levels, cell population distribution, and other markers of interest would only give us a partial idea of an evolving disease.Based on the evidence presented in this article, the study of the immune system and the coagulation cascade during COVID-19 infection could provide valuable information for approaching the diagnosis and treatment of this disease. There are many questions that remain unanswered in the context of COVID-19, including “Can we predict the progression of patients with COVID-19 by establishing their immune profiles on admission?”, “Could IC expression regulate the second phase of COVID-19?” and “Can IC inhibitors and their ligands be useful for COVID-19 therapy as they have been in many types of cancers and as they have been postulated in sepsis?” Identifying those patients with poorer prognoses will therefore facilitate the development of accurate host-directed therapies.


Seroprevalence surveys are of utmost importance to assess the proportion of a population that has developed antibodies against a newly introduced virus and could therefore potentially exhibit immunologic protection against subsequent infection. This study aims to understand the distribution of IgM and IgG antibodies in the Dominican Republic. We surveyed a total of 12,897 participants between April and June 2020 in 10 provinces of the Dominican Republic. Survey efforts in emerging hotspots yielded a positivity for all participants of anti–SARS-CoV-2 IgM of 3.8% and IgG of 5.4%, indicating that the pathogen was in circulation before the identification of those particular communities as hotspots. We found important age differences between participants who participated in the serological study where a higher mean age is associated IgM positivity and a lower age with IgG positivity. Our results highlight the need for strategies that involve community-based seroprevalence monitoring. These should preclude syndromic case identification. Also, the higher mean age of IgM-positive participants suggests that strategies based on syndromic surveillance could identify hotspots at later phases, based on the number of cases detected at the healthcare center, as such community-based seroprevalence monitoring may be an effective intervention for future outbreaks. Decisions to loosen restrictions were made based on declining positivity rates and increases in hospital capacity to admit COVID-19 cases.2The Dominican Republic reported its first confirmed case of SARS-CoV-2 on March 1, 2020.3 Besides this, the known differences in the proportion of asymptomatic and symptomatic manifestation by varying age-groups can lead to under detection in younger populations and overestimation of severity in older communities in countries with only syndromic surveillance.6 To expand testing capacities during initial phases of the pandemic caused by SARS-CoV-2, rapid detection of antibodies by the ELISA method was implemented as a screening method for active and passive surveillance. This method was mainly based on detecting specific antibodies against SARS-CoV-2 antigens, where IgM antibodies are the first antibodies that are created in response to initial exposure to an antigen and IgG antibodies appear at a later phase and serve as immunologic memory.Because of a sudden increased demand for confirmatory diagnostic testing, mildly affected and asymptomatic individuals have limited access to laboratory testing. As a result of such circumstances, the number of confirmed SARS-CoV-2 infections can significantly underestimate the actual number of cases.7 As recommended by the WHO, monitoring seroprevalence changes over time is crucial to anticipate the epidemic’s dynamics and plan an adequate public health response to contain the spread of the pathogen or prevent its reemergence.4 In addition to this, seroprevalence studies offer the benefit of saving testing costs and time and the possibility of carrying out community-based intervention in identified emerging hotspots to stop further spread of the disease.8As seen in many countries, changing testing strategies during epidemics makes it nearly impossible to estimate the extent of the population exposed to the pathogen at a given moment. However, this information is crucial in planning evidence-based strategies for lifting physical distance and confinement measures. In this context, seroprevalence surveys are of utmost importance to assess the proportion of the population that has already developed antibodies against the virus and could potentially exhibit immunologic protection against subsequent infection.This study aimed to understand the distribution of IgM and IgG antibodies within the Dominican Republic during community-based interventions. To achieve this, we analyzed the demographic characteristics of participants who received a SARS-CoV-2 IgM/IgG rapid test in emerging hotspots within the Dominican Republic. Emerging hotspots were considered as an increased rate of new infections compared with the previous epidemiological week in a given municipality or province. Because of the inherent difference in IgM and IgG function and structure, we consider that intervened communities with an increased proportion of IgM antibody positivity indicate an early identification of community circulation of SARS-CoV-2. By contrast, a high IgG combined with a low IgM positivity proportion would suggest a late community intervention. We also consider that differences in antibody profile distribution within age-groups can help identify crucial pockets of transmission within the population and guide public health policies.9 Testing efforts consisted of consecutive weekly serological interventions between April and June in communities identified as emerging hotspots for SARS-CoV-2 infections by the syndromic surveillance program of the country’s national surveillance system. We adhered to ethical guidelines by performing each test under strict confidentiality.To assess the seroprevalence of anti–SARS-CoV-2 antibodies in communities within the Dominican Republic, we contacted individuals randomly in communities identified as emerging hotspots for SARS-CoV-2 