Overall, our work suggests that more objective cluster‐based diagnostic labels combined with GNN classification may have value in clinical risk stratification and diagnosis of AD.Biomarkers for Alzheimer's disease (AD) diagnosis do not always correlate reliably with cognitive symptoms, making clinical diagnosis inconsistent. In this study, the performance of a graphical neural network (GNN) classifier based on data‐driven diagnostic classes from unsupervised clustering on heterogeneous data is compared to the performance of a classifier using clinician diagnosis as an outcome. Unsupervised clustering on tau‐positron emission tomography (PET) and cognitive and functional assessment data was performed. Five clusters embedded in a non‐linear uniform manifold approximation and project (UMAP) space were identified. The individual clusters revealed specific feature characteristics with respect to clinical diagnosis of AD, gender, family history, age, and underlying neurological risk factors (NRFs). In particular, one cluster comprised mainly diagnosed AD cases. All cases within this cluster were re‐labelled AD cases. The re‐labelled cases are characterized by high cerebrospinal fluid amyloid beta (CSF Aβ) levels at a younger age, even though Aβ data was not used for clustering. A GNN model was trained using the re‐labelled data with a multiclass area‐under‐the‐curve (AUC) of 95.2%, higher than the AUC of a GNN trained on clinician diagnosis (91.7%;  As of 2019, between 60% and 70% of all dementia cases are AD cases, with the disease predominantly affecting those aged 65 and over . As of 2, 4.With the availability of increasingly complex and heterogeneous dementia data and still sub‐optimal dementia diagnostic procedures, decision support systems, with the aid of machine learning (ML) algorithms, are gradually becoming important . HoweverIn terms of ML for classification, there has been an increase in a specific approach–graphical neural networks (GNNs) . A varieGNN studies, to date, have not made use of tau‐specific positron emission tomography (PET) neuroimaging data, one of the key lesions in AD . StudiesIn this study, we address the above limitations by first applying non‐linear dimensional reduction on a heterogeneous dataset, which includes tau PET neuroimaging data co‐registered with MRI, and individual sub‐assessments from CFAs. This is followed by data clustering, and then we investigate the feature characteristics of the individual clusters. Next, we re‐label cases based on cluster information, validated by tau‐PET data and data on Aβ, which was not used for clustering, to form new classes of AD and non‐AD for GNN's AD classification. Finally, the GNN's performance using the re‐labelled data is compared with that using clinician diagnosis.22.1The dataset used in this study was obtained from the open Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), particularly the ADNIMERGE‐3 open repository. A complete workflow of data preparation, processing and analysis is shown in Figure 18F]AV‐1451 tracer for detecting tau deposition was used [The sociodemographic and medical/family history features were selected from each participant's medical and sociodemographic profile. These features were age, gender and years of education, maternal and paternal family history of AD, number of copies of the APoE ε4 alleles (abbreviated as APoE4). Reprocessed using 1‐hot encoding, the APoE4 feature can take a value of 0, 1, or 2, representing the number of copies of the APoE4 allele. The CFA scores were collated from Alzheimer's Disease Assessment Scale (ADAS), Cognitive Battery Assessment, Clinical Dementia Rating (CDR), Mini‐Mental State Exam (MMSE), Modified Hachinski Ischemia Scale, Neuropsychological Battery Test, logical memory immediate recall test (LMIT), logical memory delayed recall test (LMDT), the Neuropsychological Inventory (NPI), and the Geriatric Depression Scale (GDS). As well as total scores for all these assessments, individual question scores from ADAS and individual subscales from NPI were included in the dataset. Other CFAs and individual CFA subscales from ADNI were not included due to the large amounts of missing data for the set of participants who had undergone PET‐MRI scans. For tau PET neuroimaging data, the [2.2Prior to data pre‐processing, sociodemographic, medical/family history, and CFAs were combined using each participant's unique identifier (ID). Inevitably, some participants were not present for a portion of the assessments without MRI and PET brain scans, which resulted in about 6% missing values. Relatively simple and sound imputation techniques were adopted in favour of more technical approaches as they tend to provide competitive performance with the absence of the computational and technical complexity Specifically, rows that contained sporadic missing values were imputed by mean imputation for numeric values and modal imputation for categorical values. For missing sociodemographic values a similarity matrix  was used2.3The pre‐processing steps for the PET and MRI data are outlined as follows. Each PET scan was co‐registered with its associated MRI scan before normalizing to a predetermined AD template, whereby the resulting images were corrected for partial volume estimation (PVE) using the SPM toolbox PETPVE12 . This re2.4After data normalization  to avoid focusing on very local structures–a minimal distance value (0) was set to improve cluster density. Five dimensions of the UMAP space were selected, based on visual inspection of the compactness of AD diagnosis cases distributed along these dimensions (see Results). Then, 3‐dimensional UMAP plots were presented for data visualization and clustering purposes. Each data point in each UMAP cluster was first assigned its original label  based on clinician diagnosis before having some of the data points re‐labelled (see below).k‐means clustering [k‐means clustering had also been applied successfully on AD datasets in previous studies [Next, we used the unsupervised learning, ustering , to iden studies , 22 incl studies  was impl studies  to identk‐means processing was subsequently identified to uniquely consist of a majority of clinically diagnosed AD cases; hence named the ‘AD’ cluster. However, there were a few clinically diagnosed CN and MCI cases in this cluster, and some clinically diagnosed AD cases outside of the cluster. We re‐labelled the CN and MCI cases in Cluster AD as AD cases. These re‐labelled cases were validated post‐hoc based on their tau‐PET and Aβ levels, which were found to be similar, while being intermediate between the non‐re‐labelled AD and non‐AD cases. The re‐labelled data was used for training the GNN model, which will be compared to GNN trained using the original clinician diagnosis labels  classifier developed by  was usedThe dataset was split into training and testing datasets, at 80% and 20%, respectively. The model was trained over 300 iterations using data batches of 64 rows before updating the loss parameter. Model performance was tested using one‐shot learning which included a single training sample in each batch . This wa2.6https://github.com/mac‐n/Clustering‐GNN which includes the Python environment set‐up, dataset visualization, and some of the dataset preparation procedures and modelling process. The original ADNI dataset was not included as part of this repository. However, the final processed (and anonymized) dataset used for GNN model development has been provided for GNN model demonstration. Requests to access the original datasets should be directed to ADNI (http://adni.loni.usc.edu/).Python and R codes for clustering, data analysis, and classification can be found in the code repository provided in the link 33.1k‐means clustering, it can be observed that there were five distinct clusters (denoted by different colours). If we indicated the data points with clinical diagnosis , we found that the distribution of clinical diagnosis did not generally conform well to the five discrete clusters : APoE4, gender, (history of) mother with AD, ctx.rh.inferiorparietal, ctx.lh.middletemporal, lh. Amygdala, ctx.lh.inferiorparietal, ctx.lh.lateraloccipital, wm.lh.entorhinal, and wm.lh.inferiortemporal. APoE4, gender and (history) of mother with AD were identified as the top three features. Moreover, the list was dominated by various tau PET‐MRI imaging features (7 out of 10 features). Hence, we analysed the clusters using the ctx.lh.inferiorparietal  and lh.Amygdala  features, based on their suggested links to early stages of MCI and AD , 28.Table 3.2When clinician diagnosis  was indicated in these clusters Figure , we obset test: p = 0.007), even though the mean age of the clinically diagnosed AD cases was older (p = 0.08). Therefore, the diagnostic re‐labelling was well supported by the tau‐PET neuromarkers and the CSF Aβ marker. We did not re‐label any cases which were diagnosed as AD by clinicians but not by our algorithm (i.e. there is no AD‐to‐CN or AD‐to‐MCI). There were 33 such cases in the data, but there appeared to be little clinical justification for re‐labelling them; they are characterized by slightly lower tau‐PET levels and older age than other AD cases.Next, based on the selected features we made use of CFA feature PHC_MEM, neuroimaging features lh.Amygdala and wm.lh.entorhinal to perform a post‐hoc check for the re‐labelled cases, given the known link of the amygdala to early‐stage AD , 29, 30.3.3t test comparing the AUC values over the 30 repetitions yielded a p value of 0.02. Balanced accuracy was also recorded: 93.6±0.05% with the re‐labelled data and 89.3 ±0.06% with the original labelled data.With the re‐labelled data, we can now train the GNN model for three‐class  classification by  Section, 2.6 usi4k‐means clustering) and supervised learning (information gain feature selection) to provide insights into heterogeneous AD data, and subsequently using supervised learning with GNN for diagnostic classification.In this work, we have successfully incorporated both unsupervised learning (UMAP and k‐means clustering (Figure Prior to applying GNN for AD classification, we made use of nonlinear dimensional reduction UMAP Figure  and projg Figure : a majorThe results of the clustering were additionally validated by data exogeneous to the clustering algorithm, specifically CSF Aβ, which is a biomarker characteristic of prodromal AD . The casFuture work, given ongoing availability of longitudinal tau‐PET data from ADNI, could use the same cluster mapping to investigate AD progression, validate the re‐labelling, and the hypothesis that the NRF cluster is a high‐risk group. Although unsupervised clustering for AD prognosis has yet to be performed on tau‐PET data, there are examples of clustering on other markers successfully predicting AD progression , 33, 34.In terms of supervised learning, GNNs have been introduced into AD classification studies in a variety of ways using different types of data, especially MRI and PET brain data , 35, 36.Taken all together, the high diagnostic accuracy of the re‐labelling approach in this work highlights the potential for data‐driven methods to be incorporated into the diagnostic process for AD. This study reinforces the value of methods such as unsupervised clustering method, to derive new patterns and sub‐groups from existing datasets and enhance the current clinical methodology for AD diagnosis and risk stratification.Niamh McCombe: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Software; Validation; Visualization; Writing‐original draft; Writing‐review & editing. Jake Bamrah: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Software; Validation; Visualization; Writing‐original draft; Writing‐review & editing. Jose M. Sanchez‐Bornot: Conceptualization; Data curation; Writing‐review & editing. David P. Finn: Validation; Writing‐review & editing. Paula L. McClean: Validation; Writing‐review & editing. KongFatt Wong‐Lin: Conceptualization; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Visualization; Writing‐original draft; Writing‐review & editing.The authors have declared no conflict of interest.


