Streptococcus suis ) and Japanese encephalitis virus (JEV) ). Commonly reported animal contact included keeping, raising or handling ) and handling, cooking or consuming raw meat, blood or viscera in the 2 weeks prior to symptom onset ), with the latter most commonly from pigs . There was no association between an unknown aetiology and exposure to animals in a multivariate logistic regression. Further testing for unknown or undetected pathogens may increase diagnostic yield, however, given the high proportion of zoonotic pathogens and the presence of risk factors, increasing public awareness about zoonoses and preventive measures can be considered.An estimated 73% of emerging infections are zoonotic in origin, with animal contact and encroachment on their habitats increasing the risk of spill-over events. In Vietnam, close exposure to a wide range of animals and animal products can lead to acquisition of zoonotic pathogens, a number of which cause central nervous system (CNS) infections. However, studies show the aetiology of CNS infections remains unknown in around half of cases. We used samples and data from hospitalised patients with CNS infections, enrolled into the Vietnam Initiative on Zoonotic Infections multicentre study, to determine the association between aetiology and animal contact including those in whom the cause was unknown. Among 933 patients, a pathogen or an antibody response to it was identified in 291 . The most common pathogens were The online version contains supplementary material available at 10.1007/s10393-022-01611-w. Streptococcus suis  infections in Vietnam are caused by a number of pathogens, many of which are zoonotic  was developed with the aim of characterizing endemic infections, novel infections and infections of unknown origin through a prospective hospital-based surveillance programme. Patients with either a respiratory tract infection, enteric infection, central nervous system (CNS) infection or jaundice were enrolled as part of the hospital surveillance component , Ha Noi; Ba Vi District Hospital, Ha Noi (Ba Vi); Hue Central Hospital, Hue (Hue); Dak Lak General Hospital, Buon Me Thuot (Dak Lak); Khanh Hoa General Hospital, Nha Trang (Khanh Hoa); and Dong Thap General Hospital, Cao Lanh (Dong Thap) Fig. .Figure 1Informed written consent was obtained from patients  included in the study. Diagnostic results from this study were previously described in a paper by Robertson et al.  who lookhttps://clires.oucru.org). GPS coordinates were also provided for the location of the hospitals.A standard Case Report Form (CRF) detailing demographics, admission to an intensive care unit, self-reported HIV status, drinking water source and animal contact was completed on enrolment (supplementary data). The questions relating to animal exposure included raising, keeping or handling animals; or slaughtering an animal; or handling, cooking or consuming the raw meat, blood or viscera from a list of twenty-nine domestic, livestock and wild animals within the 2 weeks prior to the onset of symptoms. An additional CRF was completed at discharge with the diagnosis and outcome. Global positioning system (GPS) coordinates for the centre of the patients’ commune were obtained using a conversions system via the CliRes Data Management system  were collected as soon as possible. Initial analysis was undertaken at the hospital of admission including a full blood and CSF cell count, and CSF Gram/Ziehl–Neelsen stain. Blood and CSF culture was performed at the hospital sites including Dak Lak, Dong Thap, Khanh Hoa and Hue with a repeat culture performed at the Oxford University Clinical Research Unit (OUCRU) in Ho Chi Minh City (HCMC) although a blood culture was not obtained for all patients. Discrepancies in culture results between the hospital sites and the putative causal pathogen identified at OUCRU were reviewed to predict the causal pathogen for the purpose of the analysis. In Ba Vi and NHTD, blood and CSF were transported to OUCRU Ha Noi on daily basis where these were cultured.Neisseria meningitidis, Streptococcus pneumoniae, Streptococcus suis, Haemophilus influenzae type b, herpes simplex virus (HSV), varicella zoster virus (VZV), and enteroviruses on CSF samples from all hospital sites at OUCRU HCMC  or Ha Noi. The Panbio Dengue IgM Capture Enzyme-linked immunosorbent assay (ELISA)  value of < 40, or ELISA with an optical density ratio defined as positive according to the manufacturers’ instructions.3 and those with a CSF white cell count of ≥ 5 cells/mm3 to account for any potential misdiagnoses of encephalopathy or a CNS infection without a CSF pleocytosis. Age was categorised into five categories: under 5 years; 5–17 years; 18–49 years; 50–69 years and 70 years and over. All the pathogens found in less than six patients were pooled into a category ‘other’. Statistical differences in the demographics of the patients including age category, gender and site of hospital admission by aetiology; and age category and gender by site of hospital admission were determined using Fisher’s exact test and the p value simulated with 2000 replicates. Univariate binomial regression was used to calculate the odds ratio (OR) of the presence of each of the aetiologies between age, gender, site of recruitment and type of animal exposure. Univariate binomial regression was also used to calculate the OR between each type of exposure to pigs including keeping, raising or handling; slaughtering or eating, cooking or handling the raw meat, blood or viscera and S. suis and JEV.All analyses were performed using R statistical software version 3.6.0 Core . In the 3 and a known aetiology; and patients with an unknown aetiology and a CSF white cell count of < 5 cells/mm3; age and gender were included as fixed effects. The hospital site was added as a random effect. An outcome of any type of exposure to animals was included as the sample size for the independent variables was too small to perform multivariable logistic regression modelling for exposures including slaughtering an animal or handling, cooking or consuming raw, meat, blood or viscera.A mixed-effects multivariate binomial regression model was used to determine the association between any type of animal of exposure and aetiology of the CNS infection. The four most common pathogens, patients with an unknown aetiology and a CSF white cell count of ≥ 5 cells/mmn = 296, 31.7%). An aetiology was established in 291/933 (31.2%). Among those in whom a pathogen was detected, 247/291 (87.9%) had CSF white cell count of ≥ 5 cells/mm3 and 44/291 (15.1%) had a CSF white cell count of < 5 cells/mm3. Among those in whom no pathogen was detected, 435/642 (67.8%) had a CSF white cell count of ≥ 5 cells/mm3 and 207/642 (32.2%) had a CSF white cell count of < 5 cells/mm3. A total of 12 different bacterial pathogens were found with the most common being S. suis (n = 91 (9.8%)) and S. pneumoniae (n = 49 (5.3%)). Of the five viruses tested, the most detected were JEV (n = 72 (7.7%)) and enterovirus (n = 25 (2.7%)) . Infections with S. suis were predominantly seen in adult males, with median age of 49 years [IQR = 39–58]. Infections with S. pneumoniae, JEV and enterovirus were predominantly seen in paediatric patients, with median age of 12 years [IQR = 2–33 years], 11 years [IQR = 6.8–15] and 13 years [IQR = 8–22], respectively. The median age of those in whom the aetiology was unknown was 23 years [IQR = 10–45] in those with a CSF white cell count of ≥ 5 cells/mm3 and 11 years [IQR = 2–36.5] in those with a CSF white cell count of CSF white cell count of < 5 cells/mm3. Half of the cases of JEV were recruited from Dak Lak General Hospital (n = 36 (50%)) and nearly half of the cases of S. suis were recruited from Hue Central Hospital (n = 43 (47.3%)). However, although there was statistical evidence of a difference in the aetiology of CNS infection by age of the patient and also, the site of recruitment (p = 0.001) there was not by gender of the patient (p = 0.062)  with a median age ranging from 16 years [IQR = 7–37] in Khanh Hoa to 29.5 years [IQR = 17–42] in the NHTD. No difference was seen between hospital site and gender (p = 0.102) ), and those with S. pneumoniae or an unknown aetiology with a CSF white cell count of < 5mm3 were less likely to keep, raise or handle an animal  and OR 0.67 , respectively). Those with an unknown aetiology with a CSF white cell count of < 5mm3 were less likely to have contact with an animal compared to those with a known pathogen or unknown aetiology with a CSF white cell count of > 5mm3 (OR 1.47 (1.08–2), p = 0.015)) , have slaughtered an animal (OR 5.34 (3.28–8.91), p < 0.001), and handled, cooked or consumed raw meat, blood or viscera compared to other sites (OR 2.73 (2.06–3.63), p < 0.001). Patients admitted to the hospital in Dong Thap were also more likely to have handled, cooked or consumed raw meat, blood or viscera (OR 1.51 (1.04–2.18), p = 0.028) whereas those admitted to the hospital in Hue or Khanh Hoa were more likely to have had no contact with an animal (OR 1.82 (1.34–2.47), p < 0.001 and OR 2.44 (1.72–3.48), p < 0.001, respectively) . Adults aged 50 years and over were more likely to keep, raise or handle animals compared to other age groups  and 70 years and over: OR 1.92 ) with those aged 18–69 years more likely to have slaughtered an animal  and 50–69 years: 2.5 ) and handled, cooked or consumed raw meat, blood or viscera  and 50–69 years: 1.7 . Children aged under 5 years were more likely to have had no contact with animals  . Females were more likely to have no contact with animals  with males more likely to keep, raise or handle animals  but with no difference between the genders in terms of slaughtering animals or handling, cooking or consuming raw meat, blood or viscera .Three hundred and sixty-four patients 39%) raised an animal, 78 (8.4%) had slaughtered an animal and 371 (39.8%) handled, cooked or consumed raw meat, blood or viscera. In univariate analyses, patients with 9% raisedn = 344/371 (92.7%)) again both in those with and without a known aetiology  and rat ). However, the raw meat, blood or viscera of wild/farmed wild animals were handled, cooked or consumed by those with a known aetiology  and pigeon ); those with an unknown aetiology and CSF white cell count of ≥ 5 cells/mm3 , other wild bird , pigeon  and squirrel ; and those unknown aetiology and CSF white cell count of < 5 cells/mm3  and pigeon . In the univariate binomial regression, those with S. suis were more likely to have slaughtered a pig . However, there was no difference between those with and without S. suis who kept or raised pigs; or ate, cooked or handled the raw meat, blood or viscera from pigs  and OR 1.02 , respectively). No difference was between those with and without JEV and keeping or raising pigs , slaughtering pigs  or eating, cooking or handling the raw meat blood or viscera from pigs .The most common animals kept, raised or handled were dogs (264/364 patients 72.5%)) and chickens (204/364 patients (56.0%)) both in those with and without a known aetiology. Chickens were the most common animals slaughtered (61/78 patients (78.2%)), and the raw meat, blood or viscera from pigs were the most common product handled, cooked or consumed (%) and chS. suis, S. pneumoniae, JEV or enterovirus. Similarly, there was no difference between animal exposure and an unknown aetiology, either with or without a CSF white cell count of ≥ 5 cells/mm3. Men were more likely than women to have exposure to animals ; however, there was no difference by age  of adults and 2% (n = 11) of children diagnosed with MTB  in the previous year , rats (n = 1) and squirrels  had a CNS infection with unknown aetiology and a CSF white cell count of ≥ 5 cells/mm3. Rodents are known hosts for a number of zoonotic pathogens with a study in the Mekong delta, Vietnam, detecting antibodies to Tick-borne encephalitis virus (TBEV) and hantavirus in both humans and rodents  and indirect immunofluorescence assays for O. tsutsugamushi and qPCR and microscopic agglutination tests for Leptospira spp. as described by Dittrich et al. . Figure S2. The effect of animal exposure by age of the patient as shown by odds ratios with 95% confidence intervals from univariate binomial regression with an outcome of animal contact . Figure S3. The effect of animal exposure by gender of the patient as shown by odds ratios with 95% confidence intervals from univariate binomial regression with an outcome of animal contact . (DOCX 44 kb)Below is the link to the electronic supplementary material.