by Ministry of Health reports and invited members of households to participate in the seroprevalence study. We collected demographic data for each participant and evaluated anti–SARS-CoV-2 IgM/IgG antibodies using a commercially available chromatographic test targeting the S1 domain of the SARS-CoV-2 spike protein . To establish the IgG/IgM concentration, sample, strips were subjected to a colorimetric assay using an automatic analyzer /specificity (98.8%), versus IgG sensitivity (56.7%)/specificity (100%) Confiscope G20, Genbody Inc.).n = 6,597) males with a mean age of 42 years  highlight the weakness of a reactionary response or symptomatic surveillance with this pathogen where time is essential. By contrast, IgM detection in asymptomatic participants by community intervention is an important component of the efforts to stop the community spread of SARS-CoV-2. This is shown by a decline in IgM positivity in continuous months of implementation. Novel approaches to detect emerging hotspots, such as viral detection in sewage wastewaters, have to be adopted to ensure a faster and earlier response to this pathogen.12Survey of all participants in emerging hotspots yielded positivity of anti–SARS-CoV-2 IgM of 3.8% and of anti–SARS-CoV-2 IgG of 5.5%, indicating pathogen circulation before the identification of the community hotspot . SignifiM = 43.1, SD = 14.5), compared with the 12,406 participants who tested negative , demonstrated significantly higher age at the time of the serosurvey . In addition, the 704 surveyed participants who received a positive IgG result M = 41.9, SD = 16.2), compared with the 12,406 participants who tested negative , demonstrated significantly lower age at the time of the serosurvey . Both of these findings highlight the possibility of intense pathogen circulation before the intervention of an identified hotspot, with IgG suggesting increased immune memory in younger participants, which would lead to decreased susceptible populations, hence a decreased IgM positivity.We found important age differences between those who participated in the serological study. The 491 surveyed participants who received a positive IgM result (4 We found a lower proportion of IgM positivity, 2.1%, in children, than the 3.8% mean, and a higher IgG positivity of 5.8% versus the 5.5% mean, which might indicate cryptic transmission at community levels, especially with this age-group. This scenario has also been explored in other parts of the world, where studies found that children functioned as principal transmission foci because of a high asymptomatic proportion of infections.15 With the intense debate about whether to reopen schools and colleges in the fall, it should be a public health priority to understand the role of SARS-CoV-2 transmission in seemingly unaffected population and their role in the reemergence of the pathogen.Because of substantial differences in severity, complications, and mortality across age-groups, interventions focused on stopping the spread of SARS-CoV-2 should consider different surveillance strategies to reflect such variations.16 Because our study follows seroprevalence intervention in already identified hotspots by symptomatic surveillance and contact tracing, we can assume that the differences in antibody proportions in young populations suggest important pathogen circulation before community detection and intervention. We consider that an essential limitation to our study is the lack of pre-intervention serodata, and assay-related specificity/sensitivity; this might limit our understanding of any specific age-groups that may act as transmission pockets of the SARS-CoV-2 pathogen before the emergence of the hotspot. To counteract this limitation, we base our conclusions on prior viral circulation based on IgG positivity. By doing so, our hypothesis is supported by our findings that show a younger mean age of IgG-positive individuals than negative IgG participants. Also, the higher mean age of IgM-positive participants contributes to our suggestion that strategies based on syndromic surveillance could identify a hotspot at a later phase, based on the number of cases detected at the healthcare center.Strategies involving community-based seroprevalence monitoring should preclude syndromic case identification.


This cohort study assesses the frequency and variation in low-value diagnostic testing for low back pain, headache, syncope, and sinusitis in the Veterans Health Administration. What is the frequency and degree of variation in low-value diagnostic testing for low back pain, headache, syncope, and sinusitis in the Veterans Health Administration?In this cohort study of 1 022 987 US veterans, low-value testing was performed for 5% to 21% of veterans with 1 of the 4 medical conditions. There was substantial variation in low-value testing across Veterans Affairs medical centers and significant correlation at the Veterans Affairs medical center level in veterans’ receipt of such testing.In this study, low-value diagnostic testing was common and delivered variably throughout the Veterans Health Administration, suggesting the need to address the delivery of such care, even in integrated health systems, with robust decision support and utilization management. Low-value care is associated with harm among patients and with wasteful health care spending but has not been well characterized in the Veterans Health Administration.To characterize the frequency of and variation in low-value diagnostic testing for 4 common conditions at Veterans Affairs medical centers (VAMCs) and to examine the correlation between receipt of low-value testing for each condition.This retrospective cohort study used Veterans Health Administration data from 127 VAMCs from fiscal years 2014 to 2015. Data were analyzed from April 2018 to March 2020.Continuous enrollment in Veterans Health Administration during fiscal year 2015.Receipt of low-value testing for low back pain, headache, syncope, and sinusitis. For each condition, sensitive and specific criteria were used to evaluate the overall frequency and range of low-value