Atherosclerosis is associated with various comorbidities; nonetheless, its effect on the postoperative complications of colorectal surgery in older patients with colorectal cancer (CRC) remains unclear. This study aimed to evaluate the impact of atherosclerosis on the postoperative complications of colorectal surgery in older adults with CRC.Patients aged ≥ 65 years who underwent surgery for CRC between April 2017 and October 2020 were enrolled. To evaluate atherosclerosis, we prospectively calculated the cardio-ankle vascular index (CAVI) measured by the blood pressure/pulse wave test and abdominal aortic calcification (AAC) score from computed tomography. Risk factors for Clavien–Dindo grade ≥ III postoperative complications were evaluated by univariate and logistic regression analyses.p = 0.019), AAC score ; and operative time  were identified as risk factors for postoperative complications. Based on the optimal cut-off values of CAVI and AAC score, the probability of postoperative complications was 27.8% in patients with abnormal values for both parameters, which was 17.4 times higher than the 1.6% probability of postoperative complications in patients with normal values.Overall, 124 patients were included. The mean CAVI value and AAC score were 9.5 ± 1.8 and 7.0 ± 8.0, respectively. Clavien–Dindo grade ≥ III postoperative complications were observed in 14 patients (11.3%). CAVI (odds ratio, 1.522 , Atherosclerosis, particularly that assessed using CAVI and AAC score, could be a significant predictor of postoperative complications of colorectal surgery in older adults with CRC.The online version contains supplementary material available at 10.1186/s12876-022-02600-7. The proportion of older adults in the society is rapidly increasing, and the share of the global population aged 65 years is projected to rise from 10 in 2022 to 16% in 2050 . GeneralAtherosclerosis is one of the crucial pathophysiological processes implicated in cardiovascular diseases, including coronary artery disease, stroke, and heart failure , 9, and The present study aimed to evaluate the impact of atherosclerosis on the postoperative complications of colorectal surgery in older patients with CRC using the blood pressure/pulse wave test and scoring of arterial calcifications with computed tomography (CT) scans.n = 2), and the remaining 124 patients were included in this study  class; presence of comorbid diseases, such as hypertension, hyperlipidemia, diabetes mellitus, ischemic heart disease, and chronic kidney disease; smoking history; steroid use; stroke; previous abdominal surgery; body mass index; blood test results ; prognostic nutritional index ; CAVI; AThis study was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The procedures followed were approved by the institutional review board of Tokyo Medical University (T2021-0025). Written informed consent was obtained from all patients prior to their participation in the study.The severity of atherosclerosis was evaluated using the blood pressure/pulse wave test and AAC scoring system.At the time of admission, CAVI was measured using a blood pressure pulse wave analyzer , according to the method described previously . Briefly2 . The total AAC score for each patient was determined by adding the scores from each of the eight regions [As previously described by Agatston et al. , two rad regions , 22 , major complications (class III–IV), and death (class V) . We aimep-values of < 0.05. Variables with p-values of < 0.25 in the univariate analysis were entered into the multivariate logistic regression model to determine the risk factors for postoperative complications with a criterion of p < 0.05. Receiver operating characteristic (ROC) curves were used to determine the cut-off values of CAVI and AAC score. The optimal cut-off value was defined as the point on the ROC curve plotted closest to the point from the upper left corner.All statistical analyses were performed using SPSS software version 27.0 . Categorical variables are expressed as number and percentage (%), whereas continuous variables are presented as mean ± standard deviation. The categorical variables were compared using the chi-squared test or Fisher’s exact test, as appropriate; the continuous variables were compared using the Mann–Whitney U-test. Throughout our analysis, statistical tests were two-tailed, and statistical significance was set at n = 76) than females (n = 48). Patients with ASA class ≥ 2 accounted for 96.8% of patients, as almost all patients had comorbidities. Overall, the mean AAC score was 7.0 ± 8.0 and the mean CAVI value was 9.5 ± 1.8. Table n = 5), intraperitoneal abscess (n = 3), pneumonia (n = 2), anastomotic leakage (n = 2), intestinal ischemia (n = 1), and anastomotic bleeding (n = 1).The study population comprised 124 patients with a mean age of 75.3 ± 6.4 years (Table p = 0.046), AAC score (p = 0.017), CAVI value (p = 0.006), and operative time (p = 0.005) predicted grade ≥ III postoperative complications. The results of logistic regression analysis for postoperative complications are presented in Table p = 0.019), AAC score , and operative time .Univariate analysis showed that male sex  and its radiodensity (HU). It is commonly used in studies of atherosclerosis involving the coronary arteries [Regarding AAC score, calcification occurs in the media of arteries, and the amount of calcification is directly associated with the extent of atherosclerosis . A previarteries . Regardiarteries , 34, 35.arteries , 22 simiarteries , 34, 35.CAVI and AAC score evaluate arterial stiffness and arterial calcification, respectively, although both are markers of atherosclerosis. Additionally, in this study, we evaluated the correlation between these two test results and found no significant relationship  angiography has been reported to provide information on tissue perfusion, identifying a well-perfused location for colorectal transections . This isSecondly, a previous study has reported that patients with high ligation of the inferior mesenteric artery (IMA above left colic artery) had a 3.8 times higher chance of leaking than those with low ligation (IMA below left colic artery)  in sigmoOur study had some limitations. First, this was a single-center prospective cohort study; therefore, our findings should be confirmed in a larger-scale study. Second, we only investigated calcification around the origin of the superior mesenteric artery and inferior mesenteric artery based on the AAC score. However, to assess systemic atherosclerosis, other regions may have to be investigated as well.Atherosclerosis, evaluated based on CAVI and AAC score, was a risk factor for postoperative complications of elective colorectal surgery in older patients with CRC. We emphasize that the preoperative evaluations of atherosclerosis in colorectal surgery in older patients with CRC may help surgeons perform surgery safely and appropriately tailor postoperative management.Additional file 1: Scatter plots of CAVI and AAC score. Pearson's correlation coefficient was 0.10 between AAC score and CAVI


Coronavirus disease 2019 (COVID‐19) vaccine side effects have an important role in the hesitancy of the general population toward vaccine administration. Therefore, this study was conducted to document the COVID‐19 vaccine side effects in our population.An online survey‐based, cross‐sectional study was carried out from September 1, 2021, to October 1, 2021, to document the side effects of the COVID‐19 vaccine among the general public. The questionnaire included participants’ sociodemographic data, type of vaccine, comorbidities, previous COVID‐19 infection, and assessment of side effects reported by them.p < 0.001), female gender , marital status , graduate level of education , and occupation being either retired, freelancers, or social workers (OR: 0.310 [0.106–0.909]), p = 0.033). Previous infection with COVID‐19 (p = 0.458) and comorbidities were found unrelated (p = 0.707) to the occurrence of side effects.The majority of the participants were <20 years of age (62.2%), females (74.9%), belonged to the educational sector (58.1%), residents of Sindh (65.7%), and were previously unaffected by COVID‐19 infection (73.3%). Sinovac (38.7%) followed by Sinopharm (30.4%) and Moderna (18.4%) were administered more frequently. Commonly reported side effects were injection site pain (82%), myalgia (55%), headache (46%), fatigue/malaise (45%), and fever (41%). Vaccine side effects were more likely to be reported with the first dose as compared to the second dose. On regression analysis, factors associated with occurrence of side effects included younger age (odds ratio [OR]: 6.000 [2.065–17.431], The overall prevalence of local side effects was quite higher than the systemic ones. Further large‐scale studies on vaccine safety are required to strengthen public confidence in the vaccination drive. Despite their availability, inoculation's short‐ and long‐term side effects have increased vaccine hesitancy in many parts of the world. Large‐scale surveys and meta‐analyses had predicted widespread vaccine hesitancy due to concerns regarding a potential COVID‐19 vaccine's safety and possible side effects.On the other hand, 31.5% of parents in Italy were hesitant in vaccinating their children,Before the COVID‐19 pandemic, WHO had already included vaccine hesitancy among the Top 10 threats to public health in 2019.22.1An online survey‐based, cross‐sectional study was carried out from September 1, 2021, to October 1, 2021, to estimate the prevalence of COVID‐19 vaccine side effects among the general population of Pakistan. A self‐administered online survey was circulated via social media platforms .‐Sociodemographic details of participants, including gender, age, marital status, education level, occupation, monthly household income, and the region of residence.‐Vaccine‐related data: the COVID‐19 vaccine received and the date of dosages.‐Medical histories such as comorbidities, previous COVID‐19 infection, and the time between vaccination and infection.‐The side effects of the COVID‐19 vaccine after the first or the second dose, severity, and duration , respectively.After an extensive literature search on PubMed/Medline, Embase, and Google Scholar, the questionnaire enlisted commonly reported side effects. While designing the study, Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross‐sectional studies were followed.The listed side effects were obtained from existing studiesThe study did not collect the personal information of participants and anonymity was maintained. The ethical approval was obtained by the Institutional Review Board of PAQSJ Institute of Medical Sciences, Gambat, Pakistan (Reference Number: ERC/21/9). An introduction to the study was given in the questionnaire. The participants were informed of the purpose of the study, and informed consent was obtained. Participants could withdraw from the study by reaching out to the research team.2.2p Value < 0.05 was considered statistically significant for all outcomes.The sample size was calculated using Open‐EPI, considering a response rate of 80%, with a 5% margin of error and a 95% confidence interval. Statistical analysis was performed using SPSS version 25.0 (SPSS Inc.). Descriptive statistics were performed to obtain the frequencies for side‐effects, severity, and duration of side‐effects by dosage interval of vaccines, and the occurrence of side‐effects with dosage intervals. A multiple regression analysis was performed to analyze the demographic characteristics  that affected the occurrence of post‐COVID‐19 vaccine complications. A 3n = 252, 58.1%), followed by freelancer or social workers , healthcare workers , enterprise workers , and 93 (21.4%) of the participants were currently unemployed. Among different areas of Pakistan, a majority belonged to the province of Sindh , followed by Punjab , Khyber Pakhtunkhwa , and Balochistan  as shown in Table A total of 434 responses were considered valid and contributed to the results. Two hundred seventy (62.2%) participants were under the age of 20, 141 (32.5%) participants were aged 21–30, and 23 (5.3%) participants were above the age of 30. The sample consisted of 109 male (25.1%) and 325 female patients (74.9%). Four hundred two (92.6%) were single, while 3 (0.7%) were divorced or widowed, and 29 (6.7%) participants were married. Sixty (13.8%) had a university degree, those with secondary school were 116 (26.7%), and those currently enrolled in an undergraduate program were 258 (59.4%). By occupation, most participants belonged to the education sector  were not infected with COVID‐19 previously, while a quarter  had a history of COVID‐19. Ten participants (2.3%) were infected after they received a vaccine. Among the types of vaccines received by participants, a majority received Sinovac  and Sinopharm . Sputnik (or Sputnik V) was received by two participants (0.4%). mRNA vaccines such as Pfizer BioNTech  and Moderna  were received by a total of 22.3% participants. Other vaccines included AstraZeneca , CanSinoBIO , and Johnson & Johnson (n = 1. 