Non-invasive assessment of aortic hemodynamics using four dimensional (4D) flow magnetic resonance imaging (MRI) provides new information on blood flow patterns and wall shear stress (WSS). Aortic valve stenosis (AS) and/or bicuspid aortic valves (BAV) are associated with altered aortic flow patterns and elevated WSS. Aim of this study was to investigate changes in aortic hemodynamics over time in patients with AS and/or BAV with or without aortic valve replacement.We rescheduled 20 patients for a second 4D flow MRI examination, whose first examination was at least 3 years prior. A total of 7 patients received an aortic valve replacement between baseline and follow up examination (=operated group = OP group). Aortic flow patterns (helicity/vorticity) were assessed using a semi-quantitative grading approach from 0 to 3, flow volumes were evaluated in 9 planes, WSS in 18 and peak velocity in 3 areas.p = 0.029). WSS in the outer ascending aorta was significantly higher in the OP group than in the NOP group at baseline . Peak velocity decreased from baseline to follow up in the aortic arch only in the OP group .While most patients had vortical and/or helical flow formations within the aorta, there was no significant change over time. Ascending aortic forward flow volumes were significantly lower in the OP group than in the NOP group at baseline (NOP 69.3 mL ± 14.2 mL vs. OP 55.3 mL ± 1.9 mL Aortic valve replacement influences aortic hemodynamics. The parameters improve after surgery. Indeed,D flow ca2.2.1.Participants were initially prospectively recruited , 16 and 2.2.All cardiovascular MRI examinations at both time points were performed on a 3 Tesla Scanner . 4D flow cardiovascular MRI data were acquired using a sagittal oblique volume covering the thoracic aorta. Prospective ECG gating was used in combination with a respiratory navigator placed on the lung–liver interface to allow data acquisition during free breathing. The following scan parameters were used: echo time = 2.6 ms, repetition time = 5.1 ms, bandwidth = 450 Hz/pixel, imaging acceleration using PEAK GRAPPA with a reduction factor of R = 5, net acceleration 4.17, reference lines = 20, flip angle α = 7° to 9°, temporal resolution = 40.8 ms, field of view = 360 mm × 270 mm, voxel size = 2.7 mm × 2.3 mm × 2.6 mm, phase encoding direction = anterior–posterior, number of slices = 32, encoding velocity = 1.5–2.5 m/s.Additionally, standard steady-state free-precession cine images were acquired for cardiac chamber quantification and for planimetry of the AOA. Imaging parameters were as follows: repetition time = 3.1 ms, echo time = 1.3 ms, flip angle = 45°, field of view = 276 mm × 340 mm, matrix = 156 × 192, slice thickness = 6 mm (chambers) and 5 mm , bandwidth = 704 Hz/px, parallel imaging using GRAPPA reconstruction with R = 2, 30 cardiac phases. Cardiac chamber and AOA quantification was performed using CVI42 . Maximum aortic area was defined as the product of the two orthogonal aortic diameter measurements.2.3.First, 4D flow MRI data were corrected for Maxwell terms and eddy-currents  . AliasinThen, 3D blood flow visualization and positioning of 2D cross-sectional planes for flow quantification were conducted . Nine planes were positioned perpendicular to the longitudinal axis of the aortic wall at the following locations: in the left ventricular outflow tract (P1), at the level of the sinus of valsalva (P2), at the sinotubular junction (P3), in the mid-ascending aorta (P4), defined as the level at which the pulmonary trunc divided into the left and right pulmonary artery, proximal to the brachiocephalic trunk (P5), in the mid-aortic arch between the left common carotid artery and the left subclavian artery (P6), distal to the left subclavian artery (P7), in the proximal descending aorta at the level of P4 (P8), and in the distal descending aorta P9; .3D WSS was calculated for the entire thoracic aortic wall at peak systole using a home built analysis tool , as described previously by Potter et al.  and van Peak velocities were obtained from velocity maximum intensity projections in the ascending aorta, the aortic arch and the descending aorta . Parameters were compared between B and FU for all patients as well as between the OP group and the NOP group. Parameters at B and FU were compared using Wilcoxon Signed Rank Test, parameters between groups were compared using Mann–Whitney U Test as the tested cohort was too small to reach normal distribution. Comparisons between parameters in the ascending aorta, the aortic arch and the descending aorta were performed using Friedman Test, for pairwise comparisons Wilcoxon Test was used. Nominally scaled data were compared using Chi Square Test.3.p < 0.05) and had a significantly lower Body Mass Index (BMI) . Left ventricular (LV) ejection fraction (EF) and LV mass significantly decreased in operated patients from B to FU , even after adjusting the latter for body surface area (BSA) . WSS was significantly higher in the ascending aorta than in the aortic arch and the descending aorta in all patients at both examinations , while no significant difference could be found anymore at FU. WSS in the outer ascending aorta was significantly higher in the OP group than in the NOP group at B  as well as in segments 3 and 5 , while at FU no significant difference could be found. WSS in the descending aorta was higher in the NOP group than in the OP group at B and at FU .Forward flow volumes were significantly lower in the ascending aorta in the OP group than in the NOP group at B  patients who received an aortic valve replacement between baseline and follow up (OP group), had a higher WSS along the outer curvature of the ascending aorta prior to the surgery than patients who did not undergo surgery (NOP group), (ii) peak velocity decreased after aortic valve replacement and (iii) patients, who received an aortic valve replacement between baseline and follow up, had initially a lower forward flow in the ascending aorta than patients, who did not receive an aortic valve replacement.Patients, who had an aortic valve replacement during the course of the study, had a higher WSS in the outer ascending aorta than non-operated patients, but only at baseline before surgery. Afterwards, there was no difference in WSS anymore, as WSS in the OP group decreased and WSS in the NOP group slightly increased. A higher WSS in patients with aortic stenosis than in healthy volunteers was previously described by van Ooij et al. . They foPeak velocity decreased in the OP group at follow up compared to baseline examination in the aortic arch; values in the OP group adapt almost to the NOP group. In the follow up of BAV patients over 3 years by Rahman et al.  an increAlthough there was no significant increase in forward flow volumes before and after surgery, there was a significantly lower flow in the ascending aorta at baseline in the OP group than in the NOP group, which could not be seen at follow up due to an (non-significant) increase in flow volumes in the operated group. Kamada et al.  found a It has been shown that aortic hemodynamics and the diameter of the ascending aorta in BAV patients depend on the morphology of the valve, i.e., on the type of cusp fusion , 27. We In the descending aorta we found significantly higher WSS in the NOP group than in the OP group. This might be due to the fact that the patients in the NOP group were significantly younger than patients in the OP group. WSS dependency on age with lower WSS at higher age has been shown in normal volunteers  and pati4.1.This study comprises a small number of patients in total and with aortic valve replacement between baseline and follow-up studies, which was due to the long follow up period and the age of the included patients. Additionally, the cohort was heterogenous in its clinical conditions, which might be the reason for the absence of differences between baseline and follow-up. It was conducted at one site only. Multicenter studies are necessary to confirm these findings in a larger cohort. There are several possible sources of error in our 4D flow image analysis: Aortic helical flow was only assessed using a semi-quantitative approach.4.2.In this 4-year follow up of patients with aortic valve pathology, cardiovascular 4D flow MRI revealed hemodynamic changes in the aorta in patients undergoing aortic valve replacement. WSS decreased and aortic forward flow volumes increased compared to patients who were not operated. Aortic valve replacement improves aortic hemodynamics and might decelerate aortic wall remodeling.The datasets presented in this article are not readily publically available due to German Data Protection Laws. Requests to access the datasets should be directed to the corresponding author.The studies involving human participants were reviewed and approved by Charité Ethics Committee, Charité – Universitätsmedizin Berlin, Berlin, Germany. The patients/participants provided their written informed consent to participate in this study.SW: conceptualization, formal analysis, investigation, methodology, project administration, validation, visualization, and writing – original draft. AM: data curation and investigation. RH: investigation, formal analysis, and writing – review and editing. TM: data curation. RT and FK-B: investigation, resources, and writing – review and editing. EB and MM: resources, software, and writing – review and editing. JS-M: conceptualization, methodology, project administration, resources, supervision, and writing – review and editing. All authors read and approved the final manuscript.The authors received research support from Siemens Healthineers  and Circle Cardiovascular Imaging (JS-M and SW), research grants from Circle Cardiovascular Imaging (MM) and Cryolife Inc (MM), and performed activities in consulting Circle Cardiovascular Imaging (MM).The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


During illness, adaptations of the hypothalamic–pituitary‐thyroid axis reduce energy expenditure, protein catabolism and modulate immune responses to promote survival. Lower serum free triiodothyronine‐to‐thyroxine (fT3/fT4) ratio has been linked to non‐response to treatment in a range of diseases, including in biologic‐treated patients with inflammatory bowel disease.To assess whether baseline serum fT3/fT4 ratio predicted primary non‐response (PNR) and non‐remission to infliximab and adalimumab in patients with Crohn's diseaseThyroid function tests were undertaken in stored serum from biologic‐naïve adult patients with active luminal Crohn's disease immediately prior to treatment with infliximab  or adalimumab (448) in the Personalised Anti‐TNF Therapy in Crohn's Disease study (PANTS).p < 0.001). Multivariable logistic regression analysis demonstrated that fT3/fT4 ratio was independently associated with PNR at week 14 , but not non‐remission or changes in faecal calprotectin concentrations at week 54. The optimal threshold to determine PNR was 0.31 .Baseline median [IQR] fT3/fT4 ratios were lower in women than men , in patients with more severe inflammatory disease, and in patients receiving corticosteroids (0.28 [0.25–0.33] vs. 0.32 [0.29–0.36], Lower baseline serum fT3/fT4 ratio was associated with female sex, corticosteroid use and disease activity. It predicted PNR to anti‐TNF treatment at week 14, but not non‐remission at week 54. In 995 patients with Crohn’s disease from the PANTS study, lower baseline serum fT3/fT4 ratio was independently associated with female sex, higher inflammatory burden at baseline, and baseline corticosteroid use, and predicted primary non‐response to anti‐TNF therapy at week 14, but not non‐remission or change in faecal calprotectin concentrations at week 54. Overall, however, the diagnostic accuracy of baseline fT3/fT4 ratio to predict primary nonresponse to anti‐TNF treatment was modest, limiting its clinical utility.  