testing, adjusting for sociodemographic and VAMC characteristics. VAMC-level variation was calculated using median adjusted odds ratios. The Pearson correlation coefficient was used to evaluate the degree of correlation between low-value testing for each condition at the VAMC level.P < .001) and low back pain and headache .Among 1 022 987 veterans, the mean (SD) age was 60 (16) years, 1 008 336 (92.4%) were male, and 761 485 (69.8%) were non-Hispanic White. A total of 343 024 veterans (31.4%) were diagnosed with low back pain, 79 176 (7.3%) with headache, 23 776 (2.2%) with syncope, and 52 889 (4.8%) with sinusitis. With the sensitive criteria, overall and VAMC-level low-value testing frequency varied substantially across conditions: 4.6%  for sinusitis, 12.8%  for headache, 18.2%  for low back pain, and 20.1%  for syncope. With the specific criteria, the overall frequency of low-value testing across VAMCs was 2.4%  for sinusitis, 8.6%  for headache, 5.6%  for low back pain, and 13.3%  for syncope. The median adjusted odds ratio ranged from 1.21 for low back pain to 1.40 for sinusitis. At the VAMC level, low-value testing was most strongly correlated for syncope and headache (ρ = 0.56; In this cohort study, low-value diagnostic testing was common, varied substantially across VAMCs, and was correlated between veterans’ receipt of different low-value tests at the VAMC level. The findings suggest a need to address low-value diagnostic testing, even in integrated health systems, with robust utilization management practices. Moreover, the VHA electronic medical record contains information on veterans’ care across all Veterans Affairs Medical Centers (VAMCs) and decision support tools for ordering appropriate health services, which may be associated with a decreased likelihood of VA clinicians delivering low-value care.15 Nevertheless, several studies focused on the use of individual low-value health services  have demonstrated that the delivery of low-value care still occurs at VAMCs.22Although the delivery of multiple specific low-value health services has been described among Medicare and private insurance beneficiaries, the use of such services in the Veterans Health Administration (VHA) has not been well characterized.23 Our objective was to characterize the frequency of and VAMC-level variation in the delivery of low-value care, with a focus on low-value diagnostic testing for 4 common conditions in the VHA. We also sought to examine the degree of correlation among the receipt of different types of low-value diagnostic tests for each condition at the VAMC level.As health care costs continue to increase and recent legislation provides veterans with increasing flexibility regarding whether they receive care within or outside the VHA, quantifying the frequency and patterns of low-value care delivery is essential for the VHA to reduce wasteful health care spending without compromising quality or patient satisfaction.22 This cohort study was approved by the VA Pittsburgh Healthcare System institutional review board. Because deidentified administrative data were used, a waiver of informed consent was granted by this institutional review board. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.We conducted a retrospective cohort study of a 20% national random sample of veterans who were continuously enrolled in the VHA in fiscal year 2015. Veterans were required to have at least 1 outpatient face-to-face encounter with a VA clinician in this study year to ensure that they were actively receiving care at a VAMC. We excluded nonveterans, veterans younger than 18 years, and veterans treated in non-US facilities because they are not representative of the overall VA population.International Classification of Diseases, Ninth Revision and Common Procedural Terminology codes associated with each low-value test. We used the Planning System and Support Group file to characterize each veterans’ travel time to the nearest VA facility and the following sources to identify VAMC-level characteristics: outpatient visit volume (VA Corporate Data Warehouse), training status (VHA Allocation Resource Center), VAMC Complexity Score (VHA Office of Planning), and census region (US Census).We used the VA Enrollment Data File to identify veterans continuously enrolled in the VHA in fiscal year 2015 and the VHA Corporate Data Warehouse, which is a data repository, to identify veterans’ sociodemographic characteristics, medical comorbidities, and 5 We chose these conditions and their related low-value tests because, for each condition, low-value testing is prevalent outside the VHA, the respective low-value tests are widely available at VAMCs, and the use of each low-value test is readily identifiable using VHA data  for low back pain, the sensitive criteria characterized any veteran who received low back imaging less than 6 weeks after receiving a low back pain diagnosis as having received low-value testing. This criterion was used because most cases of musculoskeletal low back pain resolve with conservative treatment within 6 weeks of onset. In acknowledging that testing may have been appropriate for some of these patients, the specific criteria further excluded veterans from the numerator who received testing with a diagnosis code for an emergent condition in the 30 days before undergoing testing  and veterans who underwent their first test more than 6 weeks after their initial back pain diagnosis. This approach allowed us to account for variations in the literature regarding the characteristics that make a diagnostic test low value, establish a range of potential low-value testing use, and establish relative sensitivity and specificity for each criteria in the absence of an established gold standard.18Because a single set of criteria to identify low-value testing for each condition may not account for the value of a health service in each unique clinical situation, we applied both sensitive and specific criteria to identify the use of low-value testing.24 We used fiscal year 2014 data to generate these variables because this provided us with a fixed baseline of covariate