0.2%). 79.3% of the participants (n = 344) reported no significant comorbidities, while 20.7% (n = 90) reported having one or more comorbidities.Nearly three‐quarters of the participants , as shown in Figure n = 241, 55.5%), headache , fatigue or malaise , and fever . The least common side‐effects were angular cheilitis , the appearance of vesicles , bleeding gingiva , urticaria , and swollen lips . While most side‐effects were mostly mild, pain at the injection site, myalgia, and fatigue or malaise were moderate in severity, as shown in Figure The most common side‐effect reported by participants was pain at the injection site n = 56, 82.0%p < 0.001) or between 21 and 30 , being married , female gender , having a university degree , and employment as a freelance worker, social workers, or being a retiree , were significantly associated with having side‐effects of postvaccination as shown in Figure p = 0.458) and comorbidities (p = 0.707) was not associated with the appearance of side effects after COVID‐19 vaccination, as shown in Table Upon regression analysis, age below 20 , headache , fatigue or malaise , and fever . The most prominent among these was having pain at the injection site, present in 82% of the involved participants. This result was comparable to a similar study conducted in Saudi Arabia and by the study conducted by the Centers for Disease Control and Prevention (CDC); however, a study conducted in the United Kingdom by Cristina et al. showed much lower rates of pain at the injection site.The most common side effects that were reported included pain at the injection site, myalgia  have been explained.


The interaction between cancer diagnoses and COVID‐19 infection and outcomes is unclear. We leveraged a state‐wide, multi‐institutional database to assess cancer‐related risk factors for poor COVID‐19 outcomes.We conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) at 17 California medical centers. We identified adults tested for SARS‐CoV‐2 from 2/1/2020–12/31/2020 and selected a cohort of patients with cancer. We obtained demographic, clinical, cancer type, and antineoplastic therapy data. The primary outcome was hospitalization within 30d after the first positive SARS‐CoV‐2 test. Secondary outcomes were SARS‐CoV‐2 positivity and severe COVID‐19 . We used multivariable logistic regression to identify cancer‐related factors associated with outcomes.p < 0.001). Among the 1781 SARS‐CoV‐2‐positive patients with cancer, BCR/ABL‐negative myeloproliferative neoplasms , venetoclax , and methotrexate  were associated with greater hospitalization risk. Cancer and therapy types were not associated with severe COVID‐19.We identified 409,462 patients undergoing SARS‐CoV‐2 testing. Of 49,918 patients with cancer, 1781 (3.6%) tested positive. Patients with cancer were less likely to test positive (RR 0.70, 95% CI: 0.67–0.74, In this large, diverse cohort, cancer was associated with a decreased risk of SARS‐CoV‐2 positivity. Patients with BCR/ABL‐negative myeloproliferative neoplasm or receiving methotrexate or venetoclax may be at increased risk of hospitalization following SARS‐CoV‐2 infection. Mechanistic and comparative studies are needed to validate findings. In a large dataset of patients who underwent SARS‐CoV‐2 testing across 17 California medical centers, we conducted multivariable analyses to evaluate cancer‐related risk factors for poor COVID‐19 outcomes. We found that patients with cancer had a lower risk of SARS‐CoV‐2 positivity than those without cancer. BCR/ABL‐negative myeloproliferative neoplasms, venetoclax use, and methotrexate use were associated with an increased risk of hospitalization after a COVID‐19 diagnosis. Though several studies have identified associations between SARS‐CoV‐2 infection or outcomes and some cancer diagnoses, such as lung and hematologic malignancies,Moreover, it remains uncertain whether recent antineoplastic systemic therapy use, particularly cytotoxic chemotherapy, is a risk factor for poor COVID‐19 outcomes. A recent meta‐analysis of 26 cohort studies found no adverse effect of various anti‐cancer therapies on COVID‐19 severity or mortality,Informatics‐based analyses of large, real‐world data sets may facilitate an understanding of the relationships between these potential risk factors and COVID‐19 outcomes. We leveraged the University of California Health COVID Research Data Set (UC CORDS),2We conducted a retrospective cohort study of patients using UC CORDS v2.0.2.1We identified patients who underwent a SARS‐CoV‐2 RT‐qPCR test between February 1, 2020, and December 31, 2020, and were ≥ 18‐years‐old at the first test date to create the first cohort. We then identified a second cohort of patients with cancer, defined as ≥1 clinical encounter associated with a cancer ICD‐10‐CM code  within 1 year prior to the test date, that is, “index date.” For patients with a positive SARS‐CoV‐2 RT‐qPCR test, the index date was the first positive date; otherwise, the index date was the first negative test date. These criteria were intended to select patients being routinely followed for their cancer. Patients with only basal and squamous cell cutaneous cancers were excluded given the extremely low morbidity and mortality of these cancers. Patients with other/unknown gender were excluded, since none of these patients had both cancer and COVID‐19. For analysis of severe COVID‐19 (defined below), a third cohort of cancer patients with COVID‐19 who were hospitalized within 30 days of index date was created to enrich for laboratory data and likelihood that outcomes are attributable to COVID‐19. Figure 2.2For demographic variables, we included birth year, gender, race, and ethnicity. For clinical variables, we included cancer type; comorbidities that are known to be associated with COVID‐19 severity in patients with cancer ; and body mass index.2.3Outcomes included SARS‐CoV‐2 positivity (at least 1 positive RT‐qPCR test); hospitalization within 30 days after the index date; and a composite endpoint for severe COVID‐19, defined as either intensive care unit admission, need for mechanical ventilation, or death within 30 days after the index date.2.4We first calculated the incidence of SARS‐CoV‐2 test positivity among all patients tested for SARS‐CoV‐2, regardless of cancer. Then, we conducted a series of multivariable logistic regression models for each of the three cohorts described above . To predict the risk of SARS‐CoV‐2 test positivity in the overall cohort, we created a multivariable model that included age, gender, race, ethnicity, comorbidities, any cancer history, and receipt of any systemic therapy. Laboratory tests were not included given the high rate of missing values. To identify cancer‐related risk factors for hospitalization in the cohort of patients with cancer and COVID‐19 (the primary outcome), we created two multivariable models: one in which systemic therapies were categorized and another in which individual therapies were delineated. Both models contained specific cancer types compared to unspecified cancer type, selected since this category comprised of non‐specific diagnostic codes that would likely span a range of cancer types . Again, laboratory tests were not included in these models given the high rate of missing values. Lastly, to evaluate the risk of severe COVID‐19, we used the cohort restricted to hospitalized patients with cancer and COVID‐19. We created a multivariable model with systemic therapies as categories and laboratory tests as continuous variables, only including tests that at least 70% of patients had completed. We did not incorporate individual therapies in this analysis because few patients were associated with each therapy.p < 0.05) were considered significant. Multiple imputations were used for imputation of missing laboratory values.Logistic regressions were visualized using forest plots with adjusted relative risk ratios, and 95% confidence intervals ; Breast ; and Prostate  per Table Overall, 24,177 of 409,462 (5.9%) patients undergoing SARS‐CoV‐2 RT‐qPCR tested positive for SARS‐CoV‐2 at any time during the study period. Of the 49,918 patients with a history of cancer, 1781 (3.6%) tested positive. The mean age of SARS‐CoV‐2‐positive patients with cancer was 59 years (SD = 16); 950 (53%) were female, 939 (53%) were White, and 636 (36%) were Hispanic or Latino (Table p < 0.001). Similarly, any systemic therapy use was associated with a decreased risk of a positive test . In terms of specific cancer types, many were associated with a decreased risk or no difference in risk of a positive test compared to unspecified cancer types  and only other targeted therapy was associated with an increased risk . In the model with individual therapies, methotrexate  and venetoclax  were associated with an increased risk of hospitalization  were hospitalized within 30 days after the index date. In the multivariable analysis, risk factors for hospitalization included older age, Asian race (compared to White), Hispanic/Latino ethnicity, and several comorbidities , as did venetoclax .In a post hoc sensitivity analysis of this model, we changed the time window of therapy receipt from 60 days before through 30 days after positive test date to 60 days before the positive test date. Methotrexate remained associated with an increased risk of hospitalization , and higher glucose was associated with an increased risk  of severe COVID‐19. Myeloproliferative neoplasm (N = 32) was not associated with severe COVID‐19  compared to unspecified cancer type.Among 388 cancer patients who were hospitalized after a positive SARS‐CoV‐2 test, 160 41.2%) had severe COVID‐19. In multivariable analysis, no demographic, comorbidity, or cancer‐related factors were associated with a decrease in risk of severe COVID‐19 ; real‐time data collection ; and data from other health care systems . Pandemic preparation should also include identification of and resource‐allocation to teams to harmonize and standardize raw data, and to query, analyze, and interpret databases. Efforts must be done ethically with potential biases in mind.This