In Crohn's disease, successful treatment leads to mucosal healing, reduced surgeries and improvements in quality of life.The biology of non‐response is complex, but being able to predict who will fail anti‐TNF therapy could help prompt concomitant immunomodulator use, anti‐TNF dose optimisation and biologic sequencing. Multiple patient, disease, and drug‐related factors have been implicated in anti‐TNF treatment failure, but few studies have been adequately powered to define their relative effects, interactions and impact on drug and anti‐drug antibody levels.Recent data reported by Bertani et al. showed that low serum triiodothyronine‐to‐thyroxine ratios (fT3/fT4) at initiation of infliximab or vedolizumab therapy predicted poor endoscopic outcomes at 54 weeks in a mixed cohort of patients with UC and Crohn's disease.We sought to assess whether baseline serum fT3/fT4 ratio predicted PNR and non‐remission to infliximab and adalimumab in patients with Crohn's disease.22.1The Personalised Anti‐TNF Therapy in Crohn's Disease study (PANTS) is a UK‐wide, multicentre, prospective observational cohort reporting the treatment failure rates of the anti‐TNF drugs infliximab  and adalimumab  in anti‐TNF‐naïve patients with active luminal Crohn's disease.Patients were recruited at the time of first anti‐TNF exposure between February 2013 and June 2016 and studied for 12 months or until drug withdrawal Table . EligiblThe choice of anti‐TNF was at the discretion of the treating physician and prescribed according to the licensed dosing schedule. Study visits were scheduled at first dose, post‐induction (week 14), and at weeks 30 and 54. Additional visits were planned for infliximab‐treated patients at each infusion and for both groups at treatment failure or exit.For this analysis, we included adult patients over the age of 17 years only because of limited or exhausted stored serum in paediatric patients. Patients who were treated with endocrine‐related medications that may have affected the hypothalamic–pituitary‐thyroid axis, including thyroxine, carbimazole, growth hormone and testosterone, or who had evidence of possible primary hypothyroidism or Grave's disease, were excluded.2.2Treatment failure endpoints were PNR at week 14, non‐remission at week 54 and adverse events leading to drug withdrawal. We used composite endpoints defined using the Harvey Bradshaw Index (HBI),PNR: exit prior to week 14 for treatment failure  or corticosteroid use at week 14 (new prescriptions or failure to taper). Patients who exhibited both a failure of CRP to fall to ≤3 mg/L or by 50% from baseline (week 0) and failure of HBI to fall to ≤4 or by 3 points were also classified as PNR.Grey zone (intermediate between PNR and response): CRP falls to ≤3 mg/L or by 50% from baseline (Week 0) or HBI falls to ≤4 or by 3 points from baseline (but not both).Response: both CRP falls to ≤3 mg/L or by 50% from baseline (Week 0) and HBI falls to ≤4 or by 3 points from baseline.Remission: CRP of ≤3 mg/L and HBI of ≤4 points, no ongoing corticosteroid therapy and no exit for treatment failure.Non‐Remission was assessed at week 54 and defined as either CRP of >3 mg/L or HBI of >4 points, ongoing corticosteroid therapy or exit for treatment failure.We defined corticosteroid therapy for the purposes of non‐remission and PNR as any systemic therapy, including prednisolone and budesonide, either oral or intravenous. We included use of corticosteroids for other conditions, but excluded use single pre‐biologic infusion dosing with hydrocortisone.Patients excluded from effectiveness analysis: Three groups of patients were excluded from our effectiveness analyses.Exit: Patients exited the study when they stopped anti‐TNF therapy or had an intestinal resection. Patients who exited the study for treatment failure were deemed to be in non‐remission for subsequent time points. Patients who exited the study for loss to follow‐up, withdrawal of consent, or elective withdrawal of drug, including for pregnancy, were censored at the time of study exit and excluded from the denominator for subsequent analyses.2.3At baseline, sites recorded demographic data , smoking status, age at diagnosis, disease duration, Montreal classification,https://www.exeterlaboratory.com/) for haemoglobin, white cell count, platelets, serum albumin, CRP, anti‐TNF drug and anti‐drug antibody concentrations and faecal calprotectin, respectively. All analysis were carried out on the Cobas 801 module of the Cobas 8000 automated platform (Roche Diagnostics).Blood and stool samples were processed through the central laboratory at the Royal Devon and Exeter NHS Foundation Trust . All tests were two tailed, and We performed univariable analyses using Fisher's exact and Mann–Whitney U tests to identify differences in baseline characteristics between infliximab‐ and adalimumab‐treated patients, and to determine categorical factors associated with fT3/fT4 ratio and predefined outcomes. Spearman's rank correlation was used to determine continuous factors, including faecal calprotectin, associated with fT3/fT4 ratio. Multivariable logistic regression models were used to identify factors independently associated with PNR at week 14 and remission at week 54. Variables identified in the PANTS study as associated with each outcome were included in the model.Youden's formula2.5PANTS is an investigator‐led study funded by the research charities CORE, Crohn's & Colitis UK and C3, and by unrestricted educational grants from Abbvie (USA), Merck Sharp and Dohme (UK), NAPP Pharmaceuticals (UK), Pfizer (USA) and Celltrion Healthcare (South Korea). No funding bodies had any role in study design, data collection or analysis, writing or decision to submit for publication.33.1. We excluded 1.8% (25/1375) patients who were treated with medications known to affect the hypothalamic–pituitary–thyroid axis, including thyroxine, carbimazole, growth hormone and testosterone. 0.2% (2/997) patients had elevated fT3 or fT4 levels with suppressed TSH concentrations, suggestive of hyperthyroidism, and were excluded. No patients had hypothyroidism. No differences were seen in baseline characteristics between patients who were included in the study and in whom we did not have sufficient serum for analysis.Overall, 86.9% 997/1146) adult patients who participated in PANTS were included: 549 (55.1%) were treated with infliximab (427 [42.8%] with originator infliximab and 122 [12.2%] with biosimilar CT‐P13) and 448 (44.9%) treated with adalimumab 146 adultDifferences between demographic and clinical characteristics of infliximab‐ and adalimumab‐treated patients are shown in Table 3.2p = 0.096; fT4: infliximab 15.60 pmol/L [IQR 14.10–17.10] vs. adalimumab 15.60 [IQR 14.30–17.50], p = 0.155; TSH: infliximab 1.34 [IQR 0.91–1.92] vs. adalimumab 1.39 [IQR 0.91–1.96], p = 0.287). Univariable analyses demonstrated that female sex, older age, lower BMI, shorter disease duration, higher CRP and faecal calprotectin concentrations and corticosteroid use at baseline, but not anti‐TNF type, smoking or immunomodulator use, were associated with lower fT3/fT4 ratio  were similar in infliximab‐ and adalimumab‐treated patients . Univariable analyses across both anti‐TNF‐treated groups demonstrated that a lower fT3/fT4 ratio was associated with PNR   and 88.4% (880/995) patients included in the effectiveness analysis of the PANTS study at weeks 14 and 54, respectively, were included here. Of 894 patients who were assessable at week 14, 25.5% (228/894) patients experienced PNR, 21.0% (188/894) patients were classified as grey zone, 13.6% (122/894) patients as having had a response and 39.8% (356/894) patients were in remission. PNR occurred in 22.7%  of infliximab‐treated and 29.0%  of adalimumab‐treated patients. Of 880 patients who were assessable at week 54, 65.1% (573/880) were classified as being in non‐remission, with no significant difference between infliximab‐ and adalimumab‐treated patients   0.51 [95% CI 0.31–0.85, ) Figure  and S2.Youden's method demonstrated that the optimal cut‐off threshold for baseline fT3/fT4 ratio to determine PNR at week 14 was 0.31, with an area under the curve of 0.57 (95% CI 0.54–0.61). The sensitivity and specificity were 0.62 (95% CI 0.41–0.74) 0.53 (95% CI 0.42–0.73), respectively. When incorporating anti‐TNF drug concentrations at week 14, in addition to fT3/fT4 ratio, we observed a marginal increase in AUC to 0.60 (95% CI 0.55–0.65).3.4p = 0.09). Furthermore, we found no difference in fT3/fT4 ratio at baseline between patients who had faecal calprotectin concentration >250 μg/g, representing active inflammation, and those who did not We performed a sensitivity analysis assessing the association between fT3/fT4 ratio at baseline and faecal calprotectin at week 54. In a subset of 51% (451/880) of patients who had week 54 faecal calprotectin data, we found no correlation between fT3/fT4 ratio at baseline and, when assessing as a continuous variable, concentrations at week 54 . Lastly, we performed a sensitivity analysis restricting the cohort to those 60 years or over only. Among 105 patients, there was no difference in fT3/fT4 ratio in those who experienced PNR compared with those who did not .As steroid use was associated with fT3/fT4 ratio and is known to affect the hypothalamic–pituitary–thyroid axis, we performed a sensitivity analysis excluding patients treated with corticosteroids at baseline. Amongst 630 patients, there was no difference in fT3/fT4 ratio in those who experienced PNR compared with those who did not ; data curation ; formal analysis ; investigation ; methodology ; writing – original draft ; writing – review and editing . Neil Chanchlani: Conceptualization ; data curation ; formal analysis ; investigation ; methodology ; writing –original draft ; writing –review and editing ; Isabel Carbery: Conceptualization ; data curation ; formal analysis ; investigation ; writing – original draft ; writing – review and editing . Malik Janjua: Data curation ; writing – review and editing (supporting). Rachel Nice: Data curation ; investigation ; writing – review and editing . Timothy J McDonald: Data curation . Claire Bewshea: Project administration . Nicholas Alexander Kennedy: Data curation ; formal analysis ; investigation ; methodology ; writing – review and editing . Tariq Ahmad: Conceptualization ; data curation ; funding acquisition ; investigation ; methodology ; supervision ; writing – review and editing . Christian P Selinger: Conceptualization ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; supervision ; writing – original draft ; writing – review and editing .Dr. Lin reports non‐financial support from Pfizer, non‐financial support from Ferring, outside the submitted work. Dr. Kennedy reports grants from F. Hoffmann‐La Roche AG, grants from Biogen Inc, grants from Celltrion Healthcare, grants from Galapagos NV, non‐financial support from Immundiagnostik, grants and non‐financial support from AbbVie, grants and personal fees from Celltrion, personal fees and non‐financial support from Janssen, personal fees from Takeda, personal fees and non‐financial support from Dr Falk, outside the submitted work.Prof. Ahmad reports grants and non‐financial support from F. Hoffmann‐La Roche AG, grants from Biogen Inc, grants from Celltrion Healthcare, grants from Galapagos NV, non‐financial support from Immundiagnostik, personal fees from Biogen inc, grants and personal fees from Celltrion Healthcare, personal fees and non‐financial support from Immundiagnostik, personal fees from Takeda, personal fees from ARENA, personal fees from Gilead, personal fees from Adcock Ingram Healthcare, personal fees from Pfizer, personal fees from Genentech, non‐financial support from Tillotts, outside the submitted work. Dr Selinger has received unrestricted research grants from Warner Chilcott, Janssen and AbbVie, has provided consultancy to Warner Chilcott, Dr Falk, AbbVie, Takeda, Fresenius Kabi, Arena, Galapagos, Celltrion, Norgine and Janssen, and had speaker arrangements with Warner Chilcott, Norgine, Galapagos, Fresenius Kabi, Celtrion, Dr Falk, AbbVie, MSD, Pfizer and Takeda. Dr. Goodhand reports grants from F. Hoffmann‐La Roche AG, grants from Biogen Inc, grants from Celltrion Healthcare, grants from Galapagos NV, non‐financial support from Immundiagnostik, outside the submitted work. The following authors have nothing to declare: Neil Chanchlani, Isabel Carbery, Malik Janjua, Rachel Nice, Timothy J McDonald and Claire Bewshea.Guarantor of the article: James R. Goodhand.Individual participant de‐identified data that underlie the results reported in this article will be available immediately after publication for a period of 5 years. The data will be made available to investigators whose proposed use of the data has been approved by an independent review committee. Analyses will be restricted to the aims in the approved proposal. Proposals should be directed to james.goodhand@nhs.net. To gain access data requestors will need to sign a data access agreement.Data S1Click here for additional data file.