values and we sought to capture low-value testing beginning in fiscal year 2015.We established variables for age, sex, race/ethnicity, marital status, driving time to the nearest VAMC, Gagne Comorbidity Index, and VA priority group, which is assigned at the time of VHA enrollment and determines a veteran’s copay status for certain services. The Gagne Comorbidity Index  integrates elements of both the Charlson and Elixhauser Comorbidity indexes and has greater prognostic accuracy in predicting 1-year mortality.18 We also identified each VAMCs’ US census region , size using outpatient visit volume, academic affiliation, and complexity score. The complexity score has 5 categories , which are determined using overall patient volume, patient case mix, number and nature of staff physician specialists, and intensive care unit capabilities; VAMCs classified as 1a are the most complex.25We assigned veterans to the 127 VAMCs nationwide where they received the majority of their outpatient care.International Classification of Diseases, Ninth Revision diagnosis code that corresponded to 1 of the 4 conditions of interest in fiscal year 2014 or before receipt of the related low-value test in fiscal year 2015. These subcohorts of eligible veterans were the denominators for each low-value testing measurement. Applying both the sensitive and specific criteria, we then determined the overall number  of veterans who underwent low-value testing for each condition and the proportion of eligible veterans from each VAMC. Using mixed-effects logistic regression modeling, we determined the adjusted VAMC rates of low-value testing, which reflect the rates of low-value testing at each VAMC after removing the effects of that VAMC’s unique veteran- and VAMC-level characteristics. We also included random effects for each veteran’s assigned VAMC in our models.26 We conducted a planned subgroup analysis to assess the receipt of low-value testing in veterans at greatest risk of 1-year mortality and thus least likely to potentially benefit from such care, defined using the top decile of Gagne scores in the overall cohort.Data analysis was performed from April 2018 to March 2020. We calculated the means and SDs for all continuous variables and frequencies for all categorical variables. From the overall cohort, we determined the number of veterans who were eligible to receive each low-value test based on their receipt of an 26 Second, to characterize the proportion of variance in veterans’ receipt of low-value testing associated with variation between VAMCs, we calculated the intraclass correlation coefficient (ICC).28 We first calculated the ICCs using unadjusted models followed by models containing only veteran-level covariates and then both veteran- and VAMC-level covariates to assess the contribution of these variables to the between-VAMC variance. Third, to characterize variation across VAMCs with a greater degree of granularity, we divided VAMCs into deciles based on their frequency of low-value testing for each condition and compared the odds of low-value testing for each condition for veterans in each of the 9 higher deciles compared with the lowest decile.We characterized variation in the delivery of low-value testing across VAMCs in 3 ways. First, to depict overall variation, we calculated the median adjusted odds ratio (aOR) of low-value testing for each condition. The median aOR characterizes the median odds of a veteran receiving a low-value test if 2 of 127 VAMCs are chosen at random and compared.P < .05 was considered statistically significant.At the VAMC level, we used the Pearson correlation coefficient to evaluate the degree of correlation between the delivery of low-value testing for each separate condition at VAMCs. We also determined the correlation between use of separate low-value tests for syncope  and uncomplicated headache  based on the algorithms available to identify specific types of testing for each of these conditions in the applied metric. Analyses were performed using SAS, version 7.1 (SAS Institute). A 2-sided There were 1 022 987 Veterans in the overall cohort. The mean (SD) age was 60 (16) years, 1 008 336 (92.4%) were male, and 761 485 (69.8%) were non-Hispanic white . A totalWhen applying the sensitive criteria, overall and VAMC-level low-value testing frequency varied substantially across conditions: 18.2%  for low back pain, 12.8%  for headache, 20.1%  for syncope, and 4.6%  for sinusitis . With thThe median aOR ranged from 1.23 among veterans with low back pain to 1.42 among veterans with acute sinusitis, meaning that the median odds of undergoing low-value testing for low back pain or acute sinusitis increased by 23% or 42%, respectively, if a veteran transferred their care to a VAMC with a higher rate of low-value testing for that service . The ICCSignificant differences were found in the odds of undergoing low-value testing by VAMC decile for each disease state. Among veterans who received care at VAMCs in the top decile for each disease state, the adjusted OR ranged from 2.0  for veterans with low back pain to 4.7  for veterans with acute sinusitis, meaning the odds of undergoing low-value testing for low back pain and sinusitis were 2 and 4.7 times as large, respectively, as the odds of undergoing such testing for veterans who received care at VAMCs in the lowest decile. When applying the specific criteria, differences across deciles were accentuated and ranged from 2.1  for veterans with low back pain to 8.4  for veterans with acute sinusitis (eTables 3 and 4 in the P = .04) for the correlation between syncope and sinusitis to 0.56 (P < .001) for the correlation between syncope and headache (P < .001) and specific  criteria. For headache, use of head imaging and electroencephalography were significantly correlated when applying the sensitive criteria  but not when applying the specific criteria .Low-value testing for different conditions was significantly correlated at the VAMC level, with ρ values ranging from 0.18 (headache . When evIn a national cohort of veterans, low-value diagnostic testing was common, affecting from 