study has several strengths, including one of the largest cancer cohorts to date, a diverse cohort, use of a novel database, and more granular categorization of cancer types and therapies. There are several limitations. Selection bias may have been introduced from the inclusion of only patients who underwent SARS‐CoV‐2 testing and from over‐representation of patients cared for at academic centers, where asymptomatic testing and COVID‐19 treatment practices may have differed from other hospitals. The database does not contain certain risk factors for severe COVID‐19, including cancer stage, cancer remission status, smoking status, poor performance status, and socioeconomic variables. Due to the limited sample size of hospitalized patients, we could not evaluate whether venetoclax and methotrexate were associated with severe COVID‐19 infection. Multiple testing was not accounted for. Lastly, we could not ascertain the outcomes of patients who sought care outside the UC health system.5As the COVID‐19 pandemic continues and new, highly transmissible variants emerge, it is important to remain vigilant of risk factors for severe infection. Close attention to patients with risk factors will allow us to better prevent and monitor COVID‐19 in high‐risk patients. We found that patients with COVID‐19 and myeloproliferative neoplasms, and those receiving methotrexate or venetoclax, may be at an increased risk of hospitalization. Further studies to confirm these associations are needed, as are studies to understand underlying mechanisms. Investigation is also needed to explain and confirm the lower risk of test positivity in patients with cancer than those without cancer. Lastly, policy makers and health systems should focus on establishing timely, live central databases of electronic health data to provide rapidly accumulating data for future pandemic preparedness, as well as the human capital needed for their maintenance and use.None.All procedures performed in studies involving human participants were in accordance with the ethical standards of UCSF and LLNL research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Conceptualization: Daniel H. Kwon, Amy L. Gryshuk, Priyadip Ray, Franklin W. Huang Data curation: Daniel H. Kwon, Franklin W. Huang Formal analysis: Daniel H. Kwon, Jose Cadena, Sam Nguyen, Kwan Ho Ryan Chan, Braden Soper, Priyadip Ray, Franklin W. Huang Funding acquisition: Priyadip Ray Investigation: Daniel H. Kwon, Jose Cadena, Sam Nguyen, Kwan Ho Ryan Chan, Braden Soper, Priyadip Ray Methodology: Daniel H. Kwon, Priyadip Ray, Franklin W. Huang, J.C.H. Project administration: Daniel H. Kwon, Priyadip Ray, Franklin W. Huang Resources: Daniel H. Kwon, Priyadip Ray, Franklin W. Huang Software: Sam Nguyen, Kwan Ho Ryan Chan, Braden Soper, Priyadip Ray Supervision: Franklin W. Huang Validation: n/a. Visualization: Daniel H. Kwon, Jose Cadena Writing – original draft: Daniel H. Kwon Writing – review & editing: all authors.FigureS1Click here for additional data file.FigureS2Click here for additional data file.FigureS3Click here for additional data file.TableS1‐S3Click here for additional data file.


V617F‐mutated essential thrombocythemia (ET). As pointed out by the authors, the occurrence of chorea has rarely been reported, and its pathogenesis remains controversial. We would like to add to this discussion by describing a 61‐year‐old woman with a 3‐month history of slowly progressive chorea involving oromandibular district, left upper limb, and ipsilateral foot and toes. Five years before, the patient was diagnosed with JAK2V617F‐mutation ET and treated with hydroxyurea . The subject autonomously interrupted the treatment right before symptoms onset. There was no history of other medical conditions or exposure to neuroleptic medication and no familiarity for neurological diseases. At admission to our department, her blood cell count showed platelet 816 × 10^3/μL, and brain magnetic resonance imaging (MRI) revealed no evidence of acute lesions but it did show a chronic vascular infarction in the right caudate nucleus. An autoantibodies panel, including ENA, ANA, ANCA, anti‐gliadin, anti‐cardiolipin, anti‐beta‐2‐glycoprotein and LAC, was negative. An electroencephalogram detected non‐specific slow‐wave activity, and genetic testing for Huntington's disease was negative.It is with great interest that we read the article by Kutty and colleaguesDuring hospitalization, hydroxyurea was reintroduced at 1000 mg OD and tetrabenazine  was started, observing substantial clinical improvement. At the 6‐month follow‐up, her neurological examination was normal, and the platelet count normalized. Three months later, tetrabenazine was gradually withdrawn. However, after 4 to 5 weeks, choreic movements reappeared in the same districts, triggered by walking. Tetrabenazine treatment was then reintroduced, with beneficial effects.Our case shares some similarities with the one described.As proposed by the Koya Kutty et al.,−V617F mutation causes the loss of its auto‐inhibitory activity, resulting in astrogliosis and inflammation. We postulate that local persistence of proinflammatory cytokines could impair striatal GABAergic signaling, generating choreic manifestations.Recently, a role for JAK2 in neuroinflammation has been proposed. JAK2 is member of a family of tyrosine kinases coupled to multiple signaling pathways. In particular, JAK2‐STAT3 pathway is required for astrocyte reactivity, and its inhibition reduces the astroglial response to quinolinic‐acid‐induced striatal lesions supporting its neuroprotective role,(1) Research project: A. Conception, B. Organization, C. Execution; (2) Data Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the First Draft, B. Review and Critique.A.C.: 1C, 3A.S.A.: 1B, 2B.A.P.: 1A, 2A, 2C, 3B.No specific funding was received for this work. The authors declare that there are no conflicts of interest relevant to this work.The authors declare that there are no conflicts of interest relevant to this work.We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. Informed patient consent was verbally obtained. The authors confirm that the approval of an institutional review board was not required for this work.


Real‐world data on atrial fibrillation (AF) ablation outcomes in obese populations have remained scarce, especially the relationship between obesity and in‐hospital AF ablation outcome.Obesity is associated with higher complication rates and higher admission trend for AF ablation.We drew data from the US National Inpatient Sample to identify patients who underwent AF ablation between 2005 and 2018. Sociodemographic and patients' characteristics data were collected, and the trend, incidence of catheter ablation complications and mortality were analyzed, and further stratified by obesity classification.p < .001). Morbidly obese patients were statistically younger, while coexisting comorbidities were substantially higher than both obese and nonobese patients (p < .01) Both obesity and morbid obesity were significantly associated with an increased risk of total bleeding, and vascular complications (p < .05). Only morbid obesity was significantly associated with an increased risk of ablation‐related complications, total infection, and pulmonary complications (p < .01). No difference in‐hospital mortality was observed among obese, morbidly obese, and nonobese patients.A total of 153 429 patients who were hospitalized for AF ablation were estimated. Among these, 11 876 obese patients  and 10 635 morbid obese patients (95% CI: 10 200–11 069) were observed. There was a substantial uptrend admission, up to fivefold, for AF ablation in all obese patients from 2005 to 2018 (Our study observed an uptrend in the admission of obese patients undergoing AF ablation from 2005 through 2018. Obesity was associated with higher ablation‐related complications, particularly those who were morbidly obese. AFatrial fibrillationBMIbody mass indexCIconfidence intervalEPelectrophysiologyGAgeneral anesthesiaLAleft atriumORodds ratioRSrespiratoryUTIurinary tract infection1Obesity is a well‐established risk factor of both new‐onset and recurrent atrial fibrillation (AF).Catheter ablation has been widely accepted as a principal treatment for AFThus, we evaluated the most recent admission trend and the impact of obesity in patients undergoing AF ablation on the hospital outcomes by utilizing the National Inpatient Sample (NIS) database, the current largest inpatient database from US inpatient settings.22.1http://www.hcup-us.ahrq.gov/db/nation/nis/NIS_2009_INTRODUCTION.pdf. Accessed January 18, 2015). Information regarding each discharge includes patient demographics, primary payer, hospital characteristics, principal diagnosis, up to 24 secondary diagnoses, and procedural diagnoses. The HCUP‐NIS does not capture individual patients but captures all information for a given admission. Institutional Review Board approval was not needed as this database was publicly available and deidentified. These data are available to other authors via the HCUP‐NIS database with the Agency for Healthcare Research and Quality.The National (Nationwide) Inpatient Sample is the largest all‐payer database of hospital inpatient stays in the United States. NIS contains discharge data from a 20% stratified sample of community hospitals and is a part of the Healthcare Quality and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research and Quality  was used for reporting diagnoses and procedures in the NIS database during the study period. For each index hospitalization, the database provides a principal discharge diagnosis, additional diagnoses, and procedures, in which total maximum numbers vary per year. We identified patients 18 years of age or older with a primary diagnosis of AF  and underwent a CA procedure (ICD‐ 9‐CM 37.34 and ICD‐10CM 02583ZZ) from 2005 to 2018. To avoid selection bias and choose only the patient who had an ablation for AF, we have excluded all the patients with other arrhythmias or potential reasons for an ablation like atrial flutter , supraventricular tachycardia , ventricular tachycardia (ICD‐9CM 427.1 and ICD‐10CM I472), Wolff‐Parkinson‐White syndrome/pre‐excitation syndrome (ICD‐9CM 426.7 and ICD‐10CM I456), “other premature beats” , and cardiac dysrhythmia (ICD‐9CM 427.89 and ICD‐10CM I49.9). Furthermore, we excluded patients with either of the following cardiac procedures during the index hospitalization to avoid attributing their complications to the ablation procedure; (1) pacemaker implantation  or (2) implantable cardioverter‐defibrillator insertion .2), obese (≥30 and ≤40 kg/m2), and morbidly obese (>40 kg/m2).2). For the purpose of our analysis, we have identified the following ICD‐9‐CM/10CM codes for nonobesity , obesity , and morbid obesity .To determine the impact of obesity in patients undergoing AF ablation, body mass index (BMI) was used to stratify patients into nonobese , an additional list of comorbidities was identified from the database using ICD‐9‐CM codes and ICD‐10‐CM codes Table . Deyo‐CC2.3We identified the common in‐hospital complications of CAs using the ICD‐9‐CM and ICD‐10‐CM diagnosis, and procedure codes using the same methodology as described in previous publications.In addition, we explored (8) gastrointestinal (GI) complications  and (9) skin complications . Owing to Cluckey et al.,2.4χ2 test and analysis of variance were used to compare categorical and continuous variables, respectively. Trends for continuous variables were tested using the nonparametric test for trend by Cuzick.Trend weight files provided by