EGFR‐mutant NSCLC.Gefitinib (G) is a recommended molecular‐targeted agent for elderly patients with epidermal growth factor receptor (EGFR)‐mutant non‐small cell lung cancer (NSCLC). Docetaxel (Doc) and pemetrexed (Pem) have similar efficacies, and either is often used as the sole agent during treatment. The efficacy of continuing G after progressive disease (PD) develops has been reported. It remains unclear whether the continuation of G in combination with a single cytotoxic agent beyond PD is beneficial for elderly patients. Here, we conducted a randomized phase II study to assess the efficacy and safety of cytotoxic chemotherapy with G for elderly patients with progressive EGFR‐mutant NSCLC with PD previously treated with G were enrolled. Patients received Pem 500 mg/m or Doc 60 mg/m every 21 days and were randomly assigned to receive chemotherapy with 250 mg G (G+ Doc/Pem arm) or without G (Doc/Pem arm) until further disease progression or unacceptable toxicity.Elderly patients with p = 0.0391). Adverse events ≥ grade 3 were more frequent in the G + Doc/Pem arm .This trial was terminated early owing to slow accrual. A group of 22 patients underwent analysis. The primary endpoint, progression‐free survival (PFS), was significantly longer in the G + Doc/Pem arm . Adverse events ≥ grade 3 were more frequent in the gefitinib with chemotherapy arm . Based on our results, combination chemotherapy with continuing gefitinib beyond PD may have potential benefit in limited elderly NSCLC without information on acquired resistance to gefitinib.We conducted a randomized phase II study to assess the efficacy and safety of cytotoxic chemotherapy with gefitinib for elderly patients with progressive  Lung cancer is the leading cause of cancer death worldwide.Docetaxel (Doc) is a third‐generation cytotoxic agent and recommended monotherapy regimen for elderly patients with advanced NSCLC.EGFR mutations.EGFR‐mutant NSCLC.Gefitinib (G) is a first‐generation epidermal growth factor receptor‐tyrosine kinase inhibitor (EGFR‐TKI) with a demonstrated strong efficacy in patients harboring active Previous studies reported the utility and safety of combination therapy with first‐generation EGFR‐TKIs, such as G and erlotinib, plus Doc or Pem for advanced NSCLC.Manegold et al. assessed the feasibility of combination therapy with G and Doc in patients with advanced NSCLC.Yoshimura et al. assessed the efficacy and toxicity of combination therapy with G and Pem in elderly patients (≥ 70 y) with previously untreated NSCLC and a performance status (PS) of 0–1.EGFR‐mutant NSCLC has been demonstrated, most patients relapse after approximately 1 year. Some EGFR‐mutant NSCLCs acquire resistance to G and show accelerated disease progression after its discontinuation.EGFR‐mutant NSCLC.Although the efficacy of G for advanced EGFR‐mutant NSCLC.Therefore, we conducted a prospective phase II study to evaluate the efficacy and safety of the continuation of G combined with Doc or Pem in elderly patients with EGFR‐mutant stage IIIB or IV NSCLC, including the exon 19 deletion mutation or L858R, after the confirmation of radiological disease progression during G therapy of at least 3 months. They were required to have a measurable or assessable disease as defined by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and a PS of 0 or 1 by the Eastern Cooperative Oncology Group (ECOG) criteria. Patients with a complete response (CR), partial response (PR), or SD for >6 months while being treated with G were eligible.We enrolled patients aged ≥70 years with adequate major organ functions and cytologically‐ or histologically‐confirmed active We excluded patients who were advised to switch to platinum‐doublet chemotherapy or patients with any of the following: a history of interstitial pneumonia and pre‐existing idiopathic pulmonary fibrosis pneumonia, active infectious disease, and uncontrolled pleural or cardiac effusion.The study protocol was approved by each institution's ethics review board, and the study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. The study was registered with University Hospital Medical Information Network Clinical Trials Registry . All patients provided written informed consent before enrollment.2 intravenously [IV] every 3 weeks) or Pem , while those assigned to the G + Doc/Pem arm continued to receive G with Doc (60 mg/m2 IV every 3 weeks) or Pem (500 mg/m2 IV every 3 weeks with the same premedication). G (250 mg daily or as tolerated) was continued on the administration schedule that had been maintained for at least three months before registration. Treatment continued until disease progression, unacceptable toxicity, or other study discontinuation criteria were met.The study design is shown in Figure The primary endpoint was PFS. The secondary endpoints were response rates (CR plus PR), disease control rates (DCR: CR plus PR plus SD), OS, and toxicity profiles. PFS was defined as the period between the date of randomization to the date of objective progression or death. OS was defined as the period between the date of randomization to the date of death due to any cause.SD had to meet the criteria of SD at least once >6 weeks after randomization. All enrolled patients were evaluated for safety and assessed for toxicity before each cycle using the Common Terminology Criteria for Adverse Events Version 4.03. They underwent full‐body computed tomography every 6 weeks until PD was achieved, and they were examined using RECIST version 1.1.With PFS as the primary endpoint, Shoenfeld's formulap‐value for differences between curves was calculated using the log‐rank test. Hazard ratios and associated 95% CIs between the treatment arms were calculated. Differences in RRs and frequency of AEs, and the associated 95% CIs were calculated and compared with Fisher's exact test. A p‐value <0.05 was considered statistically significant. Statistical analyses were performed using the software SAS version 9.2 (SAS Institute).PFS and OS were estimated using the Kaplan–Meier method, and the EGFR mutations, including exon 19 deletion and L858R.This study was closed in early July 2016 because of poor accrual. Between April 2012 and August 2015, a preliminary analysis involving 22 patients randomly assigned to receive Doc or Pem alone or the continuation of G + Doc/Pem was conducted. The patient and disease baseline characteristics are shown in Table Three patients in the Doc/Pem arm and five in the G + Doc/Pem arm received Doc, while eight patients in the Doc/Pem arm and six in the G + Doc/Pem arm received Pem. More than 50% of enrolled patients were administered G once daily and all patients continued G on the same schedule as before enrollment in each treatment arm.Ten patients in the Doc/Pem arm and four in the G + Doc/Pem arm discontinued the protocol treatment due to disease progression, which was observed after one cycle of chemotherapy in two out of the 10 patients (18%) in the Doc/Pem arm. Nonhematological AEs did not resolve until the next treatment cycle in one patient in the Doc/Pem arm. In the G + Doc/Pem arm, protocol therapy was discontinued in seven patients because of their requests or judgments by the attending physician based on a reduced PS due to nonhematological AEs .The median number of chemotherapy treatment cycles was two for each chemotherapy group in the Doc/Pem arm, six for the Doc group, and four for the pemetrexed group in the G + Doc/Pem arm Table .p = 0.0391), but not in OS .The median follow‐up time was 8.8 months. The median PFS and OS were 1.6 months (95% CI: 1.2–4.1) and 8.6 months (95% CI: 4.6–16.3) in the Doc/Pem arm and 5.6 months (95% CI: 4.2–9.1) and 14.1 months (95% CI: 5.1‐not reached) in the G + Doc/Pem arm  or DCR (p = 0.387) between the two arms (Table RR and DCR were 0% (95% CI: 0.0–28.5) and 27.3% (95% CI: 6.0–61.0) in the Doc/Pem arm, and 18.2% (95% CI: 2.3–51.8) and 54.5% (95% CI: 23.4–83.3) in the G + Doc/Pem arm. There were no significant differences in RR (n = 22). Severe toxicities (≥ grade 3) were observed in five patients (45.5%): leukopenia and neutropenia in three (27.3%) and four patients (36.4%), respectively, in the Doc/Pem arm. Lymph node infection, a grade 3 nonhematological toxicity, occurred in one patient. The frequency of severe hematological or nonhematological toxicities (≥ grade 3) was significantly higher in the G + Doc/Pem arm . The severe toxicities (≥ grade 3) in the G + Doc/Pem arm included leukopenia and neutropenia in 7 (63.6%) patients each and febrile neutropenia in two (18.2%) patients. Grade 3 toxicities, that is, anorexia and fatigue were observed in three and four patients (36.44%), respectively . The median OS was approximately 14 months in the G + Doc/Pem arm, which was 6 months longer than that in the Doc/Pem arm. Furthermore, RR and DCR were higher in the G + Doc/Pem arm. However, the frequency of severe toxicities (AEs ≥ grade 3) was significantly higher in the G + Doc/Pem arm .In this preliminary study, PFS was significantly longer in the G + Doc/Pem arm than in the Doc/Pem arm .Some studies on combination therapy with G and chemotherapy in patients with NSCLC have been conducted. The phases I and II Iressa NSCLC Trial Assessing Combination Treatment (INTACT) study, which focused on EGFR‐mutant NSCLC in the IMPRESS study.EGFR‐mutant NSCLC.Some researchers have reported the efficacy and utility of the continuation of EGFR‐TKIs beyond PD with the addition of cytotoxic agents.In this study, the efficacy of G + Doc/Pem in terms of RR, PFS, and OS is similar to that of previous studies on DOC and Pem. Rapid exacerbation after one cycle of chemotherapy, such as disease progression, was observed in approximately 20% of the patients (2 of 11 patients) in the Doc/Pem arm.EGFR‐mutant NSCLC with acquired resistance to EGFR‐TKIs has recently been suggested. EGFR‐T790M mutation is a key oncogene profile and is responsible for the majority of EGFR‐mutant NSCLC with acquired resistance to first‐ and second‐generation EGFR‐TKIs.The need for genotype profiling following rebiopsy in the management of EGFR‐mutant NSCLC,EGFR‐mutant NSCLC. Continuing EGFR‐TKIs until symptomatic progression could be an alternative treatment for patients with T790M‐negative NSCLC.In some studies which have reported the efficacy of continuation