5% to 21% of veterans based on their underlying condition and similarly affecting those at greatest risk of 1-year mortality. We also identified 2- to 5-fold variation in low-value testing across VAMCs and significant correlation at the VAMC level in veterans’ receipt of such testing.29 Our results align with studies conducted by Rubenstein et al16 and Burke et al19 that demonstrated overuse and variation of low-value upper endoscopy and neuroimaging throughout the VHA, respectively. The current findings also echo prior work examining low-value prostate cancer screening, which occurred in 17.7% of VA beneficiaries, with 10-fold variation across VAMCs.18 In taking a patient-centered approach, we identified low-value testing first by identifying subpopulations of veterans with a corresponding disease state rather than simply those who underwent testing; thus, we depicted our estimates of low-value testing as the frequency of veterans with each condition who received a corresponding low-value test. This strategy differs from prior approaches broadly assessing low-value care among Medicare beneficiaries, which depict low-value care as counts among 100 beneficiaries, making direct comparisons between VA and Medicare beneficiaries challenging.8 Nevertheless, these data, in concert with the growing body of literature on this topic, suggest that low-value care is common and occurs across multiple disease states in the VHA, as it does among Medicare and private insurance beneficiaries.Our findings build on prior studies by examining multiple low-value diagnostic tests and expand our understanding of the scope and variability of low-value testing throughout the VHA.15 Furthermore, the variation that we observed in low-value testing across VAMCs could not be easily explained by the traditional veteran- and VAMC-level covariates that we incorporated in our analyses. This suggests that unique local factors, such as those related to provider culture and variations in the robustness of decision support for ordering tests and procedures at each VAMC, are associated with low-value testing. Similar factors may also play a role in other non-US health systems, such as Canada’s, where low-value care is broadly present despite operating largely within a single payer system.30 Identifying these factors will be essential for the development of interventions to reduce the delivery of such care in the VHA and other integrated health care systems.Our findings are notable in that VA clinicians are not subject to the same incentives and circumstances commonly associated with low-value testing in non-VA settings in the US. For example, unlike VA physicians who are federal employees, non-VA clinicians may be financially incentivized for ordering certain health services, especially if they own the related equipment. VA physicians are also insulated from malpractice lawsuits and have access to an electronic medical record system that contains records from the entirety of the VHA system and incorporates decision-support tools, enhancing the ability of physicians to make clinical decisions.29 previously developed an index to characterize total systematic health care waste rather than to identify the overuse of specific low-value tests and procedures. They demonstrated that health care systems may be subject to latent overarching pressures to deliver low-value care. To the extent that low-value care delivery is specific to certain tests and procedures, directed interventions and decision-support tools integrated within the electronic medical record system may be sufficient to reduce the delivery of such care. Although the development of additional service-specific interventions is appropriate in certain circumstances, addressing those latent factors associated with the overarching provision of low-value care may require the institution of broad cultural change targeted at the national and VAMC levels. To aid in this task, our claims-based approach to identify low-value testing may help the development of future value-based metrics.33The correlations of the receipt of low-value testing among the health conditions in our study also suggest that the delivery of such care at VAMCs is associated with systemic factors that are not specific to the delivery of an individual test. Zhou et al34 the ratio of nurse practitioners and physician’s assistants to physicians was associated with overuse of low-value prostate-specific antigen testing. This finding shows the importance of characterizing practitioner- and system-level factors both qualitatively and quantitatively in future work. Third, our study only evaluated veterans’ receipt of care in the VHA. Nearly all veterans 65 years or older are dually enrolled in the VHA and Medicare, and others are increasingly receiving non-VA care that is paid for by the VHA.35 A greater understanding of veterans’ broad receipt of low-value care across all health care systems is needed to enhance the overall value of care that they receive.This study has limitations. First, value is an inherently subjective construct that cannot be readily detected by administrative claims in every circumstance. By applying both sensitive and specific criteria to identify low-value testing, we demonstrated a range in which the degree of low-value testing likely exists that is sufficient for health systems to triage specific services for interventions. Second, we did not incorporate practitioner characteristics in our models. Such data are not as readily available in the VHA, but in a prior VHA study,In this cohort study, low-value diagnostic testing for 4 conditions was common and variably delivered throughout the VHA despite the VHA being a non–fee-for-service integrated health care system. Our findings suggest the need to further characterize the unique factors associated with the overarching delivery of low-value care in the VHA to ensure that veterans may make informed health care choices and that the VHA is poised to be a model health care system in ensuring that its beneficiaries receive high-value care that is directly delivered or is paid for by the VHA.