Agency for Healthcare Research and Quality were used to reflect national estimates. The p value less than .05 was considered significant.To account for hospital‐level clustering of discharges, we generated a two‐level mixed‐effects logistic regression model to identify independent predictors of complications.33.1Applying our inclusion and exclusion criteria, a total of 153 429 patients who were hospitalized for AF ablation were estimated. Among these, 11 876 obese patients  and 10 635 morbid obese patients (95% CI: 10 200–11 069) were approximated. The majority of patients were male, White, Deyo‐CCI ≥ 1, with Medicaid or Private as a primary payer. Hypertension, congestive heart failure, diabetes, and chronic pulmonary disease were the most prevalent comorbidities. AF ablation was mostly performed in teaching hospitals (Table p ≤ .001). Coexisting comorbidities were found significantly higher among all obese patients when compared to nonobese patients. The highest proportion of patients with Deyo‐CCI ≥ 2 was observed in morbidly obese patients, followed by obese and nonobese patients (p ≤ .001). The use of anticoagulation was highest in morbidly obese patients, followed by obese and nonobese patients , while a nonstatistical decrement in the admission trend of obese patients was observed . When considering each complication category, cardiac, pulmonary, pericardial, GI, and infection complications, were all statistically uptrend from 2005 to 2018 , followed by total infection rates 2.8%) and total pericardial complications (1.7%). By obesity classifications, morbidly obese patients had the highest rates 14.3%, followed by obese patients 11.4% . and bleeding complications . Marginally, it had an increased trend toward risks of ablation‐related complications . Similar results were noted in morbid obesity compared to nonobese patients, a statistical association with increased risks ablation‐related complications , bleeding  and vascular complications . Interestingly, only morbid obesity was substantially associated with higher total infection rates , pulmonary complications .In‐hospital mortality rates in our cohort were extremely low, only 0.2%. There was no statistical difference in mortality rates between nonobese, obese, and morbidly obese patients  over left atrium (LA) for AF ablation,Of note, we found a statistical association between morbid obesity and infection complications. It is uncertain why our study failed to show an association between nonmorbid obesity and total infection rates. Uninvestigated and unknown residual biases from a retrospective design may come into play for this reason. Moreover, specifying obesity by BMI may incorrectly classify subjects with high lean but low‐fat mass into obese or nonobese groups. Owing to its poor sensitivity to detect obese patients defined by % body fat,4.2Our study has several limitations. First, as the NIS database provided only inpatient information, readmissions for any reason were unanalyzable owing to unprovided information. Also, residual biases from retrospective design cannot be excluded, despite a comprehensive covariate adjustment in our analysis. Second, owing to the same reason, this study may over/underestimate the true impact of obesity given the recent AF ablation trend in an outpatient setting. Furthermore, susceptibility to error coding is hardly evitable in administrative databases. Despite this limitation, this is the largest representative data of an inpatient AF ablation best reflecting real‐world experience. Third, as BMI was used to stratify obesity classifications, it may not truly represent adiposity in certain populations, particularly the younger population, which correlated more to lean body/muscle mass.5Based on the largest all‐payer inpatient database in the United States, our study observed an increase in trends of complications in obese patients undergoing AF ablation. Moreover, obesity was associated with higher ablation‐related complications, particularly those who were considered morbidly obese.Study design, literature review, statistical analysis, manuscript revision, intellectual revisions, and mentorship: Narut Prasitlumkum, Wisit Cheungpasitporn, Wisit Kaewput, Ronpichai Chokesuwattanaskul, Krit Jongnarangsin. Data management, data analysis, drafting manuscript: Tarun Bathini, Charat Thongprayoon, Ronpichai Chokesuwattanaskul, Saraschandra Vallabhajosyula, Krit Jongnarangsin. Access to data: Narut Prasitlumkum, Wisit Kaewput, Wisit Cheungpasitporn, Charat Thongprayoon, Tarun Bathini, Boonphiphop Boonpheng. Final approval: Narut Prasitlumkum, Wisit Cheungpasitporn, Wisit Kaewput, Charat Thongprayoon, Tarun Bathini, Boonphiphop Boonpheng, Saraschandra Vallabhajosyula, Ronpichai Chokesuwattanaskul, Krit Jongnarangsin. All authors had access to the data and a role in writing the manuscript.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.


To test the feasibility of a randomised controlled trial (RCT) of aspirin and/or vitamin D3 in active surveillance (AS) low/favourable intermediate risk prostate cancer (PCa) patients with Prolaris® testing.Newly‐diagnosed low/favourable intermediate risk PCa patients  Grade Group ≤2, maximum biopsy core length <10 mm, clinical stage ≤cT2c) were recruited into a multi‐centre randomised, double‐blind, placebo‐controlled study . Participants were randomised to oral low dose (100 mg), standard dose (300 mg) aspirin or placebo and/or vitamin D3 (4000 IU) versus placebo in a 3 × 2 factorial RCT design with biopsy tissue Prolaris® testing. The primary endpoint was trial acceptance/entry rates. Secondary endpoints included feasibility of Prolaris® testing, 12‐month disease re‐assessment , and 12‐month treatment adherence/safety. Disease progression was defined as any of the following (i) 50% increase in baseline PSA, (ii) new Prostate Imaging‐Reporting and Data System (PI‐RADS) 4/5 lesion(s) on multi‐parametric MRI where no previous lesion, (iii) 33% volume increase in lesion size, or radiological upstaging to ≥T3, (iv) ISUP Grade Group upgrade or (v) 50% increase in maximum cancer core length.Of 130 eligible patients, 104 (80%) accepted recruitment from seven sites over 12 months, of which 94 patients represented the per protocol population receiving treatment. Prolaris® testing was performed on 76/94 (81%) diagnostic biopsies. Twelve‐month disease progression rate was 43.3%. Assessable 12‐month treatment adherence in non‐progressing patients to aspirin and vitamin D across all treatment arms was 91%. Two drug‐attributable serious adverse events in 1 patient allocated to aspirin were identified. The study was not designed to determine differences between treatment arms.Recruitment of AS PCa patients into a multi‐centre multi‐arm placebo‐controlled RCT of minimally‐toxic adjunctive oral drug treatments with molecular biomarker profiling is acceptable and safe. A larger phase III study is needed to determine optimal agents, intervention efficacy, and outcome‐associated biomarkers. We wished to explore the feasibility and patient acceptability of recruitment and randomisation, assess compliance and toxicity and determine optimal disease re‐assessment parameters for future definitive studies of these and other minimally‐toxic ‘adjunctive’ treatments.22.1Patients aged >16 years with newly‐diagnosed low or favourable intermediate risk  Grade Group ≤2, maximum biopsy core length <10 mm, clinical stage ≤cT2c) Table  were enrPatients were randomly allocated using a 3 × 2 factorial design via a web‐based randomisation service  to daily oral low dose 100 mg) or standard dose (300 mg) aspirin versus placebo and/or oral vitamin D3 (4000 IU administered by 8 drops of fluid) versus placebo  was approved by the trial sponsor (Queen Mary University of London), Integrated Research Application System (IRAS) (ID 145427), National Research and Ethics Service (REC reference 14/LO/2033) and implemented in accordance with Good Clinical Practice guidelines and the Declaration of Helsinki.Treatment allocation was blinded to participants, clinicians, and the local trial team. Trial prescriptions with concealed labelling were issued at the randomisation visit and every 6 months by site pharmacies. Treatment adherence was assessed as a proportion of dispensed medication that was consumed amongst participants who returned their medication packages for an independent pharmacist review at 6 and 12 months. Patients were followed up for a total of 18 months. PSA testing was performed every 3 months, and repeat mpMRI and re‐biopsy was recommended at 12 months. Adverse events (AEs) and serious AEs (SAE) were recorded prospectively by local site teams and the central trial team was informed. The trial protocol (2.2Prolaris® cell cycle profile (CCP) testing was undertaken on surplus diagnostic tissue at baseline and on repeat 12‐month biopsy samples. CCP scores were generated as previously described2.3A 100 patient sample size was chosen for feasibility with a total recruitment period of 12 months. This was based on an assumption of a 33.3% true recruitment rate from 300 approached eligible individuals, which would achieve 89% power to show that the true recruitment rate is above 25% . The principal trial recruitment site diagnoses approximately 750 men with PCa each year, of which around 25% are managed by active surveillance. Six further recruitment sites were included based on geographical diversity. Recruitment data from this feasibility study will inform the number of centres required for a larger trial.biochemical: 50% increase in serum PSA from baseline; (ii) radiological: development of a Prostate Imaging‐Reporting and Data System (PI‐RADS) 4/5 lesionhistological: upgrade of ISUP Grade Group or a 50% increase in maximum cancer core length based on local site reports. Numbers of patient exclusions, withdrawals, and missing data on disease re‐assessment at 12 months were collected. Further secondary endpoints were tabulation of all SAEs, measurement of serum calcium to compare the effect of vitamin D with placebo, and treatment medication compliance, which was calculated for each participant by study pharmacists blinded to treatment arm allocation at 6 and 12 months.The primary endpoint was trial acceptance and entry rates over a 12‐month period. Secondary endpoints included feasibility of diagnostic tissue biomarker molecular profiling and feasibility of disease re‐assessment at 12 months. Disease progression was defined as any of the following: (i) 3Of the 469 men assessed for eligibility at seven sites between December 2016 and December 2017, 130 met the inclusion criteria. The 104 men (80%) accepted recruitment into the study and were randomised to aspirin and/or vitamin D  returned their medication packages for treatment compliance assessment by local trial pharmacy teams at 6 months and 64.5% (49/76) at 12 months Table . AmongstOf eligible patients, 9 men withdrew from the study to have RT before 12 months without evidence of disease progression according to PROVENT trial definitions, and did not undergo 12‐month reassessment. A further nine men withdrew from the study before 12‐months for non‐PCa‐related reasons , tissue biomarker molecular profiling (81%) and treatment compliance (>89%) with low rates of AEs.Interestingly, we found that overall , 43.3% showed progression within 12 months of which 9.2%, 7.9% and 41.7% had disease progression according to PSA, mpMRI or biopsy criteria, respectively. This is higher than conversion to RT rates (11.7%) in a recent non‐interventional UK AS cohort,Definitions of disease progression for AS patients can