therapy with first‐generation EGFR‐TKIs including G and erlotinib beyond PD for advanced Our limited results suggest that the termination of G therapy and a switch to subsequent chemotherapy after the confirmation of PD based on the RECIST criteria is disadvantageous. Continued G treatment in combination with chemotherapy may be reasonable and beneficial in preventing acute exacerbation due to G withdrawal. In addition, combination chemotherapy may have a synergistic effect.Rebiopsy is an indispensable technique for deciding the next treatment after progression of initial EGFR‐TKI. Unfortunately, rebiopsy was not planned for in our study and there was no information on histological findings and mutation status before administration of study treatment. Chemotherapy is recommended in cases with unknown cause of initial EGFR‐TKI resistance. Therefore, the results of our trial may show potential efficacy in limited cases in which rebiopsy test cannot be performed or sufficient samples are unavailable for rebiopsy testing analysis. Based on our limited results, combination chemotherapy with continuing gefitinib beyond PD may have potential benefit in limited elderly NSCLC without information on acquired resistance to gefitinib. However, further study is needed to assess the utility of that treatment among those population.Based on the results of a scheduled interim analysis, this randomized study had important limitations including a poor accrual rate, short follow‐up time, and premature end. Additionally, no molecular information in the enrolled patients with acquired resistance to G was obtained. We analyzed only the registered cases which lacked power to demonstrate our primary endpoint.Further, our data lacked enough power to demonstrate the clinical utility of continuation of gefitinib as a single agent chemotherapy for second‐line therapy because the study was not completed.EGFR‐mutant NSCLC.Despite accruing less than half of the planned sample size, this study showed a significantly longer PFS with combination therapy. Our study findings are not generalizable due to the small sample size and incomplete accrual. However, this study provides a basis for considering combination therapy with the continuation of G and chemotherapy beyond PD for elderly patients with advanced EGFR‐mutant NSCLC.In conclusion, combination therapy with the continuation of G and chemotherapy showed a better efficacy but was also associated with an increased toxicity in elderly Japanese patients with This study was supported by the Japan‐Multinational Trial Organization and was funded by AstraZeneca (grant no. ISSIRES0072). K.A received honoraria from AstraZeneca, M.A. received grant from Kyowa Kirin Co. Ltd. for work outside the submitted study. T.Y. and M.M. received honoraria from AstraZeneca, H.Y. received a lecture fee from AstraZeneca and Eli Lilly. S.A. reported personal fees from AstraZeneca, MSD, Eli Lilly, Chugai, Ono, Taiho, Boehringer Ingelheim, Pfizer, Bristol‐Myers Squib, Hisamitsu, and Kyowa Hakko Kirin, and received grants from AstraZeneca, MSD, Eli Lilly, Chugai, Ono, Taiho, Boehringer Ingelheim, Pfizer, F. Hoffmann‐La Roche, and Bristol‐Myers Squibb. All other authors had no conflict of interests to declare.


Pan troglodytes) in three sanctuaries in West, Central, and East Africa. Our goal was to evaluate whether viruses of human origin or other “atypical” or unknown viruses might infect these chimpanzees. We identified viruses from eight families: Anelloviridae, Flaviviridae, Genomoviridae, Hepadnaviridae, Parvoviridae, Picobirnaviridae, Picornaviridae, and Rhabdoviridae. The majority (15/26) of viruses identified were members of the family Anelloviridae and represent the genera Alphatorquevirus (torque teno viruses) and Betatorquevirus (torque teno mini viruses), which are common in chimpanzees and apathogenic. Of the remaining 11 viruses, 9 were typical constituents of the chimpanzee virome that have been identified in previous studies and are also thought to be apathogenic. One virus, a novel tibrovirus (Rhabdoviridae: Tibrovirus) is related to Bas‐Congo virus, which was originally thought to be a human pathogen but is currently thought to be apathogenic, incidental, and vector‐borne. The only virus associated with disease was rhinovirus C (Picornaviridae: Enterovirus) infecting one chimpanzee subsequent to an outbreak of respiratory illness at that sanctuary. Our results suggest that the blood‐borne virome of African sanctuary chimpanzees does not differ appreciably from that of their wild counterparts, and that persistent infection with exogenous viruses may be less common than often assumed.Infectious disease is a major concern for both wild and captive primate populations. Primate sanctuaries in Africa provide critical protection to thousands of wild‐born, orphan primates confiscated from the bushmeat and pet trades. However, uncertainty about the infectious agents these individuals potentially harbor has important implications for their individual care and long‐term conservation strategies. We used metagenomic next‐generation sequencing to identify viruses in blood samples from chimpanzees , has been associated with disease in captive or wild chimpanzees.Only one virus, rhinovirus C (Persistent infections with exotic or pathogenic blood‐borne viruses appear to be rare in sanctuary chimpanzees, suggesting that longstanding concerns over such viruses may be overstated.  BASVBas‐Congo virusEKV‐1/2Ekpoma virus 1 and 2NHPnonhuman primateNICSNgamba Island Chimpanzee SanctuaryPASAPan African Sanctuary AllianceSFVsimian foamy virusTCRCTchimpounga Chimpanzee Rehabilitation CentreTCSTacugama Chimpanzee SanctuaryTTMVtorque teno mini virusTTVtorque teno virusvRPM/kbviral reads per million per kilobase of target sequence1Approximately 75% of nonhuman primates (NHPs) are experiencing population declines in the wild  strain  Tacugama Chimpanzee Sanctuary (TCS) in Sierra Leone, (B) Tchimpounga Chimpanzee Rehabilitation Centre (TCRC) in Republic of Congo, and (C) Ngamba Island Chimpanzee Sanctuary (NICS) in Uganda. To our knowledge, this is the first study to use such methods to characterize the viromes of sanctuary‐housed chimpanzees. This work complements studies of pathogens in wild‐living apes, where such blood samples cannot routinely be obtained. The study also addresses the possibility that apes in sanctuaries harbor “unusual” viruses from exogenous sources, which could impact management options and conservation strategies.22.1The study sites included three PASA member chimpanzee sanctuaries: (A) TCS, (B) TCRC, and (C) NICS Figure . TCS, fo2.2All blood sample collection occurred during routine health examinations performed by sanctuary veterinarians for the care and welfare of the chimpanzees. For TCS, we used published data from samples collected between March 15, 2013, and July 11, 2016, as previously described  as previously described , which is a metric correlated with real‐time quantitative polymerase chain reaction data  of each virus identified . We considered an individual positive if ≥1 read of ≥50 bases matched a virus in the database . We compared viral richness  and total viral load among the three sanctuaries using Kruskal–Wallis tests  and subsequent Wilcoxon rank‐sum tests with the Benjamini‐Hochberg adjustment to control for multiple comparisons (“pairwise.wilcox.test” with p.adjust.method = “BH”) , classified as either a torque teno viruses (TTVs: genus Alphatorquevirus) or torque teno mini viruses (TTMVs: genus Betatorquevirus), and pegiviruses (Flaviviridae), previously referred to as “GB virus C” . We found members of the remaining five virus taxa in chimpanzees from only a single sanctuary: gemykivibivirus (Genomoviridae), tetraparvovirus (Parvoviridae), and rhinovirus C (Picornaviridae) at TCS, and hepatitis B virus (Hepadnaviridae) and tibrovirus (Rhabdoviridae) at TCRC.We generated an average of 3,757,384 reads per sample SD ± 2,716,857) for TCS, 1,671,859 reads per sample  for TCRC, and 2,357,429 reads per sample  for NICS after quality and length trimming. In total, we identified 26 viruses  of five genome types  . The partial or complete ORF1 sequences of the TTVs shared 48% amino acid (aa) and 52% nucleotide (nt) identity (ID) with each other, on average. The TTMVs shared 45% aa and 57% nt ID with each other, on average. For the TTVs identified in each sanctuary population, within‐population genetic distances (0.56 ± 0.01 to 0.77 ± 0.05) were similar to between‐population genetic distances (0.56 ± 0.01 to 0.64 ± 0.01). Similarly, for the TTMVs identified at TCS and NICS, within‐population genetic distance  was similar to between‐population genetic distance (0.40 ± 0.01). The complete polyprotein sequences of the three pegiviruses  shared ~88% aa and ~76% nt ID with each other. The three picobirnaviruses were classified into two genogroups  Figure . Within The TTVs shared, on average, 43% aa and 54% nt ID with select human TTVs . The TTMVs shared, on average, 43% aa and 61% nt ID with select human TTMVs . The complete polyprotein sequences of the three pegiviruses  shared ~80% aa and ~71% nt ID with human pegivirus C genotypes 1, 2, and 5, which have been detected in Africa  to 96.4%  of chimpanzees at each sanctuary  . The mean total viral load was 1.36 (SD ± 1.14) at TCS, 0.64 (SD ± 0.95) at TCRC, and 1.13 (SD ± 0.78) at NICS. The gemykibivirus (TAPTV‐6) had the highest mean viral load among infected individuals (1.7 log10vRPM/kb). A picobirnavirus (TAPTV‐11) had the lowest mean viral load among infected individuals (0.09 log10vRPM/kb). We compared the distribution of total viral load for the three sanctuaries and found a statistically significant difference    Figure ‐S5. Totap = 2.8e−09)   , the viruses we identified in these populations are “normal” members of the chimpanzee virome, in that they are known to infect chimpanzees without clinical consequences.Specifically, healthy captive and wild chimpanzees are known to be infected with anelloviruses  at TCS in 2016, coinciding with an outbreak of respiratory illness. Rhinovirus C caused a lethal outbreak of respiratory illness in the Kanyawara chimpanzees of Kibale National Park, Uganda, in 2013  would yield different results, due to tissue‐specific viral tropism ; Formal analysis ; Investigation ; Methodology ; Writing – original draft ; Writing – review and editing . Leah A. Owens: Formal analysis ; Writing – review and editing . Christopher D. Dunn: Investigation ; Writing – review & editing . Joshua Rukundo: Investigation ; Methodology ; Resources ; Writing – review and editing . Rebeca Atencia: Investigation ; Methodology ; Resources ; Writing – review and editing . Megan F. Cole: Investigation ; Writing – review and editing . Averill Cantwell: Investigation ; Writing – review and editing . Melissa E. Thompson: Conceptualization ; Funding acquisition ; Investigation ; Methodology ; Supervision ; Writing – review and editing . Alexandra G. Rosati: Conceptualization ; Funding acquisition ; Investigation ; Methodology ; Resources ; Supervision ; Writing – review and editing . Tony L. Goldberg: Conceptualization ; Funding acquisition ; Investigation ; Methodology ; Resources ; Supervision ; Writing – original draft ; Writing – review and editing .The authors declare no conflict of interest.Research at TCS was approved by the Government of Sierra Leone Ministry of Agriculture, Forestry, and Food Security. Research at TCRC was approved by the Republic of Congo Ministry of Scientific Research and Technological Innovation and Jane Goodall Institute Congo. NICS is managed by Chimpanzee Sanctuary and Wildlife Conservation Trust (CSWCT) and research was approved by CSWCT, the Uganda Wildlife Authority, and the Uganda National Council for Science and Technology. Work at TCRC and NICS was approved by the Institutional Animal Care and Use Committee at the University of Michigan (#8102) and Harvard University (#14‐07‐206‐1). All samples were shipped to the USA under Convention on International Trade in Endangered Species of Wild Fauna and Flora permits . Research procedures at all three sanctuaries complied with Pan African Sanctuary Alliance standards and adhered to the American Society of Primatologists Principles for the Ethical Treatment of nonhuman Primates.Figures S1‐S2. Maximum‐likelihood phylogenetic trees of viruses identified in blood samples from sanctuary chimpanzees at TCS, TCRC, and NICS. Viruses identified in this study and Owens et al.,Click here for additional data file.Supplementary information.Click here for additional data file.


Despite some evidence that psoriatic arthritis (PsA) may increase psychological burden in psoriasis, the mental health of this subpopulation is under‐investigated.To investigate whether PsA is associated with higher depression and anxiety in moderate‐to‐severe psoriasis; explore whether pain mediates these associations; and estimate the prevalence of undiagnosed and untreated depression.Baseline data from British Association of Dermatologists Biologic and Immunomodulators Register (BADBIR) participants completing the Hospital Anxiety and Depression Scale (HADS) were analysed.n = 540 with psoriasis only; n = 167 with PsA) were included. Depression prevalence was higher in patients with than without PsA, when a HADS‐depression subscale cut‐off ≥8 was used  = 1.64 (1.09–2.45)), but did not differ using the HADS cut‐off ≥ 11. Anxiety prevalence was higher among PsA patients, regardless of HADS cut‐off (cut‐off ≥11: adjusted OR (95% CI) = 1.62 (1.07–2.45)). Pain fully mediated the effect of PsA on depression and anxiety in psoriasis. 53.6% of participants identified as depressed did not have a known psychiatric disorder; two thirds of depressed participants were not treated.707 patients (PsA comorbidity in psoriasis is associated with higher anxiety; its association with depression appears to be robust when milder depressive syndromes are included, but less consistent for higher‐threshold depression definitions. Depression remains unrecognized and untreated in over half of moderately‐to‐severe psoriasis patients. Routine depression and anxiety screening is recommended in psoriasis and PsA. PsA comorbidity may increase depression and anxiety in psoriasis through pain experience.  It is unknown whether effects of PsA on patients' mental health can be attributed to pain experience.Depression is often underestimated in both dermatology and primary care settings. It is not clear how often depression remains undiagnosed in at‐risk populations with moderate‐to‐severe psoriasis.What does this study add?The association of PsA with anxiety among psoriasis patients is robust and independent of other comorbidities. Its association with depression is replicated when milder depressive syndromes are included. Pain appears to fully mediate these associations.Depression remains unrecognized and untreated in over half of moderately‐to‐severe psoriasis patients.Routine depression and anxiety screening is recommended in psoriasis and PsA. For PsA, joint management of pain and mental health may prove beneficial.2Depression and anxiety are overrepresented in psoriasis.n = 219) cannot be excluded.Depression prevalence and anxiety levels were higher in patients with PsA compared to patients with psoriasis only among participants we recruited from a single tertiary centre.In the current study using the BADBIR database, we first tested the generalizability of our previous findings in a larger, multi‐centre sample, and investigated whether depression and anxiety are associated with PsA among patients with moderate‐to‐severe psoriasis. Second, we estimated the proportions of patients with moderate‐to‐severe psoriasis who were identified as being depressed but did not have a corresponding depression diagnosis or antidepressant treatment by any physician at the time of assessment. Third, we explored potential mediatory effects of pain in the relationship between PsA presence and affective (depression and anxiety) burden in psoriasis, given the significant overlap and the link between pain and depression in rheumatic disease.33.1BADBIR is a prospective cohort enrolling patients with moderate‐to‐severe psoriasis in over 160 sites in the United Kingdom and the Republic of Ireland. Its design has been published previously.Data until May 2020 were used in this study. We included all participants who completed the baseline Hospital Anxiety and Depression Scale (HADS)Baseline patient characteristics, including sociodemographic and clinical data, were obtained using a combination of patient interview, examination and screening of hospital records, performed by a trained professional. Information about PsA was recorded as response to the question: ‘Has the patient a diagnosis by a rheumatologist of psoriatic arthritis?’ and the year of disease onset was reported. At baseline, participants completed the HADS and the EuroQol (EQ‐5D‐3L).3.2Our primary outcome measure was the HADS,We report two definitions for depression and anxiety, using two thresholds (≥8 and ≥11) for each HADS subscale. A cut‐off ≥8 in the HADS‐Depression subscale (HADS‐D) shows optimal psychometric properties for identifying major depression (specificity/sensitivity: 0.82/0.74). A cut‐off ≥11 is more specific, but less sensitive (0.92/0.56).To define an existing diagnosis of depression, we used the reported Medical Dictionary for Regulatory Activities (MedDRA) High Level Term ‘Depressive disorders’. We also examined the reported preferred terms for all psychiatric disorders in our sample to reduce misclassification bias.3.3t‐tests and Mann Whitney U tests where appropriate to compare baseline characteristics between the groups.We used chi‐square tests, We investigated associations of PsA with depression and anxiety prevalence using logistic regression and controlling for age, gender, ethnicity, physical comorbidities , psoriasis severity measured with the Psoriasis Area and Severity Index (PASI), and Body Mass Index (BMI).t‐tests to investigate differences in mean HADS‐subscale scores between the groups; and linear regression models to control for confounders in the associations between HADS scores and PsA.We used For our analysis, we excluded PASI scores according to a set of criteria, which took account of the temporal relationship of PASI to the HADS assessment and start of treatment. The criteria are described in the Supplement.Due to high covariate missingness (33.38% of sample), we imputed missing data (PASI and BMI) using multiple imputation (MI) before adding them to the regression models; data were assumed to be missing at random.n = 15 participants where the HADS assessment was obtained before the Euroqol.As a next step, we conducted an exploratory, statistical mediation analysis to investigate the role of pain in the association of PsA with depression and anxiety levels. For this, we hypothesized two mediational models. In both models, we used the presence of an existing PsA diagnosis as the independent variable and the presence of pain/discomfort as reported in the Euroqol 3‐level questionnaire item as a binary mediator variable (‘yes’ corresponding to having any pain or discomfort and ‘no’ corresponding to having no pain or discomfort). As outcome variable, the score in the HADS‐D depression subscale was used in the first (depression) model and the score in the HADS‐A anxiety subscale was used in the second (anxiety) model. We excluded Each hypothesized pathway was tested using nonparametric bootstrapping with 5000 iterations. We controlled for potential confounders, including age, gender, ethnicity (white/non‐white), and physical comorbidity as defined previously. Furthermore, we conducted a sensitivity analysis to investigate the degree to which our findings were robust to a potential violation of the sequential ignorability assumption, using the correlated residuals method.R.Finally, we investigated the representativeness of our sample within BADBIR using classic statistical tests. All statistical analysis was performed in 44.1n = 540 with psoriasis only and n = 167 with both psoriasis and PsA. Baseline sociodemographic and psoriasis‐related clinical characteristics are reported in Table Seven hundred and seven BADBIR participants were included in the study; Lifestyle factors and psychiatric history of the cohort are presented in Table 4.2The prevalence of depression was higher in patients with PsA than in patients without PsA using a HADS cut‐off of 8: 32.9% versus 22.7%, Odds Ratio (OR)  = 1.66 (1.13–2.43). The association between PsA and depression remained