Lung ultrasound (LUS) and chest radiography (CXR) are the most used chest imaging tools in the early diagnosis of COVID-19 associated pneumonia. However, the relationship between LUS and CXR is not clearly defined. The aim of our study was to describe the comparison between LUS interpretation and CXR readings in the first approach to patients suspected of COVID-19.In the time of the first COVID-19 pandemic surge, we prospectively evaluated adult patients presenting to an emergency department complaining of symptoms raising suspicion of COVID-19. Patients were studied by LUS and only those performing also CXR were analyzed. All the patients performed viral reverse transcriptase-polymerase chain reaction (RT-PCR). LUS studies were classified in 4 categories of probabilities, based on the presence of typical or alternative signs of COVID-19-associated interstitial pneumonia. Accordingly, the CXR readings were retrospectively adapted by 2 experts in 4 categories following the standard language that describes the computed tomography (CT) findings. Patients were divided in two groups, based on the agreement of the LUS and CXR categories. Results were also compared to RT-PCR and, when available, to CT studies.We analyzed 139 cases . The LUS vs CXR results disagreed in 60 (43.2%) cases. RT-PCR was positive in 88 (63.3%) cases. In 45 cases, a CT scan was also performed and only 4 disagreed with LUS interpretation versus 24 in the comparison between CT and CXR. In 18 cases, LUS detected signs of COVID-19 pneumonia (high and intermediate probabilities) while CXR reading was negative; in 14 of these cases, a CT scan or a RT-PCR-positive result confirmed the LUS interpretation. In 6 cases, LUS detected signs of alternative diagnoses to COVID-19 pneumonia while CXR was negative; in 4 of these cases, CT scan confirmed atypical findings.Our study demonstrated a strong disagreement between LUS interpretation and CXR reading in the early approach to patients suspected of COVID-19. Comparison with CT studies and RT-PCR results seems to confirm the superiority of LUS over a second retrospective reading of CXR. The COVID-19 pandemic poses unprecedented challenges to the health care systems even in the most advanced and rich countries. During the pandemic surge, there is the need to isolate new cases and diagnose promptly interstitial pneumonia, the most common complication of the SARS-CoV-2 infection . HoweverTo the best of our knowledge, there are no studies comparing LUS and CXR in the first diagnosis of COVID-19 pneumonia, when a systematic classification in patterns of probability is applied. The main aim of our study was to describe the agreement between LUS interpretation and CXR reading in symptomatic patients suspected of COVID-19 who presented to the ED during the peak of COVID-19 pandemic in the spring 2020. A second aim was to describe the combination with the results of CT scan and RT-PCR, in cases showing LUS/CXR disagreement.This was a monocenter observational descriptive study performed at San Luigi Gonzaga University Hospital in Torino, Italy. We received the ethical approval from the local ethical committee. During the first surge of the COVID-19 pandemic, from March 10 to May 15, adult patients (≥ 18 years old) presenting to the ED complaining of symptoms raising suspicion of SARS-CoV-2 infection and studied by LUS at presentation, were enrolled. Clinical suspicion was based on the following symptoms: at least one major criteria between fever > 37.5° C, cough, dyspnea, anosmia and/or ageusia; or at least two minor criteria between sore throat, bilateral conjunctivitis, deep weakness, rhinorrhea, headache, diffuse muscle-skeletal pain, gastrointestinal symptoms ; (2) age ≥ 18-year-old. Symptoms were to last for at least 3 days in the absence of an alternative more probable diagnosis. Exclusion criteria were: (1) a previous diagnosis of COVID-19 pneumonia; (2) a previous pneumonectomy, pleurodesis or history of fibrothorax.All patients were prospectively studied by bedside LUS at presentation for signs of COVID-19 pneumonia. Decision about performing a CXR study was taken by the physician in charge, blinded to the LUS evaluation and for clinical reasons independent from the study protocol. Only patients who performed both LUS and CXR were analyzed. Performance of a CT scan was decided by the physician in charge for clinical reasons independent from the study protocol, after having considered both the results of LUS and CXR. All the enrolled patients were submitted to RT-PCR swab test immediately after presentation.mixed phenotype), and on the presence (severe phenotype) or absence (mild phenotype) of signs and/or symptoms of respiratory failure. The list of significant chronic conditions included: severe chronic obstructive pulmonary disease, pulmonary fibrosis, lung cancer, heart failure, or cor pulmonale. Respiratory failure was determined according to the presence of dyspnea, either objective or self-reported to the attending physician at admission, and/or desaturation defined as either a PaO2/FiO2 < 300 mmHg or Sat.O2/FiO2 < 357.The clinical characteristics at presentation were simplified by considering respiratory symptoms and the clinical history. We identified 3 different clinical phenotypes based on pre-existing chronic cardiac or respiratory diseases that may entail the possibility of confounding ultrasound and radiologic signs (High probability LUS pattern (HighLUS), typical of COVID-19 pneumonia showing bilateral and multifocal clusters of separated and coalescent B-lines, large echoic bands (light beams), multifocal small peripheral consolidations, alternating regular and irregular pleural line, with or without large consolidations; these clusters should appear in patchy distribution, alternating abruptly with normal A-lines patterns ("spared areas”);Intermediate probability LUS pattern (IntLUS), less typical pattern of COVID-19 pneumonia, which includes unilateral isolated