be a challenging aspect of clinical care and research. In day‐to‐day practice, PSA, DRE, mpMRI and re‐biopsy are all routinely used for disease monitoring and to inform RT decisions, but there is limited consensus on choice of progression thresholds.In our study, we found that aspirin, at both low and high doses, and vitamin D were well‐tolerated separately and in combination, as demonstrated by high treatment compliance in all study arms. No patients developed hypercalcaemia in the vitamin D arm, despite administration at 10 times the daily recommended dose for nutritional support.Molecular and imaging prognostic biomarkers could help direct adjunctive drug treatments for patients at higher risk of disease progression. For example, mpMRI‐visible disease is associated with a greater likelihood of moving to active treatment at 5 years.We observed a higher‐than‐anticipated withdrawal rate, with 18 men of out 94 withdrawing before 12 months. Nine of these men withdrew in order to receive RT, despite not meeting PROVENT criteria for disease progression. A further nine patients withdrew for non‐PCa‐related reasons. A limitation of our study is the absence of detailed information captured on the reasons for withdrawals. Patient anxiety is a recognised reason for ceasing AS in favour of RT, which occurs in approximately 10%–36% of cases.Our feasibility study has clearly shown that adjunctive drug therapies are acceptable, safe and well‐tolerated by patients on AS for PCa. The optimal choice/combination of adjunctive agent is yet to be determined, but should be minimally‐toxic to maximise longer‐term treatment compliance. A UK randomised trial of the 5‐alpha reductase inhibitor finasteride (FINESSE) for low and favourable intermediate risk PCa will soon open to recruitment, with a primary endpoint of cessation of AS based on patient choice, mpMRI, clinical (examination) or biopsy progression, but without biomarker profiling. Future studies should consider selection of patients with higher progression risk and include multiple adjunctive drug agents, and molecular and imaging biomarkers. In addition to data on oncological, functional and quality of life outcomes, qualitative data should also be captured on patient barriers to AS, such as anxiety, reasons for trial withdrawal and drug compliance.Jack Cuzick receives royalties from and is on the advisory board of Myriad Genetics.The authors listed below have made substantial contributions to the intellectual content of the paper in the sections described below: Conception and design: ED, GS, PC, TO, PR and JC. Acquisition of data: GS, RK, NH, SS, SK, DB, AS and PR. Analysis and interpretation of data: ED, GS, PA, KF, KC, TO, PR and JC. Drafting of manuscript: ED, TO, PR and JC. Critical revision of the manuscript: GS, RK, PA, KF, KC, NH, SS, SK, DM, AS and PC. Statistical Analysis: PA, KF and KC. Obtaining funding: GS, PC, TO, PR and JC. Administrative support, technical or material support: RK, NH. Supervision: GS, PR and JC.Table S1. Inclusion and Exclusion Criteria.Table S2. Combined change in serum calcium measurement according to Vitamin D arm.Table S3. Treatment compliance as a proportion of dispensed medication that was consumed. Amongst participants who returned their medication packages for independent pharmacist review.Table S4. Patients who returned their medication packages for compliance assessment.Table S5. Reasons for Patient Exclusion (n = 10).Click here for additional data file.


Renal impairment is a common complication in coronavirus disease 2019 (COVID‐19), although its prognostic significance remains unknown.This study determines the impact of early renal impairment on the clinical outcome of COVID‐19.Patients diagnosed with COVID‐19 and hospitalized in Xiaogan Central Hospital from 20 January to 29 February 2020 were retrospectively included and grouped into two cohorts  based on the renal function detected on admission. Records of clinical manifestation, laboratory findings and clinical outcome were collected and compared between these two cohorts.P < 0.05). Patients with early renal impairment were also associated with higher incidences of haematuria/proteinuria, higher incidences of mortality and prolonged hospitalization duration. The independent risk factors for in‐hospital death included age >65 years old, complication of diabetes, renal impairment on admission (Cr > 73 μmol/L and eGFR < 60 ml/min 1.73 m2), WBC > 9.5 × 109/L and ALB < 35 g/L.A total 543 COVID‐19 patients were included. Among these patients, 70 patients developed early renal impairment, with an incidence of 12.89%. A significantly higher white blood cell (WBC) count, C‐reactive protein (CRP), erythrocyte sedimentation rate (ESR), serum creatine (Cr), blood urine nitrogen (BUN) and brain natriuretic peptide (BNP) and a significantly lower blood platelet (PLT), lymphocyte count, prealbumin and albumin (ALB) were detected in the cohort with renal insufficiency (Early renal impairment is associated with higher risk of in‐hospital death for patients with COVID‐19. Risk stratification according to renal function can better guide the clinical management of COVID‐19. ACE2angiotensin‐converting enzyme 2AKIacute kidney injuryALBprealbumin and albuminARDSacute respiratory distress syndromeBNPbrain natriuretic peptideBUNblood urine nitrogenCK‐MBcreatine kinase myocardial bandCOVID‐19clinical outcome of coronavirus disease 2019Crserum creatineCRPC‐reactive proteinCTcomputed tomographyeGFRglomerular filtration rateESRerythrocyte sedimentation rateHbhaemoglobinPCTprocalcitoninPLTsignificantly lower blood plateletSDstandard deviationWBCwhite blood cells1Since its outbreak in December 2019 in Wuhan, China, coronavirus disease 2019 (COVID‐19) has widely spread all over the word and become a pandemic that poses a great threat to the health of all human beings.Caused by the newly identified coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which dominantly target pulmonary tissues, COVID‐19 mainly manifests as pneumonia. In severe cases, acute respiratory distress syndrome (ARDS) can develop.More than 3000 cases of COVID‐19 have been detected in Xiaogan City, Hubei Province, making this one of the most severe city havocked by COVID‐19 in China, ranking only second to Wuhan. As the biggest hospital in Xiaogan City, Xiaogan Central Hospital plays an essential role in treating COVID‐19 and has hospitalized a large number of COVID‐19 patients. Utilizing the abundant clinical data of COVID‐19 in our hospital, the investigators sought to clarify the clinical features and prognostic impact of early renal impairment in COVID‐19 patients.22.1A total of 543 the patients diagnosed with COVID‐19 and hospitalized in Xiaogan Central Hospital from 20 January 2020 to 29 February 2020 were included in the present study. All patients were diagnosed according to the diagnostic criteria for COVID‐19 proposed in the Chinese Guidelines for the Diagnosis and Treatment of COVID‐19 (version 7) and were all treatment naïve when diagnosed. The investigators excluded cases that had any of the following conditions: (1) patients who have been treated elsewhere before being transferred to our hospital; (2) patients who acquired the COVID‐19 infection during the hospitalization for other diseases; (3) patients who were <18 years old. All clinical observations ended on 28 March 2020, which was the time the last case of COVID‐19 in Xiaogan City was cured. The present study was approved by the ethics committee of our hospital .2.22009Scr equation based on the Cr level detected on admission. Based on the eGFR, these patients were grouped into two cohorts: the cohort with renal insufficiency  and the cohort with normal renal function .General information, which included age, gender, height, weight, date of disease onset, date of hospitalization, date of discharge or death, complicated diseases and clinical manifestation, were collected through medical records. Furthermore, the laboratory results, which included white blood cells (WBCs), haemoglobin (Hb), blood platelet (PLT), neutrophil, lymphocytes, eosinophil, prealbumin, albumin (ALB), fibrinogen (Fib), C‐reactive protein (CRP), serum creatine (Cr), blood urine nitrogen (BUN), creatine kinase myocardial band (CK‐MB), brain natriuretic peptide (BNP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), D‐dimer, urine protein and urine occult blood that was first detected on admission, were also collected. In addition, the imaging data of the computed tomography (CT) scan that was first detected on admission was also collected for all patients. For each patient, the glomerular filtration rate (eGFR) was inferred using the GFR‐EPI2.3t test. Non‐normally distributed continuous variables are presented in median , and between‐group differences were assessed using the Mann–Whitney U test. Between‐group differences with respect to categorical variables were assessed using the Chi‐squared pair‐wise test. Univariate log rank test was performed to identify risk factors for mortality during hospitalization. For variables with statistical significance in the univariate analysis, a further multivariate survival analysis was performed using the Cox regression model to identify risk factors with an independent impact. A P value of <0.05 was considered statistically significant.The SPSS software package 21.0 for Windows  was used for the statistical analysis. Normally distributed continuous variables are presented as mean ± standard deviation (SD) and between‐group differences assessed using the independent 33.1n = 133, 24.5%), cardiovascular disease , diabetes mellitus , chronic liver disease , chronic renal disease  and malignant diseases . According to the severity of COVID‐19, these patients were classified as mild disease , general type  and critical type . The average time of disease onset before admission was 7 days, and the average hospitalization duration was 20 days. The detailed information is presented in Table A total 543 qualified cases were included in the present study. Among these patients, 291 (53.6%) patients were male, and 252 (46.4%) patients were female. The average age of these patients was 52.72 ± 15.25 years old, in which 118 (21.73%) patients were <40 years old, 305 (56.17%) patients were within 41–60 years old and 120 (22.1%) patients were >65 years old. Different complications existed among the included patients, which included hypertension . In addition, the cohort with renal impairment was associated with longer hospitalization duration and higher incidence of mortality during hospitalization (P < 0.05). More details are presented in Table A total of 70 12.89%) patients had eGFR < 60 ml/min 1.73m2.89% patP < 0.05). No significant difference in neutrophil, and eosinophil count, and serum Hb, PCT, Fib, D‐dimer and CK‐MB level were observed between the two cohorts (P ≥ 0.05). The detailed information is presented in Table In terms of laboratory findings, patients with early renal impairment had significantly higher WBC, CRP, ESR, BUN, Cr and BNP and lower lymphocytes, PLT, prealbumin and ALB, when compared with the normal group , specific complications (hypertension or diabetes mellitus), eGFR < 60 ml/min 1.73 m4Through utilizing the available abundant clinical data on COVID‐19 in our hospital, the present retrospective study was conducted to unravel the clinical significance of renal impairment, which occurs at the early stage of COVID‐19 infection. It was found that early renal impairment in COVID‐19 patients was associated with more severe disease and worse clinical outcome. Early renal