significant after adjusting for potential confounders. However, when we increased the HADS threshold to 11, PsA was no longer associated with depression.Patients with PsA had a higher prevalence of anxiety compared to patients with psoriasis only, regardless of the HADS cut‐off used to define anxiety  = 1.51 (1.04–2.19); for cut‐off ≥11: 28.7% versus 20.2%, adjusted OR (95% CI) = 1.62 (1.07–2.45))  = 1.32 , p = 0.012, Table Comparing the two patient groups for total raw HADS‐subscale scores, scores for depression and anxiety were higher in the PsA group, even after adjustment for confounders (depression: β (95% CI) = 0.84 , 4.3β = 0.77, 95% CI 0.42 to 1.13, p < 0.001. The proportion of effect mediated by pain was 0.98 .We aimed to explore the effect of experiencing pain (or discomfort) in the association between having a diagnosis of psoriatic arthritis and levels of depression at baseline. There were associations between PsA and pain, as well as pain and depression levels. As hypothesized, the effect of PsA on the HADS‐D score was fully mediated by the presence of pain, after controlling for age, gender, ethnicity and other physical comorbidities Figure . The booR2 = 0.36 for residual variance and R2 = 0.26 for total variance, indicating that an unmeasured confounder can account for up to 26% of the total variation before ACME becomes zero. A ρ value of 0.60  was needed to reverse the sign of ACME . This suggests relatively low likelihood of the observed mediated effect to be caused by an unobserved confounder and robustness of our findings to the sequential ignorability assumption.Our sensitivity analysis found β = 0.61, 95% CI 0.35 to 0.98, p < 0.001. The proportion of effect mediated by pain was 0.46 . Our sensitivity analysis found R2 = 0.20 for residual variance and R2 = 0.15 for total variance; the mediated effect of PsA group membership on anxiety through pain requires a relatively high correlation of ρ = 0.45 between residuals for the presence of the mediated effect to be invalid.Furthermore, pain presence was associated with both PsA and anxiety levels. Pain fully mediated the relationship between PsA and anxiety Figure . The boo4.460.1% of participants identified as being depressed using the HADS‐D cut‐off ≥8, did not have a recognized depressive disorder at the time of scan; the prevalence of unrecognized depression was only slightly smaller (58.5%) when using the more specific cut‐off ≥11. About two thirds of participants identified as depressed using the cut‐off ≥11 were not receiving any antidepressant medication at the time.As depression is often comorbid to other psychiatric disorders, and to further eliminate any limitations of the HADS regarding specificity for major depression, we investigated presence of any psychiatric disorder in patients demonstrating depressive symptoms; 53.6% of participants with HADS‐D scores ≥11 did not have any recognized psychiatric disorder.The prevalence of unrecognized and untreated depression was similar in the psoriasis only and the PsA group Table .4.5p < 0.0001), which is in contrast to the finding in our sample (HADS respondents). There were no differences in the prevalence of depression between non‐respondents and respondents for either group . Our sample was older than the rest of the registry, but there were no other sociodemographic or clinical differences between the HADS respondents and non‐respondents. However, the prevalence of depressive disorders recorded as baseline comorbidity in the PsA group was higher than in the psoriasis only group, among participants who had not completed the HADS , but similar among patients with psoriasis only (11.5% vs. 11.6% previously).A further explanation could be that the overrepresentation of depression in PsA predominantly concerns milder depression forms; or we may not have detected a true, smaller difference for severe depression due to its generally lower prevalence. This could also explain our findings in the wider cohort, where the groups differed for depression history, in contrast to our HADS‐completing sample. Although we cannot rule out effects of depression on non‐completion, the main reason for HADS missingness was the later introduction of the measure in the registry.Finally, we cannot exclude misclassification regarding PsA in BADBIR. In our first work, patients' medical records were scrutinized by researchers, including an experienced rheumatologist, and patients without rheumatologist‐confirmed presence or absence of PsA were excluded from the analysis; we estimated that, by relying on self‐reported and dermatology nurse‐reported PsA diagnoses only, up to 9.4% participants may be misclassified through incorrect or inconclusive diagnosis.In our hypothesized mediation models, pain fully mediated the effect of PsA on current depression and anxiety levels among patients with psoriasis. This raises the possibility that the overrepresentation of affective burden in PsA compared to the baseline skin disease may be attributable to the psychological and biological experience of pain. Our results align with findings in rheumatoid arthritis, where the evidence base is larger.Furthering our understanding of the relationship between PsA, pain and depression can have a significant impact on treatment decisions and patients' quality of life. Although pain is common and debilitating in PsA, it still constitutes an unmet need in this population, only recently coming to research focus; even among patients on biologics, moderate‐to‐severe pain persists in two thirds of patients.The rates of undiagnosed depression in our participants with moderate‐to‐severe psoriasis (58.1%) and PsA (60.0%) were higher than those found in other populations at risk for depression, such as patients with inflammatory bowel disease (35%) and diabetes (45%), and similar to those found in primary care (52.7%).The major strengths of this study lie in its sample size and inclusion of multiple centres from the UK and the Republic of Ireland, which ensures high external validity. To our knowledge, this is the largest study reporting an association of depression and anxiety with PsA in psoriasis, as well as the prevalence of undiagnosed and untreated depression in these populations to date.Using BADBIR to address our study aims is subject to some limitations. BADBIR was designed as a pharmacovigilance registry for psoriasis; HADS completion and collection of other baseline data were often asynchronous; some participants may overlap with our prior single‐centre sample; and we cannot exclude some misclassification in relation to PsA as well as under‐ or overcoding of depression history depending on the data source and study centre. Furthermore, HADS does not take into account the full diagnostic criteria of major depression, importantly suicidality, concentration, sleep/appetite disturbance and feelings of worthlessness.Our mediation analysis is subject to limitations of the cross‐sectional design, which does not allow for temporality or causality inference.6The association of PsA comorbidity with higher anxiety in psoriasis appears to be robust and independent of confounders. The association of PsA with depression in psoriasis is replicable when milder depressive syndromes are included, but a less consistent finding for more stringent/higher‐threshold depression definitions. Our findings imply that the presence of pain may play a key role in the increased affective burden of PsA. It would be important to investigate the relationship of progression to PsA with pain, inflammatory markers and depressive syndromes in psoriasis using longitudinal cohort data. Depression remains unrecognized and untreated in a very high proportion of patients with psoriasis. It is crucial for clinicians to form trusting therapeutic alliances with these patients, establish routine mental health screening, explore and manage pain symptoms and arrange psychiatric and psychotherapeutic care where appropriate.Georgia Lada: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Project administration (lead); Visualization (lead); Writing – original draft (lead); Writing – review & editing . Hector Chinoy: Conceptualization (supporting); Funding acquisition (supporting); Methodology (supporting); Project administration (supporting); Resources (supporting); Supervision ; Writing – review & editing . Peter S. Talbot: Conceptualization (supporting); Methodology (supporting); Project administration (supporting); Resources (supporting); Supervision ; Writing – review & editing . Richard B. Warren: Conceptualization (supporting); Funding acquisition (supporting); Investigation (supporting); Methodology (supporting); Project administration (supporting); Resources (supporting); Supervision ; Writing – review & editing . C. Elise Kleyn: Conceptualization (lead); Funding acquisition (lead); Investigation (supporting); Methodology (supporting); Project administration (lead); Resources (supporting); Supervision (lead); Writing – review & editing .The views expressed in this publication are those of the authors and not necessarily those of the BADBIR, NHS, the NIHR or the Department of Health.C. Elise Kleyn has received honoraria, consultant and/or research funding from Janssen, Eli Lilly, LEO, Novartis, Abbvie, UCB, Almirall, Pfizer, and L’Oréal. Hector Chinoy has received personal compensation for activities with Novartis, UCB, Lilly, Biogen, Orphazyme as a speaker, advisory board member or consultancy, grants via The University of Manchester from Novartis, UCB and MedImmune, and has received travel support from Abbvie and Janssen. Richard B. Warren has received research grants from AbbVie, Almirall, Amgen, Celgene, Janssen, Lilly, Leo, Medac, Novartis, Pfizer, and UCB and consulting fees from AbbVie, Almirall, Amgen, Arena, Astellas, Avillion, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, DiCE, GSK, Janssen, Lilly, Leo, Medac, Novartis, Pfizer, Sanofi, Sun Pharma, UCB, and UNION. Georgia Lada has received speaker honoraria from Janssen, Lilly, Leo, and Novartis. Peter S. Talbot has no conflicts of interest.BADBIR was approved by the NHS Research Ethics Committee North West England in March 2007 (reference 07/MRE08/9); all participants gave written informed consent.Supplementary MaterialClick here for additional data file.