clusters of B-lines and light beam or focal multiple B-lines, with or without small peripheral consolidations;Alternative probability LUS pattern (AltLUS), a condition more consistent with other diagnosis than COVID-19 pneumonia, such as an isolated large consolidation with dynamic air bronchograms  or without bronchograms (suggesting obstructive atelectasis), a large pleural effusion (either hydrostatic or inflammatory), diffuse homogeneously distributed B-lines (suggesting cardiogenic edema or diffuse fibrosis);Low probability LUS pattern (LowLUS), a normal or near-normal LUS pattern characterized by bilateral A-lines with respiratory sliding without significant B-lines.For many years, LUS has become a routine diagnostic tool in our institution. The exam was performed by experienced operators with documented previous experience in bedside LUS in the diagnosis of common lung pathologies seen in emergency and critical care. The operator assigned the LUS pattern at patient’s presentation being aware of symptoms but blinded to any other exam and diagnostic tool. Further details about the technique are reported in previous publications , 7. An Eground glass opacities (GGO), extensive blurred lung opacities, with indistinct margins of blood vessels; reticular alterations, visualized as a net image, formed by summation of several small linear opacities; consolidations, homogeneous increase in parenchymal attenuation that obscures the visualization of walls of vessels and airways. The distribution of CXR signs of interstitial pneumonia and the presence of additional signs atypical of COVID-19 pneumonia, were used to define 4 classes similar to those used to standardize CT readings [Typical appearance in the presence of bilateral distribution of GGO and reticular alterations, with or without presence of limited consolidations;Indeterminate appearance when opacities were limited and monolateral or smooth and not well defined;Atypical appearance when signs alternative to COVID-19 pneumonia were predominant or isolated, like a large effusion, a large lobar consolidation with evidence of well-defined air bronchograms, clear signs of atelectasis;Negative when the CXR did not demonstrate any significant alteration.All radiological studies were acquired as a digital radiograph in the bedside antero-posterior view using portable x-ray units  in the isolation wards following local protocols . The CXR images were retrospectively reviewed independently by two radiologists with long-standing experience on thoracic imaging. In case of disagreement in the reading, a consensus was reached after discussion. The findings considered were presence of few basic signs and their distribution in the two lungs. We used a standard definition of CXR alterations, according to the language approved by the Fleischner Society : ground Typical appearance, in the presence of peripheral, bilateral, multifocal GGO with or without consolidation or visible lines (“crazy-paving”);Indeterminate appearance, in the presence of multifocal perihilar or unilateral GGO with or without consolidation, or very small GGO non-rounded or non-peripheral;Atypical appearance, in the presence of isolated lobar or segmental consolidation without GGO, or discrete small nodules, or lung cavitation, or smooth interlobular septal thickening with large pleural effusion;Negative, in case of no CT features suggesting pneumonia.Patients with clinical indication to CT scan were moved to the radiology unit to perform the study. A multidetector CT  was used. The patients were in supine and head-first position and received scanning with breath held. Parameters used were: 10 0 kV; 100, mAs real-time adaptive control; layer thickness 1–2.5 mm; pitch, 1–1.5; matrix, 512 × 512. No contrast was administered. All images were transmitted to the post-processing workstation and reconstructed using high-resolution and conventional algorithms. Each study was read and interpreted by two experts with long-standing experience in chest imaging. Interpretation of CT scans was performed by radiologists not involved in CXR readings, blind to the results of LUS and CXR. Signs and nomenclature of CT scan were those recommended and reported in the Radiological Society of North America Expert Consensus document . Each CTPatients were grouped according to agreement or disagreement between LUS interpretation and CXR reading, following the reciprocal classification in the four categories. Continuous variables are expressed as mean with SD. Categorical variables were expressed as numbers and percentages. The normality of distribution for continuous numeric data was assessed with Shapiro–Wilk test. Non-continuous non-normally distributed variables were analyzed using Chi-Squared test or Fisher’s test in case of small numbers. Mann–Whitney test was used to compare continuous non-normally distributed variables. Unpaired T-Test in case of continuous normally distributed variables. P values below 0.05 were considered statistically significant. Statistical analysis was performed using MedCalc statistical software.p = 0.022).Out of 169 patients enrolled in the study, 139 (55 women and 84 men) with a mean age 59.1 ± 15.5 years old performed both CXR and LUS and were examined. The patient’s flow chart is represented in Fig. typical and indeterminate, the number of cases showing disagreement was reduced to 46 (33.1%). In the 79 patients showing agreement between LUS pattern and CXR reading, the LUS diagnoses were: 28 LowLUS, 39 HighLUS, 5 IntLUS and 7 AltLUS. Table When the two LUS classifications HighLUS and IntLUS are mixed together and matched with both CXR CT scans: out of the 45 CT studies, 24 disagreed with CXR reading and 4 with LUS interpretation. The 4 cases showing CT vs LUS disagreement, were: one case of negative CT that was interpreted AltLUS and typical CXR; one case of typical CT that was interpreted LowLUS and negative CXR; one case of indeterminate CT that was interpreted