impairment is an independent risk factor for fatality in COVID‐19.In a univariate survival analysis, it was found that male gender, age over 65 years old, complications such as hypertension or diabetes, elevation in WBC count or neutrophil count, decrease in lymphocyte count, thrombocytopenia and increase in CRP level may lead to increased risk of mortality. These present findings are consistent with the findings of the study conducted by Terpos et al.,Accumulated evidences have proven that renal impairment commonly occurs during coronavirus infection and conveys a non‐negligible clinical significance. Studies on previously identified coronaviruses, such as SARS and MERS, have revealed that AKI can occur in 5–15% of patients, and among these patients, the mortality rate can reach up to 60–90%.2 on admission) was 12.89%, and merely four cases (14.3%) were complicated with chronic renal diseases. It is noteworthy that 32.14% of patients with early renal impairment were critically ill, whereas merely 9.71% of patients with normal renal impairment developed severe COVID‐19. In addition, patients with impaired renal function had a significantly higher WBC count and CRP level, indicating that renal damage may be associated with the overwhelming inflammatory response caused by COVID‐19. Importantly, early renal impairment was significantly associated with prolonged hospitalization and higher mortality rate but was independent of all the other risk factors in predicting COVID‐19‐associated fatality, as shown by the multivariate Cox regression analysis. Similarly, a clinical trial on SRAS in 2003 revealed that concurrent AKI was associated with extremely high mortality, which can reach up to 91.7%, although the occurrence of AKI was not common in SARS patients. All these findings indicate that the concurrence of renal impairment in coronavirus diseases convey an unfavourable impact on clinical outcome. The kidney–lung crosstalk theory has been proposed to explain the interaction between kidney and lung damage and the consequent bidirectional deterioration.In the present study, the incidence of early renal impairment . All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.All data published here are under the consent for publication. A written informed consent was obtained from all individual participants included in the study.Pei‐ling Bao, Ke‐lan Deng and An‐long Yuan performed most of the investigation and data analysis and wrote the manuscript. Yi‐min Yan performed Statistical analysis. Ai‐qiao Feng and Tao Li contributed to interpretation of the data and analyses. Xiao‐an Liu directed manuscript writing. All of the authors have read and approved the manuscript.


N = 252 and N = 266) analyse how a person in persistent poverty is perceived compared to a person in poverty due to the crisis—a circumstantial poverty. We also study some feasible mechanisms underlying this different perception, as well as their effects on attitudes toward social protection policies. In Study 1, results indicated that people showed more favourable attitudes toward social protection policies when they perceived someone in poverty due to the crisis, compared to the target who had been in poverty all his/her life. Individualistic attributions for poverty mediated this effect: when people think of someone in persistent poverty, they make more individualistic attributions concerning their situation, which leads to worse attitudes toward social protection policies. Identification with the group moderates this relation. Furthermore, Study 2 showed that participants perceive people who are in poverty because of economic crisis as more deserving of help than people who have always been poor. Some theoretical and practical implications for intergroup relations and public policy are discussed.The economic crisis of 2008 severely affected the welfare states. As the economic situation of a country worsens, the resources that the public administration can devote to improve the situation of the people also decrease, endangering the advancement of those in a disadvantaged situation. People who have always lived in poverty, besides having their opportunities reduced, also face negative public views that affect the perceived legitimacy of such public aid, which can in turn be a mechanism for perpetuating their situation. Two studies (The online version contains supplementary material available at 10.1007/s12144-022-03804-6. Poverty and inequality not only persist due to inefficient economic systems or lack of resources for people in poverty, but also because they are a product of human relationships, which determine how resources are distributed  in the third quarter of 2007 to the highest peak of 5,943,400 million (an unemployment rate of 25.65%) in the third quarter of 2013  individualistic, that is, to the person in poverty; b) structural causes, such as external, social and structural forces; c) fatalistic causes, where the responsibility for poverty lies with factors such as illness or bad luck Feagin, . Other mThese attributions may vary depending on the perceived group of people in poverty. For example, Henry et al.  found thWhile attributions for poverty are important in explaining support for social protection, there are other important factors related to the characteristics of benefit recipients that influence greater or lesser support for these policies, namely the extent to which they are considered deserving of social protection. Van Oorschot , based oAttitudes towards people in poverty and towards resources distribution also depend on some characteristics of the people who maintain such attitudes. One of these important characteristics is their membership’ group. Thus, for instance, middle-class people tend to make more individualistic attributions, blaming people in poverty for their situation, while social assistance beneficiaries tend to make more structural attributions Bullock, .Another factor related to group membership is the stability with which such belonging is perceived. For instance, middle-class perceivers might identify more with people who have just fallen into poverty as consequence of the crisis -thinking that they could find in the same situation- and this identification led them to make less individualistic attributions about this group and perceive them as more deserving of social protection. Also is possible that the middle-class people seek to obtain a more positive identity by differentiating itself from those in permanent poverty.Lastly, stereotypes play an important role in the perpetuation of inequality and poverty insofar as they help maintain the system by justifying a series of social actions Tajfel, . StereotEmpirical evidence from the Stereotype Content Model also shows differences among subgroups within people in poverty, such as homeless people, recipients of charity, or poor people in general  salient in the citizens’ minds. Second, analysing the importance of attributions for poverty in the perception of various groups of people in poverty, deservingness, and stereotypes as well as the participants’ identification with each group will help explain how people perceive poverty as well as the opposition to policies to eradicate poverty. This analysis will also show where we should intervene if we want to change these attitudes.https://osf.io/ne6ug/?view_only=af98fb36c3ef4eb98fa19542cde3467b.All datasets, measures, and preregistered forms of both studies are publicly available at In Study 1, we aimed to analyse the perception of the aforementioned two groups of people in poverty in terms the perceived causes of their poverty, stereotypes about them, identification with these groups, and attitudes towards social protection policies. We also include some measures of the objective and subjective social class, system justification ideology, social-dominance orientation, and financial threat with an exploratory purpose. We present the results concerning these additional variables in the supplementary materials.d = 0.35 with a power of .80 and an alpha of .05. We determined this small to medium effect size taking into account our available resources and the necessary time to collect the sample. So this effect size was determined based on our availability of resources to obtain the sample and we relied on a benchmark. This may not be the best possible strategy, however, because we are not answering any theoretical questions . As for many other people, his loss of employment because of the crisis made him go from being in a financially comfortable situation to being considered poor (he was born and raised in a poor home). Since then (since he was old enough), he has been doing some occasional jobs to get some money or borrowing from his acquaintances to meet the needs of his household.As an attention check, we required the participants to answer a question about the nature of the poverty concerning the person in the story, which they had read immediately beforehand . This was our main independent variable in both studies.totally disagree to 5 = totally agree) to indicate their degree of agreement with every item. The scale was preceded by an introduction that clarified that we sought to know the opinion of society, not of the concrete respondent, to avoid desirability bias.We measured stereotypes for the two targets along two core dimensions, competence (α = .83) and warmth (α = .78), based on the Stereotype -Content Model . We translated the items into Spanish from Furnham  to 5 . Specifically, we used items from the solidarity subscale and the individual self-stereotyping items that best fit our purpose. Our scale contained seven items  and was preceded by the next text: “Think of someone who has been poor all his/her life (is poor due to the economic crisis) and answer the following questions.” Subjects responded to these items twice, once referring to the group perceived in their experimental condition and once referring to the other group. We subtracted from the score on the identification scale regarding poor people in the wake of the crisis (α = .67) the score on the identification scale regarding people in chronic poverty (α = .71) to obtain a global identification index. Higher positive scores indicate a stronger identification with the poor in the wake of the crisis (versus the poor in chronic poverty).We measured participants’ identification with our two groups of people in poverty by adapting to this context, changing the targets, the collective identity scale from Leach et al. . The postotally disagree to 5 = totally agree), with which subjects indicated their degree of agreement with 20 statements about social protection policies . We used a scale inspired in Furnham  and would be perceived as more competent and warmer (Hypothesis 1b); we also predicted that attitudes towards social protection policies would be better when participants perceive someone in poverty due to the economic crisis relative to those in chronic poverty (Hypothesis 1c). For this group of hypotheses, our main independent variable was the experimental condition and attributions for poverty, competence, and warmth perceptions, and attitudes towards social protection policies were our dependent variables.Identification with the group would moderate the relationship between the perceived group—person in chronic poverty versus person in poverty due to economic crisis—and the assignment of competence and warmth scores (Hypothesis 2) and attitudes towards social protection policies (Hypothesis 3). We expected that the tendency to assign greater competence and sociability