Estimation of influenza disease burden is crucial for optimizing intervention strategies against seasonal influenza. This study aimed to estimate influenza‐associated excess respiratory and circulatory (R&C) and all‐cause (AC) mortality among older adults aged 65 years and above in mainland China from 2011 to 2016.Through a systematic review, we collected influenza‐associated excess R&C and AC mortality data of older adults aged 65 years and above for specific cities/provinces in mainland China. Generalized linear models were fitted to estimate the corresponding excess mortality for older adults by province and nationwide, accounting for the potential variables of influenza virus activity, demography, economics, meteorology, and health service. All statistical analyses were conducted using R software.A total of 9154 studies were identified in English and Chinese databases, and 11 (0.1%) were included in the quantitative synthesis after excluding duplicates and screening the title, abstract, and full text. Using a generalized linear model, the estimates of annual national average influenza‐associated excess R&C and AC mortality among older adults aged 65 years and above were 111.8 (95% CI: 92.8–141.1) and 151.6 (95% CI: 127.6–179.3) per 100,000 persons, respectively. Large variations in influenza‐associated excess R&C and AC mortality among older adults were observed among 30 provinces.Influenza was associated with substantial excess R&C and AC mortality among older adults aged 65 years and above in China from 2011 to 2016. This analysis provides valuable evidence for the introduction of the influenza vaccine into the National Immunization Program for the elderly in China. However, the influenza vaccine is not included in the National Immunization Program in China, and vaccination is paid out of pocket in the majority of regions,It is difficult to quantify the exact mortality burden of influenza for the following reasons: routine laboratory tests are rarely conducted across China, and secondary bacterial infections or exacerbation of existing underlying conditions triggered by influenza virus infections, can lead to death.et al estimated the national and provincial influenza‐associated excess respiratory mortality in older adults after the 2009 influenza pandemic in mainland China.To our knowledge, Li 2Through a systematic review, we collected influenza‐associated excess R&C and AC mortality data of older adults aged 65 years and above in specific cities/provinces in mainland China. Then, we used a correlation analysis to explore the potential factors that influence influenza‐associated mortality, including influenza virus activity, demographics, economics, meteorology, and health services. Based on the results of the correlation analysis, we used generalized linear models to estimate the corresponding excess mortality stratified by province, accounting for the potential variables.2.1We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelinesEach article identified through the search strategy underwent a process of title, abstract, and full‐text screening based upon a set of inclusion and exclusion criteria Table . EligiblWe extracted publication information, methodological characteristics and outcome measurements from the included studies . To eval2.22.2.1To describe the influenza circulation intensity and pattern, annual influenza virological data  in 30 provinces  from 2005 to 2016 were extracted from the Influenza Weekly Report by the Chinese National Influenza Center and published literatures.2.2.2We obtained province‐stratified demographic data  and economic data (gross domestic product per capita) from 2000 to 2016 from the China Statistical Yearbook.Meteorological data  during 2000–2016 were obtained from the China Meteorological Data Sharing Service System.We extracted medical service data from the China Health Statistical Yearbook, including the numbers of hospital beds and healthcare workers per 1000 persons from 2000 to 2016, the number of outpatients from 2002 to 2016, and the number of inpatients from 2007 to 2016.2.3To explore the potential impact of the aforementioned predictor variables on influenza‐associated excess R&C mortality, we conducted a correlation analysis between these predictor variables and excess R&C mortality. Then, based on the results of the correlation analysis, considering that influenza‐associated excess R&C mortality approximately follows a normal distribution,To explore the impact of data quality, different models and extremely low excess mortality on the results, we conducted the following sensitivity analyses: (1) using higher quality data  to fit the model to estimate influenza‐associated excess R&C and AC mortality; (2) applying a random forest approach to estimate the results; and (3) removing the extremely low excess R&C  and AC  mortality in specific cities/provinces which was obtained through aforementioned systematic review to fit the model to estimate the results. See the details in the R squared (R2), root mean squared error (RMSE), and mean absolute error (MAE)   pp. 5–6. squared R2, root  squared R2, root  squared R2, root  squared R2, root 33.1We identified 9154 studies through the database search Figure . StudiesInfluenza‐associated excess R&C (45 province‐seasons) and AC (52 province‐seasons) mortality among older adults aged 65 years and above in eight provinces from 2000 to 2019 were included in the final model. The included data points were mainly before 2009, and six studies  reported an excess mortality burden after 2009. Influenza‐associated excess R&C and AC mortality among older adults aged 65 years and above ranged from 5.2 in Liaoning to 378.8 in Yunnan per 100,000 persons and from 10.5 in Liaoning to 477.3 in Yunnan per 100,000 persons, respectively Figure .3.2The generalized linear model showed a good fit and explained 61.6% and 64.0% of the variation in observed influenza‐associated excess R&C and AC mortality among older adults aged 65 years and above, respectively . OverallHigher than the average influenza‐associated excess mortality rate attributable to R&C, the average influenza‐associated excess AC mortality among older adults from 2011 to 2016 was estimated to be 151.6 (95% CI: 127.6–179.3) per 100,000 persons at the national level and showed similar variation patterns among the provinces Figure .3.3For the influenza‐associated excess R&C mortality among older adults aged 65 years and above, the sensitivity analyses using higher quality data, applying the random forest approach, and removing the extremely low excess mortality from the model fit produced similar results to the main analysis, with annual national average mortality estimated as 121.2 (95% CI: 99.7–151.8), 136.6 (95% CI: 135.0–137.4) and 114.9 (95% CI: 95.1–147.7) per 100,000 persons, respectively Figures  and S2. For the influenza‐associated excess AC mortality among older adults aged 65 years and above, the sensitivity analyses, conducted using higher quality data, applying the random forest approach, and removing the extremely low excess mortality in model fit, produced similar results to the main analysis, with annual national average mortality estimated as 161.3 (95% CI: 134.2–196.2), 160.5 (95% CI: 158.5–161.2), and 151.6 (95% CI: 127.6–179.3) per 100,000 persons, respectively Figures  and S2. 3.4The cross validation showed that the overall RMSE of the main analysis was slightly less than that of the sensitivity analyses, and the MAE was similar between the main analysis and the sensitivity analyses Tables  and S10.4This study estimated the excess mortality burden of seasonal influenza attributable to R&C and AC diseases among older adults aged 65 years and above at the provincial and national levels in China based on robust vital statistics and mortality data obtained from a systematic review. It was found that the mortality burden of seasonal influenza was substantial among older adults, with the annual national average excess R&C and AC mortality from 2011 to 2016 estimated as 111.8 (95% CI: 92.8–141.1) and 151.6 (95% CI: 127.6–179.3) per 100,000 persons, respectively.Our excess mortality estimates attributable to R&C and AC diseases among older adults is consistent with the influenza‐associated excess mortality burden estimated before the 2009 pandemic in China in a previous study, despite using a different modeling approach.et al have also demonstrated that the influenza‐associated excess R&C and AC mortality burdens in rural areas are significantly higher than those in urban areas.This study reveals large spatial variations in influenza‐associated excess R&C and AC mortality among the provinces in China. The higher mortality burden of influenza among older adults in Henan and Sichuan Provinces might be attributable to their higher proportion of rural residents, which is a key parameter in our model fit. Yu Likewise, the lowest excess R&C and AC mortality burden in Tianjin, Beijing, and Shanghai municipalities would be related to their low proportion of rural residents. In addition, higher socioeconomic development, better medical resources, and higher influenza vaccine coverage might contribute to the low mortality. For instance, the government has provided annual free seasonal influenza vaccination for older registered adults aged 60 years and above in Beijing since 2007, increasing vaccine coverage to 44% in 2010–2011,Compared with other age groups, older adults aged 65 years and above have the highest influenza‐associated excess R&C  and AC mortality burden .To increase influenza vaccine coverage, an increasing number of local governments have enabled older adults to receive influenza vaccination paid wholly or partially by the government or Basic Social Medical Insurance, but these are mostly concentrated in economically developed regions with rich medical resources and a low mortality burden .This study had some limitations. First, through a systematic review, we collected mortality data from eight provinces  in mainland China from different years to construct a generalized linear model to estimate average influenza‐associated excess R&C and AC mortality among older adults aged 65 years and above by province and nationwide in China. The limited mortality data varied across models and might have influenced the certainty of the generalized linear model by extrapolation, further leading to an underestimate of the national influenza‐associated mortality burden. Therefore, continued national monitoring of influenza virology and mortality surveillance are needed to further analyze the influenza‐associated mortality burden among older adults in future studies. Second, because only limited virological data were available up to 2016, we only estimated the influenza‐associated excess mortality from 2011 to 2016. Third, although the internal and external comparisons demonstrated that our estimates of influenza‐associated excess R&C and AC mortality were reliable, inconsistency between the main analysis and sensitivity analyses was observed in several provinces (such as Liaoning and Beijing). This might suggest that the variations in the estimates of these provinces were mainly affected by the optimal variables chosen by the model in the main analysis and sensitivity analyses, especially in Sensitivity Analysis 1, which had fewer data points. Therefore, influenza‐associated mortality merits more studies in the future. Fourth, after the 2009 influenza pandemic, A/H1N1pdm09 replaced the preexisting seasonal influenza A virus subtype H1N1 , circulating as a seasonal influenza virus.5This study demonstrated a substantial influenza‐associated mortality burden among older adults aged 65 years and above in 30 provinces in China from 2011 to 2016. Differences in demographic, meteorological, socioeconomic, and medical resource factors could explain spatial variations in influenza‐associated mortality burden for older adults aged 65 years and above among provinces. Our estimates of the influenza‐associated excess R&C and AC mortality provided valuable information to support policy‐makers in including influenza vaccination for older adults in the National Immunization Program to promote medical equity in China.H.Y. received research funding from Sanofi Pasteur, GlaxoSmithKline, Yichang HEC Changjiang Pharmaceutical Company, Shanghai Roche Pharmaceutical Company, and SINOVAC Biotech Ltd. All the other authors have no competing interests.Kaige Dong: Data curation; formal analysis; software; validation; visualization; writing‐original draft; writing‐review and editing. Hui Gong: Data curation; software; validation; visualization; writing‐original draft. Guangjie Zhong: Data curation; validation; visualization; writing‐original draft; writing‐review and editing. Xiaowei Deng: Methodology; software. Yuyang Tian: Formal analysis; visualization; writing‐original draft. Minghan Wang: Data curation. Hongjie Yu: Conceptualization; funding acquisition; supervision. Juan Yang: Conceptualization; funding acquisition; supervision; writing‐original draft; writing‐review and editing.Table S1. Literature search strategy and results, by database.Table S2. Inclusion and exclusion criteria of the studies.Table S3. Data category of extraction from the included studies.Table S4. Literature scoring criteria for the included studies.Table S5. Literature search strategy and the results of influenza virus‐positive rates by type in specific provinces before 2005, by database.Table S6. Basic information on the included studies.Table S7. Literature scoring results of the included studies.Table S8. Selected covariates for the next modeling process.Table S9. The results of cross validation of the main analysis and sensitivity analyses in influenza‐associated excess respiratory and circulatory among older adults aged 65 years and above.Table S10. The results of cross validation of the main analysis and sensitivity analyses in influenza‐associated excess all‐cause among older adults aged 65 years and above.Table S11. Comparison of estimates of annual influenza‐associated excess respiratory and circulatory and all‐cause mortality  among older adults aged 65 years and above after the 2009 pandemic.Figure S1. Influenza‐associated excess respiratory and circulatory (R&C) and all‐cause (AC) mortality among older adults aged 65 years and above by province and by year from the included studies.Figure S2. Estimated annual average influenza‐associated excess respiratory and circulatory and all‐cause mortality among older adults aged 65 years and above in 30 provinces in China, 2011‐2016.Figure S3. Internal comparison of influenza‐associated excess respiratory and circulatory (R&C) mortality among older adults aged 65 years and above between the estimates and published results in available provinces.Figure S4. Internal comparison of influenza‐associated excess all‐cause (AC) mortality among older adults aged 65 years and above between the estimates and published results in available provinces.Figure S5. External comparison of influenza‐associated excess respiratory and circulatory (R&C) mortality among older adults aged 65 years and above between the estimates and published results in available cities.Figure S6. External comparison of influenza‐associated excess all‐cause (AC) mortality among older adults aged 65 years and above between the estimates and published results in available cities.Figure S7. Comparison of the provincial‐level variation trend between our estimates of influenza‐associated excess respiratory and circulatory (R&C) and all‐cause (AC) mortality in the main analysis and the estimates of influenza‐associated excess respiratory (R) mortality published previouslyClick here for additional data file.