AltLUS and typical CXR; one case of atypical CT that was interpreted IntLUS but confirmed atypical at CXR.HighLUS and IntLUS with negative CXR: in 18 cases HighLUS (n = 8) and IntLUS (n = 10) corresponded to negative CXR readings  and atypical CXR (n = 3). Two of these patients performed CT scan that confirmed a negative appearance. One other was confirmed negative by the RT-PCR. The other 3 cases, of which one with atypical CXR and two indeterminate CXR, resulted RT-PCR-positive.AltLUS: in 6 cases showing disagreement, AltLUS corresponded to negative CXR and in 4 of these cases, the CT study showed atypical appearance, confirming the LUS interpretation. In the remaining 2 cases, RT-PCR resulted positive in one and negative in the other. In 5 cases, AltLUS corresponded to typical CXR, but RT-PCR resulted negative in all and in two cases CT scan confirmed the atypical appearance (Fig. indeterminate CXR with a positive RT-PCR in one and a confirmation of atypical CT in the other.Our study demonstrated a high rate of disagreement between LUS interpretation and CXR reading when the symptomatic patients are examined in the ED for suspected COVID-19 pneumonia during the pandemic surge. Disagreement between LUS and CXR was irrespective of the main patient’s characteristics, including sex, days of symptoms onset and clinical phenotype. The group with disagreement resulted only significantly slightly older.indeterminate” classification of the CT language. Like for CT, the LUS intermediate probability is assigned in cases of unilateral and mostly isolated interstitial signs. Indeed, to compare LUS probability patterns and CXR readings, it was necessary to introduce the possibility of an “indeterminate” reading also for the interpretation of the CXR exams, which may have increased LUS vs CXR disagreement. However, even mixing together typical and indeterminate CXR readings and high and intermediate probability LUS interpretation, the rate of disagreement still remained high.The high rate of disagreement may be due to the classification in 4 different LUS patterns of probability. However, the LUS probability approach demonstrated highly practical when suspected cases are examined in the ED and helps to allocate the patients during the pandemic surge , 14. IndAge may have a role, as an older age may imply an increase in confounding sonographic and radiologic signs. Indeed, the age of the group of disagreement cases was slightly but significantly older than in the other group. However, in other studies, CXR showed a low sensitivity in the early diagnosis of COVID-19 pneumonia irrespective of patient’s age, whereas sensitivity improved when symptoms had started from a longer time .Based on our data, it is reasonable to hypothesize that LUS is a more powerful tool in the early diagnosis of COVID-19 pneumonia and that the high rate of disagreement is justified by a difference in diagnostic accuracy between LUS and CXR. This hypothesis is supported also by a more detailed analysis of our disagreement cases, based on the comparison with CT studies and results of the viral swab test. Our study was not specifically designed to investigate a systematic comparison between CT scan and LUS, but the subgroup of patients submitted to all the three imaging tools showed the striking superiority of LUS to CXR, based on a higher rate of agreement between CT vs LUS interpretation than CT vs CXR reading. When a CT scan was not performed, the results of RT-PCR demonstrated a high rate of confirmation of the LUS classification. Of course, this latter result should be considered cautiously as the viral test is useful to confirm the SARS-CoV-2 infection but not necessarily this latter condition is complicated by pneumonia. Thus, a negative chest imaging test may easily be coupled with a positive RT-PCR in case of SARS-CoV-2 infection not complicated by pneumonia. On the other side, it is also well known that the molecular RT-PCR is limited by quite a low sensitivity, especially when performed early in the first approach to suspected cases , 21. It Other studies compared LUS and CXR in COVID-19, demonstrating a higher sensitivity of LUS , 23. TheIn our study, the CXR reading was performed retrospectively by expert radiologists. We did not consider the original reading performed in the ED by the radiologist in charge, which would had been more realistic of what happens in the early approach to patients during the surge. However, for intuitive reasons, the CXR readings on the field could not adhere to a strict standardization and the language used could not facilitate a systematic comparison with CT and LUS interpretations.Another limitation of our study, was the lack of a systematic comparison with CT. Indeed, CT represents the gold standard in the diagnosis of COVID-19 pneumonia. Notwithstanding, the systematic application of CT studies is not ethical in the time of a COVID-19 pandemic surge, due mainly to the problem of overexposure of personnel outside the dedicated hospital areas and lack of clinical utility during the early management of the vast majority of COVID-19 cases . We usedA further limitation may be considered the application of bedside CXR antero-posterior projections. It is well known the limitation of this view in the evaluation of the pulmonary parenchyma. However, due to the necessity of limiting the possibility of hospital cross-infections, the bedside CXR is the standard of care in the time of the COVID-19 pandemic surge.Finally, the study was conducted in a single center where LUS is used as a standard bedside diagnostic tool from many years. In other centers, the quality of LUS performance may vary depending on the level of experience of the operators.Our descriptive study showed a high rate of disagreement between LUS and CXR performed during the early evaluation of patients suspected of COVID-19. The results of this study support the idea that LUS is superior to CXR in the first approach to COVID-19.