to people in poverty in the wake of a crisis (versus people in persistent poverty) and to show a better attitude towards social protection policies when perceiving the former would be more intense among people who identify with those who are in poverty because of the economic crisis. Therefore, in this set of hypotheses, our independent variable was the experimental condition, the moderator variable was identification with the group, and the dependent variables were warmth and competence perceptions and attitudes towards social protection policies.Finally, we expected that attributions for poverty would mediate the relationship between the perceived group (poverty due to economic crisis vs. persistent poverty) and support for social protection policies (Hypothesis 4), in the sense that perceiving people falling into poverty due to economic crisis would lead to fewer individualistic attributions for their poverty and more support for social protection policies.t test for differences between two independent means (see Table t(250) = −2.16, p = .03, BCa 95% CI , d = 0.27. This effect size was smaller than the effect size that sensitivity analyses suggested we could detect, so we conducted a post hoc analysis using G*Power 3.1  = −0.83, p = .41, BCa 95% CI , d = −0.10. Our findings partially supported Hypothesis 1b because the hypothesised results were confirmed only for the competence dimension. As we expected, the participants perceived the person in persistent poverty as less competent than the person in poverty due to an economic crisis, t(250) = 4.17, p < .001, BCa 95% CI , d = −0.53. We found no statistically significant differences in the warmth subscale scores, t(250) = −1.79, p = .08, BCa 95% CI , d = −0.22. Our findings supported Hypothesis 1c: Attitudes towards social protection policies were less favorable after reading a vignette about a person who has been in poverty all his life than after reading a vignette about person in poverty as a result of the economic crisis, t(250) = 3.19, p = .002, BCa 95% CI , d = −0.40. Table To test our first hypotheses, H1a, H1b, and H1c, we conducted a e Table t50 = −2.1We tested Hypotheses 2 and 3 using the PROCESS macro for SPSS, Model 1, with 10,000 bootstrapped samples. To test Hypothesis 2, we included the experimental condition as the independent variable and the global identification index as the moderator variable. We built two models. In one model, the criteria variable was competence scores and in the other one, warmth scores. We used the same analytical strategy to test our hypotheses in this case, including attitudes towards social protection policies as a dependent variable.b = 0.20, t(248) = 1.96, p = .051, 95% CI = , and for competence, this interaction effect was b = 0.20, t(248) = 1.87, p = .062, 95% CI = . As we hypothesized in Hypothesis 3, group identification moderated the relationship between perceived group and attitudes towards social protection policies, b = 0.25, t(248) = 2.95, p = .003, 95% CI = . Perceiving someone in poverty due to economic crisis (compared to someone in persistent poverty) increased positive attitudes towards social protection policies among those who identified more with the first group (understood as a standard deviation above the mean score on the identification index), b = 0.44, SE = 0.10, p < .001, 95% CI = ; among those who identified less with people the economic crisis made poor, the effect was nonsignificant (understood as a standard deviation below the mean score on the identification index), b = 0.01, SE = 0.10, p = .91, 95% CI = .We did not find support for Hypothesis 2, that group identification will moderate the relation between perceived group and assignment of competence and warmth. For warmth, the interaction effect of poverty condition ✕ group identity was b = 0.09, BCa 95% CI . In this model, the direct effect was b = 0.14, p = .01 and the total effect b = 0.23, p = .002; the perceived condition’s effect on individualistic attributions was b = −0.20, p = .03, and the effect of the latter on attitudes towards social protection policies was b = −0.44, p < .001. Following Wen and Fan’s (PM) together with the total effect. We calculated this measure using the lavaan  although the analysis of the simple lines showed the same pattern we previously found. Individualistic attributions remained a significant mediator between experimental condition and attitudes towards social protection policies.We tested Hypothesis 4 by conducting a linear regression analysis using the PROCESS macro for SPSS, testing mediation Model 4. All confidence intervals for indirect effects are a BCa CI based on 10,000 bootstrapped samples. We found partial support for Hypothesis 4 because the hypothesised results were confirmed only for individualistic attributions, not for structural ones. Therefore, we found a significant indirect effect of the perceived group on the attitudes towards social protection policies, through individualistic attributions for poverty, nd Fan’s  recommenRosseel,  R packagRosseel, , with 10Our results showed that attitudes towards social protection policies differ depending on the nature of the perceived target’s poverty: people who have been in poverty all their lives versus people in poverty in the wake of economic crisis. Individualistic poverty attributions mediated the relationship between perceived group and attitudes towards social protection policies. When participants perceived someone who has been poor all his life, they made more individualistic attributions, which led to a worse attitude towards social protection policies (in comparison to those who perceived someone who is poor due to an economic crisis). Considering the studies that causally link individualistic attributions with lower support for the provision of funds to people in poverty ; we present results concerning these measures in the SD = 11.85; see Table We recruited a sample of 280 participants from the Spanish general population at a public-transport station. Participants agreed to participate voluntarily, without any compensation, in a study on poverty perception, and then we randomly assigned them to one of two possible conditions. As in the previous study, in each condition, they had to read a text about a poor person. In one condition, this person became poor in the wake of an economic crisis, and in the other condition, this person had been poor all his life. Next, participants had to complete other measures, listed below. After we applied our exclusion criteria, 266 participants 149 women) remained; their mean age was 30.55  and structural attributions (α = .75), a group identification index  the score on the identification scale with people in chronic poverty (α = .80), attitudes towards social protection policies (α = .89), and political ideology , with which subjects indicated their degree of agreement with statements about poor people’s deservingness of social protection. The scale is based on van Oorschot’s  and would be perceived as more deserving (Hypothesis 1b). Although the differences in structural attributions were not significant in Study 1, we preregistered this hypothesis again because we thought that perhaps the absence of effects occurred due to the nature of the sample (college students in Study 1). Participants who perceive someone in poverty due to an economic crisis would also have better attitudes towards social protection policies (Hypothesis 1c). Our independent variable for these hypotheses was the experimental condition. Attributions for poverty, deservingness of social protection, and attitudes towards social protection were the dependent variables.Identification with the group would moderate the relationship between the perceived group—person in chronic poverty versus person in poverty due to economic crisis—as well as perceived deservingness of social protection (Hypothesis 2) and attitudes towards social protection policies (Hypothesis 3). We expected that the tendency to assign greater deservingness to people in poverty in the wake of the crisis (versus people in persistent poverty) and to show a better attitude towards social protection policies when perceiving the former would be more intense in people who identify with those who are in poverty because of an economic crisis. Again, in these hypotheses, our independent variable was the experimental condition, the moderating variable was identification with the group, and the dependent variables were perceived deservingness of social protection and attitudes towards social protection policies.Finally, attributions for poverty would mediate the relationship among the perceived group (poverty due to economic crisis versus chronic poverty), perceived deservingness of social protection (Hypothesis 4), and attitudes towards social protection policies (Hypothesis 5).t test to identify differences between the two independent means (two groups) to test our first hypotheses: H1a, H1b, and H1c. Table t(264) = 5.43, p < .001, BCa 95% CI , d = 0.67 and fewer structural attributions, t(264) = −2.99, p = .003, BCa 95% CI , d = −0.36. We also found support for Hypothesis 1b: Participants who perceived a person in persistent poverty considered them less deserving of receiving support than those who were assigned to the other condition, t(264) = 5.39, p < .001, BCa 95% CI , d = 0.56. Our findings also confirmed Hypothesis 1c: Participants who perceived someone in persistent poverty showed a worse attitude towards social protection policies than participants who perceived someone in poverty due to economic crisis, t(264) = −5.23, p < .001, BCa 95% CI , d = −0.64. Table We conducted a Table t26 = 5.43, b = 0.26, t(262) = 3.71, p < .001, 95% CI = . Therefore, perceiving someone who is poor due to economic crisis (compared to someone in persistent poverty) increased deservingness perception among those who identified more with this group, b = 0.68, SE = 0.11, p < .001, 95% CI = , but not among those who identified less with this group, b = 0.11, SE = 0.11, p = .28, 95% CI =   = 3.29, p = .001, 95% CI = . As in Study 1, perceiving someone who is poor due to an economic crisis (compared to someone in persistent poverty) increased positive attitudes towards social protection policies among those who identified more with the first group, b = 0.58, SE = 0.10, p < .001, 95% CI = , but not among those who identified less with this group, b = 0.11, SE = 0.10, p = .30, 95% CI =   that structural attributions for this poverty will lead to a better attitude towards social protection policies. The moderating effect of identification with people who are in poverty because of an economic crisis was replicated, but, again, it ceased to play a significant role as a moderator when we included all mediating variables in the same model. Interestingly, we found support for our double-mediation model: The type of person in poverty influences the attributions for his poverty, which influence the target’s deservingness of social protection, which influences the attitudes towards social protection.d into Pearson’s correlation coefficients and calculated the weighted means of correlations, previously transforming correlations coefficients into Fisher’s z for normalization. Then, we combined rz values meta-analytically using the formula Goh et al.  increased deservingness perception of social protection among those who identified more with this group but not among those who identified less with this group.Somewhat surprisingly, stereotypes about people in poverty, at least measured according to the stereotype-content model